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A multiple regression analysis of these variables plus the mean home ratings of resident friendliness to staff accounted for 55% of the variation in resident-administrator differences.
A principal component analysis of these variables permits areas in Britain and Belgium with similar scores on the resulting components to be matched into pairs.
Various processing elements can be involved however, and this paper provides an analysis of these variables in order to determine which parameters should be controlled and which can be manipulated.
Analysis of these variables pooled both EOT and FUP outcomes given the paucity of homogenous data for individual variables.
In the multivariate logistic regression analysis of these variables together with RRM1 and disease stage, only low RRM2 expression emerged as an independent predictive factor for response (Table 2).
To avoid duplicate genetic data for these subjects, the entire dataset was analyzed two ways: first, with all shifts included together (un-stratified) and second, stratified for shift-type during the analysis of these variables.
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Local alignment analysis of these variable blocks revealed that their variability is not due to random mutations on a single consensus sequence.
However, a re-analysis of these variables imputing missing phone usage information, where possible, produced similar results, suggesting that this is not the reason, and that cases simply found it harder than controls to recall details of their phone use.
For this reason, we initially performed a simple analysis of all these variables according to the age decade of participants (see Results and Discussion).
In multivariate analysis, two of these variables -- household wealth as assessed by principal component analysis and water availability at handwashing locations - remained significantly associated with the hand cleanliness for both mother and child (table 5).
The simultaneous analysis of all these variables did not yield significantly raised risks for any one of the variables.
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