Week-1
1. Introduction to Discrete Data Analysis
2. Introduction to categorical data
3. Types of Data
4. Prospective and Retrospective Studies
Week-2
5. The analysis of 2x2 table
6. Ordinal data I
7. Ordinal Data II
Week-3
8. Relative Risk and Relative Difference
9. Odds Ratio
10. Simpson’s Paradox
Week-4
11. The Binary Choice model
12. Logit model
13. Probit model
Week-5
14. Predicting the failure of O Ring
15. The Generalized Linear Model
16. Components of GLM
Week-6
17. Likelihood based inference
18. IRLS equations
19. Inference for the logistic model
Week-7
20. Residual Analysis for a GLM
21. Goodness of fit
22. The glm function in R
Week-8
23. MID TERM ASSESSMENT
Week-9
24. Grouped and ungrouped binary data
25. Sparseness
26. Count data analysis I
Week-10
27. Count data analysis II
28. Case Study: Analysis of the gala dataset of the faraway library in R
29. Zero Inflated Poisson models
Week-11
30. Overdispersion
31. Quasi likelihood
32. The quasi Poisson model
Week-12
33. Polytomous regression 1
34. Polytomous regression 2
35. Polytomous regression 3
Week-13
36. Models with constant Coefficient of Variation
37. Linear Mixed Model
38. Generalized Linear Mixed Model
Week-14
39. Subject specific models for longitudinal data
40. Smoothing
41. Fitting of GAMs
Week-15
42. FINAL ASSESSMENT
43. Conclusion
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