Explaining and Predicting Outcomes with Linear Regression using Python or R.

DA25-26- EPOLR

Engels

Description

Linear regression models how a continuous dependent variable is associated with one or more predictors of any type. As many practical problems deal with continuous outcomes (e.g. income, blood pressure, temperature, affect), linear regression is a popular and flexible tool.

The first two sessions of this module introduce the conceptual framework of the method using the simple case of a single predictor. Formulas and technicalities are kept to a minimum. The main focus is on interpretation of results and assessing model validity. Our reported results will include precision statements on expected outcomes and explanatory effects (hypothesis tests and confidence intervals). We further use the regression model to predict future results. For this, we provide prediction intervals and verify model performance on independent, left out datasets.

In session 3 and 4 we allow for more than one predictor leading to the multiple linear regression model. We focus on either explanation or prediction. How to come to a parsimonious but possibly flexible model starting from a large number of predictors will be discussed in detail. In these complex linear models, special attention will be given to interpreting individual predictor effects, as they critically depend on other terms in the model and underlying relations between predictors (confounding and effect modification).

In the last session a more elaborate data analysis is discussed. We touch on problems where linear regression is too restricted and replaced by related approaches such as generalized linear models and mixed models.

Different features will be illustrated with case examples from the instructors’ practical experience, and participants are encouraged to bring examples from their own work.

Hands-on exercises are worked out behind the PC in two parallel groups using Python or the R software.

Target audience

This course targets professionals and investigators from all areas who are involved in prediction problems or need to model the relationship between an outcome and one or more explanatory variables.

Course prerequisites

Participants are expected to have an active knowledge of the basic principles underlying statistical strategies, at a level equivalent to the course "Drawing Conclusions from Data: an Introduction ". Basic knowledge of R or Python are also required (see Module 2 (Getting started with to R) and Module 4 (Getting started with Python))

Exam / Certificate

If you attend all 5 sessions you will receive a certificate of attendance via e-mail after the course ends.

Additionally, you can take part in an exam. If you succeed in this test a certificate from Ghent University is issued.
The exam consists of a take home project assignment. You are required to write a report by a set deadline.

Type of course

This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a class on campus.

Schedule

5 Friday evenings in February and March 2026: February 6, 13, 27 and March 6, 13 2026 from 5.30 pm to 9.30 pm. Each lecture is followed by a hands-on practical session.

Venue

UGent, Faculty of Science, Campus Sterre, Krijgslaan 281, Ghent

Course material

Access to lecture notes and data files

Fees

The participation fee is 1100 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations. An exam fee of 35 EUR will be applied.

Industry, private sector, profession*: € 1100
Non profit, government, higher education staff, (Doctoral) students, unemployed: € 495 Full reimbursement by Doctoral Schools is possible (see details below).

*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.

Registration

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UGent PhD students

Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.

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