The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models.  Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables a systematic approach to key steps in analysis (like assumption checking) and extension to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).

This three-day short course is aimed at applied researchers with little prior experience with R and it assumes knowledge of introductory statistics - you should know about the t-test, linear regression, analysis of variance, and know something about orthogonal and nested designs.  We will revise these methods on Day 1 so you can be a bit rusty, but should have seen them before.  

Make sure you bring your own laptop! We will sort out internet access for you.

 

 

Today we see a variety of experimental designs which we can understand as all minor variations of the same linear model.  We see how to choose between competing models based on their predictive performance, and how to deal with random vs fixed factors.

 

Today we look at how to extend the linear model framework to analyse repeated measures, non-linear data, and binary or count responses, and repeated measures in time.