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To assess relationship between the studied variables, a correlation matrix was performed (Spearman's rho).
Various plots and tables showing the interrelations between the studied variables were included in the paper.
The main aspect in multivariate analysis is the dependence between the studied variables.
Structural self-interaction matrix later on used to develop an ISM model showing relationship between the studied variables.
The strength of the association between the studied variables was estimated using the Spearman rank correlation coefficient (R).
A cross-sectional design, and correlation and factor analyses were used to study the relationship between the studied variables.
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Table 1 indicates the baseline association between occupation and the studied variables.
Logistic regression analysis was used to explore the associations between depressive symptoms and the studied variables.
Table 2 shows the associations between depressive symptoms and the studied variables.
We did not identify colinearity between any combinations of the studied variables in these models.
Univariate analyses between non-compliance and the studied variables were performed with Fisher's exact tests.
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between the examined variables
between the research variables
between the study variables
between the studied sites
between the studied fuels
between the studied WWTPs
between the studied SNPs
between the studied signals
between the studied receptors
between the studied characteristics
between the studied beams
between the studied genes
between the studied intensities
between the studied genotypes
between the studied modalities
between the studied alloys
between the studied regions
between the studied metrics
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