Syllabus

Lecture 1: Simple and multiple linear models

Lecture 2: t test, Wald test, and likelihood ratio test

Lecture 3: Analysis of variance (ANOVA)

Lecture 4: Linear models with categorical predictors

Lecture 5: Linear models with categorical predictors (cont’d)

Lecture 6: Diagnostics of linear models

Lecture 7: Logistic regression with generalized linear models

Lecture 8: Generalized linear models

Lecture 9: Iteratively reweighted least squares (IRLS)

Lecture 10: Choices of link functions and regression diagnostics

Lecture 11: IRLS for Poisson regression and quasi-likelihood

Lecture 12: Extra-Poisson model, negative binomial model, and excess zeros

Lecture 13: Random effects

Lecture 14: Linear mixed models

Lecture 15: Linear mixed models (cont’d)

Lecture 16: Fitting linear mixed models

Lecture 17: Model selection: AIC theory

Lecture 18: Model selection: AIC and BIC