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Course Detail

Course Name Categorical Data Analysis
Course Code 23DLS645
Semester 4
Credits 4

Categorical Data-nominal and ordinal random variables.

Two-way contingency tables: Table structure for two dimensions. Ways of comparing proportions. Measures of associations-odds ratio. Sampling distributions. Goodness-of-fit
tests, testing of independence. Exact and large sample inference.

Three-way contingency tables, Partial associations, Cochran-Mantel-Haenszel methods. Conditional association and related inference.

Generalized Linear Models (GLMs): components of a GLM.
Logisitc regression models for binary data, inference for logistic regression models, multiple logistic regression with qualitative predictors, exact inference for logistic regression, sample size and power of test.

Loglinear models for two-way and three-way contingency tables, inference for loglinear models, the connection between loglinear-logit regression models.
Multicategory logit models for nominal responses, cumulative logit models for ordinal responses.

Texts / References

1. Agresti, A., Categorical Data Analysis, 3rd Edition, Wiley, New York, 2013.
2. Agresti, A., An Introduction to Categorical Data Analysis, 3rd Edition, Wiley, New York, 2019.
3. Andersen, E.B., The Statistical Analysis of Categorical Data, Springer-Verlag, Berlin, 1994.
Santner, T.J. and Duffy, D., The Statistical Analysis of Discrete Data, Springer-Verlag, New
York, 1989.

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