So sánh phương pháp

	Ordinary Least Squares (OLS)	Hồi quy tuyến tính đơn giản
Lĩnh vực	Thống kê	Thống kê
Họ	Regression model	Regression model
Năm ra đời	1805	1805
Người khởi xướng	Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809)	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)
Loại	Linear parameter estimation	Parametric bivariate regression
Công trình gốc	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]
Tên gọi khác	OLS, OLS regression, linear least squares, classical linear regression	SLR, ordinary least squares regression, OLS regression, bivariate regression
Liên quan	8	7
Tóm tắt	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.