- Analysis of Variance
- Bayesian Analysis
- Categorical Data Analysis
- Cluster Analysis
- Descriptive Statistics
- Discriminant Analysis
- Distribution Analysis
- Exact Inference
- Finite Mixture Models
- Group Sequential Design and Analysis
- Longitudinal Data Analysis
- Market Research
- Missing Data Analysis
- Mixed Models
- Model Selection
- Multivariate Analysis
- Nonlinear Regression
- Nonparametric Analysis
- Nonparametric Regression
- Post Processing
- Power and Sample Size
- Predictive Modeling
- Psychometric Analysis
- Quantile Regression
- Regression
- Robust Regression
- Spatial Analysis
- Standardization
- Structural Equations Models
- Survey Sampling and Analysis
- Survival Analysis
- SAS/STAT Procedures A-Z

- ACECLUS — Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices
- ADAPTIVEREG — Fits multivariate adaptive regression splines
- ANOVA — Performs analysis of variance for balanced data
- BCHOICE — Performs Bayesian analysis for discrete choice models
- BGLIMM — provides full Bayesian inference for generalized linear mixed models (GLMMs) New Procedure!
- BOXPLOT — Creates side-by-side box-and-whiskers plots of measurements organized in groups
- CALIS — Fits structural equation models
- CANCORR — Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis
- CANDISC — Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance
- CATMOD — Performs categorical data modeling of data that can be represented by a contingency table
- CAUSALGRAPH — Examines the structure of graphical causal models New Procedure!
- CAUSALMED — Estimates causal mediation effects from observational data.
- CAUSALTRT — Estimates the average causal effect of a binary treatment,
*T*, on a continuous or discrete outcome,*Y*. - CLUSTER — Hierarchically clusters the observations in a SAS data
- CORR — Computes Pearson correlation coefficients, nonparametric measures of association, and the probabilities associated with these statistics
- CORRESP — Performs simple correspondence analysis and multiple correspondence analysis (MCA)
- DISCRIM — Develops a discriminant criterion to classify each observation into groups
- DISTANCE — Computes various measures of distance, dissimilarity, or similarity between the observations (rows) of a SAS data set. Proximity measures are stored as a lower triangular matrix or a square matrix in an output data set that can then be used as input to the CLUSTER, MDS, and MODECLUS procedures.
- FACTOR — Performs a variety of common factor and component analyses and rotations
- FASTCLUS — Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables
- FMM — Fits finite mixture models
- FREQ — Produces one-way to
*n*-way frequency and contingency (crosstabulation) tables - GAM — Fits generalized additive models
- GAMPL — Fits generalized additive models that are based on low-rank regression splines
- GEE — Fits generalized linear models for longitudinal data
- GENMOD — Fits generalized linear models
- GLIMMIX — Fits generalized linear mixed models
- GLM — Fits general linear models
- GLMMOD — Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure
- GLMPOWER — Performs prospective power and sample size analysis for linear models
- GLMSELECT — Performs effect selection in the framework of general linear models
- HPCANDISC — performs canonical discriminant analysis and is a high-performance version of the CANDISC procedure in SAS/STAT software
- HPFMM — Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions-that is, each response comes from one of several random univariate distributions that have unknown probabilities
- HPGENSELECT — Model fitting and model building for generalized linear models
- HPLMIXED — Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data
- HPLOGISTIC — Fits logistic regression models and performs model selection for binary, binomial, and multinomial data
- HPMIXED — Fits linear mixed models with simple covariance component structures by sparse-matrix techniques
- HPNLMOD — Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
- HPPLS — Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
- HPPRINCOMP — Conducts principal components analysis and provides iterative methods to calculate the principal components.
- HPREG — Fits and performs model selection for ordinary linear least squares models
- ICLIFETEST — Performs nonparametric survival analysis for interval-censored data
- ICPHREG — Fits proportional hazards regression models to interval-censored data
- INBREED — Calculates the covariance or inbreeding coefficients for a pedigree
- IRT — Fits item response models
- KDE — Performs univariate and bivariate kernel density estimation
- KRIGE2D — Performs ordinary kriging in two dimensions
- LATTICE — Analysis of variance and analysis of simple covariance for data from experiments with lattice designs
- LIFEREG — Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored
- LIFETEST — Compute nonparametric estimates of the survivor function either by the product-limit method (also called the Kaplan-Meier method) or by the lifetable method (also called the actuarial method)
- LOESS — Implements a nonparametric method for estimating regression surfaces
- LOGISTIC — Fits models with binary, ordinal, or nominal dependent variables
- MCMC — A general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models
- MDS — Fits two- and three-way, metric and nonmetric multidimensional scaling models
- MI — Performs multiple imputation of missing data
- MIANALYZE — Combines the results of the analyses of imputations and generates valid statistical inferences
- MIXED — Fits general linear models with fixed and random effects
- MODECLUS — Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates
- MULTTEST — Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests
- NESTED — Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects
- NLIN — Fits nonlinear regression models
- NLMIXED — Fits mixed models in which the fixed or random effects enter nonlinearly
- NPAR1WAY — Performs nonparametric tests for location and scale differences across a one-way classification
- ORTHOREG — Fits general linear models by the method of least squares
- PHREG — Performs regression analysis of survival data based on the Cox proportional hazards model
- PLAN — Constructs designs and randomizes plans for factorial experiments
- PLM — Performs postfitting statistical analyses
- PLS — Performs principal components regression
- POWER — Performs prospective power and sample size analyses for a variety of statistical analyses
- PRINCOMP — Performs principal component analysis.
- PRINQUAL — Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data
- PROBIT — Calculates maximum likelihood estimates of regression parameters and the natural (or threshold) response rate for quantal response data from biological assays or other discrete event data
- QUANTLIFE — Fits quantile regression models for survival data
- QUANTREG — Fits quantile regression models
- QUANTSELECT — Performs effect selection for linear quantile regression models
- REG — General purpose procedure for ordinary least squares regression
- RMSTREG — analyzes time-to-event data by using regression with respect to the restricted mean survival time (RMST) New Procedure!
- ROBUSTREG — Provides resistant (stable) results for linear regression models in the presence of outliers
- RSREG — Fits quadratic response surface regression models
- SCORE — Multiplies values from two SAS data sets, one containing coefficients and the other containing raw data to be scored using the coefficients from the first data set
- SEQDESIGN — Design interim analyses for group sequential clinical trials
- SEQTEST — Perform interim analyses for group sequential clinical trials
- SIM2D — Produces a spatial simulation for a Gaussian random field with a specified mean and covariance structure in two dimensions by using an LU decomposition technique
- SIMNORMAL — Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables
- SPP — Analyzes spatial point patterns
- STDIZE — Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure
- STDRATE — Computes directly and indirectly standardized rates and risks for study populations
- STEPDISC — Given a classification variable and several quantitative variables, the procedure performs a stepwise discriminant analysis to select a subset of the quantitative variables for use in discriminating among the classes
- SURVEYFREQ — Produces one-way to
*n*-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting - SURVEYIMPUTE — Imputes missing values of an item in a data set by replacing them with observed values from the same item and computes replicate weights (such as jackknife weights) that account for the imputation
- SURVEYLOGISTIC — Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs
- SURVEYMEANS — Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting
- SURVEYPHREG — Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs
- SURVEYREG — Performs linear regression analysis for complex survey sample designs
- SURVEYSELECT — Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection
- TPSPLINE — Provides penalized least squares estimates
- TRANSREG — Fits linear models with optimal nonlinear transformations of variables
- TREE — Produces a tree diagram, also known as a
*dendrogram*or*phenogram*, from a data set created by the CLUSTER or VARCLUS procedure that contains the results of hierarchical clustering as a tree structure - TTEST — Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design. Two-sided, TOST (two one-sided test) equivalence, and upper and lower one-sided hypotheses are supported for means, mean differences, and mean ratios for either normal or lognormal data
- VARCLUS — Divides a set of numeric variables into disjoint or hierarchical clusters
- VARCOMP — Fits general linear models with random effects (with the option of specifying certain effects as fixed)
- VARIOGRAM — Computes empirical measures of spatial continuity for two-dimensional spatial data