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Statistical Analysis of Agronomic Experiments
Sep 7, 8:45 am - Sep 9, 5:00 pm$100 – $300
You’ve designed and run your experiment and collected your data. Now what?
Details at a glance
Date: Monday 7th and Wednesday 9th of September 2020
Time: 8:45am to 5:00pm
Cost: $300 ($100 for those working on GRDC projects) including GST
Location: Online via Zoom
Max attendees: 15
Bring: A computer and headphones (headphones with microphone work best)
NB: A basic proficiency with the statistics package R is expected. If you need R training, please email Sam Rogers (firstname.lastname@example.org) to be notified when the next course will be run.
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 understand of the statistical package R (Introduction to R workshop) and the design of agronomic experiments (Introduction to Experimental Design workshop). 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 the material.
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.