課程資訊
課程名稱
 迴歸分析
REGRESSION ANALYSIS 
開課學期
 98-1 
授課對象
 理學院  數學研究所  
授課教師
 陳 宏 
課號
 MATH7606 
課程識別碼
 221 U3940 
班次
  
學分
 3 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期三2(9:10~10:00)星期五1,2(8:10~10:00) 
上課地點
 新103新103 
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/981regression 
課程簡介影片
 
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課程概述

Multiple regression and analysis of variance are the most often used statistical tools in applications.
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.
(http://www.springerlink.com/content/nujt764l16289558/fulltext.pdf)
 

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 algebra (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.)
Depth of understanding comes from a systematic use of tools from linear algebra such assubspaces, projections, and matrix decompositions that allows an astonishing variety of applications to be comprehended via a small number of geometrical pictures and algebraic manipulations. Practical understanding comes from broad experience with and probing of the methods on particular data sets through use of a flexible computer data analysis language, which for us, will be R.
 
預期每週課後學習時數
  
Office Hours
 每週三 10:00~11:00
每週五 10:10~11:00 備註： 第一,二個為陳宏老師時間; 第三個為助教時間,地點為新數103 
指定閱讀
  
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).
References:
Sen, A. and Srivastava, M. (1990). Regression Analysis: Theory, Methods, and
Applications. Springer.
Rao, C. R. and Toutenburg, H. (1999). Linear Models: Least Squares and
Alternatives. Second Edition. Springer.
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.





 
評量方式
(僅供參考)
  
No.
項目
百分比
說明
1. 
 Homework  
20% 
  
2. 
 Quiz 
20% 
 Three Quizzes 
3. 
 Midterm 
30% 
  
4. 
 Final 
30% 
  
 
課程進度
週次
日期
單元主題
第1週
 09/14  Wednesday: Review needed language and tool of probability and statistics and give an introduction of regression analysis 
第2週
 09/21  Simple Linear Regression
Wednesday: 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.) Friday: Finish 2.3~2.6.
加退選 
第3週
 09/28  Simple Linear Regression
 
第4週
 10/05  Finish Simple Linear Regression and start on Multiple Linear Regression Multiple Regression 
第5週
 10/12  Multiple Linear Regression 
第6週
 10/19  Drawing Conclusions (in Regression Analysis)
Emphasize on the last section on resampling method and measurement error in regressors 
第7週
 10/26  Weights, Lack of Fit, and More 
第8週
 11/02  Polynomials and Factors;
Quiz 1 on Tuesday which covers the materials from Chapter s 1 to 4. (quiz1.pdf is 2008's quiz for your reference.) 
第9週
 11/09  Residuals 
第10週
 11/16  Residuals; 期中考(Friday, Chapters 1-6) 
第11週
 11/23  Residuals
Quiz 2 (Friday 8:10 to 9) 
第12週
 11/30  Regression Diagnostics: Residuals  
第13週
 12/07  Transformations; Variable Selection
停修申請截止 
第14週
 12/14  Variable Selection 
第15週
 12/21  Nonlinear Regression; Logistic Regression 
第16週
 12/28  Logistic Regression; Friday:
開國紀念日 
第17週
 01/03  Outliers and Influence; Review 
第18週
 01/10  期末考: January 15th (Friday)