Comparer des méthodes

	Régression linéaire multiple	Analyse de variance à un facteur
Domaine	Statistique	Statistique
Famille	Regression model	Hypothesis test
Année d'origine	1886	1925
Auteur d'origine	Francis Galton; formalized by Karl Pearson	Ronald A. Fisher
Type	Parametric linear model	Parametric mean comparison
Source fondatrice	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Alias	MLR, OLS regression, multiple regression, linear regression with multiple predictors	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
Apparentées	8	4
Résumé	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.