Standard syllabus
Linear models · Graduate · Statistics
Topics
Matrix linear models
- Gauss–Markov theorem and BLUE
- Weighted and generalized least squares
- Partitioned regression and Frisch–Waugh
- Analysis of variance as linear models
- Multicollinearity and variance inflation
Inference and diagnostics
- F and t tests in matrix notation
- Confidence ellipsoids for coefficients
- Influence diagnostics: hat matrix and Cook's distance
- Residual analysis and assumption checking
- Variable selection criteria: AIC, BIC, Mallows Cp
Extensions
- Polynomial and spline regression
- Robust regression (introduction)
- Mixed models preview
- Regularized regression at graduate level
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.