2.1 Simple Linear Regression

Straight line fit RLINE

Simple linear regression analysis RONE

Response control by a fitted line RINCF

Inverse prediction by a fitted line RINPF

2.2 Multivariate General Linear Model Analysis

2.2.1 Model Fitting

From raw data for a single dependent variable RLSE

From covariances RCOV

From raw data without classification variables RGIVN

From raw data with classification variables RGLM

With linear equality restrictions RLEQU

2.2.2 Statistical Inference and Diagnostics

Summary statistics for a fitted regression RSTAT

Variance-covariance matrix of the estimated coefficients RCOVB

Construction of a completely testable hypothesis CESTI

Sums of crossproducts for a multivariate hypothesis RHPSS

Tests for the multivariate linear hypothesis RHPTE

Test for lack of fit based on exact replicates RLOFE

Test for lack of fit based on near replicates RLOFN

Intervals and diagnostics for individual cases RCASE

Diagnostics for outliers and influential cases ROTIN

2.2.3 Utilities for Classification Variables

Getting unique values of classification variables GCLAS

Generation of regressors for a general linear model GRGLM

2.3 Variable Selection

All best regressions via leaps-and-bounds algorithm RBEST

Stepwise regression RSTEP

Generalized sweep of a nonnegative definite matrix GSWEP

Retrieval of a symmetric submatrix from a symmetric matrix RSUBM

2.4 Polynomial Regression and Second-Order Models

2.4.1 Polynomial Regression Analysis

Polynomial fit of known degree RCURV

Polynomial regression analysis RPOLY

2.4.2 Second-Order Model Design

Generation of an orthogonal central composite design RCOMP

2.4.3 Utility Routines for Polynomial Models and Second-Order Models

Polynomial regression fit RFORP

Summary statistics for a fitted polynomial model RSTAP

Case statistics for a fitted polynomial model RCASP

Generation of orthogonal polynomials OPOLY

Centering of variables and generation of crossproducts GCSCP

Transforming coefficients for a second order model TCSCP

2.5 Nonlinear Regression Analysis

Nonlinear regression fit RNLIN

2.6 Fitting Linear Models Based on Alternative Criteria

Least absolute value regression RLAV

Least Lp norm regression RLLP

Least maximum value regression RLMV

Partial Least Squares Regression PLSR