Comparer des méthodes

	Conception Hybride Pleinement Factorielle	Méthodologie des surfaces de réponse (RSM)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1980s–2000s (building on Fisher's 1935 factorial framework)	1951
Auteur d'origine	Derived from classical factorial design theory (Fisher, 1935); hybrid extensions developed across engineering literature from the 1980s onward	George E. P. Box & K. B. Wilson
Type	Experimental design strategy	Second-order polynomial response surface model
Source fondatrice	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	hybrid factorial design, mixed full factorial design, combined factorial design, HFFD	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	3	7
Résumé	Hybrid full factorial design is an experimental strategy that applies a full factorial structure to a selected subset of factors — those believed to have the strongest interactions — while treating remaining factors with a reduced or fractional scheme. This hybrid approach balances the complete interaction information of a full factorial with the run-count efficiency of fractional designs, making it practical for studies with many factors where a pure full factorial would be prohibitively expensive.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.