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Applied Regression Analysis: Estimation, Diagnostics, and Modelling

Course Dates and Times

Monday 1 – Friday 5 August 2022
2 hours of live teaching per day
16:00 – 18:00 CEST

 

Michael Dorsch

dorschm@spp.ceu.edu

Central European University

Alexandru Moise

alexandru.moise@eui.eu

European University Institute

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.

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.

@DorschMT

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.

@alexdmoise

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.

Monday 

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.

Tuesday

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.

Wednesday 

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

Thursday

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. 

Friday

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 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.

Before the course

You must complete up to ten hours' preparatory work. This includes:

  • Becoming familiar with R
  • Viewing pre-recorded lectures

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


Disclaimer

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.