Design of Experiments (7CCM347BM)
Statistics & Research
Course Overview
This module covers essential topics on Design of Experiment, such as: randomization, replication and blocking; factorial treatment structure; fractional factorial designs; aliasing; response surface methodology; optimal designs; optimal designs for nonlinear models; incomplete block design; confounding; and nested, split-plot designs and other multi-stratum designs.
29 September 2025 - 20 December 2025
Places: Opening soon
Delivery mode: In person
Application deadline: 11 August 2025
Places: Opening soon
Course features
Examples from a range of application areas will be used to motivate and illustrate the methods covered in the module.
You will be given problem sheets with both analytical exercises and numerical problems. You will be able to measure your progress using these exercises and have the opportunity to receive feedback in tutorials. Solutions will be provided subsequent to tutorials.
The coursework consists of individual design projects to be carried out using the tools covered in the module lectures and tutorials. Problems sets will prepare you in using the necessary statistical software and in the presentation of the results.
Learning outcomes
At the end of this module, you will be able to:
- Design and critically assess an experiment based on mathematical criteria and practical objectives
- Analyse the data from experiments in a way that respects the design structure
- Apply and critically evaluate DoE methods in several application areas (e.g., industrial, agricultural, medical)
- Demonstrate expertise in using statistical software to carry out DoE analysis.
- Interpret and effectively communicate the results from a DoE analysis.

Entry Requirements
To enrol on this module, you must meet the following module-specific requirements:
- A 2:1 honours degree (or above) in Mathematics, Statistics or other mathematics-based subject, including modules covering probability theory, statistical inference and linear regression models
- A 2:2 honours degree in Mathematics, Statistics or other mathematics-based subject, including modules covering probability theory, statistical inference and linear regression models, supported by a CV and employer reference letter demonstrating a minimum of three years relevant professional experience.
Plus the following additional standard entry requirements:
- A CV and personal statement outlining reasons for study
- English language at Band D (IELTS 6.5 overall with a minimum of 6.0 in reading/writing and 6.0 in listening/speaking).
Assessment
You will be assessed via coursework and examination, as follows:
- Coursework = 20%
- Examination = 80%
Further information
This is an on-campus module that involves a two-hour lecture and one-hour tutorial per week. You will be expected to attend both in person, though lectures are lecture-captured if you have strong impediments preventing you from attending. Exact times and dates will be provided upon enrolment.
Course code:
7CCM347BM
Credit level:
7
Credit value:
15
Duration:
11 weeks
Who will I be taught by
Discover more


