Sentence examples for between variables were quantified from inspiring English sources

Degree of linear relationships between variables were quantified by Pearson's correlation coefficient (r), and evaluated in line with guidelines proposed by Cohen [ 51]: r = 0.10 - 0.29 = small (low correlation); r = 0.30 - 0.49 = medium (moderate correlation); r = 0.50 - 1.0 = large (high correlation) [ 51].

The associations between categorical variables were quantified as odds ratios (ORs) with 95% confidence intervals (95% CI).

Associations between ordinal variables were quantified by Spearman's rank correlation with Pearson's chi-squared test.

Correlations between continuous variables were quantified using Pearson's correlation coefficient for normally distributed samples or Spearman's rank correlation coefficient for non-parametric values.

Associations between markers and clinical variables were quantified using the χ test of association or Fisher's exact test for categorical variables, and t-test to compare mean age across different types of abnormalities.

The associations between the outcome and independent variables were quantified by the estimation of the odds ratios (OR) and their 95%% confidence intervals (95 % CI), for the two cut-off levels.

These variables were quantified with questionnaires before neurofeedback.

In this paper, the statistical dependence between random variables is quantified by mutual information and estimated using a k nearest neighbor based approximation.

Continuous data were compared by the nonparametric Mann–Whitney test, whereas the degree of association between continuous variables was quantified by the Spearman rank correlation coefficient.

The degree of association between two continuous variables was quantified by Spearman rank correlation coefficient.

Correlation between ADA and specified variables was quantified using Pearson correlation coefficient.