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However, 10 26% of the explainable variation in outcome was explained by the predictors, which was sufficiently high to be able to include all considered predictors in the multiple model (Table 4).
Results: Therapy intensity was mostly determined by impairment and facility type, although variances explained by the predictors were small.
The percentage of variance explained by the predictors in the models corroborates these findings.
It is clear that a large amount of variance is explained by the predictors.
As with the mathematics models, the Q-error statistic (QE) was quite large and statistically significant (see Table 3), which means a large degree of effect-size variability was not explained by the predictors.
In order to estimate the variance explained by the predictors, the additional variance explained by the added student-level or school-level variables was computed using this formula: (remaining variance without added predictors-remaining variance with added predictors)/remaining variance without added predictors.
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Some of the spatial structure can be explained by the predictor variables used in the model.
This shows that little variations in the safety indicators are explained by the predictor variables.
Less than half of the variation of foliage biomass was explained by the predictor variable(s).
The laterals roots and root system biomass models showed that more than 99% of the BGB variation was explained by the predictor variables (Tables 4, 5).
The root collar and taproot biomass models showed that more than 96 and 98% of the BGB variation, respectively, were explained by the predictor variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com