Regression
 
This section contains the following topics:
*Multiple Linear Regression
*REGRESSORS Function—Generates regressors for a general linear model.
*MULTIREGRESS Function—Fits a multiple linear regression model and optionally produces summary statistics for a regression model.
*MULTIPREDICT Function—Computes predicted values, confidence intervals, and diagnostics.
*Variable Selection
*ALLBEST Procedure—All best regressions.
*STEPWISE Procedure—Stepwise regression.
*Polynomial and Nonlinear Regression
*POLYREGRESS Function—Fits a polynomial regression model.
*POLYPREDICT Function—Computes predicted values, confidence intervals, and diagnostics.
*NONLINREGRESS Function—Fits a nonlinear regression model.
*Multivariate Linear Regression—Statistical Inference and Diagnostics
*HYPOTH_PARTIAL Function—Construction of a completely testable hypothesis.
*HYPOTH_SCPH Function—Sums of cross products for a multivariate hypothesis.
*HYPOTH_TEST Function—Tests for the multivariate linear hypothesis.
 
*Polynomial and Nonlinear Regression
*NONLINOPT Function—Fit a nonlinear regression model using Powell's algorithm.
*Alternatives to Least Squares Regression
*LNORMREGRESS Function—LAV, Lpnorm, and LMV criteria regression.