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The proper design, implementation and analysis of results of experiments are of vital importance in many disciplines. The validity and reliability of research findings can be severely compromised if a poor design or experimental procedure is followed. This course introduces principles of good design in experiments and discusses different methods of analysis of planned experiments which require the use of an appropriate statistical package. This course has relevance to all students involved in or planning to be involved in experimental projects, especially students in the general science and engineering disciplines. Previous statistical knowledge to the level of STA2300 Data Analysis only is assumed.

This course covers principles of design such as randomisation, replication, factorial arrangement and blocking. The emphasis is on general principles of design and analysis of experimental data rather than in describing the details of particular design layouts. Consideration is given to checking of assumptions and quality of data, robustness, prior and posterior analysis, contrasts, confounding, covariates, error control and reduction, and interpretation of results. Practical experience is gained in designing, carrying out, analysing and writing up the report from the results of an experimental study. Methods of analysis and different models are discussed and practised mainly using the SPSS software package.

On completion of this course students will be able to:

1. Identify and examine the principles and assumptions of a variety of experimental designs.
2. Evaluate the quality of data including underlying statistical assumptions.
3. Apply and analyse various statistical models appropriate for experimental data sets using a statistical package.
4. Communicate experimental analysis and results using appropriate statistical terminology for a wider audience.
5. Independently develop and conduct an experiment and appropriately report results.