課程資訊
 課程名稱 迴歸分析Regression Analysis 開課學期 107-1 授課對象 理學院  應用數學科學研究所 授課教師 江金倉 課號 MATH7606 課程識別碼 221 U3940 班次 學分 3.0 全/半年 半年 必/選修 必修 上課時間 星期一8,9(15:30~17:20)星期三8(15:30~16:20) 上課地點 天數101天數101 備註 總人數上限：40人 Ceiba 課程網頁 http://ceiba.ntu.edu.tw/1071MATH7606_ 課程簡介影片 核心能力關聯 核心能力與課程規劃關聯圖 課程大綱 為確保您我的權利,請尊重智慧財產權及不得非法影印 課程概述 Multiple regression and analysis of variance are the most often used statistical tools in applications. 1. Motivating Examples and Model Construction, Review of Basics such as MLE, Law of Large Numbers, and Central Limit Theorem. Simple and Multiple Linear Regressions: Estimation 2. Inference for Gaussian Linear Model. 3. Problems and Remedies - normality, unequal variances, correlated errors, outliers and influential observations, and multicollinearity. 4. Generalized Linear Model. 5. More Complicated Models- nonlinear regression model, nonparametric and semiparametric regression models. (Two chapters from http://www.springerlink.com/content/nujt764l16289558/fulltext.pdf) 6. Sparse high-dimensional regression and regularization 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 參考書目 Rao, C. R. and Toutenburg, H. (1999). Linear Models: Least Squares and Alternatives. Second Edition. Springer. Grob, J. (2003). Linear Regression. Springer. Sheather, S. (2005) A Modern Approach to Regression with R. 本校電子書 指定閱讀 待補 評量方式(僅供參考)
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