Applying the correct statistical techniques to sensory data is key to gaining a clear understanding of studies where there is a lot of natural variation. We routinely offer our training using EyeOpenR, XLSTAT, JMP®, ‘R’ (The R Project) and SPSS®
Find out more by clicking on each of the links below. Don’t see what you need? Then please get in touch and we will get back to you.
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These public courses are open for anyone to book. If you have any questions please don't hesitate to get in touch.
These courses can be run at your office, or another location, specifically for your team. If you have any questions please don't hesitate to get in touch.
Experimental Design for Product Reformulation, Optimisation and Preference Modelling
1 -3 days
This course is aimed at researchers and new product developers who need to understand how product components work together to influence consumers and to optimise performance characteristics.
Introduction to Statistics using R
2 days
This course aims to familiarize you with the R environment, and will give you freedom in running statistical analyses in R.
Statistics for Sensory Analysis
2 - 3 days
We offer training in three one day modules, any one of these can be run on its own or combined with the other modules into a two or three day training course.
Analysis of Sensory data using SenPAQ©
Half day
This short course covers the basics of how to use SenPAQ to perform popular techniques for analysing sensory and consumer data, and then understanding and interpreting the output.
Understanding Basic Statistics (Non-software based)
0.5 - 1 day
A course for professionals who need to understand statistics reports and presentations and interpret what they see correctly to make informed decisions, rather than carry out analyses themselves.
These webinar recordings are available for anyone to purchase. Please check the public course listing if you'd prefer to attend a live event. If you have any questions please don't hesitate to get in touch.
Discrimination Testing - the why and how (On Demand)
Remote training
Virtual option available
Statistical analysis for discrimination testing - focussing on Triangle and Tetrad tests - using EyeOpenR software
Sensory Statistics - S1- Panel Performance using EyeOpenR & XLSTAT (On Demand)
Remote training
Virtual option available
Statistical analysis for Panel Performance assessment using ANOVA, the MAM model and simple multivariate methods with EyeOpenR and XLSTAT software
Analysis of consumer data - C12 - Further mapping methods, combining data from different sources on the same products (On Demand)
Remote training
Virtual option available
MFA, GPA and STATIS. Applications to measuring brand effects and analysis of napping data.
Analysis of consumer data - C11 - Three Way Data Mapping using MFA + Further Applications of PCA (On Demand)
Remote training
Virtual option available
Mapping different types of data on the same products using MFA, and further applications of PCA (using supplementary variables and internal preference mapping)
Analysis of consumer data - C9 - Using "Check All That Apply" (CATA) scales to understand consumer product choices (On Demand)
Remote training
Virtual option available
Which CATA questions discriminate between sample? Visualising product difference in the CATA space using correspondence analysis.
Analysis of consumer data - C10 - Other scales, comparing products using "Rate All That Apply" (RATA), ranking and proportion data (On Demand)
Remote training
Virtual option available
Comparing product performance using percentage measures and Rate all that apply (RATA). Watch the video in our on demand training and join in with the workshops in your own time.
Applications of Experimental Design in Consumer Research - Case studies (On demand)
Online
Virtual option available
Case Studies illustrating product insights and optimisation Experimental Design methods are now widely used to optimise manufacturing processes and have an important role in product development.
Analysis of consumer data - C13 - Predicting product performance from existing data using machine learning methods - Partial Least Squares (PLS) modelling (On Demand)
Remote training
Virtual option available
Technique to build models to predict one block of correlated data from another. Applications covered include models to predict liking (from sensory/analytic data) or to predict sensory from data.