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Variables were further checked for multi-colinearity.
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For the regression analyses, only complete data sets were included (complete case analysis) and the models were checked for multi-collinearity.
We checked for multi-collinearity among all independent variables included in Model 2 by examining the variance inflation factor, which were below the recommended cut-off of 10 (Agresti and Finlay, 2009).
Goodness-of-fit measures, including the coefficient of determination R, as well as statistical tools for regression diagnostics, such as residual analysis, detection of influential cases, and checking for multi-collinearity, were applied to discover model or data problems.
Basic model-fitting techniques, including variable selection, goodness-of-fit, adjusted generalised R, and regression diagnostics (residual analysis, detection of influential cases, and check for multi-collinearity) were applied to ensure the quality of the multivariate analysis.
Data were evaluated for multi-colinearity.
We tested for multi-colinearity by examining the correlation between predictors (Additional file 1: Table S2).
Be on the lookout for multi-colinearity.
We checked for colinearity by calculating Pearson correlation coefficients.
Potential explanatory variables were checked for colinearity before inclusion in the regression models using the tolerance and variance inflation factor.
These variables were checked for colinearity prior to inclusion in the regression models using Tolerance and Variance Inflation Factor.
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