Linear Regression Models

Catalog description

This course provides a rigorous foundation in the theory and application of linear regression. The course covers key concepts in model selection, estimation, diagnostics, and interpretation through different lenses: descriptive, predictive, and causal. Additional topics include shrinkage methods, basic time series, and logistic regression. Students will implement these methods in Python. Counts toward the minor in Data Science but not toward the major (including any of the joint majors with DS-UA).