An independent variable X; is not considered highly correlated with the other independent variables if O VIF < 5 R-squared > 0 R-squared > .5 VIF > 5

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### Correlation of Independent Variables

**Question:**

An independent variable \(X_j\) is not considered highly correlated with the other independent variables if

- [ ] VIF < 5
- [ ] R-squared > 0
- [ ] R-squared > .5
- [ ] VIF > 5

**Explanation:**

In the context of multicollinearity in regression analysis, the Variance Inflation Factor (VIF) is a metric used to evaluate the extent of correlation between independent variables. A commonly accepted threshold is that a VIF less than 5 suggests that the variable \(X_j\) is not highly correlated with the other independent variables. If the VIF exceeds this threshold, it indicates a higher degree of multicollinearity which can affect the stability and interpretability of the regression coefficients.

- **VIF (Variance Inflation Factor)**: This measures how much the variance of a regression coefficient is inflated due to multicollinearity with other predictors. A VIF value less than 5 is generally accepted as an indicator that multicollinearity is not a concern.

- **R-squared**: This represents the proportion of the variance for the dependent variable that's explained by the independent variables in a regression model. Although useful, R-squared alone does not provide information about multicollinearity between independent variables.

When addressing multicollinearity, utilizing VIF is more direct and relevant than R-squared values. Hence, the correct answer to the question is:

- **VIF < 5**
Transcribed Image Text:### Correlation of Independent Variables **Question:** An independent variable \(X_j\) is not considered highly correlated with the other independent variables if - [ ] VIF < 5 - [ ] R-squared > 0 - [ ] R-squared > .5 - [ ] VIF > 5 **Explanation:** In the context of multicollinearity in regression analysis, the Variance Inflation Factor (VIF) is a metric used to evaluate the extent of correlation between independent variables. A commonly accepted threshold is that a VIF less than 5 suggests that the variable \(X_j\) is not highly correlated with the other independent variables. If the VIF exceeds this threshold, it indicates a higher degree of multicollinearity which can affect the stability and interpretability of the regression coefficients. - **VIF (Variance Inflation Factor)**: This measures how much the variance of a regression coefficient is inflated due to multicollinearity with other predictors. A VIF value less than 5 is generally accepted as an indicator that multicollinearity is not a concern. - **R-squared**: This represents the proportion of the variance for the dependent variable that's explained by the independent variables in a regression model. Although useful, R-squared alone does not provide information about multicollinearity between independent variables. When addressing multicollinearity, utilizing VIF is more direct and relevant than R-squared values. Hence, the correct answer to the question is: - **VIF < 5**
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