Complete Syllabus: Click Here
Lecture Slides
Slides | Topics | Text Coverage |
---|---|---|
C01.pdf | Likelihood and maximum likelihood estimation Inference for a binomial proportion: Wald tests, score tests, likelihood ratio tests, exact binomial tests and the corresponding confidence intervals Lab Activity: Comparing the coverage probability of Wald, score and Agresti-Coull confidence intervals |
Chapter 1 |
C02A.pdf | Diff of proportions, relative risk, odds ratio | 2.1-2.3 |
C02B.pdf | Pearson's chi-squared test of independence | 2.4 |
C02C.pdf | Fisher's exact test for small samples | 2.6 |
C02D.pdf | Association in three-way tables: partial tables and marginal tables, conditional and marginal associations, conditional and marginal odds ratios, conditional and marginal independence, homogeneous association, the Cochran-Mantel-Maenszel test for conditional independence Mantel-Haenszel estimate for the common odds ratio | 2.7, 4.3.4 |
C03.pdf | Overview of generalized linear models (GLM). Data file: falls.dat, fallsUG.dat |
3.1-3.2, 3.4 |
C04A.pdf | Simple logistic regression: interpretation and inference. Data file: horseshoecrabs.dat |
4.1-4.2 |
C04B.pdf | Multiple logistic regression | 4.4 |
C04C.pdf Multiway.pdf |
Logistic regression with categorical predictors. Logistic regression for multiway contigency tables Data file: mousemuscle.dat |
4.3 |
C05.pdf | Model selection; Model checking (deviance, residuals); Watch out for sparse categorical data | 5.1-5.3 |
C06.pdf | Multicategory logit models: Baseline-category logit models for nominal responses, Cumulative logit models for ordinal responses Lab Activity: Analysis of the wheat kernels data |
6.1, 6.2 |
Poisson.pdf | GLM for count data: Poisson regression, overdispersion and
negative binomial regression Data file: traincollisions.dat |
3.3 |
C07.pdf | Loglinear models for two-way and three-way tables; inference for loglinear models; the loglinear-logistic connection (Covered in 2016 but not in 2017) | 7.1-7.3 |
C08.pdf | Models for matched pairs: McNemar test, logistic regression for matched-pair data, population-avaraged and subject-specific models | 8.1-8.2 |