This document provides an overview of the statistical tools available in SigmaXL version 6.1. It summarizes that the tools include two-sample comparison tests, multiple regression with residual plots, binary and ordinal logistic regression, one-way ANOVA and means matrix, non-parametric tests, correlation matrix, chi-square test, multiple linear regression, power and sample size calculators, and power and sample size charts. It highlights some of the key features like significant p-values shown in red, descriptive statistics, and residual plots.

1. Statistical Tools - SigmaXL®
Version 6.1
Stats Tools: Feature Overview
Two-Sample Comparison Tests
Multiple Regression with Residuals Plots
Binary & Ordinal Logistic Regression
One-Way Anova & Means Matrix
Non-Parametric Tests
Correlation Matrix
Chi-Square Test
Multiple Linear Regression
Power & Sample Size Calculators
Multiple Regression
Predicted Response Calculator
Power & Sample Size Charts

2. Statistical Tools
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P-values turn red when results are significant (pvalue < alpha)
Descriptive Statistics including Anderson-Darling
Normality test, Skewness and Kurtosis with pvalues
1 Sample t-test and confidence intervals
Paired t-test, 2 Sample t-test
2 Sample Comparison Tests
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Normality, Mean, Variance, Median
Yellow Highlight to aid Interpretation
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3. Statistical Tools
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One-Way ANOVA and Means Matrix
Two-Way ANOVA
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Equal Variance Tests:
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Balanced and Unbalanced
Bartlett
Levene
Welch’s ANOVA
Correlation Matrix
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Pearson’s Correlation Coefficient
Spearman’s Rank
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4. Statistical Tools
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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
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5. 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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6. Statistical Tools: One-Way
ANOVA & Means Matrix
4.48
Mean/CI - Overall Satisfaction
4.28
4.08
3.88
3.68
3.48
3.28
3.08
1
2
3
Customer Type
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Index

7. Statistical Tools:
Correlation Matrix
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8. Statistical Tools:
Multiple Linear Regression
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Accepts continuous and/or categorical (discrete)
predictors.
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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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9. Statistical Tools:
Multiple Linear Regression
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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
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Fit Intercept is optional
Index

10. Statistical Tools:
Multiple Regression
Multiple Regression accepts Continuous and/or
Categorical Predictors!
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11. Statistical Tools:
Multiple Regression
Durbin-Watson Test with p-values
for positive and negative Back to
Index
autocorrelation!

12. Statistical Tools: Multiple
Regression – Predicted
Response Calculator with
Confidence Intervals
Easy-to-use Calculator with
Confidence Intervals and Prediction Intervals!
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13. Statistical Tools:
Multiple Regression with
Residual Plots
2
40
1
NSCORE
3
50
30
0
1.
10
Residuals
Regular Residuals
1.5
1.00
1
Regular Residuals
1.50
0.50
0.00
0.5
0
-0.5
Fitted Values
10
0.
00
80
.0
0
60
.0
0
40
.0
0
20
.0
0
12
0
10
0
80
40
20
60
Observation Order
0.
00
-1
-1.00
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12
0.
00
-0.50
0
Regular Residuals
0.
60
0.
10
-0
.9
0
1.
19
1.
01
0.
84
0.
67
0.
50
0.
32
0.
15
-0
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2
-3
-0
.1
9
0
-0
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7
-2
-0
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4
10
-0
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1
-1
-0
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8
20
-0
.4
0
Frequency
60

14. Statistical Tools:
Binary and Ordinal
Logistic Regression
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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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15. Statistical Tools:
Nonparametric Tests
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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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16. Statistical Tools:
Chi-Square Test
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17. Statistical Tools: Power &
Sample Size Calculators
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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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18. Statistical Tools: Power &
Sample Size Charts
Power & Sample Size: 1 Sample t-Test
1.2
Power (1 - Beta)
1
Difference = 0.5
0.8
Difference = 1
Difference = 1.5
0.6
Difference = 2
Difference = 2.5
0.4
Difference = 3
0.2
0
0
10
20
30
40
50
60
Sample Size (N)
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