Statistical Analysis with SAS/STAT® Software

Providing the foundation for SAS' analytic intelligence

Features

Analysis of variance

• Balanced and unbalanced designs; multivariate analysis of variance and repeated measurements; linear and nonlinear mixed models.

Mixed models

• Linear mixed models.
• Nonlinear mixed models.
• Generalize linear mixed models.

Regression

• Least squares regression with nine model selection techniques, including stepwise regression.
• Diagnostic measures.
• Robust regression; Loess regression.
• Nonlinear regression and quadratic response surface models.
• Partial least squares.

Categorical data analysis

• Contingency tables and measures of association.
• Logistic regression and log linear models; generalized linear models.
• Bioassay analysis.
• Generalized estimating equations.
• Weighted least squares regression.
• Exact methods.

Bayesian analysis

• Bayesian modeling and inference for generalized linear models, accelerated life failure models, Cox regression models and piecewise exponential models.
• General procedure fits Bayesian models with arbitrary priors and likelihood functions.

Multivariate analysis

• Factor analysis; principal components; canonical correlation and discriminate analysis; path analysis; structural equations.

Survival analysis

• Comparison of survival distributions; accelerated failure time models; proportional hazards models.

Psychometric analysis

• Multidimensional scaling; conjoint analysis with variable transformations; correspondence analysis.

Cluster analysis

• Hierarchical clustering of multivariate data or distance data; disjoint clustering of large data sets; nonparametric clustering with hypothesis tests for the number of clusters.

Nonparametric analysis

• Nonparametric analysis of variance. Exact probabilities computed for many nonparametric statistics.
• Kruskal-Wallis, Wilcoxon-Mann-Whitney and Friedman tests.
• Other rank tests for balanced or unbalanced one-way or two-way designs.

Survey data analysis

• Sample selection; descriptive statistics and t-tests; linear and logistic regression; frequency table analysis.

Multiple imputation for missing values

• Regression and propensity scoring for monotone missing patterns.
• MCMC method for arbitrary missing patterns.
• Combine results for statistically valid inferences.

Study planning

• Power and Sample Size application provides interface for computation of sample sizes and characterization of power for t-tests, confidence intervals, linear models, tests of proportions and rank tests for survival analysis.

Download the complete SAS/STAT Fact Sheet.