36-707: Regression Analysis

– Fall 2019 (last updated July 29, 2019) all courses · refsmmat.com

This is a course in data analysis. Topics covered include: Simple and multiple linear regression, causation, global and case diagnostics, robust regression, logistic regression and generalized linear models; Model selection: prediction risk, bias-variance tradeoff, risk estimation, model search, ridge regression and lasso, stepwise regression, maybe boosting; smoothing and nonparametric regression: linear smoothers, kernels, local regression, penalized regression, splines, wavelets, variance estimation, confidence bands, local likelihood, additive models; classification, including LDA, QDA, and trees. Students will practice real-world data analysis through several course projects.

This course is primarily for first-year PhD students in Statistics & Data Science. Students in other programs should check the syllabus for full prerequisite and waitlist information.

For course policies, consult the syllabus.

Vital information

MW 1:30–3pm, Fall 2019
Scaife Hall 208
Alex Reinhart.
Office hours
TBD and by appointment; BH 232K
Announcements and homework submission