1590 - 類別資料分析
Analysis of Categorical Data
教育目標 Course Target
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.
課程概述 Course Description
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.
參考書目 Reference Books
“Categorical Data Analysis” 3rd by Alan Agresti
“Categorical Data Analysis” 3rd by Alan Agresti
評分方式 Grading
評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
---|---|---|
Quizzes, Homework, and/or Presentations Quizzes, Homework, and/or Presentations |
30 | |
Midterm Midterm |
30 | |
Final Final |
40 |
授課大綱 Course Plan
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相似課程 Related Courses
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課程資訊 Course Information
基本資料 Basic Information
- 課程代碼 Course Code: 1590
- 學分 Credit: 3-0
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上課時間 Course Time:Tuesday/6,7,8[M108]
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授課教師 Teacher:蘇俊隆
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修課班級 Class:統計系2-4
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選課備註 Memo:生物統計群組(109-113適用),曾修習統計學下期
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