Statistical Analysis of Agronomic Experiments - Online
- Date: Thu, 12, 8:45 am - Mon, 16 Nov 2020, 4:30 pm
- Location: Online via Zoom
- Cost: $300 or $100 for those working on GRDC projects inc. GST
- Contact: Sam Rogers 08 8313 2246
- Email: firstname.lastname@example.org
NB: This is a two-day course to be run Thursday 12th and Monday 16th September 2020 (9:00AM - 5:00PM ACST both days).
You’ve designed and run your experiment and collected your data. Now what?
This workshop covers the basic topics in experimental analysis of agronomic experiments. It is intended for researchers working in the field of agronomy with an understanding of the statistical package R and the design of agronomic experiments. In this course we review ANOVA concepts (Linear Models) and learn more complex experimental analysis concepts – Linear Mixed Models – including spatial modelling for the analysis of factorial experiments, blocked designs, and split-plot agronomic experiments. Participants will learn to identify the correct model to use and perform analyses of data resulting from standard agronomic designs using R and ASReml-R and interpret the results.
Why should you do this course?
- SAGI-STH has developed the Biometry Education Initiative to tailor to the needs of researchers in the Australian Grains Industry.
- The course is run by grains industry researchers, for grains industry researchers. Researchers in other agricultural industries are also more than welcome to attend!
- The course is non-threatening, with plenty of examples and hands-on practice to help you get familiar with R.
What will you get out of it?
- Confidence to perform statistical analysis of your experiments.
- Practice using R and some relevant packages for real world examples.
- A refresher on statistics but with application to your research.
- An understanding of how the analysis of an experiment relates to its design.
Maximum number of attendees: 15. Participants are required to have their own computer and should have headphones for optimal online participation.
Students are expected to be familiar with the R statistical software, and have completed the Introduction to Experimental Design course prior to attending this workshop. If you have not completed the Introduction to Experimental Design course, please contact us to discuss options.