課程名稱 |
應用線型統計模式 (一) Applied Linear Statistical Models (I) |
開課學期 |
110-1 |
授課對象 |
共同教育中心 統計碩士學位學程 |
授課教師 |
蔡欣甫 |
課號 |
Agron5087 |
課程識別碼 |
621 U6730 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期一6,7,8(13:20~16:20) |
上課地點 |
新402 |
備註 |
建議先修:應用線性代數.統計學程C類(迴歸分析)課程三選一必修。 總人數上限:20人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1101Agron5087_2021 |
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 |
課程大綱
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課程概述 |
Linear and generalized linear models have been widely used in agronomic research. Regression models, a subset of linear models, are the most important statistical analysis tool in an agronomist's toolkit. The primary focus of this course is to introduce fundamental theory and practical techniques of regression analysis. Several important topics, including parameter estimation, hypothesis testing, model selection and diagnostics, will be detailed. In addition, two generalized linear models, including logistic and Poisson regression models, will be introduced for analyzing different types of data. R scripts will be provided to implement the analysis procedures. After successfully completing this course, students will be able to address real-world research issues using regression analysis and interpret the analysis results correctly. |
課程目標 |
The primary focus of this course is to introduce fundamental theory and practical techniques of regression analysis. |
課程要求 |
Statistics (Agron2002) and Matrix Algebra (Agron4023). |
預期每週課後學習時數 |
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Office Hours |
備註: Friday 16:00-17:00 at Biometry Laboratory 202 |
指定閱讀 |
Lecture Notes |
參考書目 |
Fahrmeir, L., Kneib, T., Lang, S. and Marx, B. (2013). Regression: Models, Methods and Applications. Springer-Verlag. (NTU eBook)
Kutner, M., Nachtsheim, C. and Neter, J. (2004). Applied Linear Regression Models. 4th Edition. McGraw-Hill.
Sen, A. and Srivastava, M. (1990). Regression Analysis: Theory, Methods, and Applications. Springer-Verlag. |
評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
Homework |
30% |
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2. |
Midterm Exam |
30% |
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3. |
Final Exam |
40% |
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週次 |
日期 |
單元主題 |
第1週 |
9/27 |
Introduction to Linear Statistical Models/Review of Matrix Algebra |
第2週 |
10/04 |
Simple Linear Regression |
第3週 |
10/11 |
Holiday (Simple Linear Regression) |
第4週 |
10/18 |
Multiple Linear Regression |
第5週 |
10/25 |
Multiple Linear Regression |
第6週 |
11/01 |
Multiple Linear Regression |
第7週 |
11/08 |
Multiple Linear Regression |
第8週 |
11/15 |
Midterm Exam |
第9週 |
11/22 |
Quantitative and Qualitative Regressors |
第10週 |
11/29 |
Model Selection, Validation and Diagnostics |
第11週 |
12/06 |
Model Selection, Validation and Diagnostics |
第12週 |
12/13 |
Shrinkage Methods |
第13週 |
12/20 |
Shrinkage Methods |
第14週 |
12/27 |
Logistic Regression and Poisson Regression |
第15週 |
1/03 |
Logistic Regression and Poisson Regression |
第16週 |
1/10 |
Final Exam |
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