Exemplos de uso de Multicollinearity problems em Inglês e suas traduções para o Português
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Multicollinearity problems were resolved prior to insertion of the variables in the model.
Therefore, the variables CDI andIBrX100 were omitted from the model to avoid multicollinearity problems.
The VIF is indicative of multicollinearity problems when values greater than 10 are present.
The variance inflation factor shows that the model does not have multicollinearity problems in the specification.
Due to the multicollinearity problems among variables FMDI-Health, FMDI-Education, FMDI-Employment and Income, Current Revenue, Current Transfers, State Indebtedness and State Fiscal Deficit/Surplus observed in preliminary tests, the factor analysis technique was applied before the multiple linear regression technique. define factor analysis as an exploratory data analysis technique to reduce the number of variables describing a phenomenon and to detect structures in the relationships between variables, thereby classifying them.
Therefore, in the final model, based on the VIF test,there are no multicollinearity problems among the explanatory variables.
In this scenario, the analyst is faced with the option of selecting individual variables orvariable intervals so to avoid or reduce multicollinearity problems.
It is important to point out that only the variables which did not present multicollinearity problems were included in the logistic regression models.
Thus, multiple regression models were all tested through the VIF statistics to check for possible multicollinearity problems.
Poisson regression models showed good overall adjustment andwe did not observe multicollinearity problems or significant interactions.
In this phase, the variables were centralized scores transformed into Z value to minimize multicollinearity problems of the data.
Ramsey's test and VIF statistics showed that the model has no omitted variable bias F 1.09; Prob>F 0.3527, nor multicollinearity problems Fávero, 2015.
All correlations are statistically different from zero at a 10% significance level except between age and educational level and they are usually low,indicating that multicollinearity problems are of lesser order.
Based on multicollinearity test by using the variation inflation factor VIF test,it was found that the VIFs found are below 3 According to, multicollinearity problems are considered when VIF above 10 are observed.
Thus, the FMDI subindicators were used to compose a new factor using the factor analysis technique and thus to eliminate the multicollinearity problem.
The correlation between the variables was checked by the Pearson correlation coefficient in order to exclude one of the self-correlated variables from the analysis r< 0.70 and, therefore,eliminate the multicollinearity problem.
Thus, we observe that there is no evidence of problems of multicollinearity between the independent variables.
The existence of possible problems of multicollinearity between independent variables was verified with the variance inflation factor VIF.
Based on this analysis,the values for Variance Inflation Factor VIF did not indicate problems of multicollinearity VIF> 5.
There were no items excessively or perfectly correlated, andit may be affirmed that there are no problems of multicollinearity and singularity, respectively.
The correlation between return and BTC is presented in Table 3 anddoes not indicate problems of multicollinearity between the explanatory variables in the model.
There were no DM values indicators of the existence of outliers orsufficiently strong correlations between variables that indicate problems with multicollinearity Variance Inflation Factors.
We can notice that the model with all independent variables included has no statistical significance at 95% confidence level, according to F test, andthere are even problems of multicollinearity between some variables, such as, for instance, adr and assets.
That's the problem of multicollinearity.
This data treatment method was selected due to the existence of dependence or mediation relationships among the independent variables that affect the dependent variables,a situation in which there may be a problem of multicollinearity and indirect effects on the predicted variable.