3.00 Credits
Description: Simple and multiple least squares regression. Curvilinear and piece-wise models. Inferences for parameters. Diagnostic, remedial, and goodness of fitmeasures. Model building techniques including best subsets, stepwise regression, cross-validation, ridge, lasso, and PCR. Categorical predictors and ANCOVA. Basic logistic regression. Appropriate statistical software used throughout.