This masterclass is an introduction to linear and non-linear predictive models. It will provide an interactive step-by-step guide to running these models and key diagnostics using the R software platform.

Regression modelling is a foundation in data science and a must for anyone wanting to venture into this space. Understanding when and how to use linear and non-linear regression models in everyday research is an essential skill for any analyst. Linear and non-linear regression models are commonly used to quantify the relationship between two or more variables by predicting a key outcome of interest. These models are used as effective and powerful tools to control for the potential confounding effect of extraneous variables and/or developing highly predictive models.

Linear regression relates to continuous outcomes and is a fundamental regression technique in data science. Logistic regression is used when the outcome of interest is categorical and a fundamental classification technique in data science. When there is no theoretical or mechanistic model to suggest a particular functional form to describe the relationship between two or more variables of interest, Generalized Additive Models (GAMs) can used as they fit a nonparametric curve to the data without requiring pre-defining any particular mathematical model to describe the nonlinearity. Gaining a sound understanding of all these models is essential to understand when it is appropriate to use these techniques.

This course assumes that participants have:

1. A basic understanding of statistical concepts pts including descriptive statistics (mean, median and interquartile range),
2. A reasonable knowledge of using the R and RStudio software
3. Some familiarity with a PC/Mac environment including keyboard skills,
4. An understanding of folder and file structures in the PC/Mac environment, and
5. Some experience in using Microsoft Word and Excel or their equivalent.

Upon completion of this masterclass, you will have the skills required to confidently run standard linear and non-linear models using the R statistical software platform. You will have gained an understanding of when each type of model is appropriate and be able to justify the use of your model using key diagnostics. The workshop is relevant to researchers and data analysts in any area of research that want to use linear and non-linear predictive models for their research work. This workshop aims to introduce these models, key diagnostics and build confidence in their use.

Participants will require their own computers and to have loaded R and RStudio loaded onto their machines. They will also need to be able to access the internet to download R libraries. This course will be taught in the PC environment but MAC users are welcome.