Implications of Multicollinearity in Regression Analysis

Medium Multiple Regression Analysis Multicollinearity

In a multiple regression analysis, a financial analyst is evaluating the impact of various independent variables on the return of a stock. The analyst finds that the variance inflation factor (VIF) for one of the independent variables is calculated to be 10. This suggests potential multicollinearity problems in the model.

Which of the following statements best describes the implications of this finding?

The high VIF value indicates that the independent variable does not contribute significantly to the regression model.

The independent variable may be highly correlated with one or more other independent variables, which could inflate the standard errors of the coefficients.

The VIF value of 10 is within acceptable limits, indicating no multicollinearity concerns.

Hint

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