As described in course description. But for computation, we will majorly use R or Splus software. We will discuss several case examples using SAS.
課程內涵 (Course Contents)
Contingency tables
Binomial and multinomial distributions
Logistic regression: estimation and applcations
Generalized linear models
Multi-Logit Model
Models for multiple categorical responses
R and SAS examples for analyzing categorical dataAs described in course description. But for computing, we will majorly use R or Splus software. We will discuss several case examples using SAS.
Course Contents
Contingency tables
Binomial and multinomial distributions
Logistic regression: estimation and applications
Generalized linear models
Multi-Logit Model
Models for multiple category responses
R and SAS examples for analyzing category data
Categorical data analysis that deals with qualitative or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing categorical data. The main topics cover :
(1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated categorical data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.
Categorical data analysis that deals with quality or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing category data. The main topics cover :
(1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated category data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.
An introduction to categorical data analysis (Alan Agresti, second edition, 華泰代理)
An introduction to category data analysis (Alan Agresti, second edition, Huatai agent)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| AttendanceAttendance Attendance |
10 | |
| Quiz 1Quiz 1 Quiz 1 |
15 | |
| Quiz 2Quiz 2 Quiz 2 |
15 | |
| Midterm ExamMidterm Exam Midterm Exam |
20 | |
| Final ExamFinal Exam Final Exam |
30 |
