Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza de Varianță cu Doi Factori (ANOVA cu Doi Factori)× | Analiza de Covarianță (ANCOVA)× | Testul H Kruskal-Wallis× | |
|---|---|---|---|
| Domeniu | Statistică | Statistică | Statistică |
| Familie | Hypothesis test | Hypothesis test | Hypothesis test |
| Anul apariției≠ | 1925 | 1932 | 1952 |
| Autorul original≠ | Ronald A. Fisher | Ronald A. Fisher | William Kruskal & W. Allen Wallis |
| Tip≠ | Parametric factorial mean comparison | Parametric group comparison with covariate control | Nonparametric group comparison |
| Sursa seminală≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574 | Kruskal, W. H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. DOI ↗ |
| Denumiri alternative≠ | factorial ANOVA, two-factor ANOVA, İki Yönlü ANOVA | analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi) | Kruskal-Wallis H test, one-way ANOVA on ranks, Kruskal-Wallis one-way analysis of variance, Kruskal-Wallis Testi |
| Înrudite≠ | 6 | 4 | 5 |
| Rezumat≠ | Two-Way ANOVA is a parametric hypothesis test that simultaneously examines the main effects of two independent categorical factors and their interaction effect on a single continuous dependent variable. The technique was developed within the broader framework of the analysis of variance established by Ronald A. Fisher in 1925 and remains the standard approach whenever an experiment or survey includes exactly two between-subjects factors. | 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). | The Kruskal-Wallis H test is a nonparametric hypothesis test that compares three or more independent groups to decide whether their distributions (typically their medians) differ. Introduced by William Kruskal and W. Allen Wallis in 1952, it works on ranks rather than raw values and is the distribution-free counterpart to one-way ANOVA. |
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