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Applied Regression Analysis: Estimation, Diagnostics, and Modelling

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Monday 3 ꟷ Friday 7 August 2020
2 hours of live teaching per day
Courses will be either morning or afternoon to suit participants’ requirements

Alexandru Moise

European University Institute

Michael Dorsch

Central European University

This course provides a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.

Purpose of the course

This course offers an introduction to regression analysis using R. You will learn the classical theory of Ordinary Least Squares (OLS) regression, as well as non-linear regression techniques. We will use a variety of data and models to show when and how regression can be useful in policy analysis as well as in other contexts.

ECTS Credits

3 credits Engage fully with class activities 
4 credits Complete a post-class assignment

Instructor Bio

Michael Dorsch is associate professor of Economics at CEU's School of Public Policy, where he teaches Applied Regression Analysis, Public Choice, and Public Sector Economics.

Michael is an applied economist whose research covers a variety of topics in political economics.


Alexandru Moise is a postdoctoral researcher at the European University Institute (2020–2025). He received his PhD in political science from Central European University in 2019. Alex's research focus is the political economy of welfare reforms. He looks at how individual perceptions and the quality of party linkages affect health care policy. Within the context of the ERC SOLID project, he looks at how crises affect European integration using a variety of quantitative models. He has been teaching quantitative analysis courses at Johns Hopkins University, the European University Institute, Central European University, Ilija University, and the ECPR’s Summer and Winter Schools for many years.


Regression is the main workhorse of statistical analysis. A solid understanding of regression will pave the way to understanding and using more sophisticated modelling techniques.


A (very) brief review of the necessary ingredients from probability and statistics. We learn the basic functionality of the statistical software R through application, starting with the generation of descriptive statistics and graphics.


We begin with the simple regression model, starting with a theoretical derivation of coefficient estimates in the Ordinary Least Squares (OLS) regression model and an overview of its properties. We will also discuss assumptions that underlie the validity of a simple linear regression model.


Once a solid understanding of the simple linear regression model has been established, we move on to statistical inference. 


We move to multiple linear regression, which allows for more than one explanatory variable. Within the context of multiple regression, we will pay particular attention to identifying models that provide the most credible estimate of the explanatory variable of interest. 


We briefly introduce non-linear regression models and other more advanced regression techniques.

How the course will work online

We will offer a number of materials online before the class for you to access at your own pace. Readings will be supplemented with around four hours of pre-recorded lectures and interactive R notebooks on the Google Colab platform. Please set up a Google account if you do not have one ready, because you will need Google Drive for this.

The pre-recorded lectures will introduce you to the major course topics that we will discuss in detail live during the course week. Similarly, R notebooks let you explore R at your own pace, but we will go through the code and models together in the live sessions. We will set up a Moodle forum on which we will post all materials.

During the course week, expect to be ‘in-class, live’ for over ten hours in total. We will get to know each other and each other's projects and explore how we can apply regression analysis to answer relevant questions in political science. Your two Instructors will work with you on the theoretical problems you will face in designing your analysis, as well as on the practical issues around using R to manipulate data, program models, and visualise data and results.
During the live week we will also ask you to complete assignments to test the knowledge you gained. We will discuss these assignments, and any issues you have, together. Your two Instructors will also host live Q&A sessions, social breaks and a SHOW YOUR BEVERAGE Zoom event. We will also designate ‘office hours’, during which you can sign up for a quick personal consultation.

This course requires a basic understanding of probability. Basic knowledge of R would also be useful. See the courses Introduction to R and Introduction to Inferential Statistics.