This is an advanced course. Participants should have seen at least one semester of statistics at university or college. Additionally, we will use the statistical package R for most of the practicals, so participants should be familiar with R. For those who are not, there are numerous courses online that introduce the basics of R. Please familiarise yourself with the package before the course.
The course is divided into a series of modules that build on each other towards more complex linear models, starting from simple linear models, through generalised linear models, to mixed effects models and, if time permits, linear models with generalised least squares. Most time is spent on mixed effects models, so participants should be reasonably competent at using these methods after the course.

Module 1: Classical methods and the linear model (~ 1 day)
The first day revises basic statistical concepts and classical methods (group tests, regression, ANOVA) and links them to the linear model. 
Module 2: Modelling with non-normal data: transformation and alternative distributions (~ 1 day)
The second day deals with transforming non-normal response data back to normality when this is possible, and introduces models for alternative types of data that cannot be normalised, the generalised linear models.
Module 3: Modelling with grouped non-independent data: mixed effects models (~ 3 days)
The bulk of this course deals with the problem of non-independent data and how to fix this appropriately using linear models. Here we introduce concepts of non-independence through experimental designs, first using classic ANOVA and then move onto mixed effect model formulations of the linear model, which deal with non-independent data points that can be grouped.
Module 4: Modelling with non-independent data that cannot be grouped: generalized least squares (~1 day)
Certain types of data are non-independent, but the non-independence cannot be corrected by grouping, e.g. autocorrelation in spatial and temporal data, which depend on individual pairwise distances between points. In this module we introduce a method for dealing non-independence between data points: the generalized least squares.