Sammenlign metoder

	Robust Response Surface Methodology	Responsflateanalyse (RSM)
Fagfelt	Forsøksdesign	Forsøksdesign
Familie	Process / pipeline	Hypothesis test
Opprinnelsesår	1990	1951
Opphavsperson	G. G. Vining and Raymond H. Myers (dual response formulation)	George E. P. Box & K. B. Wilson
Type	Experimental optimization technique	Second-order polynomial response surface model
Opprinnelig kilde	Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45.	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.
Alias	Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM	RSM, Central Composite Design, Box-Behnken Design, CCD
Relaterte	3	7
Sammendrag	Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design.	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.