Course Overview

Simple statistical methods (t-tests, chi-square tests, linear models/regression) assume independence of observations and cannot be used when dependence is present in the sample. Mixed models are extensions of linear models to dependent data. Common reasons for dependence are:

Clustering e.g. multiple patients per hospital, multiple plants per site, multiple measurements on the same individual
Time and Space - measurements taken close in time and space are more similar (dependent)

In this course we will teach mixed models as a straightforward extension of (generalised) linear models. The course will include lectures and practical sessions, and explain how to fit, check, interpret and communicate results from mixed models.

Course Pre-requisite: Ability to run, check and interpret linear models in R. You would understand linear and generalised linear models using R and have all the background you need.

Presenter and Expertise: Nancy Briggs, A/Professor, UNSW Stats Central
Course Requirements: You will need to bring and use your own computer during the workshop.

Date: 12-13 May 2026
Duration: 9.30am - 4.00pm, each day
Delivery Mode: In-person Only
Location: AGSM LG06

NOTE: Course materials will be sent out closer to the course date so ensure you provide a correct email address.

Note: If you have a funding support and would like to pay by "Internal Funding Payment", please contact us stats.central@unsw.edu.au.** Thank you!

FAQs

How can I contact the organiser with any questions?

Please send us an email at: stats.central@unsw.edu.au

Contact the host of Short Course: Mixed Models using R, 12-13 May 2026