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For the analysis of the dependence between two parameters, the Pearson correlation was used.
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The Pearson correlation r coefficient measures the proportional (i.e., linear) relationship between two parameters, where the r coefficient varies in the interval [−1.00, +1.00] and a value of 0.00 represents a lack of correlation.
The test described differences between two parameters of the two groups, with a statistical significance of p < 0.05.
This constitutes evidence of a synchronization mechanism between two parameters from the Earth's atmosphere, NC and PG, and solar cycle activity.
Then, the stepwise procedure iteratively adds or removes a parameter or an interaction between two parameters from the model (Venables & Ripley, 2002).
The correlations between two parameters were tested using the Spearman's rank correlation test.
Correlations between two parameters were studied using the two-tailed Spearman's rho-test.
Associations between two parameters were analysed with the Spearman's rank correlation test.
A significant correlation between two parameters was taken at the 95% confidence interval.
Associations between two parameters were assessed using the Pearson correlation coefficients or Spearman rank order correlation coefficients, in the cases of skewed distribution of data.
Therefore, we confirmed a very weak association between the two parameters: the higher the CEOAE level, the stronger the MOC suppression.
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