True. The Variance Inflation Factor (VIF) is a measure of collinearity among predictor variables within a multiple regression. It is calculated as the ratio of the overall model variance to the variance of a model that includes only a single independent variable.

In this case, the coefficient of determination (R^2) between the two independent variables is 0.20. This means that only 20% of the variance in one variable is explained by the other variable.

The VIF for each predictor is calculated as 1/(1-R^2). So in this case, VIF = 1/(1-0.20) = 1.25.

As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity. Here, the VIF is well below these thresholds, suggesting that collinearity is not a concern. Therefore, the statement is true.

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True. The Variance Inflation Factor (VIF) is a measure of collinearity among predictor variables within a multiple regression. It is calculated as the ratio of the overall model variance to the variance of a model that includes only a single independent variable.

In this case, the coefficient of determination (R^2) between the two independent variables is 0.20. This means that only 20% of the variance in one variable is explained by the other variable.

The VIF for each predictor is calculated as 1/(1-R^2). So in this case, VIF = 1/(1-0.20) = 1.25.

As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity. Here, the VIF is well below these thresholds, suggesting that collinearity is not a concern. Therefore, the statement is true.

Knowee AI · Accepted Answer

True. The Variance Inflation Factor (VIF) is a measure of collinearity among predictor variables within a multiple regression. It is calculated as the ratio of the overall model variance to the variance of a model that includes only a single independent variable.

In this case, the coefficient of determination (R^2) between the two independent variables is 0.20. This means that only 20% of the variance in one variable is explained by the other variable.

The VIF for each predictor is calculated as 1/(1-R^2). So in this case, VIF = 1/(1-0.20) = 1.25.

As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity. Here, the VIF is well below these thresholds, suggesting that collinearity is not a concern. Therefore, the statement is true.

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