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Statistics
Categorical Data Analysis
1. Foundations of Categorical Data
2. Descriptive Analysis of Categorical Data
3. Probability Foundations for Categorical Data
4. Single Variable Inference
5. Two-Variable Analysis
6. Stratified Analysis
7. Logistic Regression
8. Multinomial Response Models
9. Loglinear Models
10. Advanced Topics
11. Computational Methods
Logistic Regression
Binary Logistic Regression
Model Formulation
Logit Link Function
Linear Predictor
Probability Modeling
Parameter Interpretation
Log-Odds Coefficients
Odds Ratio Interpretation
Marginal Effects
Maximum Likelihood Estimation
Likelihood Function
Score Equations
Newton-Raphson Algorithm
Convergence Issues
Inference for Parameters
Wald Tests
Likelihood Ratio Tests
Score Tests
Confidence Intervals
Model Assessment
Deviance and Residuals
Goodness-of-Fit Tests
Hosmer-Lemeshow Test
Pseudo R-Squared Measures
Diagnostics
Residual Analysis
Influence Measures
Outlier Detection
Model Assumptions
Multiple Logistic Regression
Multiple Predictors
Categorical Predictor Coding
Interaction Terms
Model Building Strategies
Variable Selection Methods
Conditional Logistic Regression
Matched Case-Control Studies
Fixed Effects Approach
Conditional Likelihood
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6. Stratified Analysis
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8. Multinomial Response Models