This session will focus on how to fit regression models in R, the most common form of statistical modelling. We will examine the essential theory underpinning these models, then fit linear models with a single predictor, then multiple predictors and look at how to interpret them. We will also look at how to include categorical variables in models and what ‘interaction’ terms are. We will then extend these principles to different types of response, including binary data and count data, by using Generalized Linear Models (GLM). We will fit a logistic regression for binary outcomes and look at how our interpretation differs to linear models. We will consider how we compare model performance and chose a ‘better’ model. A final exercise will give a new dataset and encourage attendees to fit an appropriate model and work out how to improve it.