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When two or more predictor variables in a multiple regression model are taken together, multicollinearity is a statistical phenomenon. In this case, tiny changes in the model or data may cause the coefficient estimates to vary unpredictably. In order to determine whether or not there was multicollinearity, the researchers used the variance inflation factor (VIF) and its reciprocal (1/VIF) or (TOLERANCE). If VIF is less than 5%, collinearity is suspected; if it is greater than 10%, multicollinearity is assumed. The closer TOLERANCE is to 1, on the other hand, the more evidence that the regressors are not collinear. The absence of multicollinearity is indicated by the results of the multicollinearity test in Table 4.4. This is supported by the statistical findings, which reveal that none of the VIFs are closer to 10 and TOLERANCE is closer to 1. VIF has a mean value of 1.30. All of this shows that the independent variables are not multicollinear. Table.4: Multicollinearity test

Table 4 When two or more predictor variables in a multiple regression model are taken together, multicollinearity is a statistical phenomenon. In this case, tiny changes in the model or data may cause the coefficient estimates to vary unpredictably. In order to determine whether or not there was multicollinearity, the researchers used the variance inflation factor (VIF) and its reciprocal (1/VIF) or (TOLERANCE). If VIF is less than 5%, collinearity is suspected; if it is greater than 10%, multicollinearity is assumed. The closer TOLERANCE is to 1, on the other hand, the more evidence that the regressors are not collinear. The absence of multicollinearity is indicated by the results of the multicollinearity test in Table 4.4. This is supported by the statistical findings, which reveal that none of the VIFs are closer to 10 and TOLERANCE is closer to 1. VIF has a mean value of 1.30. All of this shows that the independent variables are not multicollinear. Table.4: Multicollinearity test

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