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課程名稱 |
迴歸分析
REGRESSION ANALYSIS |
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開課學期 |
97-1 |
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授課對象 |
理學院 數學系 |
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授課教師 |
陳 宏 |
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課號 |
MATH7606 |
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課程識別碼 |
221 U3940 |
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班次 |
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學分 |
3 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期一3,4(10:20~12:10)星期四@(~) |
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上課地點 |
新503新503 |
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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 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/971regression |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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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.
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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.) |
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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.) |
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預期每週課後學習時數 |
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Office Hours |
每週一 13:00~14:00
每週四 11:00~12:00 |
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指定閱讀 |
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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.
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
midterm |
30% |
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2. |
final test |
30% |
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3. |
homework |
20% |
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4. |
quizzes |
20% |
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週次 |
日期 |
單元主題 |
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第1週 |
09/15 |
Review of Basic Statistics and introduction |
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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.
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第3週 |
09/29 |
Monday: Typhoon
Thursday: Simple Linear Regression |
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第4週 |
10/06 |
Monday: Finish Simple Linear Regression and start on Multiple Linear Regression
Multiple Regression |
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第5週 |
10/13 |
Multiple Linear Regression |
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第6週 |
10/20 |
Drawing Conclusions (in Regression Analysis)
Meet on noon of Thursday and Friday. |
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第7週 |
10/27 |
Weights, Lack of Fit, and More |
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第8週 |
11/03 |
Polynomials and Factors |
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第9週 |
11/10 |
Transformations |
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第11週 |
10/17 |
Monday: Midterm;
Thursday: Residuals |
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第12週 |
11/24 |
Regression Diagnostics: Residuals |
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第13週 |
12/01 |
Outliers and Influence |
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第14週 |
12/08 |
Variable Selection |
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第15週 |
12/15 |
Variable Selection |
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第16週 |
12/22 |
Logistic Regression |
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第17週 |
12/29 |
Quiz 3; Nonlinear regression |
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第18週 |
09/01/05 |
Wrap up and Review |
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第19週 |
09/01/12 |
Monday: Final |
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