Comparer des méthodes

	Régression linéaire simple	Corrélation de Pearson par le produit des moments
Domaine	Statistique	Statistique
Famille	Regression model	Hypothesis test
Année d'origine	1805	1895
Auteur d'origine	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	Karl Pearson
Type	Parametric bivariate regression	Parametric correlation
Source fondatrice	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]	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Alias	SLR, ordinary least squares regression, OLS regression, bivariate regression	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi
Apparentées	7	4
Résumé	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.	The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association.