مقایسهٔ روش‌ها

	رگرسیون خطی ساده	رگرسیون چندجمله‌ای
حوزه	آمار	آمار
خانواده	Regression model	Regression model
سال پیدایش	1805	2012
پدیدآور	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	Montgomery, Peck & Vining (textbook treatment); classical least squares
نوع	Parametric bivariate regression	Linear regression in transformed predictors
منبع بنیادین	Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80]	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
نام‌های دیگر	SLR, ordinary least squares regression, OLS regression, bivariate regression	polynomial least squares, curvilinear regression, Polinom Regresyonu
مرتبط	7	4
خلاصه	Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods.	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.