Design of Experiments

Module overview

A well-designed experiment is an efficient way of learning about the world. Typically, an experiment may involve varying several factors and observing the value of a response at settings of combinations of values of these factors. The mathematical challenge is then to choose which settings to use in order to gain the maximum information from the resulting data. Experiments are performed in all branches of science, engineering and industry. In recent years, traditional application areas such as agriculture, manufacturing, medicine and pharmaceutical science have been joined by bioinformatics, genetics, drug discovery, finance and economics. Problems of increasing size and complexity from these new areas have led to the development of many new methods for designing and analysing experiments. The aim of this module is to provide a grounding in the statistical and mathematical methods that underpin the design and analysis of experiments, before exploring a number of areas where recent and ongoing developments are taking place. Mathematical criteria for quantifying the information available from a given design will be defined and explored, and will underpin much of the material in the module.

Linked modules

Pre-requisites: STAT6123 or (MATH6006 and MATH6183) or (MATH2011 and MATH2010)

Learning and Teaching

Teaching and learning methods

Lectures, problem classes and self-directed computer work

Study time
Type	Hours
Lecture	36
Independent Study	102
Problem Classes	12
Total study time	150

Resources & Reading list

General Resources

Website on Blackboard.

Textbooks

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

Wu, C.F.J. and Hamada, M. (2009). Experiments - Planning, Analysis and Parameter. New York: Wiley.

John, J.A. and Williams, E.R. (1995). Cyclic and computer generated designs. London: Chapman and Hall.

Montgomery, D.C. (2009). Design and Analysis of Experiments. New York: Wiley.

Atkinson, A.C., Donev, A.N. and Tobias, R.D. (2007). Optimum Experimental Designs, with SAS. Oxford: Oxford Science Publication.

Box, G.E.P., Hunter, J.S. and Hunter, W.G. (2005). Statistics for Experimenters. New York: Wiley.

Dean, A.M. and Voss, D.T. (1999). Design and Analysis of Experiments. New York: Springer-Verlag.

Max D. Morris (2011). Design of Experiments: An Introduction Based on Linear Models. CRC Press.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Exercises and Quizzes

Assessment Type: Formative
Feedback:
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Examination	80%
Coursework	20%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Examination	80%
Coursework	20%

Repeat Information

Repeat type: Internal & External