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Behavioural data analysis using maximum likelihood in R (BDML02)

4 November 2019 - 8 November 2019

Course Overview:

This 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted, many of which are fitted by maximum likelihood. Students will learn how to deal with binary, count and continuous data, including time-to-event data which is commonly encountered in behavioural analysis.

After successfully completing this course students should be able to:

  • fit a multi-parameter maximum likelihood model in R
  • derive likelihood functions for binary, count and continuous data
  • deal with time-to-event data
  • build custom models to test specific behavioural hypotheses
  • conduct hypothesis tests and construct confidence intervals
  • use Akaike’s information criterion (AIC) and model averaging
  • understand how maximum likelihood relates to Bayesian techniques

To find out more or to book online via our sister company (PS statistics) use the link below…

Behavioural data analysis using maximum likelihood in R (BDML02)


4 November 2019
8 November 2019


PS statistics head office
53 Morrison Street
Glasgow, Scotland G5 8LB United Kingdom
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