Introduction to Generalized Linear Models in R
- Level(s) of Study: Professional / Short course
- Start Date(s): Wednesday 15 May to Thursday 16 May, 2024
- Duration: 2 days, 9:30 am – 5:30 pm
- Study Mode(s): Short course
- Campus: City Campus
- Entry Requirements:
More information
Introduction:
In this two-day course, you will obtain a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is, for example, a binary, or ordinal, or count variable, etc.
This course is aimed at anyone who is interested in advanced statistical modelling as it is practiced widely throughout academic scientific research, as well as widely throughout the public and private sectors.
Level: CPD, Advanced / Professional
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.
The course will take 6 contact hours per day plus two 1-hour breaks.
The sessions will be as follows:
- Session 1: 9:30am-11:30am;
- Session 2: 12:30am-2:30pm;
- Session 3: 3:30pm-17:30pm
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.
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
Any questions? Contact kelly.smith@ntu.ac.uk, Commercial Manager, School of Social Sciences.
It was great for beginners. The combination of theory of GLM and practice in R, and details on how to interpret the results were also great.
What you’ll study
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.
Campus and facilities
Entry requirements
This course is aimed at anyone who is interested in using R for data science or statistics, such as researchers or analysts studying for/ have already studied a PhD in a field of science that involves extensive statistical analysis.
For this module, familiarity with R is assumed, however, a comprehensive introduction to R is taught in the first module, Introduction to statistics using R and Rstudio.
Getting in touch
If you need more help or information, get in touch through our enquiry form
Fees and funding
The fee for this course is (£360 VAT Inclusive) - £300 (VAT Exclusive)
Payment is due at the time of booking.
How to apply
You can book your place via the NTU online store:
For queries, please contact kelly.smith@ntu.ac.uk.