Applied Linear Models

Master 2024/2025

Type: Compulsory course (Data Analytics and Social Statistics)

Area of studies: Applied Mathematics and Informatics

Delivered by: International Laboratory for Applied Network Research

Where: Faculty of Social Sciences

When: 1 year, 2, 3 module

Mode of studies: distance learning

Online hours: 52

Open to: students of one campus

Instructors: Irina Pavlova, Anna Semenova

Master’s programme: Аналитика данных и прикладная статистика

Language: English

ECTS credits: 6

Full Syllabus Ask Question

Abstract

The objective of the discipline "Applied Linear Models" is to ensure that students understand topics and principles of applied linear models and includes two parts. The first part is devoted to practical regression analysis introducing basic concepts and providing basic knowledge of regression models building and analysis with SAS. The second part covers theoretical foundations of linear models providing more advanced concepts, considering diversity of regression types, including model selection process both with SAS and R. The course is strongly related and complementary to other compulsory courses provided in the first year of the master's programme and sets a crucial prerequisite for later courses and research projects as well as for the master thesis.

Learning Objectives

The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

Expected Learning Outcomes

Be able to explore the advantages and disadvantages of various linear modeling instruments, and demonstrate how they relate to other methods of analysis
Be able to work with major linear modeling programs, especially SAS, so that they can use them and interpret their output.
Have an understanding of the basic principles of linear models and lay the foundation for future learning in the area
Have the skill to meaningfully develop an appropriate model for the research question
To know modern extensions to applied regression, including working with “problem data”
To know the basic principles behind working with all types of data for building regression models
To know the theoretical foundation of applied linear modeling, starting with the univariate models
Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
Be able to criticize constructively and determine existing issues with applied linear models in published work

Course Contents

Section 1. Software for regression analysis. Intro to SAS
Section 2. Analysis of Variance
Section 3. Two-Way ANOVA
Section 4. Simple Regression
During this lecture, the learners will get the essence of the linear regression model. After understanding the mathematical meaning of Regression, we will talk about the rules of usage of linear Regression, what tasks it is used for, and what results it can give. Also, we will talk about possible cases when Regression can be used.This lecture will give the learners not only a theoretical understanding of Regression but practical experience. The whole week will be dedicated to working with SAS, from modeling to the finished regression model.
Section 5. Multiple Regression
Section 6. How to Choose the Right Regression Model
Section 7. Theoretical foundations of linear models
Section 8. Building Regression “by hand”
Section 9. Linear Regression Assumptions
Section 10. Categorical Predictors, Effects, Unusual Observations
Section 11. Categorical Dependent Variables and Generalized Linear Models
Section 12. Model Building and Model Selection

Assessment Elements

Quizzes
Quizzes
Mid-term project
Final project

Interim Assessment

2024/2025 3rd module
0.26 * Final project + 0.26 * Mid-term project + 0.24 * Quizzes + 0.24 * Quizzes

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
Weisberg, S. (2014). Applied Linear Regression (Vol. Fourth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=771773
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

Elliott, A. C., & Woodward, W. A. (2016). SAS Essentials : Mastering SAS for Data Analytics (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1051725
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

Authors

Pavlova Irina Anatolevna

Course Syllabus