Сравнение методов

	Многофакторный дробный факторный эксперимент	Методология поверхности отклика (RSM)
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Hypothesis test
Год появления	1961 (fractional factorial foundation); 1980 (multi-response desirability approach)	1951
Автор метода	George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)	George E. P. Box & K. B. Wilson
Тип	Experimental design with simultaneous multi-response optimization	Second-order polynomial response surface model
Основополагающий источник	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	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.
Другие названия	MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial	RSM, Central Composite Design, Box-Behnken Design, CCD
Связанные	4	7
Сводка	Multi-response fractional factorial design (MRFFD) applies a resolution-efficient fractional factorial experiment to study multiple response variables simultaneously. By running only a carefully chosen fraction of the full factorial treatment combinations, the experimenter gathers enough information to fit individual response models for each output and then optimize all responses jointly — typically via a composite desirability function — while keeping the number of experimental runs tractable.	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.