SAS/STAT® Software Features

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

Distribution analysis

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

High performance

Longitudinal data analysis

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

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

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.

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