Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Velča dispersijas analīze× | Vienvirziena dispersijas analīze× | Velča t-tests (nevienādas dispersijas)× | |
|---|---|---|---|
| Nozare | Statistika | Statistika | Statistika |
| Saime | Hypothesis test | Hypothesis test | Hypothesis test |
| Izcelsmes gads≠ | 1951 | 1925 | 1947 |
| Autors≠ | B. L. Welch | Ronald A. Fisher | B. L. Welch |
| Tips≠ | Parametric mean comparison (heteroscedastic) | Parametric mean comparison | Parametric mean comparison (unequal variances) |
| Pirmavots≠ | Welch, B.L. (1951). On the Comparison of Several Mean Values. Biometrika, 38(3/4), 330–336. link ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ | Welch, B. L. (1947). The generalization of Student's problem when several different population variances are involved. Biometrika, 34(1/2), 28–35. DOI ↗ |
| Citi nosaukumi≠ | Welch's F-test, heteroscedastic one-way ANOVA, Welch ANOVA — Heterojen Varyans ANOVA | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA | unequal variances t-test, Welch-Satterthwaite t-test, Welch t-Testi (Eşit Olmayan Varyans) |
| Saistītās≠ | 3 | 4 | 4 |
| Kopsavilkums≠ | Welch ANOVA is a parametric hypothesis test that compares the means of three or more independent groups when their variances are not equal. Introduced by B. L. Welch in 1951, it replaces classic one-way ANOVA whenever the homogeneity-of-variance assumption fails, while still requiring approximately normal data. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. | Welch's t-test is a parametric hypothesis test that compares the means of two independent groups without assuming their variances are equal. It was introduced by B. L. Welch in 1947 as a more robust generalization of Student's two-sample test for situations where the two groups have different spread. |
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