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Experimental Design and Analysis with STAVEX, Part A

Thu/Fri, 14-15/5/2020

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Statistically designed experiments yield a maximum of information for a minimum of effort. The course is aimed at scientists who wish to optimize products or processes and use statistical design and analysis with the help of STAVEX.
STAVEX is a user friendly PC software system that makes it possible for scientists in research, development and production to apply statistical experimental designs for process or product optimisation without the aid of a statistician.
The course introduces the basic statistical principles involved and shows the practical application and use of the STAVEX software.


What will you learn ?

STAVEX is a user-friendly PC software system that was originally developed by the Mathematical Applications Group in the former Ciba-Geigy company. It runs under Windows. STAVEX is designed as an Expert system which guides the user through the entire optimization process, i.e. from the initial design to the final data analysis, completely independent of a statistician. Today, STAVEX is supported and extended by AICOS Technologies AG.

The course provides, on the one hand, basic knowledge to better understand the principles of statistical designs and, on the other hand, shows the operation of STAVEX using practical examples. The course is offered in two parts, A and B, each of which lasts two days. The two parts can be taken separately. In Part B, the participants have the opportunity to evaluate their own data.


Who should attend ?
  • Chemists, physicists and engineers in Research, Development and Production
  • No previous statistical or mathematical knowledge necessary
  • Part A of the course can also be attended by participants who do not plan to use STAVEX
  • Part A or experience in using STAVEX is required for Part B

Which topics are covered ?
Concepts of statistical experimental design
Why is statistical design superior to trial-and-error methods?
User inputs (Response variables, Factors etc.)
Factor Screening and Modelling
Optimization of a response variable
Experimental designs: factorial designs, fractional designs, designs for optimization
Analysis and interpretation of results (Half normal plot, Regression)
Sequential Experimentation


Any questions ?
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