課程資訊

REGRESSION ANALYSIS

97-1

MATH7606

221 U3940

Ceiba 課程網頁
http://ceiba.ntu.edu.tw/971regression

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.

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.)

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.)

Office Hours

Textbook: Applied Linear Regression, 3rd Ed.

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.
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.

(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