Applied Linear Models

2026/2027

Type: Mago-Lego

Delivered by: International Laboratory for Applied Network Research

Where: Faculty of Social Sciences

When: 2, 3 module

Online hours: 52

Open to: students of one campus

Language: English

ECTS credits: 6

Contact hours: 28

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 and R. 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

This course aims to cultivate a robust, assumption-driven analytical mindset to shift students away from treating statistical software as a "black box" and to provide to the students an important foundation in the applied linear models domain to develop and conduct their own research as well as to evaluate research of others.
The course aims to develop knowledge to help students to justify the selection of a linear model given a specific research question and data structure by critically evaluating assumptions (linearity, independence, homoscedasticity, normality, etc.) and applying appropriate diagnostic tools to identify violations as well as to prepare students to confidently handle messy datasets through appropriate variable selection and model justification.

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 1.1. Theoretical foundations of linear models
Section 1.2. Software for regression analysis. Intro to SAS
Section 2.1. Building Regression “by hand”
Section 2.2. Analysis of Variance
Section 3.1. Linear Regression Assumptions
Section 3.2. Two-Way ANOVA
Section 4.1. Categorical Predictors, Effects, Unusual Observations
Section 4.2. 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 5.1. Categorical Dependent Variables and Generalized Linear Models
Section 5.2. Multiple Regression
Section 6.1. Model Building and Model Selection
Section 6.2. How to Choose the Right Regression Model

Assessment Elements

Quizzes Part 2
Final project
Mid-term project
Quizzes Part 1

Interim Assessment

2026/2027 3rd module
0.24 * Quizzes Part 1 + 0.26 * Final project + 0.26 * Mid-term project + 0.24 * Quizzes Part 2

Bibliography

Recommended Core Bibliography

Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=941245
Applied regression modeling, Pardoe, I., 2012
Eric Goh Ming Hui. (2019). Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics. Apress.
Keith McNulty. (2021). Handbook of Regression Modeling in People Analytics : With Examples in R and Python. Chapman and Hall/CRC.
Linear Regression Using R - An Introduction to Data Modeling - CCBY4_059 - David Lilja - 2022 - Open Educational Resources: libretexts.org - https://ibooks.ru/products/390845 - 390845 - iBOOKS
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

Applied Regression Analysis, Linear Models, and Related Methods, Fox, J., 1997
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
Lindsey, J. K. (1997). Applying Generalized Linear Models. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=104525
Linear regression models. Applications in R, Hoffman, J. P., 2022
Petris, G., Campagnoli, P., & Petrone, S. (2009). Dynamic Linear Models with R. Dordrecht: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=285596

Authors

PETROV ILIYA VLADIMIROVICH
PAVLOVA IRINA ANATOLEVNA

Course Syllabus