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Statistical Analysis of Agronomic Experiments
Feb 5, 8:45 am - Feb 8, 5:00 pm$100 – $300
You’ve designed and run your experiment and collected your data. Now what?
Details at a glance
Date: Friday 5th AND Monday 8th of February 2021
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 (email@example.com) to be notified when the next course will be run.
This two-day 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.