Salīdzināt metodes

	Metodoloģija virsmas atbildes (RSM)	Vairākkārtējā lineārā regresija
Nozare	Eksperimentu plānošana	Statistika
Saime	Hypothesis test	Regression model
Izcelsmes gads	1951	1886
Autors	George E. P. Box & K. B. Wilson	Francis Galton; formalized by Karl Pearson
Tips	Second-order polynomial response surface model	Parametric linear model
Pirmavots	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.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
Citi nosaukumi	RSM, Central Composite Design, Box-Behnken Design, CCD	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Saistītās	7	8
Kopsavilkums	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.	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.