IMSL Statistics Reference Guide > Regression
  

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

Version 2017.0
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