SAS/STAT®

SAS/STAT User's Guide - Procedures

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Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

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Basic ODS Graphics Examples
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SAS/STAT 15.1 User's Guide - Procedures

For the complete SAS/STAT 15.1 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure   PDF   |   HTML
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  • The ADAPTIVEREG Procedure    PDF   |   HTML
    Fits multivariate adaptive regression splines.
  • The ANOVA Procedure    PDF   |   HTML
    Performs analysis of variance for balanced data.
  • The BCHOICE Procedure   PDF   |   HTML
    Performs Bayesian analysis for discrete choice models.
  • The BGLIMM Procedure   PDF   |   HTML  New Procedure!
    High-performance, sampling-based procedure that provides Bayesian inference for generalized linear mixed models (GLMMs).
  • The BOXPLOT Procedure   PDF   |   HTML
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  • The CALIS Procedure    PDF   |   HTML
    Fits structural equation models.
  • The CANCORR Procedure    PDF   |   HTML
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  • The CANDISC Procedure    PDF   |   HTML
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  • The CATMOD Procedure    PDF   |   HTML
    Performs categorical data modeling of data that can be represented by a contingency table.
  • The CAUSALGRAPH Procedure    PDF   |   HTML  New Procedure!
    Examines the structure of graphical causal models and suggests statistical strategies or steps that enable researchers to estimate causal effects that have valid causal interpretations.
  • The CAUSALMED Procedure   PDF   |   HTML
    Estimates causal mediation effects from observational data.
  • The CAUSALTRT Procedure   PDF   |   HTML
    Implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies
  • The CLUSTER Procedure    PDF   |   HTML
    Hierarchically clusters the observations in a SAS data set.
  • The CORRESP Procedure    PDF   |   HTML
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  • The DISCRIM Procedure    PDF   |   HTML
    Develops a discriminant criterion to classify each observation into groups.
  • The DISTANCE Procedure    PDF   |   HTML
    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.
  • The FACTOR Procedure    PDF   |   HTML
    Performs a variety of common factor and component analyses and rotations.
  • The FASTCLUS Procedure    PDF   |   HTML
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  • The FMM Procedure   PDF   |   HTML
    Fits finite mixture models.
  • The FREQ Procedure    PDF   |   HTML
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  • The GAM Procedure    PDF   |   HTML
    Fits generalized additive models.
  • The GAMPL Procedure  PDF   |   HTML
    Fits generalized additive models that are based on low-rank regression splines.
  • The GEE Procedure   PDF   |   HTML
    Provides weighted generalized estimating equations.
  • The GENMOD Procedure    PDF   |   HTML
    Fits generalized linear models.
  • The GLIMMIX Procedure   PDF   |   HTML
    Fits generalized linear mixed models.
  • The GLM Procedure   PDF   |   HTML
    Fits general linear models.
  • The GLMMOD Procedure    PDF   |   HTML
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  • The GLMPOWER Procedure    PDF   |   HTML
    Performs prospective power and sample size analysis for linear models.
  • The GLMSELECT Procedure    PDF   |   HTML
    Performs effect selection in the framework of general linear models.
  • The HPCANDISC Procedure    PDF   |   HTML
    Performs canonical discriminant analysis.
  • The HPFMM Procedure    PDF   |   HTML
    Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
  • The HPGENSELECT Procedure    PDF   |   HTML
    Provides model fitting and model building for generalized linear models.
  • The HPLMIXED Procedure   PDF   |   HTML
    Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
  • The HPLOGISTIC Procedure    PDF   |   HTML
    Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
  • The HPMIXED Procedure    PDF   |   HTML
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  • The HPNLMOD Procedure    PDF   |   HTML
    Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
  • The HPPLS Procedure    PDF   |   HTML
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  • The HPPRINCOMP Procedure    PDF   |   HTML
    Performs principal component analysis.
  • The HPQUANTSELECT Procedure    PDF   |   HTML
    Fits and performs effect selection for quantile regression analysis.
  • The HPREG Procedure    PDF   |   HTML
    Fits and performs model selection for ordinary linear least squares models.
  • The HPSPLIT Procedure   PDF   |   HTML
    Builds tree-based statistical models for classification and regression.
  • The ICLIFETEST Procedure   PDF   |   HTML
    Performs nonparametric survival analysis for interval-censored data.
  • The ICPHREG Procedure    PDF   |   HTML
    Fits proportional hazards regression models to interval-censored data and can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
  • The INBREED Procedure    PDF   |   HTML
    Calculates the covariance or inbreeding coefficients for a pedigree.
  • The IRT Procedure   PDF   |   HTML
    Fits item response models.
  • The KDE Procedure    PDF   |   HTML
    Performs univariate and bivariate kernel density estimation.
  • The KRIGE2D Procedure    PDF   |   HTML
    Performs ordinary kriging for point-referenced spatial data.
  • The LATTICE Procedure    PDF   |   HTML
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  • The LIFEREG Procedure    PDF   |   HTML
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  • The LIFETEST Procedure    PDF   |   HTML
    Computes 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).
  • The LOESS Procedure    PDF   |   HTML
    Implements a nonparametric method for estimating regression surfaces.
  • The LOGISTIC Procedure    PDF   |   HTML
    Fits regression models with binary, ordinal, or nominal dependent variables.
  • The MCMC Procedure    PDF   |   HTML
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  • The MDS Procedure    PDF   |   HTML
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  • The MI Procedure    PDF   |   HTML
    Performs multiple imputation of missing data.
  • The MIANALYZE Procedure    PDF   |   HTML
    Combines the results of the analyses of imputations and generates valid statistical inferences.
  • The MIXED Procedure    PDF   |   HTML
    Fits general linear models with fixed and random effects.
  • The MODECLUS Procedure    PDF   |   HTML
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  • The MULTTEST Procedure    PDF   |   HTML
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  • The NESTED Procedure    PDF   |   HTML
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  • The NLIN Procedure    PDF   |   HTML
    Fits nonlinear regression models.
  • The NLMIXED Procedure   PDF   |   HTML
    Fits mixed models in which the fixed or random effects enter nonlinearly.
  • The NPAR1WAY Procedure    PDF   |   HTML
    Performs nonparametric tests for location and scale differences across a one-way classification.
  • The ORTHOREG Procedure    PDF   |   HTML
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when the data are ill-conditioned.
  • The PHREG Procedure    PDF   |   HTML
    Performs regression analysis of survival data based on the Cox proportional hazards model.
  • The PLAN Procedure    PDF   |   HTML
    Constructs designs and randomizes plans for factorial experiments.
  • The PLM Procedure   PDF   |   HTML
    Performs postfitting statistical analyses.
  • The PLS Procedure    PDF   |   HTML
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  • The POWER Procedure    PDF   |   HTML
    Performs prospective power and sample size analyses for a variety of statistical analyses.
  • The Power and Sample Size Application  PDF   |   HTML
    The functionality of the Power and Sample Size application has been replaced by tasks in SAS Studio.
  • The PRINCOMP Procedure    PDF   |   HTML
    Performs principal component analysis.
  • The PRINQUAL Procedure    PDF   |   HTML
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  • The PROBIT Procedure    PDF   |   HTML
    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.
  • The PSMATCH Procedure   PDF   |   HTML
    Provides a variety of tools for propensity score analysis.
  • The QUANTLIFE Procedure   PDF   |   HTML
    Performs quantile regression analysis for survival data.
  • The QUANTREG Procedure    PDF   |   HTML
    Fits quantile regression models.
  • The QUANTSELECT Procedure    PDF   |   HTML
    Performs effect selection in the framework of quantile regression.
  • The REG Procedure    PDF   |   HTML
    General-purpose procedure for ordinary least squares regression.
  • The RMSTREG Procedure   PDF   |   HTML  New Procedure!
    Analyzes time-to-event data by using regression with respect to the restricted mean survival time (RMST), using specialized methods such as those developed by Andersen, Hansen, and Klein and Tian, Zhao, and Wei.
  • The ROBUSTREG Procedure    PDF   |   HTML
    Provides resistant (stable) results for linear regression models in the presence of outliers.
  • The RSREG Procedure    PDF   |   HTML
    Fits quadratic response surface regression models.
  • The SCORE Procedure    PDF   |   HTML
    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.
  • The SEQDESIGN Procedure    PDF   |   HTML
    Designs interim analyses for group sequential clinical trials.
  • The SEQTEST Procedure    PDF   |   HTML
    Performs interim analyses for group sequential clinical trials.
  • The SIM2D Procedure   PDF   |   HTML
    Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
  • The SIMNORMAL Procedure    PDF   |   HTML
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  • The SPP Procedure   PDF (8.03MB)  |   HTML
    Analyzes spatial point pattern data and fits spatial process models.
  • The STDIZE Procedure    PDF   |   HTML
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  • The STDRATE Procedure    PDF   |   HTML
    Computes directly standardized rates and risks for study populations.
  • The STEPDISC Procedure    PDF   |   HTML
    Performs a stepwise discriminant analysis of a specified classification variable and several quantitative variables to select a subset of the quantitative variables for use in discriminating among the classes.
  • The SURVEYFREQ Procedure    PDF   |   HTML
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  • The SURVEYIMPUTE Procedure   PDF   |   HTML
    Imputes missing values of an item in a data set by replacing them with observed values from the same item.
  • The SURVEYLOGISTIC Procedure    PDF   |   HTML
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  • The SURVEYMEANS Procedure    PDF   |   HTML
    Estimates statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  • The SURVEYPHREG Procedure    PDF   |   HTML
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  • The SURVEYREG Procedure    PDF   |   HTML
    Performs linear regression analysis for complex survey sample designs.
  • The SURVEYSELECT Procedure    PDF   |   HTML
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  • The TPSPLINE Procedure    PDF   |   HTML
    Provides penalized least squares estimates.
  • The TRANSREG Procedure    PDF   |   HTML
    Fits linear models with optimal nonlinear transformations of variables.
  • The TREE Procedure    PDF   |   HTML
    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.
  • The TTEST Procedure    PDF   |   HTML
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  • The VARCLUS Procedure    PDF   |   HTML
    Divides a set of numeric variables into disjoint or hierarchical clusters.
  • The VARCOMP Procedure    PDF   |   HTML
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  • The VARIOGRAM Procedure    PDF   |   HTML
    Fits and selects spatial covariance models for point-referenced spatial data.


More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

For more SAS/STAT books, visit the bookstore.


Free Related Books

Basic ODS Graphics Examples
By Warren Kuhfeld

Advanced ODS Graphics Examples
By Warren Kuhfeld

SAS/STAT 14.3 User's Guide - Procedures

For the complete SAS/STAT 14.3 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure   PDF   |   HTML
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  • The ADAPTIVEREG Procedure    PDF   |   HTML
    Fits multivariate adaptive regression splines.
  • The ANOVA Procedure    PDF   |   HTML
    Performs analysis of variance for balanced data.
  • The BCHOICE Procedure   PDF   |   HTML
    Performs Bayesian analysis for discrete choice models.
  • The BOXPLOT Procedure   PDF   |   HTML
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  • The CALIS Procedure    PDF   |   HTML
    Fits structural equation models.
  • The CANCORR Procedure    PDF   |   HTML
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  • The CANDISC Procedure    PDF   |   HTML
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  • The CATMOD Procedure    PDF   |   HTML
    Performs categorical data modeling of data that can be represented by a contingency table.
  • The CAUSALMED Procedure   PDF   |   HTML  New Procedure!
    Estimates causal mediation effects from observational data.
  • The CAUSALTRT Procedure   PDF   |   HTML
    Implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies
  • The CLUSTER Procedure    PDF   |   HTML
    Hierarchically clusters the observations in a SAS data.
  • The CORRESP Procedure    PDF   |   HTML
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  • The DISCRIM Procedure    PDF   |   HTML
    Develops a discriminant criterion to classify each observation into groups.
  • The DISTANCE Procedure    PDF   |   HTML
    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.
  • The FACTOR Procedure    PDF   |   HTML
    Performs a variety of common factor and component analyses and rotations.
  • The FASTCLUS Procedure    PDF   |   HTML
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  • The FMM Procedure   PDF   |   HTML
    Fits finite mixture models.
  • The FREQ Procedure    PDF   |   HTML
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  • The GAM Procedure    PDF   |   HTML
    Fits generalized additive models.
  • The GAMPL Procedure  PDF   |   HTML
    Fits generalized additive models that are based on low-rank regression splines.
  • The GEE Procedure   PDF   |   HTML
    Provides weighted generalized estimating equations.
  • The GENMOD Procedure    PDF   |   HTML
    Fits generalized linear models.
  • The GLIMMIX Procedure   PDF   |   HTML
    Fits generalized linear mixed models.
  • The GLM Procedure   PDF   |   HTML
    Fits general linear models.
  • The GLMMOD Procedure    PDF   |   HTML
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  • The GLMPOWER Procedure    PDF   |   HTML
    Performs prospective power and sample size analysis for linear models.
  • The GLMSELECT Procedure    PDF   |   HTML
    Performs effect selection in the framework of general linear models.
  • The HPCANDISC Procedure    PDF   |   HTML
    Performs canonical discriminant analysis.
  • The HPFMM Procedure    PDF   |   HTML
    Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
  • The HPGENSELECT Procedure    PDF   |   HTML
    Provides model fitting and model building for generalized linear models.
  • The HPLMIXED Procedure   PDF   |   HTML
    Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
  • The HPLOGISTIC Procedure    PDF   |   HTML
    Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
  • The HPMIXED Procedure    PDF   |   HTML
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  • The HPNLMOD Procedure    PDF   |   HTML
    Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
  • The HPPLS Procedure    PDF   |   HTML
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  • The HPPRINCOMP Procedure    PDF   |   HTML
    Performs principal component analysis.
  • The HPQUANTSELECT Procedure    PDF   |   HTML
    Fits and performs effect selection for quantile regression analysis.
  • The HPREG Procedure    PDF   |   HTML
    Fits and performs model selection for ordinary linear least squares models.
  • The HPSPLIT Procedure   PDF   |   HTML
    Builds tree-based statistical models for classification and regression.
  • The ICLIFETEST Procedure   PDF   |   HTML
    Performs nonparametric survival analysis for interval-censored data.
  • The ICPHREG Procedure    PDF   |   HTML
    Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
  • The INBREED Procedure    PDF   |   HTML
    Calculates the covariance or inbreeding coefficients for a pedigree.
  • The IRT Procedure   PDF   |   HTML
    Fits item response models.
  • The KDE Procedure    PDF   |   HTML
    Performs univariate and bivariate kernel density estimation.
  • The KRIGE2D Procedure    PDF   |   HTML
    Performs ordinary kriging for point-referenced spatial data.
  • The LATTICE Procedure    PDF   |   HTML
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  • The LIFEREG Procedure    PDF   |   HTML
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  • The LIFETEST Procedure    PDF   |   HTML
    Computes 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).
  • The LOESS Procedure    PDF   |   HTML
    Implements a nonparametric method for estimating regression surfaces.
  • The LOGISTIC Procedure    PDF   |   HTML
    Fits regression models with binary, ordinal, or nominal dependent variables.
  • The MCMC Procedure    PDF   |   HTML
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  • The MDS Procedure    PDF   |   HTML
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  • The MI Procedure    PDF   |   HTML
    Performs multiple imputation of missing data.
  • The MIANALYZE Procedure    PDF   |   HTML
    Combines the results of the analyses of imputations and generates valid statistical inferences.
  • The MIXED Procedure    PDF   |   HTML
    Fits general linear models with fixed and random effects.
  • The MODECLUS Procedure    PDF   |   HTML
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  • The MULTTEST Procedure    PDF   |   HTML
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  • The NESTED Procedure    PDF   |   HTML
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  • The NLIN Procedure    PDF   |   HTML
    Fits nonlinear regression models.
  • The NLMIXED Procedure   PDF   |   HTML
    Fits mixed models in which the fixed or random effects enter nonlinearly.
  • The NPAR1WAY Procedure    PDF   |   HTML
    Performs nonparametric tests for location and scale differences across a one-way classification.
  • The ORTHOREG Procedure    PDF   |   HTML
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  • The PHREG Procedure    PDF   |   HTML
    Performs regression analysis of survival data based on the Cox proportional hazards model.
  • The PLAN Procedure    PDF   |   HTML
    Constructs designs and randomizes plans for factorial experiments.
  • The PLM Procedure   PDF   |   HTML
    Performs postfitting statistical analyses.
  • The PLS Procedure    PDF   |   HTML
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  • The POWER Procedure    PDF   |   HTML
    Performs prospective power and sample size analyses for a variety of statistical analyses.
  • The Power and Sample Size Application  PDF   |   HTML
    The functionality of the Power and Sample Size application has been replaced by tasks in SAS Studio.
  • The PRINCOMP Procedure    PDF   |   HTML
    Performs principal component analysis.
  • The PRINQUAL Procedure    PDF   |   HTML
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  • The PROBIT Procedure    PDF   |   HTML
    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.
  • The PSMATCH Procedure   PDF   |   HTML
    Provides a variety of tools for propensity score analysis.
  • The QUANTLIFE Procedure   PDF   |   HTML
    Performs quantile regression analysis for survival data.
  • The QUANTREG Procedure    PDF   |   HTML
    Fits quantile regression models.
  • The QUANTSELECT Procedure    PDF   |   HTML
    Performs effect selection in the framework of quantile regression.
  • The REG Procedure    PDF   |   HTML
    General-purpose procedure for ordinary least squares regression.
  • The ROBUSTREG Procedure    PDF   |   HTML
    Provides resistant (stable) results for linear regression models in the presence of outliers.
  • The RSREG Procedure    PDF   |   HTML
    Fits quadratic response surface regression models.
  • The SCORE Procedure    PDF   |   HTML
    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.
  • The SEQDESIGN Procedure    PDF   |   HTML
    Designs interim analyses for group sequential clinical trials.
  • The SEQTEST Procedure    PDF   |   HTML
    Performs interim analyses for group sequential clinical trials.
  • The SIM2D Procedure   PDF   |   HTML
    Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
  • The SIMNORMAL Procedure    PDF   |   HTML
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  • The SPP Procedure   PDF (8.03MB)  |   HTML
    Analyzes spatial point pattern data and fits spatial process models.
  • The STDIZE Procedure    PDF   |   HTML
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  • The STDRATE Procedure    PDF   |   HTML
    Computes directly standardized rates and risks for study populations.
  • The STEPDISC Procedure    PDF   |   HTML
    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.
  • The SURVEYFREQ Procedure    PDF   |   HTML
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  • The SURVEYIMPUTE Procedure   PDF   |   HTML
    Imputes missing values of an item in a data set by replacing them with observed values from the same item.
  • The SURVEYLOGISTIC Procedure    PDF   |   HTML
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  • The SURVEYMEANS Procedure    PDF   |   HTML
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  • The SURVEYPHREG Procedure    PDF   |   HTML
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  • The SURVEYREG Procedure    PDF   |   HTML
    Performs linear regression analysis for complex survey sample designs.
  • The SURVEYSELECT Procedure    PDF   |   HTML
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  • The TPSPLINE Procedure    PDF   |   HTML
    Provides penalized least squares estimates.
  • The TRANSREG Procedure    PDF   |   HTML
    Fits linear models with optimal nonlinear transformations of variables.
  • The TREE Procedure    PDF   |   HTML
    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.
  • The TTEST Procedure    PDF   |   HTML
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  • The VARCLUS Procedure    PDF   |   HTML
    Divides a set of numeric variables into disjoint or hierarchical clusters.
  • The VARCOMP Procedure    PDF   |   HTML
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  • The VARIOGRAM Procedure    PDF   |   HTML
    Fits and selects spatial covariance models for point-referenced spatial data.


More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

For more SAS/STAT books, visit the bookstore.


Free Related Books

Basic ODS Graphics Examples
By Warren Kuhfeld

Advanced ODS Graphics Examples
By Warren Kuhfeld

SAS/STAT 14.2 User's Guide - Procedures

For the complete SAS/STAT 14.2 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF   |   HTML
  • The ADAPTIVEREG Procedure
    Fits multivariate adaptive regression splines.
    PDF   |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF   |   HTML
  • The BCHOICE Procedure
    Performs Bayesian analysis for discrete choice models.
    PDF   |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF   |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF   |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF   |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF   |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF   |   HTML
  • The CAUSALTRT Procedure New Procedure!
    Implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies
    PDF   |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF   |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF   |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF   |   HTML
  • The DISTANCE Procedure
    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.
    PDF   |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF   |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF   |   HTML
  • The FMM Procedure
    Fits finite mixture models.
    PDF   |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF   |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF   |   HTML
  • The GAMPL Procedure
    Fits generalized additive models that are based on low-rank regression splines.
    PDF   |   HTML
  • The GEE Procedure
    Provides weighted generalized estimating equations.
    PDF   |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF   |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF   |   HTML
  • The GLM Procedure
    Fits general linear models.
    PDF   |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF   |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF   |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF   |   HTML
  • The HPCANDISC Procedure
    Performs canonical discriminant analysis.
    PDF   |   HTML
  • The HPFMM Procedure
    Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
    PDF   |   HTML
  • The HPGENSELECT Procedure
    Provides model fitting and model building for generalized linear models.
    PDF   |   HTML
  • The HPLMIXED Procedure
    Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
    PDF   |   HTML
  • The HPLOGISTIC Procedure
    Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
    PDF   |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF   |   HTML
  • The HPNLMOD Procedure
    Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
    PDF   |   HTML
  • The HPPLS Procedure
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
    PDF   |   HTML
  • The HPPRINCOMP Procedure
    Performs principal component analysis.
    PDF   |   HTML
  • The HPQUANTSELECT Procedure
    Fits and performs effect selection for quantile regression analysis.
    PDF   |   HTML
  • The HPREG Procedure
    Fits and performs model selection for ordinary linear least squares models.
    PDF   |   HTML
  • The HPSPLIT Procedure
    Builds tree-based statistical models for classification and regression.
    PDF   |   HTML
  • The ICLIFETEST Procedure
    Performs nonparametric survival analysis for interval-censored data.
    PDF   |   HTML
  • The ICPHREG Procedure
    Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
    PDF   |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF   |   HTML
  • The IRT Procedure
    Fits item response models.
    PDF   |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF   |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging for point-referenced spatial data.
    PDF   |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF   |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF   |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF   |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF   |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF   |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF   |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF   |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF   |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF   |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF   |   HTML
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF   |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF   |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF   |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF   |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF   |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF   |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF   |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF   |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF   |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF   |   HTML
  • The PLS Procedure
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
    PDF   |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF   |   HTML
  • The Power and Sample Size Application
    PDF   |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF   |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF   |   HTML
  • The PROBIT Procedure
    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.
    PDF   |   HTML
  • The PSMATCH Procedure New Procedure!
    Provides a variety of tools for propensity score analysis.
    PDF   |   HTML
  • The QUANTLIFE Procedure
    Performs quantile regression analysis for survival data.
    PDF   |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF   |   HTML
  • The QUANTSELECT Procedure
    Performs effect selection in the framework of quantile regression.
    PDF   |   HTML
  • The REG Procedure
    General-purpose procedure for ordinary least squares regression.
    PDF   |   HTML
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF   |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF   |   HTML
  • The SCORE Procedure
    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.
    PDF   |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF   |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF   |   HTML
  • The SIM2D Procedure
    Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
    PDF   |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF   |   HTML
  • The SPP Procedure
    Analyzes spatial point pattern data and fits spatial process models.
    PDF (8.03MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF   |   HTML
  • The STDRATE Procedure
    Computes directly standardized rates and risks for study populations.
    PDF   |   HTML
  • The STEPDISC Procedure
    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.
    PDF   |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF   |   HTML
  • The SURVEYIMPUTE Procedure
    Imputes missing values of an item in a data set by replacing them with observed values from the same item.
    PDF   |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF   |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF   |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF   |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF   |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF   |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF   |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF   |   HTML
  • The TREE Procedure
    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.
    PDF   |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF   |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF   |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF   |   HTML
  • The VARIOGRAM Procedure
    Fits and selects spatial covariance models for point-referenced spatial data.
    PDF   |   HTML


More about This Product

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Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

For more SAS/STAT books, visit the bookstore.


Free Related Books

Basic ODS Graphics Examples
By Warren Kuhfeld

Advanced ODS Graphics Examples
By Warren Kuhfeld

SAS/STAT 14.1 User's Guide - Procedures

For the complete SAS/STAT 14.1 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF   |   HTML
  • The ADAPTIVEREG Procedure
    Fits multivariate adaptive regression splines.
    PDF   |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF   |   HTML
  • The BCHOICE Procedure
    Performs Bayesian analysis for discrete choice models.
    PDF   |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF   |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF   |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF   |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF   |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF   |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF   |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF   |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF   |   HTML
  • The DISTANCE Procedure
    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.
    PDF   |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF   |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF   |   HTML
  • The FMM Procedure
    Fits finite mixture models.
    PDF   |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF   |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF   |   HTML
  • The GAMPL Procedure New Procedure!
    Fits generalized additive models that are based on low-rank regression splines.
    PDF   |   HTML
  • The GEE Procedure
    Provides weighted generalized estimating equations.
    PDF   |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF   |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF   |   HTML
  • The GLM Procedure
    Fits general linear models.
    PDF   |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF   |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF   |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF   |   HTML
  • The HPCANDISC Procedure
    Performs canonical discriminant analysis.
    PDF   |   HTML
  • The HPFMM Procedure
    Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
    PDF   |   HTML
  • The HPGENSELECT Procedure
    Provides model fitting and model building for generalized linear models.
    PDF   |   HTML
  • The HPLMIXED Procedure
    Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
    PDF   |   HTML
  • The HPLOGISTIC Procedure
    Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
    PDF   |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF   |   HTML
  • The HPNLMOD Procedure
    Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
    PDF   |   HTML
  • The HPPLS Procedure
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
    PDF   |   HTML
  • The HPPRINCOMP Procedure
    Performs principal component analysis.
    PDF   |   HTML
  • The HPQUANTSELECT Procedure
    Fits and performs effect selection for quantile regression analysis.
    PDF   |   HTML
  • The HPREG Procedure
    Fits and performs model selection for ordinary linear least squares models.
    PDF   |   HTML
  • The HPSPLIT Procedure
    Builds tree-based statistical models for classification and regression.
    PDF   |   HTML
  • The ICLIFETEST Procedure
    Performs nonparametric survival analysis for interval-censored data.
    PDF   |   HTML
  • The ICPHREG Procedure
    Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
    PDF   |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF   |   HTML
  • The IRT Procedure
    Fits item response models.
    PDF   |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF   |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging for point-referenced spatial data.
    PDF   |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF   |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF   |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF   |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF   |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF   |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF   |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF   |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF   |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF   |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF   |   HTML
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF   |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF   |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF   |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF   |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF   |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF   |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF   |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF   |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF   |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF   |   HTML
  • The PLS Procedure
    Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
    PDF   |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF   |   HTML
  • The Power and Sample Size Application
    PDF   |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF   |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF   |   HTML
  • The PROBIT Procedure
    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.
    PDF   |   HTML
  • The QUANTLIFE Procedure
    Performs quantile regression analysis for survival data.
    PDF   |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF   |   HTML
  • The QUANTSELECT Procedure
    Performs effect selection in the framework of quantile regression.
    PDF   |   HTML
  • The REG Procedure
    General-purpose procedure for ordinary least squares regression.
    PDF   |   HTML
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF   |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF   |   HTML
  • The SCORE Procedure
    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.
    PDF   |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF   |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF   |   HTML
  • The SIM2D Procedure
    Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
    PDF   |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF   |   HTML
  • The SPP Procedure
    Analyzes spatial point pattern data and fits spatial process models.
    PDF (8.03MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF   |   HTML
  • The STDRATE Procedure
    Computes directly standardized rates and risks for study populations.
    PDF   |   HTML
  • The STEPDISC Procedure
    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.
    PDF   |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF   |   HTML
  • The SURVEYIMPUTE Procedure New Procedure!
    Imputes missing values of an item in a data set by replacing them with observed values from the same item.
    PDF   |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF   |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF   |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF   |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF   |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF   |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF   |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF   |   HTML
  • The TREE Procedure
    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.
    PDF   |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF   |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF   |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF   |   HTML
  • The VARIOGRAM Procedure
    Fits and selects spatial covariance models for point-referenced spatial data.
    PDF   |   HTML


More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

For more SAS/STAT books, visit the bookstore.


Free Related Books

Basic ODS Graphics Examples
By Warren Kuhfeld

Advanced ODS Graphics Examples
By Warren Kuhfeld

SAS/STAT 13.2 User's Guide - Procedures

For the complete SAS/STAT 13.2 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF (4.28MB)  |   HTML
  • The ADAPTIVEREG Procedure
    Fits multivariate adaptive regression splines.
    PDF (6.10MB)  |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF (6.15MB)  |   HTML
  • The BCHOICE Procedure
    Performs Bayesian analysis for discrete choice models.
    PDF (4.79MB)  |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF (2.83MB)  |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF (33.9MB)  |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF (3.6MB)  |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF (3.93MB)  |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF (10.4MB)  |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF (9.07MB)  |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF (6.04MB)  |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF (12.6MB)  |   HTML
  • The DISTANCE Procedure
    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.
    PDF (3.75MB)  |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF (10.1MB)  |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF (8.37MB)  |   HTML
  • The FMM Procedure
    Fits finite mixture models.
    PDF (10MB)  |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF (7.5MB)  |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF (4.35MB)  |   HTML
  • The GEE Procedure (Experimental) New Procedure!
    Provides weighted generalized estimating equations.
    PDF (3.61MB)  |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF (10.5MB)  |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF (19.8MB)  |   HTML
  • The GLM Procedure
    Fits general linear models.
    PDF (16.1MB)  |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF (3.43MB)  |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF (4.23MB)  |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF (12.2MB)  |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF (4.6MB)  |   HTML
  • The ICLIFETEST Procedure New Procedure!
    Performs nonparametric survival analysis for interval-censored data.
    PDF (3.09MB)  |   HTML
  • The ICPHREG Procedure
    Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
    PDF (3.82MB)  |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF (3.73MB)  |   HTML
  • The IRT Procedure
    Fits item response models.
    PDF (5.81MB)  |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF (4.6MB)  |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging for point-referenced spatial data.
    PDF (8.18MB)  |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF (2.42MB)  |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF (7.91MB)  |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF (5.95MB)  |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF (5.28MB)  |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF (20.7MB)  |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF (11.7MB)  |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF (3.6MB)  |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF (10.9MB)  |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF (5.99MB)  |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF (13.9MB)  |   HTML
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF (9.66MB)  |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF (4.92MB)  |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF (2.46MB)  |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF (7.12MB)  |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF (7.5MB)  |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF (5.86MB)  |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF (3.5MB)  |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF (14.3MB)  |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF (5.29MB)  |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF (5.23MB)  |   HTML
  • The PLS Procedure
    Performs principal component regression.
    PDF (6.62MB)  |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF (10MB)  |   HTML
  • The Power and Sample Size Application
    PDF (4.26MB)  |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF (5.4MB)  |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF (4.39MB)  |   HTML
  • The PROBIT Procedure
    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.
    PDF (6.87MB)  |   HTML
  • The QUANTLIFE Procedure
    Performs quantile regression analysis for survival data.
    PDF (3.62MB)  |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF (6.79MB)  |   HTML
  • The QUANTSELECT Procedure
    Performs effect selection in the framework of quantile regression.
    PDF (6.36MB)  |   HTML
  • The REG Procedure
    General-purpose procedure for ordinary least squares regression.
    PDF (14.9MB)  |   HTML
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF (9.69MB)  |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF (4.47MB)  |   HTML
  • The SCORE Procedure
    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.
    PDF (3.97MB)  |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF (15.3MB)  |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF (15.5MB)  |   HTML
  • The SIM2D Procedure
    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.
    PDF (5.4MB)  |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF (3.33MB)  |   HTML
  • The SPP Procedure New Procedure!
    Analyzes spatial point pattern data and fits spatial process models.
    PDF (8.03MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF (3.39MB)  |   HTML
  • The STDRATE Procedure
    Computes directly standardized rates and risks for study populations.
    PDF (5.15MB)  |   HTML
  • The STEPDISC Procedure
    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.
    PDF (5.05MB)  |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (3.8MB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF (4.47MB)  |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (4.48MB)  |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF (6.14MB)  |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF (7.57MB)  |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF (4.06MB)  |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF (6.85MB)  |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF (27.4MB)  |   HTML
  • The TREE Procedure
    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.
    PDF (3.35MB)  |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF (6.02MB)  |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF (4.55MB)  |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF (3.97MB)  |   HTML
  • The VARIOGRAM Procedure
    Fits and selects spatial covariance models for point-referenced spatial data.
    PDF (7.76MB)  |   HTML


More about This Product

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Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
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SAS/STAT 13.1 User's Guide - Procedures

For the complete SAS/STAT 13.1 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF (2.39MB)  |   HTML
  • The ADAPTIVEREG Procedure
    Fits multivariate adaptive regression splines.
    PDF (4.42MB)  |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF (2.26MB)  |   HTML
  • The BCHOICE Procedure (Experimental)
    Performs Bayesian analysis for discrete choice models.
    PDF (2.71MB)  |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF (2.72MB)  |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF (7.62MB)  |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF (2.11MB)  |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF (2.23MB)  |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF (2.45MB)  |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF (4.44MB)  |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF (2.57MB)  |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF (3.91MB)  |   HTML
  • The DISTANCE Procedure
    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.
    PDF (2.59MB)  |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF (2.78MB)  |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF (2.66MB)  |   HTML
  • The FMM Procedure
    Fits finite mixture models.
    PDF (3.74MB)  |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF (3.33MB)  |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF (3.01MB)  |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF (4.42MB)  |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF (6.11MB)  |   HTML
  • The GLM Procedure
    Fits general linear models.
    PDF (3.75MB)  |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF (2.03MB)  |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF (2.29MB)  |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF (5.91MB)  |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF (2.58MB)  |   HTML
  • The ICLIFETEST Procedure
    Performs nonparametric survival analysis for interval-censored data.
    PDF (2.75MB)  |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF (2.12MB)  |   HTML
  • The IRT Procedure (Experimental)
    Fits item response models.
    PDF (2.56MB)  |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF (3.78MB)  |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging in two dimensions.
    PDF (7.06MB)  |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF (2.0MB)  |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF (3.68MB)  |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF (3.53MB)  |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF (4.31MB)  |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF (5.02MB)  |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF (6.07MB)  |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF (3.16MB)  |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF (3.21MB)  |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF (2.23MB)  |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF (4.17MB)  |   HTML
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF (5.95MB)  |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF (2.97MB)  |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF (2.05MB)  |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF (3.66MB)  |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF (2.81MB)  |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF (2.49MB)  |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF (2.20MB)  |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF (4.39MB)  |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF (2.09MB)  |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF (2.53MB)  |   HTML
  • The PLS Procedure
    Performs principal component regression.
    PDF (4.62MB)  |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF (7.03MB)  |   HTML
  • The Power and Sample Size Application
    PDF (4.26MB)  |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF (3.79MB)  |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF (3.58MB)  |   HTML
  • The PROBIT Procedure
    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.
    PDF (3.22MB)  |   HTML
  • The QUANTLIFE Procedure
    Performs quantile regression analysis for survival data.
    PDF (2.78MB)  |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF (4.77MB)  |   HTML
  • The QUANTSELECT Procedure
    Performs effect selection in the framework of quantile regression.
    PDF (3.17MB)  |   HTML
  • The REG Procedure
    General-purpose procedure for ordinary least squares regression.
    PDF (6.92MB)  |   HTML
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF (4.15MB)  |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF (2.67MB)  |   HTML
  • The SCORE Procedure
    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.
    PDF (2.08MB)  |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF (6.74MB)  |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF (5.54MB)  |   HTML
  • The SIM2D Procedure
    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.
    PDF (4.13MB)  |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF (2.17MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF (2.19MB)  |   HTML
  • The STDRATE Procedure
    Computes directly standardized rates and risks for study populations.
    PDF (3.02MB)  |   HTML
  • The STEPDISC Procedure
    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.
    PDF (2.07MB)  |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (2.67MB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF (2.50MB)  |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (2.55MB)  |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF (2.42MB)  |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF (3.0MB)  |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF (2.35MB)  |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF (4.19MB)  |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF (10.0MB)  |   HTML
  • The TREE Procedure
    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.
    PDF (2.54MB)  |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF (4.04MB)  |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF (2.15MB)  |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF (2.16MB)  |   HTML
  • The VARIOGRAM Procedure
    Computes empirical measures of spatial continuity for two-dimensional spatial data.
    PDF (5.43MB)  |   HTML


More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

For more SAS/STAT books, visit the bookstore.


Free Related Books

Basic ODS Graphics Examples
By Warren Kuhfeld

Advanced ODS Graphics Examples
By Warren Kuhfeld

SAS/STAT 12.3 User's Guide - Procedures

Note: SAS/STAT 12.3 is essentially a maintenance release, with the exception that high-performance procedures for use in single-machine mode have been added. The SAS/STAT 12.3 documentation applies to both SAS/STAT 12.3 and 12.1.


For the complete SAS/STAT 12.3 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF (2.35MB)  |   HTML
  • The ADAPTIVEREG Procedure (Experimental)
    Fits multivariate adaptive regression splines.
    PDF (4.19MB)  |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF (2.21MB)  |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF (2.66MB)  |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF (4.7MB)  |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF (2.07MB)  |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF (2.22MB)  |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF (2.41MB)  |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF (4.41MB)  |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF (2.53MB)  |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF (3.86MB)  |   HTML
  • The DISTANCE Procedure
    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.
    PDF (2.54MB)  |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF (2.74MB)  |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF (2.62MB)  |   HTML
  • The FMM Procedure
    Fits finite mixture models.
    PDF (3.22MB)  |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF (3.24MB)  |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF (2.98MB)  |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF (4.35MB)  |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF (6.04MB)  |   HTML
  • The GLM Procedure
    Fits general linear models.
    PDF (3.71MB)  |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF (1.98MB)  |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF (2.92MB)  |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF (4.76MB)  |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF (2.43MB)  |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF (2.09MB)  |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF (3.67MB)  |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging in two dimensions.
    PDF (6.99MB)  |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF (1.98MB)  |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF (3.6MB)  |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF (3.49MB)  |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF (4.22MB)  |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF (4.82MB)  |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF (6.02MB)  |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF (3.13MB)  |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF (2.93MB)  |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF (2.17MB)  |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF (3.93MB)  |   HTML
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF (5.74MB)  |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF (2.87MB)  |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF (2.01MB)  |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF (3.26MB)  |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF (2.54MB)  |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF (2.45MB)  |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF (2.16MB)  |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF (4.2MB)  |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF (2.05MB)  |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF (2.47MB)  |   HTML
  • The PLS Procedure
    Performs principal component regression.
    PDF (4.47MB)  |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF (6.89MB)  |   HTML
  • The Power and Sample Size Application
    PDF (4.23MB)  |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF (3.78MB)  |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF (3.6MB)  |   HTML
  • The PROBIT Procedure
    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.
    PDF (3.13MB)  |   HTML
  • The QUANTLIFE Procedure (Experimental)
    Performs quantile regression analysis for survival data.
    PDF (2.7MB)  |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF (4.27MB)  |   HTML
  • The QUANTSELECT Procedure (Experimental)
    Performs effect selection in the framework of quantile regression.
    PDF (2.49MB)  |   HTML
  • The REG Procedure
    General-purpose procedure for ordinary least squares regression.
    PDF (7.03MB)  |   HTML
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF (3.92MB)  |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF (2.6MB)  |   HTML
  • The SCORE Procedure
    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.
    PDF (2.05MB)  |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF (6.75MB)  |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF (5.53MB)  |   HTML
  • The SIM2D Procedure
    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.
    PDF (4.07MB)  |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF (2.13MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF (2.16MB)  |   HTML
  • The STDRATE Procedure
    Computes directly standardized rates and risks for study populations.
    PDF (2.92MB)  |   HTML
  • The STEPDISC Procedure
    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.
    PDF (2.02MB)  |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (2.6MB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF (2.45MB)  |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (2.31MB)  |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF (2.38MB)  |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF (2.43MB)  |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF (2.29MB)  |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF (4.2MB)  |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF (9.91MB)  |   HTML
  • The TREE Procedure
    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.
    PDF (2.52MB)  |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF (4.01MB)  |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF (2.12MB)  |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF (2.12MB)  |   HTML
  • The VARIOGRAM Procedure
    Computes empirical measures of spatial continuity for two-dimensional spatial data.
    PDF (5.36MB)  |   HTML


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Categorical Data Analysis Using SAS, Third Edition
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SAS/STAT 12.1 User's Guide - Procedures

For the complete SAS/STAT 12.1 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF (10.1MB)  |   HTML
  • The ADAPTIVEREG Procedure (Experimental)
    Fits multivariate adaptive regression splines.
    PDF (11.9MB)  |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF (9.99MB)  |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF (10.4MB)  |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF (12.2MB)  |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF (9.86MB)  |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF (10MB)  |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF (10.1MB)  |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF (12.1MB)  |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF (10.3MB)  |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF (11.6MB)  |   HTML
  • The DISTANCE Procedure
    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.
    PDF (10.3MB)  |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF (10.5MB)  |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF (10.4MB)  |   HTML
  • The FMM Procedure
    Fits finite mixture models.
    PDF (11MB)  |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF (10.9MB)  |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF (10.7MB)  |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF (12MB)  |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF (13.7MB)  |   HTML
  • The GLM Procedure
    Fits general linear models.
    PDF (11.4MB)  |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF (9.8MB)  |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF (10.7MB)  |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF (12.5MB)  |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF (10.2MB)  |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF (9.9MB)  |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF (11.4MB)  |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging in two dimensions.
    PDF (14.7MB)  |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF (9.8MB)  |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF (11.3MB)  |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF (11.2MB)  |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF (12MB)  |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF (12.5MB)  |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF (13.7MB)  |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF (10.9MB)  |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF (10.6MB)  |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF (10MB)  |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF (11.8MB)  |   HTML
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF (13.5MB)  |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF (10.6MB)  |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF (9.8MB)  |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF (11MB)  |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF (10.3MB)  |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF (10.2MB)  |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF (10.0MB)  |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF (11.9MB)  |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF (9.8MB)  |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF (10.2MB)  |   HTML
  • The PLS Procedure
    Performs principal component regression.
    PDF (12.2MB)  |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF (14.7MB)  |   HTML
  • The Power and Sample Size Application
    PDF (12MB)  |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF (11.5MB)  |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF (11.3MB)  |   HTML
  • The PROBIT Procedure
    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.
    PDF (10.8MB)  |   HTML
  • The QUANTLIFE Procedure (Experimental)
    Performs quantile regression analysis for survival data.
    PDF (10.5MB)  |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF (12MB)  |   HTML
  • The QUANTSELECT Procedure (Experimental)
    Performs effect selection in the framework of quantile regression.
    PDF (10.2MB)  |   HTML
  • The REG Procedure
    General-purpose procedure for ordinary least squares regression.
    PDF (14.7MB)  |   HTML
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF (11.6MB)  |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF (10.4MB)  |   HTML
  • The SCORE Procedure
    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.
    PDF (9.8MB)  |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF (14.4MB)  |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF (13.2MB)  |   HTML
  • The SIM2D Procedure
    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.
    PDF (11.8MB)  |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF (9.9MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF (9.9MB)  |   HTML
  • The STDRATE Procedure
    Computes directly standardized rates and risks for study populations.
    PDF (10.7MB)  |   HTML
  • The STEPDISC Procedure
    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.
    PDF (9.8MB)  |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (10.3MB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF (10.2MB)  |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (10MB)  |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF (10.1MB)  |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF (10.2MB)  |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF (10MB)  |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF (11.9MB)  |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF (17.6MB)  |   HTML
  • The TREE Procedure
    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.
    PDF (10.3MB)  |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF (11.7MB)  |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF (9.9MB)  |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF (9.9MB)  |   HTML
  • The VARIOGRAM Procedure
    Computes empirical measures of spatial continuity for two-dimensional spatial data.
    PDF (13.1MB)  |   HTML


More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Categorical Data Analysis Using SAS, Third Edition
By Maura Stokes, Charles Davis, and Gary Koch

SAS for Mixed Models, Second Edition
By Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger

Statistical Programming in SAS
By John Bailer

Survival Analysis Using SAS: A Practical Guide, Second Edition
By Paul Allison

For more SAS/STAT books, visit the bookstore.


Free Related Books

Basic ODS Graphics Examples
By Warren Kuhfeld

Advanced ODS Graphics Examples
By Warren Kuhfeld

SAS/STAT 9.3 User's Guide - Procedures

For the complete SAS/STAT 9.3 User's Guide, go to the SAS/STAT product documentation page.

  • The ACECLUS Procedure
    Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
    PDF (10.1MB)  |   HTML
  • The ANOVA Procedure
    Performs analysis of variance for balanced data.
    PDF (10.1MB)  |   HTML
  • The BOXPLOT Procedure
    Creates side-by-side box-and-whiskers plots of measurements organized in groups.
    PDF (10.7MB)  |   HTML
  • The CALIS Procedure
    Fits structural equation models.
    PDF (12.5MB)  |   HTML
  • The CANCORR Procedure
    Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
    PDF (9.92MB)  |   HTML
  • The CANDISC Procedure
    Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
    PDF (10MB)  |   HTML
  • The CATMOD Procedure
    Performs categorical data modeling of data that can be represented by a contingency table.
    PDF (10.3MB)  |   HTML
  • The CLUSTER Procedure
    Hierarchically clusters the observations in a SAS data.
    PDF (12.2MB)  |   HTML
  • The CORRESP Procedure
    Performs simple correspondence analysis and multiple correspondence analysis (MCA).
    PDF (10.3MB)  |   HTML
  • The DISCRIM Procedure
    Develops a discriminant criterion to classify each observation into groups.
    PDF (11.7MB)  |   HTML
  • The DISTANCE Procedure
    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.
    PDF (10.4MB)  |   HTML
  • The FACTOR Procedure
    Performs a variety of common factor and component analyses and rotations.
    PDF (10.6MB)  |   HTML
  • The FASTCLUS Procedure
    Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
    PDF (10.4MB)  |   HTML
  • The FMM Procedure (Experimental)
    Fits finite mixture models.
    PDF (11.1MB)  |   HTML
  • The FREQ Procedure
    Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
    PDF (11.2MB)  |   HTML
  • The GAM Procedure
    Fits generalized additive models.
    PDF (10.7MB)  |   HTML
  • The GENMOD Procedure
    Fits generalized linear models.
    PDF (12.3MB)  |   HTML
  • The GLIMMIX Procedure
    Fits generalized linear mixed models.
    PDF (14.36MB)  |   HTML  |   Purchase chapter
  • The GLM Procedure
    Fits general linear models.
    PDF (11.8MB)  |   HTML
  • The GLMMOD Procedure
    Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
    PDF (9.84MB)  |   HTML
  • The GLMPOWER Procedure
    Performs prospective power and sample size analysis for linear models.
    PDF (10.8MB)  |   HTML
  • The GLMSELECT Procedure
    Performs effect selection in the framework of general linear models.
    PDF (12.6MB)  |   HTML
  • The HPMIXED Procedure
    Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
    PDF (10.3MB)  |   HTML
  • The INBREED Procedure
    Calculates the covariance or inbreeding coefficients for a pedigree.
    PDF (9.92B)  |   HTML
  • The KDE Procedure
    Performs univariate and bivariate kernel density estimation.
    PDF (11.4MB)  |   HTML
  • The KRIGE2D Procedure
    Performs ordinary kriging in two dimensions.
    PDF (14.9MB)  |   HTML
  • The LATTICE Procedure
    Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
    PDF (9.8MB)  |   HTML
  • The LIFEREG Procedure
    Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
    PDF (11.2MB)  |   HTML
  • The LIFETEST Procedure
    Computes 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).
    PDF (11.2MB)  |   HTML
  • The LOESS Procedure
    Implements a nonparametric method for estimating regression surfaces.
    PDF (12.0MB)  |   HTML
  • The LOGISTIC Procedure
    Fits regression models with binary, ordinal, or nominal dependent variables.
    PDF (12.8MB)  |   HTML
  • The MCMC Procedure
    Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
    PDF (14.3MB)  |   HTML
  • The MDS Procedure
    Fits two- and three-way, metric and nonmetric multidimensional scaling models.
    PDF (11.0MB)  |   HTML
  • The MI Procedure
    Performs multiple imputation of missing data.
    PDF (10.7MB)  |   HTML
  • The MIANALYZE Procedure
    Combines the results of the analyses of imputations and generates valid statistical inferences.
    PDF (9.98MB)  |   HTML
  • The MIXED Procedure
    Fits general linear models with fixed and random effects.
    PDF (12.26MB)  |   HTML  |   Purchase chapter
  • The MODECLUS Procedure
    Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
    PDF (13.6MB)  |   HTML
  • The MULTTEST Procedure
    Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
    PDF (10.7MB)  |   HTML
  • The NESTED Procedure
    Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
    PDF (9.84MB)  |   HTML
  • The NLIN Procedure
    Fits nonlinear regression models.
    PDF (10.8MB)  |   HTML
  • The NLMIXED Procedure
    Fits mixed models in which the fixed or random effects enter nonlinearly.
    PDF (10.4MB)  |   HTML
  • The NPAR1WAY Procedure
    Performs nonparametric tests for location and scale differences across a one-way classification.
    PDF (10.2MB)  |   HTML
  • The ORTHOREG Procedure
    Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
    PDF (10.0MB)  |   HTML
  • The PHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model.
    PDF (12.0MB)  |   HTML
  • The PLAN Procedure
    Constructs designs and randomizes plans for factorial experiments.
    PDF (9.91MB)  |   HTML
  • The PLM Procedure
    Performs postfitting statistical analyses.
    PDF (10.3MB)  |   HTML
  • The PLS Procedure
    Performs principal component regression.
    PDF (12.3MB)  |   HTML
  • The POWER Procedure
    Performs prospective power and sample size analyses for a variety of statistical analyses.
    PDF (15.3MB)  |   HTML
  • The Power and Sample Size Application
    PDF (12.2MB)  |   HTML
  • The PRINCOMP Procedure
    Performs principal component analysis.
    PDF (11.9MB)  |   HTML
  • The PRINQUAL Procedure
    Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
    PDF (11.2MB)  |   HTML
  • The PROBIT Procedure
    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.
    PDF (10.9MB)  |   HTML
  • The QUANTREG Procedure
    Fits quantile regression models.
    PDF (12.0MB)  |   HTML
  • The REG Procedure
    General purpose procedure for ordinary least squares regression.
    PDF (14.7MB)  |   HTML  |   Purchase chapter
  • The ROBUSTREG Procedure
    Provides resistant (stable) results for linear regression models in the presence of outliers.
    PDF (11.7MB)  |   HTML
  • The RSREG Procedure
    Fits quadratic response surface regression models.
    PDF (10.4MB)  |   HTML
  • The SCORE Procedure
    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.
    PDF (9.88MB)  |   HTML
  • The SEQDESIGN Procedure
    Designs interim analyses for group sequential clinical trials.
    PDF (14.5MB)  |   HTML
  • The SEQTEST Procedure
    Performs interim analyses for group sequential clinical trials.
    PDF (13.0MB)  |   HTML
  • The SIM2D Procedure
    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.
    PDF (11.9MB)  |   HTML
  • The SIMNORMAL Procedure
    Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
    PDF (9.92MB)  |   HTML
  • The STDIZE Procedure
    Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
    PDF (9.98MB)  |   HTML
  • The STEPDISC Procedure
    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.
    PDF (9.87MB)  |   HTML
  • The SURVEYFREQ Procedure
    Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (10.5MB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
    PDF (10.3MB)  |   HTML
  • The SURVEYMEANS Procedure
    Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
    PDF (10.1MB)  |   HTML
  • The SURVEYPHREG Procedure
    Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
    PDF (10.3MB)  |   HTML
  • The SURVEYREG Procedure
    Performs linear regression analysis for complex survey sample designs.
    PDF (10.3MB)  |   HTML
  • The SURVEYSELECT Procedure
    Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
    PDF (10.2MB)  |   HTML
  • The TPSPLINE Procedure
    Provides penalized least squares estimates.
    PDF (12.0MB)  |   HTML
  • The TRANSREG Procedure
    Fits linear models with optimal nonlinear transformations of variables.
    PDF (18.1MB)  |   HTML
  • The TREE Procedure
    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.
    PDF (10.5MB)  |   HTML
  • The TTEST Procedure
    Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
    PDF (11.9MB)  |   HTML
  • The VARCLUS Procedure
    Divides a set of numeric variables into disjoint or hierarchical clusters.
    PDF (9.95MB)  |   HTML
  • The VARCOMP Procedure
    Fits general linear models with random effects (with the option of specifying certain effects as fixed).
    PDF (9.95MB)  |   HTML
  • The VARIOGRAM Procedure
    Computes empirical measures of spatial continuity for two-dimensional spatial data.
    PDF (3.15MB)  |   HTML


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For the complete SAS/STAT User's Guide, go to the SAS/STAT product documentation page.

SAS/STAT 9.22 User's Guide - Procedures

  • The ACECLUS Procedure
    PDF (10.2MB)  |   HTML
  • The ANOVA Procedure
    PDF (13.0MB)  |   HTML
  • The BOXPLOT Procedure
    PDF (10.6MB)  |   HTML
  • The CALIS Procedure
    PDF (12.5MB)  |   HTML
  • The CANCORR Procedure
    PDF (9.9MB)  |   HTML
  • The CANDISC Procedure
    PDF (10.1MB)  |   HTML
  • The CATMOD Procedure
    PDF (10.4MB)  |   HTML
  • The CLUSTER Procedure
    PDF (12.5MB)  |   HTML
  • The CORRESP Procedure
    PDF (10.4MB)  |   HTML
  • The DISCRIM Procedure
    PDF (11.7MB)  |   HTML
  • The DISTANCE Procedure
    PDF (10.5MB)  |   HTML
  • The FACTOR Procedure
    PDF (10.7MB)  |   HTML
  • The FASTCLUS Procedure
    PDF (10.4MB)  |   HTML
  • The FREQ Procedure
    PDF (11.0MB)  |   HTML
  • The GAM Procedure
    PDF (10.8MB)  |   HTML
  • The GENMOD Procedure
    PDF (12.3MB)  |   HTML
  • The GLIMMIX Procedure
    PDF (14.2MB)  |   HTML
  • The GLM Procedure
    PDF (11.8MB)  |   HTML
  • The GLMMOD Procedure
    PDF (9.8MB)  |   HTML
  • The GLMPOWER Procedure
    PDF (10.5MB)  |   HTML
  • The GLMSELECT Procedure
    PDF (12.6MB)  |   HTML
  • The HPMIXED Procedure (Experimental)
    PDF (10.3MB)  |   HTML
  • The INBREED Procedure
    PDF (9.9MB)  |   HTML
  • The KDE Procedure
    PDF (11.4MB)  |   HTML
  • The KRIGE2D Procedure
    PDF (14.7MB)  |   HTML
  • The LATTICE Procedure
    PDF (9.8MB)  |   HTML
  • The LIFEREG Procedure
    PDF (11.2MB)  |   HTML
  • The LIFETEST Procedure
    PDF (11.2B)  |   HTML
  • The LOESS Procedure
    PDF (11.9MB)  |   HTML
  • The LOGISTIC Procedure
    PDF (12.8B)  |   HTML
  • The MCMC Procedure
    PDF (14.0MB)  |   HTML
  • The MDS Procedure
    PDF (11.0MB)  |   HTML
  • The MI Procedure
    PDF (10.4MB)  |   HTML
  • The MIANALYZE Procedure
    PDF (10.0MB)  |   HTML
  • The MIXED Procedure
    PDF (12.3MB)  |   HTML
  • The MODECLUS Procedure
    PDF (13.6MB)  |   HTML
  • The MULTTEST Procedure
    PDF (10.7MB)  |   HTML
  • The NESTED Procedure
    PDF (9.8MB)  |   HTML
  • The NLIN Procedure
    PDF (10.5MB)  |   HTML
  • The NLMIXED Procedure
    PDF (10.4MB)  |   HTML
  • The NPAR1WAY Procedure
    PDF (10.3MB)  |   HTML
  • The ORTHOREG Procedure
    PDF (10.0MB)  |   HTML
  • The PHREG Procedure
    PDF (12.0MB)  |   HTML
  • The PLAN Procedure
    PDF (9.9MB)  |   HTML
  • The PLM Procedure
    PDF (10.3MB)  |   HTML
  • The PLS Procedure
    PDF (12.1MB)  |   HTML
  • The POWER Procedure
    PDF (14.2MB)  |   HTML
  • The Power and Sample Size Application
    PDF (12.2MB)  |   HTML
  • The PRINCOMP Procedure
    PDF (11.9B)  |   HTML
  • The PRINQUAL Procedure
    PDF (11.3MB)  |   HTML
  • The PROBIT Procedure
    PDF (11.3B)  |   HTML
  • The QUANTREG Procedure
    PDF (12.0MB)  |   HTML
  • The REG Procedure
    PDF (15.1MB)  |   HTML
  • The ROBUSTREG Procedure
    PDF (11.6MB)  |   HTML
  • The RSREG Procedure
    PDF (10.4MB)  |   HTML
  • The SCORE Procedure
    PDF (9.9MB)  |   HTML
  • The SEQDESIGN Procedure
    PDF (14.3MB)  |   HTML
  • The SEQTEST Procedure
    PDF (13.0MB)  |   HTML
  • The SIM2D Procedure
    PDF (11.8MB)  |   HTML
  • The SIMNORMAL Procedure
    PDF (9.9MB)  |   HTML
  • The STDIZE Procedure
    PDF (10.0MB)  |   HTML
  • The STEPDISC Procedure
    PDF (9.9MB)  |   HTML
  • The SURVEYFREQ Procedure
    PDF (10.5MB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    PDF (10.4MB)  |   HTML
  • The SURVEYMEANS Procedure
    PDF (10.2MB)  |   HTML
  • The SURVEYPHREG Procedure
    PDF (10.3MB)  |   HTML
  • The SURVEYREG Procedure
    PDF (10.4MB)  |   HTML
  • The SURVEYSELECT Procedure
    PDF (10.2MB)  |   HTML
  • The TPSPLINE Procedure
    PDF (11.8MB)  |   HTML
  • The TRANSREG Procedure
    PDF (18.2MB)  |   HTML
  • The TREE Procedure
    PDF (10.0MB)  |   HTML
  • The TTEST Procedure
    PDF (11.8MB)  |   HTML
  • The VARCLUS Procedure
    PDF (10.0MB)  |   HTML
  • The VARCOMP Procedure
    PDF (9.9MB)  |   HTML
  • The VARIOGRAM Procedure
    PDF (13.4MB)  |   HTML

SAS/STAT 9.2

  • The ACECLUS Procedure
    PDF (1.0MB)  |   HTML
  • The ANOVA Procedure
    PDF (955KB)  |   HTML
  • The BOXPLOT Procedure
    PDF (1.33MB)  |   HTML
  • The CALIS Procedure
    PDF (1.62MB)  |   HTML
  • The CANCORR Procedure
    PDF (698KB)  |   HTML
  • The CANDISC Procedure
    PDF (441KB)  |   HTML
  • The CATMOD Procedure
    PDF (1.24MB)  |   HTML
  • The CLUSTER Procedure
    PDF (2.70MB)  |   HTML
  • The CORRESP Procedure
    PDF (1.19MB)  |   HTML
  • The DISCRIM Procedure
    PDF (2.50MB)  |   HTML
  • The DISTANCE Procedure
    PDF (937KB)  |   HTML
  • The FACTOR Procedure
    PDF (1.42MB)  |   HTML
  • The FASTCLUS Procedure
    PDF (1.24MB)  |   HTML
  • The FREQ Procedure
    PDF (2.14MB)  |   HTML
  • The GAM Procedure
    PDF (1.75MB)  |   HTML
  • The GENMOD Procedure
    PDF (3.07MB)  |   HTML
  • The GLIMMIX Procedure
    PDF (5.27MB)  |   HTML
  • The GLM Procedure
    PDF (2.74MB)  |   HTML
  • The GLMMOD Procedure
    PDF (612KB)  |   HTML
  • The GLMPOWER Procedure
    PDF (912KB)  |   HTML
  • The GLMSELECT Procedure
    PDF (2.72MB)  |   HTML
  • The HPMIXED Procedure (Experimental)
    PDF (642KB)  |   HTML
  • The INBREED Procedure
    PDF (289KB)  |   HTML
  • The KDE Procedure
    PDF (1.79MB)  |   HTML
  • The KRIGE2D Procedure
    PDF (2.40MB)  |   HTML
  • The LATTICE Procedure
    PDF (160KB)  |   HTML
  • The LIFEREG Procedure
    PDF (1.63MB)  |   HTML
  • The LIFETEST Procedure
    PDF (1.64MB)  |   HTML
  • The LOESS Procedure
    PDF (2.34MB)  |   HTML
  • The LOGISTIC Procedure
    PDF (3.14MB)  |   HTML
  • The MCMC Procedure
    PDF (5.16MB)  |   HTML
  • The MDS Procedure
    PDF (1.36MB)  |   HTML
  • The MI Procedure
    PDF (819KB)  |   HTML
  • The MIANALYZE Procedure
    PDF (360KB)  |   HTML
  • The MIXED Procedure
    PDF (2.75MB)  |   HTML
  • The MODECLUS Procedure
    PDF (4.49MB)  |   HTML
  • The MULTTEST Procedure
    PDF (520KB)  |   HTML
  • The NESTED Procedure
    PDF (209KB)  |   HTML
  • The NLIN Procedure
    PDF (923KB)  |   HTML
  • The NLMIXED Procedure
    PDF (821KB)  |   HTML
  • The NPAR1WAY Procedure
    PDF (688KB)  |   HTML
  • The ORTHOREG Procedure
    PDF (209KB)  |   HTML
  • The PHREG Procedure
    PDF (2.41MB)  |   HTML
  • The PLAN Procedure
    PDF (286KB)  |   HTML
  • The PLS Procedure
    PDF (1.70MB)  |   HTML
  • The POWER Procedure
    PDF (4.52MB)  |   HTML
  • The PRINCOMP Procedure
    PDF (2.28MB)  |   HTML
  • The PRINQUAL Procedure
    PDF (1.73MB)  |   HTML
  • The PROBIT Procedure
    PDF (1.43MB)  |   HTML
  • The QUANTREG Procedure
    PDF (2.49MB)  |   HTML
  • The REG Procedure
    PDF (5.70MB)  |   HTML
  • The ROBUSTREG Procedure
    PDF (1.26MB)  |   HTML
  • The RSREG Procedure
    PDF (826KB)  |   HTML
  • The SCORE Procedure
    PDF (241KB)  |   HTML
  • The SEQDESIGN Procedure
    PDF (5.42MB)  |   HTML
  • The SEQTEST Procedure
    PDF (4.09MB)  |   HTML
  • The SIM2D Procedure
    PDF (710KB)  |   HTML
  • The SIMNORMAL Procedure
    PDF (276KB)  |   HTML
  • The STDIZE Procedure
    PDF (372KB)  |   HTML
  • The STEPDISC Procedure
    PDF (251KB)  |   HTML
  • The SURVEYFREQ Procedure
    PDF (669KB)  |   HTML
  • The SURVEYLOGISTIC Procedure
    PDF (721KB)  |   HTML
  • The SURVEYMEANS Procedure
    PDF (553KB)  |   HTML
  • The SURVEYREG Procedure
    PDF (587KB)  |   HTML
  • The SURVEYSELECT Procedure
    PDF (540KB)  |   HTML
  • The TCALIS Procedure (Experimental)
    PDF (2.21MB)  |   HTML
  • The TPSPLINE Procedure
    PDF (999KB)  |   HTML
  • The TRANSREG Procedure
    PDF (9.26MB)  |   HTML
  • The TREE Procedure
    PDF (326KB)  |   HTML
  • The TTEST Procedure
    PDF (2.18MB)  |   HTML
  • The VARCLUS Procedure
    PDF (295KB)  |   HTML
  • The VARCOMP Procedure
    PDF (292KB)  |   HTML
  • The VARIOGRAM Procedure
    PDF (3.06MB)  |   HTML