RSS01.D5 Introduction to Regression and Inferential Statistics

Regression analysis is the most commonly used statistical technique in the social sciences, Popular more advanced quantitative approaches are also often rooted in regression analysis. Regression analysis is used to explain variation in your dependent variable by one, or multiple independent variables through fitting the best linear (unbiased) estimates to whatever relationship(s) the researcher specifies.

But it is not this simple. In this course, we will learn how to conduct regression analysis in a way that it makes sense. We will go through how to avoid common mistakes and how to avoid the production of nonsensical regression results, or more generally: bad science. To do this, I assume that course participants have a basic understanding of inferential statistics and understands hypothesis testing, variance, central limit theorem, z and t-tests, and Pearson correlations. But if you don't, no worries. As part of this course, I will provide a full online self-guided workshop on all these topics so you can either learn or review the basics. (Just make sure you allocate adequate time for this in the two-three weeks before the start of the course.)

All analyses for the course will be conducted in R but examples can easily be implemented in other software. We will not cover all aspects of data management in R. We will simply load up a clean example dataset and run the models and diagnostics in R. If you want to run regressions on your own data I strongly urge you to also take our R class, though it is not absolutely necessary to do before this course.

Detailed program (pdf, 85 kB)


19 June 2023 - 23 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

May 1st

*Your application is only completed when the course fee has been paid

Course leader Levente Littvay
Level of participant
  • Master
  • PhD
  • PostDoc
  • Professional
Admission requirements The course assumes no prior knowledge and minimizes math burden. If you can add, subtract, multiply, and divide positive and negative numbers, you will be fine. What I do assume is that if you were not previously familiar with any statistical basics, you take the time to go through the self-guided workshop I will provide. Just know that this workshop is as long as, and as much work as this whole week-long regression course itself. Assume the materials will become available only 2-3 weeks before the class and plan accordingly with your time.
Admission documents
  • To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study On Campus
ECTS 2 or 4 Find more information here
Location Radboud University