Statistical Analysis with SAS/STAT^® Software

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.
• Generalized 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.
• Quantile regression.

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.
• Zero-inflated Poisson regression.
• Zero-inflated negative binomial regression.

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.
• Cox proportional hazards model.

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.