SAS/STAT Software Features List

Analysis of variance

• Balanced and unbalanced designs.
• Multivariate analysis of variance and repeated measurements.
• Linear models.
• More analysis of variance capabilities.

Bayesian analysis

• Built-in Bayesian modeling and inference for generalized linear models, accelerated failure time models, Cox regression models and finite mixture models.
• Wide range of Bayesian models available via general-purpose MCMC simulation procedure.
• Bayesian discrete choice modeling.
• More Bayesian analysis capabilities.

Categorical data analysis

• Contingency tables and measures of association.
• Bioassay analysis.
• Generalized linear models.
• More categorical data analysis capabilities.

Causal inference

• Propensity score analysis.
• Estimation of causal treatment effects.

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.
• More cluster analysis capabilities.

Descriptive statistics

• Box-and-whisker plots.
• Compute directly and indirectly standardized rates and risks for study populations.
• More descriptive statistics capabilities.

Discriminant analysis

• Canonical discriminant analysis.
• Stepwise discriminant analysis.
• More discriminant analysis capabilities.

Distribution analysis

• Univariate and bivariate kernel density estimation.
• More distribution analysis capabilities.

Exact inference

• Exact p-values and confidence intervals for many test statistics and measures based on one-way and n-way frequency tables.
• Exact tests for the parameters of a logistic regression model.
• Exact tests for the parameters of a Poisson regression model.
• More exact inference capabilities.

Finite mixture models

• Modeling of component distributions and mixing probabilities.
• Maximum likelihood and Bayesian methods.
• More finite mixture capabilities.

Group sequential design and analysis

• Design of interim analyses.
• Perform interim analyses.
• More group sequential design and analysis capabilities.

High performance

• 14 SAS/STAT procedures are multithreaded.
• 12 SAS^® High-Performance Statistics procedures are available with SAS/STAT for single machine use.
  

Longitudinal data analysis

• Marginal and mixed models.
• Continuous and categorical outcomes.
• More longitudinal data analysis capabilities.

Market research

• Simple and multiple correspondence analysis.
• Two-way and three-way metric and nonmetric multidimensional scaling models.
• Discrete choice models.
• More market research capabilities.

Missing data analysis

• Multiple imputation.
• Weighted generalized estimating equations.
• Imputation for survey data.
• More missing data analysis capabilities.

Mixed models

• Linear and nonlinear mixed models.
• Generalized linear mixed models.
• Nested models.
• More mixed models capabilities.

Model selection

• Linear models.
• Generalized linear models.
• Quantile regression models.
• More model selection capabilities.

Multivariate analysis

• Exploratory and confirmatory factor analysis.
• Principal components analysis.
• Canonical correlation and partial canonical correlation.
• More multivariate analysis capabilities.

Nonlinear regression

• Automatic derivatives.
• Bootstrapped confidence intervals.
• More nonlinear regression capabilities.

Nonparametric analysis

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

Nonparametric regression

• Multivariate adaptive regression splines.
• Generalized additive models.
• Local regression.
• Thin-plate smoothing splines.
• More nonparametric regression capabilities.

Post processing

• Hypothesis tests.
• Prediction plots.
• Scoring.
• More post-processing capabilities.

Power and sample size

• Computations for linear models including MANOVA repeated measurements.
• Computations for many hypothesis tests, equivalence tests and correlation analysis.
• Computations for binary logistic regression and survival analysis.
• More power and sample size capabilities.

Predictive modeling

• Classification and regression trees.
• Partitioning of data into training, validation and testing roles.
• Modern model selection methods such as elastic net and group LASSO.
• More predictive modeling capabilities.

Psychometric analysis

• Multidimensional scaling.
• Conjoint analysis with variable transformations.
• Item response theory (IRT) models.
• More psychometric analysis capabilities.

Quantile regression

• Simplex, interior point and smoothing algorithms.
• Analysis of censored data.
• Model selection for linear regression models.
• More quantile regression capabilities.

Regression

• Least squares regression.
• Principal components regression.
• Quadratic response surface models.
• Accurate estimation for ill-conditioned data.
• More regression capabilities.

Robust regression

• M estimation and high-breakdown methods.
• Outlier diagnostics.
• More robust regression capabilities.

Spatial analysis

• Ordinary kriging in two dimensions.
• Spatial point pattern analysis.
• Variogram diagnostics.
• More spatial analysis capabilities.

Standardization

• 18 standardization methods.
• More standardization capabilities.

Statistical graphics

• Hundreds of statistical graphs available with analyses.
• Customization provided.
• Base SAS “SG” procedures create user-specified statistical graphics.
• More statistical graphics capabilities.

Structural equations

• Structural equation models specified with popular modeling languages.
• Parameter estimation and hypothesis testing for constrained and unconstrained problems.
• More structural equation capabilities.

Survey sampling and analysis

• Sample selection.
• Descriptive statistics.
• Linear and logistic regression.
• Proportional hazards regression.
• Missing value imputation.
• More survey sampling and analysis capabilities.

Survival analysis

• Nonparametric survival function estimates.
• Competing-risk models.
• Accelerated failure time models.
• Proportional hazards models.
• Interval-censored data analysis.
• More survival analysis capabilities.

For more information, see the SAS/STAT documentation.