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Chapter Contents
Introduction to Regression Procedures


Many SAS/STAT procedures, each with special features, perform regression analysis. The following procedures perform at least one type of regression analysis:

analyzes data that can be represented by a contingency table. PROC CATMOD fits linear models to functions of response frequencies, and it can be used for linear and logistic regression. The CATMOD procedure is discussed in detail in Chapter 5, "Introduction to Categorical Data Analysis Procedures."

fits generalized linear models. PROC GENMOD is especially suited for responses with discrete outcomes, and it performs logistic regression and Poisson regression as well as fitting Generalized Estimating Equations for repeated measures data. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," and Chapter 29, "The GENMOD Procedure," for more information.

uses the method of least squares to fit general linear models. In addition to many other analyses, PROC GLM can perform simple, multiple, polynomial, and weighted regression. PROC GLM has many of the same input/output capabilities as PROC REG, but it does not provide as many diagnostic tools or allow interactive changes in the model or data. See Chapter 4, "Introduction to Analysis-of-Variance Procedures," for a more detailed overview of the GLM procedure.

fits parametric models to failure-time data that may be right censored. These types of models are commonly used in survival analysis. See Chapter 10, "Introduction to Survival Analysis Procedures," for a more detailed overview of the LIFEREG procedure.

fits logistic models for binomial and ordinal outcomes. PROC LOGISTIC provides a wide variety of model-building methods and computes numerous regression diagnostics. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," for a brief comparison of PROC LOGISTIC with other procedures.

builds nonlinear regression models. Several different iterative methods are available.

performs regression using the Gentleman-Givens computational method. For ill-conditioned data, PROC ORTHOREG can produce more accurate parameter estimates than other procedures such as PROC GLM and PROC REG.

performs partial least squares regression, principal components regression, and reduced rank regression, with cross validation for the number of components.

performs probit regression as well as logistic regression and ordinal logistic regression. The PROBIT procedure is useful when the dependent variable is either dichotomous or polychotomous and the independent variables are continuous.

performs linear regression with many diagnostic capabilities, selects models using one of nine methods, produces scatter plots of raw data and statistics, highlights scatter plots to identify particular observations, and allows interactive changes in both the regression model and the data used to fit the model.

builds quadratic response-surface regression models. PROC RSREG analyzes the fitted response surface to determine the factor levels of optimum response and performs a ridge analysis to search for the region of optimum response.

fits univariate and multivariate linear models, optionally with spline and other nonlinear transformations. Models include ordinary regression and ANOVA, multiple and multivariate regression, metric and nonmetric conjoint analysis, metric and nonmetric vector and ideal point preference mapping, redundancy analysis, canonical correlation, and response surface regression.

Several SAS/ETS procedures also perform regression. The following procedures are documented in the SAS/ETS User's Guide.

implements regression models using time-series data where the errors are autocorrelated.

performs regression analysis with polynomial distributed lags.

handles linear simultaneous systems of equations, such as econometric models.

handles nonlinear simultaneous systems of equations, such as econometric models.

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