Comparer des méthodes

	Analyse de sensibilité avec plan composite centré	Plan d'expériences factoriel complet
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1951 (CCD); SA integration throughout 1970s–2000s	1926
Auteur d'origine	G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners	R. A. Fisher
Type	Quantitative experimental design with post-hoc sensitivity assessment	Parametric factorial experiment
Source fondatrice	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.	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130
Alias	SA-CCD, CCD sensitivity analysis, RSM sensitivity analysis, response surface sensitivity study	factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k)
Apparentées	4	5
Résumé	Sensitivity analysis with Central Composite Design (CCD) combines a structured, space-filling experimental layout with a systematic examination of how much each input factor drives changes in the response. CCD supports estimation of a full quadratic response surface model; sensitivity analysis then interrogates that model to rank factors by influence, identify interactions, and map the performance landscape — guiding engineers and researchers toward robust operating conditions and efficient optimisation.	A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.