Skip to Content


Last modified: 28 Jun 2018 10:27

Course Overview

This highly regarded course will take your understanding of statistics to the next level and provide you with the skills and confidence to analyse your complex biological data. Through a combination of lectures, computer based practicals and group work you will gain an understanding of how to deal with pervasive issues in the analysis of real world biological data such as heterogeneity of variance and spatial and temporal non-independence. Hands on computer tutorials will allow you to apply statistical models using modern statistical software (R) to real data, collected by researchers to answer real biological questions.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 7.5 credits (3.75 ECTS credits)
Campus None. Sustained Study No
  • Professor David Lusseau
  • Dr Alex Douglas

Qualification Prerequisites


What courses & programmes must have been taken before this course?

  • Either Any Postgraduate Programme (Studied) or BI5009 Experimental Design and Analysis (Studied)
  • One of MRes Ecology & Environmental Sustainability or MRes Applied Marine and Fisheries Ecology or MSc Ecology & Environmental Sustainability or MSc Applied Marine and Fisheries Ecology or Master Of Science In Ecology & Conservation or MSc Forestry (Taught) or Master Of Science In Environmental And Forest Management or MSci Biological Sciences
  • Either Any Postgraduate Programme (Studied) or BI4015 Grant Proposal (Passed)

What other courses must be taken with this course?


What courses cannot be taken with this course?


Are there a limited number of places available?


Course Description

This course will be divided into themed weeks during which you will gain experience in understanding complex sampling methodologies and dealing with pervasive issues in the analysis of real world biological data. You will be taught using a combination of lectures, computer practicals and directed group work and emphasis will be placed on the practical implementation of various modelling strategies using the statistical programming environment R.

Week 1: Following a recap of linear models, you will be introduced to some the limitations of using standard linear models for analysing biological data and gain experience in identifying common issues arising from model misspecification. During this week you will focus on dealing with the common issue of heterogeneity of variance using a generalised least squares (GLS) approach.

Week 2: During this week you will learn how to fit models which can account for correlated data arising from repeated measurements from the same sampling unit or from sampling units that are not spatially independent. You will learn to extend the GLS approach introduced in week 1 to model this non-independence.

Week 3: The final week will bring together concepts introduced during the first two weeks and introduce you to analysing data from complex experimental or survey designs using the linear mixed effects modelling framework.

Associated Costs


Further Information & Notes

The assessment for this course will be based on one independent report (100%) which will test your conceptual understanding of the topics raised throughout the course. 

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers

Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

The module will be assessed based on an independent report (100%).

Formative Assessment

There are no assessments for this course.



Course Learning Outcomes


Compatibility Mode

We have detected that you are have compatibility mode enabled or are using an old version of Internet Explorer. You either need to switch off compatibility mode for this site or upgrade your browser.