Salīdzināt metodes

	Vairākuzdevumu atbildes virsmas metodoloģija	Metodoloģija virsmas atbildes (RSM)
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)	1951
Autors	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)	George E. P. Box & K. B. Wilson
Tips	Experimental optimization technique	Second-order polynomial response surface model
Pirmavots	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.
Citi nosaukumi	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Saistītās	6	7
Kopsavilkums	Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.	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.