Dates: 6-10 September 2021
In this course, we will introduce Generalised Linear Models as a unified, coherent, and easily extendable framework for the analysis of many different types of data, including Normal (Gaussian), binary, and discrete (count) responses, and both categorical (factors) and continuous predictors.
The course is aimed at at graduate students and researchers with little statistical knowledge but willing to learn how to extract knowledge from data using statistical models, how to use statistical models to increase our understanding and make predictions about natural phenomena, and acquiring a toolbox to analyse many different types of data (beyond the typical Gaussian responses) using R.
1) Being able to fit, understand, and use statistical models to make predictions and extract knowledge from data
2) Learn how to analyse data with different statistical distributions, estimating the effects of both categorical (factors) and continuous predictors
3) Visualise data and fitted models to check assumptions, communicate results, and increase understanding
4) Practise R programming, particularly applied to data visualisation and analysis (statistical modelling)
5) Acquire the statistical knowledge required to move on to more complex models (e.g. Generalised Linear Mixed Models, Generalised Additive Models, Bayesian modelling) in the future.
Should you have any questions, please feel free to contact us at :firstname.lastname@example.org