Stats tools

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An overview of SigmaXL's Statistical Tools

Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.

SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.

Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course. 

DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.

DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.

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Stats tools

  1. 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. 2. Statistical Tools      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   Normality, Mean, Variance, Median Yellow Highlight to aid Interpretation Back to Index
  3. 3. Statistical Tools   One-Way ANOVA and Means Matrix Two-Way ANOVA   Equal Variance Tests:     Balanced and Unbalanced Bartlett Levene Welch’s ANOVA Correlation Matrix   Pearson’s Correlation Coefficient Spearman’s Rank Back to Index
  4. 4. 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 Back to Index
  5. 5. Statistical Tools: Two-Sample Comparison Tests P-values turn red when results are significant! Rules based yellow highlight to aid interpretation! Back to Index
  6. 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 Back to Index
  7. 7. Statistical Tools: Correlation Matrix Back to Index
  8. 8. 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. Back to Index
  9. 9. 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 Back to Fit Intercept is optional Index
  10. 10. Statistical Tools: Multiple Regression Multiple Regression accepts Continuous and/or Categorical Predictors! Back to Index
  11. 11. Statistical Tools: Multiple Regression Durbin-Watson Test with p-values for positive and negative Back to Index autocorrelation!
  12. 12. Statistical Tools: Multiple Regression – Predicted Response Calculator with Confidence Intervals Easy-to-use Calculator with Confidence Intervals and Prediction Intervals! Back to Index
  13. 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 Back to Index 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 .0 2 -3 -0 .1 9 0 -0 .3 7 -2 -0 .5 4 10 -0 .7 1 -1 -0 .8 8 20 -0 .4 0 Frequency 60
  14. 14. 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. Back to Index
  15. 15. 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 Back to Index
  16. 16. Statistical Tools: Chi-Square Test Back to Index
  17. 17. 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). Back to Index
  18. 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) Back to Index

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