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The Pearson product moment correlation coefficient was used to evaluate the relationship between two numerical variables.
We used Spearman's rank correlation to assess the monotonic association between two numerical variables.
The relationship between two numerical variables was tested using the Pearson's Correlation Coefficient whereas Chi square was used to test for association between differences in proportions.
In general, associations between two numerical variables of independence were examined by using Spearman's rank correlation and the Mann–Whitney U test.
Spearman's correlation coefficient was used to evaluate the relationship between two numerical variables in terms of strength and direction (association between overall and scores of each domain with number of medication errors reported in hospitals and health clinics).
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Thus, we have two numerical variables recorded for each gene in the study.
Two numerical variables were compared by means of Pearson or Spearman correlations (depending on whether or not the two variables were normally distributed).
In the present study, four numerical variables are set at three levels.
Pearson and Spearman correlations were used for the linear relation between two numerical dates.
We have considered how to compare two independent discrete numerical variables, a problem for which the difference between the two means is a suitable effect measure.
The relationship between two variables and numerical values obtained by real-time RT PCR were analysed using t-tests.
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