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Categorical Data Analysis (STAT-411)
Week 8: The Newton-Raphson Algorithm Fits GLMs. Wald and Likelihood Ratio Inference Use the Likelihood Function.
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