This course introduces students to regression techniques from a simulation-based perspective, with emphasis on applications rather than mathematical theory. Topics include linear regression with a single predictor and multiple predictors; linear regression assumptions, diagnostics, and interpretation; prediction and inference; transformations and interactions; analysis of variance (ANOVA); and logistic regression. The programming language R is used throughout the course. Appropriate for grad students interested in learning techniques for analyzing quantitative data.
