STA 4504/5503: Readings

Problems
Sections and Topics Pages Assigned Optional
1. Introduction
1.1–1.4.2 Statistical inference for a proportion 1–131–4,8,12 15,16
2. Contingency Tables
2.1 Table structure 21–25 2
2.2 Comparing proportions 25–28 3
2.3 Odds ratio 28–34 5–8,12
2.4 Chi-squared tests 34–40 17–19 21,24–26
2.6 Exact tests for small samples 45–48 29
2.7 Association in three-way tables 49–54 33–36,39 37,38
3. Generalized Linear Models
3.1 Components of a generalized linear model 65–68 1,22ab
3.2 GLMs for binary data 68–73 2,5 6
3.3 GLMs for count data 74–84 11,12,16 17,18,20,21
3.4 Inference and model checking 84–87 9,13 14
3.5 Fitting generalized linear models 88–90
4. Logistic Regression
4.1 Interpreting logistic regression 99–106 1,4 35,36
4.2 Inference for logistic regression 106–110 2,8
4.3 Categorical predictors 110–115 11,16,17,37
4.4 Multiple logistic regression 115–120 19,21,23,24
4.5 Summarizing effects 120–121 28 27
5. Building and Applying Logistic Regression Models
5.1 Strategies in model selection 137–144
5.2 Model checking 144–150 4,15,19,30 20
5.3 Effects of sparse data 152–156 22
6. Multicategory Logit Models
6.1 Logit models for nominal responses 173–179 1,2,3,6
6.2 Cumulative logit model for ordinal responses 180–189 5,7,12,22abd
8. Models for Matched Pairs
8.1 Comparing dependent proportions 244–247 2,4 7,8,10
8.5.5 Measuring agreement 264 20ac
9. Modeling Clustered Responses (Repeated Measures)
9.1 Marginal models vs. conditional models 276–279
9.2 Marginal modeling: The GEE approach 279–284 3,4,7,18
9.3 GEE for multinomial responses 285–287
9.4 Transitional models 288–290
10. Random Effects: Generalized Linear Mixed Models
297–309
7. Loglinear Models
7.1 Loglinear models for 2-way and 3-way tables 204–212 27
7.2 Inference for loglinear models 212–223 5–7 8

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