Introducing SigmaXL Version 7

Introducing SigmaXL®
Version 7
 Powerful.
 User-Friendly.
 Cost-Effective. Priced at $249, 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.

3
SigmaXL has added some exciting, new and
unique features:
“Traffic Light” Automatic Assumptions Check
for T-tests and ANOVA
What’s New in SigmaXL Version 7
 A text report with color
highlight gives the status of
assumptions: Green (OK),
Yellow (Warning) and Red
(Serious Violation).
 Normality, Robustness,
Outliers, Randomness and
Equal Variance are
considered.

4
 “Traffic Light” Attribute Measurement
Systems Analysis: Binary, Ordinal and
Nominal
 A Kappa color highlight is used to aid interpretation: Green (> .9),
Yellow (.7-.9) and Red (< .7) for Binary and Nominal.
 Kendall coefficients are highlighted for Ordinal.
 A new Effectiveness Report treats each appraisal trial as an
opportunity, rather than require agreement across all trials.

5
 Automatic Normality Check for Pearson
Correlation
 A yellow highlight is used to recommend significant Pearson or
Spearman correlations.
 A bivariate normality test is utilized and Pearson is highlighted if
the data are bivariate normal, otherwise Spearman is highlighted.

6
 Small Sample Exact Statistics for One-Way
Chi-Square, Two-Way (Contingency) Table
and Nonparametric Tests
 Exact statistics are appropriate when the sample size is
too small for a Chi-Square or Normal approximation to
be valid.
 For example, a contingency table where more than 20%
of the cells have an expected count less than 5.
 Exact statistics are typically available only in advanced
and expensive software packages!

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 Lean Six
Sigma training. Used by Motorola University
and other leading consultants.
 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.

What’s Unique to
SigmaXL?
 Template: Minimum Sample Size for Robust
Hypothesis Testing
 It is well known that the central limit theorem enables the t-Test
and ANOVA to be fairly robust to the assumption of normality.
 A question that invariably arises is, “How large does the sample
size have to be?”
 A popular rule of thumb answer for the one sample t-Test is
“n = 30.” While this rule of thumb often does work well, the
sample size may be too large or too small depending on the
degree of non-normality as measured by the Skewness and
Kurtosis.
 Furthermore it is not applicable to a One Sided t-Test, 2 Sample t-
Test or One Way ANOVA.
 To address this issue, we have developed a unique template that
gives a minimum sample size needed for a hypothesis test to be
robust.

What’s Unique to
SigmaXL?
 Powerful Excel Worksheet Manager
 List all open Excel workbooks
 Display all worksheets and chart sheets in selected workbook
 Quickly select worksheet or chart sheet of interest
 Process Capability and Control Charts for Nonnormal data
 Best fit automatically selects the best distribution or transformation!
 Nonnormal Process Capability Indices include Pp, Ppk, Cp, and Cpk
 Box-Cox Transformation with Threshold so that data with zero or
negative values can be transformed!

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.

Worksheet Manager
 List all open Excel
workbooks
 Display all worksheets
and chart sheets in
selected workbook
 Quickly select
worksheet or chart
sheet of interest

Data Manipulation
 Subset by Category, Number, or Date
 Random Subset
 Stack and Unstack Columns
 Stack Subgroups Across Rows
 Standardize Data
 Random Number Generators
 Normal, Uniform (Continuous & Integer),
Lognormal, Exponential, Weibull and Triangular.
 Box-Cox Transformation

Templates & Calculators
 DMAIC & DFSS Templates:
 Team/Project Charter
 SIPOC Diagram
 Flowchart Toolbar
 Data Measurement Plan
 Cause & Effect (Fishbone) Diagram and Quick
Template
 Cause & Effect (XY) Matrix
 Failure Mode & Effects Analysis (FMEA)
 Quality Function Deployment (QFD)
 Pugh Concept Selection Matrix
 Control Plan

Lean Templates:
 Takt Time Calculator
 Value Analysis/Process Load Balance
 Value Stream Mapping
Basic Graphical Templates:
 Pareto Chart
 Histogram
 Run Chart

 Basic Statistical Templates:
 Sample Size – Discrete and Continuous
 Minimum Sample Size for Robust t-Tests and ANOVA
 1 Sample t-Test and Confidence Interval for Mean
 2 Sample t-Test and Confidence Interval (Compare 2
Means) with option for equal and unequal variance
 1 Sample Chi-Square Test and CI for Standard Deviation
 2 Sample F-Test and CI (Compare 2 Standard Deviations)
 1 Proportion Test and Confidence Interval
 2 Proportions Test and Confidence Interval

 Basic Statistical Templates:
 1 Poisson Rate Test and Confidence Interval
 2 Poisson Rates Test and Confidence Interval
 One-Way Chi-Square Goodness-of-Fit Test
 One-Way Chi-Square Goodness-of-Fit Test - Exact
 Probability Distribution Calculators:
 Normal, Lognormal, Exponential, Weibull
 Binomial, Poisson, Hypergeometric

 Basic MSA Templates:
 Gage R&R Study – with Multi-Vari Analysis
 Attribute Gage R&R (Attribute Agreement Analysis)
 Basic Process Capability Templates:
 Process Sigma Level – Discrete and Continuous
 Process Capability & Confidence Intervals
 Basic DOE Templates:
 2 to 5 Factors
 2-Level Full and Fractional-Factorial designs
 Main Effects & Interaction Plots
 Basic Control Chart Templates:
 Individuals
 C-Chart

Templates & Calculators:
Cause & Effect Diagram

Quality Function
Deployment (QFD)

Pugh Concept Selection
Matrix

Lean Takt Time Calculator

Value Analysis/
Process Load Balance Chart

Value Stream Mapping

Pareto Chart Quick Template
Pareto Chart
0
10
20
30
40
50
60
70
80
90
100
Return-calls
Difficult-to-order
O
rder-takes-too-long
W
rong-color
Not-available
Category
Count
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%

Failure Mode & Effects
Analysis (FMEA)

Cause & Effect (XY)
Matrix

Sample Size Calculators

Minimum Sample Size for
Robust Hypothesis Testing

Process Sigma Level –
Discrete

Process Sigma Level –
Continuous

2 Proportions Test and
Confidence Interval

Normal Distribution
Probability Calculator

Graphical Tools
 Basic and Advanced (Multiple) Pareto Charts
 EZ-Pivot/Pivot 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
0
2
4
6
8
10
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-longCustomer Type - Customer Type: # 1 - Size of Customer:
Large
Count
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Customer Type - Customer Type: # 2 - Size of Customer:
Large
Count
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Small
Count
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Small
Count
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%

Graphical Tools: EZ-Pivot/Pivot
Charts – The power of Excel’s
Pivot Table and Charts are now
easy to use!
0
10
20
30
40
50
60
70
Difficult-to-order Not-available Order-takes-too-long Return-calls Wrong-color
3
2
1
Size of Customer (All)
Count of Major-Complaint
Major-Complaint
Customer Type

Graphical Tools:
Multiple Histograms &
Descriptive Statistics
0
2
4
6
8
10
12
1.72
1.99
2.26
2.54
2.81
3.08
3.35
3.62
3.90
4.17
4.44
4.71
4.98
Overall Satisfaction - Customer Type: 1
0
2
4
6
8
10
12
1.72
1.99
2.26
2.54
2.81
3.08
3.35
3.62
3.90
4.17
4.44
4.71
4.98
Count = 31
Mean = 3.3935
Stdev = 0.824680
Range = 3.1
Minimum = 1.7200
25th Percentile (Q1) = 2.8100
50th Percentile (Median) = 3.5600
Maximum = 4.8
95% CI Mean = 3.09 to 3.7
95% CI Sigma = 0.659012 to 1.102328
Anderson-Darling Normality Test:
A-Squared = 0.312776; P-value = 0.5306
Count = 42
Mean = 4.2052
Stdev = 0.621200
Range = 2.6
Minimum = 2.4200
50th Percentile (Median) = 4.3400
Maximum = 4.98
95% CI Mean = 4.01 to 4.4
95% 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
0
20
40
60
80
100
120
140
160
Delivery Time Deviation
Histogram and Process Capability Report
Room Service Delivery Time: Before Improvement (Baseline)
0
20
40
60
80
100
120
140
160
Count = 725
Mean = 6.0036
Stdev (Overall) = 7.1616
USL = 10; Target = 0; LSL = -10
Capability Indices using Overall Standard Deviation
Pp = 0.47
Ppu = 0.19; Ppl = 0.74
Ppk = 0.19
Cpm = 0.36
Sigma Level = 2.02
Expected Overall Performance
ppm > USL = 288409.3
ppm < LSL = 12720.5
ppm 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 Test
A-Squared = 0.708616; P-value = 0.0641
Count = 725
Mean = 0.09732
Stdev (Overall) = 2.3856
USL = 10; Target = 0; LSL = -10
Capability Indices using Overall Standard Deviation
Pp = 1.40
Ppu = 1.38; Ppl = 1.41
Ppk = 1.38
Cpm = 1.40
Sigma Level = 5.53
Expected Overall Performance
ppm > USL = 16.5
ppm < LSL = 11.5
ppm 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 Test
A-Squared = 0.189932; P-value = 0.8991

Graphical Tools:
Multiple Boxplots
1
2
3
4
5
1 2 3
Customer Type - Size of Customer: Large
OverallSatisfaction
1
2
3
4
5
1 2 3
Customer Type - Size of Customer: Small
OverallSatisfaction

Graphical Tools:
Run Charts with
Nonparametric Runs Test
Median: 49.00
32.40
37.40
42.40
47.40
52.40
57.40
62.40
67.40
1
2
3
4
5
6
7
8
9101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100
RunChart-AvgdaysOrdertodeliverytime

Graphical Tools:
Multiple Normal Probability
Plots
-3
-2
-1
0
1
2
3
1 2 3 4 5 6
NSCORE
-3
-2
-1
0
1
2
3
2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1
NSCORE

Graphical Tools:
Multi-Vari Charts
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - Size of Customer:
Large - Product Type: Consumer
OverallSatisfaction(Mean
Options)
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Customer Type - Size of Customer:
Large - Product Type: Consumer
StandardDeviation
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - Size of Customer: Small -
Product Type: Consumer
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Product Type: Consumer
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - Size of Customer: Large -
Product Type: Manufacturer
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Customer Type - Size of Customer: Large -
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3

Graphical Tools:
Multiple Scatterplots with
Linear Regression
y = 0.5238x + 1.6066
R
2
= 0.6864
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
1.01 1.51 2.01 2.51 3.01 3.51 4.01 4.51
Responsive to Calls - Customer Type: 1
OverallSatisfaction
y = 0.5639x + 1.822
R
2
= 0.6994
2.1
2.6
3.1
3.6
4.1
4.6
5.1
1.88 2.38 2.88 3.38 3.88 4.38 4.88
Responsive to Calls - Customer Type: 2
OverallSatisfaction
Linear Regression with 95%
Confidence Interval and Prediction Interval

Graphical Tools:
Scatterplot Matrix
y = 1.2041x - 0.7127
R
2
= 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
ResponsivetoCalls
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
EaseofCommunications
y = 0.1055x + 2.8965
R
2
= 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
StaffKnowledge
y = 0.567x + 1.6103
R
2
= 0.6827
1.7200
2.7200
3.7200
4.7200
1.0000 2.0000 3.0000 4.0000 5.0000
Responsive to Calls
OverallSatisfaction
y = 0.303x + 2.5773
R
2
= 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
R
2
= 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
StaffKnowledge
y = 0.64x + 1.4026
R
2
= 0.5556
1.7200
2.7200
3.7200
4.7200
1.4000 2.4000 3.4000 4.4000
Ease of Communications
OverallSatisfaction
y = 0.4743x + 2.0867
R
2
= 0.1437
1.0000
2.0000
3.0000
4.0000
5.0000
1.4000 2.4000 3.4000 4.4000
ResponsivetoCalls
y = 0.0599x + 3.0732
R
2
= 0.0026
0.9600
1.9600
2.9600
3.9600
4.9600
1.4000 2.4000 3.4000 4.4000
StaffKnowledge
y = 0.0555x + 3.6181
R
2
= 0.0059
1.7200
2.7200
3.7200
4.7200
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
OverallSatisfaction
y = 0.0893x + 3.57
R
2
= 0.0071
1.0000
2.0000
3.0000
4.0000
5.0000
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
ResponsivetoCalls
y = 0.0428x + 3.6071
R
2
= 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
 Optional Assumptions Report
 Paired t-test, 2 Sample t-test

Statistical Tools
2 Sample Comparison Tests
 Normality, Mean, Variance, Median
 Yellow Highlight to aid Interpretation
One-Way ANOVA and Means Matrix
Two-Way ANOVA
 Balanced and Unbalanced

Statistical Tools
 Equal Variance Tests:
 Bartlett
 Levene
 Welch’s ANOVA (with optional assumptions report)
 Correlation Matrix
 Pearson’s Correlation Coefficient
 Spearman’s Rank
 Yellow highlight to recommend Pearson or
Spearman based on bivariate normality test

Statistical Tools
 Multiple Linear Regression
 Binary and Ordinal Logistic Regression
 Chi-Square Test (Stacked Column data
and Two-Way Table data)
 Chi-Square – Fisher’s Exact and Monte
Carlo Exact

Statistical Tools
 Nonparametric Tests
 Nonparametric Tests – Exact and Monte
Carlo Exact
 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
3.08
3.28
3.48
3.68
3.88
4.08
4.28
4.48
1 2 3
Customer Type
Mean/CI-OverallSatisfaction

Statistical Tools: One-Way
ANOVA & Means Matrix

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
10
20
30
40
50
60
-0.88
-0.71
-0.54
-0.37
-0.19
-0.02
0.15
0.32
0.50
0.67
0.84
1.01
1.19
Regular Residuals
Frequency
-3
-2
-1
0
1
2
3
-0.90
-0.40
0.10
0.60
1.10
Residuals
NSCORE
-1
-0.5
0
0.5
1
1.5
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Fitted Values
RegularResiduals
-1.00
-0.50
0.00
0.50
1.00
1.50
0
20
40
60
80
100
120
Observation Order
RegularResiduals

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:
Nonparametric Tests - Exact
 1 Sample Wilcoxon – Exact
 2 Sample Mann-Whitney – Exact & Monte
Carlo Exact
 Kruskal-Wallis – Exact & Monte Carlo Exact
 Mood’s Median Test – Exact & Monte Carlo
Exact
 Runs Test - Exact

Statistical Tools:
Chi-Square Test

Statistical Tools: Chi-Square
Test – Fisher’s Exact

Statistical Tools: Chi-Square
Test – Fisher’s Monte Carlo

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
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60
Sample Size (N)
Power(1-Beta)
Difference = 0.5
Difference = 1
Difference = 1.5
Difference = 2
Difference = 2.5
Difference = 3

Measurement Systems
Analysis
Basic MSA Templates
Create Gage R&R (Crossed) Worksheet
 Generate worksheet with user specified
number of parts, operators, replicates
Analyze Gage R&R (Crossed)
Attribute MSA (Binary)
Attribute MSA (Ordinal)
Attribute MSA (Nominal)

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
R&R (Crossed)

Measurement Systems
Analysis:
Analyze Gage R&R with
Confidence Intervals are calculated for Gage R&R Metrics!

Measurement Systems
Analysis:
Analyze Gage R&R with

Measurement Systems
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
Part01_O
peratorA
Part01_O
peratorB
Part01_O
peratorC
Part02_O
peratorA
Part02_O
peratorB
Part02_O
peratorC
Part03_O
peratorA
Part03_O
peratorB
Part03_O
peratorC
Part04_O
peratorA
Part04_O
peratorB
Part04_O
peratorC
Part05_O
peratorA
Part05_O
peratorB
Part05_O
peratorC
Part06_O
peratorA
Part06_O
peratorB
Part06_O
peratorC
Part07_O
peratorA
Part07_O
peratorB
Part07_O
peratorC
Part08_O
peratorA
Part08_O
peratorB
Part08_O
peratorC
Part09_O
peratorA
Part09_O
peratorB
Part09_O
peratorC
Part10_O
peratorA
Part10_O
peratorB
Part10_O
peratorC
X-Bar-Part/Operator-Measurement
Gage R&R - R-Chart by Operator
0.021
0.000
0.070
-0.003
0.007
0.017
0.027
0.037
0.047
0.057
0.067
Part01_O
peratorA
Part01_O
peratorB
Part01_O
peratorC
Part02_O
peratorA
Part02_O
peratorB
Part02_O
peratorC
Part03_O
peratorA
Part03_O
peratorB
Part03_O
peratorC
Part04_O
peratorA
Part04_O
peratorB
Part04_O
peratorC
Part05_O
peratorA
Part05_O
peratorB
Part05_O
peratorC
Part06_O
peratorA
Part06_O
peratorB
Part06_O
peratorC
Part07_O
peratorA
Part07_O
peratorB
Part07_O
peratorC
Part08_O
peratorA
Part08_O
peratorB
Part08_O
peratorC
Part09_O
peratorA
Part09_O
peratorB
Part09_O
peratorC
Part10_O
peratorA
Part10_O
peratorB
Part10_O
peratorC
R-Part/Operator-Measurement

Measurement Systems
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
MeanOptions-Total
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

80
 “Traffic Light” Attribute Measurement
Systems Analysis: Binary, Ordinal and
Nominal
Attribute Measurement
Systems Analysis
 A Kappa color highlight is used to aid interpretation: Green (> .9),
Yellow (.7-.9) and Red (< .7) for Binary and Nominal.
 Kendall coefficients are highlighted for Ordinal.
 A new Effectiveness Report treats each appraisal trial as an
opportunity, rather than requiring agreement across all trials.

Process Capability
(Normal Data)
 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

Process Capability:
Capability Combination
Report
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
-4
-3
-2
-1
0
1
2
3
4
-23
-13
-3
7
17
27
NSCORE
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
Individuals-DeliveryTimeDeviation
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-DeliveryTimeDeviation

Process Capability for
Nonnormal Data
 Box-Cox Transformation (includes an automatic threshold option
so that data with negative values can be transformed)
 Johnson Transformation
 Distributions supported:
 Half-Normal
 Lognormal (2 & 3 parameter)
 Exponential (1 & 2 parameter)
 Weibull (2 & 3 parameter)
 Beta (2 & 4 parameter)
 Gamma (2 & 3 parameter)
 Logistic
 Loglogistic (2 & 3 parameter)
 Largest Extreme Value
 Smallest Extreme Value

Process Capability for
Nonnormal Data
 Automatic Best Fit based on AD p-value
 Nonnormal Process Capability Indices:
 Z-Score (Cp, Cpk, Pp, Ppk)
 Percentile (ISO) Method (Pp, Ppk)
 Distribution Fitting Report
 All valid distributions and transformations reported
with histograms, curve fit and probability plots
 Sorted by AD p-value

Nonnormal Process Capability:
Automatic Best Fit
LSL = 3.5
0
2
4
6
8
10
12
14
16
1.45
1.72
1.99
2.26
2.54
2.81
3.08
3.35
3.62
3.90
4.17
4.44
4.71
4.98
5.26
3.885
1.548
5.136
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
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
Individuals:OverallSatisfaction
(PercentileControlLimits)

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

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

Generate 2-Level Factorial
and Plackett-Burman
Screening Designs
View Power Information
as you design!

Example: 3-Factor, 2-Level
Full-Factorial Catapult DOE
Objective: Hit a target at exactly 100 inches!

Main Effects and
Interaction Plots

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

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

Example: Analyze Catapult
DOE
Pareto Chart of Coefficients for Distance
0
5
10
15
20
25
A:PullB
ack
C:Pin
Height
B:Stop
Pin
AC
AB
ABC
BC
Abs(Coefficient)

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

Response Surface Designs
 2 to 5 Factors
 Central Composite and Box-Behnken Designs
 Easy to use design selection sorted by number of
runs:

Contour & 3D Surface Plots

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)

Control Charts
 Exclude data points for control limit calculation
 Add comment to data point for assignable cause
 ± 1, 2 Sigma Zone Lines
 Control Charts for Nonnormal data
 Box-Cox and Johnson Transformations
 16 Nonnormal distributions supported (see Capability
Combination Report for Nonnormal Data)
 Individuals chart of original data with percentile based
control limits
 Individuals/Moving Range chart for normalized data with
optional tests for special causes

Control Charts:
Individuals &
Moving Range Charts

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
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
X-Bar-Shot1-Shot3
0.00000
6.30000
16.21776
0
2
4
6
8
10
12
14
16
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
R-Shot1-Shot3

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
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
Individuals-Shot1-Shot3
0.00000
3.33333
10.89000
0.00
2.00
4.00
6.00
8.00
10.00
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
MR-Shot1-Shot3
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
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
Sue
David
John
M
oe
Sally
R-Shot1-Shot3

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-Bar-Shot1-Shot3

Control Charts:
Add Comments as Data Labels

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
Individuals-DeliveryTimeDeviation
Before Improvement After Improvement

Control Charts:
Scroll Through Charts With
User Defined Window Size

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

Individuals Chart for
Nonnormal Data:
Johnson Transformation

Individuals/Moving Range
Chart for Nonnormal Data:
Johnson Transformation

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 Training in SigmaXL.
 Course Duration: 4.5 Days.
 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

Introducing SigmaXL Version 7

More Related Content

What's hot (19)

Similar to Introducing SigmaXL Version 7 (20)

Recently uploaded (20)

Introducing SigmaXL Version 7