STAT40110 Design of Experiments

Academic Year 2021/2022

Experimental design is a fundamental methodology in many areas of biological, environmental, medical and psychological science. This module builds on previous introductions to experimental design. It deals with the efficient design of experiments and the analysis of data from them. It includes description and discussion of Oneway Classification, Randomized block with and without replication within blocks, Latin square designs and extensions, Factorial, Split-Plot, Incomplete Block and Fractional Replication designs.The topics multiple comparisons and randomization tests will be covered. There is a particular emphasis in the analysis of designs of parsimonious representations of treatment effects. The emphasis is on the value of experimental design as a practical tool rather than on the mathematical basis for design. The module will include computer laboratories and tutorials alternating each week.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module students should be able to: Propose an appropriate experimental design to address a wide range of research questions. Analyse data from each of these designs. Prepare a report of the analysis for a non-statistical client. Select from a range of tools to develop a more parsimonious description of the data

Indicative Module Content:

One-way classification; randomized block design with and without replication; Latin square designs; randomization tests; multiple comparisons; factorial designs; split-plot designs; incomplete block designs; report writing

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

5
Computer Aided Lab

5
Specified Learning Activities

36
Autonomous Student Learning

60
Total

130
Approaches to Teaching and Learning:
Lectures; Computer laboratories; Problem-based learning; Report writing 
Requirements, Exclusions and Recommendations
Learning Requirements:

A knowledge of probability and statistical inference to the level of Probability Theory STAT20110 and Statistical Inference STAT20100 modules is required.
Knowledge of calculus and linear algebra at First Science level is required.


Module Requisites and Incompatibles
Additional Information:
A knowledge of probability and statistical inference to the level of Probability Theory STAT20110 and Statistical Inference STAT20100 modules is required. Knowledge of calculus and linear algebra at First Science level is required.


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Approximately 8 homeworks including approximately 5 reports based on data analytic computer laboratories Throughout the Trimester n/a Standard conversion grade scale 40% No

30
Examination: 2 hour end of trimester exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

70

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Homeworks will be returned graded and annotated. In addition assignments will be further discussed in tutorials.

Montgomery D. C. (1991) Design and Analysis of Experiments. John Wiley and Sons. pp 649.

Lawson J. (2014). Design and analysis of experiments with R. Chapman & Hall/CRC. pp596.

Ugarte M, Militino A and Arnholt A. (2106). Statistics and Probability with R. 2nd edition. CRC Press.

Mead R. (1990) The Design of Experiments. Statistical Principles for Practical Application. Cambridge University Press pp 620.

Cochran W.G. and Cox. G.M. (1957) Experimental design. Wiley, pp 611.

Federer W.T. (1967) Experimental Design – Theory and application. Oxford & IBH Publishing Co., Calcutta. (Reprint of book published by Macmillan New York 1955).

Kempthorne. O. (1952) The design and analysis of experiments. Wiley, New York.

Snedecor G.W. and Cochran. W.G. (1980) Statistical Methods. Iowa State University Press, pp 507.

Dalgaard, P. (2002) Introductory Statistics with R. Springer, pp.265

Venables, W.N. and Ripley, B.D. (2002) Modern Applied Statistics with S. Springer, pp. 495.