110年第1學期-1589 類別資料分析 課程資訊

評分方式

評分項目	配分比例	說明
Attendance	10
Quiz 1或homework	15
Quiz 2或homework	15
Midterm Exam	30
Final Exam	30

教育目標

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 data

課程概述

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.

基本資料

選修課，學分數：3-0
上課時間：三/5,6,7[M109]
修課班級：統計系2-4
修課年級：年級以上
選課備註：生物統計群組(106-110適用),曾修習統計學下期

參考書目

An introduction to categorical data analysis (Alan Agresti, second edition, 華泰代理)

Categorical Data Analysis, 3rd EditionAlan Agresti