We related canopy attributes to environmental predictors to test for climate dependencies of tropical canopies and additional impacts of anthropogenic pressure on climate – canopy structure relationships.
We used binary logistic regressions, with species and treatments as predictors, to test four additional flight behaviours: course completion, circling, landing, and returning to release point.
However, in stepwise regression, as in the hierarchical model outlined above, there are often too many predictors to test every combination.
As further independent co-variates SF-36 social functioning, HADS depression, HADS anxiety, CSQ control pain, CSQ decrease pain, CSQ catastrophizing (baseline and change at follow-up for each of the scores) were included as predictors to test the hypothesis.
Pearson correlations between all variables in the study were computed for a preliminary analysis, followed by multiple linear regressions with each of the adjustment measures as outcomes and the risk and protective factors as predictors to test the main, mediation, and moderation hypotheses.
Model 2 will adjust for the other predictor variables to test independent associations.
In a first step we calculated a 'null' model with no predictor variables to test whether the mean overall PACIC scores varied significantly across the sample (random-effect with a p-value < 0.05), and assessed the Intraclass Correlation Coefficient (ICC) to quantify similarity within the groups.
Therefore, we include interviewer identity as an additional predictor of consenting to test for HIV.
Experience with positive RDT results was pinpointed as a significant predictor in adherence to test results, which was followed by the abandonment of presumptive diagnosis.
A one-way analysis of variance (ANOVA) with GH dose as a bounded continuous predictor was performed to test the piecewise linear GH effect.
