課程資訊

REGRESSION ANALYSIS

98-1

MATH7606

221 U3940

Ceiba 課程網頁
http://ceiba.ntu.edu.tw/981regression

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.

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 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.

Office Hours

Textbook: Applied Linear Regression, 3rd Ed. 電子書http:
//www3.interscience.wiley.com/cgi-bin/bookhome/109880490
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.

(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)