At Qi Statistics we pride ourselves in our approach to training. Our emphasis is always on applying the statistical techniques and interpreting the results rather than the underlying mathematics.
Because no two companies are identical, training needs to be modular and flexible. Whether it is the industry application, a requirement for particular techniques, the number of participants, the experience of participants or simply the time available for training, there is always a need for tailoring the course and delivering it in a timely fashion. With a combination of our “On the Job” or classroom-based training and our cafeteria-style curriculum (select topics from a checklist), we can tailor a training programme to meet your exact needs. We offer a range of public courses in Europe, US, Asia and Australia/New Zealand from basic statistics to masterclasses in specialised topics.
Although the most effective training is face-to-face, we recognise the need for eLearning and Webinars, and have online offerings too.
Qi are pleased to be offering courses previously managed and jointly given by Hal Macfie and Qi – please direct all enquiries to Qi Statistics from now on.
Browse all training by topic
Showing 97–112 of 134 results
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Qi Colloquium, UK
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Qi-Sensenova International Statistics Training with Sensory Focus
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Questionnaire Design for Consumer Research and Product Testing
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Rapid Descriptive Methods, Nov 2020 – Sensory Dimensions Nottingham
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Sensory Evaluation – Statistical Methods and Interpretation, Nottingham
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Sensory Methodologies Webinar (access to recording only)
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Statistic Fundamentals for Research & Industry Using Statistical Software
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Statistic Fundamentals for Research & Industry Using Statistical Software
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Statistical analysis of consumer data – Module C01 – Basic stats refresher
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Statistical analysis of consumer data – Module C02 – Considerations when planning your consumer trial
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Statistical analysis of consumer data – Module C1 – Liking scale data – are my products different?
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Statistical analysis of consumer data – Module C1 – Liking scale data – are my products different? (On Demand)
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Statistical analysis of consumer data – Module C10 – Comparing products using “Rate All That Apply” (RATA) and Proportion data
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Statistical analysis of consumer data – Module C12 – Comparing products using “Rapid” methods: Napping data (Projective Mapping)
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Statistical analysis of consumer data – Module C13 – Predicting product performance from existing data using machine learning methods – Partial Least Squares (PLS) regression modelling
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Statistical analysis of consumer data – Module C14 – Combining data sets to visualise the link between product characteristics & preferences – External preference mapping