Сравнение на методи

	Адаптивен дробен факторен експеримент	Методология на повърхността на отклика (RSM)
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Hypothesis test
Година на възникване	1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s	1951
Създател	Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work)	George E. P. Box & K. B. Wilson
Тип	Experimental design strategy	Second-order polynomial response surface model
Основополагащ източник	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130	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.
Други названия	adaptive FFE, sequential fractional factorial design, adaptive screening design, adaptive factor screening	RSM, Central Composite Design, Box-Behnken Design, CCD
Свързани	2	7
Резюме	An adaptive fractional factorial experiment combines the resource-efficiency of fractional factorial designs with a sequential, data-driven strategy for selecting which factors and interactions to investigate next. Rather than committing all experimental runs upfront, the researcher analyses results from an initial fraction and uses those findings to guide subsequent rounds of experimentation — augmenting, folding, or redirecting the design until the active factors and optimal settings are identified with sufficient precision.	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.