QE Tools Software Tutorial

An excel-based Six Sigma statistical software add-in.

QETOOLS

qetools.com

QETools Menu Items............................... 3

I. Getting Started....................................... 4

II. Six Sigma Methods – Tool Roadmap....... 11

III. Process Analysis – Qualitative Tools ....... 14 1. SIPOC Diagram...................................................... 16 2. Cause-and-Effect Diagram...................................... 17 3. Control Plan Template ............................................ 18

IV. Process Capability Summary .................. 20 1. Sigma Level Calculator ........................................... 22 2. DPM Calculator – Normal........................................ 24 3. Process Capability Summary ................................... 25

V. Descriptive Statistics ............................. 36

VI. Graphical Tools..................................... 40 1. Run Chart.............................................................. 41 2. Pareto Analysis ...................................................... 44 3. Histogram ............................................................. 48 4. Box Plot (single or multi) ........................................ 51 5. Scatter Plot............................................................ 56

VII. Correlation and Simple Regression ......... 59 1. Correlation ............................................................ 60 2. Simple Regression.................................................. 63

VIII. Measurement Systems Analysis Tools..... 66 1. Gage R&R Template...............................................67 2. Repeated Measurement Template...........................73 3. Attribute Agreement Analysis Template ...................77

IX. Control Charts ..................................... 80 1. X-bar/Range Chart .................................... 81 2. Individual/Moving Range Chart ....................... 85 3. P-Chart ................................................. 88 4. NP-Chart ............................................... 92 5. U-Chart................................................. 94 6. C-Chart ................................................. 98

X. Tabulation ..........................................100 1. Cross Tabulation...................................... 101 2. Binary Cross Tabulation .............................. 105

XI. Hypothesis Tests .................................109 1. Test Two Variances (F-Test) ........................ 110 2. Test Two Means (Independent

Sample t-Test) ....................................... 114 3. Paired t-Test .......................................... 118 4. Test Two Proportions ................................ 121

Table of Contents

Note: Not all tools are shown in this tutorial.See help files for additional examples.

QE Tools Menu Items

I. Getting Started

Getting Started – Excel Menu

QE Tools appears as a menu option in the main Excel toolbar.

Getting Started – New Data Sheet

QE Tools uses its own data sheet when performing analyses.

You may begin by creating an initial blank datasheet using the New Data Sheet menu pick.

QE Tools uses its own data sheet when performing analyses.

You may begin by creating an initial blank datasheet using the New Data Sheet menu pick.

Data Sheets

A new, pre-formatted worksheet is inserted with the name DataSheet.

After you create a data sheet, you can add and manipulate data in most of the ways familiar to you in Excel (e.g., copy, paste, add formulas, etc.).

Note: You must define a variable name for each data series in the Row: “Variable Name”.

Optional, you may include upper and lower specification limits (USL and LSL) as well as a target (nominal) value for each variable.

These will automatically be referenced for those tools that require specification limits for analysis.

A new, pre-formatted worksheet is inserted with the name DataSheet.

After you create a data sheet, you can add and manipulate data in most of the ways familiar to you in Excel (e.g., copy, paste, add formulas, etc.).

Note: You must define a variable name for each data series in the Row: “Variable Name”.

Optional, you may include upper and lower specification limits (USL and LSL) as well as a target (nominal) value for each variable.

These will automatically be referenced for those tools that require specification limits for analysis.

Data Format in “DataSheet”

Data variables may either be values or calculations of other variables.

Examples: ‘TotalVisit’ list values ‘TotalWait’ and ‘WattoVisit’ are formula. Data for any variable may be constructed using standard Excel formulas.

Data variables may either be values or calculations of other variables.

Examples: ‘TotalVisit’ list values ‘TotalWait’ and ‘WattoVisit’ are formula. Data for any variable may be constructed using standard Excel formulas.

Variable Names

When entering variable names, QE Tools may update after you enter them or paste from another worksheet. QE Tools uses an algorithm to standardize variable names. The algorithm ensures that:

Certain characters are not allowed in variable names. The following characters are stripped from variable names:

:, \, /, ?, *, [, ], ‘ (apostrophe), <space>

Variable names are no longer than 16 characters. Names that are longer are shortened by using the first 8 and last 8 characters of whatever is entered.

Duplicate names are not allowed to insure QE Tools knows which variable you wish to analyze.

Number of Worksheet Warnings

QETools warns of having too many active worksheets because performance may be diminished with increasing file size.After 30 worksheets, QE tools issues a warning message.Recommend creating a second analysis file or removing unused worksheets.

II. Six Sigma Methods –Tool Roadmap

Six Sigma Tool Roadmap

QE Tools provides a Six Sigma problem solving roadmap with common analysis steps and hyperlinks to analysis tools and templates.

Example: Measure Phase

Blue TextRepresentHyperlinksTo VariousAnalysis and Templatesin QE Tools

III. Process Analysis –Qualitative Analysis Tools

Working with ideas / text

Process Analysis – Qualitative ToolsSIPOC DiagramCause-Effect DiagramQFD - House of Quality*FMEA Table*Process Control Plan Manufacturing or Transactional*

* Sample Templates Provided

Process Analysis Tools > SIPOC

Sample Excel Data File: qetools-sampledata.xls

Select Variables UsingSIPOC Dialogue Box

SIPOC Diagram - Loan Process

Suppliers Inputs Process Outputs Customers

• Appraisers

• Insurance Companies • Title Companies

• Government

• Lender Programs• Interest Rates

• Type of Loan

• Loan Value

Step 1:

•Prepare Loan

Step 2:

•Process Loan

Step 3:

•Underwrite Loan

Step 4:

•Clear Conditions

Final:

•Close Loan

• Loan Documents

• Mortgage

• Mortgage Customers • External Underwriter• Lending Institution

OUTPUT:

Process Analysis Tools > Cause-Effect Diagram

However, we recommendentering reasons foreach cause categoryin data sheet column.

May enter dataDirectly in dialogueBox.

Process Analysis Tools > Control Plan

Select Control Plan Template

QE Tools Control Plan Template

Note: worksheets may be modified per user preference.

IV. Process Capability Summary

Process Capability Summary

Data Analysis ToolsSigma Level CalculatorDPM Calculator - Normal Process Capability Graphical Summary*

Variable is NormalVariable is Non-Normal – Best Fit with Weibull DistributionVariable is Binary – Assume Binomial Distribution

Note: Process Capability Graphical Summary includes:summary statistics, observed DPM, expected DPM (distribution), histogram, run charts, box plot, control charts where applicable

Process Capability Summary > Sigma Level Calculator

Sigma Level Calculator - Example

Three different methods are available to calculate the “sigma level”depending on the format of information available from your process. Enter the appropriate information in white boxes and sigma level is calculated automatically.

Process Capability Summary > DPM Calculator - Normal

If data may be assumed to be normal, you may input the average, standard deviation and specification limits in white boxes and QE Tools automatically estimates Defects per million.

Process Capability –Graphical Summary*

Different Process Capability Summaries are available depending on data / distribution.

Continuous Variable and Normal DistributionContinuous Variable and Non-Normal –Best Fit with Weibull DistributionBinary Variable – Distribution assumed Binomial

*Note: Process Capability Graphical Summary includes:summary statistics, observed DPM, expected DPM (distribution), histogram, run charts, box plot, control charts where applicable

Process Capability Summary -Normal

Process Capability Summary –Normal – Dialogue Box

Select one or more variables from the variable list to analyze (note: each variable is output to its own results worksheet).

Select type of control charts to display on the results worksheet (note: subgroup size is assumed 1 for “Ind / Moving Range”.

Options –-- show out-of-control patterns.-- manual scale run chart-- enter specification limits if not already entered on “data sheet”.

Select type of control charts to display on the results worksheet (note: subgroup size is assumed 1 for “Ind / Moving Range”.

Options –-- show out-of-control patterns.-- manual scale run chart-- enter specification limits if not already entered on “data sheet”.

Process Capability Summary –Normal – Using Data Ranges

Optionally select a range of data to analyze from a worksheet other than the DataSheet (note: the first row is assumed to be a label used as the variable name).

Process Capability Summary –Normal Results

The output contains several sections:• Statistical summary• Expected Defects per Million

(distribution)• Observed Defects per Million• Histogram• Run chart• Box plot• Control charts

The output contains several sections:• Statistical summary• Expected Defects per Million

(distribution)• Observed Defects per Million• Histogram• Run chart• Box plot• Control charts

Sample Excel Data File: qetools-sampledata.xlsOutput: Time in Waiting Room

Results – Summary Stats - Histogram

Notice that the Upper Specification Limit (USL) from the datasheet is displayed on the chart and summarized in the data output.

Results – Run Chart – Box Plot

The Run Chart provides a time trend.

Box Plot summarizes basic distribution. Example shown is skewed right (more points > median).

The Run Chart provides a time trend.

Box Plot summarizes basic distribution. Example shown is skewed right (more points > median).

Process Capability Summary –Non-normal (Weibull)

Results- Non-normal (Weibull)

The output contains:• Statistical summary• Expected Defects per Million

(distribution)• Observed Defects per Million• Histogram• Run chart• Box plot

The output contains:• Statistical summary• Expected Defects per Million

(distribution)• Observed Defects per Million• Histogram• Run chart• Box plot

Process Capability Summary –Binary (Binomial)

Process Capability Summary –Binary (Binomial) Dialogue Box

Select two variables for the analysis (one variable represents the number of units and the second is for the number defective).

Do not enter defective percentages – QE tools automatically calculates.

Alternatively, select one variable for the number defective and enter a constant sample size.

Select two variables for the analysis (one variable represents the number of units and the second is for the number defective).

Do not enter defective percentages – QE tools automatically calculates.

Alternatively, select one variable for the number defective and enter a constant sample size.

Specify a target for the process (note: the target does not figure into any calculations but does appear on the results worksheet for reference).

V. Descriptive Statistics

Descriptive Statistics > Basic Descriptive Statistics

Descriptive Statistics > Basic Descriptive Statistics Dialogue Box

Select one or more variables from the variable list to analyze.

Stats may also be calculated by a “grouping variable.” Select a grouping variable from the variable list or select a range from a worksheet.

Descriptive Statistics > Basic Descriptive Statistics – Results

Basic statistics are calculated for one or more variables that are entered into the analysis.

Here, output is shown for the variable “TotalWait” with a grouping variable of “Team” (which has values of “A,” “B” and “C”).

One Variable Multiple Output Variables

One Output Variable Stratified by Grouping Variable

VI. Graphical Tools

Graphical Tools > Run Chart

Graphical Tools > Run Chart Dialogue Box

Select one or more variables from the variable list to analyze (note: all output appear on a single run chart).

Optionally, modify the scale setting for the Y axis.

Run Chart – Sample Results

Single Variable

Or, You May SelectMultiple Variables

Graphical Tools > Pareto Analysis

Graphical Tools > Pareto Analysis Dialogue Box

Select a data and a category variable to create a Pareto chart.

Optionally, modify the output setting for the left and right Y axes.

Graphical Tools > Pareto Analysis Data Format

Category Sum Data*

*Note: sum data may be calculated by summing up data columns for different categories, or using the tabulation tool inside QETools.

Graphical Tools > Pareto Analysis – Sample Result

Tool Option: Show Relative and Cumulative Frequency

Graphical Tools > Histogram

Histogram – Dialogue Box

Use either Absolute or Relative Frequency for Y-axis.

Select a variable from the variable list to analyze (note: if select more than one variable, each variable is output to its own results worksheet).

Replace

Histogram – Sample Results

Enter a “1st Bin” and/or “Width” value to adjust the output to your liking. “1st Bin”and the slider adjustment (for bin width) can be used simultaneously.

Slide the slider to dynamically adjust the bin widths (and update the data table and the histogram)

The output contains:• Frequency table (frequency of observations

falling within a certain data range or bin)• Frequency count: (previous bin ~

current bin]• Histogram

The output contains:• Frequency table (frequency of observations

falling within a certain data range or bin)• Frequency count: (previous bin ~

current bin]• Histogram

Graphical Tools > Box Plot (single or multi)

Box Plot (single or multi) –Dialogue Box Example Single

Box Plot (single) – Sample Results

Graphical Tools > Box Plot (multi)Dialogue Box -- Include Grouping Variable

Box Plot (multi) – Sample Results

Graphical Tools > Scatter Plot

Graphical Tools > Scatter PlotDialogue Box

Select an input (X) and output (Y) variable to plot.

Alternatively, select a 2-column data range from any worksheet (include data labels in the first row, and use Column 1 for X, Col 2 for Y).

Scatter plot chart options:• Trend line – show linear, quadratic, or no trend line.• Show R2

• Show best fit equation

Scatter plot chart options:• Trend line – show linear, quadratic, or no trend line.• Show R2

• Show best fit equation

Graphical Tools > Scatter Plot Sample Results

Linear ModelR R 2

0.86 0.73 Timeinwatingroom TotalWait Scatter Plot

y = 1.6054x + 17.045R2 = 0.7313

0 20 40 60 80 100

Timeinwatingroom

Correlation Coefficient, R R-squared

Note: Scatter Plot also providedunder simple regression tool.

VII. Correlation and Simple Regression

Regression and Correlation > Correlation Matrix

Regression and Correlation > Correlation Matrix Dialogue Box

Enter a threshold to highlight data with a strong correlation (|correlation| > threshold will be highlighted). Typically, a 0.7 cutoff is standard to indicate a strong correlation.

Variables must contain numeric data to be included in a correlation matrix. “Text” data will be omitted [more].

Regression and Correlation > Correlation Matrix, R, Results

Matrix shows all pairwise comparisons of selected variables (Max 50 variables).

Data with a correlation stronger than the threshold is emphasized with bold text (note: either a strong positive or strong negative will be emphasized).

Regression and Correlation > Simple Linear Regression

Regression and Correlation > Simple Regression Dialogue Box

Select the variables to analyze.Select the variables to analyze.

X and Y variables must have the same N or the analysis will terminate.

Regression and Correlation > Simple Linear Regression Results

Response optimizer allows you to input an X and solve for Y (or Vice Versa) based on best fit equation.Alternatively, you may use slider buttons directly in graph.

VIII. Measurement Systems Analysis Tools

1. Measurement Systems Analysis > Gage R and R

Measurement Systems Analysis > Gage R and R Template

Measurement Systems Analysis > Gage R and R – Header Section

Needed for Calculations

Enter Study Variation Multiplier: K=5.15 or 65.15 (predict 99% of the area under normal distribution curve),

Or 6 (predict 99.73% of the area under normal distribution curve)

Width = USL- LSL

Measurement Systems Analysis > Gage R and R – Data Entry Section

Data Entry Section for Measurement System StudyMax 10 parts, 3 operators, 3 trials

Gage R&R -- Sample Data

Measurement Systems Analysis > Gage R and R -- Calculations

See Notes for Formulas

Sample Output

2. Measurement Systems Analysis > Repeated Measurements Study

Measurement Systems Analysis >> Repeated Measurements Study Template

Enter Datain White Cells

Data Entry

Max = 50 pairs Note: % tolerance calculationsRequires a USL and LSL to obtain tolerance width for

Note: Calculations in Yellow

Sample Results

Solid Line: Best FitTo remove best fit line, click on line on graph and hit delete.

3. Measurement Systems Analysis > Attribute Matching Study

Measurement Systems Analysis > Attribute Matching Study - Template

Enter Data --Must include a Standard (or reference)in 1st Column

Enter Data for1..3 Appraisersin additional columns

Max 50 samples

Enter Alpha(Type I) error

Note:CalculationsIn yellow box

Measurement Systems Analysis > Attribute Matching Study - Template

OK, ifconfidenceintervalsoverlap

IX. Control Charts

1) Control Charts > X-bar / Range

Control Charts > X-bar / RangeDialogue Box – Data in 1 column

Data Entry Options

All data in 1 columnSubgroup size every XX rows

Sample Data Sheet

Control Charts > X-bar / RangeDialogue Box – Data across columns

Data Across ColumnsEach row is a subgroup

Sample Data Sheet

Select 2 or more variables

Control Charts > X-bar / Range Sample Output

Note: after creatingA chart, you may Exclude Points and hit“UPDATE CHARTS” toreconfigure control charts.

2) Control Charts > Individual / Moving Range

Control Charts > Individual / Moving Range Dialogue Box

Select one or more variables from the variable list to analyze (note: each variable is outputted to its own results worksheet).

Check this box to show out-of-control data points on the control charts

Data can also be selected using a 1-column data range in any worksheet (Include a label name in the first row).

Control Charts > Individual / Moving Range – Sample Output

Note: after creatingA chart, you may Exclude Points and hit“UPDATE CHARTS” to

reconfigure charts.

etc ..

3) Control Charts > P-chart

Control Charts > P-chartDialogue Box

Select two variables –one with number of units inspected and another with number of defective units. Do not enter the “Percent Defective (p)” –QETools calculates this in the analysis.

Alternatively, specify a constant sample size and select a single variable with the number of defective units.

Data also can be selected using a 2-column data range.(Col 1: Number of Units; Col2: Number of Defects;Include label names in the first row).

Select two variables –one with number of units inspected and another with number of defective units. Do not enter the “Percent Defective (p)” –QETools calculates this in the analysis.

Alternatively, specify a constant sample size and select a single variable with the number of defective units.

Data also can be selected using a 2-column data range.(Col 1: Number of Units; Col2: Number of Defects;Include label names in the first row).

Control Charts > P-chart Sample Results (Unequal Sample Size)

Note: after creatinga chart, you may Exclude Points and hit“UPDATE CHARTS” to

reconfigure charts. Only appears if unequal sample size

Control Charts > P-chart Sample Results (Constant Sample Size)

P Chart

1 3 5 7 9 11 13 15 17 19 21 23 25

Subgroup

Example with constant subgroup sample size

4) Control Charts > NP-chart

Control Charts > NP-chartDialogue Box and Results

Requires entering constant subgroup sample size

5) Control Charts > U-chart

Control Charts > U-chartDialogue Box

Enter variable with the # units (subgroup sizes) orconstant sample size.

Enter # defects (errors)

Control Charts > U-chart Sample Output (Unequal Sample Size)

Note: after creatinga chart, you may Exclude Points and hit“UPDATE CHARTS” to

reconfigure charts.

Only appears if unequal sample size

Control Charts > U-chartwith Constant Sample Size

6) Control Charts > C-chart

Control Charts > C-chartDialogue Box and Results

Control Chart for Attributes

C Chart

1 2 3 4 5 6 7 8 9 10 11 12

Subgroup

Enter column of defects

X. Tabulation

1) Tabulation > Cross Tabulation

Tabulation > Cross Tabulation –Dialogue Box

Data Entry Options:

One or more variables from Datasheet

Data range in excel worksheet

Data Entry Options:

One or more variables from Datasheet

Tabulation may also be performed using a “grouping variable.”

A grouping variable may be selected from the variable list or using a worksheet range.

Tabulation > Cross Tabulation –Sample Output (binary data)

Sample output (binary data)

Data are tallied by a grouping variable (“LikelyReturn”)

Overall binary data are summed to the right.

Totals are calculated for each variable in the analysis.

Tabulation >Cross Tabulation –Sample Output(non-binary data)

Data are tallied by a grouping variable (“#Concerns”)

Group totals and overall data are summed to the right.

Totals are calculated for each variable in the analysis.

2) Tabulation > Binary Cross Tabulation

Tabulation > Binary Cross Tabulation – Dialogue Box

Data Entry Options:

One or more variables from DatasheetOr,Data range in excel worksheet

Note: Data must be binary (0/1).

Data Entry Options:

One or more variables from DatasheetOr,Data range in excel worksheet

Note: Data must be binary (0/1).

Tabulation > Binary Cross Tabulation – Sample Output(no grouping variable)

Overall DPMO is calculated when a defect (failure) is coded as 1.

Binary data and overall data are summed to the right.

Data are tallied by 0/1 and totaled for each variable in the analysis. Row% and Column% are also calculated.

Ex: PoorMealServiceRow% = 111/381Column% = 111/447

Data are tallied by 0/1 and totaled for each variable in the analysis. Row% and Column% are also calculated.

Ex: PoorMealServiceRow% = 111/381Column% = 111/447

Tabulation > Binary Cross Tabulation – Sample Output(w/ grouping variable)

Overall DPMO is calculated when a defect (failure) is coded as 1.

Data are tallied by a grouping variable (“LikelyReturn”)

Grouped (binary) and overall data are summed to the right.

Data are tallied by 0/1 and totaled for each variable in the analysis.

XI. Hypothesis Tests

1) Hypothesis Tests > Test Two Variances

Hypothesis Tests > Test Two Variances Dialogue Box

Data Entry Options:

Two Variables from Datasheet

Or, Using Summary Statistics

Data Entry Options:

Need to enter if one or two-tail hypothesis test

Two-tail: Alt Hypothesis is <>

One-tail: Alt Hypothesis is Max > Min

Alpha: Type I error (default = 0.05)

Hypothesis Tests > Test Two Variances – Sample Results

Statistical Test Conclusion:Returns either “Difference exists” or “No difference”

Hypothesis Tests > Test Two Variances –Summary Data and Results

Example: 1 Sided Test

2) Hypothesis Tests > Test Two Means - Independent

Hypothesis Tests > Test Two Means –Independent Dialogue Box

Data Entry Options:

Hypothesis Tests > Test Two Means – Independent Results

Hypothesis Tests > Test Two Means – Summary Statistics

3) Hypothesis Tests > Test Paired Data

Hypothesis Tests > Test Paired Data Dialogue Box

Data Entry Options:

Recommend 2 tail for paired

Hypothesis Tests > Test Paired Data Sample Results

Note: Box Plot of Differences

Hypothesis Test: Mean Difference = 0Alternative Hypothesis: Difference <> 0

*Note: Repeated Measurements Analysis under Measurement Systems performs also performs a Paired t-test analysis

4) Hypothesis Tests > Test Two Proportions

Hypothesis Tests > Test Two Proportions Dialogue Box

Data Entry Options:

Two Variables from Datasheet(Note: each variable must be a column of 0 and 1)

Data Entry Options:

Two Variables from Datasheet(Note: each variable must be a column of 0 and 1)

One-tail: Alt Hypothesis isLarge Prop > Small Prop

Hypothesis Tests > Test Two Proportions Sample Results

Hypothesis: P1 = P2 (2-Tail)Alt Hypothesis: P1 not equal P2

Hypothesis: P(Large) > P(small)Alt Hypothesis: P(Large) <= P(small)

Alt Hypothesis Identified

Hypothesis Tests > Test Two Proportions

Example Shown Below:Data fail normal approximation test for proportions

Here, a warning message is given.

QE Tools Software Tutorial

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