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Therefore, it is advisable that each of the studied models explains a unique pathway in the development of hypertension.
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The final regression models explained a high degree of variance in predicting isokinetic shoulder flexion (R2 = 0.59) and extension (R2 = 0.67) with a subset of four and five inputs, respectively.
Additionally, our models explain a novel concept for partial agonism, termed dynamic ligand binding.
First, though all models explained a significant amount of variability in effects (as shown by QM), all model fit tests (QE results) were very large and statistically significant.
Both science models explained a significant amount of effect-size heterogeneity, as respectively indicated by QM 3) = 18.9, p <.05 and QM 2) = 13.8, p <.05.
Both models explained a significant amount of heterogeneity in the math gaps, as indicated by QM 3) = 16.3, p <.05 and QM 2) = 8.0, p <.05, respectively.
Overall models explained a modest amount of LOS variation, with an r(2) of 4.8%, with PC responsible for 1.3% of variation and together with SL explained 2.2% of variation.
The models explained a substantial portion of this variation using relatively easily collected and intuitive predictors.
LUR models explained a large fraction of the spatial variation of the two OP metrics.
Both models explained a high proportion of between-subject variation in HRQoL, and both models displayed excellent predictive ability.
None of the multivariable-adjusted models explained a substantial portion of the variability in resistin concentration (R range 3 6%).
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