課程資訊
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
生物資源暨農學院  農藝學系  
授課教師
蔡欣甫 
課號
Agron5087 
課程識別碼
621 U6730 
班次
 
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.
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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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). 
Office Hours
備註: Friday 16:00-17:00 at Biometry Laboratory 202 
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. 
指定閱讀
Lecture Notes 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Homework 
30% 
 
2. 
Midterm Exam 
30% 
 
3. 
Final Exam 
40% 
 
 
課程進度
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