d e f es 
AICOS News
05.01.2020
Our training schedule for spring / summer 2020 is on-line!
You want to deepen your knowledge in data... 
more Linie
Search
Go
Courses

Experimental Design and Analysis with STAVEX (A)

Thu/Fri, 14-15/5/2020

Statistically designed experiments yield a maximum of information for a minimum of effort. Moreover, the systematic approach ensures the highest possible product quality from the very first R&D step - quality by design (QbD).
The course is aimed at scientists who wish to optimize products or processes and use statistical design and analysis (DoE). It introduces the basic statistical principles involved and illustrates these with various case studies using the DoE tool STAVEX.
STAVEX allows scientists in research, development and production to apply statistical experimental designs (DoE) for process or product optimisation without the aid of a statistician.


What are you going to learn?

The course provides, on the one hand, an introduction to the principles of statistical design of experiments (DoE) and, on the other hand, trains the deployment of the method using a user-friendly DoE tool, STAVEX,

on practice-oriented examples. Various case studies are discussed, showing the applicability of DoE in different areas.

Even after only two days of training, you will be comfortable using the DoE expert system STAVEX. It guides the user through the entire optimization process, i.e. from the initial design to the final data analysis, completely independent of a statistician.

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, RSM designs for optimization
Analysis and interpretation of results (Half normal plot, Regression)
Sequential Experimentation


Any questions ?
powered by Swisscom AG
c