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Monday 1 – Friday 5 August 2022
2 hours of live teaching per day
16:00 – 18:00 CEST
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 can cater to the specific needs of each individual.
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.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
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 we have established a solid understanding of the simple linear regression model, 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.
We will offer a number of online pre-course materials for you to access at your own pace. Readings will be supplemented with around four hours of pre-recorded lectures and interactive R notebooks.
We will set up an RStudio Cloud account for you, where you will find all the data and a ready-made set-up for RStudio. With the help of pre-recorded videos you can start exploring R before the live sessions. You can keep all course materials for future reference.
Pre-recorded lectures will introduce the major topics we will discuss in detail during the course. Similarly, R notebooks let you explore R at your own pace, but we will go through the code and models together during the live sessions. We will set up Canvas forums for each topic where you can discuss, share code and ask questions!
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. Two Instructors will work with you to tackle the theoretical problems you will face in designing your analysis. They will also help you use R to manipulate data, program models, and to visualise data and results.
During the live week you will complete assignments to test the knowledge you have gained. We will discuss these assignments, and any problems you may have, together.
The Instructors will host live Q&A sessions and social breaks. We will also designate ‘office hours’, during which you can sign up for a quick one-to-one consultation.
This course requires a basic understanding of probability. Basic knowledge of R would also be useful. If you don't have this knowledge, consider taking the courses Introduction to R and Introduction to Inferential Statistics.
You must complete up to ten hours' preparatory work. This includes: