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Contents
Front Matter
Introduction
1.
Introduction to Categorical Data
2.
The analysis of 2x2 tables
3.
Types of Data
4.
Ordinal categorical data
5.
Ordinal data II
6.
Relative Risk and Relative Difference
7.
Measures of association for categorical data- Odds Ratio
8.
Prospective and Retrospective design
9.
Logit and probit models
10.
Binary Choice Model
11.
Introduction to Generalized Linear Model
12.
Components of GLM
13.
Likelihood based inference
14.
Inference for the logistic model
15.
Iteratively Reweighted Least Squares
16.
The glm function in R
17.
Residual Analysis for a GLM
18.
Goodness Of Fit
19.
Grouped and ungrouped binary data
20.
Count data analysis II
21.
Sparseness
22.
The O-rings dataset
23.
Overdispersion
24.
Quasi likelihood estimation
25.
The quasi Poisson model
26.
Zero In ated Poisson models
27.
Case Study
28.
Models with constant Coeffcient of Variation
29.
Polytomous regression I
30.
Polytomous regression II
31.
Polytomous Regression III
32.
Scatterplot Smoothing
33.
Generalized additive Models(GAM)
34.
Linear Mixed Model
35.
Subject specic models for longitudinal data
Back Matter
Appendix
Regression analysis III
21
Sparseness
Sayantee Jana
you can view video on Sparseness
