FREE COURSE Introduction to Generalised Linear Models for Ecologists
Online course introducing Generalised Linear Models (GLMs) in R. Ideal for ecologists and applied scientists working with binary or count data.
Duration: 3.5 Hours
Format: Recorded ‘on-demand’ Format
£0Registration Fee
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- Overview
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Course Description
This free one–day course, a standalone introduction and part of our longer, more comprehensive course GLME01, offers an accessible overview of Generalised Linear Models (GLMs) using the R programming language. GLMs are a powerful extension of traditional linear models that allow for response variables with non-normal error distributions, such as binary, count, or proportion data. During the course, we will explore different types of regression, their applications in ecological data analysis, and how to choose the right model for your data. The day will conclude with a hands-on practical session where you’ll learn how to fit a GLM in R.
No prior experience with GLMs is required, but basic familiarity with R and linear models is recommended if you want to follow the practical session live.
All code, datasets, and presentation slides will be shared with participants prior to the course via GitHUb.
What You’ll Learn
During the course will cover the following:
- The theoretical foundations of Generalised Linear Models (GLMs).
- How to choose appropriate error structures and link functions.
- How to fit logistic and Poisson GLMs in R.
- How to interpret model coefficients.
- How to apply GLMs to real-world problems.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is intended for anyone, in particular, ecologists, data analysts, postgraduate students, and early-career researchers who have a basic background in using R and RStudio, such as importing data and running simple functions. Participants are expected to have a foundational understanding of statistics, including concepts like mean, variance, correlation, and linear regression. But even if you have none of these you are still welcome attend and listen.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Niamh Mimnagh
Niamh is a statistician working at the interface of ecology, epidemiology, and data science. Her research focuses on applying and developing statistical and machine learning methods to address real-world challenges such as estimating species population sizes from count and trace data and predicting livestock disease re-emergence using sparse or imbalanced datasets. She works with a wide array of statistical approaches, including Bayesian hierarchical models, N-mixture models, anomaly detection algorithms, and spatial analysis techniques.
Niamh earned her PhD in Statistics, with a focus on multispecies abundance modelling, and holds a first-class MSc in Data Science. Alongside her research, she is actively engaged in science communication and education, running a popular blog on applied statistics for non-specialists, and regularly delivering workshops and guest lectures on topics such as GLMs and machine learning with imbalanced data.
Education & Career
- PhD in Statistics (Multispecies Abundance Modelling)
- MSc in Data Science (First Class Honours)
- Instructor, consultant, and science communicator in statistical ecology and epidemiology
Research Focus
Niamh’s work centres on extracting meaningful insights from complex ecological and epidemiological data. She is particularly interested in population estimation techniques and predictive modelling for conservation and disease management, using advanced statistical tools and reproducible workflows.
Current Projects
- Development of Bayesian and ML approaches for estimating species abundance from imperfect data
- Modelling livestock disease risk using spatial and temporal predictors
- Creating accessible educational materials for teaching applied statistics in R
Professional Consultancy
Niamh provides expert statistical support to academic and applied research projects, with a focus on ecological monitoring, conservation planning, and disease modelling. She also advises on study design and data workflows for interdisciplinary teams.
Teaching & Skills
- Teaches topics including GLMs, Bayesian statistics, machine learning for imbalanced data, and spatial statistics in R
- Advocates for reproducibility, open science, and accessible statistical training
- Experienced in communicating complex methods to broad audiences
Links
An introduction to GLM’s for ecologists
Session 1- 01:15:00 – Introduction to Linear Models
Overview of the GLM framework; why linear regression fails for binary and count data; distributions, link functions, and the three GLM components.
Session 2- 01:15:00 – From Linear Models to GLMs
Logistic regression for binary data; log-odds and odds ratios; interpreting coefficients, making predictions, and visualising probability curves.
Session 3- 01:15:00 – Introduction to GLMs in R
Poisson regression for count data; Practical GLM workflow; model fitting, diagnostics, interpretation; preview of advanced topics in the full course.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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