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In statistics, Spearman's rank correlation coefficient or Spearman's ρ, named after Charles Spearman [1] and often denoted by the Greek letter (rho) or as , is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables).
Gene Glass (1965) noted that the rank-biserial can be derived from Spearman's . "One can derive a coefficient defined on X, the dichotomous variable, and Y, the ranking variable, which estimates Spearman's rho between X and Y in the same way that biserial r estimates Pearson's r between two normal variables” (p. 91). The rank-biserial ...
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
The Spearman–Brown prediction formula, also known as the Spearman–Brown prophecy formula, is a formula relating psychometric reliability to test length and used by psychometricians to predict the reliability of a test after changing the test length. [1] The method was published independently by Spearman (1910) and Brown (1910). [2] [3]
Spearman's rank correlation coefficient is a measure of how well the relationship between two variables can be described by a monotonic function. The Kendall tau rank correlation coefficient is a measure of the portion of ranks that match between two data sets.
A correlation matrix appears, for example, in one formula for the coefficient of multiple determination, a measure of goodness of fit in multiple regression. In statistical modelling , correlation matrices representing the relationships between variables are categorized into different correlation structures, which are distinguished by factors ...
"In statistics, Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter ρ (rho) or as rs, is a non-parametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function.
Charles Spearman developed in 1904 a procedure for correcting correlations for regression dilution, [10] i.e., to "rid a correlation coefficient from the weakening effect of measurement error". [11] In measurement and statistics, the procedure is also called correlation disattenuation or the disattenuation of correlation. [12]