Introduction to ANOVA

Time: Start 9am, Finish 5pm
Duration: 2 days.
Price includes all materials and lunches.

Who Should Attend	Individuals familiar with GenStat and who need to gain knowledge and experience of Analysis of Variance techniques. Previous experience of simple statistical concepts such as means, standard errors and probability would be advisable. However, there is no need for experience with complicated maths as the course adopts a practical and applied approach.
Overview	Analysis of Variance (ANOVA) is one of the most widely used statistical techniques, with application areas that include biology, medicine, industry and finance. GenStat can claim to be the premier vendor for ANOVA techniques with its association with Rothamsted going back to R.A. Fisher. To many GenStat IS ANOVA! GenStat has a very powerful set of ANOVA techniques that are nevertheless very straightforward and easy to use. This course introduces both the theory of ANOVA and its application within GenStat.
Learning Objectives	By the end of the course you should be able to: Select an experimental design appropriate to the requirement of your investigations. Use randomization to avoid bias in the allocation of units to treatments, to ensure that results are reliable and unaffected by any systematic patterns in the units. Determine how many replicates are required for your designs. Use blocking to increase the accuracy of an experiment by forming the basic units (e.g. plots or subjects) into groups with similar properties. Analyse simple to sophisticated designs, explaining ideas such as balance, and to recognise the GenStat features available for the analysis of unbalanced designs. Compare several types of treatment in the same experiment. Interpret experimental results and produce relevant tables, graphs and figures for publication in reports and papers.
Methods of Evaluation	Subsequent to instructor facilitated sessions, participants will be expected to complete example analyses and exercises unaided.
Training Methods	Formal lectures, GenStat based practical sessions which utilise real data. Participants are encouraged to bring their own experimental data to further enhance practical sessions.

Next steps

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