STA 4504/5503: Readings

		Problems
Sections and Topics	Pages	Assigned	Optional
1. Introduction
1.1–1.4.2 Statistical inference for a proportion	1–13	1–4,8,12	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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