The University of Southampton
Mathematical SciencesPostgraduate study

MATH6027 Design of Experiments

Module Overview

When planning experiments, it is essential that the data collected are as relevant and informative as possible. The statistical principles for the design of experiments include the choice of optimal or good treatments sets and appropriate replication of them, randomization to ensure unbiasedness and the use of blocking and other methods for reduction of variance.

Aims and Objectives


Having successfully completed this module, you will be able to:

  • 1) appreciate the advantages and disadvantages of a design for a particular experiment;
  • 2) construct optimal or good designs for a range of practical experiments;
  • 3) understand the potential practical problems in its implementation; and
  • 4) describe how the analysis of the data from the experiment should be carried out.


Emphasis throughout will be on the statistical principles underlying the methods and how they can be applied to and adapted for practical experiments. The following methods will be discussed and practiced.

1)    Basic ideas: objectives leading to choice of treatments; randomization to ensure validity of analysis; blocking to separate sources of variation in order to ensure efficiency of analysis.

2)    Choice of treatments: replication for unstructured treatments; optimal design for quantitative treatments; the factorial treatment structure and its advantages; incomplete factorial structures, including regular fractional factorials; screening experiments; response surface treatment designs for multiple quantitative factors; optimal design algorithms for choosing multifactor treatment sets.

3)    Randomization: randomization theory as a justification of linear models; extension to block designs; inter-block analysis; practical constraints on randomization.

4)    Blocking: incomplete block designs for unstructured treatments, including balanced incomplete block designs; confounding for factorial designs; optimal blocked factorial and response surface designs; split-plot and other multi-stratum designs.

5)    Special topics: sequential design; computer experiments.

Learning and Teaching

Teaching and learning methods

Lectures, computer practical sessions and self-directed computer work.

Resources and reading list

Key text: Mead, R, Gilmour, SG, and Mead, A (2012) Statistical Principles for the Design of Experiments. Cambridge.


Many other texts are available and all contain some useful information for parts of the course.


Website on Blackboard.


Assessment methods

Assessment Method Hours % contribution to final mark Feedback
Other 2 hour written examination   80%
Coursework   20%

Referral Method: By examination

Method of Repeat Year: Repeat year externally.


Costs associated with this course

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

Course texts are provided by the library and there are no additional compulsory costs associated with the module.

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