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The first phase of the analysis was to detect the most important variables for group separation between GR and BR by means of stepwise variable selection.
Equation 12 was also the most accurate for R estimation, though its mathematical formula differs from the BEF model, because both equations were fitted by means of stepwise regression.
The association of these variables with climate, topography, landscape, human settlement, and geographic position was explored by means of stepwise regression models applied on the axes obtained from principal component analysis.
This paper analyses the relationship between fragment use by martens (measured through scat presence) and a series of variables related to the size, isolation and vegetation structure of each fragment by means of stepwise logistic regression.
Variables with p-values <0.10 were incorporated into the multivariate model by means of stepwise selection.
Independent predictors of the outcome of BSI were identified by means of stepwise logistic regression analysis, with a limit for entering and removing variables at 0.05.
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Combinations of moderator variables were evaluated by means of a stepwise meta-regression analysis in a random effects model.
Then a SPCE metamodel is built with respect to the reduced dimensionality by means of the stepwise regression technique.
The best and most parsimonious model was chosen by means of a stepwise approach using Akaike's Information Criterion (AIC) as a measure of relative goodness of fit, where smaller values represent better fits, and variables remained in the final model if the associated P value was < 0.05.
We generated simplified models by means of backward stepwise elimination.
We generated parsimonious models by means of backward stepwise elimination.
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