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
Multivariate General Linear Model Analysis
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
Statistical Inference and Diagnostics
Summary statistics for a fitted regression.................................................................... RSTAT
Variance-covariancematrix 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
Utilities for Classification Variables
Getting unique values of classification variables........................................................... GCLAS
Generation of regressors for a general linear model..................................................... GRGLM
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
Polynomial Regression and Second-Order Models
Polynomial Regression Analysis
Polynomial fit of known degree.................................................................................. RCURV
Polynomial regression analysis.................................................................................. RPOLY
Second-Order Model Design
Generation of an orthogonal central composite design................................................. RCOMP
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
Nonlinear Regression Analysis
Nonlinear regression fit............................................................................................... RNLIN
Fitting Linear Models Based on Criteria Other Than Least Squares
Least absolute value regression.................................................................................... RLAV
Least Lp norm regression............................................................................................. RLLP
Least maximum value regression................................................................................. RLMV
Partial Least Squares Regression................................................................................. PLSR
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