Introduction to Generalised Linear Models
Introductory on-demand course in Generalised Linear Models using R, covering logistic and Poisson regression, overdispersion, and model interpretation for applied research.
Duration: 12 Hours
Format: Recorded ‘on-demand’ Format
£250Registration Fee
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- Overview
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Course Description
This course is designed to provide attendees with a comprehensive understanding of statistical modelling and its applications in various fields, such as ecology, biology, sociology, agriculture, and health. We cover all foundational aspects of modelling, including all coding aspects, ranging from data wrangling, visualisation and exploratory data analysis, to generalized linear mixed models, assessing goodness-of-fit and carrying out model comparison.
What You’ll Learn
During the course will cover the following:
- General linear model.
- Logistic regression.
- Poisson regression.
- Overdispersion.
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 designed for anyone interested in using R for data science or applied statistics. R is a powerful and widely used tool across academic research, government, and industry for data analysis, modelling, and visualisation. Participants should have a basic understanding of key statistical concepts such as hypothesis testing, statistical significance, and generalised linear regression models. They should also be familiar with R, including the ability to import and export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.
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.
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.
Dr. Rafael De Andrade Moral
Rafael is a statistician working at the intersection of ecological science, environmental research, and applied statistical modelling. His work focuses on developing and applying statistical and mathematical tools to understand ecological dynamics, improve wildlife management strategies, and support sustainable agricultural and environmental practices. With a strong foundation in both biology and statistics, Rafael’s research spans areas such as hierarchical modelling, population dynamics, and the integration of ecological theory with real-world data.
Rafael holds a PhD in Statistics from the University of São Paulo, building on an undergraduate background in Biology. He is currently an Associate Professor of Statistics at Maynooth University, Ireland, where he also leads the Theoretical and Statistical Ecology Group — a multidisciplinary research hub dedicated to advancing quantitative ecology.
In addition to his academic work, Rafael is deeply invested in science communication and innovative teaching. He produces educational music videos and statistical parodies, using creative media to make statistical concepts more engaging and accessible to students and the public alike.
Education & Career
• PhD in Statistics – University of São Paulo
• BSc in Biology
• Associate Professor of Statistics – Maynooth University
• Director – Theoretical and Statistical Ecology Group
Research Focus
Rafael’s research is rooted in ecological and environmental statistics, particularly:
• Statistical modelling of species distributions and abundance
• Applications of Bayesian and hierarchical models in wildlife and agricultural contexts
• Integrative approaches combining field data, simulation, and theory to inform policy and conservation
• Methodological innovation in data-poor or complex ecological systems
Current Projects
• Statistical methods for population modelling and biodiversity monitoring
• Quantitative frameworks for wildlife management under uncertainty
• Modelling ecological responses to climate and land-use changes
• Public outreach through creative science communication in Statistics
Professional Activites
Rafael collaborates widely with ecologists, conservationists, and agricultural scientists, providing expert statistical input on study design, modelling, and data analysis. He also supervises postgraduate research across interdisciplinary projects in quantitative ecology.
Teaching & Skills
• Teaches courses in statistical modelling, environmental statistics, and data analysis in R
• Promotes engaging and inclusive teaching practices, including music-based educational content
• Advocates for open science, reproducibility, and the integration of theory with application
Links
• ResearchGate
• Google Scholar
• ORCID
• GitHub
Session 1 – 01:20:00 – The general linear model.
We begin by providing an overview of the normal, as in normal distribution, general linear model, including using categorical predictor variables. Although this model is not the focus of the course, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models.
Session 2 – 01:20:00 – Binary logistic regression.
Our first generalized linear model is the binary logistic regression model, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression, implement it using R’s glm, and then show how to interpret its results, perform predictions, and (nested) model comparisons.
Session 3 – 01:20:00 – Binomial logistic regression.
Here, we show how the binary logistic regression can be extended to deal with data on discrete proportions. We will also present alternative link functions to the logit, such as the probit and complementary log-log links.
Session 4 – 02:00:00 – Categorical logistic regression.
Categorical logistic regression, also known as multinomial logistic regression, is for modelling polychotomous data, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression, categorical logistic regression is also based on an extension of the binary logistic regression case.
Session 5 – 02:00:00 – Poisson regression.
Poisson regression is a widely used technique for modelling count data, i.e., data where the variable denotes the number of times an event has occurred.
Session 6 – 02:00:00 – Overdispersion models.
The quasi-likelihood approach for both the Poisson and binomial models. Negative binomial regression. The negative binomial model is, like the Poisson regression model, used for unbounded count data, but it is less restrictive than Poisson regression, specifically by dealing with overdispersed data. Beta-binomial regression. The beta-binomial model is an overdispersed alternative to the binomial.
Session 7 – 02:00:00 – Zero inflated models.
Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models.
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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