105年第2學期-1763 類別資料分析 課程資訊
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)
Binomial and multinomial distributions
Logistic regression: estimation and applcations
Generalized linear models
Models for multiple categorical responses
R and SAS examples for analyzing categorical data
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.