Subsections:

- Introduction
- Introductory Example: Linear Regression
- Model Selection Methods
- Linear Regression: The REG Procedure
- Model Selection: The GLMSELECT Procedure
- Response Surface Regression: The RSREG Procedure
- Partial Least Squares Regression: The PLS Procedure
- Generalized Linear Regression
- Ill-Conditioned Data: The ORTHOREG Procedure
- Quantile Regression: The QUANTREG and QUANTSELECT Procedures
- Nonlinear Regression: The NLIN and NLMIXED Procedures
- Nonparametric Regression
- Robust Regression: The ROBUSTREG Procedure
- Regression with Transformations: The TRANSREG Procedure
- Interactive Features in the CATMOD, GLM, and REG Procedures

This chapter provides an overview of SAS/STAT procedures that perform regression analysis. The REG procedure provides extensive capabilities for fitting linear regression models that involve individual numeric independent variables. Many other procedures can also fit regression models, but they focus on more specialized forms of regression, such as robust regression, generalized linear regression, nonlinear regression, nonparametric regression, quantile regression, regression modeling of survey data, regression modeling of survival data, and regression modeling of transformed variables. The SAS/STAT procedures that can fit regression models include the ADAPTIVEREG, CATMOD, GAM, GENMOD, GLIMMIX, GLM, GLMSELECT, LIFEREG, LOESS, LOGISTIC, MIXED, NLIN, NLMIXED, ORTHOREG, PHREG, PLS, PROBIT, QUANTREG, QUANTSELECT, REG, ROBUSTREG, RSREG, SURVEYLOGISTIC, SURVEYPHREG, SURVEYREG, TPSPLINE, and TRANSREG procedures. Several procedures in SAS/ETS software also fit regression models.