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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課程資訊 Course Information

基本資料 Basic Information

  • 課程代碼 Course Code: 1590
  • 學分 Credit: 3-0
  • 上課時間 Course Time:
    Tuesday/6,7,8[M108]
  • 授課教師 Teacher:
    蘇俊隆
  • 修課班級 Class:
    統計系2-4
  • 選課備註 Memo:
    生物統計群組(109-113適用),曾修習統計學下期
選課狀態 Enrollment Status

目前選課人數 Current Enrollment: 39 人

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