Maximising the value of your data using EyeOpenR®

Product code: SEN1-2

TBC

September 11, 2024 - September 11, 2024

Pre-requisite
A basic awareness of how statistics is used is assumed, however we do go back to the beginning and there will be plenty of reminders of statistical concepts along the way. This course also serves as a useful refresher to those who previously studied statistics or who have used these techniques with other software and would like to find out how EyeOpenR® works compared to other packages.

Course Summary
This course covers the easy to use features in the recently launched new EyeOpenR® software for investigating, visualising and performing common statistical techniques on data sets typical to sensory research plus other applications.

Software
Participants will gain experience using the software through illustrative examples with data from a variety of sensory tests. In this way the course ensures that statistical concepts are understood in a non-mathematical way and then practiced using real data examples.

Flexibility
This course can be customized for those who have specific sensory/consumer data analysis needs.

Course Content
Basic concepts need to be learned before statistics can be used to full potential to give useful and informative answers. By keeping mathematical details to a necessary minimum we can focus on the concepts and interpretation of the statistical results from EyeOpenR®. We aim to explain the objectives of the techniques and to send you away with a better understanding of which technique to use when and how to run these in the EyeOpenR® software package.

There will be plenty of opportunity for practice using EyeOpenR® through “hands-on exercises” for which annotated solutions will be provided.

Course Outline

  • Introduction to EyeOpenR®
  • Statistics Inference – Visualisations, Estimation & ANOVA for Sensory
  • Panel Performance
  • Discrimination Tests
  • Multivariate data – Correlation & Association
  • Principal Components Analysis (PCA)
  • Analysis of ‘Rapid Methods’ data