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IMSL Routines for Regression

IMSL Routines for Regression

Multiple Linear Regression


IMSL_MULTIPREDICT: Computes predicted values, confidence intervals, and diagnostics.

IMSL_MULTIREGRESS: Fits a multiple linear regression model and optionally produces summary statistics for a regression model.

IMSL_REGRESSORS: Generates regressors for a general linear model.

Variable Selection


IMSL_ALLBEST: All best regressions.

IMSL_STEPWISE: Stepwise regression.

Polynomial and Nonlinear Regression


IMSL_NONLINREGRESS: Fits a nonlinear regression model.

IMSL_POLYPREDICT: Computes predicted values, confidence intervals, and diagnostics.

IMSL_POLYREGRESS: Fits a polynomial regression model.

Multivariate Linear Regression: Statistical Inference and Diagnostics


IMSL_HYPOTH_PARTIAL: Construction of a completely testable hypothesis.

IMSL_HYPOTH_SCPH: Sums of cross products for a multivariate hypothesis.

IMSL_HYPOTH_TEST: Tests for the multivariate linear hypothesis.

Polynomial and Nonlinear Regression


IMSL_NONLINOPT: Fit a nonlinear regression model using Powell's algorithm.

Alternatives to Least Squares Regression


IMSL_LNORMREGRESS: LAV, Lpnorm, and LMV criteria regression.



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