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Join us for our Expert Course in Modern Design of experiments

  • Online-Live training
  • Advanced course for statisticians
  • 4 x half a day 8.30AM -12.30 PM (CET and CST)
  • 5 - 7 - 12 - 14 May 2025
  • Instructors:
    • Prof. Peter Goos (KU Leuven and University of Antwerp)
    • Dr. Jose Nunez Ares (Co-founder and CSO Effex)

INCLUDED

  • Use of software
  • All course materials

This course dives deep into advanced DoE methodologies to streamline experimental design, optimize processes, and extract maximum insight. Expect high-level discussions on complex designs, model refinement, and real-world applications—all with a focus on efficiency and precision.

Let’s push DoE to its limits.

Register for the course

Is this course for me?


    • You have previous experience in DoE and want to deepen your expertise.
    • You want to stay ahead of the latest evolutions in DoE.
    • You know methodologies evolve fast and want to stay updated with the latest trends.
    • You are looking for practical guidance with real-world illustrations.
    • You work with complex experimental designs and need advanced techniques.
    • You are interested in screening, optimization, and model selection criteria.
    • You analyze multi-response datasets and work with random effects.
    • Your models don’t always perform as expected – You know model selection is key, but it feels like guesswork rather than a structured approach.
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I want to join the course!

Day 1: Optimal design of Experiments

Optimals Design Criteria, Pareto optimal design, Hard-to-Change factors

Day 3: OMARS designs

Uniform precision OMARS designs, Non-uniform precision OMARS designs, Mixed-level OMARS designs, Strong OMARS designs, Blocking

Day 2: Advances in Doe

Screening and optimization in a single experiment, Criterial for selecting designs, Correlation measures, Replicates. Alternatives to Plackett-Burman designs

Day 4: Analyzing data from designed experiments

All subset regression and MIO, Raster plots, Criteria for model selection, Pareto optimal model selection, Datasets with random effects, Multi-response optimization