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In addition, collinearity diagnostics was conducted using indices including tolerance, variance inflation (VIF), condition index (CI), and eigenvalue.
Multicolinearity between the variables included in the models was assessed using tolerance variance inflation factor (VIF) post-estimation diagnostic tests.
These correlations did not result in multicollinearity since the tolerance (variance of OC not explained by other predictors) was at least 53%.
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To check for multicollinearity problems, the tolerances, variance inflation factors (VIFs), and correlation coefficients were inspected.
Therefore, we used the commonly used measures tolerance and variance inflation factor to test for multicollinearity [19].
All the explanatory variables were above the conventional cut-off point for tolerance and variance inflation factor (VIF): none of them had tolerance less than 0.10 and greater than 10 for VIF.
Multicollinearity among independent variables was tested by examining tolerance and Variance Inflation Factors for all variables in the model.
Cumulative prospect theory [10] and rank-dependent utility models [11] have attempted to account for skewness by overweighting unlikely extreme positive or negative events, but do so by sacrificing the ability to explain tolerance for variance [12].
Multicollinearity diagnostics (tolerance and variance inflation factors: VIF) were checked.
Moreover, tolerance and variance inflation factor (VIF) were used to check for multicollinearity.
We evaluated colinearity by tolerance and variance inflation factor in the linear regression analyses.
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