Introduction to Generalized Linear Models in R (online)

Course start date: Wednesday 24 May 2023 to Thursday 25 May 2023

Course Description

The course will cover these key topics: 

• The general linear model. We begin by providing an overview of the normal, as in normal distribution, general linear model, including using categorical predictor variables. Although this model is not the focus of the course, it is the foundation on which generalized linear models are based. 
• Binary logistic regression. We will cover the binary logistic regression model, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression, implement it using R's `glm`, and then show how to interpret its results, perform predictions, and (nested) model comparisons. 
• Ordinal and categorical logistic regression. Here, we show how the binary logistic regression can be extended to deal with ordinal and categorical data.  
• Generalized linear models for count data. Here we cover Poisson and negative binomial regression in particular, which are widely used techniques for modelling count data, i.e., data where the variable denotes the number of times an event has occurred. 
• Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model.  

During the course you’ll: 

• gain a practical and theoretical introduction to generalised linear models using R, including learning about seven major examples of these models 
• learn how to choose between the different kinds of generalized linear models depending on the data being modelled  
• learn how to perform model comparison and model evaluation in generalized linear models 

What will I gain?   

By the end of the course, you’ll have comprehensive knowledge and understanding of the principles associated with generalised linear models, how to choose suitable candidate model families for analysing categorical, ordinal, and count datasets and how to evaluate model fit and compare different models. 

You’ll be proficient in the use of regression models to analyse a wide variety of different data types, and be able to evaluate model fit on a case-by-case basis and compare competing models of the same data. 

On completion of at least 80% of the course, you’ll receive a certificate of attendance. 

Where you'll learn: The course is delivered through interactive online workshops via Zoom.

It will be practical, hands-on, and workshop based.

There will be some brief lecture style presentations throughout, i.e., using slides or blackboard, to introduce and explain key concepts and theories.

Throughout the course, and we will use real-world data sets and coding examples. 

Tutor Profile: Mark Andrews is an Associate Professor at Nottingham Trent University whose research and teaching is focused on statistical methodology in research in the social and biological sciences.

He is the author of 2021 textbook on data science using R that is aimed at scientific researchers, and has a forthcoming new textbook on statistics and data science that is aimed at undergraduates in science courses.

His background is in computational cognitive science and mathematical psychology.  

Any questions?  Contact [email protected], Commercial Manager, School of Social Sciences 

Other available online CPD courses in this series include 

Introduction to statistics using R and Rstudio 

Introduction to Data Wrangling using R and tidyverse 

Introduction to Data Visualization with R using ggplot   

Introduction to Multilevel (hierarchical, or mixed effects) Models in R 

Introduction to Bayesian Data Analysis with R