Comparer des méthodes

	Régression polynomiale	Méthodologie des surfaces de réponse (RSM)
Domaine	Statistique	Plans d'expériences
Famille	Regression model	Hypothesis test
Année d'origine	2012	1951
Auteur d'origine	Montgomery, Peck & Vining (textbook treatment); classical least squares	George E. P. Box & K. B. Wilson
Type	Linear regression in transformed predictors	Second-order polynomial response surface model
Source fondatrice	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	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	polynomial least squares, curvilinear regression, Polinom Regresyonu	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	4	7
Résumé	Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.	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.