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These models explained more than 83% of the observed variability, with mean errors of less than 2.5 m.
The DGH and DG models explained more than 90% of tree AGB variation, while the D and DH models explained less than 90% of the variation.
In his empirical analysis, he estimated neoclassical models where growth of GDP was a function growth of capital and labor and ELG models that also included growth of aggregate real exports; the ELG models explained more of the growth than the neoclassical models.
The three models explained more than 98% of the total variance, calculated without considering the initialized point at breast height, and the model with a value of 2 for self-thinning line resulted in the best values of the goodness-of-fit statistics.
The variables included in the models explained more of the variance for women than for men.
Three of the five regression models explained more than 35% (R > 0.35) of the variance of the outcome variables.
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This means that these models explain more than 96.0% of the variation in emission index when compared to the total variation.
The PCM models explain more than 59% of the variance (({R^{2}_{0}} = 0.59)), thus highlighting the ability of the PCM models to predict the potency of compounds displaying uncorrelated bioactivity profiles on human cyclooxygenases.
According to the R 2 values in this table, it can be observed that, in the three set of experiments carried out, the linear models explain more than 95% of the variance found in the maximum adsorption rate data.
All five of the positive models explain more than 11% of the variance in the data (16%, 42%, 22%, 11%, 22%) while none of the negative models succeeds in explaining more than 10% of the variance.
A quadratic model explained more of the variance (R2=0.58 0.61) than a linear model (R2=0.37 0.52) between metabolic power and individual leg mechanical power during step-to-step transitions across all velocities.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com