Introducing SigmaXL Version 7 - Aug 13 2014
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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.
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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.
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“Traffic Light” Attribute Measurement Systems Analysis: Binary, Ordinal and Nominal
What’s New in SigmaXL Version 7
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.
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Automatic Normality Check for Pearson Correlation
What’s New in SigmaXL Version 7
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.
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Small Sample Exact Statistics for One-Way Chi-Square, Two-Way (Contingency) Table and Nonparametric Tests
What’s New in SigmaXL Version 7
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!
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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.
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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.
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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.
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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!
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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.
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Worksheet Manager
List all open Excel workbooks
Display all worksheets and chart sheets in selected workbook
Quickly select worksheet or chart sheet of interest
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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
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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
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Templates & CalculatorsLean Templates:
Takt Time Calculator Value Analysis/Process Load Balance Value Stream Mapping
Basic Graphical Templates: Pareto Chart Histogram Run Chart
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Templates & Calculators 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
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Templates & Calculators 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
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Templates & Calculators 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
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Templates & Calculators: Cause & Effect Diagram
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Templates & Calculators: Quality Function Deployment (QFD)
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Templates & Calculators: Pugh Concept Selection Matrix
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Templates & Calculators: Lean Takt Time Calculator
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Templates & Calculators: Value Analysis/Process Load Balance Chart
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Templates & Calculators: Value Stream Mapping
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Templates & Calculators: Pareto Chart Quick Template
Pareto Chart
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Templates & Calculators: Failure Mode & Effects Analysis (FMEA)
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Templates & Calculators: Cause & Effect (XY) Matrix
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Templates & Calculators: Sample Size Calculators
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Templates & Calculators: Sample Size Calculators
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Templates & Calculators: Minimum Sample Size for Robust Hypothesis Testing
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Templates & Calculators: Process Sigma Level – Discrete
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Templates & Calculators: Process Sigma Level – Continuous
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Templates & Calculators: 2 Proportions Test and Confidence Interval
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Templates & Calculators: Normal Distribution Probability Calculator
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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, %)
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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
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Graphical Tools: Multiple Pareto Charts
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Graphical Tools: EZ-Pivot/Pivot Charts – The power of Excel’s Pivot Table and Charts are now easy to use!
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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
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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)
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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
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Graphical Tools: Multiple Boxplots
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Graphical Tools:Run Charts with Nonparametric Runs Test
Median: 49.00
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Graphical Tools:Multiple Normal Probability Plots
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Graphical Tools:Multi-Vari Charts
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Graphical Tools:Multiple Scatterplots with Linear Regression
y = 0.5238x + 1.6066R2 = 0.6864
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Responsive to Calls - Customer Type: 2
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Linear Regression with 95% Confidence Interval and Prediction Interval
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Graphical Tools: Scatterplot Matrix
y = 1.2041x - 0.7127R2 = 0.6827
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4.0000
5.0000
1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200
Overall Satisfaction
Res
pons
ive
to C
alls
y = 0.8682x + 0.4478R2 = 0.5556
1.4000
2.4000
3.4000
4.4000
1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200
Overall Satisfaction
Ease
of C
omm
unic
atio
ns
y = 0.1055x + 2.8965R2 = 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
Overall Satisfaction
Staf
f Kno
wle
dge
y = 0.567x + 1.6103R2 = 0.6827
1.7200
2.7200
3.7200
4.7200
1.0000 2.0000 3.0000 4.0000 5.0000
Responsive to CallsO
vera
ll Sa
tisfa
ctio
n
y = 0.303x + 2.5773R2 = 0.1437
1.4000
2.4000
3.4000
4.4000
1.0000 2.0000 3.0000 4.0000 5.0000
Responsive to Calls
Ease
of C
omm
unic
atio
ns
y = 0.0799x + 2.9889R2 = 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
Staf
f Kno
wle
dge
y = 0.64x + 1.4026R2 = 0.5556
1.7200
2.7200
3.7200
4.7200
1.4000 2.4000 3.4000 4.4000
Ease of Communications
Ove
rall
Satis
fact
ion
y = 0.4743x + 2.0867R2 = 0.1437
1.0000
2.0000
3.0000
4.0000
5.0000
1.4000 2.4000 3.4000 4.4000
Ease of Communications
Res
pons
ive
to C
alls
y = 0.0599x + 3.0732R2 = 0.0026
0.9600
1.9600
2.9600
3.9600
4.9600
1.4000 2.4000 3.4000 4.4000
Ease of Communications
Staf
f Kno
wle
dge
y = 0.0555x + 3.6181R2 = 0.0059
1.7200
2.7200
3.7200
4.7200
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
Ove
rall
Satis
fact
ion
y = 0.0893x + 3.57R2 = 0.0071
1.0000
2.0000
3.0000
4.0000
5.0000
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
Res
pons
ive
to C
alls
y = 0.0428x + 3.6071R2 = 0.0026
1.4000
2.4000
3.4000
4.4000
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
Ease
of C
omm
unic
atio
ns
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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 Optional Assumptions Report
Paired t-test, 2 Sample t-test Optional Assumptions Report
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Statistical Tools2 Sample Comparison Tests
Normality, Mean, Variance, Median Yellow Highlight to aid Interpretation
One-Way ANOVA and Means Matrix Optional Assumptions Report
Two-Way ANOVA Balanced and Unbalanced
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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
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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
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Statistical Tools
Nonparametric Tests
Nonparametric Tests – Exact and Monte Carlo Exact
Power and Sample Size Calculators
Power and Sample Size Charts
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Statistical Tools: Two-Sample Comparison Tests
P-values turn red when results are
significant!Rules based
yellow highlight to aid interpretation!
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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
Mea
n/C
I - O
vera
ll Sa
tisfa
ctio
n
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Statistical Tools: One-Way ANOVA & Means Matrix
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Statistical Tools: Correlation Matrix
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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.
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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
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Statistical Tools: Multiple Regression
Multiple Regression accepts Continuous and/or Categorical Predictors!
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Statistical Tools: Multiple Regression
Durbin-Watson Test with p-values for positive and negative
autocorrelation!
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Statistical Tools: Multiple Regression – Predicted Response Calculator with Confidence Intervals
Easy-to-use Calculator with Confidence Intervals and Prediction Intervals!
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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
Freq
uenc
y
-3
-2
-1
0
1
2
3
-0.90
-0.40 0.10
0.60
1.10
Residuals
NSCO
RE
-1
-0.5
0
0.5
1
1.5
0.00
20.00
40.00
60.00
80.00
100.0
0
120.0
0
Fitted Values
Regu
lar R
esid
uals
-1.00
-0.50
0.00
0.50
1.00
1.50
0 20 40 60 80 100
120
Observation Order
Regu
lar R
esid
uals
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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.
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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
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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
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Statistical Tools:Chi-Square Test
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Statistical Tools: Chi-Square Test – Fisher’s Exact
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Statistical Tools: Chi-Square Test – Fisher’s Monte Carlo
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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).
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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)
Pow
er (1
- Be
ta) Difference = 0.5
Difference = 1
Difference = 1.5
Difference = 2
Difference = 2.5
Difference = 3
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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)Attribute MSA (Ordinal)Attribute MSA (Nominal)
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Measurement Systems Analysis: Gage R&R Template
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Measurement Systems Analysis: Create Gage R&R (Crossed) Worksheet
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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)
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Measurement Systems Analysis: Analyze Gage R&R (Crossed)
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Measurement Systems Analysis: Analyze Gage R&R with Confidence Intervals
Confidence Intervals are calculated for Gage R&R Metrics!
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Measurement Systems Analysis: Analyze Gage R&R with Confidence Intervals
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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 01
_Opera
tor A
Part 01
_Opera
tor B
Part 01
_Opera
tor C
Part 02
_Opera
tor A
Part 02
_Opera
tor B
Part 02
_Opera
tor C
Part 03
_Opera
tor A
Part 03
_Opera
tor B
Part 03
_Opera
tor C
Part 04
_Opera
tor A
Part 04
_Opera
tor B
Part 04
_Opera
tor C
Part 05
_Ope
rator
A
Part 05
_Opera
tor B
Part 05
_Opera
tor C
Part 06
_Opera
tor A
Part 06
_Opera
tor B
Part 06
_Opera
tor C
Part 07
_Opera
tor A
Part 07
_Opera
tor B
Part 07
_Opera
tor C
Part 08
_Opera
tor A
Part 08
_Opera
tor B
Part 08
_Opera
tor C
Part 09
_Opera
tor A
Part 09
_Opera
tor B
Part 09
_Opera
tor C
Part 10
_Opera
tor A
Part 10
_Opera
tor B
Part 10
_Ope
rator
C
X-Ba
r - P
art/O
pera
tor -
Mea
sure
men
t
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
Part 01
_Opera
tor A
Part 01
_Ope
rator
B
Part 01
_Opera
tor C
Part 02
_Opera
tor A
Part 02
_Opera
tor B
Part 02
_Opera
tor C
Part 03
_Opera
tor A
Part 03
_Opera
tor B
Part 03
_Opera
tor C
Part 04
_Opera
tor A
Part 04
_Opera
tor B
Part 04
_Opera
tor C
Part 05
_Opera
tor A
Part 05
_Ope
rator
B
Part 05
_Opera
tor C
Part 06
_Opera
tor A
Part 06
_Opera
tor B
Part 06
_Opera
tor C
Part 07
_Opera
tor A
Part 07
_Opera
tor B
Part 07
_Opera
tor C
Part 08
_Opera
tor A
Part 08
_Opera
tor B
Part 08
_Opera
tor C
Part 09
_Opera
tor A
Part 09
_Opera
tor B
Part 09
_Opera
tor C
Part 10
_Opera
tor A
Part 10
_Opera
tor B
Part 10
_Ope
rator
C
R - P
art/O
pera
tor -
Mea
sure
men
t
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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
Mea
n O
ptio
ns -
Tota
l
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
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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
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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.
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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
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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
NSC
ORE
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
Indi
vidu
als
- Del
iver
y Ti
me
Devi
atio
n
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
- Del
iver
y Ti
me
Dev
iatio
n
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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
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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
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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
Overall Satisfaction
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
Indi
vidu
als:
Ove
rall
Satis
fact
ion
(Per
cent
ile C
ontro
l Lim
its)
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Process Capability: Box-Cox Power Transformation
Normality Test is automatically applied to transformed data!
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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
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Basic DOE Templates
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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
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Design of Experiments: Generate 2-Level Factorial and Plackett-Burman Screening Designs
View Power Informationas you design!
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Design of Experiments Example: 3-Factor, 2-Level Full-Factorial Catapult DOE
Objective: Hit a target at exactly 100 inches!
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Design of Experiments: Main Effects and Interaction Plots
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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
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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
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Design of Experiments Example: Analyze Catapult DOE
Pareto Chart of Coefficients for Distance
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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
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Design of Experiments: Response Surface Designs
2 to 5 Factors Central Composite and Box-Behnken Designs Easy to use design selection sorted by number of
runs:
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Design of Experiments: Contour & 3D Surface Plots
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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)
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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.
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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)
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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
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Control Charts: Individuals & Moving Range Charts
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Control Charts: X-bar & R/S Charts
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Control Charts: I-MR-R/S Charts (Between/Within)
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Control Chart Selection Tool
Simplifies the selection of appropriate control chart based on data type
Includes Data Types and Definitions help tab.
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Control Charts: Use Historical Limits; Flag Special Causes
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Control Charts: Add Comments as Data Labels
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Control Charts: Summary Report on Tests for Special Causes
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Control Charts: Use Historical Groups to Display Before Versus After Improvement
Mean CL: 0.10
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Control Charts: Scroll Through Charts With User Defined Window Size
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Control Charts: Process Capability Report (Long Term/Short Term)
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Individuals Chart for Nonnormal Data: Johnson Transformation
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Individuals/Moving Range Chart for Nonnormal Data: Johnson Transformation
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Control Charts: Box-Cox Power Transformation
Normality Test is automatically applied to transformed data!
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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.
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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.
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SigmaXL® TrainingCourse 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