Regression Analysis
Course No.: SMI1132004 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 | |
