Meeting Times: Old Chem 101 Tues-Thur 1:15-2:30
Instructor: Merlise Clyde
|Office Hours:||Mon/Tues 1:30-2:30 or by appointment|
Teaching Assistant: Nick Jarrett (firstname.lastname@example.org)Office Hours in 211A Old Chem (SECC) See the SECC Schedule for availability
This course investigates the essential concepts of linear models from both Bayesian and classical viewpoints, using a coordinate free approach where possible. Topics include: simple linear and multiple regression, parameter estimation and interpretation, distribution theory for ANOVA and testing, variable transformations, prediction, model diagnostics, variable selection, Bayes factors and model selection, and Bayesian model averaging. and Bayesian hierarchical linear models. Selected topics in Markov chain Monte Carlo simulation will be introduced as required. Extensions to multivariate models and nonparametric models if time permits.
Prerequisite: Statistics 213 and Linear Algebra (Math 104), Corequisite: Statistics 290
Grading will be based on Homeworks, Midterms (In-class/Takehome) and Final.
The following books will be useful for the course:
Purchase books from the bookstore or order directly from Springer, Amazon or other sites.