Regression
Multiple Linear Regression
Generates regressors for a general linear model
Fits a multiple linear regression model and optionally produces summary statistics for a regression model
Computes predicted values, confidence intervals, and diagnostics
Variable Selection
All best regressions
Stepwise regression
Polynomial and Nonlinear Regression
Fits a polynomial regression model
Computes predicted values, confidence intervals, and diagnostics
Fits a nonlinear regression model.
Inference and Diagnostics
Constructs an equivalent completely testable multivariate general linear hypothesis HbU = G from a partially testable hypothesis HpbU = Gp.
Computes the matrix of sums of squares and cross products for the multivariate general linear hypothesis HbU = G given the regression fit.
Performs tests for a multivariate general linear hypothesis HbU = G given the hypothesis sums of squares and cross products matrix SH.
Polynomial and Nonlinear Regression
Fit a nonlinear regression model using Powell's algorithm.
Alternatives to Least Squares Regression
LAV, Lpnorm, and LMV criteria regression
