100年第2學期-1395 類別資料分析 課程資訊
|Ongoing (Part I)||20||Including homework assignment and class attendance& participation|
|Ongoing (Part II)||25||Including review quizzes/project report|
Independent thinking is a key component in your development as an excellent undergraduate student. The purpose of this courses are to assist you in this development. We have three specific aims:
� to provide an overview of statistical vocabulary
� to describe statistical methodology and interpretation and
� to introduce statistical computing techniques (SAS/SPSS/MINITAB/EXCEL)
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.
Office HourOffice hour: 15:20—16:10 (W1) /15:20—16:10 (W3) /14:10—16:10 (W4) Classroom: M449
1. Agresti, A. (2007), An Introduction to Categorical Data Analysis, 2nd edition, John Wiley & Sons, Inc. （華泰文化有限公司代理）
2. Hosmer, D.W.& Lemeshow, S.(2000), Applied Logistic Regression, 2nd ed., John Wiley & Sons, Inc.
3. Agresti, A. (2002), Categorical Data Analysis, 2nd ed., Hohn Wiley & Sons, Inc.