Comparar métodos

	Experimento Fatorial Completo Adaptativo	Metodologia de Superfície de Resposta (RSM)
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Hypothesis test
Ano de origem	1950s (factorial foundations); adaptive extensions prominent from 1990s onward	1951
Autor original	Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory	George E. P. Box & K. B. Wilson
Tipo	Experimental design	Second-order polynomial response surface model
Fonte seminal	Atkinson, A., Donev, A., & Tobias, R. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606	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.
Outros nomes	adaptive full-factorial design, sequential full factorial experiment, adaptive complete factorial design, dynamic full factorial trial	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	5	7
Resumo	An adaptive full factorial experiment is an experimental design that starts with a complete crossing of all factors and all their levels, then uses interim data to modify subsequent runs — dropping unpromising factor levels, adding new ones, or re-allocating replication — while preserving the full factorial structure within each phase. This integration of full factorial coverage with adaptive decision rules allows researchers to explore all main effects and interactions without committing to a fixed, inefficient run plan before any data are observed.	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.