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The inclusion of both biaxial and uniaxial tests in model fits improved the accuracy of model predictions.
We found that yellowfin model fits improved with zero-inflated negative binomial models.
However, the differences in urine dilution still explained some of the observed variance in BPA concentrations of the spot samples, because when we accounted for urine dilution, the model fits improved (models B and C vs. A).
These results suggest that differences in urine dilution explained some of the variance in the metabolite concentrations, because the model fits improved when we accounted for dilution by correcting or adjusting for creatinine concentration.
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Based on the DIC values, model fits improve as more variables were included; thus, the full model has the best fit.
The model fit improved significantly by linearly standardizing whole blood concentrations to a hematocrit of 45%% (ΔOFV = −78.3, p < 0.001).
The model fit improved quantitatively (RMSD = 0.012) with the inclusion of criterial variability.
We stratified for the APACHE II scores into three groups [15, 16], when the P value of the interaction term was significant and the model fit improved (based on Akaike Information Criterion).
Our findings indicate that devising transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit, improved estimates of key transmission parameters, and more realistic epidemic forecasts.
All measures of model fit improved for the PRI model.
The model fit improved as expected, with R-squared ranging from 0.11 to 0.16.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com