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Large variability in the response of those variables and interactions between the explanatory variables were observed.
The bivariate relationships just discussed may overlook relevant interactions between the explanatory variables that can affect the correlations uncovered above.
This finding indicates that a significant relationship exists between the explanatory variables and forest dependency (outcome variable).
Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable.
We applied classification trees at either scale to capture a hierarchy of relationships between the explanatory variables and brush-tailed rock-wallaby presence/absence.
According to this result, it is proved that there is no multicollinearity problem between the explanatory variables used in these models.
The high correlations between the explanatory variables showed that the variables were interrelated and thus that AGB variation cannot be explained by one single variable.
To show how the bivariate random effects meta-analysis model can be used to study the relation between the explanatory variables and the performance of diagnostic tests as characterized by a summary receiver operating characteristic curve (SROCC).
The analytical construction of optimal designs for generalized linear models with interaction between the explanatory variables is challenging and the optimal designs reported in the statistical literature for such models are invariably constructed numerically.
We included multiple biophysical and anthropogenic variables in the analyses, based on data at a regional scale so that the interrelationships between the explanatory variables could be accounted for.
The interaction terms between the explanatory variables was also significant, indicating that the difference between edges and centres is more pronounced in organic fields.
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