Comparar métodos

	Prueba de Durbin-Watson para Autocorrelación	Regresión Lineal Múltiple
Campo	Econometría	Estadística
Familia	Regression model	Regression model
Año de origen	1950	1886
Autor original	James Durbin & Geoffrey Watson	Francis Galton; formalized by Karl Pearson
Tipo	Test for first-order residual autocorrelation	Parametric linear model
Fuente seminal	Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
Alias	DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Relacionados	4	8
Resumen	The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations.	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.