Confronta i metodi

	Regressione Lineare Multipla	Analisi della Varianza a una Via
Campo	Statistica	Statistica
Famiglia	Regression model	Hypothesis test
Anno di origine	1886	1925
Ideatore	Francis Galton; formalized by Karl Pearson	Ronald A. Fisher
Tipo	Parametric linear model	Parametric mean comparison
Fonte seminale	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
Correlati	8	4
Sintesi	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.