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| Robust ANCOVA× | Varianzanalyse mit Kovariaten (ANCOVA)× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Hypothesis test | Hypothesis test |
| Entstehungsjahr≠ | 1990s–2000s | 1932 |
| Urheber≠ | Rand R. Wilcox and colleagues | Ronald A. Fisher |
| Typ≠ | Robust parametric covariate-adjusted comparison | Parametric group comparison with covariate control |
| Wegweisende Quelle≠ | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838 | Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574 |
| Aliasnamen≠ | robust ANCOVA, heteroscedastic ANCOVA, trimmed-mean ANCOVA, resistant ANCOVA | analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi) |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Robust ANCOVA is a covariate-adjusted group comparison that replaces classical ANCOVA's ordinary least squares estimation with resistant methods — typically trimmed means or M-estimators — so that the test retains valid Type I error control and reasonable power when data contain outliers, heavy-tailed distributions, or heteroscedastic errors. | ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013). |
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