Comparar métodos

	Regresión Lineal Simple	Regresión Lineal Múltiple
Campo	Estadística	Estadística
Familia	Regression model	Regression model
Año de origen	1805	1886
Autor original	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	Francis Galton; formalized by Karl Pearson
Tipo	Parametric bivariate regression	Parametric linear model
Fuente seminal	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]	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
Alias	SLR, ordinary least squares regression, OLS regression, bivariate regression	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Relacionados	7	8
Resumen	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.	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.