Understanding Multicollinearity in Multiple Regression

Hard Multiple Regression Analysis Multicollinearity

In a multiple regression analysis, the researcher is attempting to understand the impact of three independent variables, X₁, X₂, and X₃, on a dependent variable Y. Upon performing the regression, the researcher notices that the Variance Inflation Factor (VIF) for X₁ is significantly higher than 10, while those for X₂ and X₃ are below 5. In this context, what can be inferred about the multicollinearity among the independent variables?

There is severe multicollinearity among X1, X2, and X3, potentially distorting the regression results.

X1 is not highly correlated with X2 and X3, indicating that multicollinearity is not an issue.

The high VIF for X1 implies that it is redundant, but the overall multicollinearity is acceptable due to low VIFs for X2 and X3.

Hint

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