105年第2學期-1763 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	15
Quiz 2	15
Midterm Exam	20
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

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.

基本資料

選修課，學分數：0-3
上課時間：一/3,4,8[M121]
修課班級：統計系2-4
修課年級：年級以上
選課備註：大數據資料群組(105適用),A群組(101-104適用)；需先修習統計學下期

參考書目

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