This course covers regression techniques from a simulation-based perspective, with an emphasis on applications rather than mathematical theory. Topics include linear regression with single and multiple predictors; linear regression assumptions, diagnostics, and interpretation; prediction and inference; transformations and interactions; ANOVA; global tests for coefficients; contingency tables; and information criteria and model comparison. R will be used throughout the course.