This is a course in data analysis. Topics covered include: 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, stepwise regression; smoothing and nonparametric regression: linear smoothers, kernels, local regression, penalized regression, splines, variance estimation, confidence bands, local likelihood, additive models. Students will practice real-world data analysis through several course projects.
This course is primarily for first-year PhD students in Statistics & Data Science.
For all other information, view the course website.