Applied Regression Analysis: Estimation, Diagnostics, and Modelling

About

This course offers an immersive online learning environment that employs state-of-the-art pedagogical tools. With a maximum of 16 participants, our teaching team can provide personalised attention to each individual, catering to their specific needs. The course is designed for a demanding audience, including researchers, professional analysts, and advanced students.

ECTS Credits
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment

Course leader

Daniel Kovarek is a Research Fellow at the European University Institute, at the Florence School of Transnational Governance. He holds a PhD in Political Science from the Central European University. He studies political behaviour at the voter and the elite level; his expertise lies in the intersection of political geography and distributive politics. Previously, Daniel has been teaching a wide variety of graduate-level courses on quantitative methods, applied statistics, experiments, programming, research design, as well as comparative politics. His research has appeared in Research & Politics, The ANNALS of the American Academy of Political and Social Science and Democratization, among others

Target group

Social science researchers

Course aim

This course will introduce you to regression analysis using R. You will learn about the classical theory of Ordinary Least Squares (OLS) regression, along with non-linear regression techniques. Through the use of various data sets and models, you will gain an understanding of when and how regression can be valuable in policy analysis and other contexts

Fee info