Linear Models
An exploration of practical applications of the projection approach to linear models, building from a review of essential linear algebra concepts to the theory of linear models from a projection-based perspective. Introduction to Bayesian ideas. Additional topics include analysis of variance, generalized linear models, and variable selection techniques. Statistics and Data Sciences 387 and Statistics and Scientific Computation 387 may not both be counted. Three lecture hours a week for one semester.
Topics
- analysis of variance
- generalized linear models
- and variable selection techniques
Topics explicitly listed in official catalogue description
Prerequisites & course information
An exploration of practical applications of the projection approach to linear models, building from a review of essential linear algebra concepts to the theory of linear models from a projection-based perspective. Introduction to Bayesian ideas. Additional topics include analysis of variance, generalized linear models, and variable selection techniques. Statistics and Data Sciences 387 and Statistics and Scientific Computation 387 may not both be counted. Three lecture hours a week for one semester.
Sources
- Course catalogue ↗2026-2027