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
109-1 
授課對象
學程  生物統計學程  
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
生統教室 
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/1091Agron5087_alsm2 
課程簡介影片
 
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課程概述

Linear and generalized linear models, which have been widely used in the analysis of field trials and breeding studies, are useful tools for agronomic research. 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 covered. In addition, two generalized linear models, including logistic and Poisson regression models, will be introduced for analyzing different types of data. Students will also learn how to use R to analyze real-world data. After successfully completing this course, students will be able to address real-world research issues using regression analysis and interpret the analysis results appropriately. 

課程目標
The objective 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: Thursday 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 e-Book)
Faraway, J. J. (2014). Linear Models with R. Second Edition. Chapman & Hall/CRC.
Searle, S. R. and Khuri, A. I. (2017). Matrix Algebra Useful for Statistics. Second Edition. Wiley. 
指定閱讀
Kutner, M., Nachtsheim, C. and Neter, J. (2004). Applied Linear Regression Models. 4th Edition. McGraw-Hill. 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Homework 
30% 
 
2. 
Exam 1 
20% 
 
3. 
Exam 2 
20% 
 
4. 
Final Exam 
20% 
 
5. 
Final Report 
10% 
 
 
課程進度
週次
日期
單元主題
第1週
9/14  Introduction to Linear Statistical Models 
第2週
9/21  Review of Matrix Algebra 
第3週
9/28  Review of Matrix Algebra 
第4週
10/05  Simple Linear Regression 
第5週
10/12  Simple Linear Regression 
第6週
10/19  Multiple Linear Regression 
第7週
10/26  Multiple Linear Regression 
第8週
11/02  Exam 1 
第9週
11/09  Quantitative and Qualitative Regressors 
第10週
11/16  Quantitative and Qualitative Regressors 
第11週
11/23  Model Selection, Validation and Diagnostics 
第12週
11/30  Model Selection, Validation and Diagnostics 
第13週
12/07  Model Selection, Validation and Diagnostics 
第14週
12/14  Exam 2 
第15週
12/21  Regularization Techniques 
第16週
12/28  Regularization Techniques 
第17週
1/04  Logistic and Poisson Regression (if time permits) 
第18週
1/11  Final Exam