Mod 5 03 Intro to Data Mar 02

8/12/2019 Mod 5 03 Intro to Data Mar 02

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© Visteon Corporation Module #5 Intro to Data Rev 1.0 3/021

Introduction To Data

Scales of Measure




The Breakthrough Strategy

Define BB Works with Management

1 Select Output Characteristic and identifykey process input and output variables

2 Define Performance Standards

3 Validate Measurement System

4 Establish Product Capability

5 Define Performance Objectives

6 Identify Variation Sources

7 Screen Potential Causes

8 Discover Variable Relationships9 Establish Operating Tolerances

10 Validate Measurement System

11 Determine Process Capability

12 Implement Process Controls

Measure

Analyze

Improve

Control

Characterize

Optimize




Measurement Phase

• Project Definition: – Problem Description (Authorize.ppt) – Project Metrics (Metrics.xls)

• Process Exploration: – Process Flow Diagram

– C&E Matrix, PFMEA, Fishbones – Data collection system

• Measurement System(s) Analysis (MSA): – Attribute / Variable Gage Studies

• Capability Assessment (on each Y) – Capability (Cpk, Ppk, s Level, DPU, RTY)

• Graphical & Statistical Tools• Project Summary

– Conclusion(s) – Issues and barriers – Next steps

• Completed “Local Project Review”




What do you want to know?

In form at ion Sources:

• Words (A to Z)

• Pictorial

• Verbal

• Numeric (0-9)

Grouping Measurement

Discrete Continuous

Nominal Ordinal

Interval Ratio

1) What do you want to know?

2) How do you want to see what it is thatyou need to know?

3) What type of tool will generate what itis that you need to see?

4) What type of data is required of theselected tool ?

5) Where can you get the required typeof data?

Critical

Questions

Plan

Execute

“Data does not provide information – You have to

torture data for i t to give inform at ion. And th e

instrument of torture is statistics”. Dr. Mikel Harry.




Discrete Vs. Continuous Data

FAIL PASS

Electrical Circuit

TEMPERATURE

Thermometer

Time

CONTINUOUSDISCRETE

NO-GO GOCaliper

QTY UNIT DESCRIPTION TOTAL

1 $10.00 $10.00

3 $1.50 $4.50

10 $10.00 $10.00

2 $5.00 $10.00

SHIPPING ORDER

ERROR

$$




Continuous

$ $

Sparse

Information

Rich With

Information

The Advantage of Continuous Data

• To obtain the same level of understanding regarding aprocess


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Nominal: unrelated categorieswhich representmembership or non-membership.

Ordinal: ordered categories with noinformation about distancebetween categories.

Interval: ordered categories withequal distance between

categories, but no absolutezero point.

Ratio: ordered categories withequal distance betweencategories with anabsolute zero point.

• Discrete data (usually)• Grouping / sorting’ • Yes / no, pass / fail• Arithmetic not possible

• Discrete data• Ranking• Seldom used• Very little arithmetic possible

• Continuous data• Most common scale

• Use arithmetic with caution

• Continuous data• Proportional relationship• Most forms of arithmetic apply

• Categories• Labels

• 1st, 2nd, 3rd

• Relative height• Alphabetic order• 1<2<3<4

• Temperature scales• Dial indicator

• Velocity = distance/time• Ruler

Categories of scale Description Examples

Categories of Scales



Equipment Application

Environment Materials

Poor paintadhesion

Application form

Select one from each group

Nationality

Marital status

Occupation

List of Field Reps

Access toa digitalcamera

Withoutaccess toa digitalcamera

Fred W.

Bill S.

John D.

Sam C.

Bob T.

Jim C.

Joe W.

Diane A.

Nominal Scale

• Nominal scales of measure are used to classify elements into categories withoutconsidering any specific property.

• Examples of nominal scales include “causes” on fish bone diagrams, yes / no,

pass / fail, etc.



9

Example 1: Pareto Chart - Paint Adhesion Test

R e

l a t i v e S i z e

Ordinal Scale

Order ofimportance

Example 2: Customer Survey

Question: How would yourate our service?

Excellent

Very Good

Good

Fair

Poor

Ordinal Scale

• Ordinal scales of measure are used to order or ranknominal (pass / fail) data based on a specific property.

• Examples of ordinal scales include relative height, Paretocharts, customer satisfaction surveys, etc.



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Types o f Quest ions fo r Quest ionnaires

• Strongly agree

• Somewhat agree

• Neither agree or disagree

• Somewhat disagree

• Strongly disagree

• Somewhat less than expected

• Much less than expected

• One of the best of all

• Better than most

• About the same as most

• Worse than many

• One of the worst of all

• Much better than expected

• Somewhat better than expected

• As expected

Likert Scale (Ordinal)

• Example Rating Scale Ranges – Five-point school grading system (A B C D E)

– Seven-point numerical rating (1 2 3 4 5 6 7)

– Verbal scale (excellent, good, average, fair, poor)



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Interval and Ratio Scale

Examples of interval scale:(no absolute zero)

1. Displaced Scale

50

40

30

20

10

0

0

0.10

0.20

2. Dial Gage

3. Relative Velocity

Examples of Ratio Scale(absolute zero)

1. Ruler 10090

80706050403020100

2. Position Vs Timeat Constant Speed

3. Weight As a Function of

Number of Bricks

Gageblock

• Interval scales of measure are used to express numerical information on ascale with equal distance between categories, but no absolute zero. – a dial gage sitting on top of a gage block, comparison of differences, etc.

• Ratio scales of measure are used to express numerical information on ascale with equal distance between categories, but with an absolute zero inthe range of measurement. – a tape measure, ruler, position Vs time at constant speed, etc.



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Discrete (Attribute) Continuous (Variable)

Continuous(Variable)

Discrete

(Attribute)

Outputs

I n p u t s

Chi-squareAnalysis of

Variance

Discriminate Analysis

Logistic regression

Correlation

Multiple Regression

Selecting Statistical Techniques

• There are statistical techniques to cover allcombinations of data types.

These are some of the statistical techniques that youwill use to drive Process Improvement

Documents

Mod 5 03 Intro to Data Mar 02