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For three different scenarios tested, correlation coefficients (R2) for linear regression between the estimated and known values ranged from 0.968 to 0.993 for mixing proportions and from 0.839 to 0.998 for end-member compositions.
(Table 6; P < 0.001 in both tested correlation combinations).
We tested correlation between number of hydrophobin-encoding genes, overall genome size and their ecological strategy.
Clearly, this has a big influence on the P-MRS data, contributing to the nonsignificance of their tested correlation.
In addition to the continuous energy values correlation, we also tested correlation of a binary classification ("inhibitor - noninhibitor") between calculated and experinhibitor - noninhibitor
The Pearson correlation coefficient (R ) and the p value associated with the slope term (p ) are shown for each tested correlation, with correlations with a p value less than 0.05 in bold italics.
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We also tested correlations between teachable moment constructs and motivation to change behaviors.
We tested correlations between miR-182 and angiogenic and hypoxia molecular markers.
To test correlation of different parameters, Spearman's rank correlation coefficients were calculated.
Spearman's method was used to test correlation between numerical variables.
Pearson correlation was used to test correlation between sleep, symptoms severity, and ferritin levels.
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