The Statistical Discovery Software™

TM

User’s GuideUser’s Guide

version5

SAS, JMP, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA �and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Six Sigma �is a registered trademark of Motorola, Inc. 47976US_0402

SAS Japan Inui Bldg Kachidoki1-13-1 Kachidoki Chuo-ku Tokyo 104-0054 Japan Tel: (81) 3 3533 3887 Fax: (81) 3 3533 1600 jmpjapan@sas.com www.jmpdiscovery.com/japan

JMP Sales SAS Campus Drive Cary, NC 27513 US: 877-59GO JMP International: 919-677-8000Fax: 919-677-4444 jmpsales@sas.com www.jmpdiscovery.com

Worldwide, JMP® is used in the following markets: pharmaceuticals, medical devices, semiconductors, electronics manufacturing, consumer products manufacturing, chemical manufacturing, agriculture, business and in various areas of industrial and academic research.

The first edition of JMP software was developed in 1989 linking statistics with graphics to explore data, make discoveries, and gain knowledge �for better decision making. JMP provides a complete and flexible design of experiments environment to help engineers, researchers, and �Six Sigma® practitioners create designs with fewer runs, providing quicker results at less cost.

SAS is the market leader in providing a new generation of business intelligence software and services that create true enterprise intelligence. SAS solutions �are used at more than 38,000 sites—including 99 of Fortune 100 businesses—to develop more profitable relationships with their customers and suppliers, �to make better, more accurate and informed decisions, and to drive their organizations forward.

Software that

makes innovation

a continuous process.

Supports all phases of the Six Sigma® (DMAIC) process and root cause analysis

Prepare custom experimental designs with Design of Experiments (DOE)

Analytics for all phases of manufacturing from design concept to production through quality control

Retrieve data from other software packages by ODBC

User interface supports all levels of users

Supports all phases of the Six Sigma® (DMAIC) process and root cause analysis

Prepare custom experimental designs with Design of Experiments (DOE)

Analytics for all phases of manufacturing from design concept to production through quality control

Retrieve data from other software packages by ODBC

User interface supports all levels of users

www.jmpdiscovery.com

A BUSINESS UNIT OF sas

TM

58813version5

User’s GuideUser’s Guide

TM

“The real voyage of discovery consists not in seeking newlandscapes, but in having new eyes.”

Marcel Proust

User’s Guide

JMP, A Business Unit of SASSAS Campus DriveCary, NC 27513

Version 5

JMP

®

User’s Guide

, Version 5

Copyright © 2002 by SAS Institute Inc., Cary, NC, USA.

ISBN 1-59047-070-2

All rights reserved. Printed in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher, SAS Institute Inc.

Information in this document is subject to change without notice. The software described in this document is furnished under the license agreement packaged with the software. The software may be used or copied only in accordance with the terms of the agreement. It is against the law to copy the software on any medium except as specifically allowed in the license agreement.

First printing, April 2002

JMP®, SAS®, and all other SAS Institute Inc. product or service names are registered trademarks of SAS Institute Inc. All trademarks above are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

Other brand and product names are registered trademarks or trademarks of their respective compa-nies.

Imageman® is a registered trademark or trademark of Data Techniques, Inc. All rights reserved.

Microsoft Text to Speech Engine® is a registered trademark or trademark Microsoft Corporation. All rights reserved.

Installer VISE™, Updater VISE®, and MindExpander® are trademarks of MindVision Inc. All rights reserved worldwide.

Install Shield® is a registered trademark of Install Shield Software Corporation. All rights reserved.

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Contents

JMP User’s guide

Credits and Acknowledgments

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

1 Preliminaries 1

What you need to know

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

…about your computer

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

…about statistics

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Learning about JMP

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

…on your own with JMP Help

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

…hands-on tutorials

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

…reading about JMP

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

…if you are a previous JMP user

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Conventions and Organization

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Starting JMP 7

Overview of the JMP Starter Window

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

JMP Starter and the File Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

JMP Starter and the Analyze Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

The Basic Stats Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

The Modeling Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

The Multivariate Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

The Survival Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

JMP Starter and the Graph Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

The Graphs Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

The QC Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

JMP Starter and the DOE Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

JMP Starter and the Tables Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

The JMP Starter Index Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3 The Menu Bar 25

The File Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

The Edit Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

The Tables Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

The Rows Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

The Cols Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

The DOE Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

The Analyze Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

The Graph Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

iv

The Tools Menu and Tool Bars

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

The View Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

The Window Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

The Help Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

The Layout Menu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4 Characteristics of Data 75

JMP Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Types of Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Data Types

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Modeling Types

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Analysis Roles

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Freq Variables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Weight Variables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Numeric Formats

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Date, Time and Date-Time Formats

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Row States

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Row State Columns

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Selection Status

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Data Validation

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Specialized Properties of Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5 JMP Data Tables 93

Elements of a JMP Data Table

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Table Variables and Table Properties

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

New Table Variable

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

New Property/Script

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

The Columns Panel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

The Rows Panel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

The Data Grid

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Cursor Forms

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Selecting and Deselecting Rows and Columns

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Creating a New Table

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Adding and Deleting Rows

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Adding and Deleting Columns

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Filling a Data Table with Values

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Entering and Editing Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Finding and Replacing Cell Values

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Importing Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Exporting Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

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Accessing a Database

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Opening a JMP File from a Web Address

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Cut and Paste

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Drag and Drop

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

6 The Tables Menu 119

Overview of Tables Menu Commands

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

The Summary Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

The Summary Table

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Summary Statistics

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Marginal Statistics

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

The Subset Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Selecting Rows

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

The Sort Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

The Stack Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

The Split Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

The Transpose Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

A Simple Transpose Example

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Transpose with a Label

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

The Concatenate Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Concatenate Tables with the Same Column Names

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Concatenate Tables with Different Column Names

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

The Join Command

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Join by Row Number

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Keep a Subset of Columns

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Join by Matching Columns

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Cartesian Join

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Update a Table

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

7 Using the Formula Editor 143

A Quick Example

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Formula Editor Terminology

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

The Formula Work Panel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Formula Element Browser

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Keypad

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Function Browser

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150The Formula Display Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Keypad Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Formula Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

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Table Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Table Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Working with Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Computational Order of Precedence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Building a Formula in Order of Precedence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156Constant Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156Cutting and Pasting Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Selecting Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Dragging Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Tips on Editing a Formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Table Templates and Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158Grid Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Actuarial Life Table Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Central Limit Theorem Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Nonlinear and Loss Function Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Keyboard Shortcuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

8 Formula Editor Functions 163The Function Browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Row Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166Numeric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168Transcendental Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170Trigonometric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172Character Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Comparison Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178Conditional Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Probability Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184Statistical Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194Random Number Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Date Time Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Row State Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205Assignment Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

9 Report Windows and Surface Features 213Report Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

Standard Window Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215Report Disclosure Icons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

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Popup Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Cursor Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Features and Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

Formatting Analysis Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220Context Menu Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220Resizing Plots and Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222Selecting Points in Plots and Using Row States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Customizing Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224Using the Annotate Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229Using the Draw Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

Copy, Paste, Drag and Drop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232Journal and Layout Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

The JMP Journal Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234The JMP Layout Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

10 Personalizing JMP 237Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Background Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240Fonts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241Graphic Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241Communications Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242File Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Preferences Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Platform Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243Text Import/Export . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Tool Bars and Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244The Show Toolbars Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

The Customize Command in the Edit Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246Customizing Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246Customizing Toolbars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

A What’s New in JMP Version 5 253Data Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255Data Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256New Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Statistical Platform Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

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Internal Optimizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262JMP Scripting Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263OLE Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

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Credits and Acknowledgments

Origin

JMP was developed by SAS Institute Inc., Cary, NC. JMP is not a part of the SAS System, though por-tions of JMP were adapted from routines in the SAS System, particularly for linear algebra and proba-bility calculations. Version 1 of JMP went into production in October, 1989.

Credits

JMP was conceived and started by John Sall. Design and development was done by John Sall, Katherine Ng, Michael Hecht, Richard Potter, Brian Corcoran, Annie Dudley Zangi, Bradley Jones, Charles Soper, Craige Hales, Kevin Hardman, and Chris Gotwalt. In the SAS Institute Technical Sup-port division, Ryan Gilmore, Wendy Murphrey, Toby Trott, Peter Ruzsa, and Rosemary Lucas provide technical support and conducted test site administration. Statistical technical support is provided by Craig DeVault, Duane Hayes, and Kathleen Kiernan. Nicole Jones, Jianfeng Ding, Jim Borek, and Kyoko Tidball provide ongoing quality assurance. Additional testing and technical support is done by Noriki Inoue and Kyoko Takenaka from SAS Japan. Bob Hickey is the release engineer.

The JMP manuals were written by Ann Lehman, Lee Creighton, John Sall, Bradley Jones, and Erin Vang, with contributions from Meredith Blackwelder, Annie Dudley Zangi, and Brian Corcoran. Edit-ing, creative services, and production was done by SAS Publications. Melanie Drake implemented the help system.

Thanks also to Georges Guirguis, Warren Sarle, Gordon Johnston, Duane Hayes, Russell Wolfinger, Randall Tobias, Robert N. Rodriguez, Ying So, Warren Kuhfeld, George MacKensie, Bob Lucas, Donna Fulenwider, and Mike Stockstill for statistical R&D support.

Acknowledgments

We owe special gratitude to the people that encouraged us to start JMP, to the alpha and beta testers of JMP, and to the reviewers of the documentation. In particular we thank Michael Benson, Howard Yet-ter, Andy Mauromoustakos, Al Best, Stan Young, Robert Muenchen, Lenore Herzenberg, Larry Sue, Ramon Leon, Tom Lange, Homer Hegedus, Skip Weed, Michael Emptage, Pat Spagan, John Frei, Paul Wenz, Mike Bowen, Lori Gates, Georgia Morgan, David Tanaka, Zoe Jewell, Sky Alibhai, David Cole-man, Linda Blazek, Michael Friendly, Joe Hockman, Frank Shen, J.H. Goodman, David Ikle, Lou Valente, Robert Mee, Barry Hembree, Dan Obermiller, Lynn Vanatta, and Kris Ghosh. Also, we thank Dick DeVeaux, Gray McQuarrie, Robert Stein, George Fraction, Al Fulmer, Cary Tuckfield, Ron This-ted, Nancy McDermott, Veronica Czitrom, Tom Johnson, and Avigdor Cahaner.

We also thank the following individuals for expert advice in their statistical specialties: R. Hocking and P. Spector for advice on effective hypotheses; Robert Mee for screening design generators; Jason Hsu for advice on multiple comparisons methods (not all of which we were able to incorporate in JMP); Ralph O’Brien for advice on homogeneity of variance tests; Ralph O’Brien and S. Paul Wright for advice on

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statistical power; Keith Muller for advice in multivariate methods, Harry Martz, Wayne Nelson, Ramon Leon, Dave Trindade, Paul Tobias for advice on reliability plots; Lijian Yang and J.S. Marron for bivariate smoothing design; George Milliken and Yurii Bulavski for development of mixed models; Will Potts and Cathy Maahs-Fladung for data mining; and Clay Thompson for advice on contour plot-ting algorithms.

For sample data, thanks to Patrice Strahle for Pareto examples, the Texas air control board for the pollu-tion data, and David Coleman for the pollen (eureka) data.

Translations

For our localized releases, Erin Vang coordinated localization. Noriki Inoue, Kyoko Takenaka, and Yusuke Ono of SAS Japan were indispensable throughout the project. Thanks to Junji Kishimoto of SAS Japan, and special thanks to Professor Toshiro Haga (retired, Science University of Tokyo) and Pro-fessor Hirohiko Asano (Tokyo Metropolitan University). Thanks to Hui Di for Chinese translation review. Finally, thanks to all the members of our outstanding translation teams.

Past Support

Many people were important in the evolution of JMP. Special thanks Jeffrey Perkinson, David DeLong, Mary Cole, Kristin Nauta, Aaron Walker, Ike Walker, Eric Gjertsen, Dave Tilley, Curt Yeo, Mike Pez-zoni, Ruth Lee, Annette Sanders, Tim Christensen, Xan Gregg, Jeff Polzin, Alissa Aungvibool, and Eric Wasserman. SAS Institute quality assurance by Jeanne Martin, Fouad Younan, and Frank Lassiter. Additional testing for Versions 3 and 4 was done by Li Yang, Brenda Sun, Katrina Hauser, and Andrea Ritter.

Also thanks to Jenny Kendall, Elizabeth Shaw, John Hansen, Eddie Routten, David Schlotzhauer, and James Mulherin. Thanks to Steve Shack, Greg Weier, and Maura Stokes for testing JMP Version 1.

Thanks for support from Charles Shipp, Harold Gugel, Jim Winters, Matthew Lay, Tim Rey, Rubin Gabriel, Brian Ruff, William Lisowski, David Morganstein, Tom Esposito, Susan West, Chris Fehily, Dan Chilko, Jim Shook, Ken Bodner, Rick Blahunka, Dana C. Aultman, and William Fehlner.

Technology License Notices

JMP for the Power Macintosh was compiled and built using the CodeWarrior C compiler from Metro-Works Inc.

SAS INSTITUTE INC.’S LICENSORS MAKE NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANT-ABILITY AND FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE SOFTWARE. SAS INSTITUTE INC.’S LICENSORS DO NOT WARRANT, GUARANTEE OR MAKE ANY REPRESENTATIONS REGARDING THE USE OR THE RESULTS OF THE USE OF THE SOFTWARE IN TERMS OF ITS CORRECTNESS, ACCURACY, RELIABILITY, CURRENT-NESS OR OTHERWISE. THE ENTIRE RISK AS TO THE RESULTS AND PERFORMANCE OF THE SOFTWARE IS ASSUMED BY YOU. THE EXCLUSION OF IMPLIED WARRANTIES IS NOT PERMITTED BY SOME STATES. THE ABOVE EXCLUSION MAY NOT APPLY TO YOU.

IN NO EVENT WILL SAS INSTITUTE INC.’S LICENSORS AND THEIR DIRECTORS, OFFICERS, EMPLOYEES OR AGENTS (COLLECTIVELY SAS INSTITUTE INC.’S LICEN-SOR) BE LIABLE TO YOU FOR ANY CONSEQUENTIAL, INCIDENTAL OR INDIRECT

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DAMAGES (INCLUDING DAMAGES FOR LOSS OF BUSINESS PROFITS, BUSINESS INTERRUPTION, LOSS OF BUSINESS INFORMATION, AND THE LIKE) ARISING OUT OF THE USE OR INABILITY TO USE THE SOFTWARE EVEN IF SAS INSTITUTE INC.’S LICENSOR’S HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. BECAUSE SOME STATES DO NOT ALLOW THE EXCLUSION OR LIMITATION OF LIABILITY FOR CONSEQUENTIAL OR INCIDENTAL DAMAGES, THE ABOVE LIMITATIONS MAY NOT APPLY TO YOU. SAS INSTITUTE INC.’S LICENSOR’S LIABILITY TO YOU FOR ACTUAL DAMAGES FOR ANY CAUSE WHATSOEVER, AND REGARDLESS OF THE FORM OF THE ACTION (WHETHER IN CONTRACT, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR OTHERWISE), WILL BE LIMITED TO $50.

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Preliminaries

JMP is statistical software that gives you an extraordinary graphical interface to display and analyze data. JMP is for interactive statistical graphics and includes

• a spreadsheet for viewing, editing, entering, and manipulating data

• a broad range of graphical and statistical methods for data analysis

• an extensive design of experiments module

• options to select and display subsets of the data

• data management tools for sorting and combining tables

• a calculator for each table column to compute values

• a facility for grouping data and computing summary statistics

• special plots, charts, and communication capability for quality improvement techniques

• tools for moving analysis results between applications and for printing

• a scripting language for saving frequently used routines.

JMP is easy to learn. Statistics are organized into logical areas with appropriate graphs and tables, which help you find patterns in data, identify outlying points, or fit models. Appropriate analyses are defined and performed for you, based on the types of variables you have and the roles they play.

JMP offers descriptive statistics and simple analyses for beginning statisticians and complex model fit-ting for advanced researchers. Standard statistical analysis and specialty platforms for design of experi-ments, statistical quality control, ternary and contour plotting, and survival analysis provide the tools you need to analyze data and see results quickly.

1Contents

What you need to know . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3…about your computer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3…about statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Learning about JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3…on your own with JMP Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3…hands-on tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4…reading about JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4…if you are a previous JMP user . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Conventions and Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1 Preliminaries—What you need to know 31

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What you need to know

…about your computer

Before you begin using JMP, you should be familiar with standard operations and terminology such as click, double-click, Control-click and Alt-click under Windows (Command-click and Option-click on the Macintosh), Shift-click, drag, select, copy, and paste. This version of JMP also uses context-click popup menus on many report surfaces (right mouse-click under Windows and Control-click on the Macin-tosh). These menus have commands specific to the area where you click. In some cases the context-click menus have commonly used commands also found on main menus.

You should also know how to use menu bars and scroll bars, how to move and resize windows, and how to manipulate files in the desktop. If you are using your computer for the first time, consult the refer-ence guides that came with it for more information.

…about statistics

Even though JMP has many advanced features, you need only a minimal background of formal statisti-cal training. All analysis platforms include graphical displays with options that help you review and interpret the results. Each platform also includes access to help windows that offer general help and some statistical details.

Learning about JMP

…on your own with JMP Help

If you are familiar with Microsoft Windows or Macintosh software, you might want to proceed on your own. After JMP is installed, you can open any of the JMP sample data files and experiment with analy-sis tools. Help is available for most menus, options, and reports.

There are several ways to see JMP Help:

• Windows users have a standard Help main menu with Contents, Search, and Index commands, which access the JMP documentation.

• On the Macintosh, select Help Center from the Help main menu. This displays all the help books in the Apple help facility on your system. Click the JMP Help Book to see a list of JMP documents. The Macintosh Help system has a search facility. Or, you can access each document through its table of contents within the Help system.

4 1 Preliminaries—Conventions and Organization

• Click the Index tab on the JMP Starter window to access the JMP statistical guide, which is a scroll-ing alphabetical reference that tells you how to generate specific analyses using JMP and lets you access further help for that topic.

• You can also choose Help from dialogs and from popup menus in JMP report windows.

• After you generate a report, context-sensitive help is available. Select the help tool (?) from the Tools menu and click the report surface. Context-sensitive help tells about the items in the report window.

…hands-on tutorials

The JMP Introductory Guide is a collection of tutorials designed to help you learn JMP strategies. Each tutorial uses a file from the Sample Data folder. By following along with these step-by-step examples, you can quickly become familiar with JMP menus, options, and report windows.

…reading about JMP

The book you are reading now is the JMP User’s Guide. It gives you reference material for all JMP menus, an explanation of data table manipulation, a description of the calculator and how to use it, and a discussion of scripting to capture and run a sequence of actions. See the following manuals for further documentation of JMP:

• The JMP Statistics and Graphics Guide gives documentation of the Analyze and Graph menus.

• JMP Design of Experiments covers the DOE menu, the experimental design facility in JMP.

• The JMP Scripting Guide is a reference guide to the JMP scripting language (JSL) that lets you auto-mate action sequences.

…if you are a previous JMP user

If you are familiar with the JMP environment, you might want to know only what’s new. The appendix “What’s New in JMP Version 5,” p. 253, gives a summary of general changes and additions in Version 5 of JMP.

Conventions and OrganizationThe following manual conventions help you relate written material to information you see on your screen:

• JMP file names appear as they show in the JMP Sample Data folder under Windows, which is usu-ally in mixed case followed by the .jmp extension. On the Macintosh the JMP sample data files have the same mixed-case name but show without an extension. Reference to names of JMP files, data tables, variable names, and items in reports show in Helvetica to help distinguish them from sur-rounding text.

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• Special information, warnings, and limitations are noted as such with the word ‘Note’.

• Reference to menu names (File menu) or menu items (Save command) appear in the Helvetica bold font, similar to the way they appear on your screen.

• The notation to select a command from a menu is sometimes written File > New, meaning “select the New command from the File menu.”

• Words or phrases that are important or have definitions specific to JMP are in italics the first time you see them.

The chapters and appendix in this book are organized as follows:

Chapter 2, Starting JMP shows how to start a JMP session and documents the JMP Starter window and its relationship to the main menus.

Chapter 3, The Menu Bar documents the main menu bar and describes each menu command.

Chapter 4, Characteristics of Data describes types of data, modeling types, data storage, informats and formats, and other special properties of data.

Chapter 5, JMP Data Tables explains how to create new JMP tables; import and export data; navigate the table to modify values; add and delete rows and columns; cut, paste, print; and save JMP table information.

Chapter 6, The Tables Menu explains the Tables menu commands and shows how to summarize data, and sort, subset, transpose, join, and concatenate JMP tables.

Chapter 7, Using the Formula Editor explains in detail how to use the JMP formula editor, describes the formula editor components, shows how to create and modify formulas, introduces table templates, and tells how to create your own table templates.

Chapter 8, Formula Editor Functions lists all the formula editor functions, and includes many examples.

Chapter 9, Report Windows and Surface Features describes the form and features of JMP results in report windows.

Chapter 10, Personalizing JMP covers preferences and describes how to customize menus and save them.

Appendix A, What’s New in JMP Version 5 gives an overview of new features in Version 5 of JMP.

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Starting JMPThe JMP Starter Window

When the JMP application first opens, you see the JMP Starter window. The JMP Starter is a set of tabs pages, as illustrated below. The JMP Starter provides a guide and gateway to the facilities in JMP. It is a good way to get started if you haven’t used JMP before. It gives access to most commands found on the main menu or on toolbars. However, you don’t have to access JMP commands through the JMP Starter. An experienced JMP user might want to close the JMP Starter window and proceed with other chapters in this book.

This chapter gives an overview of the JMP Starter window and briefly describes its tab pages and the items or commands on them. Each tab page description directs you to an appropriate chapter in this book for details, or to the Statistics and Graphics Guide.

2Contents

Overview of the JMP Starter Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9JMP Starter and the File Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9JMP Starter and the Analyze Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

The Basic Stats Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10The Modeling Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12The Multivariate Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14The Survival Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

JMP Starter and the Graph Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16The Graphs Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16The QC Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

JMP Starter and the DOE Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20JMP Starter and the Tables Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22The JMP Starter Index Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

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Overview of the JMP Starter WindowWhen the JMP application opens you first see the JMP Starter window, which is a set of tab pages as illustrated in Figure 2.1. It gives access to most commands found on the main menu or on toolbars. You can close the JMP Starter if you want; it is not required for running JMP. To open the JMP Starter window, choose the JMP Starter command from the View menu (View > JMP Starter under Win-dows) or the Macintosh Window menu (Window > JMP Starter) any time during a JMP session. This window provides easy access to a broad scope of JMP functionality. It gives descriptions and help to

• create and manipulate JMP data tables

• use statistical commands in the Analyze menu

• generate graphs, overlay plots, spinning plots, contour plots, and use statistical quality control tools from the Graph menu

• design experiments

• manipulate tables using features accessed by commands on the Tables menu.

The JMP Starter also has a tab that gives an extensive scrolling index of features in JMP.

This chapter briefly describes the JMP Starter and shows how to use it. Detailed descriptions and exam-ples of all commands follow in this book or in the Statistics and Graphics Guide. If you are already famil-iar with JMP you might want to move ahead and continue with the following chapters.

Figure 2.1 JMP Starter Tab Pages

JMP Starter and the File MenuThe JMP Starter window first appears with the File tab showing, as in Figure 2.2. The commands on the File tab page correspond to File menu commands on the main menu bar. These commands open a JMP data table or other kinds of JMP windows, which is often what you need to do first.

Note: The Open Database Table command is not currently available on the Macintosh.

Create DataAnalyze Menu

Graph MenuDesign Experiment

Tables Menu

Scrolling Index

10 2 Starting JMP—JMP Starter and the Analyze Menu

Figure 2.2 JMP Starter File Tab

JMP Starter and the Analyze MenuThe tab pages organize commands in the Analyze menu into categories or types of analyses. The four statistical tab pages that follow the File tab can lead you step by step from your data to an appropriate analysis. Once you have become more familiar JMP analyses, you might prefer to use the Analyze menu directly.

The next sections show how the tab pages relate to the Analyze menu and give an overview of the selections on each tab.

The Basic Stats Tab

The Basic Stats tab page (Figure 2.3) addresses univariate and bivariate analyses. You see how to examine variables one at a time by looking at distributions and comparing them to known distribu-tions. When there are two variables, a single response (Y) and a single factor (X), JMP performs the appropriate bivariate analysis according to whether the variables are continuous or categorical. Figure 2.3 shows the correspondence between Basic Stats tab items and Analyze menu commands.

File > New > Data Table

File > New > Script

File > Open

File > Database > Open Table

File > Open

File > Open

File > Preferences

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Figure 2.3 JMP Starter Basic Stats Tab

Distribution

Analyze > Distribution launches the Distribution platform, which describes the distribution of values in a data table column using histograms and other graphical and text reports. For details about univari-ate distributions see “The Moments Table for Continuous Variables,” p. 32 in the JMP Statistics and Graphics Guide.

Fit Y by X

Analyze > Fit Y by X launches a report platform for each pair of columns in the active data table that are assigned X and Y roles for the analysis. The type of analysis depends on the whether the variables are continuous or categorical:

Oneway (nominal/ordinal by continuous)

If X has a nominal or ordinal analysis type and Y is continuous, Fit Y by X plots the distribution of Y values for each discrete value of X and computes a one-way analysis of variance table.

Bivariate (continuous by continuous)

If both X and Y have continuous analysis types, Fit Y by X displays a scatterplot. Using options, you can explore various regression fits for the data and choose the most suitable fit for further analysis. Each fit is accompanied by tables with supporting statistical analyses and parameter esti-mates.

Contingency (nominal/ordinal by nominal/ordinal)

If both X and Y have a nominal or ordinal modeling type, a contingency table is shown. Accom-panying tables show statistical tests and frequency, proportion, and chi-square values for each cell. Optionally, you can request a correspondence analysis.

Analyze > Distribution

Analyze > Fit Y by X

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Logistic (continuous by nominal/ordinal)

If X is continuous and Y is nominal or ordinal, Fit Y by X performs a logistic regression and dis-plays a family of logistic probability curves. Accompanying tables show the log likelihood analysis and parameter estimates for each curve.

The chapters “Bivariate Scatterplot and Fitting,” p. 63, “Oneway Layout,” p. 87, “Contingency Tables Analysis,” p. 123, and “Simple Logistic Regression,” p. 139 in the JMP Statistics and Graphics Guide cover these four cases.

The Modeling Tab

The Modeling tab page (Figure 2.4) gives choices for fitting all types of models, from simple regression and analysis of variance to complex nonlinear fits.

Fit model

Analyze > Fit Model fits one or more Y variables to a model of X variables. You can select the kind of model appropriate to your data from the menu of fitting personalities. The kinds of available personali-ties depend on the kind of responses you select.

See “Introduction to Model Fitting,” p. 165, “Standard Least Squares: Introduction,” p. 179, “Standard Least Squares: Perspectives on the Estimates,” p. 211, “Standard Least Squares: Exploring the Prediction Equation,” p. 233, and “Standard Least Squares: Random Effects,” p. 251 in the JMP Statistics and Graphics Guide for a detailed discussion of the Fit Model dialog and Fit Model analyses.

Figure 2.4 JMP Starter Modeling Tab

Matched Pairs

Analyze > Matched Pairs looks at the mean difference between two (or more) columns. It plots the points in a rotated scatterplot that presents them in terms of the differences and means of two variables.

Analyze > Fit Model

Analyze > Matched Pairs

Analyze > Modeling > Nonlinear

Analyze > Modeling > Time Series

Analyze > Modeling > Partition

Analyze > Modeling > Neural Net

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The chapter “Paired Data,” p. 151 in the JMP Statistics and Graphics Guide, discusses matched pairs and shows a unique graphical representation for that kind of analysis.

Fit Nonlinear

Analyze >Modeling > Nonlinear lets you specify nonlinear models, which are models that are nonlin-ear in their parameters. Nonlinear launches an interactive fitting facility. You orchestrate the fitting pro-cess as a coordination of three important parts of JMP: the data table, the Formula Editor, and the Nonlinear Fit platform. Details on the JMP data table and Formula Editor are in the chapters “Charac-teristics of Data,” p. 75, “JMP Data Tables,” p. 93, “The Tables Menu,” p. 119, and “Using the Formula Editor,” p. 143 in this book.

See the chapter “Nonlinear Regression,” p. 335 in the JMP Statistics and Graphics Guide for more about nonlinear fitting.

Time Series

Analyze >Modeling > Time Series looks at the relationship between a value and its lagged values. The data must be sorted and have equal intervals between time periods. Time Series plots the time series and autocorrelations, shows a variogram, and shows the spectral density.

“Time Series Analysis,” p. 499 in the JMP Statistics and Graphics Guide, discusses time series analysis and gives examples and statistical details

Partition

Analyze > Modeling > Partition recursively partitions rows into groups according to X values that associate with Y values. This partitioning creates a tree of partitions.

The factor columns (X’s) can be either continuous or categorical (nominal or ordinal). If an X is con-tinuous, then the splits (partitions) are created by a cutting value, which divides the sample into values below and values above this cutting value. If the X is categorical, then the sample is divided into two groups of levels.

The response column (Y) can be either continuous or categorical (nominal or ordinal). If Y is continu-ous, then the platform fits means, and creates splits which most significantly separate the means by the sums of squares due to the means differences. If Y is categorical, then the response rates become the fit-ted value. The most significant split can be determined by the largest likelihood ratio chi-square statis-tic.

Neural Net

Analyze >Modeling > Neural Net is a particular case of a back propagation feed-forward mulit-layer-perception neural net. The neural network is a set of nonlinear equations that predict output vari-ables (Ys) from input variables (Xs) in using layers of linear regressions. JMP fits the neural net using standard nonlinear least-squares regression methods.

14 2 Starting JMP—JMP Starter and the Analyze Menu

The Multivariate Tab

The Multivariate tab page introduces ways to look at continuous variables when they are considered as responses only; there are no factor or independent variables. Multivariate exploration with correlations and cluster analysis lets you look at many variables at the same time.

Figure 2.5 JMP Starter Multivariate Tab

Multivariate

Analyze > Multivariate Methods > Multivariate gives you ways to explore how several variables relate to each other. The platform begins with a standard correlation matrix. Options give additional options for correlations and other techniques for looking at several variables.

Hierarchical and K-Means Clustering

Clustering is a technique of grouping rows together that share similar values across a number of vari-ables. It is an exploratory technique to help you understand the clumping structure of your data. Analyze > Multivariate Methods > Cluster has options in its launch dialog that provide two different clustering methods:

Hierarchical Clustering is appropriate for small tables, up to several hundred rows. It combines rows in an hierarchical sequence portrayed as a tree. In JMP, the tree, also called a dendrogram, is a dynami-cally responding graph. You can specify the number of clusters after the tree is built.

K-means/Clustering is suitable for larger tables, up to hundreds of thousands of rows. It begins with a cluster seed point. Then the clustering process iterates between assigning points to the number of clus-ters you specify and recalculating cluster centers.

After the clustering process is complete, you can save the cluster assignments to the data table or use them to set colors and markers for the data table rows.

See the chapters “Correlations and Multivariate Techniques,” p. 365, and “Clustering,” p. 379 in the JMP Statistics and Graphics Guide for a detailed discussion of multivariate relationships and clustering.

Analyze > Multivariate Methods

Analyze > Multivariate Methods

> Multivariate

> Cluster

Analyze > Multivariate Methods> Cluster

Analyze > Multivariate Methods> Discriminant

> PLSAnalyze > Multivariate Methods

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Discriminant

Analyze > Multivariate Methods > Discriminant is a platform that provides a method of predicting the level of a one-way classification based on known values of the responses. The technique is based on how close a set of measurement variables are to the multivariate means of the levels being predicted. Optionally, you can do stepwise discriminant analysis.

PLS

Analyze >Multivariate Methods > PLS fits models using the partial least squares (PLS) method that balances the two objectives of explaining response variation and explaining predictor variation. The PLS techniques work by extracting successive linear combinations of the predictors, called factors (also called components or latent vectors) that address one or both of these two goals. The PLS platform in JMP also enables you to choose the number of extracted factors by cross validation, which involves fit-ting the model to part of the data and minimizing the prediction error for the unfitted part.

The Survival Tab

Survival data contain duration times until the occurrence of a specific event and are sometimes referred to as event-time response data. The event can be failure, such as the failure of an engine or death of a patient.

Figure 2.6 JMP Starter Survival Tab

Analyze > Survival and Reliability can analyze survival data several ways.

Survival and Reliability (univariate survival analysis) is done using product-limit life table survival computations with estimation of Weibull, log normal, and exponential parameters.

Parametric Regression analysis tests the fit of an exponential, Weibull, or log normal distribution.

Proportional Hazards regression analysis fits a Cox model.

Recurrence analysis looks at the age of a system when it requires a repair. A system can have multiple repairs, each with its associated age, and is followed until it is no longer in service. A typical system might be some component of an engine or appliance.

Note: You can also use the Nonlinear platform to handle nonlinear models with loss functions for other parametric survival modeling.

Analyze >Survival and Reliability> Survival/Reliability

Analyze >Survival and Reliabi;ity> Fit Parametric Survival

Analyze >Survivall and Reliability>Fit Proportional Hazards

Analyze >Survival and Reliability> Recurrence Analysis

16 2 Starting JMP—JMP Starter and the Graph Menu

See the chapter “Survival and Reliability Analysis I,” p. 433 in the JMP Statistics and Graphics Guide, for more about the Survival platform.

JMP Starter and the Graph MenuThe Graph menu on the main menu bar lists commands that produce plots and charts of summarized data, a three-dimensional spinning plot, contour and ternary plots, and plots used in statistical quality control. The JMP Starter has separate tabs for standard types of charts and plots, and for quality control tools.

The Graphs Tab

The Graphs tab, shown in Figure 2.7, describes seven general purpose commands in the Graph menu. Other specialized Graphics menu commands are tools for statistical quality control and are discussed in the next section, “The QC Tab,” p. 18. The last two commands (not shown on the JMP starter) supple-ment the Fit Model platform and can also be found as platform options there.

The Graphs tab page launches the following plots and charts:

Chart

Graph > Chart gives a chart for every numeric Y specified where the Ys are statistics to chart. The X val-ues are always treated as discrete values. Chart automatically updates when values change in the current data table and you then activate the chart window.

Overlay Plot

Graph > Overlay Plot gives an overlaid plot of a single X column and all numeric Y variables specified in the Overlay Plot launch dialog. Optionally, the curves can be shown as separate plots for each Y with a common X axis. Plots can be modified with range and needle options, color, log axes, and grid lines. Overlay Plots automatically update when you change values in the current data table and then activate the Overlay Plot report window.

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Figure 2.7 JMP Starter Graphs Tab

Spinning Plot

Graph > Spinning Plot produces a three-dimensional view of data and, optionally, an approximation to higher dimensions through principal components. The plot is a spinnable display of the values of numeric columns in the current data table. The Spinning Plot platform displays three variables at a time from the columns you select.

To help capture and visualize variation in higher dimensions, the Spinning Plot platform displays a biplot representation of the points and variables when you request principal components.

Contour Plot

Graph > Contour Plot constructs contours of a response in a rectangular coordinate system. A contour plot required two variables for the X and Y axes and a third variable (denoted Y in the Contour Plot Launch dialog) for contours.

Profiler

The output report that results from Analyze > Fit Model has an option that produces a prediction pro-filer for the effect variables in a model, with one or more Y variables. The prediction profiler displays prediction traces for each X variable. A prediction trace is the predicted response as one variable is changed while the others are held constant at the current values. If you save the prediction equations for a model, you can access the prediction profiler for that data table directly with Graph > Profiler without rerunning the model.

Contour Profiler

The output report that results from Analyze > Fit Model has an option to do interactive contour pro-filing. This is useful for optimizing response surfaces graphically. This interactive plot lets you adjust both multiple responses and multiple factors simultaneously and shows acceptable factor setting regions

Graph > Chart

Graph > Overlay Plot

Graph > Spinning Plot

Graph > Contour Plot

Graph > Ternary Plot

Graph > Profiler

Graph > Contour Profiler

Analyze > Fit Model

Analyze > Fit Model

18 2 Starting JMP—JMP Starter and the Graph Menu

for response values you specify. If you save the prediction equations for a model, Graph > Contour Profiler accesses the contour profiler for that data table directly without rerunning the model.

More information is found in the chapters “Bar, Line, and Pie Charts,” p. 519, “Overlay Plots,” p. 533, “Three-Dimensional Viewing,” p. 543, and “Contour Plot,” p. 559 in the JMP Statistics and Graphics Guide. The chapter “Standard Least Squares: Exploring the Prediction Equation,” p. 233 in the JMP Statistics and Graphics Guide, covers the Profiler and the Contour Profiler.

The QC Tab

The QC tab page, shown in Figure 2.8, accesses the commands on the Graph menu that are used in sta-tistical quality control. Also, the Capability item on this tab page accesses the Distribution command found on the Analyze menu.

Control Charts

Graph > Control Charts offers a variety of ways to analyze and monitor process data with control charts. It displays control charts that update dynamically as samples are received. Control charts are a graphical and analytic tool for deciding whether a process is in a state of statistical quality control. Shewhart control charts are broadly classified into control charts for variables and control charts for attributes. Moving average charts and cumulative sum (cusum) charts are special kinds of control charts for variables.

See the chapters “Statistical Control Charts,” p. 567, and “Shewhart Control Charts,” p. 589 in the JMP Statistics and Graphics Guide for a discussion of statistical quality control and examples that use differ-ent kinds of control charts.

Figure 2.8 JMP Starter QC Tab

Graph > Control Chart

Graph > Variability Chart

Graph > Pareto Plot

Analyze > Distribution

Graph > Diagram

Accesses a library of JSL scriptsthat compute and plot OperatingCharacteristic curves.

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Variability Chart

Graph > Variability Chart performs a variability analysis, also called a Gage R&R analysis. In a Gage R&R analysis, a number of supposedly identical parts are taken from a production line. Each one is measured by several operators a number of times using different measuring instruments. You use vari-ability charts to examine the magnitudes of the variation due to operators, parts, and instruments.

The chapter “Variability Charts,” p. 615 in the JMP Statistics and Graphics Guide, documents the plots and reports produced by the Variability platform.

Pareto Plot

Graph > Pareto Plot creates a bar chart (Pareto chart) that displays the severity (frequency) of prob-lems in a quality-related process or operation. A Pareto chart is a statistical quality improvement tool that shows frequency, relative frequency, and cumulative frequency of problems. The bars are ordered by frequency in decreasing order from left to right, which makes a Pareto chart useful for deciding what problems should be solved first.

The chapter “Pareto Plots,” p. 633 in the JMP Statistics and Graphics Guide, discusses simple before-and-after Pareto charts and two-way comparative Pareto charts.

Ternary Plot

Graph > Ternary Plot creates ternary plots, which display the distribution and variability of three-part compositional data. Examples of compositional data are the proportion of sand, silt, and clay in soil, or the proportion of three chemical agents in a trial drug. You can use data expressed in proportions, or in absolute measures. The Ternary Plot platform converts absolute values to proportions.

The ternary plot in JMP also has a contour feature, which means you can use a response measure at each point to form a contour plot within the ternary structure.

See the chapter “Ternary Plots,” p. 647 in the JMP Statistics and Graphics Guide, for details and exam-ples of ternary plots.

Capability Analysis

The Capability item accesses the Distribution command on the Analyze menu, which has a platform option for capability analysis. The Capability Analysis option gives a capability analysis for quality control applications. A capability study measures the deviation of a process from given specification limits. A dialog prompts you for Lower Spec Limit, Upper Spec Limit, and Target. These are optional fields, and only those fields you enter are part of the resulting Capability Analysis table. Optionally, you can enter a known value for sigma, the process standard deviation.

See the chapter “Univariate Analysis,” p. 25 in the JMP Statistics and Graphics Guide, for examples of capability analysis.

Diagram

The Diagram platform is used to construct Ishikawa charts, also called fishbone charts, or cause-and-effect diagrams. These charts are useful to organize the sources (causes) of a problem (effect),

20 2 Starting JMP—JMP Starter and the DOE Menu

perhaps for brainstorming, or as a preliminary analysis to identify variables in preparation for further experimentation.

OC Curves

The OC Curves item accesses a set of JSL scripts that compute and plot operating characteristic curves. The chapter “Statistical Control Charts,” p. 567 in the JMP Statistics and Graphics Guide, gives details about how to run the OC curves scripts. The JMP Scripting Guide is the reference for scripting.

JMP Starter and the DOE MenuThe DOE tab page, shown in Figure 2.9, lists the commands on the DOE main menu. These com-mands construct classical and custom experimental designs and save them in a JMP table. Selecting a design type presents an environment for describing the factors, responses and other specifications needed to make a design of that type.

Figure 2.9 JMP Starter DOE Tab

The following list briefly describes the DOE menu commands.

Custom Design

DOE > Custom Design generates a D-optimal design for the design you specify and, optionally, an I-optimal design for repsonse surface designs. Custom designs can have continuous factors and categor-ical factors with arbitrary numbers of level, interactions, mixture ingredients. A design can also have covariates, which are factors that already have unchangeable values; a design is built around the covari-ate. There is also a facility for constructing custom design models, including interaction terms and polynomial terms for continuous factors. Factors can have inequality constraints. You can also specify the number of experimental runs, which can be any number greater than or equal to the number of terms in the model.

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Screening Design

DOE > Screening Design allows you to specify continuous factors, two- and three-level factors, and categorical factors. It creates a table of classical screening designs from which you can pick.

Response Surface Design

DOE > Response Surface Design lets you define a number of continuous factors. This environment then offers a table of classical response surface designs from which to choose.

Full Factorial Design

DOE > Full Factorial Design lets you specify a set of continuous and categorical factors with arbitrary numbers of levels. JMP creates the design that has all possible combinations of those factors.

Taguchi Arrays

DOE > Taguchi Arrays guides you through the definition of signal and noise factors. The signal factors form the inner array and the noise factors form the outer array. The inner and outer array designs are the traditional Taguchi orthogonal arrays such as L4, L8, or L16.

Mixture Design

DOE > Mixture Design lets you define a set of factors that are ingredients in a mixture. JMP displays a dialog for choosing among several classical mixture design approaches such as simplex, extreme vertices, and lattice. For the extreme vertices approach, you can supply a set of linear inequality constraints lim-iting the geometry of the mixture factor space.

Augment Design

DOE > Augment Design can modify existing designs by adding center points, replicating the design, creating a foldover design, or adding runs to the design using a model with more terms than the origi-nal design.

Sample Size and Power

DOE > Sample Size and Power computes power, sample size, or the effect size to be detected for a given alpha and error standard deviation. You supply two of these values and the Sample Size and Power feature computes the third. If you supply only one of these values, Sample Size and Power dis-plays a plot of the other two. This feature is available for single sample, two sample, and k sample situa-tions.

See JMP Design of Experiments for documentation of the DOE facility in JMP.

22 2 Starting JMP—JMP Starter and the Tables Menu

JMP Starter and the Tables MenuThe Tables tab (Figure 2.10) lists the commands on the Tables Menu. See the chapter “The Tables Menu,” p. 119 in this book, for detailed descriptions and examples for each Tables menu command.

Figure 2.10 JMP Starter Tables Tab

The following list briefly describes the Tables menu commands.

Tables > Summary creates a JMP window that contains a summary table, which summarizes columns from the active data table, called its source table. The summary table has a single row for each level of a grouping variable you specify. Optionally, you can add columns of summary statistics to this table and request summary statistics.

Tables > Subset creates a new data table formed by the selected rows and columns or by a random sample in the active spreadsheet.

Tables > Sort sorts a data table by one or more columns.

Tables > Stack creates a new data table by stacking specified columns from the active data table into a single column in the new data table.

Tables > Split creates a new data table by dividing one or more specified columns into several new col-umns according the values of one or more ID variables.

Tables > Transpose creates a new data table that has the columns of the active table as its rows and the rows of the active table as its columns.

Tables > Concatenate creates a new data table from two or more open data tables by combining them end to end.

Tables > Join creates a new data table by merging (joining) two tables side by side.

The chapter “The Tables Menu,” p. 119 in this book discusses the Tables menu commands in detail and gives examples.

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The JMP Starter Index TabThe Index tab page on the JMP Starter window is an alphabetical list of statistical tools in JMP. When you click on an item, a brief description of that method is displayed with instructions for generating results. There is also help for these items using the Help main menu.

Figure 2.11 JMP Starter Index Tab

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The Menu Bar

This chapter is a reference chapter. It contains overview documentation for all the commands in the JMP main menu bar. It is organized for easy reference. The menus and commands are presented in the order they appear on the menu bar. Each menu title appears with the page number to help you locate specific commands quickly.

Chapters later in this book describe the Rows and Cols menus in detail, give more information and examples of Tables menu commands, explain the Formula Editor with examples of how to use it, and cover details about report windows, personalizing JMP, and JMP web capabilities.

If you prefer to begin using JMP with a hands-on approach, you can skip this chapter and begin with “JMP Data Tables,” p. 93, or refer to the Introductory Guide for tutorials.

When the JMP application opens, the first thing you see is the main menu bar and the JMP Starter window (discussed in the previous chapter, “Starting JMP,” p. 7).

Default tool bars appear beneath the main menu. You can drag the tool bars and dock them on any edge of the JMP window or tear them off and place them in any convenient location (Windows only).

Under Microsoft Windows, the Rows, and Cols menus appear only when a data table is open.

Windows Main Menu - No Open Data Table

Windows Main Menu - Data Table Open

Macintosh Main Menu

3Contents

The File Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27The Edit Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32The Tables Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37The Rows Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41The Cols Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 The DOE Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49The Analyze Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51The Graph Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59The Tools Menu and Tool Bars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63The View Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65The Window Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68The Help Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71The Layout Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

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The File Menu

The File menu has commands that perform file management or affect the JMP environment. Related commands are grouped according to their functions. These groups contain commands that

• open and close data files and create new files

• save data tables

• access data from other databases

• set global options

• print data and report windows

• end a JMP session.

You modify a data table by working with its data table view in an active window. In descriptions of menu commands, data table, table, and spreadsheet are used synonymously unless specifically noted oth-erwise.

Under Windows, the New command gives a submenu to create a new data table or a new script win-dow. Use Shift-New on the Macintosh to see the New > Script command.

Macintosh Shift-FileMacintosh

Macintosh Option-File

28 3 The Menu Bar—The File Menu

Data Table

opens an empty untitled data table with one column labeled Column 1. To create more columns, use the Rows and Columns popup menu commands and type or paste data into the data table. Use Save As from the File menu to name the table and save it as a JMP file. Use the Open com-mand to reopen a JMP data table after it has been saved.

Script

opens a new empty script window, which is an editable text window to enter and submit JSL (JMP Scripting Language) commands.

Note: On the Macintosh the New command creates a new JMP table. Shift-File displays the New Script command.

The Open command displays a specialized open file dialog to locate the file you want to open and spec-ify the file format of the incoming file. The Open dialog displays information appropriate for the incoming file type. The Open command directly reads JMP data tables, JMP journal files, JMP script files, SAS data sets, SAS transport files, text files with any column delimiter, Excel® files, and Windows CMD files. CMD (.JMPCMD) files are created when you modify the JMP main menu and save the results.

Note: On the Macintosh, JMP opens SAS version 6 data sets Later versions of SAS are not supported on the Macintosh. However, JMP on the Macintosh can open any SAS data that has been saved in SAS transport format.

See the chapter “JMP Data Tables,” p. 93, for details and examples of using the Open command.

The Close command closes the active window. When you close a data table window while any of its analysis windows are open, a dialog asks if you want to hide the data table only or close it and all related analysis windows. Also, when you close the data table, a dialog asks you whether to save or discard changes to the table. The Close command is the same as clicking the close box of the active window. Close does not appear on the File menu unless a window is open.

Note: On the Macintosh, the Close command on the File menu closes the active window. Option-File displays the Close all Data Windows command, which closes all window of the same type as the active window.

Macintosh Option-File

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The Import as Data command writes an open script file (JSL file) it to a JMP data table. This is partic-ularly helpful when extracting tabular information from a web page. To do this, highlight the informa-tion on a web page and copy it. Then open a new script window in JMP, and paste it to the script window. Highlight the script text (usually a table of values) and use the Import as Data command to create a new JMP table that contains that information. The import method conforms to the preference settings you have for default import setting.

The Save command writes the active data table to a file. If the data table has been saved before, it is rewritten to the same file name and location, replacing the old information. If the data table is new, the Save command has the same effect as the Save As command. Saving a data table does not automati-cally close it.

Note: JMP analysis windows are not saved with the data table. However, you can use JMP tools to copy reports to other applications or you can use the journaling and layout features to save reports in text or word processing format.

The Save As command writes the active data table to a file after prompting you for a name and file type. It can save the data table as a JMP file, convert it to SAS Transport file, save as a Version 7 SAS data set (Windows only), save it as a text format file, or save in any database format available on your system. See the chapter “JMP Data Tables,” p. 93, for more information.

The Revert command restores the current data table to its condition when it was last saved. Revert is dimmed on the File menu unless a data table has been edited.

On the Macintosh, Shift-File displays the Save Selection As command, for saving highlighted graph-ics from an analysis report window. To highlight output, select the area selection (large plus) tool and click or drag. When you choose Save Selection As, a dialog lets you choose a Macintosh picture, PNG, or JPEG graphics format. Under Windows, the Save Selection As command is found in the Edit menu.

The Database command opens a connection to any database on your system that has an ODBC driver. File > Database initially displays the Database Open Table dialog, which lets you connect to or disconnect from a specific database source. When one or more database connections are made, the dia-log lists the connections and the tables in the selected database file or directory. Optionally, use the Advanced button on the dialog to write SQL statements to open specific subsets of a table. Use the

Macintosh Shift-File

30 3 The Menu Bar—The File Menu

dialog shown by the Database > Save Table command to save an open database table. There is a more detailed example in the chapter “JMP Data Tables,” p. 93.

Under Windows, the Internet Open command lets you open an internet browser within the JMP envi-ronment. This command displays an open dialog for you to enter a URL, and options to Browse, Edit HTML, Edit HTML with tags stripped, Open a JMP file, or Extract HTML Table as JMP file.

The Preferences command displays tab pages with the following sets of options:

• General has check boxes for JMP start-up options, analysis destination, information to include

on output, report table style, and others.

• Background Color (Windows only) sets the background color of the report window. You can select from a standard palette of colors or select a color on a color wheel and add that color to the color palette.

• Fonts displays a list of fonts to set the style for text, headings, and titles.

• Graphic Formats (Windows only) has check boxes for selecting the graphic format when you copy or drag graphics from report windows. There are also popup menus to select Metafile, JPEG, or PNG as the graphic format when saving RTF files and HTML files.

• Communications Settings (Windows only) has settings for real-time data collection.

• File Location lets (Windows only) you enter a default path for standard JMP files such as the sample data file, Help file, preferences file, and others.

• Platforms lists the analysis and graph platforms, with appropriate options for each.

• Text Import/Export lets you set default options, such as field and line delimiters, for reading and writing text files.

See “Personalizing JMP,” p. 237 for further discussion of preferences.

Note: On the Macintosh OS 9 and earlier, the Preferences command is located in the Edit menu. On the Macintosh OS X, Preferences is on the menu for the JMP application.

The Print command prints the active window. It displays the standard dialog for printing. The appear-ance of the dialog depends on your operating system and printer driver.

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The Print Preview command (Windows only) displays the active window in the form it will print. Print Preview is dimmed on the File menu unless there is an open window.

The Print Setup command (Macintosh only) displays the standard dialog for setting printed page char-acteristics. The form of the dialog depends on your operating system and current printer driver.

The Send command (Windows only) submits the open window to the mail facility you define.

Under Microsoft Windows, the Recent Files command has a submenu that lists the JMP tables most recently opened. When you click on a table’s name in this list, JMP opens the table.

The Exit command (Windows) or Quit command (Macintosh) closes all JMP windows (prompting you to save changes) and quits the JMP application. On the Macintosh OS X, the Quit is the last com-mand in the JMP application menu.

32 3 The Menu Bar—The Edit Menu

The Edit Menu

The Edit menu contains standard commands. These commands operate on rows and columns that are selected, on selected areas of reports, and on selected formula elements in the Formula Editor.

In the data table, you can select rows, columns, or both rows and columns at the same time, as shown in Figure 3.1. Edit commands operate on entire rows if no columns are selected. Likewise, they operate on whole columns if no rows are selected. When both rows and columns, Edit commands affect the subset of values defined by the intersection of those rows and columns.

Figure 3.1 Example of Selected Rows and Columns

Macintosh Option-Edit

Macintosh Shift-Edit

selected rows selected column

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To select a row in a data table, click the space that contains the row number. To select a column, click above the column name either at the top of the column or in the Columns panel to the left of the data table. To select multiple rows or columns, drag across them or Shift-click the first and last rows or col-umns of a range. To make a discontiguous selection, use Control-click (Windows) or Command-click (Macintosh) to highlight the rows and columns. To select a block of cells formed by the intersection of rows and columns, drag the cross cursor diagonally across the subset of cells.

Note: You can also select special subsets of rows using the Row Selection command in the Rows menu, described under “The Rows Menu,” p. 41 later in this chapter.

The Edit menu in conjunction with JMP tools can be used to copy all or part of active analysis report windows. See “The Tools Menu and Tool Bars,” p. 63, for more information.

The Undo command cancels the effect of the most recent reversible Edit, Rows, or Columns com-mand. If Undo is available its selection in the Edit menu appears as Undo command where command is the most recent action. Most destructive data table operations (such as cut, paste, or delete rows) are reversible. Undo dims when the most recent command is irreversible.

The Redo command reverses the effect of the Undo command.

The Cut command copies selected fields from the active data table to the clipboard and replaces them with missing values. It is equivalent to Copy, then Clear. You can also use the Cut command to copy all or part of a report window. However, Cut works like Copy in graphical displays (it does not clear the copied image).

The Copy command copies the values of selected data cells from the active data table to the clipboard. Copy copies entire rows when no columns are selected. Likewise, whole columns are copied when no rows are selected. If you select both rows and columns, Copy copies the subset of cells defined by their intersection.

The Copy command captures graphical displays or text reports defined by the area selection tool (large plus) in the Tools menu.

Note: Data you cut or copy to the clipboard can be pasted into JMP tables or into other applications. Pictures can be pasted into any application that accepts graphics.

If you use the Shift key and select Copy (Option-Copy on the Macintosh), JMP does a Copy with Labels, which preserves the data table’s column labels in the copied image. Then use Shift-Paste

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34 3 The Menu Bar—The Edit Menu

(Option-Paste with Labels on the Macintosh) to paste the information into a new column with the column name intact.

The Copy As Text command copies all text from the active report window (no graphical displays) as unformatted text on the clipboard. On the Macintosh, hold down the Shift key to see Copy as Text on the Edit menu.

The Paste command pastes information from the clipboard to the selected area in a JMP data table. Paste can be used with the Copy command to duplicate rows, columns, or any subset of cells defined by selected rows and columns.

If you use the Shift key and select Paste (Option-Paste on the Macintosh) JMP does a Paste with Labels, which uses the first line of information on the clipboard as column headers.

The Clear command clears all selected cell values from the active data table and replaces them with missing values. The values are not copied to the clipboard.

When a data table is the active window, the Select All command selects (highlights) all the rows in the current data table. When an output, a layout, or a journal is the active window, Select All selects all the objects in the window.

Under Windows, the Save Selection As command saves highlighted portions of an analysis window in a graphic format. To highlight output, select the highlighter tool (shown above) and click or drag the area you want. The Save Selection As dialog lets you choose between PNG (.PNG), JPEG (.JPG), and WMF (.WMF) graphic formats.

Note: On the Macintosh, the Save Selection As command appears in the File menu when you hold down the Shift key.

Macintosh Shift-Edit

Macintosh Option-Edit

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Run Script submits a text file containing JMP Scripting Language (JSL) commands. You create a JSL file two ways:

• Under Windows, select Script from File > New and manually key in JSL commands.

• Complete an analysis and then use Save Script to Script Window from a platform popup menu. You can save a script that describes the whole analysis window or use a Save Script to Script Window command for a single part of analysis within the output window.

To submit a portion of a script, first drag to highlight the portion you want and then use Edit > Run Script. If no script commands are highlighted, the entire script is submitted.

See the JMP Scripting Guide for details on JSL syntax.

The Search command displays the submenu shown above. It gives you the ability to find and replace text in data tables (including column names) in the usual way found in most word processing and edit-ing programs. Find and replace actions deal only with character strings; numbers are treated as text and appear to the Search command as they appear in the data table. All dialog actions also respond to the keystroke shortcuts shown on the Search popup menu. For multiple search actions, it is sometimes much more convenient to use the keystroke shortcuts instead of the Search dialog. See the chapter “JMP Data Tables,” p. 93, for more details about search commands.

Note: the Undo command works only with Replace, and Replace and Find Next.

Under Windows, when a script window is open, the Go to Line command displays a dialog that lets you enter the line number of the script, then sets the edit focus on that line.

The Journal command copies all information from the active data table or report window to an open Journal window. The Journal window appears identical to the Report window. Each subsequent Journal command appends the information from the active JMP window to the current journal file. You can journal as much information as you need into a single open journal window. The area selection tool (large plus tool) and the Cut and Paste commands in the Edit menu let you cut and paste sections of JMP analysis reports into the Journal window.

36 3 The Menu Bar—The Edit Menu

Note: You can open or close outline levels in a Journal window.

To save a Journal use the Save As command and give a name and disk location for that Journal file. By default, the suffix .JRN is automatically appended to the file. However, you can save the journal file as .PNG, .JPG, .WMF (Windows metafile), .TXT (text), .HTM, or .RTF (rich text format), and reopen a journal file with many commonly used word processors.

The Layout command creates a new window that contains all information from the report window. Under Microsoft Windows, when a Layout window is the active (front-most) window, the Tables, Rows, and Cols menus are replaced with the Layout menu. The Align command in the Layout menu has standard draw commands to Group and Ungroup selected areas of the Layout window. Ungrouped pieces of the report can be moved anywhere on the report surface to rearrange them.

Note: You can open or close outline levels in a Layout window only until you have ungrouped layout elements.

The commands in the Layout menu are discussed further in Chapter 10, “Report Windows and Sur-face Features” chapter in this book.

Under the Windows operating systems, the Customize command has a submenu to customize Menus or Toolbars. These commands each display a window that shows a sketch of all main menus or tool-bars. These facsimiles are completely interactive. You can drag any command or toolbar icon out of its position and place in any other position. Or, you can drag a command or icon out of its position and into blank area to eliminate it from showing on the application. Revert to Factory Defaults resets the menus and toolbars to the arrangement when you first installed JMP.

See “Personalizing JMP,” p. 237 for further discussion and examples of customizing JMP menus.

On the Macintosh, OS 9 and earlier systems, the last command in the Edit menu is the Preferences command. On the macintosh OS X system, the Preferences command is on the JMP application menu. Microsoft windows displays Preferences on the File menu. See the Preferences command described previously in the File menu section for more information.

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The Tables Menu

The Tables menu commands (or Tables toolbar buttons) modify or create a new JMP table from one or more existing tables. This section gives you an overview of each Tables menu command. See the chapter “The Tables Menu,” p. 119, for details and examples of each Tables command.

The Summary command creates a JMP window that contains a summary table. This table summarizes columns from the active data table, called its source table. The summary table has a single row for each level of a grouping variable you specify. When there are several grouping variables, the summary table has a row for each combination of levels of all grouping variables.

The summary table is linked to its source table; when you highlight rows in a summary table, the corre-sponding source table rows highlight, as shown in Figure 3.2.

Figure 3.2 Summary table with Special Columns Menu

The illustration in Figure 3.2 shows a source table called Companies with a summary table grouped by type of company. The Type column in the source table has the values “Computer” and “Pharmaceut”.

38 3 The Menu Bar—The Tables Menu

The Companies By (Type) summary table has two rows that show counts of 20 computer companies and 12 pharmaceutical companies in the N Rows column.

The Subset command produces a new JMP table that is a subset of the active data table. If you click Selected Rows in the Subset dialog, shown Figure 3.3, the new table has the rows and columns defined by the highlighted rows and columns from the active data table. Highlight rows by

• selecting them on the data table

• highlighting histogram bars or points on a plot generated by the data table

• using one of the Row Selection commands from the Rows menu

• selecting one or more rows in a summary table produced by the Summary command (described previously) to define a subset in its source table.

Figure 3.3 The Subset Dialog

If you click the Random Sample radio button, the sample is a random sample of the active data table. Enter the sample size you want as a number or a proportion in the Sampling Rate text edit box.

Note: When you check the Link to original data table check box on the Subset dialog, the subset table that results and any plot or graph of that subset table remain linked to the original table. Highlighting rows in this kind of subset table highlights the corresponding rows in the original table and in all its plots and graphs. If you hold down the Shift key and select Subset, a linked subset table is automati-cally generated.

Also, if there are formulas in any columns check boxes appear on the Subset dialog to Copy Formula from the source table, and Suppress Formula Evaluation of saved formulas when necessary to main-tain data integrity. If the Copy Formula box is not checked, column formulas are not transferred to the new data table.

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The Sort command sorts a JMP data table by one or more columns. The Sort command displays a dia-log for you to specify columns as sort fields. Sorting a table has these options:

• The order in which you build a list of sort variables establishes the sorting order. Each variable is sorted within the previous variable in the sort list.

• You can sort any column in the sort list in either ascending or descending order. By default, columns sort in ascending order.

• If there are formulas in any columns other than the Sort columns of the data table to be sorted, the Sort dialog shows a Copy Formula and Suppress Formula Evaluation check boxes. If the Copy Formula box is not checked, column formulas are not transferred to the new data table.These check boxes don’t appear if there are no formulas in the source table.

• By default, Sort creates a new data table. However, you have the option to sort in place; that is, the sorted table replaces the original table. This option to replace the original table with the sorted table is not available if there are any open report windows generated from the original table.

The Stack command creates a new data table from the active table by stacking specified columns into a single new column. The values in other columns are preserved in the new data table. In addition, Stack creates an ID column that identifies each row in the new table with values that are the corresponding column names from the original table.

The table on the left in Figure 3.4, has columns called Column1, Column2, and Column3 that stack to produce the table on the right. The stacked column, _Stack_, has a single column with values from the original three columns. The _ID_ variable has the original column names as its values.

Figure 3.4 Example of Stacked Columns

Also, if there are formulas in any columns other than the Stack and ID columns of the data table to be stacked, the Stack dialog shows Copy Formula and Suppress Formula Evaluation check boxes. If the Copy Formula box is not checked, column formulas are not transferred to the new data table.

40 3 The Menu Bar—The Tables Menu

The Split command creates a new data table from the active table by splitting one or more columns to form multiple columns. The new columns correspond to the values (levels) of an ID variable. Split requires one or more columns whose combined values identify each row in the new table. Optionally, the values in the other columns can be either dropped or preserved in the new data table.

Stack does the reverse of Split. If you use the Split command on the right-hand table, in Figure 3.4 (nine rows), the result is the original table on the left, with three rows.

Also, if there are formulas in any columns other than the Split and ID columns of the data table to be split, the Split dialog shows Copy Formula and Suppress Formula Evaluation check boxes.

The Transpose command creates a new JMP table that is a transpose of the active data table. That is, the columns of the original table are the rows of the new table, and the original table rows are the new table’s columns. The new table has an additional column called Label, whose values are the column names of the active table. If there is a label column in the active table, the values of that column are col-umn names in the new transposed table. If there is no label column, the column names in the trans-posed table are Row1, Row2, …Rown, where n is the number of rows in the original table.

The Concatenate command appends two or more tables end to end. The Concatenate dialog lists all open JMP data tables. Select the tables to concatenate and click Concatenate on the Concatenate dia-log. Concatenate creates a new untitled data table that consists of all rows in the first selected table fol-lowed by all rows from the second table, and so on.

The arrangement of the new data table depends on the columns in the original tables. Concatenate creates one column in the new table for each unique column name in all appended tables. Column names that are the same in multiple tables stack into a single column. See the chapter “The Tables Menu,” p. 119, for examples of concatenating tables.

Note: The Concatenate dialog also has a Save and Evaluate Formulas check box. If this box is not checked, column formulas are not transferred to the new data table.

The Join command creates a new data table by joining two tables side by side, or by updating one data table with values from another table. Tables can be joined

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• by row number

• by matching the values in one or more columns that exist in both data tables

• in a Cartesian fashion where all values in a column of one data table are merged with all values in a column of another table.

Optionally, you can include all nonmatches in the new table or eliminate nonmatches that occur in either of the original tables. Also, you can include all columns from both tables in the joined table or include only a subset of columns you specify.

See the chapter “The Tables Menu,” p. 119, for details and examples of the Join command.

Also, if there are formulas in any columns of the data table to be joined, the Join dialog shows Copy Formula and Suppress Formula Evaluation check boxes. If the Copy Formula box is not checked, col-umn formulas are not transferred to the new data table.

The Rows Menu

You can access commands that affect highlighted rows from the Rows main menu, the red triangular popup icon on the Rows panel to the left of the data table, or from the triangular popup menu in the upper-left corner of the data table above the row numbers.

Rows menu commands can

• exclude highlighted rows from further analysis

• hide highlighted points in current graphical displays

• assign special colors or markers for graphical display of highlighted points

• select all rows or selecting rows assigned specific row state characteristics

42 3 The Menu Bar—The Rows Menu

• assign automatic color and marker

• add or move highlighted rows

• delete highlighted rows.

To select (highlight) a row, click the space in the row number area to the left of the row. This area is called the row selection area. To highlight multiple contiguous rows, drag down the row numbers or Shift-click the first and last rows of a desired range. To make a discontiguous selection, use Con-trol-click under Windows or Command-click on the Macintosh to select the rows you want.

Rows are also highlighted (and selected) when you highlight points or bars in a corresponding graphical display. Because the JMP data table and any graphical display of that table are linked, selected points and rows always highlight simultaneously.

To deselect all rows, click in the upper-left corner of the data table. To deselect a single row, Con-trol-click that row’s number (use Command-click on the Macintosh).

Exclude/Unexclude is a toggle command used to exclude selected rows from statistical analyses. Data remain excluded until you choose Exclude/Unexclude again for selected rows. Excluded rows are marked by the “do not use” symbol (circle with a diagonal bar across it) showing in the row number area.

Hide/Unhide is a toggle command that suppresses the display of points in all scatterplots. To hide data, highlight their points in any plot or in the data table and select Hide/Unhide. Data remain hidden until you choose Hide/Unhide again for selected rows. Hidden rows are marked by the mask symbol showing in the row selection area, next to the row number.

Warning: Hidden points can be included in statistical analyses although they do not display in plots. Likewise, points can be excluded from an analysis but not hidden. These conditions could cause mis-leading plots and analyses.

Label/Unlabel is a toggle command that labels or identifies points on all scatter plots. To label points, highlight them in the data table or in any plot, and select Label/Unlabel. By default, the row number is used as the label value on plots. However, if you designate a column in the data table as a Label col-umn, its values show as labels in plots instead of the row numbers. Data remain labeled until you choose Label/Unlabel again for selected rows.

Exclude/Unexclude, Hide/Unhide, and Label/Unlabel status are row state characteristics. Active row states are saved when you save the data table, and can be saved permanently with the data table in a spe-cial row state column. Figure 3.5 shows examples of how active Hide, Exclude, and Labeled rows appear on a data table.

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Figure 3.5 Examples of how Labeled, Hidden, and Excluded Row States in a Data Table

Note: You can have multiple columns as label variables. The value showing on a labeled variable in a plot is the values of all label variables separated by commas. For more details about row states, see the chapter “JMP Data Tables,” p. 93.

Colors changes highlighted points in all scatterplots to the colors you choose. To color a group of points, select the appropriate rows in the data table or select the points in a plot. Then choose a color from the Colors palette in the Rows menu.

There are 65 colors including shades of gray for distinguishing data points in JMP. The default color is black.

Markers assigns a plot character to replace the standard points in scatterplots and spinning plots. To assign one of the markers, select rows in the data table or select the corresponding points in a plot and choose a marker from the Markers palette. The default marker is a dot.

Note: All row state conditions (including selection status) can be saved permanently with the data table as active row states or saved in a special row state column in the data table.

The Next Selected command locates the first selected row after the current row and makes it blink briefly. The current row is the first row when you open a data table. The current row changes to the

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most recent row you edited or identified by a Next Selected or Previous Selected command. To set the current row, Alt–click (Option–click on the Mac) anywhere in a row. Each time you choose the Next Selected command, the next selected row is found and blinks. A beep signals when the last selected row is located.

The Previous Selected command behaves the same as Next Selected but locates the first selected row before the current row and makes it blink briefly.

The Row Selection command has a submenu with options for selecting

• a specific row number

• all rows in a data table

• a subset of rows based on the excluded, hidden or labeled row states

• a subset based on criteria you enter

• randomly select rows.

See the section called Selection Status, p. 87 in the “Characteristics of Data” chapter for details about the Row Selection command.

The Clear Row States command clears all active row states in the data table. All rows become included, visible, unlabeled, and show in plots as black dots. It does not affect row states saved in row state columns.

The Color or Marker by Column command displays a dialog with a list of the variables in the current data table. JMP uses the levels of the variable you select to color or mark points in plots.

The Row Editor command displays the dialog window that is useful for browsing the columns of a data table one row at a time. When the row editor appears, its shows information for the first selected row. To navigate the data table, use the buttons on the Row Editor, or the commands in the menu accessible at the top of the dialog. For more information about using the row editor, see Entering and Editing Data, p. 106 in the “JMP Data Tables” chapter.

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The Add Rows command displays a dialog that prompts for the number of rows to add and to specify their location in the table. Radio buttons let you add rows at the beginning of the table (At start), the end of the table (At end), or after a row number you specify (After row:). The new rows have missing values that can be filled by typing or pasting in data.

The Move Rows command moves highlighted rows to the location you specify in the Move Rows dia-log. Click the appropriate radio button to move highlighted rows to the beginning of the table (At start), to the end of the table (At end), or after a specific row number (After row:).

The Delete Rows command deletes all selected rows from a JMP data table. Use the Undo command on the Edit menu to undo an accidental deletion.

The Cols Menu

You can access commands that affect selected columns from the Cols main menu, from the Columns panel to the left of the data table, or from the triangular popup menu in the upper-left corner of the data table.

Commands in the Cols menu act on selected columns in the current data table. To select a column, click the background area above the column name or click on the column name in the columns panel. This area is called the column selection area. To highlight multiple columns, drag across their column selection areas or Shift-click the first and last columns of a desired range. Use Control-click (Com-mand-click on the Macintosh) to make a discontiguous selection.

Cols menu commands can:

• create or insert new columns and access the Formula Editor to compute column values

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• rearrange the order of columns

• create a set of valid entries for a column

• use column values as labels in plots

• lock a column so it doesn’t scroll in the spreadsheet

• hide or exclude columns temporarily

• change column characteristics

• delete unwanted columns.

The New Column command gives a dialog to enter new column information. The dialog prompts for column name, column characteristics and other column properties.

The Add Multiple Columns command displays a dialog that lets you add more than one column at a time to a table. You specify the number of columns to add, their location, field width, and type. Specify whether to add columns at the beginning of the table, after the rightmost column, or inserted between columns.

By default, the new column are named Column 1, Column 2, and so forth, but the Add Multiple Columns dialog accepts any prefix to be used in place of Column. Other column characteristics are the same for all the new columns. Change the column names and characteristics by editing them in the spreadsheet or in the Column Info dialog.

The Column Info command displays the dialog used by the New Column command, except there is no Next button for adding new columns. You can use the Column Info command at any time to change the attributes of one or more columns.

See the chapter “JMP Data Tables,” p. 93, for a complete discussion of column properties.

The Formula command displays the column’s formula editor to create a formula that computes col-umn values. A plus sign shows next to the column name in the Columns panel when its values are cal-culated.

The formula can include existing columns, constants, parameters, conditional logic, and a variety of functions. A column of computed values is locked and cannot be edited. However, you can disassociate a column from its formula by clicking the Remove Property button on the Column Info dialog for that column. or clear the Formula Editor contents.

Note: You can also access a column’s Formula Editor with a context-click (right-click under Windows and Control-click on the Macintosh) in the column heading area or on the column name in the Col-

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umns panel to access the popup menu shown above. For details about the Formula Editor see the chap-ters “Using the Formula Editor,” p. 143, and “Formula Editor Functions,” p. 163.

The Preselect Role command assigns a role to the selected column and saves the role with the data table. Launch dialogs then use the role to automatically fill in the dialog role boxes. Roles have specific meanings that depend on the analysis being done. Roles are discussed in more detail in the chapter “Characteristics of Data,” p. 75. Also, each analysis chapter in the JMP Statistics and Graphics Guide talks about specific analysis roles.

The Validation command lets you enter a list of valid values (List Check) or valid range limit condi-tions (Range Check) for a column, which then restricts the values that can be entered into the column.

The Label/Unlabel command applied to a column tells JMP to use this column’s values to identify points in plots. When a column is a label column, the label icon (tag) shows next to the column name in the Columns panel to the left of the data grid. Multiple columns can be label columns; a labeled point shows the values of the label variables separated with a comma.

The Scroll Lock/Unlock command locks the selected column into the left-most position in the data grid. When multiple columns are locked, they show in a block at the left in the data grid; when you scroll through table columns, locked columns remain visible. A bold vertical line divides the locked and unlocked columns.The name of a locked column is in italics in the Columns panel to the left of the data grid.

The Hide/Unhide command hides or shows selected column in the spreadsheet but does not remove them from the data table. To hide columns, select them and choose the Hide/Unhide command. A hid-den column appears with the hidden icon (mask) next to the name in the columns panel.

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When the Exclude/Unexclude command is chosen, that variable does not show in analysis role assign-ment dialogs. The exclude icon shows next to the excluded column names in the Columns panel to the left of the data grid.

To unlabel, unlock, or show columns, select them and again select the Label/Unlabel, Scroll Lock/Unlock, or Hide/Unhide command.

Note: Hidden columns cannot be scroll locked.

The Standardize Attributes command displays a dialog with popup menus to select column attributes and properties. The selections apply to all the selected columns in the data table when you click Apply. For example, if you want to change all numeric columns to character, order the data table columns by data type (or just select all the numeric variables) select all the numeric columns, and use the Standardize Attributes command to simultaneously change them to character.

Reorder Columns lets you move columns according to the selection you make from its submenu:

Move Selected Columns

moves the selected columns To first (left-most in the data table), To last (right-most in the data table), or After: (after a column you identify) in the Move Selected Columns dialog.

Original Order

returns the columns to the order they were in at the time the data table was last saved.

Reorder by Name

arranges the columns (except for row state columns) from left to right in alphabetical order by column name.

Reorder By Data Type

arranges the columns from left to right in alphabetic order by data type (Character, Numeric, Row State).

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Reorder By Modeling Type

arranges the columns from left to right in alphabetic order by modeling type (Continuous, Ordi-nal, Nominal). Row State columns have not modeling type, and are shown last.

Reverse Order

reverses the order of the data table columns.

If you mistakenly move one or more columns, use the Undo command in the Edit menu to restore the previous order.

The Delete Columns command removes selected columns from the data table. If you accidentally remove columns, you can use the Undo command in the Edit menu to restore them.

The DOE Menu

The Design of Experiments (DOE) facility in JMP is an environment for describing the factors, responses and other specifications needed to create a designed experiment and saving them in a JMP data table. The specific designs are described briefly here and covered in detail in JMP Design of Experi-ments, the user’s guide for DOE.

The Custom Design command provides the most flexibility of all design choices. With this option you can have continuous factors, categorical factors with any number of levels, interactions, mixture ingre-dients, and covariates (factors that have unchangeable fixed values). There is also a facility for construct-ing custom-designed models that include interaction terms and polynomial terms for continuous factors. You can also specify inequality constraints on the factors, and the number of experimental runs, which can be any number greater than or equal to the number of terms in the model.

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After specifying all your requirements, the design solution generates an optimal design for those requirements.

The Screening Design command allows you to specify continuous factors, and 2- and 3-level categor-ical factors. It creates a table of classical screening designs from which you can pick.

The Response Surface Design command lets you define a number of continuous factors. This envi-ronment then offers a table of classical response surface designs from which to choose.

For Full Factorial Design command, you specify a set of continuous and categorical factors with arbi-trary numbers of levels. JMP creates the design containing all possible combinations of those factors.

The Taguchi Arrays command guides you through the definition of signal and noise factors. The sig-nal factors form the inner array and the noise factors form the outer array. The inner and outer array designs are the traditional Taguchi orthogonal arrays, such as L4, L8, L16, and so forth.

The Mixture Design command lets you define a set of factors that are ingredients in a mixture. JMP creates a new dialog for choosing among several classical mixture design approaches, such as simplex, extreme vertices, and lattice. For the extreme vertices approach you can supply a set of linear inequality constraints limiting the geometry of the mixture factor space.

The Augment Design command lets you modify existing designs. You can add center points, replicate the design a specified number of times, create a foldover design, and add runs to the design using a model with more terms than the original design.

See JMP Design of Experiments for a discussion of each type of design, with details and examples.

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The Sample Size and Power command computes power, sample size, or the effect size you want to detect for a given alpha and error standard deviation. You supply two of these values and the sample size and power facility computes the third. If you supply only one of these values, the result is a plot of the other two. This feature is available for the single sample, two sample, and sample situations.

See JMP Design of Experiments for more discussion of prospective power analysis and examples.

The Analyze Menu

Each Analyze command launches a platform. A platform is an interactive window you use to analyze data, work with points on plots, and save results. The reports in a JMP analysis are organized hierarchi-cally. Methods unfold that suit the context of your data. Many results display automatically, and more are offered through popup menus.

The Analyze commands are briefly discussed in this section. The JMP Statistics and Graphics Guide describes each command, its options, and gives examples.

Distribution describes a distribution of values with histograms and other graphical and textual reports:

• Continuous columns display a histogram and box plots. You can change the width of the histogram bars using the hand tool (grabber) from the Tools menu. Optionally, you can test the mean and standard deviation of the distribution and select from a variety of distribution fits. For continuous variables, capability analysis is available.

• Nominal or ordinal columns are shown with a histogram of relative frequency for each level of the ordinal or nominal variable and a mosaic (stacked) bar chart, with options to test probabilities.

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Text reports support each of the distribution plots. The reports show selected quantiles and moments of continuous values. Tables of counts and proportions support nominal and ordinal values. Save com-mands let you save information such as ranks, level numbers, standardized values, and other statistics as new columns in the data table.

“Univariate Analysis,” p. 25 in the JMP Statistics and Graphics Guide, covers the Distribution command in detail and gives examples.

Fit Y by X studies the relationship of two variables. This platform shows plots with accompanying anal-yses for each pair of X and Y variables. The kind of analysis done depends on the modeling types (con-tinuous, nominal, or ordinal) of the X and Y columns. Figure 3.6, illustrates each type of plot produced by the different combinations of X and Y modeling types.

• If both X and Y have continuous modeling types, Fit Y by X displays a scatterplot. Using options, you can explore various regression fits for the data and choose the most suitable fit for further analy-sis. Each fit is accompanied by tables with supporting statistical analyses and parameter estimates.

• If X is nominal or ordinal and Y is continuous, Fit Y by X plots the distribution of Y values for each discrete value of X. You can use options to see means diamonds and a box plot for each X value and to compare group means with comparison circles. Accompanying text reports show a one-way anal-ysis of variance table. Optionally, you can request nonparametric analyses, view multiple compari-sons, and test homogeneity of variance.

• If X has continuous values and Y has nominal or ordinal values, Fit Y by X performs a logistic regres-sion and displays a family of logistic probability curves. Tables show the log likelihood analysis and parameter estimates for each curve.

Note: Logistic regression of ordinal columns is parameterized differently from logistic regression of nominal columns and sometimes produces different results.

• If both X and Y are nominal or ordinal values, Fit Y by X shows a contingency table and a mosaic bar chart. Accompanying tables show statistical tests, frequency, proportion, and chi-square values for each cell. Optionally, you can request a correspondence analysis.

Individual chapters in the JMP Statistics and Grpahics Guide describe each type of analysis given by the Fit Y by X command.

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Figure 3.6 Fit Y by X Platforms

The Matched Pairs command handles bivariate data in the special situation where the two responses form a pair of measurements coming from the same experimental unit or subject. For example, a matched pair might be a before-and-after blood pressure measurement from the same subject. The responses are correlated, and the statistical method called the paired t test takes that into account.

The Matched Pairs platform displays the data as a scatterplot of the difference between each pair (Y) by the mean of each pair (X) and includes the standard paired t test, which is equivalent to testing that the mean difference between the paired values is zero.

Fit Model lets you tailor an analysis using a model specific for your data. You select columns, assign roles, and build the model to fit in the Fit Model dialog.

Fit Model performs a fit of one or more Y variables by the X variables selected. You can select the kind of model appropriate to your data from the popup menu of fitting personalities given in the Fit Model dialog. The fitting personalities available depend on the kind of responses you select. The following list briefly describes the different fitting techniques:

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Standard Least Squares

gives a least squares fit for a single continuous response, accompanied by leverage plots and an analysis of variance table.

Screening

produces an exploratory screening analysis for single or multiple Y columns with continuous val-ues.

Stepwise

gives a stepwise regression for a single continuous or categorical Y and all types of effects.

Manova

performs a multivariate analysis of variance for multiple continuous response columns. Manova displays a dialog that lets you fit multivariate models interactively.

Loglinear Variance

is for a single continuous response and estimates parameters that optimize both a mean and a variance.

Nominal Logistic

fits a single nominal response with nominal regression by maximum likelihood.

Ordinal Logistic

fits a single ordinal response with ordinal cumulative logistic regression by maximum likelihood.

Proportional Hazard

performs a proportional hazard model fit for survival analysis of censored data with a single con-tinuous response.

Parametric Survival

tests the fit of an exponential, Weibull, or lognormal distribution.

Individual chapters in the JMP Statistics and Graphics Guide document each technique offered by the model fitting platform.

The Modeling command has the submenu shown above that launches the platforms:

Nonlinear

Nonlinear fits nonlinear models, which are models that are nonlinear in their parameters. The Nonlinear command launches an interactive fitting facility. You orchestrate the fitting process as a coor-

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dination of three important parts of JMP: the data table, the Formula Editor, and the Nonlinear Fit platform.

You define the nonlinear prediction formula with the Formula Editor. Then choose Nonlinear with the response variable as Y and the model column with its fitting formula in the X role. You interact with the platform through the Nonlinear Fitting Control Panel using:

• buttons to start, stop, and step through the fitting process, and to reset parameter values

• fitting options to specify loss functions and computational methods

• a processing messages area

• a list of current and limit convergence criteria and step counts, current parameter estimates, and error sum of squares

• options to specify the alpha level for confidence intervals and delta for numerical derivatives.

The Nonlinear platform can show the model and the derivatives of the model with respect to each of its parameters, and the fitting solution reports. There are features that give confidence intervals on the parameters and plot the resulting function if it is of a single variable. You can also save the SSE values in a data table with a grid for plotting them.

The chapter “Nonlinear Regression,” p. 335 in the JMP Statistics and Graphics Guide, describes the Nonlinear command in detail and gives examples.

Partition

The Partition platform recursively partitions rows into groups according to X values that associate with Y values. This partitioning creates a tree of partitions.

The factor columns (Xs) can be either continuous or categorical (nominal or ordinal). If an X is contin-uous, then the splits (partitions) are created by a ‘cutting’ value, which divides the sample into values below and values above this cutting value. If the X is categorical, then the sample is divided into two groups of levels.

The response column (Y) can be either continuous or categorical (nominal or ordinal). If Y is continu-ous, then the platform fits means, and creates splits which most significantly separate the means by the sums of squares due to the means differences. If Y is categorical, then the response rates becomes the fit-ted value, the estimated probability for each response level. The most significant split can be deter-mined by the largest likelihood-ratio chi-square statistic. In either case, the split is chosen to maximize the difference in the responses between the two.

Neural Net

The Neural Net platform is a standard type of neural network. It is a particular case of a back propaga-tion feed-forward multilayer-perception neural net. The neural network is a set of nonlinear equations that predict output variables (Ys) from input variables (Xs) in a flexible way using layers of linear regres-sions and S-shaped functions. This model is a useful nonlinear regression model, and JMP fits it using standard nonlinear least-squares regression methods.

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Time Series

The Time Series platform lets you explore, analyze, and forecast univariate time series. The launch dia-log (role assignment dialog) requires that one or more continuous variables be assigned as the time series. Optionally, you can specify a time ID variable, which is used to label the time axis. If a time ID variable is specified it must be continuous, sorted ascending, and evenly spaced with no missing values

The analysis begins with a plot of the points in the time series. In addition, the platform displays graphs of the autocorrelations and partial autocorrelations of the series. These indicate how and to what degree each point in the series is correlated with earlier values in the series. You can interactively add

• variograms—characterizations of process disturbances

• AR coefficients—autoregressive coefficients

• spectral density plots—period and frequency plots with white noise tests.

These graphs can be used to identify the type of model appropriate for describing and predicting (fore-casting) the evolution of the time series. The model types include:

• ARIMA—autoregressive integrated moving-average, often called Box-Jenkins models

• Seasonal ARIMA—ARIMA models with a seasonal component

• Smoothing Model—several forms of exponential smoothing and Winters Method.

For more details about the Time Series platform, see the chapter “Time Series Analysis,” p. 499 in the JMP Statistics and Graphics Guide.

The Multivariate Methods submenu has the commands shown above that launch the following plat-forms:

Multivariate

Multivariate explores how multiple variables relate to each other and how points fit that relationship. This platform helps you see correlations between two or more response (Y) variables, look for points that are outliers, and examine principal components to look for factors.

The Multivariate platform appears showing correlations and a scatterplot matrix. Options give

• inverse and partial, and nonparametric correlations and pairwise correlations with accompanying bar charts

• a matrix of bivariate scatterplots with a plot for each pair of Y variables

• a Mahalanobis distance outlier plot

• a jackknifed multivariate distance outlier plot where the distance for each point is calculated exclud-ing the point itself.

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There are options with these plots to save the distance scores.

You can also request principal components, standardized principal components, rotation of a specified number of components, and factor analysis information.

The chapter “Correlations and Multivariate Techniques,” p. 365 in the JMP Statistics and Graphics Guide, describes the Multivariate command.

Cluster

The Cluster command clusters rows of a JMP table. Cluster can perform a hierarchical or a k-means clustering method.

The Hierarchical Cluster platform displays results as a tree diagram of the clusters called a dendrogram followed by a plot of the distances between clusters. The dendrogram has a sliding cluster selector that lets you identify the rows in any size cluster. There are options to save the cluster number of each row. Cluster (hierarchical) uses these five clustering methods:

• Average linkage computes the distance between two clusters as the average distance between pairs of observations, one in each cluster.

• Centroid method computes the distance between two clusters as the squared Euclidean distance between their means.

• Ward’s minimum variance method (the default) uses the distance between two clusters as the Anova sum of squares between the two clusters added up over all the variables.

• Single linkage uses the distance between two clusters that is the minimum distance between an observation in one cluster and an observation in the other cluster.

• Complete linkage uses the distance between two clusters that is the maximum distance between an observation in one cluster and an observation in the other cluster.

The k-means clustering approach finds disjoint clusters on the basis of Euclidean distances computed from one or more quantitative variables. Every observation belongs to only one cluster—the clusters do not form a tree structure as with hierarchical clustering. You specify the number of clusters you want.

The Cluster platform also has options to do normal mixture clustering and SOMs (self-organizing maps).

The chapter “Clustering,” p. 379 in the JMP Statistics and Graphics Guide, describes the Cluster com-mand in detail and shows clustering examples.

Discriminant

The Discriminant platform provides a method of predicting the level of a one-way classification based on known values of the responses. The technique is based on how close a set of measurement variables are to the multivariate means of the levels being predicted. Optionally, you can do stepwise discrimi-nate analysis.

PLS (Partial Least Squares)

PLS fits models using the partial least squares method that balances the two objectives of explaining response variation and explaining predictor variation. The PLS techniques work by extracting succes-

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sive linear combinations of the predictors, called factors (also called components or latent vectors) that address one or both of these two goals. The PLS platform in JMP also enables you to choose the num-ber of extracted factors by cross validation, which involves fitting the model to part of the data and minimizing the prediction error for the unfitted part.

The Survival and Reliability submenu has a submenu with the following commands:

Survival/Reliability

The Survival platform performs a univariate survival analysis using product-limit life table survival computations with estimation of Weibull, lognormal, and exponential parameters.

Fit Parametric Survival

The Fit Parametric Survival platform launches the Fit Model dialog, with the Parametric Survival fit-ting personality in effect. The analysis tests the fit of an exponential, Weibull, or lognormal distribu-tion.

Fit Proportional Hazards

The Fit Proportional Hazards platform launches the Fit Model dialog, with the Proportional Hazards fitting personality in effect. This regression analysis fits a Cox model.

Recurrence Analysis

The Recurrence Analysis platform looks at the age of a system when it requires a repair. A system can have multiple repairs, each with its associated age, and is followed until it is no longer in service. A typ-ical system might be some component of an engine or appliance.

Note: You can also use the Nonlinear Fit platform to handle nonlinear models with loss functions for other parametric survival modeling.

See “Survival and Reliability Analysis I,” p. 433, “Survival and Reliability Analysis II,” p. 453, and “Recurrence Analysis,” p. 477 in the JMP Statistics and Graphics Guide for a complete discussion of the Survival submenu commands.

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The Graph Menu

Graph menu commands produce windows that contain specialized graphs or plots with supporting tables and statistics. For a detailed discussion of each Graph menu command, see the JMP Statistics and Graphics Guide.

The Chart command gives a chart for every numeric Y variable specified. The Ys are the variables whose statistics you want to chart. Initially, a vertical bar chart appears, but there are options to show horizontal bar charts, line charts, step charts, needle charts, point charts, or pie charts.

You can specify up to two X variables for grouping on the chart itself. The first X is the group variable, and the second X is the level (subgroup) variable. If there is no X variable, then each row is a bar. The chapter “Bar, Line, and Pie Charts,” p. 519 in the JMP Statistics and Graphics Guide, describes the Chart command and shows examples.

The Overlay Plot command overlays a plot of a single numeric or categorical X column and all speci-fied numeric Y variables. The axis can have either a linear or a log scale. Optionally, the plots for each Y can be shown separately, with or without a common X axis.

By default, the values of the X variable are in ascending order, and the points are plotted in that order. You have the option of plotting the X values as they are encountered in the data table.

Note: For scatterplots of two variables with regression fitting options, use the Fit Y by X command instead of Overlay Plot.

The chapter “Overlay Plots,” p. 533 in the JMP Statistics and Graphics Guide, describes the Overlay Plot command in detail and shows examples of plotting data.

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Spinning Plot produces a three-dimensional spinnable display of values from any three numeric col-umns in the active data table. It also produces an approximation to higher dimensions through princi-pal components, standardized principal components, rotated components, and biplots. There are options to save principal component scores, standardized scores, and rotated scores.

The Spinning Plot platform also gives factor-analysis-style rotations of the principal components to form orthogonal combinations that correspond to directions of variable clusters in the space. The method used is called a varimax rotation, and is the same method that is traditionally used in factor analysis.

The chapter “Three-Dimensional Viewing,” p. 543 in the JMP Statistics and Graphics Guide, describes the Spinning Plot command in detail and shows examples of plotting data and computing principal components.

The Contour Plot command constructs a contour plot for a response variable, Y, for the values of two X variables. Contour Plot assumes the X values lie in a rectangular coordinate system, but the observed points do not have to form a grid. Some Contour Plot options are:

• show or hide data points

• show or hide triangulation and boundary

• specification and labeling of levels

• show a line contour or fill areas

The chapter “Contour Plot,” p. 559 in the JMP Statistics and Graphics Guide, describes the Contour Plot command in detail and shows examples of plotting data.

The Control Charts command creates dynamic plots of sample subgroups as they are received and recorded. Control charts are a graphical analytic tool used for statistical quality improvement. Control charts can be broadly classified according to the type of data analyzed:

• Control charts for variables are used when the quality characteristic to be analyzed is measured on a continuous scale.

• Control charts for attributes are used when the quality characteristic is measured by counting the number of nonconformities (defects) in an item or by counting the number of nonconforming (defective) items in a sample.

The concepts underlying the control chart are that the natural variability in any process can be quanti-fied with a set of control limits, and that variation exceeding these limits signals a special cause of varia-tion. In industry, control charts are commonly used for studying the variation in output from a

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manufacturing process. They are typically used to distinguish variation due to special causes from vari-ation due to common causes.

The Control Chart platform offers the following kinds of charts:

• mean, range, and standard deviation

• individual measurement and moving range

• p-chart, np-chart, c-chart, and u-chart

• UWMA and EWMA

• CUSUM.

The “Statistical Control Charts,” p. 567 in the JMP Statistics and Graphics Guide, describes the Control Charts command in detail.

In a Variability analysis, a number of parts assumed to be identical are taken from a production line. Each one is measured several times by a number of operators using different measuring instruments. You want to know the magnitudes of the variation due to operators, parts, and instruments. In the same way that a Shewhart control chart can identify processes which are going out of control over time, a variability chart can help identify operators, instruments, or part sources that are systematically differ-ent in mean or variance.

The chapter “Variability Charts,” p. 615 in the JMP Statistics and Graphics Guide, describes the Variability Chart command in detail.

The Pareto Plot command gives charts that display counts or the relative frequency of problems in a quality-related process or operation. Pareto plots compare quality-related measures or counts in a pro-cess or operation. The defining characteristic of Pareto plots is that the bars are in descending order of values, which visually emphasizes the most important measures or frequencies.

Pareto Plot uses a single Y variable, called a process variable, and gives

• a simple Pareto plot when you do not specify an X (classification) variable

• a one-way comparative Pareto plot when you specify a single X variable.

• a two-way comparative plot when there are two X variables.

The Pareto plot facility does not distinguish between numeric and character variables or between mod-eling types. All values are treated as discrete, and bars represent either counts or percentages.

The chapter “Pareto Plots,” p. 633 in the JMP Statistics and Graphics Guide, describes the Pareto Plot command in detail.

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The Ternary Plot command constructs a plot using triangular coordinates. The Ternary platform uses the same options as the Contour platform for building and filling contours. In addition it a specialized crosshair tool that lets you read the triangular axes values.

The chapter “Ternary Plots,” p. 647 in the JMP Statistics and Graphics Guide describes the Ternary Plot command in detail and shows ternary plot examples.

The Diagram platform is used to construct Ishikawa charts, also called fishbone charts, or cause-and-effect diagrams. These charts are useful to organize the sources (causes) of a problem (effect), perhaps for brainstorming, or as a preliminary analysis to identify variables in preparation for further experimentation. See “Ishikawa Diagrams,” p. 625 in the JMP Statistics and Graphics Guide for exam-ples of Ishikawa charts.

The Profiler is available for tables with columns whose values are computed from model prediction for-mulas. Usually, a Profiler plot results when you do a Standard Least Squares analysis and then request it. However, if you save the prediction equation from the analysis, you can access the Prediction Profile later from the Graph menu and look at the model using the response column with the saved prediction formula.

The Prediction Profiler displays prediction traces for each X variable. A prediction trace is the predicted response as one variable is changed while the others are held constant at the current values. The Predic-tion Profiler is a way of changing one variable at a time and looking at the effect on the predicted response. You interact with the Prediction Profiler; as you vary the value of an X variable, the Prediction Profiler recomputes

• The low and high values show on the X-axis for each factor, showing its current value.

• The current predicted value of each Y variable for the current values of the X variables.

• Lines and markers within the prediction plots show how the predicted value changes when you change the current value of an individual X variable and include the 95% confidence interval for the predicted values shown by error bars above and below each marker.

Prediction profiles are especially useful in multiple-response models to help judge which factor values can optimize a complex set of criteria.

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The Contour Profiler command works the same as the Profiler command. It is usually accessed from the Fit Model platform when a model has multiple response. However, if you then save the prediction formulas for the responses, you can access the Contour Profiler at a later time from the Graph menu and specify the columns with the prediction equations as the response columns. See the chapter “Stan-dard Least Squares: Exploring the Prediction Equation,” p. 233 in the JMP Statistics and Graphics Guide, for examples of the Profiler and the Contour Profiler.

The Tools Menu and Tool Bars

The Tools menu toolbars are palettes of special tools that determine the effect of mouse actions. There are general tools, and specific graphics tools that operate on points in plots. The default tool is the arrow.

• When a data table is the active window, the arrow selects items in the Rows, Columns and Tables panels. Click the arrow in the upper-left corner of the data grid to deselect rows and columns.

• When a report is the active window, the arrow highlights points in plots and histogram bars.

Other tools are listed next, with a brief description of each. The chapter “Report Windows and Surface Features,” p. 213, covers the Tools menu in more detail.

The question mark icon accesses the JMP Help system. Select the help tool and then click graphs, plots, or tables to access specific help in the JMP Help system.

64 3 The Menu Bar—The Tools Menu and Tool Bars

Use the large plus (selection tool) to select rows and columns in the data table or areas of a display win-dow. When you click the selection tool on a section of a plot or report, that section highlights and can be copied or dragged. Drag the large plus to extend the selection, or Shift-click on other report ele-ments. Clicking near the edge of the report window highlights the entire report. To deselect, click a sec-ond time in a highlighted area. The Copy command in the Edit menu copies highlighted display areas to the clipboard.

Drag the scroller tool to scroll reports up or down and display only the results you want to see. The scroller is a more precise way to perform the same function as the vertical scroll bar.

The hand (grabber) tool is for direct manipulation of plots, charts, axes, and formula components.

The brush tool is for highlighting an area of points in plots. When you click, a rectangle appears. Move the rectangle over points to highlight them. Shift-click to extend the selection. Alt-click (Option-click on the Macintosh) to change the size of the selection rectangle and also extend the selection.

The lasso tool lets you highlight an irregular area of points in plots. Drag the lasso around any set of points. When you release the lasso, it automatically closes and highlights the points within the enclosed area.

The magnifying (zoom) tool automatically zooms in on any area of a plot. When you click the magni-fier, the point or area where you click becomes the center of a new view of the data. Use Alt-click under Windows (Option-click on the Macintosh) at any time to restore the original plot. On a ternary plot you can drag the magnifier tool to zoom the triangular axes.

The crosshair is a movable set of axes used to measure points and distances in graphical displays. The values where the crosshair intersects the vertical and horizontal axis appear automatically as you drag the crosshair within a plot. On a ternary plot, this tool displays triangular crosshair lines.

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The annotate tool places a text box wherever you click in a report, journal, or layout window. You can key in notes to use as titles, footnotes, or other report annotation. The annotation box and its text can be any color or can be transparent. Context-click to see a menu of options to tailor the appearance of the annotate note.

The line, polygon, and simple shape tools work much like the Annotate tool. They behave in a similar fashion to those in many draw packages. They can be used on the surface of any report, journal, or lay-out window. Each has a context (right-click) menu of options to tailor its appearance.

• The line tool draws thin, thick, or dashed lines and can have arrows at either end or both ends.

• The polygon tool draws any shaped polygon and can be spline smoothed.

• The simple shape tool draws either oval shapes or rectangles. Polygons, rectangles, and ovals (simple shapes) can be filled and raised to give a three-dimensional appearance.

Note: The annotate tool, graphics tools, and the scroller are selected only for a single click and revert to the standard arrow cursor when you release the mouse button. Use Shift-click to cause the them to per-sist.

The View Menu

This section covers all the View commands, which differ between Microsoft Window and the Macin-tosh.

The JMP Starter command opens the JMP Starter window, discussed in the previous chapter, “Start-ing JMP,” p. 7. When you open the JMP application, the first thing you see is the main menu bar and the JMP Starter window. If you open a JMP file to start JMP, that file displays as the active window along with the JMP Starter. You can close the JMP Starter with File > Close when it is the active win-dow or by clicking its close box. Use the View > JMP Starter to reopen it. Note that on the Macintosh, the JMP Starter command appears in the Window menu and can be closed by File > Quit when it is the active window.

66 3 The Menu Bar—The View Menu

(Windows only)The Log command displays a pane at the bottom of the JMP window that monitors JSL statements (JSL scripts) as they execute. The log window is editable. For example, can use the log to locate errors in the script, correct the code, copy it and paste it back into the script window, submit the corrected script and there is a JSL error.

Note: On the Macintosh, the log window only appears after JSL statements have been run.

(Windows)There are tool bars for most main menus, and for the colors and markers palettes.The Show Toolbars command displays a window that lists all available toolbars with a check box to show or hide them. The Edit > Customize > Toolbars lets you change and rearrange the tool icons that show on each toolbar. See the chapter “Report Windows and Surface Features,” p. 213, for a complete discussion of toolbars.

(Windows)The Status Bar command turns the Windows status bar on or off at the bottom window edge.

(Macintosh)If the toolbar or palette is visible, the Hide Toolbar command hides the toolbar. Likewise, the Show Toolbar command shows the command icons as a toolbar.

The Unanchor Toolbar command unanchors the visible anchored toolbar so that it is a floating win-dow with a close box. Likewise, if the toolbar shows as a floating window, the Anchor Toolbar anchors it to the top of the JMP window. You can also anchor or unanchor the toolbar by simple dragging it to or from the top of the JMP window.

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Initially, the palette contains a default set of command icons, but you can add or delete any command for which there is an icon by using the Tool Elements Window. The Customize Toolbar window com-mand displays an alphabetical interactive list of main menu items that you can arrange any way you want on the tool palette. The example in Figure 3.7 shows the initial tool palette and a modified tool palette.

Figure 3.7 Macintosh Tool Elements Window and Toolbars

When the Tool Elements window is open, you can delete tool icons from the palette, by simply drag-ging them off the palette. Add elements from the Tool Elements Window by dragging them from the window to the tool palette. Rearrange icons on the palette by dragging them to the position you want.

The New Data View command displays a duplicate view of an open data table. The new view is linked to the original view and all corresponding analysis windows. Changes made to a new view reflect on the original view when it is made active.

Note: On Windows, the New Data View command is found on the Window menu.

(Macintosh)The Redraw Window command redraws the active window. It is useful for cleaning up both data table views and graphical displays that have accumulated stray imperfections resulting from high-speed, dynamic handling of windows.

Note: The equivalent command on Windows is Redraw, found on the Window menu.

Initial Tool Palette

Modified Tool Palette

Initial Tool Palette

Modified Tool Palette

68 3 The Menu Bar—The Window Menu

The Window Menu

The Window menu helps you organize the windows produced during a JMP session. Window menu items differ as noted below, depending on whether you use a Windows or Macintosh operating system.

(Macintosh)On the Macintosh, the first two Window menu commands use conventional Mac commands to regu-late the window size. On Macintosh OS 9 and earlier, Minimize uses the ‘windowshade’ feature to roll the window into its title bar. On Macintosh OS 10, Minimize shrinks the window into an icon on the dock. To unroll or expand the window, select Minimize again. The Zoom command enlarges the win-dow to use the available monitor screen. To reduce the window size, select Zoom again.

The New Data View command displays a duplicate view of an open data table. The new view is linked to the original view and all corresponding analysis windows. Changes made to a new view reflect on the original view when it is made active.

Note: On the Macintosh, the New Data View command is found on the View menu.

Macintosh OS 9 Macintosh OS X

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(Windows only)The Close All of Same Type closes all windows that are the same type as the active (front) window. For example, if the active window is a data table, the Macintosh command appears as Close All Data Windows, and closes only data table windows. The Windows Close All command closes all open win-dows.

Note: On the Macintosh, the Close command on the File menu closes the active window. Option-File displays the Close all Type Window command, which closes all window of the same type as the active window. Macintosh OS 10 only closes all Data windows (instead of all of any specific type window).

(Windows only)The Arrange command helps you organize the open windows within JMP:

Cascade

arranges open windows so that the title bar of each window is visible.

Tile

arranges open windows side by side so all of them are visible. Tile Horizontally stacks the win-dows and Tile Vertically arranges the windows side by side.

Arrange Icons

(Windows) arranges all program-item icons for a selected group into rows. Or, if a group icon is selected, Arrange Icons arranges all group icons into rows.

The Redraw command redraws the active window. It is useful for cleaning up both data table views and graphical displays that have accumulated stray imperfections resulting from high-speed, dynamic handling of windows.

Note: The equivalent command on the Macintosh is Redraw Window, found on the View menu.

(Windows only)The Move To Back command moves the active window behind all other windows generated by the current JMP session, leaving the next window in the sequence showing.

70 3 The Menu Bar—The Window Menu

Under Microsoft Windows, the Set Title command lets you change the name of any active JMP win-dow. This is especially useful if you generate multiple untitled windows and need to distinguish among them during the JMP session

Note: On the Macintosh, use Control-click on any disclosure icon next to a window title to see report disclosure commands. These commands include DisplayBox > Set ReportTitle, which performs the same function as the Set Title command in the Window menu under Microsoft Windows. This Set Report Title command is available to both the Windows (right-mouse click on a report disclosure icon) and Macintosh operating systems, but the Windows > Set Title command is Windows only.The report disclosure commands are discussed in Chapter 9, Report Windows and Surface Features, p. 216.

The Hide command suppresses the display of the active window but does not close it. Under Windows, to reshow a hidden window, select it from the list displayed by the Unhide command. Choosing All from the Unhide menu displays all JMP windows. On the Macintosh, select the window from the list of open windows at the bottom of the Window menu.

Note: Under Microsoft Windows, hidden windows do not appear on the list of open JMP windows listed at the end of the Window menu.

The List All command displays the dialog shown above for you to select the window you want to be the active window. This command is especially useful if you have a large number of open windows, where the windows might be data tables, analysis or graphics results, journals and layouts.

Windows only

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Under Microsoft Windows, the last section of the Window menu lists all open (unhidden) windows. On the Macintosh, this list includes all windows (both hidden and unhidden). You can bring any win-dow to the front by clicking on it in this list.

Note that on the Macintosh, the JMP Starter window is accessed from the list of windows at the bot-tom of the Window menu.

The Help Menu

The Windows version of JMP has the standard Windows Help menu. Windows users will find the menu items similar to those in other applications. For details about the Windows Help menu in gen-eral, consult your Microsoft Windows User’s Guide. Macintosh users should consult the Macintosh User’s Guide for the Macintosh OS 8 or later operating system.

The Contents, Search, and Index commands access the JMP Help system. The Help system is con-structed according to facility provided by your operating system, and also provides navigable online JMP documentation.

About JMP displays a panel that shows the release, the copyright, the operating system, and the owner of the copy of JMP that is running. Note that the About JMP and the Preferences commands show under the application (JMP) menu on Macintosh OS X.

Macintosh OS X

72 3 The Menu Bar—The Layout Menu

On Macintosh, the Help Center command displays a list of Help Books. The JMP Help book accesses the JMP documentation, with topics and a search facility. Show Balloons shows the standard Macin-tosh balloon help. The JMP Help command accesses the JMP Help system directly without showing the list of Help books.

The Layout Menu

When you choose the Layout command from the Edit menu to create a Layout window, a modified main menu appears with the Layout menu showing between Edit and Tables. This menu has com-mands similar to those found in many draw packages. The JMP report in the Layout window is com-posed of report elements grouped together forming a single object. Each title, plot, chart, axis, and table item in the analysis report can be ungrouped into separate objects; any subset of report elements can be grouped into an object. The Layout menu commands operate on selected objects. Click on a Layout object to select it. Use Shift-click to select multiple objects. You can also drag the cursor to form a stretch rectangle that selects everything it encompasses.

The Layout window begins with all report elements grouped into a single object. When you click on the Layout the group highlights and is selected. The Ungroup command ungroups selected objects. The first time you use Ungroup, the title of the analysis becomes an object, and is the outermost out-line level of the analysis. The remaining nesting levels become an object. You can continue to select any portion of Layout window and use the Ungroup command repetitively to ungroup the analysis into its smallest elements.

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The Group command combines selected objects into a single object or group; a group can contain other groups.

The Align command aligns selected objects according to the type of alignment you select from the Align submenu.You can select as many objects as you want for alignment. However, objects overlap if they are positioned on the layout window in such a way as to conflict with the alignment request.

Layout objects can be moved to any position in the Layout window, which means objects can overlap. When objects overlap, the front-most object hides some or all of the objects beneath it. The Clean Up command moves selected objects such that they are in separate positions in the layout window, and are all showing.

The Move commands work the same as they do in other draw packages. Each object has a layer relative to other objects. The Move Forward and Move Backward commands change the layer level of the selected objects forward or backward one layer at a time. Move to Front moves the selected objects to lay on top of all other objects. Likewise, Move to Back moves the selected objects to lie beneath all other objects. Note that when objects are overlaid, the front most objects can hide objects beneath them.

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Characteristics of Data

The columns of a JMP table can contain different kinds of information. However, all information in a single column must be of the same type. You create a new column with the New Column command, and define its characteristics with the New Column dialog.

JMP correctly analyzes data according to column characteristics. This chapter discusses the following attributes of data, usually defined when the column is created, and by commands in the Rows and Cols menus:

• data types and modeling types

• analysis roles

• data storage and formats

• a special format for date and time values

• properties assigned to columns that affect the way data behave.

4Contents

JMP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Types of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Modeling Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Analysis Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Freq Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Weight Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Numeric Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Date, Time and Date-Time Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82Row States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Row State Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Selection Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Data Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Specialized Properties of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

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JMP DataThe columns of a JMP table can contain different kinds of information. However, all information in a single column must be of the same type. You create a new column with the New Column command, and define its characteristics with the New Column dialog.

JMP analyzes data and presents results according to column characteristics. This chapter discusses the following attributes of data:

• data types and modeling types

• the roles data play in an analysis

• data storage and formats

• a special format for date and time values

• computed values

• row states

• specialized properties assigned to columns that affect the way data behave.

Types of DataThe New Column dialog lets you give JMP a description of a column’s data characteristics by assigning the column one of three data types and one of three modeling types. Figure 4.1 shows a New Column dialog, with menus for selecting a data type and a modeling type. These characteristics are described in detail in the following sections.

Figure 4.1 New Column Dialog

Data Types

The first characteristic assigned to a column is its data type. The data type of a column determines how its values are formatted in the data grid, how they are stored internally, and whether they can be used in calculations. The three data types are numeric, character, and row state, as shown in Figure 4.1.

You designate a data type for each column with the Data Type popup menu in the New Column dialog or change the data type using the Column Info dialog.

78 4 Characteristics of Data—Types of Data

You can change a column’s data type as long as the column values are as follows:

• Numeric columns must only contain numbers, with or without a decimal point. A numeric column can have any modeling type. Numeric column values display right-aligned and can be formatted with the Format popup menu. You can change a numeric column to character or row state with the Data Type popup menu.

• Character columns can contain any characters including numbers. In character columns, numbers are seen as characters only and are treated as discrete values instead of continuous values. You can change a character column to numeric. Any values that are numbers convert correctly. Character values become missing.

• Row State columns contain special information that can affect the appearance of graphical displays. A row state column does not have a modeling type because its values are not used in analyses. See Row States, p. 84 later in this chapter, for details about using row state information.

Note: You cannot undo the change of a modeling type.

Modeling Types

Numeric and character columns must be assigned one of the three modeling types listed by the Modeling Type popup menu on the New Column or on the Column Info dialog. The Modeling Type assigned to a column tells JMP how to treat its values in analyses. The icon to the left of the column name in the Columns panel also accesses the modeling type menu. Changing the modeling type lets you look at a variable in different ways in an analysis.

You can change a column’s modeling type as long as the assigned modeling type corresponds to the data types as follows:

• Continuous columns must contain numeric values and be assigned the Numeric data type. Contin-uous values are treated as continuous measurement values. JMP uses the numeric values directly in computations.

• Ordinal columns can have either Numeric or Character data types. JMP analyses treat ordinal val-ues as discrete categorical values that have an order. If the values are numbers, the order is the numeric magnitude. If the values are character, the order is the sorting sequence.

• Nominal columns can have either Numeric or Character data types. All values are treated in JMP analyses as though they are discrete values with no implicit order.

The table to the right in Figure 4.2 illustrates possible combinations of data types and modeling types.

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Data

Figure 4.2 Modeling Types

Analysis RolesTo analyze data, you must cast columns into roles appropriate for the analysis you want to perform. The following roles are the most typical.

• X identifies a column as an independent, classification, or explanatory variable whose values divide the rows into sample groups.

• Y identifies a column as a response or dependent variable whose distribution is to be studied.

• Weight identifies a numeric column whose values supply weights for each response.

• Freq identifies a numeric column whose values assign a frequency to each row for the analysis.

Assigning roles is usually done in the launch dialogs presented by each Analysis or Graph menu com-mand. The launch dialog uses the assigned roles only for a single analysis. However, there is a Recall button on most launch dialogs that recasts the variables as they were in the previous analysis that used that dialog.

The names of the role buttons on the different platform launch dialogs vary slightly to convey informa-tion specific to the analysis. Figure 4.3 shows the launch dialogs for a Distribution analysis (top dialog) and a Recurrence Survival analysis (second dialog). Both analyses require a response (Y) column, but the recurrence response is denoted as Y, Time to Event to indicate that the response for this kind of analysis is always a time measurement.

All platforms also support By variables entered on the Launch dialog. When there is a By variable, the analysis is performed for each level of the By variable, or combination of levels if there is more than one By variable.

80 4 Characteristics of Data—Analysis Roles

Figure 4.3 Launch Dialog Example

You can assign the most common analysis roles with the Preselect Roles command on the Cols menu. Multiple columns can have preselected roles. Preselected roles remain in effect for all analyses until you change them.

When you select an Analyze or Graph command and there are sufficient columns with preselected roles, the analysis completes without the intermediate step of showing the launch dialog. If an analysis requires more columns than have been assigned roles, the launch dialog shows the preselected roles and prompts you for additional role information.

Often, data are collected and arranged in a data table with columns that always serve as Weight or Freq variables. Preselected roles are especially useful for these kinds of columns because their values are an integral component of the data and are needed for most analyses.

Freq Variables

The Freq role identifies a variable whose values are frequencies for each row. Specifically, if f is the value of the Freq variable for a given row, then that row is used in computations f times. When there is no Freq column, each row contributes the implied value of one (1) to the frequency count. The total number of rows with nonmissing values is the denominator of the mean computation, and is used in determining degrees of freedom for tests of hypotheses.

An analysis that uses a frequency variable reflects an expanded number of rows; means and degrees of freedom reflect this expanded table. You could produce the same analysis by first creating a new data table that contains the number of observations expanded by the value of the Freq variable for each row.

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Data

For example, if the value of Freq variable is 5 for the first row, then the first 5 rows in the new expanded table would be copies of the first row of the original table.

You usually think of a frequency value as being an integer greater than 1. However, there are situations where its value has a fractional part, or may be between zero and one. If the value is not an integer, only the floor of the value (the largest integer smaller than the value) is used. If the frequency value is missing or is less than one, the floor of its value is zero and that observation is not used in the analyses.

Note: A negative frequency value causes an error message. If all frequencies are less than one, the fre-quencies for computations are all zero and generate an error condition.

Weight Variables

You assign a column the Weight role when you want the analysis to use relative weights for each row. The response in each row is multiplied by its Weight variable for all analyses. The sum of the weights is used in statistical computations; however, the weight variable does not alter the degrees of freedom.

One common use for a Weight variable is to compute weighted product-moment (Pearson) correlation coefficients. A weight variable with values proportional to the reciprocals of the variances is sometimes used when the variance associated with each row is different.

Unlike the Freq variable, the values of a Weight variable can be either integer or noninteger, positive or negative.

Numeric FormatsA numeric variable has a format associated with it. Formats for numeric column values are listed in the Format popup menu found in the New Column and Column Info dialogs, as shown in Figure 4.4. Note that there is an editable Width associated with numeric formats. Formats appear with a default width that you might need to increase to display large numbers.

Figure 4.4 Column Info Dialog and Date-Time Formats

The following numeric formats tell JMP how to display numbers in the data grid:

• The Best format means JMP considers the precision of each cell value and chooses the best way to show it. By default, the physical width of the column is 10 characters. You can increase the column width in the Column Info dialog if you need to show large values with many decimal places. If a col-

82 4 Characteristics of Data—Date, Time and Date-Time Formats

umn has computed values, the Best format often leads to messy-looking numbers. To show all val-ues with the same number of decimal points, select Fixed Decimal as the numeric format.

• Fixed Dec displays all column values rounded to the number of decimal places you specify. You can set the number of decimal places to zero to see only whole numbers.

• PValue is a specialized format that best displays probability values. It has a default width of 6 and shows 4 decimal places. If a number is less than 0.0001, it displays with a less than sign, <.0001. The format is mostly used in JSL programming scripts and rarely needed for a data table column.

• Scientific displays a number in standard scientific notation. If you enter the number 123456, it dis-plays as 1.23456e+5

• Currency formats values with two decimal positions, commas as needed, and the dollar sign ($) pre-fix. If you enter the number 123456, it shows in the data table as $123,456.00. Entering the num-ber 1234.56 formats as $1,234.56. The default width of the Currency format is 10. If you have a number that requires a wider field width, the format defaults to the Best format. Use the Column Info dialog to increase the column width sufficiently to display your data.

• Date and Time formats are a special numeric format, and are described in the next section.

Date, Time and Date-Time FormatsDates and times are a special type of numeric format. The Format menu on the New Column dialog or on the Column Info dialog has a long list of date, time, and date-time formats (called date formats for short). When you assign a date format to a numeric column, JMP assumes its numeric values are the number of seconds since January 1, 1904. For example, there are 1,234,567,890 seconds between January 1, 1904 and February 13, 1943. That is the value a simple numeric format would show in the data grid. You can leave the date values displayed as (very large) numbers, but they would not convey much date information. It is more likely you want to choose one of the date or time formats from the Format popup menu. The date is then shown in the representation indicated by the format, but stored as the numeric number of seconds.

The Date or Time format selections give the submenus in Figure 4.5 and an additional field, called In, appears on the Column Info dialog. The In formats identify the form of a date in a column so that the values will be treated as numeric JMP dates (number of seconds). This lets you read columns in any of the formats listed in the In menu as dates. The format itself specifies how a numeric JMP date (number of seconds) appears in a column.

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Figure 4.5 Date, Time, and Datetime Formats

The following examples show formats for reading or displaying the date, December 31, 2002. Its unformatted (numeric) value is 3,124,137,600.

• m/d/y has the form mm/dd/yyyy, giving 12/31/2002.

• mmddyyyy has the form mmddyyyy, giving 12312002.

• m/y has the form mm/yyyy, giving 12/2002.

• d/m/y has the form dd/mm/yyyy, giving 31/12/2002.

• ddmmyyyy has the form ddmmyyyy, giving 31122002.

• ddMonyyyy has the form ddMonyyyy, giving 31Dec2002.

• Monddyyyy has the form ddMonyyyy, giving Dec312002.

• y/m/d has the form yyyy/mm/dd, giving 2002/12/31.

• yyyymmdd has the form yyyymmdd, giving 20021231.

• Date Long and Date Abbrev are for display only.

Date Long shows a date value as weekday, month day, year, which gives Tuesday, December 31, 2002.

Date Abbrev is the same as the Long format except that weekday and month have three-character abbreviations, giving Tue, Dec 31, 2002.

You might need to increase the column width to see the entire formatted value for Date Long and Date Abbrev date formats, and for date-time formats. Edit the Width text box on the New Col-umn dialog to specify the field width needed to accommodate the largest number of digits or char-acters you think the new column could have. The maximum field width is 40 for numeric values and 255 for character values.

• m/d/y h:m, m/d/y h:m:s, d/m/y h:m, d/m/y h:m:s, y/m/d h:m, y/m/d h:m:s, ddMonyyyy h:m, ddMonyyyyh:m:s, Monddyyyy h:m, and Monddyyyy h:m:s, show the number of hours, minutes, and seconds after midnight of the prepended date. The date displays in the formats described above.

84 4 Characteristics of Data—Row States

The hours, minutes, and seconds are separated by a colon. This example (12/31/2002) has no hours or minutes. Examples of its formatted values with zero hours minutes and seconds are 12/31/2002 12:00 AM and 12/31/2002 12:00:00 AM.

• :days:hrs:mins and :days:hrs:mins:secs show the number of days, hours, minutes, and seconds since January 1, 1904. The results for December 31, 2002 are :36159:00:00 and :36159:00:00:00.

• h:m:s gives hours, minutes, and seconds portion of the date in the date field. The example used here (December 31, 2002) has no time entered, so the result is 00:00:00.

Selecting a date or time format for a column also lets you enter values using any representation recog-nized by your machine. When you press Return, JMP stores the date entry internally as the numeric number of seconds between January 1, 1904 and the date you entered. The date displays according to the format you assigned to the column.

The JMP Formula Editor offers full support for dates with functions that accept date columns as argu-ments and return date-related elements such as day of the week, day of the year, week of the year, or month. See the chapter Formula Editor Functions, p. 163, for a description of date functions.

If you are importing variables from a SAS file, JMP looks for a SAS date format and translates it to a JMP date column. When you are exporting data to a SAS file, JMP date columns become SAS date val-ues with the appropriate SAS format.

Row States

Row states are characteristics associated with a row. They can distinguish subsets of your data, exclude data from analyses, and customize the appearance of graphical displays. Row state commands are in the Rows menu and affect only highlighted rows. Row state assignments appear in the row number area at the left of the data grid (see Figure 4.6).

4 Characteristics of Data—Row States 854

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Row State Conditions

JMP uses the following row states:

Exclude/Unexclude

is a toggle that excludes selected rows from statistical analyses. To do this, select the rows that con-tain unwanted values and choose Exclude/Unexclude from the Rows menu. Data remain excluded until you choose Exclude/Unexclude again for those selected rows.

Warning: Excluded data are not automatically hidden in plots even though they are excluded from calculations in text reports and graphical displays.

Hide/Unhide

is a toggle that suppresses the display of points in all scatterplots. To hide data, select rows in the data grid and choose Hide/Unhide from the Rows menu. For example, you can exclude points from analysis and then hide those same points in scatterplots. The data remain hidden until you choose Hide/Unhide again for selected hidden rows.

Warning: Hidden points are not automatically excluded from statistical computations that affect text reports and graphical displays even though they are not displayed in the plots. To exclude hid-den observations from analyses, you must also use assign them the Exclude/Unexclude row state.

Label/Unlabel

a toggle that labels points on all scatterplots. To label points, select the rows containing the points and use the Label/Unlabel command. This row state remains in effect until you choose Label/Unlabel again for those selected rows.

Note: There is also a Label/Unlabel command in the Cols menu. JMP uses the row number as the label value on scatterplots if you don’t assign a label column. However, if you assign a label col-umn, its values appear on plots for labeled rows. If there are multiple label columns, their values appear on plots separated by a comma.

Colors

lets you assign any colors to highlighted rows. The points in scatterplots and spinning plots display using the color you select from the Colors palette. The active color assigned to a row displays next to the row number in the data grid.

Markers

assigns a character from the JMP markers palette to replace the default dot in scatterplots and spinning plots. Each active marker displays next to its row number in the data grid, as shown in Figure 4.6.

Row State Columns

Active row state characteristics can be saved permanently with the data table as active row states or in a special row state column. To save row state information in a column, create a new column and select Row State from the Data Type popup menu on the New Column dialog. Then assign the row states you want to selected rows. To save row state conditions in the column, use the Copy from Row States or Add from Row States command in the popup menu next to the row state column name. To copy

86 4 Characteristics of Data—Row States

or activate a row state condition saved in a column, use the Copy to Row States or Add to Row States command (Figure 4.6).

Copy to Row State

copies the row state characteristics from the row state column to the row number area, which makes them active. Copy to Row States replaces existing active row states.

Add to Row States

adds the row state characteristics from the row state column to the row number area, which makes them. Add to Row States preserves existing active row states.

Copy from Row States

copies the active row state characteristics from the row number area to the row state column. Copy from Row States replaces existing row state values in the column.

Add from Row States

adds the active row state characteristics from the row number area to the row state column. Add from Row States preserves existing row state values in the column.

Figure 4.6 Row States Columns and Row State Conditions

Note: To add a row state to a row state column for every row, context-click (Control-click on the Macintosh) to see Rows menu commands, and select from the list of row states.

4 Characteristics of Data—Row States 874

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Selection Status

Whether or not a row is selected is also a row state condition. Like other row state characteristics, selec-tion status can be saved in a row state column. In the data grid, the row number area of a selected row is highlighted. If you use Copy from Row States, the cell of the row state column also appears high-lighted.

The Row Selection command in the Rows menu is useful for selecting all rows or a subset of rows with specific row state characteristics.

Note that the Invert Row Selection command deselects all selected rows, and selects all previously deselected rows. The Select All Rows command selects all the rows in a JMP data table.

You can select rows with the excluded, hidden, or labeled row state characteristic using the following submenu items:

Select Excluded

selects all excluded rows regardless of their current selection status and deselects any other previ-ously selected rows.

Select Hidden

selects all hidden rows regardless of their current selection status and deselects any other previously selected rows.

Select Labeled

selects all labeled rows regardless of their current selection status and deselects any other previously selected rows.

Select Where

displays the dialog on the left in Figure 4.7. To use the Select Where command, highlight a vari-able in the variable list on the dialog, choose a condition from the list of conditions (equals, does not equal, and so forth), enter a comparison value in the text box, and click OK.

88 4 Characteristics of Data—Data Validation

Select Randomly

displays the dialog on the right in Figure 4.7 to enter the absolute sample size or proportional sam-ple size you want in the text edit box.

Figure 4.7 Select Where and Select Randomly Dialogs

See Selecting and Deselecting Rows and Columns, p. 101 in the “JMP Data Tables” chapter for more details about selecting rows.

Data ValidationThe List Check and Range Check Validation column properties the New Column dialog display the dialogs shown in Figure 4.8. You complete a dialog by entering acceptable values or an acceptable range of values for a column. For a single-sided range check, omit one of the bounds (upper or lower).

(list check)

The cursor changes to the list check form when positioned over any editable area (cell or column name) in a column with list check data validation checking in effect. When you click a cell, a blinking vertical bar appears for entering or editing text as usual. However, you can only enter values included on the validation list. A beep and a message warn if you try to enter invalid text. If you right-click a cell (Con-trol-click on the Macintosh), a popup menu of acceptable values shows. This lets you select the cell value from this menu instead of typing it into the cell.

(range check)

The cursor changes to a large I-beam on numeric column values with a specified range and range con-dition. If you try to enter a value outside the validation range, a dialog asks if you want to leave the value as is or change it to a missing value.

4 Characteristics of Data—Specialized Properties of Data 894

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Figure 4.8 List Check and Range Check Dialogs

Specialized Properties of DataSpecial column properties can be assigned by selecting them from the New Property popup menu on the Column Info dialog, shown in Figure 4.9. Many columns have no special properties, but if you want to document a column with notes, compute values with a formula, place restrictions on values that can be entered, and so on, you need to add the appropriate property to the column.

Figure 4.9 New Property Items in the New Column dialog

When you select a property from the New Property list, a panel specific to that property appears, and the property is then listed in the Current Properties list. You can select any property to see its dialog and change information about it. To delete a property, select in the list of properties and click Remove but-ton. Column properties can be added or deleted at any time.

90 4 Characteristics of Data—Specialized Properties of Data

Formula

gives the column access to the JMP Formula Editor. To display the Formula Editor, click the Edit Formula button that now appears on the Formula panel in the New Column dialog. To modify a column’s formula after the column has been created, highlight the column in the data grid and select Formula from the Cols menu.

The Formula column property locks the column so that values cannot be edited in the data table.

Notes

gives a text edit box for you to enter documentation for the column, such as the source of the data or any other helpful notes.

Range Check and List Check

display the dialogs shown in the previous section, Data Validation, p. 88, for constraining the val-ues that can be entered into a column.

Axis

displays the same panel as you see when you context-click on the axis of a graph. It lets you specify minimum, maximum, increment, number of tick marks, and so on for these axes. These values become the default whenever the column is used in an analysis.

Coding

has edit boxes to enter high and low values. When you fit the column in a model, the low and high data values are transformed into –1 and 1, which makes tests and parameter estimates more mean-ingful. Coding can be used for any continuous variable, and is the default for continuous factors generated by the DOE facility in JMP.

Mixture

is a property assigned to a column if it is one of several factors that form 100% of a mixture. When you assign the Mixture property to a set of variables, a no-intercept model is automatically gener-ated by the Fit Model facility when you analyze the data.

Row order levels

has a check box to indicate that rows be ordered by the order they are found in the data table instead of ordered by value.

Spec Limits

creates a tab with edit fields to enter Lower Spec Limit, Upper Spec Limit, and Target, as you do when you request a capability analysis in the Distribution platform. These limits are stored with the column and automatically used in analyses.

Control Limits

lets you enter values for average (Avg), lower confidence level (LCL), and upper confidence level (UCL) to be saved with the column and used automatically by the Control Charts platform. Also, a popup menu on this property panel lets you select the type of control chart to which the values apply.

4 Characteristics of Data—Specialized Properties of Data 914

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Response Limits

gives edit fields to enter Lower, Middle, and Upper limits, and Desirability values for a response column in an experimental design. There is also a popup menu listing the selections Maximize, Match Target, Minimize, and None, which are possible goals for a DOE response variable.

Design Role

provides a popup menu with selections that tell how a factor column is to be used in a model to design an experiment. Factor roles are Continuous, Categorical, Blocking, Covariate, Mixture, Constant, Signal, and Noise.

Sigma

lets you enter a known sigma to be saved with the column. It is used by applications such as con-trol charts that require a sigma value to complete computations. If no value is supplied, Sigma is calculated from the sample.

Units

gives a text edit box for entering measurement units. For example, you might want to indicate that age values are measured in months or that a monetary value is in thousands of dollars.

Other

is for user-defined properties. You assign any name you want to the property, and enter the value for the property. This property is then available for JSL programming.

5 5D

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JMP Data TablesCreate and navigate

JMP data are organized in memory as rows and columns of a table referred to as the data table. The col-umns have names and the rows are numbered. An open data table is kept in memory, and you commu-nicate with it through an active window. You can open as many data tables in a JMP session as memory allows.

Commands in the File, Edit, Tables, Rows, and Cols menus give you a broad range of data handling operations and file management tasks such as flexible data entry, data validation, text editing, and extensive table manipulation. This chapter describes the elements of a JMP data table, tells how to cre-ate a new data table, and shows how to navigate about on the data table window. The chapter “The Tables Menu,” p. 119, covers each Tables menu command in detail.

5Contents

Elements of a JMP Data Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Table Variables and Table Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

New Table Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96New Property/Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

The Columns Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97The Rows Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98The Data Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Cursor Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Selecting and Deselecting Rows and Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Creating a New Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Adding and Deleting Rows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104Adding and Deleting Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Filling a Data Table with Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Entering and Editing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Finding and Replacing Cell Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Importing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108Exporting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Accessing a Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Opening a JMP File from a Web Address . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Cut and Paste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Drag and Drop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5 JMP Data Tables—Elements of a JMP Data Table 955

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Elements of a JMP Data TableJMP data are organized in memory as rows and columns of a table referred to as the data table. The data table opens as a standard window. The following is a list of some important data table features:

• Data can be manually keyed in, calculated, imported from a text file, pasted into a JMP table, or read directly from SAS data sets, Excel files, and other file formats. Under Windows, data can be received as real-time external measurements.

• Column names use any keyboard character including spaces. The size and font for names and values is a preference setting that you control. Column names automatically wrap in the column name area to accommodate the column width you specify.

• Character fields can be up to 255 characters long. You can move column boundaries and enlarge the column to view long values.

• Commands from the Rows and Cols menus add, move, hide, and delete rows and columns.

• There is no limit to the number of rows or columns in a data table. However, the table must fit in memory.

You can start JMP by opening an existing JMP data table, which displays the data in the form shown in Figure 5.1. The table has two parts: the data table panels and the spreadsheet view of the data, called the data grid.

Figure 5.1 Active Areas of JMP Data Table Panels and Data Grid

There are three data table panels—the Table panel, the Columns panel, and the Rows panel. The data table panels are arranged to the left of the data grid as shown in Figure 5.1. These panels contain all the pertinent information about the table and its contents. Each has a popup menu with commands that perform most table management operations, column actions, and row actions.

The panels are divided by horizontal splitters, which you drag up or down to lengthen or shorten the panels. Likewise, a movable vertical splitter divides the table panels from the data table itself. You can click the diamond in the upper left of a panel to show or hide the data table panels.

Table Panel

Columns Panel

Data Table Panels

Tables menus andtable properties

Cols menu

Rows menu

rows stateinformation

Data GridCols menu click to

select rowsor columns

MenuRows

movablecolumnboundaries

Rows Panel

show or hidedata grid

96 5 JMP Data Tables—Table Variables and Table Properties

Table Variables and Table PropertiesThe top panel (the Table panel) shows the data table name and a small red triangular popup menu icon. The title bar for the table is editable. You can click on the table name to type in a new one. (All title bars are editable.)

The popup menu icon to the left of the table name lists the commands shown below. The Tables com-mand accesses the Tables menu, which is discussed in detail in the chapter “The Tables Menu,” p. 119. The next sections cover Table Variables and Table Properties.

Figure 5.2 Tables Panel Menu

New Table Variable

A Table Variable has a constant character value that is always available in the data table for which it is created. The New Table Variable command in the Table Panel menu displays a dialog with edit fields for naming a Table Variable and giving it a value. Here are examples of Table Variables automatically created by JMP.

• If you open a data table created by an earlier release of JMP and the table has table notes in it, a Table Variable called Notes appears with the notes text.

• If you create a design table with the Design of Experiments facility in JMP, it has a Table Variable named Design with the name of the design type as its value.

Table Variables are mostly used to document tables, but they are available to the Formula Editor and could be used in formulas or JSL scripts.

You can see and edit the value of a Table Variable by clicking on any portion of its verbiage. You can also edit a Table Variable name and its content by double-clicking on its name. Or, Control-click (com-mand-click on the Macintosh) anywhere on the Table Variable name or text to see a menu with Edit and Delete commands to edit or delete a Table Variable (see Figure 5.3).

New Property/Script

The New Property/Script command lets you save a JSL script with a data table. This command dis-plays a dialog to name the script and space to type in JSL commands.

5 JMP Data Tables—The Columns Panel 975

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As a simple example, suppose you create a new table property in a table that has height and weight as columns (the Big Class sample data table). Figure 5.3 shows a Property/Script namedBivariate that runs the Fit Y by X command with weight as Y and height as X, and then fits a line to the result. The Table Panel shown in Figure 5.3 displays the new Table Property, which runs the bivariate script when you click its icon and select the Run Script command. Notice in the table panel that the data table also has a Table Variable called Notes.

Table Properties are mostly used to create permanent JSL scripts for a table, but they are available to the Formula Editor and could be used in formulas.

Double-click on a Table Property to edit its name or content. Click on the triangle next to the Table Property name to see a menu with commands to Run Script, Edit or Delete the Table Property.

Figure 5.3 Create a New Table Property to Run a JSL Script

The Columns PanelThe Columns panel lists all columns in the data table, shows the modeling type of each variable (col-umn), lets you change the analysis type of a variable, and indicates special attributes if there are any. Highlight the variable name to edit it, or to apply any of the commands in the Cols menu. You can also access the Cols menu with the popup icon in the upper left of the data grid, next to the column names.

Icons to the right of each name indicate any special status or role the variable plays. You assign charac-teristics to variables using the Cols menu items, or when you use the Formula Editor to compute val-ues. Figure 5.4 shows the column characteristic icons:

• The tag icon appears when you select the Label/Unlabel command.

• The plus icon means the values in the column result from a formula.

• The exclude icon shows when you select the Exclude/Unexclude command. It means the variable will not show in analysis role assignment dialogs.

• The name of a column with the Scroll Lock in effect shows in italics at the top of the list of columns list.

• The hide icon shows when a column is hidden in the data grid.

98 5 JMP Data Tables—The Rows Panel

Figure 5.4 Column Characteristics Icons

The icon to the left of the column name assigns the modeling type: Continuous, Nominal, or Ordinal. Modeling types tell JMP how to use the variable values in an analysis. You can also assign data types in the New Column dialog or the Column Info dialog (see Figure 5.5).

Figure 5.5 Columns Panel Information

Modeling types are described in the chapter “Characteristics of Data,” p. 75. The Cols menu com-mands are covered in the chapter “The Menu Bar,” p. 25.

The Rows PanelThe Rows panel (Figure 5.6) shows the total number of rows in the data table, and lists the possible row state conditions, noting the number of rows that have row states assigned to them. The first five items in the Rows menu are row state conditions.

To apply a row state, highlight (select) rows in the data grid and select a row state command in the Rows menu. The Rows menu is also available from the popup icon in upper left of the data grid, above the row numbers.

The Rows menu commands are covered in the chapter “The Menu Bar,” p. 25. The chapter “Charac-teristics of Data,” p. 75, gives more detail about row states.

Scroll Lock - locks column to the left in the data grid

Label - values of column identify points on plots

Hide - column is hidden in data grid

Exclude - column is hidden in data grid and launch dialog

Formula - computes values in column; the formula icon appears dimmedwhen formula evaluation is suppressed for the column

5 JMP Data Tables—The Data Grid 995

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Figure 5.6 Rows Panel Information

The Data GridThis section describes the cursor forms that appear as you move the mouse on the data grid, and tells how to select rows and columns, find rows, and use keyboard arrows.

Cursor Forms

To navigate the data grid, you need to understand how the cursor works. The cursor has different forms, and the actions it performs depend on its location in the data grid:

Arrow Cursor

The cursor is the standard arrow when it is in the panels area to the left of the data table, in the triangu-lar rows and columns area in the upper-left corner of the data grid, or on the title bar of the Tables panel.

• To select a column, click once in the area above the column or on its name in the Columns panel.

• Click the table name on the title bar of the Tables panel to edit it.

• Click in the column name area to edit a name, or double-click in the column name area to see the Column Info dialog.

• Click in the triangular areas in the upper-left corner of the data grid to deselect rows and columns.

100 5 JMP Data Tables—The Data Grid

Large Plus Cursor

When the cursor is within a column heading or a row number area it becomes a large plus, indicating it is available to select rows or columns. When you click the large plus cursor, that row or column high-lights. Drag to highlight multiple rows or columns, and Control-click (Command-click on the Macin-tosh) to select discontiguous rows or columns.

• Double-click in a column heading area to see the Column Info dialog for that column.

• Click in a column name area at the top of a column to edit the name. The cursor becomes an I-beam.

• Double-click in the row number area to see the Row Editor for editing values.

The cursor then becomes an I-beam and you can edit the text if it is not in a locked column or locked data table.

You can also edit selected column names in the Columns panel to the left of the data grid.

I-beam Cursor

When you select (click) editable text, the cursor appears as the I-Beam. To edit text, position the I-beam within highlighted text. Click the I-beam to mark an insertion point, or drag it to select text for replacement. The I-beam deposits a blinking vertical bar to indicate a text insertion point or a high-lighted area of text to be replaced. The default selection is the entire cell. Use the keyboard to make changes.

Double Arrow Cursor

The cursor changes to a double arrow when it is on a column boundary. Drag this cursor left or right to change the width of a column. However, this physical width does not affect the column field width specified in the Column Info dialog.

Note: You can adjust all widths of selected column at once with Control key (Option key on the Macintosh) as you drag the double arrow cursor on any of the selected column boundaries.

List Check and Range Check Cursors

The cursor changes form when you move the mouse over values in columns that have data validation in effect. It becomes a small, downward-pointing arrow on a column with list checking, and a large I-beam on a column with range checking. When you click, the value highlights and the cursor becomes the standard I-beam; you enter or edit data as usual with any values defined as valid text or valid num-bers. List Check and Range Check values are defined in the Column Info dialog of a column.

Popup pointer cursor

5 JMP Data Tables—The Data Grid 1015

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The cursor changes to a pointer over any popup menu icon or diamond-shaped disclosure icon. Click and drag to select a menu icon and to open or close a window panel.

Selecting and Deselecting Rows and Columns

Most Rows and Cols menu commands operate only on selected rows and columns. You select rows and columns in a data grid by highlighting them, as shown in Figure 5.7.

• To highlight a row, click the space that contains the row number.

• To select a column, click the background area around the column name. You can also select col-umns by clicking in the column names on the Columns panel to the left of the data grid.

Figure 5.7 Selected Rows and Columns

To extend a selection of rows or columns, drag the cursor across the array or Shift-click the first and last rows or columns of the desired range. To make a discontiguous selection, use Control-click (Com-mand-click on the Macintosh) on the desired selections. These commands are also toggles that deselect already selected rows or columns.

Note: To select all rows or all columns at once, Shift-click in the triangular areas in the upper-left corner of the data grid. Click in the upper-left corner to deselect all rows or columns.

There are other ways to highlight specific rows:

Plots and Graphs

When you click on points in plots or bars of a graph, the corresponding rows highlight. The example in Figure 5.8 shows a histogram with the SPEEDYTYPE bar highlighted, and the corresponding rows highlighted in the table. You can extend the selection of bars by Shift-clicking them. Use Control-click under Windows (Command-click on the Macintosh) to make a selection of discontiguous histogram bars, and use the brush tool or lasso tool to select multiple points in plots.

selected column

selected rows

102 5 JMP Data Tables—The Data Grid

Figure 5.8 Select Rows Using a Histogram

Summary Tables

You can use the Summary command in the Tables menu to select subsets of rows. Summary creates a JMP window that contains a summary table. This table summarizes columns from the active data table, called its source table. It has a single row for each level of a grouping variable you specify. Each row in the summary table identifies the corresponding subset of rows in the source table. When you highlight a summary table row, all corresponding rows highlight in its source table. See the chapter “The Tables Menu,” p. 119, for more details about the Summary command.

Row Selection Command

The Row Selection command has a submenu with options for selecting all rows in a data table, a sub-set of rows based on row states, or a specified character string.

Use the Select submenu as follows:

Go to Row

displays a dialog for you to enter a single row number to select.

Invert Row Selection

deselects currently selected rows and selects rows currently not selected.

Select All Rows

selects the entire JMP data table, the same as Shift-clicking the first and last rows in the data table.

Select Excluded

selects all the excluded rows regardless of their current selection status and deselects all included rows.

Select Hidden

selects all hidden rows regardless of their current selection status and deselects all rows that are not hidden.

Select Labeled

selects all labeled rows regardless of their current selection status and deselects all unlabeled rows.

Select Where

lets you search for a specific value in a column and selects all rows where that value is found. Select Where displays the dialog shown in Figure 5.9, which prompts you to select a column, a

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comparison operation from the Select Where dialog popup menu, and a selection criterion value. When you click OK, Select Where highlights all rows that meet the search criterion.

The Select Where command looks only at text strings. Before it completes the comparison, Where converts a numeric value to the character string as it appears in the table cell. The Select Where popup menu lists the comparison operators.

Figure 5.9 Select Rows Using the Select Where Dialog

Select Randomly

randomly selects the number of rows you enter into a dialog. If the number entered is greater than 1, that many rows are selected. If the number entered is a fraction, JMP selects that fraction of the total rows. For example, entering 10 randomly selects 10 rows. Entering 0.1 randomly selects 10% of the rows.

Creating a New TableTo process data with JMP, you must first create a JMP data table. The New command in the File menu creates a new data table in memory and displays an empty data table with no rows and one numeric column, labeled Column 1.

There are several ways to fill a table with values:

• Create new rows and columns, and type or paste data into the data grid.

• Construct a formula to calculate column values.

• Under Microsoft Windows, import data from another application with the Open or the Database command in the File menu.

• Fill the table with values from the clipboard with the Paste command in the Edit menu.

• Use a measuring instrument to read external measures (Windows only).

• Drag columns from one table to another.

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Adding and Deleting Rows

To add new empty rows, use the Add Rows command from Rows menu. The Add Rows dialog prompts for the number and location of rows you want to add. By default, new rows appear at the end of the table.

You can also simply begin typing in a cell anywhere beyond the last row in a table and press Return to automatically generate new rows up to and including the row with the value you typed.

If the values in an existing column are computations, that column’s new cells automatically fill with val-ues. Otherwise, the new cells have missing values.

To delete rows from the data grid, select the rows you want to delete and choose the Delete Rows com-mand from the Rows menu. If you mistakenly delete rows, immediately select the Undo Delete Rows command from the Edit menu.

Adding and Deleting Columns

There are several ways to add columns to a JMP data table:

• Use the New Column command in the Cols menu.

• A new column appears when you double-click in the empty space to the right of the last data table column. The new column appears highlighted and the column name is immediately editable. To edit a column name at any time, highlight the column and type a new name. Or, highlight the col-umn, press return, and edit the existing name.

• Use the Add Multiple Columns command in the Cols menu.

• Use Save commands from a report window.

The New Column Command

To use the New Column dialog, select the New Column command from the Cols menu. The dialog on the left in Figure 5.10 prompts you to name the new column and provide column characteristics. Then click OK to see the new columns appear in the data table.

The New Column dialog also gives the option to compute the new column values with a formula. A Formula Editor window for the new column displays whenever you select Formula from the New Property popup menu. You use this Formula Editor to construct a formula that can include existing columns, functions, parameters, constants. Formulas can also use conditional logic with comparison operators. A column automatically locks and cannot be edited when it has a Formula Column Property. See the chapters “Using the Formula Editor,” p. 143, and “Formula Editor Functions,” p. 163, for details.

The Add Multiple Columns Command

The Add Multiple Columns command displays the dialog shown on the right in Figure 5.10, which prompts for the number of columns to add, their location, field width, and type. Column characteris-tics are the same for all new inserted columns.

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By default, the new column names are Column 1, Column 2, and so forth. However, you can specify other text that becomes the prefix of the new column names.

Using the Add Multiple Columns command to define multiple columns is different than using the New Column command because all columns you request with the Add Multiple Columns dialog have the same data characteristics. The Add Multiple Columns dialog creates the number of new columns you request with the characteristics you enter.

See the chapter “Characteristics of Data,” p. 75, for a description of each Column Property.

Figure 5.10 The New Column Dialog (left) and Add Multiple Columns Dialog (right)

The Delete Columns Command

To delete columns from the data table, select the columns to delete and choose Delete Columns from the Cols menu. If you mistakenly delete columns, immediately select the Undo Delete Columns com-mand from the Edit menu.

Filling a Data Table with ValuesA JMP table is a flexible way to prepare data. You can use the data table to enter data, edit cells, manip-ulate rows and columns, and perform other common tasks. This section tells you how to get data into a JMP table. To use a JMP table efficiently, you must be able to distinguish between character and numeric columns. Numeric columns must contain numbers only, with or without a decimal point. Character columns can contain any characters including numbers. See the chapter “Characteristics of Data,” p. 75, for more details.

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Entering and Editing Data

To enter data, move the cursor onto a cell. The cursor becomes an I-beam. The cell highlights when you click in it and you can enter a value. If the cell has a value and you click a second time, the cursor becomes a blinking text insertion bar. Position the insertion bar and click again to edit a value. Typing replaces highlighted values or inserts values if nothing is highlighted.

The Row Editor command in the Rows menu command brings up a window useful for browsing the columns of a data table one row at a time. To invoke the Row Editor, highlight a row or select a point in a plot. Then, select Rows > Row Editor. Alternatively, you can simply right-click (Control-click on the Macintosh) inside a plot. When the Row Editor appears, it displays information for the first selected row. The row editor window has buttons to browse through selected rows or the entire data set. Figure 5.11 shows the function of each button. The red triangle menu contains these functions, and a Save and a New Row command.

Note: Text in a locked column or a locked data table cannot be edited.

Figure 5.11 The Row Editor

Finding and Replacing Cell Values

The Search command in the Edit menu lets you choose from the submenu with commands to find and replace text in the usual way found in most word processing and editing programs. Find and replace actions deal only with characters. Numbers are treated as text, and appear to the Search com-mand as they show in the data table. All dialog actions also respond to the keystroke shortcuts shown on the Search popup menu. For multiple search actions, such as when using Find Next repeatedly, it is often more convenient to use the keystroke shortcuts instead of the Search dialog.

When you choose Find from the Search submenu, the dialog in Figure 5.12 appears.

previousrow

selectedrow

nextpreviousselected

row

blink row

find

next

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Figure 5.12 The Search Dialog

The Search popup menu selections function as follows:

Find

prompts you to enter a find value in the Find what box. Optionally, you can enter a replace value in the Replace with box. After you enter a find value and click Find, JMP searches the active table for the find value. The search begins with the first cell in the first column and covers every table cell until it locates a find value or reaches the end of the table. The find value highlights when located. The search beeps when it reaches the end of the table, or when it returns to the cell where the search began.

The Find command does not find values in hidden columns or locked tables. To find these values, you must unhide the column or unlock the table.

Note: Values found in locked columns can not be modified.

Find Next

continues to search for the find value. It is often convenient to use the keyboard shortcut Con-trol-G (Command-G on the Macintosh) to quickly use the Find Next command.

Replace

replaces the currently highlighted cell value with the contents of the Replace with box (the replace value). If the replace value is missing and you select Replace or use Control-H (Command-= on the Macintosh), the currently highlighted cell content becomes a missing value.

Replace & Find Next

functions the same as Replace, but continues to search for the find value.

Replace All

replaces all occurrences of the find value with the replace value except in locked columns. You can-not undo Replace All.

Check boxes on the Search dialog further refine a search:

Match Case

gives case sensitive search, useful for locating proper nouns or other capitalized words.

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Match whole words only

detects blanks, which lets you search for a series of words in a character column, or locate strings with unwanted leading or trailing blanks. Using the Match whole words only check box also locates words with at least one leading and one trailing blank.

Restrict to selected rows

restricts the search to selected rows.

Search by row

searches the data table row by row from left to right, to the rightmost cell in the last row or until you stop the search.

Search by column

searches the table column by column, from top to bottom until it reaches the last cell in the right-most column, or until you stop the search. If the search begins with a focused cell, the Search by Column option limits the search to the rows or columns following the focused cell, according to the respective radio button in effect.

Search Data

restricts search data cells only.

Search Column Names

incluces column names as text to be searched.

Note: the Undo command works only with Replace, Replace & Find Next. In particular, you cannot undo Replace All.

Importing Data

The Open command displays a specialized open file dialog that lets you locate the file you want to open and tell JMP the file format of the incoming file. The Open command then reads the file into a JMP data table.

JMP directly reads JMP data tables, JMP journal files, JMP Script files, SAS data sets, SAS transport files, text files with any column delimiter, and Excel files. Also, under Windows, the Database com-mand (described next) can access any database on your system that has an ODBC driver. This feature is not currently available for the Macintosh.

Windows Import

If you indicate what kind of file to expect with a Files of type selection appropriate for the file type to be read, JMP gives helpful information when possible. The example on the right in Figure 5.13, shows an Open Data File dialog for reading a JMP data table. The table notes (if there are any) also show.

If the incoming file is not a JMP data table and you choose All(*.*) from the Files of type drop-down list, JMP looks at the type of file given by the 3-character extension appended to its file name and opens it accordingly. This works as long as the file has the structure indicated by its name.

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Figure 5.13 The Open Data File Dialog for Macintosh (left) and Windows (right)

The Text Import selection from the Files of Type drop-down list attempts to discern the arrangement of text data. This is adequate for a rectangular text file with no missing fields, a consistent field delimiter and end-of-line delimiter.

Note: If double-quotes are encountered when importing text data, they change the delimiter rules to look for an end double-quote. Other text delimiters including spaces embedded within the quotes, are ignored and treated as part of the text string.

To see a preview of an incoming text file, choose Text Import Preview from the Files of Type drop-down list.

Then, when you click Open, a panel asks whether the data have Delimited fields or have Fixed Width fields.

If you click Delimited, the data arrangement is described as shown in Figure 5.14. This dialog begins with settings from your Preferences file. It also shows the columns’ names, data types and the first two rows of data. In this example, preferences are set that indicate the incoming table contains column headers to be used as the column names. The column names are species, subject, miles, and season. If there are no column names the Name fields are called Column 1, Column 2, and so on.

You can also identify one or more end-of-field delimiters, end-of-line delimiters, choose to strip enclos-ing quotes, and see how many rows and columns will be read.

If you click Fixed Width, you can enter the width of each field and specify column names.

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Figure 5.14 Import Delimited Field Text File

Macintosh Text Import

To import text data on the Macintosh, first select Text documents in the Show menu (see Figure 5.13). This displays a file selection list of the documents perceived by JMP as text documents, and adds an Open As menu to the File Open dialog.

Then choose one of the three text commands in the Open As menu:

• Text opens the file in a simple text editing window.

• Text Data opens the file and uses default rules (set in the preferences panel) to interpret end of field and end of line delimiters to create a JMP data table.

• Text Data with Preview presents a dialog similar to the one shown in Figure 5.14, that lets you des-ignate delimiters, and shows a preview of the resulting data table.

Importing SAS Data Sets

To read native SAS data sets, choose SAS Datasets from the Files of type drop-down list in the Open Data dialog. Optionally, you can check Use SAS Variable Names for Column Names. When this box is not checked, JMP uses the variable label as the column name. Importing SAS files is supported for SAS Version 6 (Macintosh) and Version 6 or later versions under Windows.

JMP can also write SAS files through OLEDB access.

Selecting Rows and Columns For Import

The dialog that displays for the Text Import Preview selection in the File Open dialog (Figure 5.14) has a Specify Columns button for selecting the columns you want to import from the text file. When Select Columns is checked, another dialog box (shown to the right in Figure 5.15) appears.

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To limit the number of rows to be read, enter the number in the Number of Lines edit box above the Specify Columns button (see Figure 5.14).

You can also select the columns to read from an existing JMP data table and SAS Transport files. The Select Columns checkbox shown in Figure 5.15 appears in the File Open dialog for the those file types.

Figure 5.15 Selecting Rows and Columns for Importing

Import as Data

The Import as Data command on the File menu reads scripts and text windows. Selecting this com-mand instantly imports the text in the active text window as a JMP data table using the settings from the Text Import preference. To change the Text Import preferences, select File > Preferences and select the Text Import/Export tab on the Preferences panel.

Using the Import as Data command is useful for situations where text is pasted into a text window, then subsequently formatted and adjusted in preparation for import into JMP.

Imported File Sizes

The limitations on the size of imported text files are:

• a limit of 10,000 columns that can be imported per table.

• a limit of 128K per line (row) of data that can be imported.

The size limit of a file to be imported is restricted by the amount of free RAM/Virtual Memory that the system possesses. For example, JMP doesn’t import 1 Gigabyte file into a system that has a combined 500Meg of RAM/Virtual Memory. For realistic performance, the size of the file to import should be less than the physical RAM present in the machine.

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Exporting Data

The Save As command writes the active data table to a file after prompting you for a name and file type. It can save the data table as a JMP file, convert it to a SAS Transport file, save it as a text format file, or save it in any database format available on your system.

The Save As command has the same options as the Open command. The .TXT file extension and Text Export from the Save As Type drop-down list displays the Text Export Options dialog in Figure 5.16:

JMP Data Tables

save the table in JMP format. This is the default Save As option.

SAS Transport Files

converts a JMP data table to SAS transport file format and saves it in a SAS transport library.

Append To

appends the data table to an existing SAS transport library. If you don’t use the Append To option, a new SAS transport library is created using the name and location you give. If you do not specify a new file name, the SAS transport library replaces the existing JMP data table.

Text Format Files

converts data from a JMP file to standard text format, with the rows and columns. The options button on the Save As dialog displays choices to describe specific text arrangements:

Export Column Names to Text File has Yes and No radio buttons to request that JMP column names be written as the first record of the text file, or that no labels or header information be saved with the data.

End of Field and End of Line designate the characters to identify the end of each field and end of line in the saved text file. These options are described previously in the Importing Data section.

There is an option to have no delimiter in exported text files, which exports the variables with no quotation marks or spaces between them.

When saving a SAS V7 file, the following rules apply. Suppose you are saving a JMP data table called baseball.jmp:

• If there are existing.sd2 or.sd7 files in the directory, JMP saves the file as Baseball.sd7, if V7 format is specified, and Baseball.sd2 if V6 format is specified.

• If there exists files in the directory with the longer extension (.sas7bdat), then JMP saves the file as Baseball.sas7bdat if V7 format is specified.

• If there are no SAS data sets in the directory, JMP saves the file as Baseball.sas7bdat if V7 format is specified.

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Figure 5.16 Save As Dialog and Text Formatting Options

Accessing a Database

Under Windows, the Database command opens a connection to any database on your system that has an ODBC driver. Figure 5.17 shows the dialog initially displayed when you select File > Database Open Table. This Database Open Table dialog lets you connect to or disconnect from a specific database source.

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Figure 5.17 Initial Dialog Given by Database Open Command

When one or more database connections are made, the list of connections shows in the Connections list, and a list of tables in the selected database file or directory shows in the Tables list. Optionally, you can click the Advanced button and write SQL statements to open specific subsets of a table.

To use the database importing facility in JMP, click Connect and follow these steps:

1. When the Select Data Source dialog (see Figure 5.18) appears, click the Machine Data Source tab to see a list of database types on your system.

2. Select the data source you want from the list of names and click OK. Then identify the file or the directory of the file you want to import into JMP.

Figure 5.18 Select a Database Source, and Navigate to a File or Directory

3. After you identify a directory or file location and click OK, the Database Open Table dialog shows the database connection source and available files (see Figure 5.18).

4. Select the database name and the file you want to open and click OK to import the entire table.

If you want to import only selected portions of a table, click the Advanced button. An enhanced Data-base Open Table dialog appears with a default SQL Select statement appropriate for the selected file. The example shown in Figure 5.19, reads five variables (name, age, sex, height, and weight) from the Big Class table stored in an Excel directory.

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You could type in the SQL statement yourself, or modify the generated statement with additional SQL clauses. For example, the first SQL statement below could be modified as shown in the second state-ment:

SELECT NAME, AGE, SEX, HEIGHT, WEIGHT FROM Big ClassSELECT NAME, AGE FROM Big Class where AGE>14 and SEX=‘F’

Then, when you click Execute SQL, a JMP data table appears with columns Name and Age. The SQL statement becomes the JMP data table notes.

Figure 5.19 Open a Table, or SQL Selection

Opening a JMP File from a Web Address

Under Microsoft Windows, when you choose the Internet Open command from the File menu, the dialog in Figure 5.20 appears. The Open As: dropdown list has options to browse or edit the page given by the URL you enter. In addition, the last two commands on this menu can create a JMP data table.

When you open a browser from within JMP, be sure to display the browser related toolbars. To do this, choose Show Toolbars from the View menu and check both Browser and URL_list. The Browser tool-bar has standard buttons, Forward, Backward, Home, and so on. The URL_List toolbar is an edit box to enter a URL address. It also lists all the addresses you have open. You can select from this list to bring a page to the front.

Browse HTML Page

opens the page specified by the URL address you enter in the dialog.

Edit HTML Page

displays the HTML commands in a JMP text editor for the URL address you enter in the Internet Open dialog.

Edit HTML with Tags Removed

displays the text without the HTML tags for the URL address you enter.

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Open JMP File

opens a JMP data table that resides on a web server. You enter the complete address of the data table in the Internet Open dialog.

Extract HTML Table as JMP File

creates a JMP data table from information on the page corresponding to the URL you enter. In order to create a meaningful data table, the HTML table information must consist of cells of data only. Tables on a page that include headers, or table structured used for formatting a page do not result in meaningful JMP tables. Currently, this is an experimental command.

Figure 5.20 Internet Open Dialog With Options

Cut and Paste

Edit menu commands have different effects depending on row, column, and cell selections in the cur-rent data table. These commands either affect selected rows, selected columns, or affect one or more single selected cells. If you click in a single cell, the cell highlights and can be edited. You can also high-light cells if you identify them by selecting their rows and its columns.

• If there is a selected cell, all Edit commands apply only to that cell.

• The Edit commands affect all values in selected rows if no columns are selected. They affect all val-ues in selected columns (except the header field) if no rows are selected.

• When both rows and columns are selected, Edit commands affect the subset of cells defined by the intersection of those rows and columns.

• If there is no highlighted cell or selected region, all Edit commands are dim except the Copy com-mand.

The Copy and Paste commands in the Edit menu function as follows:

Copy

copies selected rows, columns, a single cell, or combination of cells from the active data table to the clipboard. On the clipboard, fields are delimited by tab characters and a return character indi-cates an end of row.

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Paste

pastes information from the clipboard to the selected area in a JMP data table. Paste can be used with the Copy command to duplicate rows, columns, or any subset of cells defined by selected rows and columns.

To duplicate an entire row or column,

• select and Copy the row or column to be duplicated

• select an existing row or column to receive the values

• use the Paste command.

Note: You can also copy or move rows by dragging them to a new position in the data table.

To duplicate a subset of values defined by selecting both rows and columns, follow the steps above, but select the same arrangement of rows and columns to receive the copied values as originally contained them. If you paste data with fewer rows into a destination with more rows, the source values recycle until all receiving rows are filled. If you paste more information than space available, the excess values are lost.

Also, the Paste command extends a JMP data table by adding rows and columns to a data table as needed to accept values from the clipboard.

If you select rows before choosing this command, the effect is similar to joining data tables by row number; Paste adds cells to the bottom of the data table and fills them with values from the clip-board.

To transfer data from another application into a JMP data table, first copy the data to the clip-board from within the application. Then use the Paste command to copy the values to a JMP data table. Pasting automatically creates rows and columns as needed.

If you use the Shift key and select Paste (Option-Paste on the Macintosh) JMP does a Paste with Labels, which uses the first line of information on the clipboard as column headers.

See the chapter “JMP Data Tables,” p. 93, for more information about the Paste command.

Drag and Drop

Drag and drop is a convenient way to copy or rearrange columns. Rearranging columns can be done either in the Tables Panel or in the data grid.

To drag and drop columns in the Columns Panel, click on the column name to highlight it. Then click a second time on the highlighted column and drag up or down the list of column names to the position you want.

To drag and drop columns in the data grid, select the hand tool and click on a column name once to highlight it. Then click a second time and drag the column left or right to the position you want. (Note that dragging on the first click extends the selection instead of moving the column).

Note: When you drag and drop a column to a new position, the new column has the original column’s characteristics.

Use Edit > Undo to undo a drag-and-drop operation.

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You can drag multiple columns (contiguous or discontiguous) at the same time. Click to highlight the set of columns you want to move or duplicate. Click a second time and drag the column set to the new position. If you do this with discontinuous columns, they become contiguous when dropped.

To duplicate a column use Control-drag (Option-drag on the Macintosh). The new column has the original column’s display format, but not necessarily its modeling type.

You can also move or duplicate only highlighted values. Cell values appear highlighted when you select both rows and columns. Use the grabber (hand) tool, then click and drag from within a highlighted cell. All selected cells are moved or duplicated.

Note: If you drag a set of cells to an empty area of the table, new columns are automatically created. If you drag selected cells to a destination within the data grid, they are moved, leaving missing values in the original cells, and replacing the values at the destination if the data types are the same. Using Con-trol-drag (Option-drag on the Macintosh) copies the values to the destination leaving the original val-ues intact.

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The Tables MenuManipulating tables

The Tables menu has commands that perform a wide variety of data management tasks on JMP data tables. These commands let you

• sort by any number of columns

• subset by selected rows, or randomly

• stack multiple columns into a single column

• split a column into two columns

• concatenate multiple tables end to end

• join two tables side by side

• update columns in a table with values from another table

• transpose tables.

6Contents

Overview of Tables Menu Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121The Summary Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

The Summary Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Marginal Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

The Subset Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Selecting Rows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

The Sort Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129The Stack Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130The Split Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132The Transpose Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

A Simple Transpose Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Transpose with a Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

The Concatenate Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134Concatenate Tables with the Same Column Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134Concatenate Tables with Different Column Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

The Join Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Join by Row Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Keep a Subset of Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Join by Matching Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Cartesian Join . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Update a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

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Overview of Tables Menu CommandsThe Tables menu commands are accessible from the main menu and as a submenu in the Tables panel to the left of the data grid (Figure 6.1). The commands in the Tables menu modify JMP data tables or create a new table by combining one or more tables.

Figure 6.1 The Tables Panel

The following list is a brief description of each command. Details and examples are covered later in this chapter.

Summarycreates a JMP window that contains a summary table. This table summarizes columns from the active data table, called its source table. The summary table has a single row for each level of a grouping variable you specify. Optionally, you can add columns of summary statistics to this table.

Subsetcreates a subset data table defined by the selected rows and columns of the active data table win-dow.

Sort sorts a JMP data table by one or more columns in either ascending or descending order.

Stack creates a new data table by combining specified columns from the active data table into a single new column.

Split creates a new data table by dividing a specified column into several new columns according to the values of an ID variable.

Transposecreates a new data table that is the transposed version of the active data table. The columns of the active table are the rows of the new table, and its rows are the columns of the new table.

122 6 The Tables Menu—The Summary Command

Concatenate

creates a new data table from two or more data tables by combining them end to end.

Joincreates a new data table by joining (merging) two tables side by side.

The Summary CommandThe Summary command creates a JMP window that contains a summary table. This table summarizes columns from the active data table, called its source table. It has a single row for each level of a grouping variable you specify. When there are several grouping variables, the summary table has a row for each combination of levels of all the grouping variables. Each row in the summary table identifies its corre-sponding subset of rows in the source table.

You can expand the summary table by adding columns of summary statistics for any numeric column in the source table. Initially, a summary table contains the same information as frequency reports for nominal and ordinal variables given by Analyze menu commands. However, you can use the summary table produced by the Summary command for several purposes:

Create a Table of Summary Statistics

The Statistics popup menu on the Summary dialog lists standard univariate descriptive statistics. You can add columns of descriptive statistics to the summary table for any numeric column in the source table. It is often useful to use the summarized statistics for plotting and charting.

Create Subsets of Data

When you select rows in the summary table, the corresponding rows are highlighted in the source table. Commands on the Analyze menu recognize subsets identified by a summary table. A single Analyze menu command produces a separate report for each subset selected by the summary table.

The Summary Table

To illustrate the Summary command, open the Companies.jmp sample data table, as shown in Figure 6.2. It is a collection of financial information for 32 companies. The first column (Type) identi-fies the type of company with values “Computer” or “Pharmaceut.” The second column (Size Co) cat-egorizes each company by size with the values small, medium, and big. These two columns are typical examples of ways to group information.

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Figure 6.2 JMP Table for Grouping Examples

The Summary command displays the dialog in Figure 6.3. To create the summary table shown to the right of the dialog, first select the variable Type in the columns list of the dialog. Click Group to see Type in the grouping variables list. You can select as many grouping variables as you want. Click OK to see the summary table.

The new summary table appears in an active window. This table is linked to its source table and is not saved when you close it unless you use the Save As command to give it a name and location.

Figure 6.3 The Summary Dialog

A summary table has one column for each grouping variable plus a column named N Rows with counts for each grouping level. This example shows 20 computer companies and 12 pharmaceutical compa-nies. The values of the grouping variables are listed in either ascending or descending sort order. To specify the order of grouping levels, select the variable in the grouping variable list and click the a…Z/Z…a toggle. By default, the grouping levels appear in ascending order.

The example in Figure 6.4 shows the Companies.jmp data table summarized by both type of company and size of company, with the values of size listed in descending order within company type.

The summary table behaves in the following ways:

124 6 The Tables Menu—The Summary Command

• When you select rows in the summary table, the corresponding rows are highlighted in its source table.

• When a summary table is active, there is an Add Statistics Column command in the Column popup menu at the upper-left corner of the data table, or in the Columns panel to the left of the data grid. This command accesses the Summary dialog so that you can add statistical summary col-umns to the table at any time.

• The summary table, like a graphical display, is not saved when you close it. However, you can use the Save As command to specify a name and disk location for the table.

Figure 6.4 Second Grouping Variable in Descending Sequence

Summary Statistics

Initially, a summary table displays frequency counts (N Rows) for each level of the grouping variables. However, you can add columns of descriptive statistics to the table.

Click the popup menu icon in the upper-left corner of the data table and select the Add Statistics Column command. This displays the Summary dialog again. The Statistics popup menu in the Sum-mary dialog lists standard univariate descriptive statistics. Use the Statistics menu to request any of these statistics for any numeric source table column. The example in Figure 6.5 shows how to generate the Mean of Profits/Emp for each level of the grouping variable.

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Figure 6.5 Add Summary Statistics

The Statistics popup menu gives these summary statistics for numeric columns:

N is the number of nonmissing values and is used to compute statistics when there is no column assigned the weight role.

% of Total expresses each value of the selected variable as a percent.

N Missing is the number of missing values.

Min is the least value in a column, excluding missing values.

Max is the greatest value in a column.

Sum Wgt is the sum of all values in a column assigned the Weight role and is used instead of N to compute other statistics.

Sum is the sum of all values in a column.

Mean is the arithmetic average of a column’s values. It is the sum of nonmissing values divided by the number of nonmissing values (N–N Missing).

Variance is the sample variance, computed for the nonmissing values. It is the sum of squared deviations from the mean, divided by (N–N Missing–1).

126 6 The Tables Menu—The Summary Command

Std Dev is the sample standard deviation, computed for the nonmissing values. It is the square root of the sample variance.

Std Err is the standard error of the mean. It is the standard deviation divided by the square root of N. If a column is assigned the role of weight, then the denominator is the square root of the sum of the weights.

Range is the difference between Max and Min.

Medianis the 50th percentile, which is the value where half the data are below and half are above or equal to the 50th quantile (median).

Quantiles gives the value at which the specific percentage of the argument is less than or equal to. The sum-mary dialog has an edit box for entering the quantile percentage you want.

Figure 6.6 shows the result of the example in Figure 6.5. By default, the name of the statistics columns is of the form statistic(source column), where statistic is the selection made on the summary dialog, and source column is the column from the source table selected in the summary dialog. The form of the col-umn names can be changed to one of the selections for the drop-down menu found on the lower-left of the Summary dialog (see Figure 6.3)

Figure 6.6 Example of Summary Tables with Summary Statistics

See “The Moments Table for Continuous Variables,” p. 32 in the JMP Statistics and Graphics Guide for more information about summary statistics.

Another way to add summary statistics to a summary table is with the Subgroup button on the Sum-mary dialog. This method creates a new column in the summary table for each level of the variable you specify with Subgroup. The subgroup variable is usually nested within all the grouping variables. The summary table now becomes a two-way table of summary statistics.

The table that groups the Companies.jmp data table by Type and Size Co can use a subgroup variable instead of two grouping variables, as illustrated in the next example. Figure 6.7 begins with the Companies.jmp data table grouped by Size Co. The Statistics popup menu requests the mean of profit/emp with Type as the subgroup variable. This creates a column for each statistic for each level of

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subgroup variable. The result is the summary table shown in Figure 6.7. There is a row for each size company and a column for the mean of each type of company. The cells hold the mean for the sub-group defined by the intersection of the row and column.

Figure 6.7 Summary Statistics for Subgroup

Marginal Statistics

Note that there is a check box on the Summary dialog to Include marginal statistics (Figure 6.7). When marginal statistics are requested, the resulting summary table has an extra row for each level of the first variable in the Group list that gives the requested summary statistics for that level. There is also an additional row at the end of the table that shows the summary statistics for the whole table. Figure 6.8 shows the Summary Table for group variables Type and Size Co with marginal statistics.

Figure 6.8 Summary Table with Marginal Statistics

128 6 The Tables Menu—The Subset Command

The Subset CommandThe Subset command displays the dialog in Figure 6.9, and produces a new data table that is a subset of the rows and columns of the active data table.

• If you click the Selected Rows radio button, the subset table consists of the selected rows and col-umns from the active table.

• If you click the Random Sample radio button, the sample is a random sample of the active data table. Enter the size of the sample you want in the Sampling Rate text edit box. The size of the sam-ple can be either the proportion of the active table you enter or the number of rows from the active table if you enter a number greater than 1.

Figure 6.9 The Subset Dialog

Selecting Rows

To select rows, click row number areas in the data grid or define subsets with a summary table as described previously, or highlight points in plots. If no rows or columns are selected, then Subset reproduces the entire data table.

Select with Plots and Charts

Selected bars of a histogram or points on a plot identify a subset. Click a bar to select it, click or Shift-click points in a plot, or use the brush or lasso tool to encompass selected points. All graphs and plots that represent the same data table are linked to each other and to the corresponding data table. Highlighting bars or selecting points in graphical displays is often used to select a range of column val-ues. Figure 6.10 illustrates selected histogram bars and the corresponding selected rows in the data table. After selecting the rows and columns, the Subset command creates the new data table to the right in Figure 6.10. This Subset of Popcorn table lists the rows from the original table with the high-est values, as highlighted on the histogram.

Note: Double-clicking on a single histogram bar automatically creates a subset of the rows selected by the bar.

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Figure 6.10 Subsetting Highlighted Columns with a Histogram

Select with the Select Where command

You can also select specific subsets of rows using the Select Where command found in the Rows > Row Selection menu. The Where dialog lets you search for a specific value in a column and selects all rows where that value is found. Select Where displays a dialog that prompts you to select a table col-umn to search, a comparison operation from the Select Where dialog popup menu, and a selection criterion value. See the chapter “Characteristics of Data,” p. 75, and the section Selecting and Dese-lecting Rows and Columns, p. 101 in the “JMP Data Tables” chapter, for more information about using the Select Where command.

Selection from Linked Subsets

When you check the Linked check box on the Subset dialog, the subset table that results and any plot or graph of that subset table remain linked to the original table. Selecting rows in this kind of subset table highlights the corresponding rows in the original table and in all its plots and graphs. If you hold down the Shift key and select Subset, a linked subset table is automatically generated.

Note: If there are formulas in any columns in the data table to be subsetted, the Subset dialog shows a Copy Formula check box option. If you don’t check this option, no formulas are included in the subset table. There is also a Suppress Formula Evaluation check box that lets you suppress formula evaluation when the subset table is initially created.

The Sort CommandThe Sort command displays the dialog, shown in Figure 6.11, to specify columns as sort fields. To sort a JMP data table, select sort fields from the list of columns and click By to add them to the sort list. Alternatively, double-click column names to add them to the By list. To remove sort fields, select them in the By list and click Remove.

130 6 The Tables Menu—The Stack Command

The columns you add to the By list establish the order of precedence for sorting. The first column is the major sort field. Each successive column in the list sorts after the preceding column. The icon to the left of the sort column indicates whether the values sort in ascending or descending order. By default, val-ues sort in ascending order. You can toggle the sort between ascending and descending for any selected column in the By list with the a…Z/Z…a button. Click Sort when the dialog is complete.

Figure 6.11 shows the JMP sample data table Popcorn.jmp in its original form (top table) and sorted by popcorn and yield (within popcorn) (bottom table). In this example, Sort created a new data table. However, if you check the Replace Table option, the sorted table overwrites the original data table.

Figure 6.11 Sort in Ascending or Descending Order

Note: If there are formulas in any columns other than the Sort columns of the data table to be sorted, the Sort dialog shows a Copy Formula check box option. If you don’t check this option, no formulas are included in the sorted table. There is also a Suppress Formula Evaluation check box that suppresses formula evaluation when the sorted table is initially created.

The Stack CommandThe Stack command creates a new data table from the active table by stacking two or more specified columns into a single new column. The new data table preserves the values from the other columns.

Stack creates an ID column to identify each row in the new table. The values in that column are the column names from the original table that contained the values now stacked in the new table.

The example in Figure 6.12 shows the sample JMP data table called popcorn trials.jmp, with two col-umns listing popcorn yield from two popping trials conducted under various conditions. To complete the appropriate analysis of the data, the two columns called yield1 and yield2 must be stacked into a single column.

The Stack command displays the dialog shown in Figure 6.12. To stack columns, select their names from the list of columns and click Stack. The column names appear in the stack list on the right of the dialog. This example dialog specifies that the two columns, yield1 and yield2, be stacked into a single column called yield. You can enter any name for the new stacked column in the Stacked Data Column box. If you do not enter a name for the new stacked column, its name is _Stack_ by default.

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The Stack command creates a special Source Lable Column (ID column) to identify each row in the new table. Its values are the column names in the original table from which the stacked values origi-nated. In the example below, the name of the ID column is the default name (_ID_), and it has the lit-eral values ‘yield1’ or ‘yield2.’

Note: Be aware that if you delete the default column name, _Stack_ and _ID_, and do not specify new names, these new columns are not written to the data table.

You can enter a name for the data table in the Output table name box. This example uses the default table name, Untitled.

Click Stack when the dialog is complete. In this example, the stack operation creates the new untitled data table shown at the bottom in Figure 6.12. This new table has twice as many rows as the original table because two columns are stacked. The values of _Stack_ are the experimental results, and values in the _ID_ column tell which trial each row represents.

Figure 6.12 Stack Command Dialog and Stacked Tables Example

Check box options on the Stack dialog affect the rows and columns in the new stacked table,

• If no options are used, the rows in the first column in the Stack list are first in the new stacked table, followed by all the rows in the second column.

• If Stack by Row is checked, as in Figure 6.12, the stacked results are interleaved.

• If all values to be stacked are missing for a row, the Eliminate Missing Rows option drops that row from the new table.

• When Drop All Other Columns is checked, only the stacked column and the ID column appear in the new table.

Note: If there are formulas in any columns other than the Stack and ID columns of the data table to be stacked, the Stack dialog shows a Copy Formula check box option to save formulas from the original tables. If you don’t check this option, no formulas are included in the new table. There is also a Suppress Formula Evaluation check box that suppresses formula evaluation when the new table is ini-tially created.

132 6 The Tables Menu—The Split Command

The Split CommandThe Split command creates a new data table from the active table by dividing a specified column into several new columns according to

• values (levels) of the Split Label Col (Col ID) variable

• values (levels) of one or more Grouping variables.

As an example, suppose you want to rearrange the top table in Figure 6.13 to be like the table below it. (The first table is actually the result of the Stack example in the previous section, with new variable names yield and trial).

Split displays the dialog in Figure 6.13. In this example the Split dialog specifies that the values of yield form a new column for each of its values. trial is the Split Label Col variable. Its values are used as the column names (yield1 and yield2) in the new table.

Complete the dialog by specifying columns from the table as follows:

• Select the column or columns whose values are to form multiple new columns and click Add.

• Select a single column whose values are to be used as the new column names and click Split Label Col.

• Optionally, select one or more columns whose values can uniquely identify each row in the new table and click Group. If you don’t use a grouping variable, Split assumes that the Split Label Col values correctly split the rows.

• Use the radio buttons to specify other columns for the new table.

Figure 6.13 Split Columns Dialog and Split Example

Note: If there are formulas in any columns other than the Split and ID columns of the data table to be split, the Split dialog shows a Copy Formula check box option to save formulas from the original table. If you don’t check this option, no formulas are included in the new table. There is also a Suppress Formula Evaluation check box that suppresses formula evaluation when the new table is initially cre-ated.

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The Transpose CommandThe Transpose command creates a new JMP table that is a transposed version of the active data table. The columns of the active table are the rows of the new table, and its rows are the new table’s columns.

The new table has an additional column called Labels whose values are the column names of the origi-nal table. If there is no label column, the column names in the transposed table are Row 1, Row 2, …, Row n where n is the number of rows in the original table. If there is a variable assigned the label role, the values of that column are column names in the new transposed table.

The Transpose command has the following characteristics:

• The columns of the original table must be either all character or all numeric, except a column used as an ID variable or columns used for grouping.

• Transpose can transpose any selected subset of rows.

• Transpose can transpose groups of rows. Subsets defined by a summary table created with the Summary command (described previously in this section) transpose independently and stack to form the new transposed table.

• Transposing a table with columns but no rows gives a new table with one column that lists those column names. Likewise, if you create a table with one column and assign it the Label role, its val-ues become the column names in the transposed table.

A Simple Transpose Example

The simplest kind of transpose is illustrated by the left-hand tables shown in Figure 6.14. The top left- table has two rows and three continuous columns called plastic, tin, and gold. A simple Transpose cre-ates the bottom-left table. The transposed table has a row for each of the three columns in the example table and columns named Row 1 and Row 2 for the original table’s rows. The additional column called Labels has the column names (plastic, tin, and gold) from the original table as values, ‘plastic,’ ‘tin,’ and ‘gold’.

Transpose with a Label

The right-hand top table in Figure 6.14 has the same columns as the table used in the previous example and an additional column called Item with the label (Label) role. Transpose creates the same table as before but uses values in the label column of the Example table as column names in the transposed table.

134 6 The Tables Menu—The Concatenate Command

Figure 6.14 Simple Transpose (left) and Transpose with a Label (right)

The Concatenate CommandWhen two or more tables are appended end to end, they are concatenated. The Concatenate command appends data tables and creates one column in the new table for each column name in the original tables.

If a column name is the same in all tables, the column in the new table lists the values from all tables in the order of concatenation. If a column name is not in all tables, it has missing values for the tables where it does not exist.

Concatenate Tables with the Same Column Names

Suppose you want to concatenate two data tables with the same column names, as shown in Figure 6.15.

When you select the Concatenate command, the dialog in Figure 6.15 appears and lists all the open JMP data tables, Trial1 and Trial2. Note that these tables have the same column names. To concatenate, select them from the list on the left in the dialog and click Add. Then click Concatente to see the data tables combine into a new untitled table with all rows from the first data table followed by all rows from the second data table.

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Figure 6.15 Concatenate Tables with the Same Column Names

Concatenate Tables with Different Column Names

Concatenated tables always have a column for every column name found in the original data tables. For example, in the tables shown to the left in Figure 6.16, the columns yield1 in the Trial1.jmp table and yield2 in the Trial2.jmp table contain similar information (the column names have been changed to illustrate concatening tables with different names). However, the new concatenated table shown to the right has columns for both variables. These columns have missing values for rows from the table in which the column did not exist. The column called trial occurs in both tables and concatenates to form a single column in the new table.

Figure 6.16 Concatenate Tables With Different Column Names

You can concatenate as many tables as you want. The number of rows in the new table is the sum of the number of rows in all the tables.

136 6 The Tables Menu—The Join Command

Note: The Concatenate dialog (Figure 6.15) has a check box option to Save and Evaluate Formulas from the original tables. If you don’t check this option, no formulas are included in the new table. When that option is checked, all formulas found in the first table in the concatenate list on the dialog are included in the new table.

The Join CommandThe Join command creates a new table by joining two open data tables side by side. Tables can be joined

• by row number

• by matching the values in one or more columns that exist in both data tables

• in a Cartesian fashion where each value in a column of one data table joins with each value in a col-umn of another table to form new rows.

Join by Row Number

The simplest join operation combines tables by row number. For example, suppose you want to com-bine the eight rows from each data table shown in Figure 6.17 into a single table. This example uses the sample data tables Trial1.jmp and Trial2.jmp. (The batch and oil amt columns are hidden in some of the figures that follow.)

Figure 6.17 The Trial 1 and Trial 2 Data Tables

When you select Join, the dialog in Figure 6.18 displays the name of the active table next to the word Join. You select an open data table to join With from the table selector list.

After you select a With table, choose a matching specification from the Join dialog. The default match-ing option is By Row Number. Click Join to see the new data table shown in Figure 6.18. The two original tables are joined side by side, and the new table has all columns from both tables.

If a column name is the same in the original tables, the names of these columns in the new table are of the form variable name of table name.

If a name (such as oil amt) occurs in only one table, it is written directly to the new table.

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Figure 6.18 Join Dialog and Joined Table

Note: The Join dialog (Figure 6.18) has the Copy Formula and Suppress Formula Evaluation check box options to save and evaluate formulas from the original tables. If you don’t check this option, no formulas are included in the new table. When the Copy Formula option is checked, for-mulas in columns other than matching columns are included in the new table. The Suppress Formula Evaluation suppresses formula evaluation when the new table is initially created.

Keep a Subset of Columns

If you don’t want all columns from the original data tables to be in the joined table, click Select Columns in the Join dialog to specify the subset of columns you want. This displays the Select Col-umns dialog, shown on the left in Figure 6.19. This dialog shows a column selector list for both tables. Select all the columns you want from both tables in the column selector lists and click Add. The box under the new table name lists the columns you want in the new table. In this example, the tables Trial1 and Trial2 (Figure 6.17) have identical data in the popcorn column, so only one of them is needed in the joined table. Click Done when you are ready to return to the Join dialog.

The new table has only the selected columns. You can now rename the columns or the data table, and save it. The table on the right in Figure 6.19 shows the joined table with selected columns.

138 6 The Tables Menu—The Join Command

Figure 6.19 The Select Columns Dialog

Join by Matching Columns

Often, you need to join two tables that have different numbers of rows. This means that columns with similar information do not have all matching values. For example, suppose the popcorn trials experi-ment is complete for the first trial but partially complete for the second trial. The Trial1.jmp data table (Figure 6.20) has values for eight experimental conditions. The Little.jmp table has values for only four of those conditions. Further, columns having similar information in the two tables do not have the same names (oil amt and oil) and are not in the same relative positions in the data tables.

Figure 6.20 Join Tables with Different Columns

To join these tables correctly, the values for popcorn and batch must match in both tables, and oil amt in the Trial1.jmp table must match with oil in the Little.jmp table. The By Matching Cols option on the Join dialog lets you specify columns whose values must match to complete a join.

By Matching Cols displays the Match Columns dialog, which prompts you to select columns whose values must match in both tables for rows to be joined. After selecting a column from each upper list, click Match to see the selected pair of columns displayed in the lower box. Select additional pairs of

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columns as needed. If you want to remove matched columns, select them in the lower lists and click Remove.

Matching columns do not have to have the same names and do not have to be in the same relative col-umn position in both tables. When you click Done, the first column in the left-hand list pairs with the first column in the right-hand list, the second columns are paired, and so on. Rows join only if values match for all the column pairs.

Figure 6.21 Join by Matching Columns Dialog

After you choose columns whose values must match, additional options show beneath each table’s col-umn list to further qualify the join operation. For example, some rows in the Trial1.jmp table (see Figure 6.17) do not have a match in the Little.jmp table.

Click Include Non matches box for the Trial1.jmp table so that the new table has all rows in Trial1.jmp even if there is no match in the Little.jmp table.

After the Match Columns dialog is complete, click Done to return to the Join dialog. When you click Join, JMP creates the table shown in Figure 6.22. The yield column from the Little.jmp data table (Yield of Little) has missing values whenever there were no matching values in the Trial1 table.

Notice that the new table is now sorted by the matching columns. JMP automatically sorts the data table so that matching takes place properly. You do not need to sort tables before joining them with matched columns.

Figure 6.22 Table Joined by Matching Columns

There are additional Join options:

• If you specify Drop Multiples in both tables, only the first match found is written to the new table.

140 6 The Tables Menu—The Join Command

If you specify this option for one table, the first match value is joined with all matches in the other table.

• When you select Include Non Matches for a data table, each row from that data table is included in the new data table even when there is no matching value, as in the previous example. You can spec-ify this option for either or both data tables being joined.

If you do not select either the Drop Multiples or the Include Non Matches option, a Cartesian join operation is performed within each group of matching column values.

Cartesian Join

If you choose the Cartesian Join option, each row in the Join data table joins with each row in the With data table. To illustrate this, suppose you want to construct a JMP table that has a row for each combination of levels of experimental conditions in the popcorn example.

You can begin with the three small tables shown at the top in Figure 6.23. Each table has two rows and one column. The values are the experimental categories for popcorn yield trials.

The Join command joins the active data table with any other open data table you choose. In this exam-ple, you must use the Join command twice. The first join combines the oil amt.jmp data table with the batch.jmp table using the Cartesian option. The lower-left table in Figure 6.23 has all columns from the original tables. Each value in the oil amt table pairs with each value in the batch table, giving a new table with four rows.

Next, Join the new table (the result of the Cartesian join of oil amt and batch) with popcorn. Again use the Cartesian Join option, and click Join to produce the lower-right table in Figure 6.23.

This final data table has a row for each experiment condition and is ready for recording results of the corn popping trials. Keep in mind that the number of rows produced by a Cartesian join is the product of the number of rows in the original tables.

Note: Another way to produce a factorial design table is to use the Full Factorial command on the DOE menu.

Figure 6.23 A Cartesian Join

6 The Tables Menu—The Join Command 1416

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Update a Table

The Join command has an option to update the Join table with information you select from a table in the With list. The tables at the top in Figure 6.24 could be an update example. Suppose you want to change the values of oil amt to be “1 tablespoon” instead of “little” and “4 tablespoons” instead of “lots”. To do this, complete the join dialog as shown, with the Update first table with data from second table box checked.

When you click Join, a new table is created that has the information from the second table written over the information in the first table for columns with the same names.

The other options in the join dialog are the same as other join options. If the column names in the update table are different than those to be updated in the first table, change the Matching Specification to By Matching Columns and complete the Match Columns dialog.

Figure 6.24 Example of Updating a table with the Join command

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Using the Formula Editor

This chapter describes the Formula Editor and shows how to build formulas. The next chapter, For-mula Editor Functions, p. 163, gives definitions and examples of the functions supported by the For-mula Editor.

Formulas can be simple assignments of numeric, character, or row state constants, or can contain com-plex evaluations based on conditional clauses.

When you create a formula for a column, that formula becomes an integral part of the data table. The formula is stored as part of a column’s information when you save the data table, and it is retrieved when you reopen the data table. You can examine or change a column’s formula at any time by opening its Formula Editor window.

A column whose values are computed by a formula is both linked and locked. It is linked to (or depen-dent on) all other columns that are part of its formula. Its values are automatically recomputed when-ever you edit the values in these columns. It is also locked so that its data values cannot be edited, which would invalidate its formula.

The Formula Editor window operates like a pocket calculator with buttons, displays, and an extensive list of easy-to-use features. This chapter begins with a simple example and shows how to build formu-las.

7Contents

A Quick Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Formula Editor Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147The Formula Work Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Formula Element Browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Keypad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Function Browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

The Formula Display Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Keypad Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Formula Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152Table Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Table Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Working with Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Computational Order of Precedence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Building a Formula in Order of Precedence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156Constant Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156Cutting and Pasting Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Selecting Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Dragging Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Tips on Editing a Formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Table Templates and Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158Grid Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Actuarial Life Table Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Central Limit Theorem Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Nonlinear and Loss Function Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Keyboard Shortcuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

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A Quick ExampleThe following example gives you a quick look at the basic features of the Formula Editor. Keypad and function browser definitions are in this chapter. The next chapter, Formula Editor Functions, p. 163, documents the function library.

For this example, open the Students data table in the sample data folder. It has a column called weight. Suppose you want a new column that computes standardized weight values. Begin by selecting New Column from the Cols menu.

The New Column dialog (Figure 7.1) lets you set the new column’s characteristics. Type the new name, Std. Weight, in the Column Name area. Then select Formula from the New Property popup menu, which displays formula information on the New Column dialog, and the Formula Editor window shown in Figure 7.2. The other column characteristics define a numeric continuous variable and are correct for this example.

Figure 7.1 Create a New Column with the Formula Property

Click Edit Formula on the formula information panel to see the Formula Editor window, as shown in.

Figure 7.2 The Formula Editor Window

To build a formula, you first click the empty formula element in the formula editing area to select it. When you click a formula and Show Boxing is checked, the selected portion of the formula is outlined

formula elementbrowser

keypad

formula editingarea

function browser

146 7 Using the Formula Editor—A Quick Example

with a thin red line. All terms within the smallest nesting box relative to the place you clicked become selected, and the subsequent action applies to those combined elements. Boxing becomes very useful when you are building complex formulas and want to modify specific terms within the formula, or need to determine the order evaluation takes place.

Enter the formula that standardizes the weight values by following these steps.

1. While the initial missing term is selected, click on the column named weight in the column selector list. Then Click on the minus button in the keypad. A new missing term appears after the minus sign as shown in Figure 7.3.

2. While the new missing term is selected, click on weight again.3. Click in the function browser topics list, and scroll to locate Statistical functions. Click on this

topic to see a list of statistical functions, and then select Col Mean in this list.4. Click anywhere in the white space of the total boxed area to select the entire expression. The red box

should now enclose the whole formula.5. While the entire expression is selected, click on the division button in the keypad. The result gives a

selected missing denominator for the whole expression.6. Choose weight again from the column selector list. 7. While weight is still selected in the denominator, choose Col Std Dev from the Statistical func-

tions.

You have now entered your first formula. Close the Formula Editor window to see the new column fill with values. When a weight value changes, the calculated std weight value automatically recalculates.

Figure 7.3 Building a Formula

The Column Info dialog for a computed column displays its formula (see Figure 7.4). If the column’s formula is larger than four inches wide or five inches tall, it will be shrunk to fit within these limits. The formula can be resized by clicking and dragging from the right or bottom border. To preserve the pro-portions of the formula picture, use Shift-drag.

To see the Formula Editor again, click Edit Formula on the Column Info dialog. Or, highlight one or more columns that have formulas you want to see and choose Formula from the Cols menu. You can also Right-click (Control-click on the Macintosh) at the top of any column to bring up its Formula Editor.

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Figure 7.4 Column Info Dialog for Column with Formula

What if you make a mistake?

If you make a mistake entering a formula, first choose Undo from the Edit menu. Undo reverses the effect of the last (undoable) command. There are other editing commands to help modify formulas, including Cut, Copy and Paste. The delete key removes selected expressions. Note that the hand tool appears when you move the cursor over a selected term. To rearrange terms or expressions, click to see the hand grab the term. Drag to move formula pieces.

Text Mode

You also have the option of entering or changing any part of a formula in text mode. Text mode displays the formula in JMP scripting language (JSL). The entire formula (or any of its terms) appears in text mode when you double click in the white space inside the boxed formula area. The elements of that box appear as plain text and you can then edit the formula as you would in any text editor. Any element of a formula can be displayed as a scripting command and then edited. After editing formula scripting commands, click the formula again to see its formatted form. For example, the text for the standardiza-tion of weight is

(:weight – Col Mean(:weight)) / Col Std Dev(:weight)

Formula Editor TerminologyThe following list is a glossary of terminology used throughout this chapter and the next chapter, For-mula Editor Functions, p. 163. See Figure 7.5 for an illustration of formula terminology.

elementThe name of a constant, Table Variable, Table Column, Local Variable, or Parameter that shows in the element browser list on the left side of the Formula Editor can be an element in a formula.

148 7 Using the Formula Editor—The Formula Work Panel

argumentAny element or an entire expression (including mathematical operands) that is operated on by a func-tion is an argument to that function.

termAn indivisible part of an expression such as Constants and variables are terms.

expressionAn expression is any part of a formula that can be selected as a single unit, including terms, missing terms, and functions grouped with their arguments, as well as the entire formula.

ClauseA complete segment in a conditional function.

functionA mathematical or logical operation that performs a specific action on one or more arguments. Func-tions include most items in the function browser and all keypad operators.

missing termAny empty place holder for an expression, represented by an empty box in the formula.

missing valueExcluded or null data consisting of the missing value mark (•) for numeric data, or null character strings for character data.

Figure 7.5 Formula Terminology

The Formula Work PanelThe formula work panel (the top section of the formula editor, shown previously in Figure 7.2, is com-posed of buttons and selection lists for constructing a formula of elements, operators, and functions.

missing valuesmissing termfunction

formula element browser

expressionargument term

clause

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The following paragraphs briefly describe each component of the work panel. Formula elements are covered in detail later in this chapter, and the next chapter, Formula Editor Functions, p. 163, which documents all functions.

Formula Element Browser

The formula element browser on the right in the formula work panel displays selection lists of table col-umns, constants, variables or parameters. By default, the list of table columns shows. You can change the kind of elements listed by choosing from the popup menu at the top of the formula element browser. To choose a formula element, select an expression in the formula and click an element (see Figure 7.5).

Keypad

The keypad is a set of buttons used to help build formulas. Some of the buttons, such as the arithmetic operators, are familiar. Others have special functions, described in the next section. The popup menu icon has the commands shown in Figure 7.6.

Figure 7.6 Keypad and Formula Editor Menu Commands

The popup menu above the keypad has these commands.

Show Boxing

outlines specific terms within the formula. Boxing is important when you want to select and mod-ify a specific portion of a formula, or need to determine the order of evaluation that takes place.

Suppress Eval

suppresses formula evaluation unless you specifically click Apply on the Formula Editor. This is a useful formula development mode for building complex formulas. You can turn off evaluation and build sections of a formula, and evaluate only to test it. In particular, you can close the Formula Editor and reopen it at a later time to continue building a formula without the formula attempting to evaluate.

Ignore Errors

suppresses error messages while a formula is under development. This is useful in situations where you want to see an evaluation for some rows and don’t want to see an error message for every row where the formula evaluation finds problems. If you don’t select Ignore Errors, an error message

150 7 Using the Formula Editor—The Formula Display Area

dialog appears when there is an error and asks if you want to ignore further errors. This has the same effect as the Ignore Errors menu selection.

Larger Font and Smaller Font

incrementally increases or decreases the font used to display the formatted formula.

Derivative

finds and displays the derivative of a function you enter into the Formula Editor. The derivative is found with respect to a function argument (a single variable name) you highlight. Thus, in order to differentiate with respect to x, it must be one of the immediate arguments in an expression you enter.

Function Browser

The function browser groups collections of functions and features in lists organized by topic. To enter a function in a formula, select an expression and click any item in one of the function browser topics. You can also see a list of ungrouped functions in alphabetical order by selecting Functions (All) from the popup menu above the function list.

The Formula Display AreaThe formula display is the area where you build and view a formula. To compose a formula, select expressions in the formula display and apply functions and terms from the formula work panel.

Functions always operate upon selected expressions, terms always replace selected expressions, and argu-ments are always grouped with functions. To find which expressions serve as a function’s arguments, select that function in the formula. The boxed groupings also show how order of precedence rules apply and show which arguments are deleted when you delete a function. See Computational Order of Pre-cedence, p. 155 later in this chapter, for more information.

• enter function into column

• select Derivative from menu• highlight variable

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Keypad FunctionsThe keypad is composed of common operators (referred to as functions in all Formula Editor docu-mentation). Enter a keypad function by selecting an expression in the formula display and clicking on the appropriate keypad buttons. The keypad buttons are shown in Figure 7.7, followed by a description of each button function.

Figure 7.7 Keypad Keys and Explanations

• The four arithmetic functions work as they normally do on a pocket calculator.

• The Insert keypad function inserts a new empty formula clause or function argument. To insert a clause into a formula, first select the existing clause or argument you want the new element to fol-low. When you click the insert button, the new clause appears and is selected. You can also insert a new clause or argument by typing a comma. The Delete keyboard function deletes an elements value, or deletes a clause from a formula. The delete button functions the same as the Delete key on the keyboard.

• The Exponent keypad function raises a given value to a specified power. It has an exponent of two by default. The power is initially highlighted and can be changed to another value.

• The Root keypad Function calculates the specified root of the radicand. It has an implied index of 2 (a square root), which is not displayed. The index area is initially highlighted so that you can enter a different index value.

• The Switch Terms keypad function looks at the operator that is central to the selected expression and switches the expressions on either side of that operator.

• The Unary Sign keypad function inverts the sign of its argument. Apply the function to variable expressions or use it to enter negative constants.

• The Local Variable keypad function creates a local variable and assigns it the value of the selected expression. Its value can be as simple as an empty term, or as complicated as a complex formula. See Local Variables, p. 153 in this chapter, for more details.

• To use the Delete Expression key, begin by selecting any expression. When you click the Delete but-ton, the selected expression is deleted leaving a selected empty term in its place. This process repeats each time the key is clicked. In this way you can delete a formula term by term, in the precedence order of the formula, beginning with the first term you select.

exponent orpower

keypadarithmetickeys

add or insertdelete

root switch terms

unary sign function local variablekey

delete expression

152 7 Using the Formula Editor—Formula Elements

In the example shown in Figure 7.8, Col 2 is first selected (step 2). Repeated clicks on the Delete Expression key then produce the sequence of steps from 3 to 10.

Figure 7.8 Deleting Terms from a Formula

The Delete key also lets you peel functions from their arguments. To do this, select a function and its arguments, then click the delete button. The function and all but its first argument are deleted.

Formula ElementsThe formula elements browser displays the categories of items to build formulas. The categories are accessed from the menu at the upper-left of the calculator, as illustrated in Figure 7.9.

Figure 7.9 Formula Elements Browser

Constants

The Constants list includes commonly used numeric constants such as e and π, as well as some com-monly used integers such as –1, 0, 1 and 2. To use a constant, select a term in the model and select an item in the constant list. Also, when you double-click a term in a formula it becomes a text box and you can enter numeric, character, or row state constants by typing them.

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Table Variables

Table variables are character strings that are available to the entire table. Table Variable names are dis-played in the Tables panel at the left of the data grid. The Formula Editor can refer to a table variable in a formula. Many of the sample data files have a Table variable called Notes. It has as its value the table notes carried over from previous versions of JMP. The Table Variables command in the Formula Ele-ments popup menu lists all the Table variables that exist for a table. You can create additional Table variables with the New Table Variables command in the Tables panel of the data table.

Table Columns

Table columns, the default element list, are the data table columns. To insert columns into a formula, select a formula term and click the column name in the Table Columns list. If you delete a column used in a formula, an empty term appears in its place. The Table Column list updates whenever you add or delete a column, or edit a column name.

Local Variables

Local variables lets you define temporary numeric variables to use in expressions. Local variables exist only for the column in which they are defined.

To create a new local variable, select Local Variables from the Formula Elements popup menu and complete the dialog, as illustrated in Figure 7.10. By default, local variables have the names t1, t2, and so on, and have missing values. Optionally, you can assign a starting value in the Local Variable dialog. Local Variables appear in a formula as bold italic terms.

Local Variables are most often used with Assignment functions. Assignment functions can assign expressions to local variables, which are then used in a complex equation. This technique can some-times simplify building an equation and improve the efficiency of its evaluation.

As an example, suppose you have variable X and Y and you want to compute the slope in a simple lin-ear regression of Y on X using the standard formula shown in Figure 7.10.

One way to do this is to create two local variables, call them XY and Xsqrd, as described in Figure 7.10. Then assign them to the numerator and the denominator calculations of the slope formula. Delimit each assignment with a semicolon. The slope computation is simplified to XY divided by Xsqrd.

154 7 Using the Formula Editor—Formula Elements

Figure 7.10 Local Variables in a Formula

Note: Statements in the Formula Editor are actually JSL programming statements. Multiple statements in a formula must be separated by a semicolons.

Note: Another way to create local variables is to use the local variable button (t=) on the keypad, which automatically creates and displays a local variable with the default name, t1, in an expression and places a semicolon after it.

Use Alt-click (Control-click on the Macintosh) on the name in the local variable selection list to see a popup menu with commands to copy, edit or delete, as described next under Parameters, p. 154.

Parameters

A Parameter is a special type of numeric local variable created in the Formula Editor and can be used in any formula. Numeric parameters are most useful in formulas created for nonlinear fitting. You can set the initial value of a parameter when you create it, but its final value is determined by the formula com-puting process that contains it.

To create a parameter, select Parameters from the formula element popup menu and click New Parameter to bring up the dialog shown in Figure 7.11. You use the dialog to assign a name and value to the new parameter. You can create as many parameters as you need, but they are only available to the Formula Editor (column) that created them. Click OK when the dialog is complete or Cancel to exit the dialog without creating a new parameter.

To change parameter settings or remove a parameter, Control-click (Option-click on the Macintosh) the parameter name and chose Edit from the menu (see Figure 7.11).

Parameters are added to formulas in much the same way that variables are added. To insert parameters into a formula, select a formula term and click the parameter name in the parameter list.

Parameters are easy to recognize in formulas because they are displayed in bold type. If you open the US Population.jmp table from the Nonlinear Examples in the Sample Data folder and display Parameters, you see the parameters shown in Figure 7.11, with the formula that uses them.

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When a parameter is used in a model for the Nonlinear platform, an initial value or starting value should be given to the parameter. After completing a nonlinear fit or after using the Reset button in the nonlinear control panel, the parameter’s value is the most recent value computed by the nonlinear platform.

A new parameter shows in the parameter list with an initial value if there is one. When a computing process changes the value of a parameter, the new value then appears in the parameter list.

When you paste a formula with parameters into a column, the parameters are automatically created for that column unless it has existing parameters with the same names.

Figure 7.11 Using Parameters in a Formula

Working with Formulas

Computational Order of Precedence

As you build a formula, keep in mind that all functions have an order of precedence defined by levels 1 through 6 as described in Table 7.1 Precedence Order of Functions, where level 1 is the highest order of precedence. Expressions with a high order of precedence are evaluated before those at lower levels. When an expression has operators of equal precedence, it is evaluated from left to right. You can use parentheses to override other precedence rules when necessary because any expression within parenthe-ses is always evaluated first.

Terms have no order of precedence because they cannot be evaluated further.

Table 7.1 Precedence Order of FunctionsLevel 1 ParenthesesLevel 2 Functions in the Function Browser lists except for those listed in other levelsLevel 3 *, ÷, ModuloLevel 4 +, -Level 5 Comparisons: <, ≤, =, ≠, ≥, >, ≤ x <, < x ≤, < x <Level 6 Logical Functions: And, Not, Or

156 7 Using the Formula Editor—Working with Formulas

Note: When a function has an expression as its argument, the argument has a higher order of prece-dence than it would if enclosed in parentheses outside the function.

Building a Formula in Order of Precedence

It is best to build a formula starting with any expression that serves as an argument. This is because functions have a high order of precedence and are always grouped with their corresponding arguments. It is a good idea to create expressions working from highest to lowest order of precedence when possi-ble. If you need parentheses, be sure to type the left parenthesis before entering the expression to be enclosed.

For example, given a data table with the columns A, B, and C, use the following steps to compose the expression A(B + C). Note that this expression is not the same as A x B + C, which evaluates as (A x B) + C.

1. To begin entering the formula, click column A in the column selector list.2. Click the multiplication button in the keypad. 3. While the new missing term is selected, type the left parenthesis to apply a set of parentheses.4. Next, click column B in the column selector list5. While column B is still selected, click the addition button in the keypad.6. While the new missing term is selected, click column C in the column selector list.

Because order of precedence determines which arguments are affected by each function, order of prece-dence also affects the grouping of expressions. Select functions in the formula to verify how the order of precedence rules have been applied.

Constant Expressions

Once JMP has evaluated a formula, you can select an expression to see its value. This is true for both parameters and expressions that evaluate to a constant value. To do this, select the expression you want to know about and Right-click (Control-click on the Macintosh) on it. This displays a popup menu as shown in Figure 7.12. When you select the Evaluate command and release the mouse click, the current value of the selected expression shows until you move the cursor.

Figure 7.12 Context-Click to See the Value of an Expression

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Cutting and Pasting Formulas

You can Cut or Copy any expression or an entire formula and Paste it into another formula display. The formula is saved on the clipboard as a JSL statement. When you paste it into another formula dis-play, it appears in formatted form. If you copy it into other applications, it appears as a JSL statement.

Selecting Expressions

You can use the keyboard arrow keys to select expressions for editing or to view the order of precedence within a formula when parentheses are not present or the boxing option is not in effect. You can click on any single term in an expression to select it for editing. Clicking an operand (+, –, *, ÷) in an expression selects the operand and its operator.

Once an operand is selected, the left and right arrow keys move the selection across other associative operands having equal precedence within the expression. The left arrow extends the selection to include the next operand and operator of higher precedence to the selection, until the entire formula is selected.

The up arrow extends the current selection by adding the operand and operator of higher precedence to the selection. The down arrow reduces the current selection by removing an operand and operator from the selection.

Dragging Expressions

You can drag any part of a formula that can be selected to any other location than can be selected. When you place the arrow cursor inside an expression and click, the expression highlights and the cur-sor changes to a hand cursor. As you drag across the formula, destination expressions are highlighted, and then you can drag the selected expression to the new location you want. When you drag, the selected expression is copied to the new location where it replaces the existing expression when the mouse button is released.

Tips on Editing a Formula

If you need to change a formula, highlight the column and select Formula from the Cols menu. Alter-natively, Right-click (Control-click on the Macintosh) at the top of the column or on the column name in the Columns panel and select Formula.

Deleting a function also deletes its arguments. Deleting a required argument or missing term from a function sometimes deletes the function as well. You can save complicated expressions that serve as arguments and paste them where needed. Use the Copy command to copy the arguments to the clip-board or scrapbook.

You can peel a function to delete the function from its argument. To peel a function from a single argu-ment, first click to select the function. Then, choose the peel button from the keypad (Figure 7.13). Or, you can use the hand tool to drag the argument on top of its function. See Dragging Expressions, p. 157 for a description of using the hand tool.

158 7 Using the Formula Editor—Table Templates and Scripts

Figure 7.13 Peeling an Argument (boxing option is on)

After you complete formula changes, the new values fill the column automatically when you click Apply or close the Formula Editor window.

Once you have created a formula, you can change values in columns that are referenced by your for-mula. JMP automatically recalculates all affected values in the formula’s column.

Efficiency

Usually, it is not necessary to structure formulas with efficient evaluation in mind. Most formulas eval-uate almost instantaneously regardless of their structure. This is because statistical functions and con-stant expressions are evaluated only once when a column’s values are calculated.

However, when you are creating conditional expressions, keep in mind that Match evaluates faster and uses less memory than an equivalent Condition function, If.

Consider the following two formulas for predicting a child’s height from his age. In each case there is a base height of 58.125 inches to which a quantity is added depending on the value of the age variable.

The Match conditional evaluates faster than the If function because the age variable is evaluated only once for each row in the data table. The If condition must evaluate the age variable at each If clause for each row until a clause evaluates as true.

Table Templates and ScriptsJMP data table templates store formulas that are frequently applied to data. If you find yourself repeat-edly entering the same formulas into different data tables, try creating a template table into which you can paste new data. A table template is a table, usually without any rows, but with columns that store formulas. When you add or change rows of data, the formula columns automatically evaluate. A collec-tion of JMP table templates are in the Templates Folder of the Sample Data.

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In addition, most table templates also have JSL scripts in the Templates folder that accomplish the same function as the template. See the JMP Scripting Guide for documentation of script commands.

Grid Templates

The grid templates provide a quick way to compute an evenly spaced x, y grid for three-dimensional plotting. There are two grid templates in the Templates folder of the Sample Data:

• The template Grid Template (x=y) produces a grid in which x and y both range from columns called Start Grid to End Grid.

• The template Grid Template (x !=y) allows x to range from columns called Start x to End x, while y ranges from Start y to End y.

To use a grid template, first save it under a new name. Next, specify the grid endpoints in the first row of the new table. Then, enter a function of x and y into the Surface Formula column. For example, you might want to paste a prediction formula from a response surface analysis into the Surface Formula.

At this point you still have only one row in your table. To obtain the grid, choose Add Rows from the Rows menu. Enter the number of rows you want minus 1 into the How Many Rows to add box. As soon as you add rows, JMP evaluates all column formulas. To view the data in three dimensions, use the Spinning Plot platform.

Note: To create an n-by-n square grid, you add n2 – 1 add rows to the data table because there is already one row in the table.

If you need to alter the grid size or several axis ranges, remove all but the first row from your data table before doing so. This allows JMP to reevaluate formula columns for only the first row until all changes are made. See the Cowboy Hat Template and Odor Control Surface template data tables for examples that use grids.

Note: Adding colors to your points based on the value of the column Surface Formula can enhance your spinning plot and is simple to do. Use the Distribution of Y to create a histogram of Surface Formula column. Get the hand tool and adjust the number of bins until there are at most twelve bins. Choose Save Level Numbers from the Surface formula popup menu to save the bin levels with values 1 to 12 into the data table. Now create a row state column and give it the formula:

Hue State(Level Surface Formula)

Actuarial Life Table Template

Lifetime or survival data are often analyzed by computing actuarial life tables. Life tables estimate the survival, failure or probability, and hazard distribution functions of right-censored lifetime data. Because life tables are a series of formulas applied to a few columns of data, a table template is possible.

To use the Actuarial Life Table template, save the template under a new name and then paste in your own data for the columns Years, # out, # lost, # dying, and Sample Size. The formula columns com-pute values for the survival, failure, and hazard distribution functions along with their standard devia-tions, and 95% confidence limits. Descriptions of each column are on the Notes tab in the Column Info dialog.

160 7 Using the Formula Editor—Keyboard Shortcuts

After you enter or paste your data into the life table template, you can graph the distribution functions using the Overlay Plot command in the Graph menu.

See the JMP data table called Angina Life Table Data table (Lee, 1980) for a life table example. It is in the Example Data for Templates folder in the Templates folder of the Sample Data.

Note: The complete reference for (Lee, 1980) is in the appendix “References,” p. 679 of theJMP Statis-tics and Graphics Guide.

Central Limit Theorem Template

The Central Limit Theorem table template is used to demonstrate that the frequency distribution of the sample mean becomes normal as the sample size n increases. Columns of the table contain formulas to compute sample means for n equal to 1, 5, 10, 50 and 100. The samples are generated from a distribu-tion of uniform random numbers raised to the fourth power (a highly skewed distribution). After you add rows (choose Add Rows from the Rows menu), each cell contains a sample mean. The number of samples taken is equal to the number of rows you add to the template.

To view the frequency distributions of the sample means, use the Distribution platform to plot all col-umns. Use the Normal option in the Fit Distribution commands found on the each distribution’s popup menu to overlay a normal curve. It is easy to see that the frequency distributions of large sample means follow a normal distribution more closely than the distributions for smaller sample means.

Nonlinear and Loss Function Templates

Since the Nonlinear Fit command requires that you enter fitting and loss functions as formula columns in a data table, you might find it convenient to create templates for frequently used models. See “Non-linear Regression,” p. 335 of theJMP Statistics and Graphics Guide, for more information about the non-linear table templates. A collection of templates with nonlinear fitting functions and loss functions can be found in the Sample Data Nonlinear Templates folder, Loss Function Templates folder, and the Nonlinear Examples folder.

Keyboard ShortcutsYou can use the mouse or the keyboard shortcuts shown in Table 7.2 Keyboard Shortcuts, to apply Formula Editor features. Replace a selected expression with the function or argument by entering the corresponding key command.

Table 7.2 Keyboard ShortcutsItem ShortcutMissing Element • DeleteSubscript [

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() set of parentheses (() set of parentheses )• * on keypad+ + on keypad– – on keypad÷ / on keypad+/– Shift-minusAnd &Not !Or |Power ^Delete DeleteNew argument ,x<y <x=y =x>y >

Table 7.2 Keyboard ShortcutsItem Shortcut

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Formula Editor Functions

Formulas can be simple assignments of numeric, character, or row state constants or can contain com-plex evaluations based on conditional clauses.

When you create a formula for a column, that formula becomes an integral part of the data table. The formula is stored as part of a column’s information when you save the data table, and it is retrieved when you reopen the data table. You can examine or change a column’s formula at any time by opening its Formula Editor window.

A column whose values are computed by a formula is both linked and locked. It is linked to (or depen-dent on) all other columns that are part of its formula. Its values are automatically recomputed when-ever you edit the values in these columns. It is also locked so that its data values cannot be edited, which would invalidate its formula.

The Formula Editor window operates like a pocket calculator with buttons, displays, and an extensive list of easy-to-use features. This chapter begins with a simple Formula Editor example and serves as a reference guide for all Formula Editor functions and features.

8Contents

The Function Browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Row Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166Numeric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168Transcendental Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170Trigonometric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172Character Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Comparison Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178Conditional Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Probability Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184Statistical Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194Random Number Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Date Time Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Row State Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205Assignment Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

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The Function BrowserThe function browser groups the Formula Editor functions by the topics as shown at the left in Figure 8.1. When you click a function group, a popup menu lists the functions in that group. Alterna-tively, you can select a choose Functions (All) from the menu above the function browser to see all func-tions listed in alphabetical order.

Figure 8.1 The Function Browser

To use a function, select an expression in a formula by clicking on it and then click any item in the function browser topics. When the formula appears in the editing area, highlight a term in its argument and click on columns, other functions, or other formula elements to enter them into the argument. Most formulas give hints about appropriate arguments, and show a small caret in the argument area if additional arguments can be added.

The function categories are briefly described in the following list. The remaining sections in this chap-ter give a detailed description and examples of each function in the function categories. They are pre-sented in the order you find them in the function browser.

• Row functions lists miscellaneous functions such as Lag, Dif, Subscript, Row, and NRow

• Numeric functions are terms commonly used in formulas.

• Transcendental functions are functions such as natural log, common log, exponential, root, facto-rial, combinatorial, beta, and gamma.

• Trigonometric functions are the standard trigonometric functions: sine, cosine, tangent, inverse functions, and hyperbolic functions.

• Character functions operate on character arguments for trimming, finding the length of a string, changing numbers to characters or characters to numbers, and more.

• Comparison functions are the standard logical comparisons such as less than, less than or equal to, not equal to, and so forth.

166 8 Formula Editor Functions—Row Functions

• Conditional functions are programming-like functions such as If, Match, and Choose.

• Probability functions compute probabilities and quantiles for the normal, Student’s t, chi-square, and F distributions.

• Statistics functions calculate standard statistical quantities such as the mean or standard deviation.

• Random functions generate random numbers based on predefined distributions such as the uni-form, normal, Cauchy, and so on. There is also a function to randomize the order of table rows.

• Date Time functions require arguments with the date data type, which is interpreted as the number of seconds since January 1, 1904. Date Time functions return values such as day, week, or month of the year. They can also compute dates and can find data intervals.

• Row State functions assign or detect row state status of color, marker, label, hidden, excluded, or selected.

• Assignment functions place the value on the right side of the assignment operator into the variable on the left side of the operator.

Row Functions

Row Functions let you reference specific rows or cells within specific rows:

Lag

The Lag function returns the value of the first argument in the row defined by the current row less the second argument. The default Lag is one, which you can change to any number. The value returned for any lag that identifies a row number less than one is missing. Note that Lag(X, ßn) gives the same result as the subscripted notation, XRow( )–n.

Dif

The Dif function returns the difference between the value of the first argument in the current row and its value in the row defined by the current row less the second argument. The default Dif is one, which

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you can change to any number. Note that Dif(X, n) gives the same result as XRow()–XRow()-n, or as XRow()–Lag(X, n).

Subscript

The Subscript function enables you to use a column’s value from a row other than the current row. To use a subscript, enter a column name in the formula display and click Subscript to display the col-umn’s empty subscript argument. You can enter any numeric expression into the subscript argument. Subscripts that evaluate to nonexistent row numbers produce missing values. A column name without a subscript always refers to the current row.

To remove a subscript from a column, select the subscript and delete it. Then delete the missing box.

The formula CountRow() – CountRow()–1, where Row() is the row number as described below, uses subscripts to calculate the difference between each pair of values from the column named count. This result is the same as that given by the Dif() function. When Row() is 1, the computation produces a missing value.

The formula

calculates a column called Fib, which contains the terms of the Fibonacci series (each value is the sum of the two preceding values in the calculated column). It shows the use of subscripts to do recursive calcu-lations. A recursive formula includes the name of the calculated column, subscripted such that it refer-ences only previously evaluated rows (rows 1 through (i–1)).

The calculation of the Fibonacci series shown above includes a conditional expression and a compari-son. See the sections Comparison Functions, p. 178, and Conditional Functions, p. 179, for more information about these functions.

Row

The Row function is the current row number when an expression is evaluated for that row. You can use Row() in any expression, including column name subscripts. The default subscript of a column name is Row() unless otherwise specified.

NRow

The NRow function is the total number of rows in the active data table.

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Numeric Functions

The Numeric functions are terms commonly used in formulas.

Abs

The Abs (absolute value) function returns a positive number of the same magnitude as the value of its argument. For example, |5| and |–5| both result in 5.

Modulo

The Modulo function returns the remainder when the second argument is divided into the first. For example, Modulo(6, 5) results in 1.

Ceiling

The Ceiling function returns the smallest integer greater than or equal to its argument. For example, Ceiling(2.3) results in 3, while Ceiling(–2.3) results in –2.

Floor

The Floor function returns the largest integer less than or equal to its argument. For example, Floor(2.7) results in 2, but Floor(–.5) results in –1.

Round

The Round function rounds the first argument to the number of decimal places given by the second argument. For example, Round(3.554, 2) rounds to 3.55 and Round(3.555, 2) rounds to 3.56.

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Count

The Count function creates a list of values beginning with the from value and ending with the to value. The number of steps specifies the number of values in the list between and including the from and to values. Each value determined by the first three arguments of the Count function occurs con-secutively the number of times you specify with the times argument. When the to value is reached, count starts over at the from value.

Optionally, you can add the times argument with the insert button on the keyboard. This argument is on by default, but repeats the count process as many times as you specify, as illustrated by the Count4 column in the data table in Figure 8.2. To add any argument to the Count function, highlight the argu-ment preceding the one you want to enter and either type a comma or use the insert button.

The columns in the data table shown in Figure 8.2 result from the following formulas:

• Count (1, 9, 2) gives Count 1• Count (1, 9, 3) gives Count 2• Count (1, 9, 9) gives Count 3 • Count (1, 9, 3, 3) gives Count 4Figure 8.2 Example of the Count Function

The Count function is especially useful for generating a column of grid values. For example, the follow-ing formulas create a square grid of increment NRow() (the Row function that gives the total number of rows in the data table) and axes that range from –5 to 5. There are table templates in the Sample Data folder for grids. See the previous chapter, Using the Formula Editor, p. 143, for details.

Count (–5, 5, Root(NRow()))Count (–5, 5, Root(NRow(), Root(NRow()))

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Transcendental Functions

In the Transcendental functions category, the Formula Editor supports logarithmic functions for any base, functions for combinatorial calculations, the Beta function, and several gamma functions.

Exp

The Exp function raises e to the power you specify. Thus, Exp(1) = e.

Log Log10

The Log function calculates the natural logarithm (base e). To change the default base, highlight the argument and type a comma or click the insert key on the keypad. The base appears and is editable. The Log argument can be any numeric expressions. The expression Log(e) evaluates as 1, and Log2(32) is 5. The Log10 function calculates the logarithm of base 10 only.

Squish

The Squish function is an efficient computation of the function, 1 / (1+e-x), where x is any numeric column, variable, or expression.

Root

The Root function calculates the root of its argument as specified by the index. Root initially shows with an index of 2. To change the index, highlight the index argument and enter the value you want.

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Factorial

The Factorial function returns the product of all numbers 1 through the argument you specify. For example, Factorial(5) evaluates as 120.

NChooseK

The NChooseK function returns the number of n things taken k at a time (n choose k). and is com-puted in the standard way using factorials, as n! / (k!(n – k)!). For example, NChooseK(5,2) evaluates as 10.

Beta(m, n)

The two parameter Beta function, B(m, n), is written terms of the Gamma function as

See the discussion of the Gamma function below.

Gamma

The Gamma function, denoted Γ(i) is defined by

In JMP, this formula computes gamma for each row using the current row number as i. The current row number is used in the formula for the gamma distribution and other probability distributions. Gamma with a single argument is the same as Gamma(x, infinity). The optional second argument changes the upper integrand from infinity to the value you enter. Other interesting gamma function relationships are

• for any α > 1, Γ(α) = (α–1) • Γ(α–1)

• for any positive integer, n, Γ(n–1) = n factorial, denoted n!

• Γ(0.5) = the square root of π.

B m n ),( ) Γ m( )Γ n( )

Γ m n+( )--------------------------=

Γ i( ) xi 1–( ) e

x–( )dx0

∞∫=

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LGamma

The LGamma function is the natural log of the result of the gamma function evaluation. You get the same result using the natural log function in the Formula Editor with the Gamma function. However, the LGamma function computes more efficiently than do the Log (natural log) and the Gamma func-tions together.

Trigonometric Functions

The Formula Editor supports most Trigonometric functions. The following list shows how each func-tion appears in the Formula Editor, and gives an example.

Sine, Cosine, Tangent

The Sine and Cosine functions calculate the sine and cosine of their respective arguments given in radians. For example, the expression Sine(0) evaluates as 0, and Cosine(0) evaluates as 1. The Tangent function calculates the tangent of an argument given in radians. The expression Tan(.25) evaluates as 0.255342.

ArcSine, ArcCosine, ArcTan

The ArcSine and ArcCosine functions return the inverse sine and inverse cosine of their respective arguments. The returned value is measured in radians. For example, both expressions ArcSine(1) and ArcCosine(0) evaluate as 1.57080. The ArcTan function returns the inverse tangent of its argument. The returned value is measured in radians. The expression ArcTan(0.5) evaluates as 0.46364.

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SinH, CosH, TanH

The SinH and CosH functions return the hyperbolic sine and hyperbolic cosine of their respective arguments. The expression SinH(1) evaluates as 1.175201, and CosH(0) evaluates as 1.0. The TanH function returns the hyperbolic tangent of its argument. The expression TanH(1) evaluates as 0.761594.

ArcSinH, ArcCosH, ArcTanH

The ArcSinH and ArcCosH functions return the inverse hyperbolic sine and inverse hyperbolic cosine of their respective arguments. The expression ArcSinH(1) evaluates as 0.881374, and ArcCosH(1) is 0. The ArcTanH function returns the inverse hyperbolic tangent of its argument. The expression ArcTanH(0.5) evaluates as 0.549306.

Character Functions

Character functions accept character arguments or return character strings and can convert the data type of a value from numeric to character, or character to numeric.

Note: Character functions can result in either character or numeric data. If you calculate a data type different than the one specified, the data type of the computed column is automatically changed to match the result.

Note: When character functions result in character values greater than the Field Width in the Column Info dialog, the length of the character field is automatically increased to hold the computed result.

Note: Arguments which are literal character strings must be enclosed in quotation marks.

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Char

The Char function produces a character string that corresponds to the digits in its numeric argument. For example,

Char(1.123)

evaluates as “1.123”.

Concat ||

The Concat function (shown as two vertical bars) concatenates character strings to produce a new string with the function’s second character argument appended to the first. For example,

"Dr." || " " || name

produces a new string consisting of the title “Dr.” followed by a space and the contents of the name string.

Contains

The Contains function returns the numeric position within the first argument of the first instance of the second argument, if it exists. The second argument can contain one ore more characters. If the sec-ond argument does not exist, Contains returns a zero. For example,

• Contains("Veronica Layman", "ay") evaluates as 11.

• Contains("Lillie Layman", "L") evaluates as 1.

Munger

The Munger function computes new character strings from existing strings by inserting or deleting characters. It can also produce substrings, calculate indices, and perform other tasks depending on how you specify its arguments.

Note: The Munger function treats uppercase and lowercase letters as different characters.

Text is a character expression. Munger applies the other three arguments to this string to compute a result.

Offset is a numeric expression indicating the starting position to search in the string. If offset is greater than the position of the first instance of the find argument, the first instance is disregarded. If offset is greater than the search string’s length, the string’s length is the offset.

Find/Length is a character or numeric expression. Use a character string as search criterion, or use a pos-itive integer to return that number of consecutive characters starting from the offset position. If you specify a negative integer as the Length value, Munger returns all characters from the offset through to the end of the string.

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Replace (optional argument) can be a string or unspecified. If it is a string and the Find/Offset value is numeric, Munger replaces the search criterion with the Replace string to form the result. If the Find/Offset value is numeric and no string is specified, Munger calculates a substring. If the Find/Length value is a character string, Munger always returns the numeric offset, disregarding the Replace value if it exists. To insert the Replace argument, click on any argument in the Munger function and then click on the insert button. Use the delete button to remove the Replace argument.

The following examples show uses of the Munger function. Assume a character column of names with “Veronica Layman” as one of its values. To simplify the examples, the literal name “Veronica Layman” is the search string instead of a column name.

Insert Characters

This Munger example finds the blank between the first and last name, and inserts the middle initial “J.”. The formula

Munger("Veronica Layman", 1, " ", " J. ")

inserts the middle initial J., and evaluates as Veronica J. Layman.

Note: Double quotes are required by the Munger function for literal strings, including strings that con-sists of a blank or when leading or trailing blanks are part of a string.

Delete Characters

To delete one or more characters from a string, designate the characters to delete as the find string in the Munger function, and enter a replace string that is two quotation marks to indicate a null string. For example, the function,

Munger("Veronica Layman", 1, "onic", "")

removes the “onic” from Veronica and evaluates as Vera Layman.

Note: A replace field with a null (no value) string enclosed in quotation marks is different from a replace field with no value. If you delete the replace string altogether, Munger shows the argument name ("replace") in the Formula Editor window and behaves as if that optional argument doesn't exist, as in the next example. The resulting data type may also change from character to numeric, depending on the value of the Find/Replace argument.

Find the Position (Index) of a Value

When the Find/Length field contains characters, Munger behaves like an index function and returns the numeric position of the first instance of the search string if it exists. For example,

Munger("Veronica Layman", 1, " ")

searches for a single blank and finds it in position 9. If the search string is not found, Munger returns a zero. This use of Munger produces the same result as the Contains function, described previously in this section.

Find a substring

Munger can extract substrings. For example, to see the first name only

Munger("Veronica Layman", 1, 8,)

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starts at position 1, reads through position 8, and ignores the remaining characters because the replace function is not defined. This yields “Veronica.” This produces the same result as the Substring, described later in this section.

An alternative way to find a substring is with a start value, any negative find value, and a no replace argument. This example returns “Layman.”

Munger("Veronica Layman", 9, –1,)

Lowercase, Uppercase

The Lowercase function converts any upper case character found in its argument to the equivalent lowercase character. For example, Lowercase("VERONICA LAYMAN") evaluates as “veronica layman”. The Uppercase function converts any lower case character found in its argument to the equivalent uppercase character. For example, Uppercase("Veronica Layman") evaluates as “VERONICA LAYMAN”.

Length

The Length function calculates the length of its argument. For example, Length("Veronica") eval-uates as 8.

Num

The Num function produces a numeric value that corresponds to its character string argument when the character string consists of numbers only. If a character string contains a non-numeric value, the result is a missing value. For example, Num(“1.123”) evaluates as the number 1.123.

Substr

The Substr function extracts the characters that are the portion of the first argument beginning at the position given by the second argument and ending based on the number of characters specified in the third argument. The first argument can be either a character column or a literal value. The starting argument and the length argument can be numbers of expressions that evaluate to numbers.

For example, to show the first name only, Substr("Veronica Layman", 10, 6) starts at position 11 and reads through position 16, which yields “Layman.”

Trim

The Trim function produces a new character string from its argument, removing any trailing blanks. For example, Trim("john ") evaluates as “john”.

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Word

The Word function extracts the nth word from a character string. One or more spaces define where each word begins and ends unless the optional delimiters argument is specified.

For example, Word(2, "Veronica Layman") returns "Layman".

To insert the delimiters argument, click on any argument in the Word function and then click on the insert button on the keypad. Use the delete button to remove the delimiters argument.

Most special characters act as single delimiters. You can enter any character or set of characters to act as an word delimiter. For example, to extract the last name in the following example, use a comma and blank together as the delimiting characters and ask for the first word. The following function returns the word “Layman”.

Word(1, "Layman, Veronica", ", ")

Item

The Item function is different than the Word function because of the way it treats word delimiters. If a delimiter is found multiple times, or you enter a delimiter with multiple characters, the Word function treats them as a single delimiter. The Item function uses each delimiter to define a new word position.

To compare, suppose a name is of the form lastname, firstname. The delimiter is a comma followed by a blank.

Item(2, "Layman, Veronica", ", ")Word(2, "Layman, Veronica", ", ")

The Item function returns a missing value because it treats the comma and blank separately and finds nothing between them. The Word function treats the comma and blank as a single delimiter and finds "Veronica" as the second word.

Hex

The Hex function returns the hex representation of its argument. If the argument is character (in quotes), then the result is a character string twice as long containing the hexadecimal codes for the char-acter values. For example, Hex("A") returns the string “41”.

If the argument is numeric, the Hex function returns an 8-hex-character representation of the integer returned. For example, Hex(12) returns the string "0000000C" (Mac) or "0C000000"(PC).

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Repeat

The Repeat function creates a string that is the first argument repeated the number of times specified by the second argument. The first argument can be either a character literal, a character variable, or a character expression. For example, Repeat(“Katie”, 3) creates “KatieKatieKatie”.

Note: The third argument applies when Repeat is used in a JSL script to repeat a matrix. When the first argument is a matrix, the second argument is the row-wise repeat and the third argument is the column-wise repeat.

Comparison Functions

Comparison functions compare the values of their arguments. Each comparison relationship evaluates as true or false based on numeric magnitudes or character rankings.

Comparisons are most useful when you include them in conditional expressions, but they can also stand alone as numeric expressions if neither term in comparison is missing. A true relationship evalu-ates as 1, and false evaluates as 0.

A relational symbol’s arguments can be any two expressions. However, both arguments in a Comparison function must be of the same data type. The relational symbols used in comparison expressions are:

Note: JMP displays an error if you use a singe “=” in a conditional where “==” is expected.

Table 8.1 Comparison functions< less == equal> greater != not equal<= less or equal a<X<=b X greater than a and less than or equal b>= greater or equal a<=X<b X greater than or equal a and less than b

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The Formula Editor uses the International Utilities package when comparing character strings. This package contains different rankings for each international character set and takes diacritical marks into consideration.

Note: Do not use comparison operators to specifically compare to a missing value. If either term in a comparison is missing, the result of the comparison is missing (not 0 or 1). Use the Is Missing func-tion to detect a missing value.

Is Missing

The Is Missing function returns a one (1) if the value of the argument for the current row is missing, and a zero if the value is not missing. The Formula Editor excludes missing numeric values from its sta-tistical calculations.

Note: You can write a comparison function using the == function (see Comparison Functions, p. 178 earlier in this chapter) and compare a numeric variable to the numeric missing value (•), or a character variable to a null string. The result is missing if either of the comparison arguments are missing.

Conditional Functions

JMP offers conditional expressions (called conditionals for short) in the Conditional functions list. These expressions let you build a sequence of clauses paired with result expressions. Constructing a sequence of clauses is the way you conditionally assign values to cells in a calculated column. With If and Match, the Formula Editor searches down from the top of the sequence for the first true clause and evaluates the corresponding result expression. Subsequent true clauses are ignored. For maximum effi-ciency, list the most frequently evaluated clause/result pairs first in the sequence. When no clause is true, the Formula Editor evaluates the result expression that accompanies the else clause. With Choose the Formula Editor goes directly to the correct choice clause and evaluates the result expression.

To build a conditional expression, first select a type of conditional. Then, use the insert and delete clause buttons on the Formula Editor panel to expand the expression. Each feature is described below.

Note: All result expressions in a conditional expression must evaluate to the same data type. A missing term matches any data type. By definition, expressions that evaluate as zero are false. If an expression

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evaluates as missing, no clauses are executed and missing is returned. All other numeric expressions are true.

The following examples show how to use conditionals.

If

When you select If from the Conditions list, the Formula Editor shows a single If condition with a missing expression and a missing then clause, as shown above. You can highlight either the expr or then clause to enter an expression. When you highlight an expression in any formula and click the insert button on the keypad, the Formula Editor creates a new conditional expression with the original high-lighted expression as expr and a missing else clause.

To add a new condition to the If conditional, highlight then clause and click the insert button on the keypad. Initially this changes the existing else condition to an expr clause. Click the insert button again to add an else clause. Repetitively clicking then or else clauses alternately changes the else to expr or adds a new expr clause. To delete a clause, select the then clause above it and click the delete button on the keypad or the delete key on your keyboard.

A conditional expression is usually a comparison. However, any expression that evaluates as a numeric value can be a conditional expression.

Note: By definition, expressions that evaluate as zero are false. If an expression evaluates as missing, no clauses are executed and missing is returned. All other numeric expressions are true.

To enter a conditional expression, fill the missing terms with expressions. For example, to calculate count as a percentage of total when total is not 0, enter the conditional expression (using columns called count and total)

In text form, the if statement above looks like this:

If(total!==0, (count/total)*100, 0)

To enter an if statement in text form, add pairs of arguments for each if/then clause in the state-ment, and a single last argument for the else clause if needed.

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Match

When you select Match, the Formula Editor shows a single Match condition with a missing expression and a missing then clause. You can highlight either expr, value, or the then clause to enter an expres-sion. When you highlight an expression in any formula and click Match, the Formula Editor creates a new Match conditional with the original highlighted expression as expr and nothing for the value and else clause.

You add and delete clauses in a Match conditional the same way as in the If conditional described pre-viously. To delete a clause, select the then clause above it and click the delete button on the keypad.

Match compares an expression to a list of clauses and returns the value of the result expression for the first matching clause encountered. With Match, you provide the matching expression only once and then give a match value for each clause. The Match expression is often just a column name, but can be any valid formula expression.

For example, to assign character literals to age values in the Big Class data table, enter the conditional expression

Note: Shift-click is a shortcut for building Match conditionals. Select the Match column in the column selector list. Then hold down the Shift-key and select Match in the Conditional functions list. For example, suppose you want a Match conditional for the nominal variable Type from the Hot dogs data table, with values Beef, Meat, and Poultry. First choose the column Type from the column selector list. Then with Type selected in the formula display, Shift-click on Match to see the following formula:

.

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Note: Match evaluates faster and uses less memory than an equivalent If. See Efficiency, p. 158 in the “Using the Formula Editor” chapter, for a comparison of Match and If conditionals.

Choose

Choose is a special case of Match in which the arguments of the condition are a sequence of integers starting at 1. The value of clause replaces the match condition.

When you highlight an expression and click Choose, the Formula Editor creates a new conditional expression with one clause. Use the insert and delete buttons on the keypad to add new clauses or remove unwanted clauses, as described previously for the If conditional.

Choose evaluates the choose expression and goes immediately to the corresponding result expression to generate the returned value. With Choose, you provide a choosing expression that yields sequential integers starting at 1 only once, then you give a choice for each integer in the sequence.

For example, to assign character class values to protein to fat ratios in the Hot dogs table, which has rat-ings 1, 2, and 3, enter the conditional expression

The logical functions (And, Or and Not) evaluate as either true or false, and are usually used in con-junction with conditional statements. The next examples show how the logical functions evaluate.

And &

The And function evaluates as 1 when both of its arguments are true. Otherwise it evaluates as 0 (see Figure 8.3). The formula

labels participants as drivers only if both comparisons are true.

Or |

The Or function evaluates as 1 when either of its arguments is true. If both of its arguments are false, then the Or expression evaluates as 0 (see Figure 8.3). The formula

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assigns males and all participants over 13 to cabin 1.

Not !

When its argument is false, the Not function evaluates as 1. Otherwise, Not evaluates as 0.

When you apply the Not function, use parentheses where necessary to avoid ambiguity. For example, !(weight==64) can be either true or false (either 1 or 0), but (!weight)==64 is always false (0) because Not can only return 0 or 1. Expressions such as !(weight==64) can also be entered as weight != 64.

The truth tables on the left in Figure 8.3 illustrate the results of the And (&) and Or (|) functions when both arguments have nonmissing values that evaluate to true or false. The table on the right illustrates the result when either the left or right expression (call them a and b) or both have missing values.

Figure 8.3 Evaluations of And and Or Expressions

Note: The following statements (Interpolate, Step, For, and While) are most often used in conjunc-tion with other commands to build a JSL script. You can use the column Formula Editor to create and execute a script in that column, but this is not recommended because of dependencies and ambiguities that can result. Most often, scripts are stored as .JSL files, and can be saved with a data table as a Table Property. See the JMP Scripting Guide for documentation of all scripting commands.

Interpolate Step

The Interpolate function linearly interpolates the y value between two points, x1, y1 and x2, y2 that corresponds to the arguments you give. You can insert additional pairs of X, Y arguments with the insert key. Interpolate finds the pair of X, Y points that correspond to the X value and completes the interpolation.

The Step function is like Interpolate except that it returns the Y value corresponding to the greatest X value less than or equal to the X, Y arguments. That is, it finds the corresponding Y for a given X from a step function rather than a linear fit between points. Like Interpolate, you can have as many X, Y argument pairs as you want.

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For

For repeats the statements in the body argument as long as the while condition is true. The init and next control the iterations.

while

While repeatedly tests the expr condition and executes the body until expr is no longer true.

Probability Functions

Probability functions calculate quantiles and probabilities for beta, chi-square, F, gamma, normal, Stu-dent’s t, and Weibull distributions, and quantiles for the Tukey HSD.

Beta Density, Beta Distribution, Beta Quantile

The Beta Density function requires a quantile argument, and optionally accepts a shape parameter alpha > 0, and a scale parameter, beta > 0. It returns the value of the Beta probability density function (pdf ) for the given arguments. The beta density is sometimes useful for modeling the probabilistic behavior of random variables such as proportions constrained to fall in the interval [0, 1]. Examples of densities for several combinations of α and β are shown in Figure 8.4.

The Beta Distribution has a positive density only for an X interval of finite length, unlike normal and gamma which have positive density over an infinite interval. The theoretical beta distribution has a shape parameter, α > 0 and a scale parameter, β > 0, and constants A ≤ x ≤ B that define the interval for which the distribution has values. The Beta Distribution function accepts the response variable argu-ment x, whose range defines the interval for the distribution. The standard beta distribution occurs in the interval [0, 1]. The Beta Distribution function is the inverse of the Beta Quantile function.

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The Beta Quantile function accepts a probability argument p, and shape and scale parameters, α > 0 and β > 0. It returns the pth quantile from the standard beta distribution. The Beta Quantile function is the inverse of the Beta Distribution function.

Figure 8.4 Overlay Plot of Three Beta Density Curves

ChiSquare Density, ChiSquare Distribution, ChiSquare Quantile

The ChiSquare Density function accepts a quantile argument from the range of values for the Chi Square distribution, a degrees-of-freedom argument, and optionally, a noncentrality parameter. It returns the value of the Chi Square density function (pdf ) for the arguments. Figure 8.5 shows the shape of three Chi Square curves, with degrees of freedom 4, 6, and 10.

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Figure 8.5 Overlay Plot of Three Chi-Square Density Curves, DF=4, DF=6, DF=10

The ChiSquare Distribution function accepts a response argument (range of X values) and three parameter arguments: a quantile, a degrees-of-freedom, and a noncentrality parameter. It returns the probability that an observation from the chi-square distribution with the specified noncentrality parameter and degrees-of-freedom is less than or equal to the given quantile. For example, the expres-sion

ChiSquare Distribution(11.264, 5)

returns the probability that an observation from the chi-square distribution centered at 0 with 5 degrees-of-freedom is less than or equal to 11.264. The expression evaluates as 0.95361.

The ChiSquare Distribution function accepts integer and noninteger degrees-of-freedom. It is centered at 0 by default. The ChiSquare Distribution function is the inverse of the ChiSquare Quantile func-tion.

The ChiSquare Quantile function accepts three arguments: a probability p, a degrees-of-freedom, and a noncentrality parameter. It returns the pth quantile from the chi-square distribution with the speci-fied noncentrality parameter and degrees-of-freedom. For example, the expression

ChiSquare Quantile(.95, 3.5, 4.5)

returns the 95% quantile from the chi-square distribution centered at 4.5 with 3.5 degrees-of-freedom. The expression evaluates as 17.50458.

The ChiSquare Quantile function accepts integer and noninteger degrees-of-freedom. It is centered at 0 by default. The ChiSquare Quantile function is the inverse of the ChiSquare Distribution function.

F Density, F Distribution, F Quantile

The F Density function accepts a quantile argument from the range of values for the F distribution, numerator and denominator degrees-of-freedom arguments , and optionally, a noncentrality parameter.

DF=10DF=6

DF=4

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It returns the value of the F density function (pdf ) for the arguments. Figure 8.6 shows the shape of three F density curves, with degrees of freedom (5, 10), (10, 20), and (20, 50).

Figure 8.6 Overlay Plot of Three F Density Curves

The F Distribution function accepts four arguments: a quantile, a numerator and denominator degrees-of-freedom, and a noncentrality parameter. It returns the probability that an observation from the F distribution with the specified noncentrality parameter and degrees-of-freedom is less than or equal to the given quantile. For example, the expression

F Distribution(3.32, 2, 3)

returns the probability that an observation from the central F distribution with 2 degrees-of-freedom in the numerator and 3 degrees-of-freedom in the denominator is less than or equal to 3.32. The expres-sion evaluates as 0.82639.

The F Distribution function accepts integer and noninteger degrees-of-freedom. By default it is cen-tered at 0 and has 1 numerator degree of freedom. The F Distribution function is the inverse of the F Quantile function.

The F Quantile function accepts four arguments: a probability p, a numerator and denominator degrees-of-freedom, and a noncentrality parameter. It returns the pth quantile from the F distribution with the specified noncentrality parameter and degrees-of-freedom. For example, the expression

F Quantile(0.95, 2, 10, 0)

returns the 95% quantile from the F distribution centered at 0 with 2 degrees-of-freedom in the numerator and 10 degrees-of-freedom in the denominator. The expression evaluates as 4.1028.

The F Quantile function accepts integer and noninteger degrees-of-freedom. By default, it is centered at 0 and has 1 numerator degree of freedom. The F Quantile function is the inverse of the F Distribution function.

F(20, 50)

F(5, 10)F(10, 20)

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Gamma Density, Gamma Distribution, Gamma Quantile

The Gamma Density function (gamma pdf ) requires a quantile argument.

Optionally the Gamma Density function accepts a shape parameter α, which defaults to 1, a scale parameter β with a default 1, and a threshold parameter with default zero.

Figure 8.7 shows the shape of gamma probability density functions for shape parameters of 1, 3, and 5. The standard gamma density function is strictly decreasing when α (shape) ≤1. When α > 1 the den-sity function begins at zero when x is 0, increases to a maximum, and then decreases.

The Gamma Distribution function is based on the standard gamma function, and accepts a single argument with a quantile value. Optionally, the Gamma Distribution function accepts shape, scale, and threshold parameters, with defaults as described previously in the discussion of the Gamma Density. It returns the probability that an observation from a standard gamma distribution is less than or equal to the specified x. The Gamma Distribution function is the inverse of Gamma Quantile func-tion.

The Gamma Quantile function accepts a probability argument p, and returns the pth quantile from the standard gamma distribution with the shape parameter you specify. The Gamma Quantile function is the inverse of the Gamma Distribution function.

Figure 8.7 Overlay Plot of Gamma Density with Shape Parameter 1, 3, and 5

Shape=1

Shape=3Shape=5

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Normal Density, Normal Distribution, Normal Quantile

The Normal Density function accepts a quantile argument from the range of values for the standard normal distribution. It returns the value of the standard normal probability density function (pdf ) for the argument. For example, you can create a column of quantile values (X) with the formula count(-3, 3, nrow()), and a second column computed as Normal Density(X) to generate den-sity values. Then use Graph > Overlay to plot the normal density by X. Figure 8.8 shows an overlay plot of normal density curves with various means and standard deviations.

The Normal Distribution function accepts a quantile argument from the range of values for the stan-dard normal distribution with mean 0 and standard deviation 1. It returns the probability that an observation from the standard normal distribution is less than or equal to the specified quantile. For example, the expression Normal Distribution(1.96) returns 0.975, the probability that an observation from the standard normal distribution is less than or equal to the 1.96th quantile. Optionally, you can specify mean and standard deviation parameters to obtain probabilities from nonstandard normal dis-tributions. The Normal Distribution function is the inverse of the Normal Quantile function.

The Normal Quantile (Probit) function accepts a probability argument p, and returns the pth quantile from the standard normal distribution. For example, the expression Normal Quantile(0.975) returns the 97.5% quantile from the standard normal distribution, which evaluates as 1.96. Optionally, you can specify parameter values for the mean and standard deviation to obtain quantiles from nonstandard normal distributions. The Normal Quantile function is the inverse of the Normal Distribution function.

Figure 8.8 Overlay Plots of Normal Density Curves with Mean and Std (0,1) (1,1) and (0.5,0.75)

Normal(1,1)

Normal(0.5,0.75)Normal(0,1)

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t Density, t Distribution, t Quantile

The t Density function accepts a quantile argument from the range of values for the t distribution, a degrees-of-freedom argument, and optionally, a noncentrality parameter. It returns the value of the t density function (pdf ) for the arguments. To compare a t density with 5 df with a standard normal dis-tribution you can create a column of quantile values (X) with the formula count(-3, 3, nrow()), a second column computed as t Density(X), and a third column computed as Normal Density(X). Then use Graph > Overlay to plot the t density and the normal density by X to see the plot shown in Figure 8.9. You can see that the t density has slightly more spread than the normal.

The t Distribution function accepts three arguments: a quantile, a degrees-of-freedom, and a noncen-trality parameter. It returns the probability that an observation from the Student’s t distribution with the specified noncentrality parameter and degrees-of-freedom is less than or equal to the given quantile. For example, the expression t Distribution(.9, 5) returns the probability that an observation from the Student’s t distribution centered at 0 with 5 degrees-of-freedom is less than or equal to 0.9. The expression evaluates as 0.79531.

The t Distribution function accepts integer and noninteger degrees-of-freedom. It is centered at 0 by default, but you can enter a value for the noncentrality parameter. The t Quantile function is the inverse of the t Distribution function.

The t Quantile function accepts three arguments: a probability p, a degrees-of-freedom, and a noncen-trality parameter. It returns the pth quantile from the Student’s t distribution with the specified non-centrality parameter and degrees-of-freedom. For example, the expression Student’s t Quantile(.95, 2.5) returns the 95% quantile from the Student’s t distribution centered at 0 with 2.5 degrees-of-freedom. The expression evaluates as 2.558219. The t Quantile function is the inverse of the t Distribution function.

The t Quantile function accepts integer and noninteger degrees-of-freedom. It is centered at 0 by default, but you have the option to enter a value for the noncentrality parameter. The t Distribution function is the inverse of the t Quantile function.

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Figure 8.9 Comparison of Normal Density and t Density

Weibull Density, Weibull Distribution, Weibull Quantile

The Weibull Density function accepts a quantile argument from a range of values for the Weibull dis-tribution. It returns the value of the Weibull probability density function (pdf ), which is the probabil-ity that an observation from a Weibull distribution is less than or equal to the specified quantile argument.

The Weibull distribution has different shapes depending on the values of α (a scale parameter that affects the x direction) and β (a shape parameter). It often provides a good model for estimating the length of life, especially for mechanical devices and in biology. The two-parameter Weibull is the same as the three-parameter Weibull with a threshold of zero. Figure 8.10 shows examples of several combi-nations of shape and scale parameter values, with the threshold parameter equal to zero. The Extreme Value distribution is a two-parameter Weibull (α, β) distribution with the transformed parameters: δ = 1 / α and γ = ln(β).

The Weibull distribution has two parameters, α > 0 and β > 0. When α = 1 the pdf reduces to the exponential distribution (with γ = 1 / β). The exponential distribution is a special case of both the gamma and Weibull distributions. However, there are Weibull distributions that are not exponential distributions.

The Weibull Distribution function uses an argument with a quantile value and, optionally, a value for the scale parameter α and the shape parameter β. It returns the probability that an observation is less than or equal to the specified X for Weibull distribution with the shape and scale parameters you speci-fied The Weibull Distribution function is the inverse of Weibull Quantile function.

normal densityt density

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The Weibull Quantile function accepts a probability argument p, and returns the pth quantile from the Weibull distribution with the shape and scale parameters you specify. The Weibull Quantile function is the inverse of the Weibull Distribution function.

Figure 8.10 Comparison of Weibull Curves

Binomial Distribution, Binomial Probability

The binomial functions accept arguments that are the probability of success p (the event of interest), the number of trials n, and the number of successes k.

The Binomial Distribution function returns the probability that an observation from a binomial distri-bution with parameters p and n is less than or equal to k.

The Binomial Probability function computes the probability that a random variable from a binomial distribution is equal to p.

NegBinomial Distribution, NegBinomial Probability

The negative binomial functions accept an argument that is the probability of success p, a number of trials n, and the count of interest k, which is the number of failures that precede the ith success.

The NegBinomial Distribution function computes the probability that a random variable from a nega-tive binomial distribution is less than or equal to p.

The NegBinomial Probability function computes the probability that a random variable from a nega-tive binomial distribution is equal to p.

Weibull(2,1)

Weibull(1,1)

Weibull(1,2)

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Hypergeometric Distribution, Hypergeometric Probability

The hypergeometric distribution models the total number of successes in a fixed sample drawn without replacement from a finite population. The hypergeometric functions accept as arguments the size of the population N, the total number of items with the desired characteristic in the population, K, the num-ber of samples drawn n, and the number of successes in the sample x.

Hypergeometric Distribution computes the probability that a random variable from a hypergeometric distribution is less than or equal to x.

Hypergeometric Probability computes the probability that a random variable from a hypergeometric distribution is equal to x.

Poisson Distribution, Poisson Probability

The Poisson functions accept an argument that is the count of interest, and lambda, the mean param-eter.

Poisson Distribution computes the probability that a random variable from a Poisson distribution with mean lambda is less than or equal to the count of interest.

Poisson Probability computes the probability that a random variable from a Poisson distribution with mean lambda is equal to the count of interest.

Tukey HSD Quantile

The Tukey HSD Quantile function accepts a probability argument p, and returns the pth quantile from Tukey’s HSD test for the parameters you specify. The alpha argument is the significance level you want, nGroups is the number of groups in a study, and dfe is the error degrees-of-freedom (based on the total study sample). This is the quantile used to calculate least significant difference in Tukey’s multiple com-parisons test.

F Power, F Sample Size

F Power calculates the power from a given situation that involves an F test or t test and F Sample Size computes the sample size. The arguments are the values you specify for computation of a prospec-tive power analysis.

alpha

is the significance level you are willing to tolerate (often 0.05).

dfh

is the hypothesis degrees-of-freedom. It is one (1) for a t test.

dfm

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is the model degrees-of-freedom (such that dfe = n – dfm).

SquaredSize

is the squared effect size scaled by the error variance, which is used for making the noncentrality argument for the F distribution. For this argument, use squared size = ∆2/σ2 where σ2 is the error variance. That is

for a one-sample t test.

for a two-sample t test.

for a k-sample F test.

n

(in the F Power function) is the total number of observations (runs, experimental units, or samples) you expect to have.

(in the F Sample Size function) is the probability you want to have of declaring a significant result.

These functions perform the same computations as the Sample Size, Power command found in the DOE main menu. See the chapter “Prospective Power and Sample Size,” p. 139 of JMP Design of Exper-iments, for a discussion of power and sample size.

Statistical Functions

There are two kinds of Statistical functions:

• The functions with names that have the prefix Col compute statistics for a column of numbers or expressions involving columns.

∆2

x µ–( )2

=

∆2

x1 x2–( )2

2------------------------=

∆2 xi x–( )

2

k---------------------

i 1=

k

∑=

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• The Mean, Std Dev, Number, Sum, Quantile, Maximum, Minimum, and N Missing functions evaluate across columns or arguments. The statistic is computed for each row across the series of arguments. You can use the insert key on the Formula Editor panel, or type a comma to add argu-ments to the functions that accept multiple arguments. When there are multiple contiguous argu-ments, select the function and the first argument, then Shift-click the last argument in the group. These functions then automatically shows with the complete list.

The following paragraphs give descriptions and examples of the Statistical functions.

Col Mean

The Col Mean function calculates the mean (or arithmetic average) of the numeric values identified by its argument. The formula Mean(age) calculates the average of all nonmissing values in the age col-umn.

Col Std Dev

The Col Std Dev function measures the spread around the mean of the distribution identified by its argument. In the normal distribution, about 68% of the distribution is within one standard deviation of the mean, 95% of the distribution is within two standard deviations of the mean, and 99% of the distribution is within three standard deviations of the mean.

Col Number

The Col Number function counts the number of nonmissing values in the column you specify. A miss-ing numeric value occurs when a cell has no assigned value or as the result of an invalid operation (such as division by zero). Missing values show on the spreadsheet as a missing value mark (•). Missing char-acter values are null character strings.

In formulas for row state columns, an excluded row state characteristic is treated as a missing value. The Formula Editor interprets other missing values according to their data types.

Col NMissing

The Col Nmissing function counts the number of missing values in the column you specify. A missing numeric value occurs when a cell has no assigned value or as the result of an invalid operation (such as division by zero). Missing values show in the data grid with a missing value character •. Missing charac-ter values are null character strings.

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Col Sum

The Col Sum function computes the sum of the values in its numeric argument. Missing values are ignored.

Col Minimum and Col Maximum

The Col Minimum function takes the minimum of its numeric arguments. Col Minimum ignores miss-ing values.

The Col Maximum function takes the maximum of a numeric column argument, and ignores missing values.

Col Quantile

The Col Quantile function computes the value at which a specific percentage of the values is less than or equal to that value. For example, the value calculated as the 50% quantile, also called the median, is greater than or equal to 50% of the data. Half of the data values are less than the 50th quantile.

The Col Quantile function’s quantile argument represents the quantile percentage divided by 100. The 25% quantile, also called the lower quartile, corresponds to p = 0.025, and the 75% quantile, called the upper quartile, corresponds to p = 0.075. The default value of p is 0.5 (the median).

The Formula Editor computes a quantile for a column of N nonmissing values by arranging the values in ascending order. The subscripts of the sorted column values, y1, y2,...,yn, represent the ranks in ascending order.

The pth quantile value is calculated using the formula p(N + 1) where p is the quantile and N is the total number of nonmissing values. If I is an integer, then the quantile value is yp = yi. If I is not an inte-ger, then the value is interpolated by assigning the integer part of the result to i, and the fractional part to f and by applying the formula qp = (1 – f )yi + (f )yi+1.

For example, suppose a column has values 2, 4, 6, 8, 10, 12, 14, 16, 18, and 20. The 50% quantile is calculated as 0.5(10 + 1) = 5.5.

Because the result is fractional, the 50% quantile value is interpolated as

(1 – 0.5) x 10 + (0.5) x 12 = (0.5)10 + (0.5)12 = 6 + 5 = 11

The following are example Quantile formulas:

Quantile(age, 1) calculates the maximum age.

Quantile(age, 0.75) calculates the upper quartile age.

Quantile(age, 0.5) calculates the median age.

Quantile(age, 0.25) calculates the lower quartile age.

Quantile(age, 0) calculates the minimum age.

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The Quantile argument can be any expression that evaluates to a value between (and including) 0 and 1. For example, the first formula Figure 8.11 calculates quantile values of age in axcending order for each row. The column then contains the interpolated values of age in ascending order in the calculated column. The second formula lists the interpolated values of age in descending order.

Figure 8.11 Examples of the Quantile Function

Col Standardize

Col Standardize performs the usual standardization on its numeric expression. For each row i, Col Standardize(height) is (HeightRow()–Col Mean(Height))/Col Standardize(Height).

Mean

Mean calculates the arithmetic average of the nonmissing arguments you specify. The arguments can be constants, numbers, or expressions. The Mean function initially shows with a single argument. You add arguments with the insert button or by typing a comma.

Std Dev

Std Dev computes standard deviation of the list of arguments you specify. The arguments can be con-stants, numbers, or expressions. The Std Dev function initially shows with a single argument. You add arguments with the insert button or by typing a comma.

Number

Number counts the number of nonmissing values in the list of arguments you specify.

Sum

Sum returns the sum of the arguments.

Quantile

m

Quantile calculates the quantile given by its first argument for all the following arguments given.

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Summation (Σ ) and Product (Π)

The Summation and Product functions always evaluate for an explicit range of values in a column, as given by the summation indices. All other statistical functions always evaluate on every row.

The Summation function uses the summation notation shown in Figure 8.12. To calculate a sum, replace the missing body term with an expression containing the index variable i, or an index variable you assign. Summation repeatedly evaluates the expression for i = 1, i = 2, through i = NRow() and then adds the nonmissing results together to determine the final result.

You can replace NRow(), the number of rows in the active spreadsheet, and the index constant, i, with any expression appropriate for your formula. For example, the second summation formula in Figure 8.12 computes the total for each row of all revenue values for rows 1 through the current row number, filling the calculated column with the cumulative totals of the revenue column.

The Product function uses the notation shown in the formulas on the right in Figure 8.12. To calculate a product, replace the missing body term with an expression containing the index variable j. Product repeatedly evaluates the expression for i = 1, i = 2, through i = n and multiplies the nonmissing results together to determine the final result.

You can replace NRow(), the number of rows in the active spreadsheet and the index constant, i, with any expression appropriate for your formula.

For example, the expression second product example in Figure 8.12 calculates i! (each row number’s factorial).

Figure 8.12 Examples of Summation and Product Functions

Minimum and Maximum

Minimum and Maximum return the minimum and maximum value, respectively, from the list of non-missing arguments you specify.

N Missing

The N Missing function counts the number of missing values in the list of arguments you specify.

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Random Number Functions

Random Number functions generate real numbers by effectively “rolling the dice” within the con-straints of the specified distribution. Each time you click Apply in the Formula Editor window, these functions produce a new set of random numbers. This section describes the random functions.

Note: Random numbers are generated using the Mersenne-Twister technique. This technique has a period length of 219937-1 (as opposed to 231-1 for the generator used in MP 4.03 and earlier). Details on the generators is found in Matsumoto and Nishimura (1998). The new generators are verified to pass all the DIEHARD tests as documented in Marsaglia (1996).

The previously used routines are still available as RandomSeededUniform() and RandomSeededNormal() and work with the RandomSeed() function call. For compatibility, Version 3 data tables with Random Uniform() and Random Normal() functions are converted to the new names for the old functions, RandomSeededUniform() and RandomSeededNormal().

The seeded functions are important then you want the same series of random numbers generated repeatedly for testing.

Random Uniform

Random Uniform generates random numbers uniformly between 0 and 1. This means that any num-ber between 0 and 1 is as likely to be generated as any other. The result is an approximately even distri-bution. You can shift the distribution and change its range with constants. For example, 5 + Random Uniform()*20 generates uniform random numbers between 5 and 25.

Random Normal

Random Normal generates random numbers that approximate a normal distribution with a mean of 0 and variance of 1. The normal distribution is bell shaped and symmetrical. You can modify the Normal function with constants to specify a normal distribution with a different mean and standard deviation. For example, the formula Random Normal()*5 + 30 generates a random normal variable with a mean of 30 and a standard deviation of 5.

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Random Exp

Random Exp generates a single parameter exponential distribution for the distribution parameter lambda=1. You can modify the Exponential function to use a different lambda. For example, 0.1*Random Exp()-0.1 generates an exponential distribution for lambda=0.1. The exponential dis-tribution is often used to model simple failure time data, where lambda is the failure rate.

Random Gamma

Random Gamma gives a gamma distribution for the parameter, alpha, you enter as the function argument. The gamma distribution describes the time until the kth occurrence of an event. The gamma distribution can also have a scale parameter, beta. A gamma variate with shape parameter alpha and scale beta can be generated with the formula beta*Random Gamma(alpha). If 2*alpha is an integer, a chi-square variate with 2*alpha degrees-of-freedom is generated with the formula 2*Random Gamma(alpha).

Random Cauchy

Random Cauchy generates a Cauchy distribution with location parameter 0 and scale parameter 1. The Cauchy distribution is bell shaped and symmetric but has heavier tails than the normal distribu-tion. A Cauchy variate with location parameter alpha and scale parameter beta can be generated with the formula alpha+beta*Random Cauchy().

Random Triangular

Random Triangular generates a triangular distribution of numbers between 0 and 1, with the midpoint you enter as the function argument. You can add a constant to the function to shift the distribution, and multiply to change its span.

Random Integer

Random Integer generates a uniform distribution of integers between 1 and the argument you enter as n1, if nothing is entered for n2. If you enter both n1 and n2 (n1<n2), Random Integer generates a uni-form distribution of the integers between and including n1 and n2.

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Random Binomial

Random Binomial generates random numbers from a binomial distribution with parameters you enter as function arguments. The first argument is n, the number of trials in a binomial experiment. The sec-ond argument is p, the probability that the event of interest occurs. When n is 1, the binomial function generates a distribution of Bernoulli trials. For example, n =1 and p = 0.5, give the distribution of toss-ing a fair coin. The mean of the binomial distribution is np, and variance is np(1 – np).

Random Negative Binomial

Random Negative Binomial generates a negative binomial distribution for the parameters you enter as function arguments. The first parameter is the number of successes of interest ( r) and the second argu-ment is the probability of success (p). The random variable of interest is the number of failures that pre-cede the ith success. In contrast to the binomial variate where the number of trials is fixed and the number of successes is variable, the negative binomial variate is for a fixed number of successes and a random number of trials. The mean of the negative binomial distribution is (r(1 – p))/p and the vari-ance is (r(1 – p))/p2.

Random Geometric

Random Geometric returns random numbers from the geometric distribution with the parameter you enter as the function argument. The parameter, p, is the probability that a specific event occurs at any one trial. The number of trials until a specific event occurs for the first time is described by the geomet-ric distribution. The mean of the geometric distribution is 1 / p, and the variance is (1 – p) / p2.

Random Poisson

Random Poisson generates a Poisson variate based on the value of the parameter, lambda, you enter as the function argument. Lambda is often a rate of events occurring per unit time or unit of area. Lambda is both the mean and the variance of the Poisson distribution.

Random Seed

This function lets you start a random number sequence with a seed you specify. To use the Random Seed function, assign its argument and use it in an If conditional function. Then use the random number function you want as the else clause. This example uses the number 1234567 as the seed to generate a sequence of uniform random numbers.

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Random Seeded Uniform and Random Seeded Normal

These functions generate uniform and normal random numbers with the random number generator used in JMP 3. However, for compatibility, JMP 3 data tables with Random Uniform() and Random Normal() functions are converted to the new names for the old functions, RandomSeededUniform() and RandomSeededNormal. The random numbers in JMP 5 are generated using the Mersenne-Twister technique. See Matsumoto and Nishimura (1998) in the reference section of the JMP Statistics and Graphics Guide for details.

Col Shuffle

Col Shuffle selects a row number at random from the current data table. Each row number is selected only once. When Col Shuffle is used as a subscript, it returns a value selected at random from the col-umn that serves as its argument. Each value from the original column is assigned only once as Shuffle’s result.

For example, to identify a 50% random sample without replacement, use the following formula:

This formula chooses half the values (n / 2) from the column X and assigns them to the first half of the rows in the computed column. The remaining rows of the computed column fill with missing values.

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Date Time Functions

JMP stores dates and times in numeric columns, using the Macintosh standard of the number of seconds since Jan 1, 1904. When a column has date values, you can assign a date format to that column using the Date Time format popup menu in the Column Info dialog so that they display in a familiar form. See the chapter JMP Data Tables, p. 93, for more details about using dates.

The Formula Editor supports JMP dates with the following Date Time functions:

In Minutes, In Hours, In Days, In Weeks, In Years

Each of the functions listed above converts from the units of the function name to the equivalent num-ber of seconds for the argument. The argument must be a number or numeric expression. For example, In Minutes(2) yields 120, and In Years(1) yields 31,557,600 (60 seconds * 60 minutes * 24 hours * 365.25 days).

Date DMY, Date MDY

These Date DMY and Date MDY functions accepts numeric expressions for day, month, and year and return the associated JMP date. For example, Date DMY (20, 3, 1991) and Date MDY(3, 20, 1991) evaluate to 2,752,272,000.

Today

The Today function returns the number of seconds between January 1, 1904 and the current date. For example, at midnight on March 20, 1991 (a Wednesday), the Today function returns 2752272000, (2,752,272,000 seconds) and continues counting. If you evaluate the Today function later in the day, it reflects the additional seconds.

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Day, Month, Year

The argument for the Day, Month, and Year functions is interpreted as a JMP date. These functions returns the day of the month, the month as a number from 1 to 12, and a four-digit year, respectively.

For Example, on March 20, 1991,

• Day(2752272000) returns the number 20.

• Month(2752272000) returns the number 3.

• Year(2752272000) returns the nubmer 1991.

Day of Week, Day of Year, Week of Year

The argument for Day Of Week, Day Of Year, and Week Of Year functions is a JMP date. Day Of Week returns a number from 1 to 7 where 1 represents Sunday, Day Of Year returns the number of days from the beginning of the year, and Week Of Year returns a number from 1 to 52.

For example, on Wednesday, March 20, 1991,

• Day Of Week(2752272000) returns the number 4.

• Day Of Year(2752272000) returns the number 79.

• Week Of Year(2752272000) returns the number 12.

Informat

The argument for the Informat function is a date character string. For example, Informat("03/20/1991") returns the appropriate JMP date value, 2752272000. JMP can read all the date formats except for Abbrev Date and Long Date.

Abbrev Date, Long Date, Short Date

The argument for these date functions is a JMP date. They return character strings that are the format-ted representation of the argument.

For example,

• Abbrev Date(2752272000) returns “Wed, Mar 20,1991”,

• Long Date(2752272000) returns “Wednesday, March 20, 1991”,

• Short Date(2752272000) returns “3/20/91”.

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Format

The first argument in the Format function is a JMP date. This function returns the character string representation of the date by the date format you specify in the second argument. If you apply this for-mula to a numeric column, JMP automatically changes the column’s data type to character.

MDYHMS

The argument of MDYHMS is a JMP date. This function shows all date and time fields, appending zeros as time fields if no time information is present. This can be useful if a date column is formulated such that not all date information is displayed. The MDYHMS function can be used to see all available date and time information.

Row State Functions

Formulas process row state data just as they process character and numeric data. There are six row state conditions: selected, hidden, excluded, labeled, colored, and marked. You assign row states to rows using commands in the Rows menu. You can also create row state data table columns and use Row State functions to compute and save row state conditions. Row states identify rows in the following ways:

• Select rows to identify data applicable to JMP commands.

• Hide rows to hide points in report displays.

• Exclude rows to eliminate data from analysis calculations and displays.

• Label rows to identify points in plots.

• Use colors to distinguish points in report displays.

• Use markers to distinguish points in report displays.

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Note: A row can be assigned any combination of row states; a row state column can have multiple row states as a value. Table 8.2 “Summary of State Functions,” p. 209 at the end of this section summarizes the Row State functions by listing the type of argument each requires, and what each function returns.

The Function Browser has the following Row State functions.

Row State

The Row State function returns the active row state condition of the current row as true or false. You can use this function to conveniently write conditional clauses that depend on the status of the current row. For example,

assigns a 1 to rows that are currently selected and labeled and a zero otherwise.

As Row State

The As Row State function converts a numeric argument to a row state or set of row state conditions. Row states are stored internally in JMP as a 16-bit number, with each bit assigned to represent one of the possible row states. For example, the binary representation of 1327 is 0000010100101111. As Row State(1 3 2 7) would therefore set the row state as Selected, Excluded, Hidden, Labeled, with marker 2 and color 10.

Combine States

The Combine States function generates a row state combination with two or more arguments. Use the Insert button on the keypad or type a comma to add arguments to the Combine States function. The currently selected expression becomes the first argument when you choose Combine States. Replace each argument with an expression that evaluates to a row state.

Figure 8.13 Binary Row State CodesBit Function

0 Unselected (0) or Selected (1)1 Unexcluded(0) or Excluded (1)2 Unhidden(0) or Hidden(1)3 Unlabeled (0) or Labeled(1)4-7 Marker8-14 Color15 Internal Use

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The formula

Combine States(Selected State(Modulo(Row(),2), Labeled State(Modulo(Row()+1,2))

alternately labels or selects each row in the calculated row state column. The Selected State and Labeled State functions are defined later in this section.

Use the insert and delete buttons in the Formula Editor panel to add more arguments or remove unwanted arguments.

Note: If you include conflicting row states in a combination, the results are unpredictable.

Excluded State, Hidden State, Labeled State, Selected State

These Row State functions are used in a column that has Row State as its data type. They interpret a numeric argument as true or false.

• When an argument evaluates as true, the Excluded State function assigns the excluded condition as the value of the column for that row. For example, Excluded State(Modulo(Row(),2), assigns the excluded row state as the value of the row state column for each odd numbered row.

• The Hidden State function assigns the hidden row state condition when its argument is greater than zero. If the argument is zero the value in the column for that row is not hidden.

• The Labeled State function gives the labeled row state condition when its argument is greater than zero. If the argument is zero the row value in the column for that row is not labeled.

• The Selected State function gives the selected row state condition when its argument is greater than zero. If the argument is zero, the value in the column for that row is not selected.

Color State

The Color State function returns the color from the JMP Color Map that corresponds to its integer argument. JMP colors are numbered 0 through 12, but larger integers map to color indices are treated as modulo 12. Zero maps to black.

Marker State

The Marker State function returns markers from the JMP Marker Map that correspond to its integer argument. JMP markers are numbered 0 through 16, but larger integers that map to marker indices are treated as modulo 8.

The formula

Marker State(Row())

assigns all the row state markers in a repeating sequence determined by the current row number to the calculated row state column.

208 8 Formula Editor Functions—Row State Functions

Note: A row state column can have multiple row states as a value.

Hue State and Shade State

The Hue State function is used with a Row State data type column. The Hue State function returns the color from the JMP hue map that corresponds to its integer argument. JMP hues are numbered 0 through 11 but larger integers are treated as modulo 12. The Hue State function does not map to black, gray, or white. A Hue of 0 maps to red and Hue of 11 maps to magenta. The formula on the left in Figure 8.14 assigns row state colors in a chromatic spread based on the value of z.

The Shade State function assigns 5 shade levels to a color or hue. A shade of –2 is darkest and shade of +2 is lightest. A shade of zero is a pure color. The formula on the right in Figure 8.14 assigns shade val-ues based on the value of z.

Figure 8.14 Examples of Hue and Shade Functions

However, to assign all shades of all the colors in the colors palette, you need to use the Hue State and Shade State assignments together. The formula in Figure 8.15 uses the Combine States function described at the beginning of this section. The first argument in the Combine State function is the Hue State formula shown previously, and the second argument is the Shade State formula. In addi-tion, the Marker State function with an argument of 2 assigns the X marker to each row, and the Selected State function with an argument of 1 selects each row.

Figure 8.15 Combine States Example For Using Both Hue State and Row State

Excluded, Hidden, Labeled, and Selected

The Excluded, Hidden, Labeled, and Selected functions accept a row state expression argument (row state column or row state constant) that evaluates as either 1 or 0 (true or false). These characteris-tics are inactive by default. Often, the Row() function is the argument, which detects the active row

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state condition of each row. For example, in a numeric column this formula assigns 99 whenever a row is actively selected, and 0 otherwise.

Another example assigns row state conditions to a row state column. The formula for the row state col-umn (in the columns called row state col) in Figure 8.16 checks to see if the active row state is either Hidden or Excluded, and if so, assigns the Labeled row state.

Figure 8.16 Calculate Row state Information in a Row State Column

Color Of and Marker oF

The Color Of function accepts any row state expression or column, or the Row State() function as its argument and returns a number from the JMP color map that corresponds to the active color state, or a missing value if there is no assigned color.

The Marker Of function accepts any row state expression or column, or the Row State() function as its argument and returns a number from the JMP marker map that corresponds to the active marker or a missing value if there is no assigned marker.

Table 8.2 Summary of State FunctionsFunction name Argument type Returns (column type needed)Row State none row state of current rowAs Row State numeric all row states of current rowCombine States multiple row state arguments multiple row state assignmentsExcluded State positive integer or zero row state-excluded or not excludedHidden State positive integer or zero row state-hidden or not hiddenLabeled State positive integer or zero row state-labeled or not labeledColor State integer row state colorMarker State integer 0-15 (dark) 16-31 (gray) row state marker

Assignment Functions

Assignment functions work in place. That is, the result returned by the operation (on the right of the operator) is stored in the argument on the left of the operator and replaces its current value. Assignment statements are most often used in conjunction with other commands to build a JSL script. You can use the column Formula Editor to create and execute a script in that column, but this is not recommended because of dependencies and ambiguities that can result. Most often, scripts are stored as .JSL files, and can be saved with a data table as a Table Property. See the JMP Scripting Guide for documentation of all scripting commands.

Note: The first argument of an assignment function must be capable of being assigned. This means you cannot have an assignment such as 3+=4, because 3 is a constant value that cannot be reassigned. You must first create a variable (a table variable or column) whose value is 3. Then use that variable as the left-hand argument of the assignment function.

assign function (a=b) puts the value of b into a.

add to function (a+=b) adds the value of b to a and puts the result back into a.

Selected State positive integer or zero row state-selected or not selectedHue State integer row state hueShade State integer 1-5 row state intensityExcluded Row State() or row state column Numeric 0 (not excluded) or 1

(excluded)Hidden Row State() or row state column integer 0 (not hidden) or 1 (hidden)Labeled Row State() or row state column integer 0 (not labeled) or 1 (labeled)Color Of Row State() or row state column color map integer

Marker Of Row State() or row state column marker map integer

Selected Row State() or row state column integer 0 (not selected) or 1 (selected)

Table 8.2 Summary of State FunctionsFunction name Argument type Returns (column type needed)

assignadd tosubtract tomultiply todivide topost incrementpost decrement

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subtract to function (a–=b) subtracts the value of b and puts the result back into a.

multiply to function (a*=b) multiplies b with a and puts the result back into a.

divide to function (a/=b) divides b into a and puts the result back into a.

post increment function (a++) adds one (1) to a, in place. For example, if the initial value of a is 4, the expression a++ changes a to 5.

post decrement function (a– – ) subtracts one (1) from a, in place. For example, if the initial value of a is 4, the expression a– – changes a to 3.

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Report Windows and Surface Features

This chapter describes features common to all report windows in JMP. These include standard window controls and special additional JMP features.

To make the most of each analysis, all report windows have button controls and popup menus. In addi-tion, you can select from tools that change the function of the mouse. This puts report features literally at your fingertips.

You can customize the appearance of report windows by changing text styles, adding colors and labels to plots, and more. Once you’ve explored the report windows on your monitor, you can print them directly from JMP. If you want to include analyses in your own comprehensive report, you can journal report windows to word-processing files or copy any part of a report window into another application.

9Contents

Report Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215Standard Window Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215Report Disclosure Icons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216Popup Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

Cursor Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Features and Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

Formatting Analysis Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220Context Menu Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220Resizing Plots and Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222Selecting Points in Plots and Using Row States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Customizing Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224Using the Annotate Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229Using the Draw Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

Copy, Paste, Drag and Drop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232Journal and Layout Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

The JMP Journal Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234The JMP Layout Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

9 Report Windows and Surface Features—Report Windows 2159

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Report WindowsAll analysis platforms produce report windows. These windows contain both text reports and graphical displays. They are designed to be convenient in several ways:

• Although report windows consist of complex plots and calculations, they appear on the screen almost instantaneously most of the time.

• Individual plots can be resized and text reports can be closed to optimize the use of space on the monitor.

• Report windows always print each individual plot and text report completely on one page whenever possible.

• Many areas of report windows have context-sensitive help.

• Report windows can be displayed in a journal window or a layout window. A layout window lets you ungroup reports and graphs and rearrange them.

All or part of any report window can be copied to other applications or printed from JMP.

Standard Window Features

JMP reports are displayed in standard windows with scroll bars, a resize box, a zoom box, and a close box. Most JMP report windows also have other special buttons and popup menus like those illustrated in Figure 9.1, and discussed in the following sections.

Figure 9.1 Report Window

resizehorizontal

Macintoshwindow shade

zoom box

verticalscroll bar

Windowsminimizebutton

reportdisclosurebuttons

box

horizontal scroll barvertical resize

popupmenus

window resize box

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Report Disclosure Icons

JMP reports are organized in a hierarchical outline. Each level of the outline has a diamond-shaped dis-closure button like those shown above. Click a disclosure button to alternately open and close the corre-sponding graphs or reports for that level of the report outline. When you close an outline level, all its sublevels are also hidden.

If you right-click on a disclosure button (open or closed), the menu shown above appears. These com-mands let you rearrange the analysis report and give you control over report outline levels. You can open or close multiple levels instead of managing them one at a time.

Horizontal

By default, most reports are arranged with the outline levels showing vertically with indented outline levels. The Horizontal command attempts to horizontally align the next lower outline levels.

Open All Below, Close All Below

Open and Close All Below open or close all outline levels beneath the level where this command is selected, including that level.

Open All Like This, Close All Like This

If you analyze several variables at a time, or select multiple options on a single analysis, you often have many of the same type report tables. You can select the Open or Close All Like This command to open or close all of the same type of report present in the analysis window. For example, a distribution for several continuous variables produces a Moments table and a Quantiles table for each variable. Select-ing the Close All Like This command on any Moments table disclosure button closes all the Moments tables.

Close Where No Outlines

This command is usually used at the top level of the report outline. It closes all parts of the report that do not have sublevels. This is a quick way to see a nesting structure overview of a report.

DisplayBox

DisplayBox has the submenu shown above, with commands that affect all reports at the outline level where they are used.

• Select and Deselect select and deselect all reports for that outline level. You can perform the same function with the area selection tool (large plus) in the Tools menu.

open orclosereports

right-click disclosurebutton to see menu

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• Journal creates a journal window of all reports at that outline level, whether or not they are selected. The Journal command in the Edit menu does the same thing, but only on selected report areas.

• Copy Picture copies the entire report at that outline level to the clipboard as a single picture.

• Page Break inserts a page break for printing purposes. The page break appears as a gray line on the report.

• Set Report Title displays a text box to type a new title for the entire report window. Under Win-dows, you can do the same thing with the Set Title command in the Window menu.

Popup Menus

Many report outline levels have popup menus indicated by a red downward pointing triangle. Each popup menu icon displays a list of options that apply for that particular report.

Popup menus have several modes that give the same commands in different ways:

• Click the menu icon and select a command to affect a single analysis.

• If you Alt-click (Option-click on the Macintosh) the popup icon, a panel of all commands and options appears with check boxes so that you can select multiple actions at the same time.

• Control-click (Command-click on the Macintosh) the popup icon on any analysis to broadcast the command you select to all like reports in the analysis window. For example, for a bivariate analysis, if you Control-click and select Means/Anova/t Test, an analysis of variance is performed for all bivariate analyses in the active report window.

Chapters in the Statistics and Graphics Guide describe the popup menus for each statistics and graphics platform.

Cursor Tools

To expand the capabilities of the mouse in report windows, you can choose special mouse cursors from the Tools menu or palette. Mouse actions have different effects depending on which tool you choose and where you use it.

There are general tools and specific graphics tools that affect points in plots and histogram bars. The default tool is the arrow. The tools are briefly described here, with more discussion and examples in the section Features and Formatting, p. 220, later in this chapter.

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Help Tool

The question mark icon accesses JMP Help. Select the help tool and then click graphs, plots, or tables to access specific help topics in the JMP Help system. JMP uses the standard Windows or Macintosh Help systems. For details about the Windows Help system, consult a Microsoft Windows User’s Guide. Macintosh users should consult the Macintosh User’s Guide for the Macintosh OS 8.6 or later operat-ing system.

Report Selection Tool

The fat plus is for selecting rows and columns in the data table, or components of analysis tables or graphs. When you click on a section of a plot or report, that section highlights and is selected. To extend the selection, drag the selection tool or Shift-click on other report elements. Clicking near the edge of the report window selects the entire report. To deselect, click a second time in a highlighted area. The Copy command in the Edit menu copies selected display areas to the clipboard.

Annotate Tool

The annotate tool places a text box wherever you click in a JMP window. You can key in notes and remove them at a later time, draw lines to make a special point, or use the annotate tool to enhance a JMP graphical display. The annotate tool is selected for only a single click and then reverts to the arrow cursor. Use Shift-select to cause the annotate tool to persist. See the section Using the Annotate Tool, p. 229 in this chapter for details on the annotate tool.

Scroller Tool

Dragging the scroller tool scrolls reports up or down to display only the results you want to see. The scroller is a precise way to perform the same function as the vertical and horizontal scroll bars. The scroller reverts to the arrow when you release the mouse button. Use Shift-select to cause the scroller tool to persist.

Hand (Grabber) Tool

The hand tool (or grabber) is for direct manipulation or grabbing in plots and charts. It works like the arrow on all other parts of report displays.

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Here are some examples of the way the hand behaves in graphs and plots:

• On axes in all plots, you can use the hand tool to change the range of the axis values, which then changes the part of the plot that shows. Using the hand tool in a plot frame changes the X and Y axes together.

• On histograms, use the hand tool to change the number of bars in a histogram or to shift the boundaries of the bars on the axis.

• On spinning plots, the hand tool spins the plot. To spin a plot, grab the plot with the hand by hold-ing down the mouse button and then move the hand about.

• In a multivariate scatterplot matrix, the hand can drag the position of a column of scatterplots to a new position in the matrix by dragging along the diagonal.

• In a CUSUM control chart the hand relocates the origin of the V-mask.

• In all reports, use the hand tool and drag columns to rearrange the report.

Point Selection Tools

The brush tool and lasso tool are for selecting points in plots. When you click the brush tool on a plot, a rectangle appears. Move the rectangle over points to select and highlight them in the plot and in the active data table. Shift-click to extend the selection. Alt-click (Option-click on the Macintosh) to change the size of the selection rectangle. If the brush tool is not in a plot area, it behaves the same as the arrow tool.

The lasso tool lets you select points in an irregular area of a plot. Drag the lasso around any set of points. When you release the lasso it automatically closes and highlights the points within the enclosed area. Use Shift-lasso to extend a selection.

There are more details about selecting points with the cursor, the brush tool, and the lasso tool in the section Selecting Points in Plots and Using Row States, p. 223.

Magnifier Tool

The magnifier tool automatically zooms in on any area of a plot. When you click the center of a new view of the data. The scale of the new view is enlarged approximately 25%, giving you a closer look at interesting points or patterns. Click and drag the magnifier to focus in on a particular region of the plot. You can zoom repeatedly to look closer at the data. Use Alt-click under Windows (Option-click on the Macintosh) at any time to restore the original plot.

Crosshair Tool

The crosshair makes movable set of axes used to measure points and distances in graphical displays. For example, on a fitted line or curve the crosshair identifies the response value for any predicted value. The

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values where the crosshair intersects the vertical and horizontal axis appear automatically as you drag the crosshair within a plot. On a ternary plot, this tool displays small triangular crosshair lines and dis-plays the three axis values.

Features and Formatting

Formatting Analysis Tables

You can format columns of numbers in analysis tables. Double-click anywhere in a column to see the Column Numeric Format dialog shown in Figure 9.2. Select a format from the Format menu and edit the Width and Decimal boxes with the format you want. When you click OK, the entire column for-mats accordingly.

By default, JMP picks what it considers the best fixed-decimal format for numbers both in statistical reports and in the data table. For long decimals this format usually displays four places and is as wide as necessary to show the largest number in the column. It also truncates trailing zeros.

Figure 9.2 Formatting Report Table Columns

Context Menu Commands

Right-mouse click on a report window (Control-click on the Macintosh) to see the context popup menu shown in the following examples. The context menu changes depending on where you click. If you are not over a display element with its own context menu, the menu for the whole platform is shown.

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Context Commands for Report Tables

By default, an analysis table is plain, with no lines or other style to separate rows and columns. Context menu items for report tables let you tailor the appearance and content of the tables as follows:

Table Styleenhances the appearance of a table by drawing borders or other visual style to the table rows and columns (Figure 9.3).

Figure 9.3 Table Style Context Menu and Beveled Table

Columnslets you specify which columns you want to show in the analysis table. Analysis tables often have many columns. The leftmost table in Figure 9.4 is a Parameter Estimates table showing only the estimate name, the estimate itself, and the probability associated with the estimate. The standard error and Chi-square values are hidden.

Make into Data Tablecreates a JMP data table from an analysis table. The rightmost data table in Figure 9.4, is the JMP data table result of sorted Parameter Estimates table with hidden columns.

Sort by Columnrearranges the rows of a report table. This command displays a list of visible columns in a report and you choose one or more columns as sort variables. The middle table in Figure 9.4 is the Parameter Estimates table sorted by Prob>ChiSq.

Figure 9.4 Commands for Analysis Tables

Context Commands for Plots and Charts

The context menu within a graphics frame gives the menu shown in Figure 9.5, which has many of the Rows menu commands, and additional commands to change the background color of the plot, add a

Hidden Report Columns Sorted Columns Report Report as JMP Data Table

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legend with colors or markers based on the values of a variable you choose, or add information to the plot with JSL commands.

The example in Figure 9.5 shows a bivariate analysis of variables height and weight. Context menu commands added a plot legend (Row Legend) that identified gender with different colored markers. The Add Graphics Script (or Edit Graphics Script) has commands that draw dashed horizontal lines on the plot at the maximum and minimum of height.

Figure 9.5 Analysis Window Modified with JSL Statements

Resizing Plots and Graphs

Special options are available to manipulate plots and graphs in each analysis window. To resize a plot or graph, place the cursor on the right edge, bottom edge, or lower-right corner of the plot frame. The cursor changes to a small double-arrow pointer. Drag to change the size of the plot horizontally or ver-tically or both at the same time. When you resize, the height and width of all plots in that frame adjust independently of other frames in the same report window.

When you use resize, the Alt, Control, and Shift keys (Option, Command, and Shift on the Macin-tosh) have the following special effects:

• Alt-resize under Windows or Option-resize on the Macintosh adjusts a plot in 8-pixel increments.

• Control-resize under Windows (Command-resize on the Macintosh) adjusts all like plots in that window simultaneously. For example, you can have scatterplots and mosaic plots in the same win-dow. If you use Control-resize (Command-resize) in one scatterplot, all scatterplots resize together. The mosaic plots are unchanged.

• Shift-resize adjusts the plot frame and preserves the proportions (aspect ratio).

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Selecting Points in Plots and Using Row States

Most graphs display columns from the data table. Points in scatterplots have different appearances depending on the active row state characteristics. Row state conditions include select, exclude, hide, label, marker, and color. There are 16 JMP markers and 65 JMP colors. Row states are described in the chapter “Characteristics of Data” on page 75. To apply a row state characteristic you must first select rows or points in plots.

Selecting Points

Clicking a point in a plot with the arrow cursor selects (highlights) the point as well as the correspond-ing row in the current data table. The label for that row appears on the display and persists as long as you hold down the mouse button. (The label also shows if you move the mouse and linger briefly over a point, then disappears when you move)

When you click the arrow in a display, previously selected data are no longer selected. You can either select a new point or no points depending on where you click. To extend a selection, hold down the shift key while you click new points.

The brush tool in JMP selects a group of points delineated by a rectangle. As you drag the brush over a plot, points that fall within the rectangle are temporarily selected. As the brush tool passes over points, selected points are highlighted and appear larger. When you release the mouse button, the points within the rectangle remain selected.

You can Shift-drag the brush tool to extend the current selection. The new selection includes the previ-ous selection and all points that pass within the rectangle while the Shift key is pressed. The additional selected areas need not be contiguous.

You can Alt-drag (Option-drag on the Macintosh) the brush tool to resize its rectangular selection area. This feature lets you drag a rectangle around the area containing all the points you want to select. You can also use Alt-drag to form a thin vertical or horizontal rectangle. This shape acts like a slicing tool that can traverse and highlight slices of points across either axis.

If you hold down the Command key and drag the brush tool, the selection status of points within the rectangle continuously inverts. This causes the points to blink on all representations of the data.

If you Control-Alt-drag (Command-Option-drag on the Macintosh) with the brush tool and then let go (push the rectangle), the rectangle bounces around in the frame on its own. The speed and direction you push (or drag) the mouse determines the speed and direction of the bouncing rectangle. Click the mouse button in the report window to stop the rectangle from bouncing.

The lasso tool lets you select an irregular area of points in plots. To select points, drag the lasso around any set of points. When you release the lasso, it automatically closes and highlights the selected points within the enclosed area. Use Shift-lasso to extend a selection.

Labeling Points

Label is a special row state that signals plots to show a label for selected rows. Row numbers are the default labels when there is no column assigned as a label column.

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To designate a label using values in a column, select Label/Unlabel from the Cols menu for the selected columns whose values you want to show as labels.

To apply a row state, select rows in any representation of the data table and choose the row state you want from the Rows menu. The data table and plots automatically show the effect of the row state.

Saving Row States

You can save row states permanently in a row state column. The popup menu that shows with a row state column in the Columns panel to the left of the data grid has these commands.

Copy to Row StatesCopy the row state condition from the row state column to the row number area of the data grid, which activates the row states. This command writes over any existing active row state informa-tion.

Add to Row States Add the row state conditions from the row state column to the row number area of the data grid, but preserves all active row state information.

Copy from Row StatesCopy the row state condition showing in the row number area of the data grid to the row state column. This command writes over any row state information already in the row state column.

Add from Row StatesAdd the row state conditions showing in the row number area of the data grid to the row state column, leaving any row state information in the row state column intact.

Customizing Axes

You can double-click in the numeric axis areas of most plots or charts to see an Axis Specification dia-log. Customization features on the dialog depend on the data type of the axis and with the specific plat-form. Figure 9.6 shows a typical Axis Specification dialog for numeric axes.

If an axis is a JMP date value, the Format popup menu gives you options for the date display and an additional popup lists date increments for tick marks. JMP dates are numeric values that correspond to the number of seconds since January 1, 1904. JMP dates and the date display formats are described in the chapter “Characteristics of Data” on page 75.

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Figure 9.6 The Axis Specification Dialog

Tailoring the Plot Appearance

The example in the right in Figure 9.7 illustrates customization of both numeric axes given by the Fit Y by X platform:

• The X axis has rotated tick marks.

• Both axes have minor tick marks.

• Horizontal grid lines on the major tick marks of the Y axis, and vertical tick marks on the major and minor tick marks of the X axis define the geography of a dense cluster of points in the first plot.

The plot on the right in Figure 9.7, is an enlargement of the point cluster that shows between 0 and 500 in the plot to the left. The enlarged plot is obtained by reassigning the maximum and minimum axis values and changing the number of minor tick marks.

Figure 9.7 Rescale Axis to Enlarge a Plot Section

Formatting Dates As Axis Values

Figure 9.8, shows the axis specification settings to plot daily data with major tick marks every 2 days and one minor tick between them. Selecting a date interval from the date increment popup menu

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divides the JMP date (number of seconds) into the appropriate units to give the plot scale you want for your data. The date axis must be a column with a JMP data value and appears in the axis specification dialog in the date format found in the Column Info dialog. However, you can use the Axis Specifica-tion dialog to format the date any way you want it to be displayed in the plot.

Figure 9.8 Format Axis with Dates

Context Options for Axes

Context-click on a plot axis to see the menus for modifying the axis of numeric or categorical variables. There are options to add axis labels, rotate tick labels, and add an outline box to nominal or ordinal axes. In addition, context-click directly on the axis label text of a numeric variable to see the menu shown on the right in Figure 9.9.

Fontaccesses the standard font dialog for choosing a font, font size, and font style.

Font Coloraccesses the JMP color palette for choosing a text color.

Rotate Text has commands to rotate the axis text Horizontal, or to the Left or Right of its current orienta-tion.

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Figure 9.9 Context Menus for Nominal and Continuous Axes

The Context menu for a continuous axis has these options:

Axis Settingsdisplays the axis specification dialog discussed previously in the section Customizing Axes, p. 224.

Revert Axisrestores the default axis settings. This option is dimmed if the axis is in its default state.

Add Axis Labeldisplays a text edit box to key in a second line of axis label. This command can be used multiple times. The axis area enlarges to hold the number of label lines you enter.

Remove Axis Labelremoves the most recently added label.

DisplayBoxhas a submenu of options, and was previously discussed in this chapter (see p. 216).

Nominal axes can be modified with the following commands:

Rotated Tick Labelsdisplays the axis labels vertically or horizontally.

Divider Linesseparates categories with a vertical line. This feature can be especially useful when there are many levels of a nominal or ordinal variable. The x-axis in Figure 9.9 shows rotated tick labels and divider lines.

Lower Framedraws a frame around the axis area.

DisplayBoxhas a submenu of options, and was previously discussed in this chapter (see p. 216).

options to modifylabel text ofnumeric columns

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Scrolling an Axis

The hand tool (grabber tool) is another way to change the axes of a plot, and the view of the plot. You can drag the hand back and forth on the X or Y axis of a plot and slide the axis range accordingly. Drag-ging the hand tool in a plot frame changes the X and Y axes together and changes the position of the plot in the plot frame.

Note: The grab-and-drag axis feature of the hand tool is locked in a journal if the axis is dependent on output that is no longer active. For example, when a distribution is journaled, the histogram is no longer active. The histogram axis must be locked to correspond with the histogram bars.

Zooming

The magnifying glass tool lets you automatically zoom in on any area of a plot. When you click the magnifier, the point or area where you click becomes the center of a new view of the data. The plot enlarges approximately 40-45%, focusing on the area in to give you a closer look at interesting points or patterns. When you drag the magnifier to form a rectangular area, this area enlarges and occupies the whole axis.

Option-click on the Macintosh or Alt-click under Windows at any time anywhere in the plot frame to restore the original plot.

The Pollen.jmp sample data table dramatically illustrates the magnifier. The data were devised with a hidden pattern (message) buried in over 3,800 points. The Fit Y by X platform displays the whole table as the dense cloud shown to the left in Figure 9.10. Clicking the magnifier twice produces the plot in the middle, and clicking twice more reveals the surprise message shown in the plot on the right.

Figure 9.10 Example of Zooming with the Magnifier Tool

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Using the Annotate Tool

The annotate tool creates an editable note area wherever you click and drag it in a JMP report, journal, or layout. You can key in notes and remove them at a later time, or use the annotate tool to enhance a JMP graphical display.

To use the annotate tool, select it from the Tools menu or toolbar, and click or drag a rectangle in a dis-play window to create a white editable text box. Enter text and click outside the note to see a yellow sticky note. By default, the position of the note is fixed (or anchored) to its place on the report. It moves when you scroll the report, resize a graph, or shift an axis.

The annotation can be manipulated in the following ways:

• Click inside the text box at any time to add or edit its text.

• When you click outside the text box, it is no longer editable.

• Click and drag inside the annotation box or on its edge to move it. When an annotation is moved, it becomes selected, as indicated by a double blue line with handles around the perimeter of the note (see Figure 9.11)

• Place the cursor on the handle of a note (showing in the middle of the edges and in the corners) and it appears as a single crossed arrow; drag to resize the note.

• Place the cursor on the edge of a note, but not on a handle, and it appears as a double crossed arrow; drag to move the note.

Right-click (Control-click on the Macintosh) on a note for a menu of options (Figure 9.11) to tailor the appearance of the annotation.

Background Color and Text Color display the JMP color palette to change the background color of the note and the color of the Font used for the message. The color of the font also defines the color of the note outline. If you choose the same color for both the background and the font, the font does not show except for black and white. A black font on a black background changes to white, and a white font on a white background appears black.

Fontdisplays the standard dialog for choosing a font type, size, and style.

Tagline draws a line from the nearest side, initially the left side, of the note to the report surface as illus-trated in Figure 9.11. The note moves slightly down and to the right, and the line is positioned from where the upper left corner of the note was to the edge of the note in its new position. By default, the end of the line that is not attached to the note stays attached to its original position when you move the note. To move the line with the note to a new position, hold down the Con-trol key (Command key on the Macintosh) and drag the note.

Filledgives the option of having the note filled with the background color you chose, or of having a transparent note. A transparent note is handy for putting titles and footnotes on a graph.

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Reanchor and Unanchorfixes(Reanchor) or releases(Unanchor) the note to or from an object in a layout window. In a report window, a note is initially anchored where you created it. If you resize a plot, the note resizes proportionally (the text does not change). If you scroll an axis, a note in the plot frame moves also. However, in a layout window, the components of a report can be ungrouped and moved around. When rearranging these ungrouped pieces of a report you might need to free a note from its position and reanchor it in a new position. Reanchor becomes available in a report window when the note is repositioned.

Deleteremoves the note from the report surface. You can also remove the selected note by pressing the Delete (or Backspace) key.

Figure 9.11 Examples of Annotations

Using the Draw Tools

The Line, Polygon, and Simple Shape (oval or rectangle) tools work much like the Annotate tool. They behave in a similar fashion to those in many draw packages.

Line Tool

Select the Line tool and drag anywhere on a report, journal, or layout window. The line appears selected, showing handles on both ends. Click and drag on the line to move it. Click on a handle and drag to rotate the line. Click on the line to alternately select and deselect it.

Right-click (Control-click on the Macintosh) on a line for a menu of options to tailor the appearance of the line:

Point to and Point fromplace arrows on either end (or both ends) of the line.

Thickalternately displays the width of a line as thick or thin. A line is thin by default.

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Dashedalternately displays a line as dashed or solid. A line is solid by default.

Colordisplays the JMP color palette to change the color of the line.

Reanchor and Unanchorfixes(Reanchor) or releases(Unanchor) the line to or from an object in a layout window. These two commands work as described above for the Annotate tool.

Deleteremoves the line from the report surface. You can also remove the line by pressing the Delete (or Backspace) key.

Polygon or Spline Tool

Select the Polygon tool and click anywhere on a report, journal, or layout window to create the begin-ning point for the first side of a polygon. Move or drag the cursor and click where you want to com-plete the first side and begin an adjacent side. When the second side is complete, a square selection box with handles appears around the polygon area. Continue this process until the polygon is the way you want it. Each time a side is complete, the selection box adjusts to encompass the polygon sides. Dou-ble-click at any time to release the polygon tool.

Click on the edge of a completed polygon to alternately select and deselect the polygon. To reshape the polygon, select it and drag one of the handles of its selection box. To resize, drag from an edge of the selection box between the handles.

You can change the configuration of a polygon by dragging from one of its edges or vertices. If you drag from a vertex, the polygon retains the same number of sides but its shape changes. When you drag or click between vertices, a new vertex is created and you change both the shape and dimension (number of sides) of the polygon. When you move the cursor onto a polygon, it changes to a thin cross when it is on an existing vertex, and to the finger pointer when it is on an edge.

Right-click (Control-click on the Macintosh) on a polygon for a menu of items to tailor the appearance of the polygon:

Filledalternately fills or empties the area of a polygon.

Raiseddisplays thick shaded polygon lines. If the polygon is also filled, the lower edge of the figure appears raised, giving it a three-dimensional look.

Smoothsmooths the vertices of a polygon to produce a Bezier curve. The smoothed figure is reshaped and resized the same way as the polygon, and can be filled and raised.

Closedalternately opens or closes the last segment of a polygon.

Colordisplays the JMP color palette to change the color of the polygon sides, and its fill color when the Filled option is in effect.

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Reanchor and Unanchorfixes(Reanchor) or releases(Unanchor) the polygon to or from an object in a layout window. These two commands work as described above for the Annotate tool.

Deleteremoves the polygon from the report surface. You can also remove the polygon by pressing the Delete (or backspace) key.

Simple Shape Tool (oval or rectangle)

Select the Simple Shape (oval or rectangle) and drag anywhere on a report, journal, or layout window to create an oval or circle. The oval appears with a selection box around it. Click on the edge of an oval to alternately select and deselect it. To reshape or resize the oval, select it and drag one of the handles of its selection box. To move the oval, drag from an edge of the selection box between the handles.

Note: To create a rectangle, right-click on the edge of the oval and choose the Shape command from the menu that appears. The other context menu commands are the same as those described for the polygon tool, except the Close command, which is not needed.

Graphics Tools Memory

Each graphics tool remembers the last options chosen. This is useful if you need many annotations or other graphics with the same characteristics. For example, suppose you want many thick green lines with an arrow on one end. Create a line the way you want it, set the options, and subsequent lines will appear with those options in effect. The options persist until you change them.

Note: The annotate and graphics tools are only selected for a single click and revert to the standard arrow cursor when you release the mouse button. Use Shift-click to cause the them to persist.

Copy, Paste, Drag and DropYou can use the standard copy and paste operations to move data and graphical displays from JMP to other applications. The Edit menu includes Cut and Copy commands that perform the same tasks in the data table and document windows. Cut copies all or part of the active report window onto the clip-board, but does not delete the image from JMP.

If you want to copy part of a report window, use the area selection tool in the Tools menu. Click and drag across the area you want to copy to select and highlight it. Use Shift-click to extend the selection. The example below shows the selection of a histogram. They are two report elements that can be selected separately, or together, as illustrated in Figure 9.12.

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Figure 9.12 Selection of Graph Elements

If nothing is selected, the Copy command copies the entire window to the clipboard. If you right-click (Control-click on the Macintosh) when this tool is active, a popup menu gives you commands to Copy, Paste, or Select All.

Any element in a JMP report window that can be selected can be dragged. When you drag report ele-ments within the report frame, they are copied to the destination area where you drop them. As you drag, an outline of the element shows and the edge of the report frame shows if you attempt to drag outside it.

To delete a copied report element, select it and click the Delete key.

You can copy and paste any report element to other applications, and drag-and-drop JMP reports and graphs to any other application that supports drag-and-drop operations.

The format used when pasting depends on the application you paste into. If the application has a Paste Special command, you can select a paste format such as: text (RTF), unformatted text (TXT), picture (PICT or WMF), bitmap (BMP), and enhanced picture (EMF).

Journal and Layout WindowsThe Journal command and the Layout command in the Edit menu are ways to capture results of a JMP analysis. Each of these operations creates a new window that contains JMP results. The journal window and the layout window have different properties as described next.

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The JMP Journal Window

Journaling is a special feature of JMP that helps you collect, edit, and save analysis results. When you select the Journal command from the Edit menu, all text and pictures from the active JMP analysis window are written to a new journal document and displayed. If an area of an analysis window is selected, the Journal command saves only the selected area instead of saving the entire window. Once a journal window is open, you can select any portion of the Analysis window and drag it to the journal window.

A JMP journal window opens after you first select the Journal command. The reports in the journal window look like the live analysis, but they are disconnected from the data, and the analysis commands are no longer available. However, you can still customize the appearance of the reports. JMP appends text and graphs from the active analysis window to the end of the journal window each time you select the Journal command.

When a JMP journal is open you can

• use disclosure buttons to open and close report outline levels

• resize plots and graphs

• select and move any element of the report using copy-and-paste or drag-and-drop operations

• right-click (Control-click on the Macintosh) on plots and graphs and use any of the context menu commands such as assign markers, change marker size, or change background color

• tailor plot axes with the axis specification dialog or by dragging them

• select and click Delete to delete any element of the report from the Journal, or use Edit menu com-mands (Cut, Copy, Clear, and Paste) on report elements

• use the Annotate tool and other draw tools

• copy individual graphs and text reports from a JMP analysis window and paste them in a journal window

• copy text and pictures from other applications and paste them at the end of a journal window

• print the contents of the journal window using the Print command in the File menu

• rerun an analysis from the journal window.

Note: Text and graphs from the active analysis window append to the end of the journal window each time you select the Journal command.

When you close the journal window (using the close box or Close command), a Save As dialog prompts you for a disk location, file format, and name for the JMP journal. You select the file format you want from the list shown in Table 9.1 “Available Save As file Formats,” p. 235.

Note: You can open an existing journal document from within JMP. After you close your journal it has the journal file format (.JRN). Use the Open Journal command on the JMP Starter to open a saved journal.

On the Macintosh, the only supported formats are JRN, RTF, and HTML.

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The JMP Layout Window

The Layout command performs the same initial operation as described for the Journal command. When you select Layout from the Edit menu, a layout window opens that contains the contents of the active report window. You can do the same things in a layout window as those listed previously for the journal window.

However, the first difference to note is that the Layout menu is inserted before the Tables menu in the main menu bar. When you click on a layout window with the arrow cursor, the Layout menu and its commands become active.

To begin using the layout window to rearrange results, click inside the layout window and choose Ungroup from the Layout menu. This performs the first stage of ungrouping report elements, which ungroups (or disconnects) the topmost title bar from its reports and subreports. Choose Ungroup again to ungroup the next level of the report outline. You can continue to select specific report elements (or all elements) and ungroup them until each title, and each piece of a report or plot is an object. Then click on an object to select it and move it anywhere in the layout window. The layout window has as many pages as you want, outlined with gray boundary lines.

Table 9.1 Available Save As file FormatsSuffix Format Use.PNG Portable Network Graphics compressed bitmap picture, successor to GIF.JPG Joint Photographics Expert

Groupcompressed bitmap pictures, standard for photographs

.WMF Window Metafile pictures stored as commands, resolution independent

.JRN JMP Journal file allows JMP to read and show as originally created

.TXT Plain Text plain text, no pictures

.HTM Hypertext Markup Lan-guage

world wide web format, marked up text that refers to separate picture files

.RTF rich text format word processing, mixture of pictures, text, and tables

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You can ungroup a report from its title bar, and ungroup all its major pieces, but you cannot ungroup a column in a report table from its column header. However, you can double-click on a title bar or report table column header and edit it.

Note: As with most drawing software packages, keyboard shortcuts are very handy, especially for ungrouping, To quickly ungroup a layout to its smallest objects, use Control-U (Command-U on the Macintosh) repeatedly.

Each time you ungroup a report outline level title from its reports, the disclosure icon for that level dis-appears and you can no longer close it. However, you can do many of the surface operations available in the Analysis window and in the journal window, use the context menu in plots, tailor axes, and so forth. Also, the popup menu on the title bar has two active commands: Rerun in a new window and Edit Script.

You can use the area selection tool as described above for the layout window, but the Layout menu is dimmed unless you are using the arrow cursor.

The example in Figure 9.13 shows results extracted, combined, and organized in a compact form from a bivariate analysis (scatterplot and polynomial fit), a distribution analysis (histogram and frequency count), and a one-way analysis (means and t-test). The result gives a neat-looking summary of oxygen uptake as a function of run time in an exercise experiment. This was done by forming a layout window for one analysis, then dragging the other desired analysis elements into the layout window, arranging the desired pieces, and deleting unwanted elements if there are any.

Figure 9.13 Multiple Platform Results in a Layout Window

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Personalizing JMP

This chapter describes ways to tailor JMP to give a comfortable working environment.

• JMP Preferences enable you to specify general and specific settings and save the settings so they are associated with your JMP session.

• Under Microsoft Windows, complete customization of menus and toolbars give the flexibility to show only the commands you need, or set up JMP for groups with special interests.

10Contents

Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Background Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240Fonts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241Graphic Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241Communications Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242File Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Preferences Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Platform Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243Text Import/Export . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Tool Bars and Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244The Show Toolbars Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

The Customize Command in the Edit Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246Customizing Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246Customizing Toolbars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

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PreferencesThe Preferences command in the File menu (Edit menu on Macintosh OS 9 and the JMP application menu on OS X) displays the tab dialog in Figure 10.1. Most of the tab items are straightforward. You can turn them on and off to see the effect. This section describes the preferences, and explains those that might not be obvious. The following descriptions are for a Windows environment. Some of these are Windows-specific preferences, but most are also available on the Macintosh.

To change preferences, click a tab page and check the items you want or select from a menu of items. Click Apply to see the results, and then click OK.

Figure 10.1 The General Panel Preferences Dialog

General

When you select Preferences, the dialog appears with the General preferences tab (Figure 10.1) show-ing. Most General preferences customize your JMP session environment as follows:

• Initial Splash Window shows or hides the initial splash window at start up.

• Initial JMP Starter Window shows or hides the JMP Starter window at start up. The default prefer-ence shows the JMP Starter window. If you don’t use this tool, turn it off. The JMP Starter com-mand in the View menu (Window menu on the Macintosh) can be used at any time in a JMP session.

• Suppress Formula Eval On Open suppresses evaluation of formulas when a data table is opened.

• Suppress OnOpen Script Eval suppresses any table property that automatically executes a JSL script when the table opens.

240 10 Personalizing JMP—Preferences

• Allow Short Numeric uses 8 bits to store an integer instead of 16 bits.

• Scroll bar arrow style and Scroll bar thumb style give the options shown below to adjust the appearance of the scroll bars and thumb.

• Auto match brackets in script editor automatically includes a closed bracket whenever you type an open bracket in a script.

• Enter Key moves down causes the data entry cursor to move to the next cell down instead of the next cell to the right.

• Print Data Grid as is prints the JMP data grid as it appears on the screen instead of listing the data without lines separating the rows and columns.

• Date Title on Output displays the date and time the analysis occurred at the top of the output page.

• Data Table Title on Output places the name of the data table and notes, if there are any, at the top of its output.

• Report Table Style has the submenu shown below that sets the output table style. This is the same menu displayed by a context click in a report window.

• Show Explanations shows explanatory verbiage with some analysis results.

• Formula Boxing turns on the boxes around formula components in the formula editor.

• Graph Marker Size sets the default marker size for all plots. You can change the marker size for any individual plot by using the context-click popup menu in the plot frame.

Background Color

Under Microsoft Window, the Report window and data table can have any color background. When you check the Background Color tab, you see the JMP color palette (Figure 10.2) and a sample of the current background color. To set the background color, click a color in the palette, or choose Other and create a custom background color. Click the Apply button on the Preferences dialog to preview the result.

Note: The background of the plot frame is white by default, and is not affected by the window back-ground color. You can change the plot frame background by using the context popup menu in the frame, or set a default graph background color using the Graphs radio button in the preferences dialog.

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Figure 10.2 Background Color Preferences Tab

Fonts

One way you can customize the appearance of reports is to change text styles using the Fonts prefer-ences tab. This is the standard Windows or Macintosh font preference dialog, which lists the fonts and sizes available in your system, and the regular, bold, italic, and bold italic styles. You can specify font, style and size characteristic for the items listed on the Fonts tab.

The following areas in a JMP can be given a specific font, font size, and font style.

Text is the text portion of a JMP analysis report

Heading is the heading of columns in an analysis report

Title is the title shown in all title bars

Small sets the font for small text such as that showing in the upper left corner of the data grid if when you have closed the side panels of the data table.

Mono is for the font used in the JSL editor for script commands

Calculator is for the expressions entered into the Formula Editor

Annotation sets the font information for the first annotation (note) you create in a JMP session. In a single JMP session, the appearance of the first annotation carries to all following annotations you cre-ate. Thus, to set a default using this preference, you must set the preference before creating any anota-tions.

Graphic Formats

The check boxes on the Graphics Formats tab (see Figure 10.3) tell the Copy command what formats to use when copying the contents of the selected area in a JMP report to the clipboard. You can check any or all of these formats. When you paste from the clipboard, the receiving application uses the for-

242 10 Personalizing JMP—Preferences

mat the clipboard has as its default. If the application does not recognize any of the formats on the clip-board, a dialog appears and prompts you to select a format.

Also, you can save a JMP document as an RTF file or as an HTML file, and choose the format you want (PNG, JPG, or Metafile) from the menus at the bottom of the Graphics Formats tab page.

Figure 10.3 The Graphics Format Tab for Preferences

Communications Settings

Under Microsoft Windows, the Communications Settings only need to be specified if you are using an instrument to read data from an external source. Refer to the documentation for your instrument to find the appropriate settings.

File Location

Under Microsoft Windows, the File Location tab lets you change the default locations of JMP system files. Usually, the files can stay where JMP installs them. However, you might want to move large files, such as the Help files (jmp.chm), to somewhere other than your hard drive. Information in the Installa-tion Directory is sometimes needed by technical support. You should leave most installed files in the default installation directory.

Note: If you have more than one version of JMP installed, the Reset Association button lets you assign JMP-specific file extensions (.JMP, .JSL, and so on) to the JMP application.

Preferences Path

Under Microsoft Windows, you can designate a directory where the preferences file (JMP.pfs) should be stored. To do so, select the File Location tab from the Preferences panel. Then, select Preferences file directory from the listbox, and click the Browse button. Navigate into a directory where the JMP.pfs should be stored and click the Select button. You can point to a directory even if there is no

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JMP.pfs file already in it. When JMP saves the preferences, it will create the JMP.pfs file in that direc-tory.

Figure 10.4 The File Location Tab for Preferences

Platform Settings

Each Analysis and Graph platform has a variety of different plots and tables in its report. Some of these results are shown by default. You can set the default output by using the check boxes in the Options checkbox list that displays for each platform. As an example, a bivariate analysis shows points by default. If you always want to see a linear fit each time the platform is launched, check the Fit Line pref-erence, as shown in Figure 10.5. All bivariate analyses will then automatically appear with a linear fit.

Figure 10.5 Platform Settings Preferences Tab

Text Import/Export

The Open command displays a specialized open file dialog that lets you locate the file you want to open, and tell JMP the format of the incoming file.

244 10 Personalizing JMP—Tool Bars and Menus

For text files, you can use options to identify the field and line delimiters and strip enclosing quotes from character strings. The defaults for these options are in the Text Import/Export Preferences dialog shown in Figure 10.6. You can change the defaults to suit the most common type of text file you read or write. The field and line delimiters can be any combination of the characters indicated by check boxes. Of particular importance at this time is the option to pick the 2-digit year rule you want to use. The default 2-digit year interpretation appends 20 to the front of 2-digit years indicated by 00–10, and 19 otherwise.

Figure 10.6 Text Import/Export Preferences Tab

Tool Bars and MenusYou might need to frequently select certain items from menus or palettes, and would prefer to access them as buttons instead of menu commands. Under Windows, JMP has tool bars available for most main menus and palettes for the colors and markers.

The Show Toolbars Command

The Show Toolbars command in the View menu displays the window shown in Figure 10.7 with check box options that let you show or hide tool bars.

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Figure 10.7 Show Toolbars Check Box Options

In addition to tool bars for main menu items, there are also check boxes for the following options:

• The Data_Tables_List option displays all open data tables. This is especially useful when you have multiple data tables open. You select a data table in this list to make it the current table. The current table is the one to which analyses apply.

Note: The current table is not necessarily the front window. To bring a table (or any window) to the front, select its name from the list of open windows in the Window menu.

• The Browser option displays standard browser buttons such as forward, backward, and home, to use when an internet page is open in a browser within JMP. When this toolbar item is open, you can type a URL into the text area to open a new page in the JMP browser. Also, the Internet Open com-mand on the File menu prompts you for a URL and opens an internet browser in the JMP session for the location you enter.

• The URL_List option lists the browser pages you have requested during the current JMP session (see Figure 10.8). You can also use the Internet Open command on the File menu, which prompts you for a URL and opens an internet browser in the JMP session for the location you enter, or opens a file at the location as a data table.

Figure 10.8 Toolbars for Tools, Browser, URL List, Data Table List, and Search List Control

• The Search_List_Control option keeps track of all the data table searches performed on the current data table. When you enter a value in this text area and click Enter, the action is the same as using the Edit > Search > Find command. You can select from this list to do the same as Edit > Search > Find Next.

246 10 Personalizing JMP—The Customize Command in the Edit Menu

The Customize Command in the Edit Menu

(Windows only)

The Customize command in the Edit menu lets you completely tailor all menus and toolbars to make your JMP environment best fit your work activities.

Customizing Menus

All main menus and their submenus can be rearranged or eliminated from the menu bar with the Edit > Customize > Menus window. When you select Menus from the Customize menu, the window in Figure 10.9 appears. It contains drawings of all the main menus. Using drag-and-drop actions and context menu commands, you can reposition or eliminate any menu or menu item, or create new menus and menu items. Each menu or menu item has properties that you assign using its Properties dialog. To see a properties dialog, right-click on the menu or menu item and select the Properties com-mand from the context menu that appears. Customized menus can be saved and used anytime during a JMP session. You can restore the default menus at any time with Edit > Customize > Revert to Factory Defaults.

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Figure 10.9 The Customize Menus Palette

Modify or Delete a Menu

Customizing the menus is a simple drag-and-drop action. As an example, suppose you want to simplify the Analyze menu by eliminating Survival and Reliability and Modeling except for its Nonlinear command. Figure 10.10 shows how to do this:

• Select Edit > Customize > Menus. When the Customize Menus window displays, scroll to the Analyze menu and click to open it.

• Click on Survival and Reliability and drag it anywhere on the palette to tear it off the menu.

• Optionally, you could tear off the divider line as well.

• Click on Modeling to tear it off the menu.

• Click on the Modeling submenu icon to open it.

• Drag Nonlinear from the Modeling submenu and place it anywhere in the main Analyze menu.

The result of rearranging should look similar to the center figure in Figure 10.10. Click the close box on the palette. You are then prompted to save the changes, and to apply them. The Analyze menu on the menu bar now looks like the menu shown on the right in Figure 10.10

To delete an entire menu, drag if off the menu bar.

Scroll right to see all menus.

248 10 Personalizing JMP—The Customize Command in the Edit Menu

Figure 10.10 Rearrange the Analyze Menu

Add New Commands to a Menu

You can also add new commands to any menu. To do this, right-click anywhere in the background area of the customize palette. A menu displays the commands New Item, Paste, and Apply Changes.

New Item

creates a single command item on the palette called Untitled. Double-click on the new item to see its Menu Properties dialog (Figure 10.11).

Paste

lets you duplicate an existing command. First select the command on the palette and Copy it. Then choose Paste to see the duplicate on the palette. You can then edit the new item with the Menu Item Properties dialog.

Apply Changes

applies the current menu configuration in the menu palette to JMP. This command has the same function as the prompt after you close the palette window, but it keeps the window open for fur-ther menu work.

If you right-click on a menu name or the menu item itself (as opposed to the palette background), the menu shown on the right in Figure 10.11 appears. This menu can also be used to access the Menu Item Properties dialog. Other commands on this menu help to position the new command, copy it, or delete it.

The Menu Item Properties dialog has these features:

• On the General tab, use radio buttons to specify whether the new menu item is a Command, is a Submenu, or is a Separator line.

• Give the new item a Caption (name).

• Give a Tooltip when the new item is a tool (instead of a menu item).

• If you want the item to use an existing JMP command, select it from the Builtin command list.

Factory Default Analyze Menu Customized Analyze Menu

drag to new position

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• If you want the menu item to execute a JSL script, either give the path to the file containing the JSL (Run JSL in this file), or key the JSL into the text area called Execute this JSL.

The other tabs on the Menu Item Properties dialog are for optionally assigning your own icon to the menu item, or assigning a shortcut key.

Figure 10.11 Menu Item Properties Dialog

Save a Customized Menu

Menu arrangements (including the factory defaults) are saved in the same folder as the JMP applica-tion. They have a file type called JMPCMD file. The factory defaults are in a file called JMPCMD. If you create a customized menu and want to save it, use the Save As command in File menu. The Save As dialog appears with the JMPCMD file type showing. You name the custom menu and save it. If you also applied the new menu, either with the Apply Changes context menu command or by responding to the prompt when closing the menu palette, JMP uses the saved JMPCMD file. Use Customize > Revert to Factory Defaults at any time to restore the default main menu. You can also open any JMPCMD file at any time in a JMP session, which brings up the saved configuration in the menu cus-tomization palette.

Customizing Toolbars

Customizing tool bars is similar to customizing menus. Choose Edit > Customize > Toolbars to see the Customize Toolbars palette. You manipulate the toolbars and individual tools the same way as the menus and menu items, described in the previous section. You can rearrange existing toolbars, elimi-nate tools, or create new toolbars and new tools.

The customize tools palette has context menus accessible from the palette surface with commands to create a New Toolbar, a New Item (tool), Paste, which duplicates a tool you have copied with Edit > Copy, or Apply Changes, and import a JMP Version 4 toolbar (described below).

250 10 Personalizing JMP—The Customize Command in the Edit Menu

There are also context menus for the whole toolbar, accessible by right-clicking on the toolbar name area above the tools, and for individual tools.

The menu for the whole toolbar has a Delete Toolbar command and a Properties command. The Properties command lets you name the toolbar, and use radio buttons to designate whether the tool-bar will initially be visible or not, and whether it is to be docked or floating when visible. If it is not vis-ible, its name appears unchecked in the View > Show Toolbars list.

The menu for individual tools has commands for positioning the tool, Copy, Paste, and Delete com-mands, and a Properties command that displays the Toolbar Button Properties dialog, similar to the one shown previously in Figure 10.11. The Toolbar Button Properties dialog has these features:

• On the General tab, use radio buttons to specify whether the new menu item is a Command or a Separator line.

• Give the new item a Caption (name).

• Optionally, give the new tool a Tooltip.

• If you want a tool to access an existing JMP command, select it from the Builtin command list. This list has all platform level commands in JMP, including those that appear in submenus, such as Nonlinear or Partition.

• If you want the menu item to execute a JSL script, either give the path to the file containing the JSL (Run JSL in this file), or enter the JSL statements directly.

• The Toolbar Button Properties dialog is for optionally assigning your own icon to the tool, or giving it a keyboard shortcut.

Importing JMP Version 4 Customized Toolbars

If you were a Version 4 JMP user and had customized toolbars, you must import them into JMP Ver-sion 5 to use them. To do this, right-mouse click in the Customize Toolbars window and select the Import V4 Toolbar File command, as shown in Figure 10.12. This command displays a standard open file dialog. By default, customized toolbars files are named User1, User2, and so forth, and are stored in your JMP Version 4 folder. You might have to navigate to find the customized toolbar files if they are named differently than the default names, or stored in a different location.

After you select the Version 4 toolbar you want, be sure and use the Apply Changes command to implement that toolbar.

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Figure 10.12 Import JMP Version 4 Toolbars

right-mouseclick to ImportV4 Toolbar File

Appendix A AW

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What’s New in JMP Version 5

JMP Version 5 builds on the improved interface of Version 4, adding new data access, statistics, and graphics commands. JMP’s scripting language has been enhanced. The menu structure and tool bars are now fully customizable, and new drawing tools have been added to aid in presentation-style results.

This chapter provides an overview of the enhancements. For complete details of each command, see the appropriate entry in the main documentation.

AContents

Data Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255Data Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256New Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Statistical Platform Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261Internal Optimizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262JMP Scripting Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263OLE Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

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Data Access

Internet Access (Windows Only)

JMP can get HTML, JMP, or text files from an http address. In addition, you can browse web pages from within JMP using a built-in browser.

All of these features are accessed through the File > Internet Open command. See section “Opening a JMP File from a Web Address,” p. 115 in the JMP User’s Guide for details on internet data access.

Native SAS Files (Windows Only)

When opening SAS data sets, there is now an option to use the SAS variable names for the column names in the resulting JMP data table.

When saving a SAS V7 file, the following rules apply. Suppose you are saving a JMP data table called baseball.jmp:

• If there are existing .sd2 or .sd7 files in the directory, JMP saves the file as Baseball.sd7 if V7 format is specified, and Baseball.sd2 if V6 format is specified.

• If files exist in the directory with the longer extension (.sas7bdat), then JMP saves the file as Baseball.sas7bdat if V7 format is specified.

• If there are no SAS datasets in the directory, JMP saves the file as Baseball.sas7bdat if V7 format is specified.

Column Selection

Under Microsoft Windows, you can select which columns are imported from other JMP or SAS files.

Text Import Preview

Text Import Preview now allows the specification of the number of rows in the text file.

Instant Importing of Text Files (Windows Only)

There is now an Import as Data command on the File menu for scripts and text windows. Selecting this command instantly imports the text in the active text window as a JMP data set using the settings from the Text Import/Export preference.

Imported File Sizes

The limitations on the size of imported text files has been expanded. The limitations are now:

• a limit of 10,000 columns that can be imported per table.

• a limit of 128K per line (row) of data that can be imported.

256 Appendix A

Text Export

There is now an option to have no delimiter in exported text files. When selected, the variables are exported with no quotation marks or spaces between them.

Data Manipulation

Tables Menu Commands

• Join and Stack have been enhanced with new options.

• The Summary command dialog allows the user to specify the name of summary statistics columns. For example, for the mean of a column col, you can choose to name its summary column Mean(col), Mean of col, or col mean.

• The Summary command allows the inclusion of marginal statistics.

Column Formulas

Save Formula and Suppress Formula Evaluation commands have been added to JMP.

Row Editor

The Row Editor brings up a window useful for browsing the columns of a data table one row at a time.

Random Selection

You can select rows of a JMP data table at random by using the new Select Randomly command. This command allows selection of a random number or random proportion of rows.

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New Platforms

Partial Least Squares

The PLS procedure fits models using partial least squares (PLS). It balances the two objectives of explaining response variation and explaining predictor variation and is useful in situations where the number of x-variables is larger than the number of observations—when ordinary least-squares is impos-sible.

JMP’s PLS implementation allows the user to specify the number of factors and use cross-validation to examine the model fit. It also allows for interactive exploration of the meaning of the underlying latent factors that explain the factors and responses. See “Partial Least Squares,” p. 403 in the JMP Statistics and Graphics Guide for details on the PLS platform.

Discriminant Analysis

Although the Manova platform has always contained discriminant analysis in previous versions of JMP, it didn’t produce any displays and had few features. JMP 5 includes a new platform for discriminant analysis and has features for stepwise selection, canonical plots, and identification of rows by scoring profiles. See “Discriminant Analysis,” p. 423 in the JMP Statistics and Graphics Guide for details about the Discriminant platform.

Partition

The Partition platform recursively partitions data according to a relationship between the X and Y val-ues, creating a tree of partitions. Variations of this technique go by many names: CARTTM, CHAIDTM, C4.5, C5, and others. See “Recursive Partitioning,” p. 483 in the JMP Statistics and Graphics Guide for details about the Partition platform.

Neural Net

The Neural Net platform allows for simple neural net modeling using a single hidden layer. See “Neural Nets,” p. 413 in the JMP Statistics and Graphics Guide for details about the Neural Net platform.

Diagram

The Diagram platform’s main intended use is to do Ishikawa charts, also called fishbone charts, or cause-and-effect diagrams. See “Ishikawa Diagrams,” p. 625 in the JMP Statistics and Graphics Guide for details about the Diagram platform.

258 Appendix A

Statistical Platform Improvements

Distribution (Single-Variable Analysis)

• The Pvalue and Power animations, accessible after testing a mean, are enhanced to allow sample size and alpha levels to be changed.

• Tolerance Intervals can now be computed.

Capability Analysis

Capability analysis can now use four different options for estimating σ:

• Long-term

• Specified

• Short-term, grouped by fixed subgroup size

• Short-term, grouped by column.

Fit Y By X (Two-Variable Analysis)

• The Bivariate platform now provides a way to turn off polynomial centering, using the Fit Special command.

• The Version 3 paired t-test is accessible by holding the Shift key down when opening the Bivariate Platform menu.

• The Fit Spline command now results in a report with an attached slider, used to vary the stiffness of the fitted curve.

• The text box for the equation in bivariate reports is now editable.

• A CDF plot has been added as an option in one-way analysis of variance.

• When you do multiple comparisons on means in the Oneway or Fit Model platforms, a connecting letter report (similar to the reports in SAS PROC ANOVA) shows.

Fit Model (General Linear Models)

• The new All Possible Regressions command shows the results of running all possible subsets of a linear model.

Appendix A 259A

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• CV (coefficient of variation) has been added as a hidden column in REML and EMS results.

Clustering

The following new commands have been added in JMP Version 5.

• An Ordering column can now (optionally) be specified in the Hierarchical Clustering launch dialog. In the ordering column, clusters are sorted by their mean value.

• Color Map is an option to add a color map showing the values of all the data colored across it's value range. There are several color theme choices in a submenu.

• Geometric X Scale is an option useful when there are many clusters and you want the clusters near the top of the tree to be more visible than those at the bottom. This option is the default for more than 256 rows.

• Save Cluster Hierarchy saves information needed if you want to create a custom dendrogram with scripting.

• Orientation allows you to rotate the dendrogram.

• Biplots are now available for k-means clusters.

• Self-Organizing Maps (SOMs) have been implemented in k-means clustering.

When there are a large number of clusters and the frame is squeezed down, Cluster now adapts the cluster labels to smaller fonts, as needed.

Survival and Reliability Analysis

• Failure Plots have been added to univariate analyses.

• The Fitted Distribution Plots command, in conjunction with the fitted distributions, shows Sur-vival, Density, and Hazard plots corresponding to the fitted distributions.

• JMP can now constrain the values of the Beta (Weibull) and Sigma (LogNormal) parameters for use in a Weibull Analysis.

• The Proportional Hazards platform now supports a Frequency column.

ROC Curves

The positive level in a ROC curve in now specified by the user.

Design of Experiments• You can add effects to the model that aren’t required to be estimable. JMP 5 allows these effects to

only be estimated if possible.

• Supersaturated designs are now practical to obtain.

• I-optimality is now an alternative to D-optimality in appropriate situations.

260 Appendix A

• For certain situations with covariate factors, the row exchange algorithm using a candidate set is available in the custom designer.

• Desirability Functions are much improved. New desirability functions have been implemented for both maximizing targets and minimizing targets.

• JMP now (optionally) shows a table with D, G, and A efficiencies for a custom design.

• The design matrix can be stored in a data table.

• Random starts have been implemented when searching for optimal designs.

The following scripting commands that let you predefine guidelines for the design search by the cus-tom designer. See the JMP Design of Experiments guide for details on the new features in Version 5.

DOE Mixture SumDOE StartsDOE Starting DesignDOE Search Points Per FactorDOE K Exchange ValueDOE Bayes Diagonal

Graphics

Charting

Bar charts that show means can have standard error bars attached to them, as shown below.

Overlay Plot

• The Overlay Plot platform can use character variables as X variables.

• Overlay plots can have two y-axes, as shown below.

Appendix A 261A

Wh

at’s

New

Control Chart

Capability analyses are included as an option in control charts.

Pareto Plot

Pareto Plot includes an Ungroup Plots option to allow a group of Pareto charts to be split up into sep-arate plots.

Drawing Tools

In addition to JMP’s traditional annotation tool, JMP 5 has tools for drawing lines, ovals, rectangles, and polygons. These graphics can appear on reports, in journals, and in layouts.

User Interface

Menu Customization

Under Microsoft windows, the way in which the user customizes menus and toolbars has been changed. At the bottom of the Edit menu, there is a new submenu named Customize. This brings up a drag-and-drop editor for customization of menus and toolbars.

Layout and Journal

• You can now undo most editing operations in Layouts.

• There is an option to rerun an analysis from within a Journal.

262 Appendix A

Preferences Path

Under Microsoft Windows, the user can now designate a directory where the preferences file (JMP.pfs) should be stored.

Formulas

Formulas now provide a visual clue (a caret) when optional parameters can be included.

Pictures of formulas now appear in the Column Info dialog.

Other Improvements

To move columns in a report around, drag them with the hand tool.

If you have nominal axes and ask for dividers, JMP now automatically wraps the text across several lines, if needed.

Pictures that you have dragged or copied into JMP reports can now be resized. (To restore a picture to normal size, Alt-click on it.)

Internal OptimizationsAlthough they are not feature enhancements, it is worth noting the optimizations that took place between JMP 4 and JMP 5.

• The executable has been virtually halved in size. It now takes up approximately 7 megabytes of disk space.

• The help system was reworked to reduce image sizes and to include a more useful navigation struc-ture.

• Formulas that calculated across the rows of a data table (using, for example, subscripted variables or the Lag function) stressed the formula dispatcher and dependency system for large data tables. This resulted in a marked decrease in performance. The formula dependency system has been rewritten completely and now performs quickly.

• The Cluster platform is much faster than before, making it now practical to do problems with sev-eral thousand rows. One large test situation involved 4700 rows. Although requiring 125 meg of memory because of the large distance matrix, the calculations completed in about two minutes.

• ODBC is much faster for importing large database tables, including Excel files.

• The JSL Try function now intercepts errors better.

Random Number Functions

Random numbers are now generated using the Mersenne-Twister technique. This technique has a period length of 219937-1 (as opposed to 231-1 for the former generator). Details on the generators are

n2

Appendix A 263A

Wh

at’s

New

found in Matsumoto and Nishimura (1998). The new generators are verified to pass all the DIEHARD tests as documented in Marsaglia (1996).

The old routines are still available as RandomSeededUniform and RandomSeededNormal and work with the RandomSeed function call. For compatibility, Version 3 data tables with Random Uniform and Random Normal functions are converted to the new names for the old functions, RandomSeededUniform and RandomSeededNormal.

JMP Scripting Language

Script Editor (Windows Only)

There have been enhancements to the script editor.

• Control-] now performs “fence matching”. When pressed, the script editor searches forward for the first parenthesis or bracket it can find. It then highlights backward to the corresponding open paren-thesis.

• There is a new Go To Line... command that highlights a specified line in the script. This is useful for finding error messages that are reported by line number.

• There is a command to keep script code from being displayed to script end-users.

Formulas

New functions make setting column values possible without the use of a formula. column<<Set Each Value(expression) sets each value in a column equal to the value of expression. The expression is not stored in the column.

Invisible Reports

Reports can now be generated without displaying them. This is useful when a large number of reports are generated that a script user does not need to see.

Foreground Analyses

Analyses initiated from Nonlinear or Stepwise commands can be forced to complete their execution in the foreground before proceeding to the rest of the script using the Finish command.

An EvalFormula command can now be (optionally) added in a New Column function to cause the formula in the column to be evaluated in the foreground.

dt<<Run Formulas runs all the formulas in the data table in the foreground until completion.

264 Appendix A

Menu Commands (Windows Only)

All commands present in the main menu are scriptable through the Main Menu function. For example,

Main Menu(window:close all journals)

closes all currently open journal files.

Fit Model - Least Squares

Certain scripting-only commands were added so that internal matrices can be accessed from scripts.

• Get X Matrix;• Get Parameter Names;• Get Estimates;• Get Std Errors;• Get XPX Inverse;

Spline Functions

Several new spline functions allow access to the spline features in the bivariate platform directly.

Pictures

JSL has a new data type Picture that holds pictures of generated reports.

Matrices

The following new matrix commands have been implemented in JSL.

• LocMin and LocMax• Contains• VecQuadratic• InvUpdt• GInverse• QR

In addition,

• The Set Matrix command has been added to easily convert a matrix to a data table.

• Interpolate and Step now support x- and y-values that are matrices.

• The Mail command supports attachments (Windows only).

Data Table

Column attributes, locking, and formatting are accessible through JSL.

You can revert to a saved version of a data table through the File > Revert command.

Appendix A 265A

Wh

at’s

New

DLLs

JSL can now pass a single parameter to and from functions embedded in DLLs.

Other Commands

The following other commands have been implemented.

• AsDate for date conversion

• AsName for string conversion

• Find and Window for window manipulation

• a graphical GradientFunction

• Text positioning

• Web for displaying web pages in JMP

• Quit and Exit for interactively shutting down JMP

• Journal and Layout messages are supported in JSL

• Squish, a logistic function

• Derivative now directly reports symbolic derivatives

• Type(expression) returns a string naming the type of expression.

Other Improvements

• Any structure of a list can be used in scalar operations.

• Evaluation of names of columns has been improved.

• Multiple left-side subscripts (e.g. a[i][j]) have been implemented.

• JSL references to the Distribution and Fit Model platforms can be subscripted by number or the name of the y variable.

• It is now possible to launch Nonlinear when there is only a loss function.

OLE AutomationA vast number of new automation methods have been added to JMP 5, corresponding to other improvements in JMP.

Ind

exIndex

JMP User’s Guide

Symbols4!= 178" 109% of Total 125& 183' 109+ icon 97+ tool 218, 232< 178< <= 178<= 178<= < 178== 178–179> 178>= 178? tool 4, 63, 218^ see Insert key, exponential_ID_ 131_Stack_ 130| 183

Numerics2-digit year 244

AA tool 218, 229Abbrev Date 204About JMP 71Absolute Value 168acknowledgments ix–xActuarial Life Table template 159add column 104–105Add Columns vs. New Column 105Add from Row States 224Add from Row States 86Add Graphics Script 222Add Multiple Columns 46

Add Rows 45, 104Add To 210Add to Row States 86, 224Align 73Allow Short Numeric 240Analyze menu 10, 51–56Anchor Tool Palette 66And 182Angina Life Table Data 160annotate tool 65, 218, 229annotate tool options

Background Color 229Delete 230Filled 229Font 229Reanchor 230Tagline 229Text Color 229Unanchor 230

append horizontally see Joinappend vertically see ConcatenateApply 158, 240ArcCosine 172ArcCosineH 173ArcSine 172ArcSineH 173ArcTan 172ArcTanH 173area selection tool 218, 232argument 148, 156Arrange 69Arrange Icons 69array see Taguchi Arraysarrow keys 157arrow tool 99, 223, 232As Row State 206ascending sort 121ASCII file 108, 112, 233Assign 210assign role 79–80

268 Index

Assignment functions 153, 210–211Augment Design 21, 50Axis

column property 90axis

change 224change with hand 219example 225scroll 228

Axis Customization dialog 224axis options

Add Axis Label 227Axis Setting 227DisplayBox 227Divider Lines 227Font 226Font color 226Lower Frame 227Remove Axis Label 227Revert Axis 227Rotate Text 226Rotated Tick Labels 227

BBackground Color preference 30, 240bar chart see Chart, ParetoBasic Stats 10Basic Stats tab of JMP Starter 10–12Best format 81Beta 171Beta Density 184Beta Distribution 184Beta Quantile 185Binomial Distribution 192Binomial Probability 192bitmap 233, 235Bivariate 11BMP 233bounce selection rectangle 223broadcast command 217Browse HTML Page (internet open option) 115browser of functions 146Browser toolbar 115brush tool 64, 128, 219, 223build formula 143–160By group 79

statistics 121

Ccalculator window 143–160Capability 19Cascade 69cast column into role 79–80cdf functions 184–194Ceiling 168cell chart see ChartCentral Limit Theorem table template 160change axes 224Char 174character 78Character functions 173–177characteristics of data 75–91Chart 16chart statistics 122Charts 59ChiSquare Distribution 185–186ChiSquare Quantile 186Choose 179, 182Clean Up 73Clear 34Clear Row States 44Close 28, 234Close All 69Close All Below 216Close All Like This 216Close Where No Outlines 216Cluster 14, 57Coding column porperty 90Col Maximum 196Col Mean 195Col Minimum 196Col Nmissing 195Col Number 195Col Quantile 196Col Shuffle 202Col Standardize 197Col Std Dev 195Col Sum 196collect results 233–234Color of 209Color or Marker by Column 44Color State 207Colors 43, 85Cols 36Cols menu 45column

in formula 153Column Info 46, 100

Index 269 In

dex

Column Name 145column name 104, 147Columns 221Columns panel of data table 97–98columnwise statistics functions 194–198combinatoric functions 170–172combine columns into one 121Combine Row States 206Combine States 208Communications Settings 30, 242Comparison functions 178–179compositional data see Ternary Plotcompressed 235computed-values column 143–160Concat 174Concatenate 22, 40, 122, 134–135Conditional functions 179–184

efficiency 158conjunction 179–184Connections 114constant 152, 156Contains 174Contents 71context menus 220–222Context-click menus 221–222Contingency 11continuous 78Contour Plot 17, 60Contour Profiler 18, 62–63Control Charts 18, 60Control Limits column property 90Control-click menus 220–222conventions 4Copy 33, 116, 157, 218, 232Copy As Text 34Copy from Row States 86–87, 224Copy Picture 217copy pictures 234copy text 234Copy to Row States 86Copy to Row States 224Copy with Labels 33copy/paste results 233–234correlation see MultivariateCosine 172CosineH 173Count 167, 169Create Columns 104create data table 93–118create formula 143–160create new data table 103

credits ix–xcross cursor 33crosshair tool 64, 219cursor tools 217, 244Custom Design 20, 49customize axes 224customize JMP 239–243customize menus 246customize results 235–236customize toolbars 244, 249Cut 33, 157, 232

Ddashed rectangle tool 64, 219data

add/delete column 104–105create 93–118enter and edit 105export 112import 108–109navigate 93–118

data characteristics 75–91data properties 89–91data table

select and deselect rows/columns 101–102data table template 158–160data types 77Database 29, 103, 113Database Open Table dialog 114datafeed 242Date DMY 203Date MDY 203Date Time functions 203–205date-time data 224

formats 82–84functions 203–205text import/export 244

Day 204Day Of Week 204Day Of Year 204dBase 108–112Decimal 220default path 30, 242Delete button 151delete column 104–105Delete Columns 49, 105delete formula 46Delete key 152delete results 233–234Delete Rows 45, 104

270 Index

Delete Selected Items 97delimiter 109dependent column (formula) 143descending sort 121descriptive statistics 122Deselect 216deselect rows/columns 101–102Design 96Design Role 91diacritical marks 179diamond control 216–217Dif 166disclosure control 100, 216–217, 234discontiguous selection 33Discriminant 15, 57disjunction 179–184Display Box 216DisplayBox 227Distribution 11, 51, 159–160distribution functions 184–194divide columns into several 121Divide To 211DOE menu 49–51DOE tab of JMP Starter 20–21D-optimal design see Custom Designdouble arrow button 151double quotation marks 109down arrow 100, 157drag and drop 233–234

in formula 157drawing tool options

Color 231Dashed 231Delete 231Point from 230Point to 230Reanchor 231Thick 230Unanchor 231

duplicate column 118

Eedit data 105Edit Formula 90, 145edit formula 46, 143–160Edit Graphics Script 222Edit HTML Page (internet open option) 115Edit HTML with Tags Removed (Internet

Open Option) 115Edit HTML with Tags Removed option 115

Edit menu 32–36, 116–118edit report 235–236Edit Selected Items 96–97edit text 100effect size see Sample Size and Powerefficient evaluation 158element 145, 147elements browser 149, 152–155EMF 233end-to-end merge see Concatenateenhanced metafile 233enter data 105equality functions 178–179equation editor see formula editorEuclidean distances see ClusterEvaluate 156evaluation of formulas 155–156Excel file 108–112exclude icon 97Exclude/Unexclude 42Exclude/Unexclude columns 48Exclude/Unexclude rows 42, 85Excluded 208Exit 31Exp 170export data 112Exporting Data 112expression 148, 156extension 108Extract HTML Table as JMP File (internet open

option) 116extreme vertices see Mixture Design

FF Density 186F Distribution 187F Power 193F Quantile 187F Sample Size 193Factorial 171factorial design table 140fat plus tool 64, 100, 218, 232Fibonacci series 167field width 104, 109File locations preference 30, 242File menu 9, 27–32filename extension 108fill data table 105Fit Distribution 160Fit Model 12, 17, 53

Index 271 In

dex

Fit Modelfitting personalities 53

Fit Nonlinear 13, 54Fit Y by X 11, 52, 225Fixed Decimal format 82flip data table 121Floor 168foldover design see Augment DesignFonts preferences 30, 241For 183–184Format 205Format 224format analysis table 220formatting dates as axis values 225Formula 46, 90, 104, 157formula

drag and drop 157elements 152–155functions 163–211keyboard shortcuts 160store 158–160tips 157–158

Formula Boxing 240formula display area 150formula editor 143, 145–160formula work panel 148–150four-digit year 244Freq role 79–81Full Factorial Design 21, 50function 148, 156, 163–211function browser 146, 150, 165

GGage R&R see VariabilityGamma 171Gamma Density 188Gamma Distribution 188Gamma Quantile 188General preferences 239generate random numbers 199GIF 235Go to Row 102grabber tool (hand tool) 218, 228graph file formats 235Graph Marker Size 240Graph menu 59–62Graph tab of JMP Starter 16–18Graphic 241Graphic Formats preferences 30graphics formats 241

Graphics Formats preferences 242Graphs tab of JMP Starter 16Grid Template (x !=y) 159Grid Template (x=y) 159Group 73grouped statistics 121grouping of expressions 156

Hhand tool 51, 157, 159, 228

in formula 147hand tool (grabber tool) 218Help 3, 71–72, 215, 218Help Center 72Hex 177Hidden 208Hide 70Hide Tool Palette 66Hide/Unhide 42, 47, 85hide/unhide results 216–217Hierarchical clustering 14higher dimensions see Spinning Plothistogram

change bars with hand 219Horizontal 216HTM 235HTML file 242Hue State 208Hypergeometric Distribution 193Hypergeometric Probability 193Hypertext Markup Language 235

II-beam cursor 100, 106

range check 100I-beam cursor (range check) 88ID column 130idf functions 184–194If 179–180, 201Ignore Errors 149Import as Data command 111import data 108–109Importing

Instant Text Importing 255In Days 203In Hours 203In Minutes 203In Weeks 203In Years 203

272 Index

Index 71Index tab of JMP Starter 4, 23inequality functions 178–179Informat 204Initial JMP Starter 239Initial Splash Window 239Insert button 151insertion point 100, 106instrument 242integer weight 81international characters 179internet file formats 235Internet Open command 115Interpolate 183intranet file formats 235Invert Row Selection 102invert rows/columns 121invert selection 223Is Missing 179Item 177

JJMP Journal file 235JMP Starter 65JMP Starter window 7, 9–23JMP.pfs file 242Join 22, 40, 122, 136Joint Photographics Expert Group 235Journal 35, 217, 233Journal window 215, 233–234JPG 235JPG graphics format 242JRN 235JSL 35

in formula editor 147, 154, 157in graph 222in report window 222saved in data table 96

Kkeyboard shortcuts

for Layout 236in formula editor 160

keypad operators 151–152K-means clustering 14

LL16 see Taguchi Arrays

L4 see Taguchi ArraysL8 see Taguchi ArraysLabel role 133Label/Unlabel 42, 47–48, 85, 97, 224Labeled 208Labels column 133Lag 166lasso tool 64, 128, 219, 223lattice see Mixture DesignLayout 36, 72–73, 233, 235Layout

keyboard shortcuts 236Layout window 215, 235–236left arrow 157left to right evaluation 155Length 176LGamma 172library see SAS Transportlife table example 160line tool 65, 230linked column 143linked data table 128–129List Check 47, 88, 100List Check column property 90local variable 147, 151

in formula 153Local Variable dialog 153locked column 143Log 170Log 66logarithmic functions 170–172logical operators 179–184Logistic 12Loglinear Variance 54Long Date 204loss functions 160Lowercase 176

MMachine Data Source 114magnifying glass tool 64, 219, 228major tick marks 225Make into Data Table 221manipulate plots/graphs 218, 222Manova 54Marker Of 209Marker State 207Markers 43, 85marquee tool 64, 219Match 158, 179, 181–182

Index 273 In

dex

match values of two data tables see JoinMatched Pairs 12, 53Matching Cols 138Max 125Maximum 198MDYHMS 205Mean 125Mean 197Median 126menu bar 25merge horizontally see Joinmerge vertically see ConcatenateMersenne-Twister technique 199, 202metadata 89–91Min 125Minimum 198–201minor tick marks 225missing term 148missing value

comparison 179Mixture column property 90Mixture Design 21, 50Modeling tab of JMP Starter 12modeling types 77Modulo 168Month 204Move Backward 73Move Forward 73Move Rows 45Move To Back 69Move to Back 73Move to Front 73multiple actions 217Multiply To 211Multivariate 56Multivariate Methods 14Multivariate tab of JMP Starter 14Munger 174–176

NN 125N Missing 125N Missing 198navigate data table 93–118NChooseK 171needle plot see Overlay Plotnegation 179–184negative frequency 81negative weight 81NegBinomial Distribution 192

NegBinomial Probability 192nesting box in formula editor 146Neural Net 55Neural Net 13, 55New Column 46, 77–84, 104, 145New Column vs. Add Columns 105New Data Table 28new data table 103New Data View 67–68New Files 31New Property 104New Script 28New Table Property 96New Table Variable 96Next Selected 43node controls 216–217nominal 78Nominal Logistic 54noninteger weight 81Nonlinear Fit 13, 54, 160nonlinear fit parameter 154–155nonlinear table templates 160normal curve 160Normal Density 189Normal Distribution 189normal distribution 160Normal Quantile 189Not 183notation in manual 4Notes 96Notes column property 90NRow 167Num 176Number 197numeric 78numeric column 78numeric formats ??–82Numeric functions 168–169

OOC Curves 20ODBC import 108–112Oneway 11Open 108Open 28, 103, 243Open

preferences 244Open All Below 216Open All Like This 216open cross cursor 100

274 Index

Open JMP File (internet open option) 116operating characteristic curves see OC Curvesoperator 163–211optimize use space in report windows 215Or 182order of operations 155–157ordinal 78Ordinal Logistic 54orthogonal arrays see Taguchi ArraysOther column property 91Outline Connecting Lines 240outline controls 216–217oval tool 65, 232oval tool (simple shape tool) 230Overlay Plot 16, 59, 160overview of JMP 1

PPage Breaks 215, 217Page Setup 31paintbrush tool 64panels see Column, Row, Tables panelparameter 147, 149, 154–155Parameters 154Parametric Regression 15, 58parentheses 155–156Pareto Plot 19, 61Partition 13, 55Paste 103, 117, 157paste results 233–234Paste Special 233Paste with Labels 34, 117path preference 30pdf functions 184–194Pearson correlation 81peel function 152, 157personalize JMP 239–243photographs 235PICT 233picture file formats 235plain text 235plain text file 108, 112, 233platform preferences 30, 243plot statistics 122PLS (partial least squares) 15, 57plus icon 97PNG 235PNG graphics format 242pocket calculator see formulapoint chart see Chart

Poisson Distribution 193Poisson Probability 193Pollen.jmp 228polygon (spline) tool 65, 231polygon tool options 231popup menus 217, 220–222popup pointer cursor 100Portable Network Graphics 235Post Decrement 211Post Increment 211power see exponential, Sample Size and Powerprecedence 155–157prediction profiler see Profilerprediction traces see ProfilerPreferences 30, 239–243preferences path 242prerequisites 3–5Preselect Roles 80Previous Selected 44principal components see Spinning PlotPrint 30, 234Print Preview 31print report 233–234Probability functions 184–194Probit 189Product 198Profiler 17, 62properties 89–91, 97Proportional Hazards 15, 54, 58publish on WWW 235PValue format 82

QQC tab of JMP Starter 18–20quality improvement see QCQuantile 197quantile functions 184–194question mark tool 63, 218Quit 31quotation marks 109

RR&R see VariabilityRandom Binomial 201Random Cauchy 200Random Exp 199–201Random Gamma 200Random Geometric 201Random Integer 200–201

Index 275 In

dex

Random Negative Binomial 201Random Normal 199Random Normal() 202Random Number functions 199Random Poisson 201random sample 128Random Seed 201Random Triangular 200Random Uniform 199Random Uniform() 202RandomSeededNormal 202RandomSeededNormal() 199RandomSeededUniform() 199, 202Range 126Range Check ??–88Range Check 47, 88, 100Range Check column property 90range plot see Overlay Plotreal-time data 30rearrange results 234–236rearrange terms in formula 147rectangle tool (simple shape tool) 230Recurrence 15, 58Redraw 67, 69reference of functions 163–211relative frequency see ParetoRemove Property 46, 89Reorder Columns 48–49reorder rows see SortReplace 35, 107Replace Table 130report windows

customize 235–236disclosure button 216–217features 215–217

resize plots/graphs 222, 234Response Limits column property 91Response Surface Design 21, 50response surfaces see Contour ProfilerRevert 29rich text format 233, 235right arrow 157Right-click menus 220–222roles 79–80Root 170rotated tick marks 225Round 168Row 167Row Editor 44, 100Row functions 166–167Row Order Levels column property 90

Row Selection 44, 102row state column 78Row State functions 205–209row states 84–88, 223–224

summary of 209Rows 36, 41–45Rows panel 98rowwise statistics functions 194–198RTF 233, 235, 242Run Script 35

SSample Size and Power 21, 51SAS data 108–112

date-time formats 84SAS Datasets 110SAS files, writing 110SAS Transport 108, 112SAS Variable Names 110Save 29, 104Save As 29, 36, 112Save As

preferences 244Save Level Numbers 159save report 233–234Save Selection As 34scatterplot matrix

rearrange 219Screening 54Screening Design 21, 50script see JSLscroll axis 228Scroll bar arrow style 240Scroll bar thumb style 240Scroll Lock/Unlock 47scroller tool 64, 218Search 35, 71, 106search also see FindSelect 87, 216Select All 34, 233Select All Rows 87, 102select bars of histogram 128select block of cells 33Select Excluded 87, 102select expression 157select graph 218Select Hidden 87, 102Select Labeled 87, 102select multiple rows/columns 33select points 128, 219, 223–224

276 Index

select result 218select rows/columns 33, 99–102

for new data table 121select subset 102select table 218Select Where 102, 129Selected 208Selected State 207selection cursor 99selection tool 218, 223Send 31Set Report Title 217Set Role 47Set Title 70severity analysis see ParetoShade State 208Short Date 204shortcuts see keyboard shortcutsShow Boxing 145, 149Show Explanations 240Show Hidden 70Show Tool Palette 66Show Toolbars 66show/hide results 216–217sideways data table 121Sigma column property 91signal and noise factors see Taguchi Arrayssimple shape tool (oval or rectangle) 230simplex mixture design see Mixture DesignSine 172SineH 173sinusoid functions 172–173slope computation 153Sort 22, 39, 121, 129–130Sort by Column 221sort order 179source table 102, 121Spec Limits column property 90Spinning Plot 17, 60Spinning Plot platform 159Split 22, 40, 121, 132SQL statement 114square grid 159Squash 170Stack 22, 39, 121, 130–131Standard Least Squares 54Standardize Attributes 48start-up options 30Statistical functions 194–198Status Bar 66Std Dev 197

Std Dev 126Std Err 126Step 183Stepwise 54store formula 158–160Subscript 167Subset 22, 38, 121, 128–129subset of columns see JoinSubstr 176Subtract To 211Sum 125Sum Wgt 125Summary 22, 37, 102, 121–122summary table 102, 121Summation 198Suppress Eval 149Suppress OnOpen Processing 239Surface Formula 159Survival 15, 58survival data 159Survival Distribution 15Survival tab of JMP Starter 15–16switch terms 151

Tt Density 190t Distribution 190t Quantile 190t= button 154table

format 220table property 97Table Style 221Table variable 96, 147, 153Tables 36–37, 121, 235Tables panel of data table 96–97Tables tab of JMP Starter 22tag icon 97Taguchi Arrays 21, 50Tangent 172TanH 173template 158–160term 148Ternary Plot 19, 62Text Export Options dialog 112text file 108, 112, 233text functions 173–177text import/export

preferences 244text mode for formula 147

Index 277 In

dex

three-dimensional plot see Spinning PlotTile 69Tile Horizontally 69Tile Vertically 69time functions 203–205Time Series 13, 15, 56title bar 236Today 203Tool Elements Window 67Toolbars 63, 217, 244Toolbars

customize 244Tools 63, 216–217, 229, 232, 244Transcendental functions 170–172Transpose 22, 40, 121, 133triangle control 216–217Trigonometric functions 172–173Trim 176truth table 183Tukey HSD Quantile 193two-digit year 244TXT 233, 235type 104

UUnanchor Tool Palette 66Undo 33, 49, 104–105, 108, 117, 147Ungroup 72, 235Unit column property 91univariate statistics 122up arrow 157Uppercase 176URL_List toolbar 115

VValidation 47validation 88Variability 19, 61vector graphics 235View menu 65–66V-mask origin 219

W-ZWeek Of Year 204Weibull Density 191Weibull Distribution 191Weibull Quantile 192Weight role 79, 81

While 183–184Width 220Window menu 68–71Window Metafile 235window panel 101Windows Metafile 233WMF 233, 235Word 177world wide web authoring 235X role 79Y role 79Y2K date-handling 244Year 204yx tool 219zoom tool 219, 228