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A mediating variable is intermediate in the causal relationship between two variables such that an independent variable causes the mediating variable which causes a dependent variable.
However, to analyze the difference between two variables such as two different professional years, Mann-Whitney was used.
The relationships between two variables, such as seizure frequency and age, were analysed using Pearson's correlation coefficient analysis.
Bivariate global Moran's I value determines the strength and direction of the relationship between two variables such as SMR and AOD in each county and measures the overall clustering.
Correlation analysis was used to determine whether there was a direct linear relationship between two variables, such that the variation in one data set corresponds to the variation in the other.
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Thus, we conclude that, while we find no strong evidence to suggest that metabolic rate and mutation rate (when measured as the root to tip path length) are correlated directly, there is an indirect relationship between these two variables, such that lineages with related mtDNA also have similar metabolic rates.
As with any analysis of association between two variables over such a long time period, there may be a number of confounding variables for which it was not possible to completely adjust.
Such an approach aims at deriving the partial correlations between two variables conditioning on the remaining ones, and building a network where the variables are connected by an edge if and only if their partial correlation is significantly non-zero.
And simply because of such variations, no fixed degree of correlation can be assumed to exist 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).
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