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Member rate £492.50
Non-Member rate £985.00
Date: Monday 16 – Friday 20 December 2024
Time: 09:30 – 12:30 CET
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.
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.
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Daniel Kovarek is a postdoctoral research fellow at the European University Institute. He studies political behaviour at the voter and the elite level; his expertise lies in the intersection of political geography and distributive politics. Within the context of the ERC SOLID project, he looks at how crises affect European integration using a variety of quantitative models. Daniel has been teaching a wide variety of graduate-level courses on applied statistics, research design and programming at Central European University and colleges of Corvinus University of Budapest. His research has appeared in Research & Politics, The ANNALS of the American Academy of Political and Social Science and Environmental Politics, among others.
The concept of regression is fundamental to statistical analysis and serves as the foundation for more advanced modelling techniques. Gaining a firm grasp on regression analysis is essential for building a strong understanding of statistical analysis overall.
A brief review of the necessary ingredients from probability and statistics. You will learn the basic functionality of the statistical software R through application, starting with the generation of descriptive statistics and graphics.
Beginning with the simple regression model, you will learn about a theoretical derivation of coefficient estimates in the Ordinary Least Squares (OLS) regression model and an overview of its properties. You will also discuss assumptions that underlie the validity of a simple linear regression model.
Once you have established a solid understanding of the simple linear regression model, the instructor will move on to statistical inference.
Covering multiple linear regression, which allows for more than one explanatory variable. Within the context of multiple regression, you will pay particular attention to identifying models that provide the most credible estimate of the explanatory variable of interest.
Non-linear regression models will be introduced along with other more advanced regression techniques.
You will have access to a number of online pre-course materials for you to work through at your own pace. Readings will be supplemented with around four hours of pre-recorded lectures and interactive R notebooks.
You will be provided with an RStudio Cloud account , 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 you will discuss in detail during the course. R notebooks will let you explore R at your own pace, along with discussing the code and models together during the live sessions. Moodle forums will be created for each topic where you can discuss, share code and ask questions.
During the course week, expect to be in live sessions, which will be delivered through Zoom, amounting to 15 hours in total. You will get to know each other and each other's projects, and explore how you can apply regression analysis to answer relevant questions in political science. The instructor 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.
You will complete assignments to test the knowledge you have gained. You can discuss these assignments, and any problems you may have, together. The instructor will host live Q&A sessions and social breaks. There will also be designated ‘office hours’, during which you can sign up for a quick one-to-one consultation.
You must have a basic understanding of probability.
You must complete up to ten hours' preparatory work. This includes:
As a participant in this course, you will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.
Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings. The time commitment required to familiarise yourself with the content and complete any pre-course tasks is estimated to be approximately 20 hours per week leading up to the start date.
During the course week, you are expected to dedicate approximately two-three hours per day to prepare and work on assignments.
Each course offers the opportunity to be awarded three ECTS credits. Should you wish to earn a 4th credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.
This comprehensive approach ensures that you not only attend the live sessions but also engage deeply with the course material, participate actively, and complete assessments to solidify your learning.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.