Applied Regression Analysis
Date: 2018-10-24 Views: 13

Applied Regression Analysis

Course No.: SMI1131003    Credit(s): 3

Course Description

Regression Analysis refers to a statistical analysis of the quantitative relationship between two or more variables and more than two variables. Regression Analysis can be divided into a regression analysis and multiple regressions according to the number of variables involved; and can be divided into simple regression analysis and multiple regression analysis according to the dependent variable. According to the independent and dependent relationship among variables, it also can be divided into linear regression analysis and nonlinear regression analysis. This course mainly studies the basic methods of regression analysis and practice in combination with R software.

Course Learning Outcomes

After studying the course, Students will grasp the models of regression analysis, measure the models which violate the classical regression models, and do regression analysis with practical problems. Students can apply various methods to write academic papers independently.

Relationship to Other Courses

The prerequisite for this course is Linear Algebra, Statistical Software, and Probability Theory and Mathematical Statistics etc.

Textbook and Reading Lists

Textbook:

Zina Li, Econometrics. Tsinghua University Press, September 2010.

Suggested reading lists:

Liming Wang, Application Regression Analysis. Fudan University Press, 2008.

Jixiang Zhou, regression analysis. East China Normal University Press, 1993.

Niansheng Tang, Applied regression analysis. Science Press, 2014

  

Course Assessment

Item

Title

Weighting (%)

1

Task   in home

10%

2

Test   and Questions in class

10%

3

Final   exam

80%

Course Schedule

Week

Topics

Text

1

Overview   of Regression Analysis

Chapters 1

2

Linear   Regression of One Element

Chapters 2

3

Multiple   Linear Regression

Chapters 3

4 -5

A Violation   of the Basic Assumptions

Chapters 4

6

Independent   Variable Selection and Stepwise Regression

Chapters 5

7

The Case   of Multiple Collinearity and its Treatment

Chapters 6

8

Ridge Regression

Chapter 7

9

Principal   Component Regression and Partial Least Squares

Chapter 8

10

Nonlinear   Regression

Chapter 9

11-12

Regression   Model with Qualitative Variables

Chapter 10

13-14

Panel Data   Regression Model

Chapters 11

15-16

Time Series   Econometric Model

Chapters 12

17

Cointegration   and Error Correction Model

Chapters 13

18

Review