Short Course: Mixed Models using R

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, completed our Fundamentals of Regression course in February 11-13. You would understand linear and generalised linear models using R and have all the background you need.

Presenter and Expertise: Gordana Popovic, Senior Statistical Consultant, UNSW Stats Central

Course Requirements: You will need to bring and use your own computer during the workshop.

Date: Tuesday 13 and Wednesday 14 May 2025

Duration: 9.30am - 4.00pm, each day

Delivery Mode: In-person Only

Location: Wallace Wurth Building

You will receive a certificate of completion for the course.

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

FAQs

How can I contact the organiser with any questions?

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