เปรียบเทียบวิธี

	การวิเคราะห์ความไวด้วยการออกแบบแฟกทอเรียลแบบเศษส่วน	ระเบียบวิธีพื้นผิวการตอบสนอง (Response Surface Methodology - RSM)
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Hypothesis test
ปีกำเนิด	1935 (factorial design); 1990s–2000s (systematic SA integration)	1951
ผู้ริเริ่ม	R. A. Fisher (factorial design foundations); combined with sensitivity analysis frameworks developed by A. Saltelli and colleagues	George E. P. Box & K. B. Wilson
ประเภท	Quantitative experimental screening method	Second-order polynomial response surface model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	FFD sensitivity analysis, fractional factorial sensitivity screening, SA-FFD, screening design sensitivity analysis	RSM, Central Composite Design, Box-Behnken Design, CCD
ที่เกี่ยวข้อง	1	7
สรุป	Sensitivity analysis with fractional factorial design (SA-FFD) is an experimental screening method that uses a carefully chosen fraction of all possible factor combinations to identify which input variables most strongly influence a system's output. By running only 2^(k-p) experiments instead of a full 2^k factorial, it makes sensitivity ranking feasible when many factors are present. The approach is widely used in engineering, product development, simulation modeling, and process optimization.	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.