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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
3.
Probability Foundations for Categorical Data
3.1.
Discrete Probability Distributions
3.1.1.
Bernoulli Distribution
3.1.1.1.
Definition and Parameters
3.1.1.2.
Mean and Variance
3.1.2.
Binomial Distribution
3.1.2.1.
Definition and Parameters
3.1.2.2.
Probability Mass Function
3.1.2.3.
Mean and Variance
3.1.2.4.
Relationship to Bernoulli
3.1.3.
Multinomial Distribution
3.1.3.1.
Definition and Parameters
3.1.3.2.
Probability Mass Function
3.1.3.3.
Mean Vector and Covariance Matrix
3.1.4.
Hypergeometric Distribution
3.1.4.1.
Definition and Parameters
3.1.4.2.
Sampling Without Replacement
3.1.4.3.
Applications in Categorical Analysis
3.2.
Sampling Distributions
3.2.1.
Distribution of Sample Proportions
3.2.2.
Central Limit Theorem Applications
3.2.3.
Standard Error of Proportions
3.2.4.
Finite Population Corrections
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2. Descriptive Analysis of Categorical Data
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4. Single Variable Inference