Design of Experiments
with STAVEX (Part A)
Next Date
Wed/Thu, 4-5/11/2026
Participants
3 - 12
Duration
2 Days
Course Form
online, classroom in Basel or in-house ℹ
He has over 25 years of experience in the application of statistical methods in the process industry. He has coached many training sessions on various topics, including Design of Experiments (DoE) for process and product optimization. His wide practical knowledge allows him to make even technical concepts easily understandable by participants.
Dr. Solot holds an M.Sc. and a Ph.D. in Applied Mathematics, both from the EPFL (Swiss Federal Institute of Technology, Lausanne).
Course Description
Statistical Design of Experiments (DoE) is the method of choice for obtaining maximum information from the shortest possible list of experiments. Its application is recommended by authorities and guidelines in numerous industrial sectors, e.g. in the pharmaceutical and biotechnological industry for Quality by Design (QbD), because it leads to the following advantages:
- Efficiency: the superfluous experiments are eliminated, but all the necessary ones are carried out!
- Accuracy: the highest possible accuracy is obtained for the experimental effort made.
- Interactions: synergies or antagonisms between influence factors are detected and understood.
The course constitutes an introduction to the Statistical Design of Experiments (DoE) methodology and to its application in practice. It is aimed at scientists who wish to optimize processes or products efficiently and to use DoE for this purpose.
What Are You Going to Learn?
The course explains the Statistical Design of Experiments (DoE) methodology. It also illustrates its application in practice by means of many software demonstrations and PC-based exercises derived from various industrial case studies. Throughout the course, emphasis is placed on a good balance between the two aspects, as well as on making the concepts presented easily understandable by scientists, even those with limited statistical knowledge. Mathematical formalism is avoided as much as possible.
Each step of the DoE workflow – from problem definition to data analysis over the experimental design – is treated. The course also highlights how to ideally adapt the approach depending on the complexity of the problem studied, to ensure that the experimental effort remains reasonable.
Even after only two days of training, you will feel comfortable using a user-friendly DoE software such as the web-based tool (for the intranet) STAVEX. Designed as an expert system, it guides the user through the whole DoE process, completely independently of a statistician. This makes the application of Design of Experiments extremely easy, as simple as an online hotel booking: STAVEX is therefore particularly suitable for learning (but also for later use – a free license is provided for the course and the subsequent four weeks). However, the course is not software-specific, so that the knowledge learnt can easily be transferred to other software tools.
Who Should Attend?
Scientists and engineers in Research, Development and Production (e.g. in chemistry, pharmacy, biotechnology, process technology, physics etc.) who want to develop or optimize processes and products more efficiently
No prior statistical or mathematical knowledge is required.
Course Fees
- CHF 795.- per day + 8.1% VAT (incl. course documents and individual participant coaching during the practical exercises) for online courses
- CHF 865.- per day + 8.1% VAT (incl. course documents, individual participant coaching during the practical exercises, break refreshments and lunch) for classroom courses.
Which Topics Are Covered?
Introduction
Concepts of Statistical Design of Experiments
Why is statistical design superior to trial-and-error methods?
Sequential approach in three phases: screening, modelling, optimization
Problem Definition
STAVEX user specifications: response variables, factors ...
Modelling
Effects and interactions
Full and fractional factorial designs
Design selection
Multiple linear regression: analysis of the experimental results
Verification of the model fit
Graphical model diagnosis
Complements About the Problem Specification
Linear restrictions
Forced blocking
Optimization
Optimization designs
Analysis of the experimental results
Confirmatory experiments
Screening
Screening designs
Analysis with the half-normal plot
Sequential Experimental Design
Summary
Any Questions about STAVEX or Design of Experiments (DoE)?
We are always at your disposal if you have any questions. Simply call us on +41 61 686 98 77 or use the Contact Form.