DoE (Design of Experiments)
Systematic experiment planning so that a minimum number of runs yields maximum insight into factors, main effects, and interactions.
Also known as: experimental design, designed experiment, DoE
Design of Experiments (DoE) studies several inputs simultaneously under a fixed experimental plan. Compared to the classical "one factor at a time" approach: fewer runs, statistically anchored effects, and interactions are detected instead of missed.
- Screening — which of the many candidate factors matter at all?
- Optimisation — for the few important factors, find the best setting (Response Surface, CCD, Box-Behnken)
- Robustness — settings where the response stays insensitive to noise factors (Taguchi, dispersion DoE)
- Mixtures — recipes whose proportions sum to 100 % (Simplex-Lattice, Simplex-Centroid)
See also
Used in
In the Algorithm Lab
Sources
- Montgomery, D. C. — Design and Analysis of Experiments, 10th Edition, Wiley
- Box, G. E. P., Hunter, W. G., Hunter, J. S. — Statistics for Experimenters, 2nd Edition, Wiley
- Kleppmann, W. — Versuchsplanung, 10. Auflage, Hanser