課程資訊

Applied Linear Statistical Models (I)

106-1

Agron5087

621 U6730

3.0

Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1061Agron5087_ALSM1

Linear and generalized linear models are useful tools for agronomic research, which have been widely used in the analysis of field trails and breeding studies. The aim of this course is to introduce linear regression model and its applications. The contents of this course cover two parts: matrix algebra and regression analysis. Matrix algebra is a necessary basis for studying linear and other more complicated models. Based on matrix algebra, several important topics in regression analysis, including parameter estimation, hypothesis testing, model selection and diagnostics, will then be introduced. Students will also learn how to use R to analyze real-life data.

After successfully completing this course, students will be able to tackle real-world problems by using regression analysis, and interpret the analysis results correctly.

Statistics
Office Hours

1. Searle, S. R. and Khuri, A. I. (2017). Matrix Algebra Useful for Statistics. 2nd Edition. Wiley.
2. 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 40% 2. Quiz 1 20% 3. Quiz 2 20% 4. Final Exam 20%

 課程進度
 週次 日期 單元主題 第1週 9/11 Fundamentals of Matrix Algebra 第2週 9/18 Determinant 第3週 9/25 Inverse 第4週 10/02 Rank 第5週 10/09 雙十節調整放假 第6週 10/16 Partitioned Matrices 第7週 10/23 Eigenvalues and Eigenvectors 第8週 10/30 Quiz 1 第9週 11/06 Simple Linear Regression 第10週 11/13 Multiple Linear Regression 第11週 11/20 Multiple Linear Regression 第12週 11/27 Quiz 2 第13週 12/04 Quantitative and Qualitative Predictors 第14週 12/11 Quantitative and Qualitative Predictors 第15週 12/18 Model Selection, Validation and Diagnostics 第16週 12/25 Model Selection, Validation and Diagnostics 第17週 1/01 元旦放假 第18週 1/08 Multicollinearity and Remedies (if time permits)