36-707: Regression Analysis

– Fall 2020 (last updated August 25, 2020) 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 3:20-4:40pm, Fall 2020
Baker Hall 136A, and remotely (see syllabus)
Alex Reinhart
Teaching assistant
Office hours
areinhar@stat.cmu.edu. (Please include [707] at the beginning of the subject line when you email me, so I can prioritize your email. Also, I prefer answering questions in office hours if possible.)
Announcements and homework submission