مقایسهٔ روش‌ها

	حداقل مربعات معمولی (OLS)	رگرسیون خطی ساده
حوزه	آمار	آمار
خانواده	Regression model	Regression model
سال پیدایش	1805	1805
پدیدآور	Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809)	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)
نوع	Linear parameter estimation	Parametric bivariate regression
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.]	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]
نام‌های دیگر	OLS, OLS regression, linear least squares, classical linear regression	SLR, ordinary least squares regression, OLS regression, bivariate regression
مرتبط	8	7
خلاصه	Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority from 1795), OLS is provably optimal under the Gauss-Markov theorem: given its assumptions, it yields the Best Linear Unbiased Estimator (BLUE) of the regression coefficients.	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.