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In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient (after the Greek letter τ, tau), is a statistic used to measure the ordinal association between two measured quantities.
Dave Kerby (2014) recommended the rank-biserial as the measure to introduce students to rank correlation, because the general logic can be explained at an introductory level. The rank-biserial is the correlation used with the Mann–Whitney U test, a method commonly covered in introductory college courses on statistics. The data for this test ...
The Kendall tau rank distance is a metric (distance function) that counts the number of pairwise disagreements between two ranking lists. The larger the distance, the more dissimilar the two lists are.
Note that Kendall's tau is symmetric in X and Y, whereas Somers’ D is asymmetric in X and Y. As τ ( X , X ) {\displaystyle \tau (X,X)} quantifies the number of pairs with unequal X values, Somers’ D is the difference between the number of concordant and discordant pairs, divided by the number of pairs with X values in the pair being unequal.
The Kendall tau rank correlation coefficient is a measure of the portion of ranks that match between two data sets. Goodman and Kruskal's gamma is a measure of the strength of association of the cross tabulated data when both variables are measured at the ordinal level.
Kendall's Tau also refers to Kendall tau rank correlation coefficient, which is commonly used to compare two ranking methods for the same data set. Suppose r 1 {\displaystyle r_{1}} and r 2 {\displaystyle r_{2}} are two ranking method applied to data set C {\displaystyle \mathbb {C} } , the Kendall's Tau between r 1 {\displaystyle r_{1}} and r ...
Kendall tau rank correlation coefficient, also called the Kendall tau test A test of the strength of the abdominal muscles during a physical examination Topics referred to by the same term
Some correlation statistics, such as the rank correlation coefficient, are also invariant to monotone transformations of the marginal distributions of X and/or Y. Pearson/Spearman correlation coefficients between X and Y are shown when the two variables' ranges are unrestricted, and when the range of X is restricted to the interval (0,1).