Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Wald-Wolfowitz Runs Test× | Durbin-Watson-test voor Autocorrelatie× | |
|---|---|---|
| Vakgebied≠ | Statistiek | Econometrie |
| Familie≠ | Hypothesis test | Regression model |
| Jaar van ontstaan≠ | 1940 | 1950 |
| Grondlegger≠ | Abraham Wald & Jacob Wolfowitz | James Durbin & Geoffrey Watson |
| Type≠ | Nonparametric randomness test | Test for first-order residual autocorrelation |
| Oorspronkelijke bron≠ | Wald, A. & Wolfowitz, J. (1940). On a test whether two samples are from the same population. Annals of Mathematical Statistics, 11(2), 147–162. DOI ↗ | Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428. DOI ↗ |
| Aliassen | Wald-Wolfowitz test, runs test for randomness, Runs Testi (Wald-Wolfowitz) | DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | The Wald-Wolfowitz runs test is a nonparametric hypothesis test that determines whether a sequence of observations — coded as a series of binary symbols — follows a random pattern or contains systematic structure. Introduced by Abraham Wald and Jacob Wolfowitz in 1940, the test counts the number of uninterrupted runs of identical symbols and asks whether that count is consistent with random arrangement. | 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. |
| ScholarGateGegevensset ↗ |
|
|