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This should not represent a problem for multivariate models, as variables exhibit of variance above the recommended levels (R 2 > 0.50) and low standard error (SE < 0.08) Table 6 Descriptive statistics and correlation Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1. tamUse – 2.
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The optimal local correlation variables for very low Re flows were found after a lengthy calibration process.
After analysis we performed a multiple linear regression analysis with the positive correlation variables.
Table 1 Means, standard deviations, and correlations Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 1.
Table 1 Means, standard deviations, and correlations Variable M SD 1 2 3 4 5 6 7 8 9 1.
Before adding the independent variables, correlation of variables was checked using Spearman's correlation coefficient.
Table 3 Correlation matrix Correlations Variables 1 2 3 4 5 6 7 8 9 10 1. System quality 1 2.
High-correlation variables removal, SVM machine learning algorithm, and surface area ROC analysis were also applied.
Table 2 Means, Standard Deviations, and Correlations Variables Mean SD 1 2 3 4 5 6 7 8 1. Age 2.05.98 2.05.98
Correlation of variables was analyzed using Pearson correlation coefficient.
Correlation between variables: NI has significant correlation with all variables identified except for antibiotic use.
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