Week 8: The Newton-Raphson Algorithm Fits GLMs. Wald and Likelihood Ratio Inference Use the Likelihood Function.
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Course Material
- 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.
- Chapters 16
- Department Statistics
- Teacher
Summera Kinat
