Prerequisites: graduate standing; or consent of instructor

Description: Covers linear models; theory of least squares; general prediction theory; testing lack of fit; model assessment and selection; mixed models and variance components; collinearity and alternative estimates; generalized linear models; lasso for linear models and generalized linear models; and Bayesian linear models.