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The study showed that four out of five main effects as well as an interaction between two parameters were significant.
Correlations between two parameters were performed using Spearman rank correlation.
Correlations between two parameters were searched for by linear regression analysis.
Associations between two parameters were analysed with the Spearman's rank correlation test.
Linear correlations between two parameters were described by Spearman's rank correlation coefficient.
Correlations between two parameters were studied using the two-tailed Spearman's rho-test.
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A moderate correlation between two parameters is observed in the plot of the conditional distribution, whereas no clear correlation is observed in that of the background distribution (Table 3).
different from zero, and correlation between two parameters was tested according to Pearson's coefficient.
Furthermore Spearman's rank correlation coefficient ρ between two parameters was calculated.
The Pearson correlation test was used to determine whether the correlation between two parameters was significant.
A significant correlation between two parameters was taken at the 95% confidence interval.
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