Introducing statistical models for categorical data used by statistical practitioners.
1. Introduction: Distributions and Inference for Categorical Data
2. Describing Contingency Tables
3. Inference for Contingency Tables
4. Introduction to Generalized Linear Models
5. Logistic Regression
6. Building,Checking, and Applying Logistic Regression Models
7. Alternative Mideling of Binary Response Data
8. Models for Multinomial Responses
9. Loglinear Models for Contingency Tables
10. Building and Extending Loglinear Models
11. Models for Matched Pairs
12. Cluster Categorical Data: Marginal and Transitional Models
13. Cluster Categorical Data: Random Effect Models
14. Other Mixture Models for Discrete Data
15. Non-Model-Based Classification and Clustering
16. Large- and Small Sample Theory for Multinomial Models
17. Historical Tour of Categorical Data Analysis.
In this semester, we may include some topics related to Data Mining such as Decision Trees, Bagging, Random Forests, and/or Boosting.

Introducing statistical models for category data used by statistical practicers.
1. Introduction: Distributions and Inference for Categorical Data
2. Describing Contingency Tables
3. Inference for Contingency Tables
4. Introduction to Generalized Linear Models
5. Logistic Regression
6. Building,Checking, and Applying Logistic Regression Models
7. Alternative Mideling of Binary Response Data
8. Models for Multinomial Responses
9. Loglinear Models for Contingency Tables
10. Building and Extending Loglinear Models
11. Models for Matched Pairs
12. Cluster Categorical Data: Marginal and Transitional Models
13. Cluster Categorical Data: Random Effect Models
14. Other Mixture Models for Discrete Data
15. Non-Model-Based Classification and Clustering
16. Large- and Small Sample Theory for Multinomial Models
17. Historical Tour of Categorical Data Analysis.
In this semester, we may include some topics related to Data Mining such as Decision Trees, Bagging, Random Forests, and/or Boosting.

Categorical data analysis that deals with qualitative or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing categorical data. The main topics cover :
(1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated categorical data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.

Categorical data analysis that deals with quality or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing category data. The main topics cover :
(1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated category data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.

“Categorical Data Analysis” 3rd by Alan Agresti

“Categorical Data Analysis” 3rd by Alan Agresti