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26 Nov 2026 (Available)

Apply by: 25 Nov 2026

In-person: Wednesday 26th November 2025, 09:30-17:00

University of Hertfordshire, Hatfield, Hertfordshire

Course overview

This course is aimed at those with a working knowledge of basic statistical techniques and introduces methods for building analytical and predictive models involving multiple grouping factors, covariates and confounding variables. The General Linear Modelling (GLM) framework allows a wide range of models to be fitted, including multiple regression, factorial ANOVA, analysis of covariance, and models involving interactions. Models for binary, categorical and ordinal outcome variables are also discussed.


For simplicity, the course makes use of the jamovi software for demonstration and exercises. However, if preferred, any other advanced statistical software can be used (e.g. SPSS or R).


See our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk.

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Funding

If you are unable to pay by card and require an invoice, please email statistics@herts.ac.uk.

Why choose Herts?

Over 25 years' experience of delivering quality short courses in Statistics

1st in the UK for support and top 10 in every other section, Postgraduate Taught Experience Survey (PTES), 2024

25 mins north of London: Exciting learning and social experiences within easy access

78% of research ranked as world-leading or internationally excellent, Research Excellence Framework (REF), 2021

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Course details

Course leader

Matt Coates

Administrator

Neil Spencer

Telephone

Please email for prompt response

Course delivery

Online/In-person

Downloads

Prerequisites

It is assumed that delegates have attended either the “Survey Design & Analysis” stream of courses or “Quantitative Data Analysis” stream of courses or already have a good knowledge of multiple regression and analysis of variance (ANOVA).

 

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Hertfordshire Business School

ADM [Short Course] 2026/27

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0 Credits

Academic Level:

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