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Spearman's rank correlation coefficient measures the statistical dependence between two variables by assessing to what extent those variables can be modelled using a monotonic function.
Pearson correlation coefficient measures the relationship between two variables by dividing the covariance of the two variables by their standard deviation [13].
2) Test the correlation between two variables by calculating the zeroth-order of the partial correlation coefficient (Pearson's correlation coefficient).
Briefly, regression analysis using GEE assesses the association between two variables by correction for the within-subject dependence as a result of the repeated observations on wheezing, eczema and functional constipation, since repeated measurements within one individual are frequently correlated.
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4) Test the correlation between four variables by calculating the second-order of the partial correlation coefficient of the variable pairs, given two variables.
3) Test the correlation between three variables by calculating the first-order of the partial correlation coefficient of the variable pairs, given one variable.
Another way to evaluate the linear dependence between two variables is by adopting a linear regression framework.
Considering Bollen ([ 14] page 22), Duncan ([ 8] page 14), or Hayduk ([ 15] page 31; [ 16] pages xvi,10) will convince you that the covariance between two variables effected by a common cause must equal the product of the two effects and the variance of that common cause.
The degree of association that exists between two variables is measured by correlation coefficient, one taken as dependent variable (Chatterjee 2010).
Correlations between two variables were computed by Pearson correlation coefficient.
The relationship between two variables was estimated by Spearman's nonparametric correlation.
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