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Statistical Modelling using Generalised Linear Models (GLMs) - Part 1

Target audience

The course is for University of St Andrews staff, and students matriculated on a doctoral degree or the following research degree programmes – MSc (Res), MSt (Res), and MPhil (by research). The course is not open to students on an undergraduate degree or to students on a taught postgraduate degree programme (MSc, MLitt, or MRes).

Qualifications needed

The prerequisites for this course are: • to understand concepts of basic statistics, in particular hypothesis testing and simple linear regression. The “Introduction to Statistics” course would be an ideal introduction, and this should be completed before enrolling on this course. • be able to write basic commands in R to undertake an exploratory data analysis, fit simple linear regression model and interpret the R output. You are encouraged to install R and RStudio on your laptop before the course starts.

Course pre-work

none

Course information

Statistical modelling using generalised linear models (GLMs) is a free course for University of St Andrews research degree students (see eligibility advice below) and staff. The course covers extensions to linear models and generalised linear models; these models describe some form of response as a function of one, or more, explanatory variables and are powerful tools in understanding complex systems or predicting an outcome. The whole course is split into two parts (one part per semester) and participants can do one or both parts (see entry requirements).

The course is facilitated by St Leonard's Postgraduate College and the Centre for Educational Enhancement and Development (CEED) and delivered by the Centre for Research into Ecological and Environmental Modelling (CREEM).

Aims and objectives

Content and Structure

Semester 1

1. Introduction and Extension of Linear Models
   1. A re-cap of multiple linear regression
   2. Generalized least squares and maximum likelihood
2. Introduction to Generalised Linear Models
   1. Poisson response data
      1. For data with response values that are count

Semester 2

Continuation of GLMs

2. 2. Binomial response data (also known as logistic regression)
      1. For data with response values that are proportions
      2. or binary (0 or 1 responses only)
3. More advanced Generalised Linear Models
   1. Multinomial response data (also known as multiple outcome or multi-category models)
      1. data with unordered categorical responses (nominal), and
      2. ordered categorical responses (ordinal)

The course includes a substantial practical component and the methods covered will be implemented using the statistical programming language R and user-friendly environment RStudio.

The whole course is spread over semesters 1 and 2 and there will be a guided programme for self-study. Course material (lecture notes, exercises and computer practicals) will be available on Moodle.

There will be an in-person introductory session to get you started at 11am on Friday 3^rd October 2025 and throughout the course there will be online help sessions (the exact times and dates TBC), and a final in-person session to finish the course. You are encouraged to attend the introductory and final sessions in-person to meet the course tutor and provide feedback. Participation in the help sessions is optional. 

Your weekly time commitment is anticipated to be about 2-3 hours learning through the course notes and associated computer practical and attending the optional help session.

There is an option for participants to obtain a certificate for each part by attempting all assessed quizzes and obtaining a pass. See the course timetable for the deadline for submitting responses to the assessed quizzes.

Course provider

St Leonard's Postgraduate CollegeEmail: stlc@st-andrews.ac.uk