This module will explain the major principles and techniques of statistical analysis of research data without becoming too involved in the underlying mathematics. Now that computer software is very well established for data analysis, it is more important to understand what a statistical test is doing (and thus whether or not it is appropriate) than to be able to perform the underlying calculations by hand. It is equally important to collect data in an appropriate and planned manner for later analysis.
At the end of the course, participants should have an overall grasp of the major analytical techniques available, and how they relate to each other, and have developed abilities in experimental design, data analysis using appropriate software and presentation of results.
Areas covered will include:
- Introduction to basic statistics.
- Linear Regression.
- ANOVA, ANCOVA, Multiple Regression, Log-linear & Logistic analysis.
- Introduction to GenStat: Regression, ANOVA, ANCOVA in Genstat.
- How to, and not to, present results of statistical analyses.
- Experimental design simulation exercise: design an experiment and use a simulation model to generate data. Feedback will be provided on your preliminary designs which you can then use to improve future designs.