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Spearman's rank correlation coefficient was used to measure the statistical dependence between two variables in alcohol-dependent persons.
The Pearson product-moment correlation coefficient measures the linear dependence between two variables.
The dependence between two variables is best represented through the copula of their joint distribution.
It is a non-parametric measurement of the statistical dependence between two objects.
Correlation distance [19] is a measure of statistical dependence between two time series objects.
For the analysis of the dependence between two parameters, the Pearson correlation was used.
Mutual information, which measures the dependence between two variables, is different from the correlation function.
Phase synchrony analysis is a useful measure of linear dependence between two stochastic signals.
However, these dependence measures can only measure the dependence between two scalar random variables.
Therefore, the distance covariance test of independence detects any nonlinear and nonmonotone dependence between two random variables [63].
This type of dependence between two random variables is called here a rotational or correlation type dependence.
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