Introducing statistical models for categorical data used by statistical practitioners.
1. Introduction: Distributions and Inference for Categorical Data
2. Describing Contingency Tables
3. Inference for Contingency Tables
4. Introduction to Generalized Linear Models
5. Logistic Regression
6. Building,Checking, and Applying Logistic Regression Models
7. Loglinear Models for Contingency Tables
In this semester, we may include some topics related to Data Mining such as Decision Trees, Bagging, Random Forests, and/or Boosting.Introducing statistical models for category data used by statistical practicers.
1. Introduction: Distributions and Inference for Categorical Data
2. Describing Contingency Tables
3. Inference for Contingency Tables
4. Introduction to Generalized Linear Models
5. Logistic Regression
6. Building,Checking, and Applying Logistic Regression Models
7. Loglinear Models for Contingency Tables
In this semester, we may include some topics related to Data Mining such as Decision Trees, Bagging, Random Forests, and/or Boosting.
Objective: Introducing statistical models for categorical data used by statistical researchers and practitioners.
Prerequisites:(a) Elementary Statistics(b).At least one of the following packages(SAS, R/Splus, or SPSS).
Contents :
1.Statistical inference for Two-way and Three-way Contigency tables under different assumptions.
2.Logit/Loglinear models and their extensions.
3.Generalized linear models with random effects for categorical responses.
4.Models checking and selection.
5.Asymptotic results and other advanced topics.
Sofewares:
1.SAS: PPRC FREQ, GENMOD, LOGISTIC, CATMOD, and NLMIXED.
2.S-PLUS or R: chisq.test, glm, fisher. test, gee, and glmmPQL.
3.SPSS: crosstabs, logistic, and plum.
Objective: Introducing statistical models for category data used by statistical researchers and practicers.
Prerequisites:(a) Elementary Statistics(b).At least one of the following packages(SAS, R/Splus, or SPSS).
Contents:
1.Statistical inference for Two-way and Three-way Contigency tables under different assumptions.
2.Logit/Loglinear models and their extensions.
3. Generalized linear models with random effects for category responses.
4.Models checking and selection.
5.Asymptotic results and other advanced topics.
Softwares:
1.SAS: PPRC FREQ, GENMOD, LOGISTIC, CATMOD, and NLMIXED.
2.S-PLUS or R: chisq.test, glm, fisher. test, gee, and glmmPQL.
3.SPSS: crossstabs, logistic, and plum.
“Categorical Data Analysis” 3rd by Alan Agresti
“Categorical Data Analysis” 3rd by Alan Agresti
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Quizzes, Homework, and/or PresentationsQuizzes, Homework, and/or Presentations Quizzes, Homework, and/or Presentations |
30 | |
| MidtermMidterm Midterm |
30 | |
| FinalFinal Final |
40 |
