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Description
This course provides a thorough introduction to the General Linear Model (GLM), which incorporates analyses such as multiple regression, ANOVA, ANCOVA, repeated-measures ANOVA. We will also cover extensions to linear mixed-effects models, and Bayesian hypothesis testing. All techniques will be discussed within a general framework of building and comparing statistical models. Practical experience in applying the methods will be developed through exercises with statistical software, with a choice of either R or JASP.
Module aims: This module is intended to give a more advanced and flexible understanding of the statistical methods to analyse experimental data. The aim is to give students with the skills and confidence to analyse their own data, even in complex and non-standard cases.
Module objectives: Through the course, students are expected to develop the ability to: - structure and summarise quantitative data - construct appropriate statistical models - analyse data with the General Linear Model and extensions into mixed-effects models - test hypotheses through comparing statistical models -- use statistical software for data visualisation and analysis
Key skills provided: Flexible statistical thinking - modelling data with linear models - applying and interpreting statistical hypothesis tests - use of statistical software (R or JASP).
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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