Introduction to Generalised Linear Models in R - Online
- Level(s) of Study: Short course; Professional
- Course Fee:
£360
- Start Date(s): Tuesday 3 June 2025
- Duration: Two days (Tuesday and Wednesday) 9:30 am – 5:30 pm
- Study Mode(s): Part-time
- Entry Requirements: More information
Introduction:
Develop your skills in Generalised Linear Models (GLMs) using R and learn how to handle more complex forms of data analysis.
This two-day course offers a practical introduction to GLMs, equipping you with the knowledge and confidence to apply these models effectively. Moving beyond ordinary linear regression, GLMs allow you to model a wide range of data types, including binary, ordinal, categorical, and count-based outcomes.
Through expert led online instruction and hands-on coding exercises, you'll gain a solid understanding of how and when to use GLMs and how to interpret and evaluate your results in a meaningful way.
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A structured introduction to GLMs – Understand the key principles and how they extend standard regression models. -
Practical experience using R – Learn how to fit, interpret, and assess GLMs with real-world datasets. -
A focus on real-world application – Develop the confidence to apply GLMs in research, industry, and professional settings. -
Guidance from an expert tutor – Gain insights from an experienced statistician with a strong background in applied data analysis.
What you’ll study
By the end of the course, you will have a solid grasp of the major GLMs used in modern statistical analysis. You will be able to identify when a particular model is appropriate, fit it using R, interpret the results responsibly, and critically evaluate its assumptions—skills that will serve you well in any field where data are more complex than a simple linear trend.
Topic 1: The General Linear Model
Understand the basics of the ordinary linear model and how it serves as the foundation for GLMs.
Topic 2: Binary Logistic Regression
Learn how to derive, implement, and interpret binary logistic regression models using glm() in R.
Topic 3: Ordinal Logistic Regression
Apply cumulative logit ordinal models using the ordinal package’s clm() function.
Topic 4: Categorical (Multinomial) Logistic Regression
Model multi-category outcomes and extend your knowledge beyond binary cases.
Topic 5: Poisson Regression
Model count data effectively using the Poisson regression framework.
Topic 6: Binomial Logistic Regression
Apply binomial regression for count data with a fixed upper limit.
Topic 7: Negative Binomial Regression
Address overdispersion in count data with negative binomial models.
Topic 8: Zero-Inflated Models
Learn how to handle datasets where excess zeros require specialised modelling approaches.
How you’re taught
This course is designed to provide a practical, engaging learning experience incorporating hands-on workshops and coding sessions, concise expert led lectures, and real-world data examples to reinforce key concepts.
Delivery Format: Online via Zoom allowing you to interact with instructors and fellow participants in real time. You’ll also have access to downloadable resources including code, datasets, and exercises for you to continue practicing and applying your skills after the sessions.
Contact hours
The course will take 6 contact hours per day plus two 1-hour breaks.
The sessions will be as follows:
- Session 1: 9:30am - 11:30am
- Session 2: 12:30pm - 2:30pm
- Session 3: 3:30pm - 5:30pm.
Careers and employability
Certificate of attendance and digital badge
Upon successful completion of the course, you will receive a digital certificate of attendance and a digital badge powered by .
Your is more than just a certificate – it’s secure, verifiable, and protected against fraud through encryption and blockchain technology.
They also come with detailed metadata, including an overview of the skills you have achieved on the course, evidence of completion, and assessment criteria if appropriate.
Share your achievements seamlessly with friends, customers, and potential employers online, and proudly add your badge or certificate to social media platforms such as LinkedIn, so all the right people can see it.
Entry requirements
This course is designed for those who already possess a basic understanding of linear modelling and are ready to take the next step in their statistical toolkit. Our approach emphasises clarity, careful reasoning, and practical skills over unnecessary complexity, ensuring that you can confidently apply these techniques to your own research questions.
The course is particularly well-suited for the following groups:
Early-Career Researchers and PhD Students
If you’re beginning your research journey, this course will help you develop robust data analysis skills essential for conducting high-quality, reproducible studies. Learn to navigate RStudio confidently and apply statistical techniques that will elevate your research output.
Postdoctoral Researchers and Academics
As a Postdoc or Academic, this course provides an opportunity to deepen your expertise in data science. Enhance your ability to analyse complex datasets, visualise results, and streamline your workflow with RMarkdown. These skills can improve your efficiency, strengthen your grant applications, and make your research more impactful.
Data Analysts and Statisticians
For professionals working in data-heavy roles, such as those in government, healthcare, or private sector organisations, this course offers tools and techniques to tackle statistical challenges efficiently. Gain skills in data wrangling, visualisation, and foundational statistical modelling that will add value to your current role and career progression.
Industry Professionals Transitioning to Data Science
If you’re transitioning into data science or statistical analysis roles, this course provides an excellent foundation. Learn how to use RStudio effectively for data manipulation, creating visualisations, and conducting statistical analyses that are increasingly in demand across industries like finance, marketing, and technology.
Prerequisites: Familiarity with R is assumed. Foundational concepts are covered in our course, Introduction to Statistics Using R and RStudio.