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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
SigmaXL® Version 5.2 – What’s New?
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.
SigmaXL® Version 5.2 – What’s New?
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!
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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
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Graphical Tools:Multiple Histograms & Descriptive Statistics
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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
Overall Satisfaction - Customer Type: 2
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
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Histogram and Process Capability ReportRoom Service Delivery Time: Before Improvement (Baseline)
LSL = -10 USL = 10Target = 0
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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
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Graphical Tools:Run Charts with Nonparametric Runs Test
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Graphical Tools:Multiple Scatterplots with Linear Regression
y = 0.5238x + 1.6066
R2 = 0.6864
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R2 = 0.6994
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Linear Regression with 95% Confidence Interval and Prediction Interval
Graphical Tools: Scatterplot Matrix
y = 1.2041x - 0.7127
R2 = 0.6827
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R2 = 0.5556
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R2 = 0.0059
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R2 = 0.6827
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Responsive to Calls
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R2 = 0.0071
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Ease of Communications
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Staff Knowledge
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R2 = 0.0071
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Staff Knowledge
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R2 = 0.0026
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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
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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
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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
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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
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Gage R&R Multi-Vari
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1.40879
1.45879
1.50879
Operator A Operator B Operator C
Operator - Part 01
Mea
n O
pti
on
s -
To
tal
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
LSL = -10 USL = 10Target = 0
0
10
20
30
40
50
60
70
80
90
-11.
9
-10.
3
-8.6
-7.0
-5.4
-3.8
-2.1
-0.5 1.1
2.7
4.4
6.0
7.6
9.2
10.9
12.5
14.1
15.7
17.4
19.0
20.6
22.2
23.9
25.5
Delivery Time Deviation
-4
-3
-2
-1
0
1
2
3
4
-23
-13 -3 7 17 27
Delivery Time Deviation
NS
CO
RE
Mean CL: 6.00
-15.60
27.61
-17.66
-12.66
-7.66
-2.66
2.34
7.34
12.34
17.34
22.34
27.34
1 21 41 61 81 101
121
141
161
181
201
221
241
261
281
301
321
341
361
381
401
421
441
461
481
501
521
541
561
581
601
621
641
661
681
701
721
Ind
ivid
ual
s -
Del
iver
y T
ime
Dev
iati
on
8.12
0.00
26.54
-1.72
3.28
8.28
13.28
18.28
23.28
28.28
33.28
1 21 41 61 81 101
121
141
161
181
201
221
241
261
281
301
321
341
361
381
401
421
441
461
481
501
521
541
561
581
601
621
641
661
681
701
721
MR
- D
eliv
ery
Tim
e D
evia
tio
n
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
0
5
10
15
20
25
A: Pull
Bac
k
C: Pin
Height
B: Sto
p Pin AC AB
ABC BC
Ab
s(C
oef
fici
ent)
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
32.58
Mean CL: 49.02
65.46
29.32
34.32
39.32
44.32
49.32
54.32
59.32
64.32
69.32
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
Ind
ivid
ual
s -
Avg
day
s O
rder
to
del
iver
y ti
me
0.00000
6.18182
20.19600
0.00
5.00
10.00
15.00
20.00
25.00
1
MR
- A
vg d
ays
Ord
er t
o d
eliv
ery
tim
e
Control Charts: X-bar & R/S Charts
93.92
100.37
106.81
84.52921561
89.52921561
94.52921561
99.52921561
104.5292156
109.5292156
114.5292156
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
X-B
ar -
Sh
ot
1 -
Sh
ot
3
0.00000
6.30000
16.21776
0
2
4
6
8
10
12
14
16
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
R -
Sh
ot
1 -
Sh
ot
3
Control Charts: I-MR-R/S Charts (Between/Within)
91.50
100.37
109.23
82.35
87.35
92.35
97.35
102.35
107.35
112.35
117.35
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
Ind
ivid
ual
s -
Sh
ot
1 -
Sh
ot
3
0.00000
3.33333
10.89000
0.00
2.00
4.00
6.00
8.00
10.00
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
Sue
MR
- S
ho
t 1
- S
ho
t 3
0.00000
6.30000
16.21776
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
SueDavid
John
Moe
Sally
R -
Sh
ot
1 -
Sh
ot
3
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
1
1
5
100.37
93.92
106.81
93.15
95.15
97.15
99.15
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
X-B
ar -
Sh
ot
1 -
Sh
ot
3
Control Charts: Summary Report on Tests for Special Causes
Control Charts: Use Historical Groups to Display Before Versus After Improvement
Mean CL: 0.10
-6.80
7.00
-19
-14
-9
-4
1
6
11
16
21
26
31
Ind
ivid
ua
ls -
De
live
ry T
ime
Dev
iati
on
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