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In statistics, D'Agostino's K 2 test, named for Ralph D'Agostino, is a goodness-of-fit measure of departure from normality, that is the test aims to gauge the compatibility of given data with the null hypothesis that the data is a realization of independent, identically distributed Gaussian random variables.
I have K^2=8.7 (K=2.95), Critical value(0.95,2)=5.991 - so it's an unlikely event. Clarification is requested.155.198.66.7 15:17, 30 October 2009 (UTC) I just ran this test, for a sample of size n = 100 the test statistic K² has mean of 2.014 and standard deviation of 2.261 (versus theoretical 2.0 and 2.0) over B = 100,000 simulations. For a ...
Cochran's test is a non-parametric statistical test to verify whether k treatments have identical effects in the analysis of two-way randomized block designs where the response variable is binary. [ 1 ] [ 2 ] [ 3 ] It is named after William Gemmell Cochran .
Illustration of the Kolmogorov–Smirnov statistic. The red line is a model CDF, the blue line is an empirical CDF, and the black arrow is the KS statistic.. In statistics, the Kolmogorov–Smirnov test (also K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions.
Both the k-dimensional Weisfeiler-Leman (k-WL) and the k-dimensional folklore Weisfeiler-Leman algorithm (k-FWL) are extensions of 1-WL from above operating on k-tuples instead of individual nodes. While their difference looks innocent on the first glance, it can be shown that k-WL and (k-1)-FWL (for k>2) distinguish the same pairs of graphs.
To test whether allele a is recessive to allele A, the optimal choice is t = (0, 1, 1). To test whether alleles a and A are codominant, the choice t = (0, 1, 2) is locally optimal. For complex diseases, the underlying genetic model is often unknown. In genome-wide association studies, the additive (or codominant) version of the test is often used.
Ripley's K and L functions introduced by Brian D. Ripley [2] are closely related descriptive statistics for detecting deviations from spatial homogeneity. The K function (technically its sample-based estimate) is defined as ^ = (<),
I think an article purporting to describe the M-K test needs to actually describe the eponymous statistical test. I don't have access to the McDonald and Kreitman article, but according to this article it is a 2x2 Chi-square test performed on the table given in the McDonald-Kreitman test page.