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It can be seen from Table 5, most VIF values are slightly over 1.000 and the maximum is 5.226, indicating model (2) has obvious statistical significance.
All the model terms, except for B 2, owned a high F value with Prob > F < 0.05, indicating model term significances.
That is, well-fitting models show non-significance on the goodness-of-fit test, indicating model prediction that is not significantly different from observed values.
No evidence for the inaccurate overall prediction of mortality by Model 1 was found, given that the SMR was not significantly different from 1 (SMR = 1.003, 95%% CI 0.959 1.050) (Table 3). Figure 2a displays predicted and observed hospital mortality in the derivation set across each 10th of the observed risk of death for Model 1, indicating Model 1 was well calibrated.
Observed versus predicted response in genetic variation (AFLP markers) between sampling localities, indicating model performance.
Observed versus predicted response in morphological variation between sampling localities, indicating model performance.
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The residuals plots indicated model residuals did not violate the statistical modeling assumptions.
Positive values indicate model overestimation bias, and negative values indicate model underestimation bias.
Symbols indicate experimental data and lines indicate model data.
Values of "Prob > F" <0.0500 indicate model terms are significant.
P value less than 0.05 indicates model terms are significant.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com