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Different mix of patients did not explain this variance between physicians.
It is possible that characteristics of mothers and/or newborn infants could explain this variance.
As PCAIMs are chosen to explain this variance, even a small number of markers are able to predict outlying populations well.
Three types of models have been put forward to explain this variance: the clinical model, the neuropsychological model, and the psychological denial model [ 23].
The wide range of adoption times revealed, and the lack of simple predictors to explain this variance, suggest that factors other than condition/class and order of entry affect a healthcare system's response to the introduction of new medicines.
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Explaining this variance can elucidate challenges and opportunities confronting developing nations sharing limitations that previously constrained China.
There are a number of reasons that may go some way to explaining this variance with the prevailing literature.
If the GLM did not explain this amount of variance in a voxel's data, the voxel was removed from the mask.
This might explain the variance, but not the decline.
Given the different conductances of the Aβ channels, this could explain the variance in conductance measured in the electrical recordings.
From Figure 1, we noted that chromosome 2 was the most important chromosome in explaining the variance of this trait.
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