Centre for Research Informatics Training
Generalised linear models
Event details
Sessions
- 2 December 2026 — 09:30 to 17:00 — Zoom
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About the course
A practical introduction to generalised linear models for researchers analysing biological, experimental, or other research data with non-continuous response variables.
This course teaches how to construct, interpret, and assess generalised linear models using R or Python. The focus is on practical application and interpretation rather than mathematical derivation.
Topics include logistic regression, proportional response models, Poisson regression, negative binomial regression, significance testing, goodness-of-fit, and model diagnostics.
By the end of the course, participants should be able to:
- choose appropriate models for binary, proportional, and count response data
- construct logistic, Poisson, and negative binomial models in R or Python
- plot observed data and fitted model predictions
- assess model significance, goodness-of-fit, and assumptions
- interpret generalised linear model outputs confidently
Teaching is primarily hands-on, with short explanations introducing the statistical ideas needed to apply the methods appropriately.
Intended audience
This course is suitable for:
- researchers and students who analyse research data
- participants who encounter non-continuous response variables, including binary, proportional, or count data
- people who already have experience with standard statistical models and want to extend this to generalised linear models
- participants with a working knowledge of either R or Python
Prerequisites
Participants should have a solid understanding of statistics, ideally equivalent to completion of the Core statistics course covering linear models, regression, hypothesis testing, and model assumptions.
A good working ability in either R or Python is required (for example through completion of the Data analysis in R or Data analysis in Python course). This is not an introductory programming course.
Course fees
All fees are per full training day.
| Category | Fee |
|---|---|
| Industry full charge | £130.00 |
| Academic / Government / charity concessionary | £65.00 |
| Cambridge University staff members / postdocs / visitors | £65.00 |
| Cambridge University registered students | Free |
| Cambridge University registered students non-attendance | £22.00 |
| Special events | Per event |
Payment options will be provided in booking confirmation emails sent after registration.
General information
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