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Further, the multiple regression explained 8.6% of the total variance.
The late rate was, further, positively correlated with estimates of the percentage of nulliparous women (9 populations) and, together with terms for sugar and fat intakes, the multiple regression explained 90% of the variation.
Most weather variables were highly correlated with each other, so combining variables through stepwise multiple regression explained no more of the variance in fungal abundance and richness than did the average number of consecutive days with relative humidity less than 50%.
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In both ant species, multiple regressions explaining the production of individuals of the different castes gave very similar results (Table 2).
Deprivation has been identified as an important factor within multiple regressions, explaining variation in emergency admissions.
Addition of stoichiometric measures and other soil attributes (e.g. soil C N, C P, δ15N) in a multiple regression model explained more of the variation than a single factor plus the land use effect (though soil order still explained a small, but significant amount of variance for measures of microbial biomass).
Results: The model of 7 variables, analyzed by using multiple regression analysis, explained 10.3% of the variance in self-care.
Multiple regression models explained 35% to 60% of the variance in the regional BMDs; the Singh Index, weight, and subtrochanteric cortical thicknesses were significant contributors to all regional hip BMD models.
A multiple regression model explained 79% of the variance in placebo variability (F = 59.7; p < 0.0001).
Multiple regression models explained 37 77 % of the variation in plant species richness.
Multiple regression models explained a low to moderate portion of the variability in microbial biomarker levels (R = 4.2 19.0%).
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