Master
2021/2022
Linear Regression and Modeling
Type:
Elective course (Social Policy and Administration)
Area of studies:
Public Administration
Delivered by:
Institute for Social Policy
Where:
Institute for Social Policy
When:
2 year, 1 module
Mode of studies:
distance learning
Online hours:
9
Open to:
students of one campus
Instructors:
Elena Selezneva
Master’s programme:
Social Policy and Administration
Language:
English
ECTS credits:
3
Contact hours:
2
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
- Learn to model numerical response variables using multiple predictors
- To introduce linear regression
- To introduce linear regression and variability partitioning
- To introduce Statistics with R and Linear Regression and Modeling.
Course Contents
- About Linear Regression and Modeling
- More about Linear Regression
- Linear Regression
- Multiple Regression
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.