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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
7.
Logistic Regression
7.1.
Binary Logistic Regression
7.1.1.
Model Formulation
7.1.1.1.
Logit Link Function
7.1.1.2.
Linear Predictor
7.1.1.3.
Probability Modeling
7.1.2.
Parameter Interpretation
7.1.2.1.
Log-Odds Coefficients
7.1.2.2.
Odds Ratio Interpretation
7.1.2.3.
Marginal Effects
7.1.3.
Maximum Likelihood Estimation
7.1.3.1.
Likelihood Function
7.1.3.2.
Score Equations
7.1.3.3.
Newton-Raphson Algorithm
7.1.3.4.
Convergence Issues
7.1.4.
Inference for Parameters
7.1.4.1.
Wald Tests
7.1.4.2.
Likelihood Ratio Tests
7.1.4.3.
Score Tests
7.1.4.4.
Confidence Intervals
7.1.5.
Model Assessment
7.1.5.1.
Deviance and Residuals
7.1.5.2.
Goodness-of-Fit Tests
7.1.5.3.
Hosmer-Lemeshow Test
7.1.5.4.
Pseudo R-Squared Measures
7.1.6.
Diagnostics
7.1.6.1.
Residual Analysis
7.1.6.2.
Influence Measures
7.1.6.3.
Outlier Detection
7.1.6.4.
Model Assumptions
7.2.
Multiple Logistic Regression
7.2.1.
Multiple Predictors
7.2.2.
Categorical Predictor Coding
7.2.3.
Interaction Terms
7.2.4.
Model Building Strategies
7.2.5.
Variable Selection Methods
7.3.
Conditional Logistic Regression
7.3.1.
Matched Case-Control Studies
7.3.2.
Fixed Effects Approach
7.3.3.
Conditional Likelihood
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6. Stratified Analysis
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8. Multinomial Response Models