This course is for the graduate students enrolled in the deparment of Statistics master program. This course focuses on building a greater understanding, median level of theoretical underpinning, and tools for applying the linear regression model and its generalizations. With a practical focus, it explores the workings of multiple regression and problems that arise in applying it, as well as going deeper into the theory of inference underlying regression and most other statistical methods. The course also covers several new directions of model selection methods and shrikange methods. Through the course, computation skills of utilizing R or Splus are expected.

This course is for the graduate students enrolled in the deparment of Statistics master program. This course on building a greater understanding, median level of theoretical underpinning, and tools for applying the linear regression model and its generalizations. With a practical focus, it explores the workings of multiple regression and problems that arise in applying it, as well as going deeper into the theory of inference underlying regression and most other statistical methods. The course also covers several new directions of model selection methods and shrikange methods. Through the course, computation skills of utilizing utilization R or Splus are expected.

本課程主要目標在介紹迴歸分析之相關方法以及其理論。除此之外，如何利用所學迴歸方法來做實際資料分析亦是本課程之重點，課程主要涵蓋如下：
1.簡單以及多重迴歸之方法及理論
2.迴歸模式適合度檢定以及診斷
3.反應變數之轉換
4.迴歸與變異數分析
5.模式選取
6.利用迴歸相關方法之實例分析

The main goal of this course is to introduce the related methods and theories of regression analysis. In addition, how to use the regression methods learned to do actual data analysis is also the focus of this course. The course mainly covers the following:
1. Simple and multiple regression methods and theories
2. Regression model fitness test and diagnosis
3. Conversion of reaction variables
4. Regression and variation analysis
5. Mode selection
6. Example analysis using regression correlation method

No textbook

No textbook