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
Linked modules
Pre-requisites: MATH6174 or STAT6123 or (MATH2011 and MATH2010)
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- appreciate the advantages and disadvantages of a design for a particular experiment
- construct optimal or good designs for a range of practical experiments
- describe how the analysis of the data from the experiment should be carried out
- understand the potential practical problems in its implementation
Syllabus
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 practised.
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, ANOVA methodology.
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.
3) Randomization: practical constraints on randomization.
4) Blocking: incomplete block designs for unstructured treatments, including balanced incomplete block designs; confounding for factorial designs.
Learning and Teaching
Teaching and learning methods
Lectures, problem classes and self-directed computer work
| Type | Hours |
|---|---|
| Problem Classes | 12 |
| Lecture | 36 |
| Independent Study | 102 |
| Total study time | 150 |
Resources & Reading list
General Resources
Website on Blackboard.
Textbooks
Atkinson, A.C., Donev, A.N. and Tobias, R.D. (2007). Optimum Experimental Designs, with SAS. Oxford: Oxford Science Publication.
Max D. Morris (2011). Design of Experiments: An Introduction Based on Linear Models. CRC Press.
John, J.A. and Williams, E.R. (1995). Cyclic and computer generated designs. London: Chapman and Hall.
Box, G.E.P., Hunter, J.S. and Hunter, W.G. (2005). Statistics for Experimenters. New York: Wiley.
Mead, R, Gilmour, SG, and Mead, A (2012). Statistical Principles for the Design of Experiments. Cambridge.
Montgomery, D.C. (2009). Design and Analysis of Experiments. New York: Wiley.
Wu, C.F.J. and Hamada, M. (2009). Experiments - Planning, Analysis and Parameter. New York: Wiley.
Dean, A.M. and Voss, D.T. (1999). Design and Analysis of Experiments. New York: Springer-Verlag.
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.
| Method | Percentage contribution |
|---|---|
| Written assessment | 50% |
| Coursework | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Written assessment | 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.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Written assessment | 50% |
Repeat Information
Repeat type: Internal & External