Linear Regression and Modeling

2020/2021

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered by:

Department of Applied Economics

Type:

Elective course

When:

1 year, 3, 4 module

Course Syllabus

Abstract

The course “Linear Regression and Modeling” is taught on educational online platform “Coursera.org”. Discipline studies are carried out by students independently on the basis of an online course “Linear Regression and Modeling”, https://www.coursera.org/learn/linear-regression-model, Duke University. This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.

Learning Objectives

• Learn the fundamental theory behind linear regression and, through data examples
• Learn to fit and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio

Expected Learning Outcomes

• To introduce Statistics with R and Linear Regression and Modeling.
• To introduce linear regression
• To introduce linear regression and variability partitioning
• Learn to model numerical response variables using multiple predictors

Course Contents

• About Linear Regression and Modeling
  This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through.
• More about Linear Regression
  In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression.
• Linear Regression
  In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.
• Multiple Regression
  In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. Hope you enjoy!

Assessment Elements

• Online tests
• Final interview
• Online tests
• Final interview

Interim Assessment

• Interim assessment (4 module)
  0.3 * Final interview + 0.7 * Online tests

Bibliography

Recommended Core Bibliography

• Montgomery, D. C., Vining, G. G., & Peck, E. A. (2012). Introduction to Linear Regression Analysis (Vol. 5th ed). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1021709
• Yan, X., Su, X., & World Scientific (Firm). (2009). Linear Regression Analysis: Theory And Computing. Singapore: World Scientific. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=305216

Recommended Additional Bibliography

• Hocking, R. R. (2013). Methods and Applications of Linear Models : Regression and the Analysis of Variance (Vol. Third edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=603362
• Meyer C. D. Matrix analysis and applied linear algebra. – Siam, 2000. – 718 pp.