Course Overview

Date: 10-12 February 2026
Time: 9.30am to 4.00pm - each day
Delivery Mode: In-Person ONLY
Location: UNSW Sydney, Kensington Campus
Course Requirements:
- We assume knowledge of introductory statistics. Our Introductory Statistics for Researchers short course covers all of the assumed knowledge.
- Participants must have basic R skills prior to workshop. If you do not have basic R skills but want to do this short course, you can enrol in our Introduction to R short course.
- You will need to bring and use your own computer during the workshop with both R and RStudio installed.

You will receive a certificate of completion for the course.

This course provides a comprehensive hands-on introduction to regression analysis techniques The course content is designed for researchers with some prior knowledge of basic statistical testing, such as t-tests, p-values, confidence intervals and simple linear regression. The primary focus is on developing a conceptual understanding of regression models through numerous examples. There will be a strong emphasis on practical implementation in R, and interpretation of output. Approximately half the time will be dedicated to practical hands-on sessions.

The core content starts from linear models with more than one variable, enabling research questions like "What is the effect of this treatment/intervention after adjusting for confounding variables?" or "What is the relationship between two variables while controlling for other factors?" We then cover interactions between variables in linear models, enabling research questions like: "How does the effect of the treatment depend on some other variable? Is the treatment effect different between groups?" and "How is the relationship between two variables modified by some other variable?"

Fundamental regression concepts and skills that arise in regression, like multicollinearity, multiple testing, model selection, generalizing the linear model to data that is non-normal (e.g., binary response and count data), are all covered in this course. By the end of this course, you will have a foundation in regression modelling techniques with the practical experience in R needed for more advanced regression methods like mixed models, longitudinal data analysis, survival analysis, meta-analysis, generalised additive models, multivariate analysis, ordinal and multinomial regression, spatial regression and other extensions.

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Course Outline

• Day 1: Revision, Multiple Regression Introduction and Extensions
• Day 2: Morning/Afternoon: Multiple Comparisons/Model Selection]
• Day 3: Morning/Afternoon: Generalized Linear Models (GLMs)/Generalized Additive Models (GAMs)c