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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
Single Variable Inference
Confidence Intervals for Proportions
Large Sample Methods
Wald Interval
Formula and Assumptions
Coverage Properties
Limitations
Wilson Score Interval
Derivation and Formula
Improved Coverage Properties
Computational Methods
Agresti-Coull Interval
Modified Wald Approach
Practical Implementation
Exact Methods
Clopper-Pearson Interval
Beta Distribution Approach
Conservative Nature
Small Sample Considerations
Rule of Thumb Guidelines
Method Selection Criteria
Hypothesis Testing for Single Proportions
Large Sample Tests
One-Sample Z-Test
Null and Alternative Hypotheses
Test Statistic
Assumptions and Conditions
P-value Calculation
Continuity Correction
When to Apply
Effect on Results
Exact Tests
Binomial Test
Exact P-value Calculation
One-Tailed and Two-Tailed Tests
Small Sample Applications
Goodness-of-Fit Testing
Pearson Chi-Square Test
Test Statistic Formula
Expected Frequency Calculation
Degrees of Freedom
Assumptions and Conditions
Interpretation Guidelines
Likelihood Ratio Test
G-Statistic
Comparison with Pearson Chi-Square
Asymptotic Properties
Exact Goodness-of-Fit Tests
Multinomial Exact Tests
Small Sample Applications
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3. Probability Foundations for Categorical Data
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5. Two-Variable Analysis