View a PowerPoint demonstration here

Introducing SigmaXL® Version 5.2

Powerful. User-Friendly. Cost-Effective. Priced at $199, SigmaXL is a fraction

of the cost of any major statistical product, yet it has all the functionality most professionals need.

Quantity, Educational, and Training discounts are available.

Visit www.SigmaXL.com or call 1-888-SigmaXL (1-888-744-6295) for more information.

SigmaXL® Version 5.2 – What’s New?

Compatible with Excel 2007 and Windows Vista

Lean and Six Sigma DMAIC Templates: Team/Project Charter SIPOC Diagram Data Measurement Plan Quality Function Deployment (QFD) Pugh Concept Selection Matrix Control Plan Lean Templates: Takt Time, Value Analysis and Process

Load Balance Chart

Menu Layout Option – Classical or DMAIC: Use SigmaXL’s Classical

Menu (default). Tools are grouped by category.

Use the DMAIC Menu. Tools are grouped by the Six Sigma DMAIC format.

Control Chart Selection Tool: Simplifies the

selection of appropriate control chart based on data type

Includes Data Types and Definitions help tab.

Why SigmaXL?

Measure, Analyze, and Control your Manufacturing, Service, or Transactional Process.

An add-in to the already familiar Microsoft Excel, making it a great tool for Six Sigma training. Used by Motorola University and other leading providers.

SigmaXL is rapidly becoming the tool of choice for Quality and Business Professionals.

What’s Unique to SigmaXL?

User-friendly Design of Experiments with “view power analysis as you design”.

Measurement Systems Analysis with Confidence Intervals.

Two-sample comparison test - automatically tests for normality, equal variance, means, and medians, and provides a rules-based yellow highlight to aid the user in interpretation of the output.

Low p-values are highlighted in red indicating that results are significant.

Recall Last Dialog

Recall SigmaXL Dialog This will activate the last data worksheet and recall

the last dialog, making it very easy to do repetitive analysis.

Activate Last Worksheet This will activate the last data worksheet used

without recalling the dialog.

EZ-Pivot: The power of Excel’s Pivot Table and Charts are now easy to use!

Difficult-to-order Not-available Order-takes-too-long Return-calls Wrong-color

Size of Customer (All)

Count of Major-Complaint

Major-Complaint

Customer Type

Data Manipulation

Subset by Category, Number, or Date Random Subset Stack and Unstack Columns Stack Subgroups Across Rows Standardize Data Normal Random Number Generator Box-Cox Transformation

Templates & Calculators

Team/Project Charter SIPOC Diagram Data Measurement Plan Cause & Effect (XY) Matrix Failure Mode & Effects Analysis (FMEA) Quality Function Deployment (QFD) Pugh Concept Selection Matrix Control Plan Lean Templates – Takt Time, Value Analysis and

Process Load Balance Chart

Templates & Calculators Sample Size – Discrete Sample Size – Continuous Gage R&R Study (MSA) Gage R&R: Multi-Vari & X-bar R Charts Attribute Gage R&R (Attribute Agreement Analysis) Process Sigma – Discrete Process Sigma – Continuous Process Capability Process Capability & Confidence Intervals Standard Deviation Confidence Interval 1 Proportion Confidence Interval 2 Proportions Test

Templates & Calculators: Quality Function Deployment (QFD)

Templates & Calculators: Pugh Concept Selection Matrix

Templates & Calculators: Value Analysis/Process Load Balance Chart

Templates & Calculators: Failure Mode & Effects Analysis (FMEA)

Templates & Calculators: Cause & Effect (XY) Matrix

Templates & Calculators: Sample Size Calculators

Templates & Calculators: Process Sigma Level – Discrete & Continuous

Templates & Calculators: Two-Proportions Test

Graphical Tools

Basic and Advanced (Multiple) Pareto Charts Run Charts (with Nonparametric Runs Test allowing

you to test for Clustering, Mixtures, Lack of Randomness, Trends and Oscillation.)

Basic Histogram Multiple Histograms and Descriptive Statistics

(includes Confidence Interval for Mean and StDev., as well as Anderson-Darling Normality Test)

Multiple Histograms and Process Capability (Pp, Ppk, Cpm, ppm, %)

Graphical Tools

Multiple Boxplots and Dotplots Multiple Normal Probability Plots (with 95%

confidence intervals to ease interpretation of normality/non-normality)

Multi-Vari Charts Scatter Plots (with linear regression and

optional 95% confidence intervals and prediction intervals)

Scatter Plot Matrix

Graphical Tools: Multiple Pareto Charts

ngCustomer Type - Customer Type: # 1 - Size of Customer:

0%10%20%30%40%50%60%70%80%90%100%

Customer Type - Customer Type: # 2 - Size of Customer: Large

0%10%20%30%40%50%60%70%80%90%100%

Customer Type - Customer Type: # 1 - Size of Customer: Small

0%10%20%30%40%50%60%70%80%90%100%

Customer Type - Customer Type: # 2 - Size of Customer: Small

0%10%20%30%40%50%60%70%80%90%100%

Graphical Tools:Multiple Histograms & Descriptive Statistics

Overall Satisfaction - Customer Type: 1

Count = 31Mean = 3.3935Stdev = 0.824680Range = 3.1

Minimum = 1.720025th Percentile (Q1) = 2.810050th Percentile (Median) = 3.560075th Percentile (Q3) = 4.0200Maximum = 4.8

95% CI Mean = 3.09 to 3.795% CI Sigma = 0.659012 to 1.102328

Anderson-Darling Normality Test:A-Squared = 0.312776; P-value = 0.5306

Count = 42Mean = 4.2052Stdev = 0.621200Range = 2.6

Minimum = 2.420025th Percentile (Q1) = 3.827550th Percentile (Median) = 4.340075th Percentile (Q3) = 4.7250Maximum = 4.98

95% CI Mean = 4.01 to 4.495% CI Sigma = 0.511126 to 0.792132

Anderson-Darling Normality Test:A-Squared = 0.826259; P-value = 0.0302

Graphical Tools:Multiple Histograms & Process Capability

Histogram and Process Capability Report Room Service Delivery Time: After Improvement

LSL = -10 USL = 10Target = 0

Delivery Time Deviation

Histogram and Process Capability ReportRoom Service Delivery Time: Before Improvement (Baseline)

Count = 725Mean = 6.0036Stdev (Overall) = 7.1616USL = 10; Target = 0; LSL = -10

Capability Indices using Overall Standard DeviationPp = 0.47Ppu = 0.19; Ppl = 0.74Ppk = 0.19Cpm = 0.36Sigma Level = 2.02

Expected Overall Performanceppm > USL = 288409.3ppm < LSL = 12720.5ppm Total = 301129.8% > USL = 28.84%% < LSL = 1.27%% Total = 30.11%

Actual (Empirical) Performance% > USL = 26.90%% < LSL = 1.38%% Total = 28.28%

Anderson-Darling Normality TestA-Squared = 0.708616; P-value = 0.0641

Count = 725Mean = 0.09732Stdev (Overall) = 2.3856USL = 10; Target = 0; LSL = -10

Capability Indices using Overall Standard DeviationPp = 1.40Ppu = 1.38; Ppl = 1.41Ppk = 1.38Cpm = 1.40Sigma Level = 5.53

Expected Overall Performanceppm > USL = 16.5ppm < LSL = 11.5ppm Total = 28.1% > USL = 0.00%% < LSL = 0.00%% Total = 0.00%

Actual (Empirical) Performance% > USL = 0.00%% < LSL = 0.00%% Total = 0.00%

Anderson-Darling Normality TestA-Squared = 0.189932; P-value = 0.8991

Graphical Tools: Multiple Boxplots

Customer Type - Size of Customer: Large

Customer Type - Size of Customer: Small

Graphical Tools:Run Charts with Nonparametric Runs Test

Median: 49.00

1 2 3 4 5 6 7 8 9101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100

Graphical Tools:Multiple Normal Probability Plots

1 2 3 4 5 6

2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1

Graphical Tools:Multi-Vari Charts

# 1 # 2 # 3

Customer Type - Size of Customer: Large - Product Type: Consumer

# 1 # 2 # 3

Customer Type - Size of Customer: Large - Product Type: Consumer

# 1 # 2 # 3

Customer Type - Size of Customer: Small - Product Type: Consumer

# 1 # 2 # 3

Customer Type - Size of Customer: Small - Product Type: Consumer

# 1 # 2 # 3

Customer Type - Size of Customer: Large - Product Type: Manufacturer

# 1 # 2 # 3

Customer Type - Size of Customer: Large - Product Type: Manufacturer

# 1 # 2 # 3

Customer Type - Size of Customer: Small - Product Type: Manufacturer

# 1 # 2 # 3

Customer Type - Size of Customer: Small - Product Type: Manufacturer

Graphical Tools:Multiple Scatterplots with Linear Regression

y = 0.5238x + 1.6066

R2 = 0.6864

1.01 1.51 2.01 2.51 3.01 3.51 4.01 4.51

Responsive to Calls - Customer Type: 1

y = 0.5639x + 1.822

R2 = 0.6994

1.88 2.38 2.88 3.38 3.88 4.38 4.88

Responsive to Calls - Customer Type: 2

Linear Regression with 95% Confidence Interval and Prediction Interval

Graphical Tools: Scatterplot Matrix

y = 1.2041x - 0.7127

R2 = 0.6827

1.0000

2.0000

3.0000

4.0000

5.0000

1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200

Overall Satisfaction

y = 0.8682x + 0.4478

R2 = 0.5556

1.4000

2.4000

3.4000

4.4000

1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200

y = 0.1055x + 2.8965

R2 = 0.0059

0.9600

1.9600

2.9600

3.9600

4.9600

1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200

y = 0.567x + 1.6103

R2 = 0.6827

1.7200

2.7200

3.7200

4.7200

1.0000 2.0000 3.0000 4.0000 5.0000

Responsive to CallsO

y = 0.303x + 2.5773

R2 = 0.1437

1.4000

2.4000

3.4000

4.4000

1.0000 2.0000 3.0000 4.0000 5.0000

Responsive to Calls

y = 0.0799x + 2.9889

R2 = 0.0071

0.9600

1.9600

2.9600

3.9600

4.9600

1.0000 2.0000 3.0000 4.0000 5.0000

Responsive to Calls

y = 0.64x + 1.4026

R2 = 0.5556

1.7200

2.7200

3.7200

4.7200

1.4000 2.4000 3.4000 4.4000

Ease of Communications

y = 0.4743x + 2.0867

R2 = 0.1437

1.0000

2.0000

3.0000

4.0000

5.0000

1.4000 2.4000 3.4000 4.4000

y = 0.0599x + 3.0732

R2 = 0.0026

0.9600

1.9600

2.9600

3.9600

4.9600

1.4000 2.4000 3.4000 4.4000

y = 0.0555x + 3.6181

R2 = 0.0059

1.7200

2.7200

3.7200

4.7200

0.9600 1.9600 2.9600 3.9600 4.9600

Staff Knowledge

y = 0.0893x + 3.57

R2 = 0.0071

1.0000

2.0000

3.0000

4.0000

5.0000

0.9600 1.9600 2.9600 3.9600 4.9600

Staff Knowledge

y = 0.0428x + 3.6071

R2 = 0.0026

1.4000

2.4000

3.4000

4.4000

0.9600 1.9600 2.9600 3.9600 4.9600

Staff Knowledge

Statistical Tools

P-values turn red when results are significant (p-value < alpha)

Descriptive Statistics including Anderson-Darling Normality test, Skewness and Kurtosis with p-values

1 Sample t-test and confidence intervals Paired t-test, 2 Sample t-test 2 Sample Comparison Tests

Normality, Mean, Variance, Median Yellow Highlight to aid Interpretation

Statistical Tools

One-Way ANOVA and Means Matrix Two-Way ANOVA

Balanced and Unbalanced Equal Variance Tests:

Bartlett Levene Welch’s ANOVA

Correlation Matrix Pearson’s Correlation Coefficient Spearman’s Rank

Statistical Tools

Multiple Linear Regression Binary and Ordinal Logistic Regression Chi-Square Test (Stacked Column data and

Two-Way Table data) Nonparametric Tests Power and Sample Size Calculators Power and Sample Size Charts

Statistical Tools: Two-Sample Comparison Tests

P-values turn red when results are

significant!Rules based

yellow highlight to aid interpretation!

Statistical Tools: One-Way ANOVA & Means Matrix

Customer Type

Statistical Tools: Correlation Matrix

Statistical Tools: Multiple Linear Regression

Accepts continuous and/or categorical (discrete) predictors. Categorical Predictors are coded with a 0,1 scheme

making the interpretation easier than the -1,0,1 scheme used by competitive products.

Interactive Predicted Response Calculator with 95% Confidence Interval and 95% Prediction Interval.

Statistical Tools: Multiple Linear Regression

Residual plots: histogram, normal probability plot, residuals vs. time, residuals vs. predicted and residuals vs. X factors

Residual types include Regular, Standardized, Studentized

Cook's Distance (Influence), Leverage and DFITS Highlight of significant outliers in residuals Durbin-Watson Test for Autocorrelation in Residuals with

p-value Pure Error and Lack-of-fit report Collinearity Variance Inflation Factor (VIF) and Tolerance

report Fit Intercept is optional

Statistical Tools: Multiple Regression

Multiple Regression accepts Continuous and/or Categorical Predictors!

Statistical Tools: Multiple Regression

Durbin-Watson Test with p-values for positive and negative

autocorrelation!

Statistical Tools: Multiple Regression – Predicted Response Calculator with Confidence Intervals

Easy-to-use Calculator with Confidence Intervals and Prediction Intervals!

Statistical Tools: Multiple Regression with Residual Plots

-0.02 0.

Regular Residuals

-0.40 0.

Residuals

Fitted Values

0 20 40 60 80 100

Observation Order

Statistical Tools:Binary and Ordinal Logistic Regression

Powerful and user-friendly logistic regression. Report includes a calculator to predict the response event

probability for a given set of input X values. Categorical (discrete) predictors can be included in the

model in addition to continuous predictors. Model summary and goodness of fit tests including

Likelihood Ratio Chi-Square, Pseudo R-Square, Pearson Residuals Chi-Square, Deviance Residuals Chi-Square, Observed and Predicted Outcomes – Percent Correctly Predicted.

Statistical Tools: Nonparametric Tests

1 Sample Sign 1 Sample Wilcoxon 2 Sample Mann-Whitney Kruskal-Wallis Median Test Mood’s Median Test Kruskal-Wallis and Mood’s include a graph of

Group Medians and 95% Median Confidence Intervals

Runs Test

Statistical Tools:Chi-Square Test

Statistical Tools: Power & Sample Size Calculators

1 Sample t-Test 2 Sample t-Test One-Way ANOVA 1 Proportion Test 2 Proportions Test The Power and Sample Size Calculators

allow you to solve for Power (1 – Beta), Sample Size, or Difference (specify two, solve for the third).

Statistical Tools: Power & Sample Size Charts

Power & Sample Size: 1 Sample t-Test

0 10 20 30 40 50 60

Sample Size (N)

Difference = 0.5

Difference = 1

Difference = 1.5

Difference = 2

Difference = 2.5

Difference = 3

Measurement Systems Analysis

Basic MSA TemplatesCreate Gage R&R (Crossed) Worksheet

Generate worksheet with user specified number of parts, operators, replicates

Analyze Gage R&R (Crossed)Attribute MSA (Binary)

Measurement Systems Analysis: Gage R&R Template

Measurement Systems Analysis: Create Gage R&R (Crossed) Worksheet

Measurement Systems Analysis: Analyze Gage R&R (Crossed)

ANOVA, %Total, %Tolerance (2-Sided or 1-Sided), %Process, Variance Components, Number of Distinct Categories

Gage R&R Multi-Vari and X-bar R Charts Confidence Intervals on %Total, %Tolerance,

%Process and Standard Deviations Handles unbalanced data (confidence

intervals not reported in this case)

Measurement Systems Analysis: Analyze Gage R&R (Crossed)

Measurement Systems Analysis: Analyze Gage R&R with Confidence Intervals

Confidence Intervals are calculated for Gage R&R Metrics!

Measurement Systems Analysis: Analyze Gage R&R with Confidence Intervals

Measurement Systems Analysis: Analyze Gage R&R – X-bar & R Charts

Gage R&R - X-Bar by Operator

1.4213

1.3812

1.4615

1.1930

1.2430

1.2930

1.3430

1.3930

1.4430

1.4930

1.5430

Part 0

1_Oper

Part 0

1_Oper

Part 0

1_Oper

Part 0

2_Oper

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2_Oper

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3_Oper

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9_Oper

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0_Oper

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0_Oper

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0_Oper

Gage R&R - R-Chart by Operator

-0.003

Part 0

1_Oper

Part 0

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Part 0

1_Oper

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2_Oper

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0_Oper

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Measurement Systems Analysis: Analyze Gage R&R – Multi-Vari Charts

Gage R&R Multi-Vari

1.20879

1.25879

1.30879

1.35879

1.40879

1.45879

1.50879

Operator A Operator B Operator C

Operator - Part 01

Gage R&R Multi-Vari

1.20879

1.25879

1.30879

1.35879

1.40879

1.45879

1.50879

Operator A Operator B Operator C

Operator - Part 02

Measurement Systems Analysis: Attribute MSA (Binary)

Any number of samples, appraisers and replicates

Within Appraiser Agreement, Each Appraiser vs Standard Agreement, Each Appraiser vs Standard Disagreement, Between Appraiser Agreement, All Appraisers vs Standard Agreement

Fleiss' kappa

Process Capability

Process Capability/Sigma Level Templates Multiple Histograms and Process Capability Capability Combination Report for

Individuals/Subgroups: Histogram Capability Report (Cp, Cpk, Pp, Ppk, Cpm, ppm, %) Normal Probability Plot Anderson-Darling Normality Test Control Charts

Box-Cox Transformation

Process Capability: Capability Combination Report

-0.5 1.1

-13 -3 7 17 27

Mean CL: 6.00

-15.60

-17.66

-12.66

1 21 41 61 81 101

Process Capability: Box-Cox Power Transformation

Normality Test is automatically applied to transformed data!

Design of Experiments

Basic DOE Templates Automatic update to Pareto of Coefficients Easy to use, ideal for training

Generate 2-Level Factorial and Plackett-Burman Screening Designs

Main Effects & Interaction Plots Analyze 2-Level Factorial and Plackett-

Burman Screening Designs

Basic DOE Templates

Design of Experiments: Generate 2-Level Factorial and Plackett-Burman Screening Designs

User-friendly dialog box 2 to 19 Factors 4,8,12,16,20 Runs Unique “view power analysis as you design” Randomization, Replication, Blocking and

Center Points

Design of Experiments: Generate 2-Level Factorial and Plackett-Burman Screening Designs

View Power Informationas you design!

Design of Experiments Example: 3-Factor, 2-Level Full-Factorial Catapult DOE

Objective: Hit a target at exactly 100 inches!

Design of Experiments: Main Effects and Interaction Plots

Design of Experiments: Analyze 2-Level Factorial and Plackett-Burman Screening Designs

Used in conjunction with Recall Last Dialog, it is very easy to iteratively remove terms from the model

Interactive Predicted Response Calculator with 95% Confidence Interval and 95% Prediction Interval.

ANOVA report for Blocks, Pure Error, Lack-of-fit and Curvature

Collinearity Variance Inflation Factor (VIF) and Tolerance report

Design of Experiments: Analyze 2-Level Factorial and Plackett-Burman Screening Designs

Residual plots: histogram, normal probability plot, residuals vs. time, residuals vs. predicted and residuals vs. X factors

Residual types include Regular, Standardized, Studentized (Deleted t) and Cook's Distance (Influence), Leverage and DFITS

Highlight of significant outliers in residuals Durbin-Watson Test for Autocorrelation in

Residuals with p-value

Design of Experiments Example: Analyze Catapult DOE

Pareto Chart of Coefficients for Distance

A: Pull

C: Pin

Height

B: Sto

p Pin AC AB

ABC BC

Design of Experiments: Predicted Response Calculator

Excel’s Solver is used with the Predicted Response Calculator to

determine optimal X factor settings to hit a target distance of

100 inches.

95% Confidence Interval and Prediction Interval

Control Charts

Individuals Individuals & Moving Range X-bar & R X-bar & S P, NP, C, U P’ and U’ (Laney) to handle overdispersion I-MR-R (Between/Within) I-MR-S (Between/Within)

Control Charts

Tests for Special Causes Special causes are also labeled on the control

chart data point. Set defaults to apply any or all of Tests 1-8

Control Chart Selection Tool Simplifies the selection of appropriate control chart

based on data type Process Capability report

Pp, Ppk, Cp, Cpk Available for I, I-MR, X-Bar & R, X-bar & S charts.

Control Charts

Add data to existing charts – ideal for operator ease of use!

Scroll through charts with user defined window size

Advanced Control Limit options: Subgroup Start and End; Historical Groups (e.g. split control limits to demonstrate before and after improvement)

Box-Cox Transformation

Control Charts: Individuals & Moving Range Charts

Mean CL: 49.02

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99

0.00000

6.18182

20.19600

Control Charts: X-bar & R/S Charts

100.37

106.81

84.52921561

89.52921561

94.52921561

99.52921561

104.5292156

109.5292156

114.5292156

SueDavid

0.00000

6.30000

16.21776

SueDavid

Control Charts: I-MR-R/S Charts (Between/Within)

100.37

109.23

102.35

107.35

112.35

117.35

SueDavid

0.00000

3.33333

10.89000

SueDavid

0.00000

6.30000

16.21776

SueDavid

Control Chart Selection Tool

Simplifies the selection of appropriate control chart based on data type

Includes Data Types and Definitions help tab.

Control Charts: Use Historical Limits; Flag Special Causes

100.37

106.81

101.15

103.15

105.15

107.15

109.15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Control Charts: Summary Report on Tests for Special Causes

Control Charts: Use Historical Groups to Display Before Versus After Improvement

Mean CL: 0.10

Before Improvement After Improvement

Control Charts: Scroll Through Charts With User Defined Window Size

Control Charts: Process Capability Report (Long Term/Short Term)

Control Charts: Box-Cox Power Transformation

Normality Test is automatically applied to transformed data!

Reliability/Weibull Analysis

Weibull Analysis Complete and Right Censored data Least Squares and Maximum Likelihood

methods Output includes percentiles with confidence

intervals, survival probabilities, and Weibull probability plot.

SigmaXL® Training

We now offer On-Site and Public Training in SigmaXL.

Course Duration: 4.5 Days. Tuition is $1500 per participant, 20% discount for

groups of 3 or more from the same company. Tuition includes a perpetual license of SigmaXL! Instructor is John Noguera, SigmaXL co-founder,

Six Sigma Master Black Belt, Motorola University Senior Instructor.

Hands-on exercises with catapult.

SigmaXL® Training

Course Contents: Day 1: Introduction to SigmaXL, Basic

Graphical Tools and Descriptive Statistics Day 2: Measurement Systems Analysis,

Process Capability Day 3: Comparative Methods, Multi-Vari

Analysis Day 4: Correlation, Regression and

Introduction to DOE Day 5: Statistical Process Control

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