Regression Refresher (before you take a more advanced stats course)

Friday
We cover the conceptual and mathematical foundations of bivariate regression, going briefly into the associated hypothesis test. Then we turn to regression’s multivariate form, focused mainly on the functioning of ordinary least squares.

Saturday
We review the assumptions of regression models, examining why these assumptions exist and what happens if you violate them.

I will offer practical tips on how to deal with assumption violations. We start by discussing model specification, from the perspective of omitted variable bias and the inclusion of unnecessary variables.

We will discuss measurement scales and measurement errors of the variables in the model. We cover homoscedasticity, mean independence, autocorrelation, linearity of relationships and collinearity.

Though strictly speaking not assumptions, I will also outline good practices that prevent assumption violations like the treatment of outliers and diagnosis of all variable distributions.

We cover these topics both in theory and through examples (presented in R, though no knowledge of R is assumed; it really could be done in any software).

This course is a refresher about regression. This means that I assume you know regression. You may just need more context, more in-depth knowledge, maybe just a review before you take a more advanced class.

If you don't know regression, this course is NOT for you.

I expect you to know regression and the basics of inferential statistics, including:

• hypothesis testing
• central limit theorem
• t-test
• correlations.

If you don’t, I recommend instead the week-long regression class, or, if you have no statistical background, the intro class.

If, however, you are ready to venture into a more advanced statistical topic, such as:

• panel data
• time series
• structural equations
• multilevel modelling

this is the course for you.

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.