השוואת שיטות

	מבחן דרבין-וואטסון לאוטוקורלציה	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1950	2019
הוגה השיטה	James Durbin & Geoffrey Watson	Wooldridge (textbook treatment); classical least squares
סוג	Test for first-order residual autocorrelation	Linear regression
מקור מכונן	Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
כינויים	DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	4	5
תקציר	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).