課程資訊

Applied Linear Statistical Models (I)

109-1

Agron5087

621 U6730

3.0

Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1091Agron5087_alsm2

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.

The objective of this course is to introduce fundamental theory and practical techniques of regression analysis.

Statistics (Agron2002) and Matrix Algebra (Agron4023).
Office Hours

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.

Kutner, M., Nachtsheim, C. and Neter, J. (2004). Applied Linear Regression Models. 4th Edition. McGraw-Hill.

(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