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For example, the covariance between CBCL7 and DSM is for 51% explained by A, 25% by D, and 24% by E. To obtain the unstandardized amount of variances explained, the standardized influences should be multiplied with the phenotypic covariance between the measures, which is.51 for CBCL7 and DSM.
*** p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05 ***p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05 According to the results of the regression with all independent variables included in the model, physical health status variables and socio-demographic factors, the overall results yielded significant amount of variances explained for the functioning indexes, ranging from 32.6% to 63.9% (Table 7).
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The second level partitioned the amount of variance explained by plot, patch, and landscape factors alone and in combination.
The amount of variance explained increased only ~ 2 percentage points (pp), less than that obtained by including the kriging step, which explained 4 pp.
Then, multiple regression models and variance partitioning techniques were applied to assess the amount of variance explained by the landscape metrics on plant species richness for a range of extents.
Along the same lines, the amount of variance explained by all the environmental (overstorey, soil and topography) and spatial variables together was higher in the models of cover than in those of diversity.
Differences in the amount of variance explained in models for the two groups warrant further research.
The amount of variance explained by each principal component is shown in brackets.
Component scores indicate the amount of variance explained by each PC.
The total amount of variance explained by all the independent variables in the regression models was computed.
Thus, the amount of variance explained by person variables was only about half as much for numeracy compared to literacy.
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