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• Week 1: Categorical Data and its Types, Probability Distributions for Categorical Data.
• Week 2: Two-way Contingency Tables, Probability Structure for Contingency Tables.
• Week 3: Comparing Proportions in Two-by-Two Tables.
• Week 4: Independence: Chi-square Test and Fisher's Exact Test.
• Week 5: Generalized Linear Models (GLMs) and its Components.
• Week 6: GLMs for Binary Data: Linear Probability Model, Logit Model and Probit Model.
• Week 7: GLMs for Count Data: Log-linear Model. Model Comparison Using the Deviance.
• Week 8: The Newton-Raphson Algorithm Fits GLMs. Wald and Likelihood Ratio Inference Use the Likelihood Function.
• Week 9: Logistic Regression: Binary Logistic Regression.
• Week 10: Building, Applying and Interpreting Binary Logistic Regression Model.
• Week 11: Multinomial Logistic Regression.
• Week 12: Ordinal Logistic Regression.
• Week 13: Poisson Regression.
• Week 14: Log-linear Models for Contingency Tables.
• Week 15: Models for Matched Pairs.
• Week 16: Analyzing Repeated Categorical Response Data.