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| Robuste ANOVA (Welch & Getrimmter Mittelwert)× | Methode der kleinsten Quadrate (OLS)× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1951 | 2019 |
| Urheber≠ | Welch (1951); robust trimmed-mean approach popularised by Wilcox | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Robust one-way analysis of variance | Linear regression |
| Wegweisende Quelle≠ | Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliasnamen≠ | Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads. | 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). |
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