Exemplos de uso de Multicollinearity em Inglês e suas traduções para o Português
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That's the problem of multicollinearity.
Multicollinearity was assessed using the VIF statistic.
Indicating the absence of severe cases of multicollinearity.
Multicollinearity among the independent variables was not a problem;
The VIF did not show evidence of multicollinearity for the adjusted model.
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VIF values of> 2.5 were used as indicators of considerable multicollinearity.
No multicollinearity was observed among the studied variables(Table 3). Thumbnail.
And, still, there were no problems about the singularity and multicollinearity.
This fact may suggest that multicollinearity issues can have affected the results.
Multicollinearity was excluded using the variance inflation factor before modeling.
However, the test did not detect multicollinearity among the independent variables under study.
Multicollinearity was evaluated by the correlation matrix between independent variables.
VIF values indicate that there is no multicollinearity among the model variables VIFs< 5.
Multicollinearity problems were resolved prior to insertion of the variables in the model.
The analyses revealed no evidence of multicollinearity between the independent variables Table 7.
Multicollinearity, heteroskedasticity, econometric modeling, advanced forecasting tools.
However, the test showed no evidence of multicollinearity among the independent variables of the study.
Multicollinearity tests were carried out between the independent variables that remained in the final model.
However, overall literature has shown that a new econometric issue arises: multicollinearity.
The VIF is indicative of multicollinearity problems when values greater than 10 are present.
The correlation between the explanatory variables allows aninference on the assumption of multicollinearity.
Multicollinearity in the models was avoided by excluding variables with variance inflation factors> 4.
This work presents a comparative study of multicollinearity identification methodologies in multivariate analyzes.
The multicollinearity is detected in regression models on which independent variables are strongly correlated.
Based on this analysis,the values for Variance Inflation Factor VIF did not indicate problems of multicollinearity VIF> 5.
The multicollinearity diagnosis was performed via estimation of the variance inflation factors VIF.
However, the variance inflation factor values were< 4,indicating that there was no such multicollinearity in the alternate models.
To check the degree of multicollinearity between the variables in the final model, tolerance and variance inflation factor were used.
This work proposes the use of some biased estimators to investigate whether is possible minimize the multicollinearity effects in logistic regression models.
Due to their multicollinearity, the independent variables were changed into z-scores to conduct the regression analysis, using the stepwise method.