Stats Central (UNSW) is offering a short course on multivariate statistics.

Date: 4 July 2024

Duration: 9.00 - 4:30pm

Location: UNSW Sydney Kensington Campus.

Registration: https://events.humanitix.com/short-course-multivariate-statistics-using-r

Course Overview

This workshop extends on the Fundamentals of Regression course and introduces multivariate statistics in a model based framework. We move beyond a single response variable to visualising and analysing a collection of correlated response variables. In this course, many of the motivating applications come from ecology, though the methods do generalise to multivariate data in other settings. Methods available to you depend on the number of responses relative to your sample size; and like in univariate regression we also need to think about the response variable type (binary, count etc). There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. It is designed to help you, the researcher. There will be plenty of practical work.

Regression, with normal and non normal responses, and basic R skills are assumed knowledge. These are covered in our R course, Fundamentals of Regression Course and Mixed Models Course. Please note - this course is NOT about regression with multiple predictor variables - for example when many predictor variables might be correlated with your outcome variable. Our brief pre-enrolment questionnaire is designed to help you decide if you are ready for this course.

Course outline

Introduction to multivariate data – with fewer response variables

• What is a multivariate research question
• Why use multivariate methods
• Covariance matrices
• Analysis with manova
• Checking model assumptions

Multivariate data – with lots of response variables

• Reducing the rank of the covariance matrix
• Reduced Rank Analysis with PCA
• Reduced Rank Analysis with generalised latent variable models (glmmTMB)
• Visualising high dimensional data

Multivariate data – hypothesis testing with LOTS and LOTS of response variables

• Design based inference in mvabund
• Analysing Compositional data – row effects and offsets
• Correlation types