36-707 Regression Analysis

12 units, Carnegie Mellon University

This is a course in applied data analysis using regression. Students learn to answer real applied questions using methods including: Simple and multiple linear regression, causation, diagnostics, logistic regression and generalized linear models; Model selection: prediction risk, bias-variance tradeoff, risk estimation, model search, ridge regression and lasso; smoothing and nonparametric regression: linear smoothers, kernels, local regression, splines, additive models. Students will practice real-world data analysis through several course projects culminating in written reports, and the course emphasizes the development of skills in interpreting and explaining statistical results. This course is primarily for first year PhD students in Statistics & Data Science; it requires an appropriate background for entering that program.

Prerequisites: none listed

Offered: Fall 2026, Fall 2025

Courseweave needs JavaScript to show sections, workloads and planning tools.