107年第2學期-6192 類別資料分析 課程資訊
評分方式
| 評分項目 | 配分比例 | 說明 |
|---|---|---|
| Homework assignments | 20 | |
| Midterm, Quizzes, or Projects | 50 | |
| Final | 30 |
選課分析
本課程名額為 70人，已有5 人選讀，尚餘名額65人。
登入後可進行最愛課程追蹤 [按此登入]。
教育目標
Introducing advanced statistical models and theories for categorical data used by statistical researchers and 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. Alternative Mideling of Binary Response Data
8. Models for Multinomial Responses
9. Loglinear Models for Contingency Tables
10. Building and Extending Loglinear Models
11. Models for Matched Pairs
12. Cluster Categorical Data: Marginal and Transitional Models
13. Cluster Categorical Data: Random Effect Models
14. Other Mixture Models for Discrete Data
15. Non-Model-Based Classification and Clustering
16. Large- and Small Sample Theory for Multinomial Models
17. Historical Tour of Categorical Data Analysis.
In this semester, we may include some topics related to Data Mining such as Decision Trees, Bagging, Random Forests, and/or Boosting.
課程概述
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.
課程資訊
基本資料
選修課，學分數：0-3
上課時間：四/5,6,7[M438]
修課班級：統計碩1,2
修課年級：年級以上
選課備註：
教師與教學助理
授課教師：蘇俊隆
大班TA或教學助理：尚無資料
Office HourOffice Hours : 二/9,10; 三/9,10
Location : 新管院 M434
授課大綱
授課大綱：開啟授課大綱(授課計畫表)
(開在新視窗)
參考書目
“Categorical Data Analysis” by Alan Agresti
開課紀錄
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