As a result, factor analysis outcomes showed that the response variance on the A&SQ questions could for a large amount be explained without redundant domains, which implied that the collected complaints were all-encompassing, in spite of the fact that the number of subjects was not very large [ 14].
■ In the case of the methodology based on annual average cases, differences in the amounts are explained by differences in the type and quantity of inputs used by each institution depending on the production function in question and the quality standards attention as intervening variable in the control or management cost average.
Disability was predicted by sex, presence of leg pain, and pain intensity; however, the largest amount of variance was explained by pain intensity (27% of a total 40%).
A substantial amount of heterogeneity was explained by separate analyses of groups of trials.
The enhancement of the amount of grafting was explained by an initiation of the pendent double bonds and autoacceleration of graft polymerization as indicated by differential scanning calorimetry (DSC).
As expected, a high amount of variation was explained by the combined effect of hosts-soil, hosts-topoclimate and soil-topoclimate, since there is a close relationship between each pair of factors, such as the NDVI linking topoclimate and soil.
Partitioning complex factors (topoclimate and hosts) into their components (topography/climate and wild/domestic hosts, respectively) demonstrated that within topoclimate the highest amount of variation was explained exclusively by climate (Figure 4b), whereas wild ungulates explained a higher variation than livestock within host factors (Figure 4c).
As indicated by the adjusted R a considerable amount of variance was explained by the model variables.
A significant amount of variance was explained by changes in the present and future gaps for actual and aspirational QoL (52.4%, 59,9% respectively both p < 0.01).
The largest amount of variance was explained by the pure effect of regional-scale variables (9.9% for streams and 7.8% for lakes), followed by local-scale variables (2.9% and 5.8%) and geographic position (1.8% and 3.7%).
