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The Bayes factor for each predictor reflects evidence for the null based on the full model compared to a model without the predictor (Rouder & Morey, 2012).
To identify the most parsimonious models, we used the likelihood-ratio test to determine whether each variable and interaction term significantly improved model fit (P<0.05), compared to a model without that variable.
This model had 22 points lower OFV compared to a model without scaling.
The explained variance is the percentage reduction in unexplained variance as compared to a model without any independent variables.
Explained variance is the percentage reduction in unexplained variance compared to a model without any independent variables.
Parameters for these possible gender differences were included along their respective paths in the overall path model, resulting in a model with reduced DIC compared to a model without such gender differences (from 7182 to 7172).
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The mixed model presented a better overall model fit when compared to a similar model without random components (P <0.001 by likelihood ratio test).
The in vitro model of the stomach, using a plastic bag to model the gastric mucosa, was initially compared to a similar model without the plastic bag, to validate the modeling of the mucosa.
This interaction model was then compared to a reduced model without the "sample" interaction and significance was assessed with a partial F-test.
However, some of the odds ratios for the current number of drugs were in the opposite direction to the corresponding odds ratios in Model 8. Adding age and sex into Model 8 under the assumption of non-ignorable missing data did not have any effect on the growth estimates and their standard errors when compared to a similar model without external covariates (Table 4, Model 10).
Negative transfer is knowledge transfer that results in a worse performance compared to a regression model without knowledge transfer.
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