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Mutual information, which measures the dependence between two variables, is different from the correlation function.
The larger the absolute value of r is, the stronger correlation between two variables is.
The dependence between two variables is best represented through the copula of their joint distribution.
The relation curve between two variables is usually an approximate straight line.
As a first step, correlation analysis which reveals the relationship between two variables is calculated for the major ion chemical data from the study area.
cTo ensure that a relationship between two variables is not due to reversed causality it is necessary to extract the truly exogenous fraction of the explanatory variable.
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Interactions between two variables were analyzed while keeping the other variable at midpoint.
The 3D plots for interactions between two variables were presented in Figure 3.
Correlations between two variables were determined with Pearson's correlation analyses.
Comparisons between two variables were done using a T test.
Pearson Product Moment Correlation Pearsonn's correlation), reflecting the degree of linear relationship between two variables were calculated with Matlab.
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