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To test for independence between two variables based on observed response frequencies, we use chi-square tests.
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The cluster model retained twenty-three variabasedbased on their importance for the characterization of the participants and their discriminative results between clusters.
The cluster model retained fifteen variables based on their importance for the characterization of participants and their discriminative results between clusters.
The divergence rate measure developed by Lee and Kim, 2006 [6] is based on the idea of quantification of the information difference between two process (variables) based on the notion of conditional entropy.
The Spearman rank correlation coefficient measures the statistical dependence between two variables and creates clusters based on the effect of each assembly statistic on the others (Tables 1 and 2).
Correlations are based on the covariance between two variables, which is a function of the variances of the two variables.
The association between module pairs were estimated with partial correlation coefficient in order to reveal 'direct' correlations between two variables after removing the effects of other variables, and a network was then constructed based on Gaussian graphical model [26] [28].
These paths represent tradoffs between two variables.
The correlation coefficient, r, measures the extent of the correlation between two variables.
A correlation between two variables (ice-cream consumption and shark attacks) may well be due to a third variable (summer weather).
The shortest pathway between two variables indicates the direct or indirect connection between those two variables along the strongest connection(s), or in other words the 'quickest' way to traverse the network from the one variable to the other.
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