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STA4006 Categorical Data Analysis September, 2004

STA4006 Categorical Data Analysis September, 2004. Instructor: Xin-Yuan Song (LSB114) Teaching Assistant: Yan-Hui Wang (LSB 130) Assessment Scheme Exercise 10% Mid-term examination 40%

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STA4006 Categorical Data Analysis September, 2004

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  1. STA4006 Categorical Data AnalysisSeptember, 2004 Instructor: Xin-Yuan Song (LSB114) Teaching Assistant: Yan-Hui Wang (LSB 130) • Assessment Scheme Exercise 10% Mid-term examination 40% Final examination 50% • Reference Books Agresti, A. Categorical Data Analysis Agresti, A. An Introduction to Categorical Data Analysis Agresti, A. Analysis of Ordinal Categorical Data Fienbberg, S. E. The Analysis of Cross-Classified Data

  2. Course Outline • Chapter 1: Introduction: Distribution and Inference for Categorical Data • Chapter 2: Basic results for Contingency Tables • Chapter 3: Analysis of multidimensional Tables • Chapter 4: Introduction to Generalized Linear Models • Chapter 5: Logistic Regression • Chapter 6: Logit Models for Multinomial Response

  3. Chapter 1 Introduction: Distributions and Inference for Categorical Data 1.1 Categorical Response Data 1.2 Distributions for Categorical Data 1.3 Statistical Inference for Binomial Parameters 1.4 Statistical Inference for Multinomial Parameters 1.5 Statistical Inference for Poisson Parameters

  4. Chapter 2 Basic Results for Contingency Tables 2.1 Notation and Definition 2.2 Sampling Models 2.3 Test of Independence and Test of Homogeneity 2.4 Odds Ratio 2.5 Other Measures of Association in 2£ 2 Table 2.6 Measures of Association in I £ J Table 2.7 Loglinear Model in Two Way Table

  5. Chapter 3 Analysis of Multidimensional Tables 3.1 Partial Association 3.2 Loglinear Model for Three Dimensions 3.3 Collapsibility 3.4 Maximum Likelihood Estimates of Expected Cell Frequencies 3.5 Loglinear Model for Higher Dimensions

  6. Chapter 4 Introduction to Generalized Linear Models 4.1Generalized Linear Model 4.2 Generalized Linear Models for Binary Data 4.3 Generalized Linear Models for Counts 4.4 Extension of Notation in Generalized Linear Models

  7. Chapter 5 Logistic Regression 5.1 Logistic Regression 5.2 Multiple Logistic Regression 5.3 Logit Models 5.4 Model Selection

  8. Chapter 6 Logit Models for Multinomial Response 6.1 Nominal Responses: Baseline-Category Logit Models 6.2 Ordinal Responses: Cumulative Logit Models 6.3 Ordinal Responses: Cumulative Link Models ( End of Course Outline)

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