Comparer des méthodes

	Régression polynomiale	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	2012	2019
Auteur d'origine	Montgomery, Peck & Vining (textbook treatment); classical least squares	Wooldridge (textbook treatment); classical least squares
Type	Linear regression in transformed predictors	Linear regression
Source fondatrice	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	polynomial least squares, curvilinear regression, Polinom Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	4	5
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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).