107年第1學期-1761 類別資料分析 課程資訊
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.
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).
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.
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.