課程資訊
課程名稱
迴歸分析
REGRESSION ANALYSIS 
開課學期
97-1 
授課對象
理學院  數學系  
授課教師
陳宏 
課號
MATH7606 
課程識別碼
221 U3940 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期一3,4(10:20~12:10)星期四@(~) 
上課地點
新503新503 
備註
研究所統計科學組基礎課。
總人數上限:50人
外系人數限制:15人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/971regression 
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課程概述

0. Review of Basics.
1. Motivating Examples and Model Construction.
2. Simple and Multiple Linear Regressions.
3. Problems and Remedies - normality, unequal variances, correlated errors, outliers and influential observations, and multicollinearity.
4. More Complicated Models.
5. Generalized Linear Model.
 

課程目標
1. Give you some experience with basic regression techniques that you can apply in your research.
2. Expose you to situations where regression analysis is useful (and perhaps not useful).
3. Give you enough understanding that you can evaluate regression in papers your read. (it requires you to know how regression works to be able to evaluate a regression solution in a particular research situation.) 
課程要求
calculus, one semester of linear albegra (matrix theory), some programming experience, one semester introductory probability, and one semester mathematical statistics
(Statistical Concepts: Random variables, normal and t distributions, mean and variance of a linear
combination of random variables, hypothesis-testing including the concepts of significance level and
p-value, t-tests and confidence intervals, sampling error, and the standard error of the mean.) 
預期每週課後學習時數
 
Office Hours
每週一 13:00~14:00
每週四 11:00~12:00 
參考書目
Textbook: Applied Linear Regression, 3rd Ed.
電子書http://www3.interscience.wiley.com/cgi-bin/bookhome/109880490
Sanford Weisberg, published by Wiley/Interscience in
2005 (ISBN 0-471-66379-4).
(電子書) Rao, C. R. and Toutenburg, H. (1999). Linear Models:
Least Squares and Alternatives. Second Edition. Springer
Sen, A. and Srivastava, M. (1990). Regression Analysis:
Theory, Methods, and Applications. Springer.
http://www.springerlink.com/content/h4tk1j/?
p=c6cb2f6b81394ae28ab15c93254b0327&pi=552
Grob, J. (2003). Linear Regression. Springer.
Ramsey, F. L. and Schafer, D.W. (2002). The Statistical Sleuth
– A Course in Methods of Data Analysis. Second Edition. Duxbury.
Venables, W.N. and Ripley, B.D. (2002). Modern Applied Statistics with S.
Fourth Edition. Springer.
 
指定閱讀
 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
midterm 
30% 
 
2. 
final test 
30% 
 
3. 
homework 
20% 
 
4. 
quizzes 
20% 
 
 
課程進度
週次
日期
單元主題
第1週
09/15  Review of Basic Statistics and introduction 
第2週
09/22  Simple Linear Regression
Monday: Finish up Chapter 1 and demo on setting up R-program to do linear regression, Finish the derivation of LS estimate and show that the estimator of $beta_1$ is consistent.
(Study A2.1~A2.3, A3.)
Thursday: Finish 2.3~2.6.
 
第3週
09/29  Monday: Typhoon
Thursday: Simple Linear Regression 
第4週
10/06  Monday: Finish Simple Linear Regression and start on Multiple Linear Regression
Multiple Regression 
第5週
10/13  Multiple Linear Regression 
第6週
10/20  Drawing Conclusions (in Regression Analysis)
Meet on noon of Thursday and Friday. 
第7週
10/27  Weights, Lack of Fit, and More 
第8週
11/03  Polynomials and Factors 
第9週
11/10  Transformations 
第11週
10/17  Monday: Midterm;
Thursday: Residuals 
第12週
11/24  Regression Diagnostics: Residuals 
第13週
12/01  Outliers and Influence 
第14週
12/08  Variable Selection 
第15週
12/15  Variable Selection 
第16週
12/22  Logistic Regression 
第17週
12/29  Quiz 3; Nonlinear regression 
第18週
09/01/05  Wrap up and Review 
第19週
09/01/12  Monday: Final