Statistical Modelling

Module titleStatistical Modelling
Module codeBIOM4025
Academic year2017/8
Module staff

Dr Erik Postma (Convenor)

Duration: Term123
Duration: Weeks


Number students taking module (anticipated)


Description - summary of the module content

Module description

Biological, environmental and social data are famously complicated. However, modern statistical methods are able to accommodate many of these complications, and others can be avoided through careful study design and data collection. This module uses a series of lectures, practical work and discussion sessions to guide you through modern statistical philosophies and methods. The main software platform for the module is “R”, which is powerful, flexible and free. By the end of the module you will understand how to design experiments or surveys, handle the data, analyse them, interpret the results and provide graphical summaries. Most examples used will be drawn from recent research in ecology, evolution, environmental and social sciences.

Module aims - intentions of the module

Statistical modeling is an integral part of all quantitative research. Thereby this module provides key transferable skills in experimental design, data collection and handling, statistical modelling and programming. More generally, it will promote quantitative and logical thinking.

The modern, powerful methods of (generalised) linear and linear mixed effects modelling will be taught using a mixture of lectures and computer exercises, often using the ‘R’ programming language and software environment. Using a combination of real and simulated data, the module will emphasise the possibilities and limitations of the various statistical approaches, without losing sight of their real-world application, and the importance of careful experimental design and data collection.

The module introduction will include lecture material on the history and philosophy of statistical modelling, and on the special considerations (ethical, logistical, behavioural) required when collecting quantitative information on the features of biological and social entities.

Statistical modelling will focus on the use of model simplification and model comparisons in the framework of general and generalised linear modelling. You will be exposed to data that challenge the traditional assumptions of normality and constant variance; hence non-normal error structures, transformations and link functions will become part of your quantitative vocabulary. Extensions will include blocked and nested data structures, working with random effects, and nonparametric analyses.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. Discuss, with a scientific vocabulary, the philosophy of statistical analysis in research
  • 2. Formulate testable hypotheses to clarify theory and solve problems in scientific applications
  • 3. Debate the relative merits of different experimental designs to test relevant hypotheses
  • 4. Comprehend the importance of statistical power
  • 5. Optimise the collection and summary of information
  • 6. Analyse and interpret the results of experiments
  • 7. Criticise, and adapt, statistical models to cope with atypical error structures and non-independence
  • 8. Use command-line interactions competently and program using the powerful statistical software ’R’
  • 9. Assess critically the presentation of your own and others’ statistical methods and results

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 10. Communicate knowledge and understanding in ecology, evolution, environmental and social sciences
  • 11. Describe and critically evaluate aspects of research and communication with reference to reviews and research articles
  • 12. With limited guidance, deploy established techniques of analysis and enquiry in scientific endeavour

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 13. Communicate ideas effectively and professionally by written, oral and visual means
  • 14. Study autonomously and undertake projects with minimum guidance
  • 15. Select and properly manage information drawn from books, journals, and the internet
  • 16. Interact effectively in a group

Syllabus plan

Syllabus plan

Topic 1: Lectures on statistical history and philosophy, including the importance of exploratory analysis, the generation of hypotheses and the use of confidence intervals. Computer exercises introduce ‘R’ software and command-line interactions.

Topic 2: Lectures on hypothesis testing and ANOVA-type techniques (including analogous non-parametric methods) and model simplification. Computer exercises on data handling, graphing, simple statistics (t-test, ANOVA) and bootstrapping.

Topic 3: Linear regression modelling, including interpretation, interactions, model diagnostics, transformations and prediction. Computer exercises on more complex statistical models (multiple regression).

Topic 4: Generalised linear models (GLMs). Lectures on error structures (e.g. Poisson, binomial, negative binomial) and link functions, analysis of deviance and prediction. Computer exercises on fitting GLMs in R.

Topic 5: Lectures on non-independence, nested designs and mixed-effects models. Computer exercise on the analysis of mixed effects models and dealing with non-independence.

Statistics clinics and research seminars will take place throughout the module.

Learning and teaching

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching10Lectures on statistical and quantitative methods
Scheduled Learning and Teaching5Seminars reviewing quantitative methodologies used in research seminars and selected scientific publications
Scheduled Learning and Teaching15Computer practical sessions
Guided independent study120Additional research and reading, and preparation for module assessments.


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Short-answer questions during lectures and practical sessionsOngoing throughout the moduleAllOral
Problem sheets available on ELEMade available throughout the moduleAllWritten

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Statistical modelling problem sheet 130Question sheet1-13Written
Statistical modelling problem sheet 230Question sheet1-13Written
In-class ELE quiz401 hour1-13Written


Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Statistical modelling problem sheet 1Statistical modelling problem sheet 11-13During an appropriate specified time period before the end of July
Statistical modelling problem sheet 2Statistical modelling problem sheet 21-13During an appropriate specified time period before the end of July
In-class ELE quizELE quiz1-13During an appropriate specified time period before the end of July

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a 2000 word report. The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 50%.


Indicative learning resources - Basic reading

  • Crawley, M. (2005) Statistics: An Introduction Using R. John Wiley and Sons.

Indicative learning resources - Web based and electronic resources

Module has an active ELE page

Indicative learning resources - Other resources

  • Class contributions to web forum (peer support).

Key words search

Statistics, ‘R’ software, experimental design, randomisation, replication, independence, general linear modelling, mixed effects modelling, t-test, regression, analysis of variance, analysis of covariance, multiple regression

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date