# SigmaStat 4.0 Statistical Features

Below are some of the many statistics features found in new SigmaStat Version 4:

## Analysis of Variance and Covariance

• Independent and paired two samplet-tests, one-sample t-test
• One, two, and three way ANOVA
• One and two way repeated measures and mixed ANOVA
• One way ANCOVA with multiple covariates

## Nonparametric Tests

• One-sample signed rank test
• Mann-Whitney rank sum test
• Wilcoxon signed rank test
• Kruskal-Wallis ANOVA on ranks
• Friedman repeated measures ANOVA

## Correlation

• Pearson product moment
• Spearman rank order

## Descriptive Statistics

• Sample mean, standard deviation, standard error of mean, median, percentiles, sum and sum of squares, skewness, kurtosis, confidence interval for the mean, range, maximum and minimum values, normality tests, sample size, missing value content

## Regression

• Linear and multiple linear
• Polynomial for a specific order and model comparisons for several orders
• Stepwise, forward and backward
• Best subsets
• Multiple logistic
• Deming
• Nonlinear,using built-in and user-defined models

## Rates and Proportions

• Chi-Square analysis of contingency tables
• McNemar’s test
• Fisher exact test
• Z-test
• Odds Ratio
• Relative Risk

## Survival Analysis

• Kaplan-Meier, including single group, LogRank, and Gehen-Breslow
• Cox Regression, including proportional hazards and stratified models

## Principal Components Analysis

• Covariance or correlation matrix analysis, multiple methods of component selection

## Power and Sample Size in Experimental Design

• T-tests, ANOVA, proportions, chi-square and correlation

## Normality

• Shapiro-Wilk
• Kolmogorov-Smirnov with Lilliefors correction

## Equal Variance

• Brown-Forsythe test for ANOVA analysis
• Spearman rank correlation test for regression analysis

## ANOVA Multiple Comparison Procedures

• Holm-Sidak
• Tukey
• Duncan’s Multiple Range
• Fisher LSD
• Student-Newmann-Keuls
• Bonferroni t-test
• Dunnett’s t-test
• Dunn’s test

## Test Options

• Check test assumptions like normality and equal variance that, if violated, may prompt the user to choose an alternate test
• Criterion options that change the way the analysis for a test is performed
• Options for running multiple comparison tests depending on the significance of main test effects
• Options for placing residuals, confidence intervals, predicted values, and weights in the worksheet
• Additional statistics and diagnostics in reports that explore your data and enhance the results, including outlier detection, multicollinearity, retrospective power and residual analysis
• Result graph options for scaling, error bars and color

## Statistical Transforms

• Stack data
• Index and Un-index data for one or two factor variables
• Center, standardize, and rank data
• Apply simple transforms to data using arithmetic operations and basic numerical functions
• Create dummy variables with either reference coding or effects coding
• Create sequences of random numbers that are uniform or normally distributed
• Filter data from worksheet columns using a key column 