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.

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 Multi-Logit Model R and SAS examples for analyzing categorical data

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

Categorical Data Analysis, 3rd EditionAlan Agresti