Implications of Multicollinearity in Regression Analysis

Hard Multiple Regression Analysis Multicollinearity

Consider a multiple regression model aimed at predicting the sales volume of a product based on three independent variables: advertising spend (X1), price (X2), and competitor pricing (X3). After running the regression analysis, the analyst finds that the variance inflation factors (VIFs) for X1 and X3 are significantly larger than 10, while the VIF for X2 is close to 1.

Which of the following statements best describes the implications of the analysis concerning multicollinearity?

High multicollinearity between the independent variables X1 and X3 may lead to unreliable coefficient estimates, but the model can still make accurate predictions.

Since the VIF for X2 is close to 1, it indicates that X2 is not affected by multicollinearity and hence, has strong predictive power.

The presence of high multicollinearity suggests that the results for coefficients related to X1 and X3 should be interpreted with caution, as they may be unstable.

Hint

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