Regression can be seen as one of the core techniques used in statistics, where we are interested in predicting an outcome variable based upon a set of predictor variables. We then have different kinds of regression for predicting different kinds of outcome variables.

In this workshop we will explore the foundation for linear and logistic regression. The workshop will cover:

• the basics of statistics (including concepts such as p-values and the role of statistical tests)

• the fundamental equations for linear and logistic regression

• the assumptions that need to be checked when performing regression (linearity, normality, constant variance, no outliers)

• multiple regression and the effect of confounders

• variable selection (choosing which variables to select for a linear regression model)

• the differences between linear and logistic regression

• interpreting the regression coefficients for logistic regression

• nominal and ordinal regression

• Poisson regression (for count variables)

This workshop consists of 4 x 1.5 hour sessions in PowerPoint, and 4 x 1.5 hour practical sessions in R.

This workshop is intended for non-statisticians wishing to develop a solid foundation in the use of these techniques. A basic knowledge of statistics is assumed, but participants need not have a prior knowledge of linear or logistic regression.

These workshops are designed for a broad range of participants, researchers from various application fields who only spend a small portion of their time doing statistics (but researchers who when they do statistics want to be doing statistics well). Communicating complex statistical ideas to a non-statistical audience requires significant skill. Equations for example are vital for statisticians who want to describe statistical models accurately but succinctly, however these equations can be a stumbling block for a larger audience who are not used to working with complex equations. We think one sign of a good statistician is that they know the pictures that go on behind the equations, and in our workshops we focus on the pictures rather than the equations.

Working with this broad audience we teach complex statistical theory (we find it heart-breaking when excellent research studies are undone by a poor understanding of underlying statistical theory), but we teach this theory using a number of practical real-world examples. We are also a strong believer in the use of humour in a training context, where humour means that participants are more likely to relax in a training context, to engage with the training content and the presenter, and are more comfortable to ask the questions that they really want to ask about the content as a result.

Each workshop will be capped at 20 participants.