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Results showed clearly that all variance components presented were quite sensitive to changes in selection intensity.
As selection intensity increased, all variance components declined by differing extents in a quadratic fashion.
The PCA showed that almost all variance (>98%) was explained by a single component.
All variance inflation factors (VIFs) were less than 1.30, indicating no multicollinearity.
For (gamma = 0), in contrast, the late signal is completely deterministic and all variance is attributable to early reporting.
First of all, variance in mathematics performance as the dependent variable can be partitioned into components for which students and schools are responsible.
We found that all variance inflation factors (VIFs) were less than 10, indicating that there was no serious multi-collinearity and that model construction could progress.
PC1 (principal component 1) and PC2 (principal component 2) capture 46% of all variance in the dataset and are related to molecular size (PC1) and charge and lipophilicity (PC2).
Note, in the results presented herein, the native state ensemble is extended slightly to include a range over θ near θ nat (equation not shown), which increases statistics and accounts for all variance within the entire free energy basin.
A larger amount of variance can be attributed to variance across origin groups: 20% of all variance in the educational achievement of migrant pupils can be attributed to the countries migrant children migrated from.
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We questioned whether a one-size-fits-all variance structure was appropriate.
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