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Although the frequency of detection of HBoV species varied among the periods studied, variance analysis indicated no significant differences (p = 0.099) (Table).
The final models explained about 85%95%5% CI 77, 96%, P = <0.001) of the studied variance in carotid measures and had excellent goodness-of-fit as described in the ROC curves, but the performance of the models improved only modestly with the addition of low C4 to the known traditional risk factors.
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Two-factor ANOVA for studying variance between and within samples was applied using Microsoft Excel 2010.
It is often natural to assume that expectations are unknown and thus to additionally be estimated when studying variances.
Moderate data heterogeneity of the included studies and small between-studies variance were also observed here.
The random effects model provides a more conservative estimate because it accounts for between-study variance.
The between-study variance was assessed using Cochran Q test and I2.
Yet, data heterogeneity observed in the included studies, as well as the between-studies variance, was high.
However, data heterogeneity of the included studies was moderate to high, and the between-studies variance was moderate.
However, between-study variance is significant, and there are signs of publication bias among published studies on pair programming.
R 2 was calculated to explain the percentage of between study variance explained by any particular co-variate.
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