方法对比

	敏感性分析集成全因子设计	响应面方法 (RSM)
领域	实验设计	实验设计
方法族	Process / pipeline	Hypothesis test
起源年份	1990s–2000s (formalized combination)	1951
提出者	Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)	George E. P. Box & K. B. Wilson
类型	Experimental design with factor importance ranking	Second-order polynomial response surface model
开创性文献	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons. ISBN: 978-0470059975	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.
别名	SA-FFD, full factorial design with sensitivity analysis, factorial-based sensitivity analysis, FFD-SA	RSM, Central Composite Design, Box-Behnken Design, CCD
相关	3	7
摘要	Sensitivity analysis-integrated full factorial design combines exhaustive factorial experimentation — where every combination of factor levels is tested — with systematic sensitivity analysis to quantify how much each input factor drives variation in the output response. This hybrid approach provides both reliable effect estimates and a ranked picture of factor importance, guiding engineers and scientists toward the levers that truly matter for system performance.	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.