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科目代碼 |
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科目名稱 |
應用統計線性模型 |
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授課老師 |
曹振海 |
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開課班級 |
碩一、碩二、博士班 |
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每週授課時數 |
三小時 |
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校內分機 |
3520 |
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教師電子郵件 |
chtsao@mail.ndhu.edu.tw |
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教師辦公室 |
理工學院A411 |
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會談時間 |
Tues 1310-1500 |
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課程助教 |
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助教電子郵件 |
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助教工作項目 |
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課程目標 |
Applied linear models for regression, analysis of variance and experimental design are widely used in various application domains. Starting with the simple linear models, we will cover the main topics in linear statistical models such as multiple linear regressions, ANOVA, logistic regression, etc. Students will be exposed to both theoretical and practical aspects of applied linear models. Selected topics such as statistical machine learning, variable selection and PCA will also be briefly addressed if time permits. |
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教學方法 |
Some possible topics/problems for group projects will be announced early in the class. These projects will be integrated with lectures, data analysis, class discussion and presentation. The statistical freeware R will be used for data analysis. |
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教學評量 |
Homework （15％）>/FONT>Project and presentation（30%）Midterm （20%）Final （35%） |
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課堂教材 |
Kutner, M.H., Nachtsheim, C.J., Neter, J. and Li, W. (2005). Applied Linear Statistical Models, 5th edition. McGraw-Hill. (TextBook) Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W. (1990). Applied Linear Statistical Models, 4th edition. Irwin Inc.
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其他教材 |
Draper,
N.R. and Smith, H. (1981). Applied Regression Analysis, 2 Montgomery, D.C. and Peck, E. (1991). Introduction to Linear Regression Analysis, 2nd edition. Wiley. Sen, A. and Srivastava, M. (1990). Regression Analysis: Theory, methods and applications. Springer. Lehmann, E.L. (1986). Testing Statistical Hypotheses. 2nd edition. Wiley. Scheffe, H. (1959). The Analysis of Variance. Wiley. R website: http://www.r-project.org/ |
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>FONT FACE="標楷體, cursive">作業備註 |
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其他標題 |
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其他內容 |
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週次 |
日期 |
進度 |
重要事項 |
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1 |
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Introduction: Overview and motivation Review of Simple Linear Regression |
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2 |
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Simple Linear Regression: Inference and Diagnostics for Simple Linear Regression |
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3-4 |
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Multiple Regression: Matrix Notation, Inference and Diagnostics for Multiple Regression |
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5 |
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Model Selection: Forward, Backward Selection, etc. |
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6-7 |
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Categorical Independent Variables: Bridge to ANOVA |
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8-9 |
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Midterm, One-way ANOVA |
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10-11 |
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Two-way ANOVA, Multi-way ANOVA: Inference, Model validation |
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12-13 |
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Logistic Regression: Inference and application; Generalized Linear Models |
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14-15 |
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Special Topics: Discussion and Presentation Topics such as statistical machine learning, variable selection under multicollinearity, PCA, etc. |
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16-18 |
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Project Presentation/Discussion |
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18 |
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Final Exam |
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