Understanding Multicollinearity Implications

Medium Multiple Regression Analysis Multicollinearity

In a study conducted to determine the factors affecting the sales of a retail store, analysts employed a multiple regression model with the following independent variables: advertising budget, pricing strategy, and competitor pricing. However, upon estimating the model, the analysts noticed that the variance inflation factor (VIF) for both the advertising budget and competitor pricing was greater than 10, indicating potential multicollinearity.

Based on this context, which of the following statements best describes the implications of multicollinearity in this regression analysis?

Multicollinearity may lead to larger standard errors for the coefficients, but it does not impact the overall fit of the model.

Multicollinearity indicates that the independent variables are uncorrelated, which increases the interpretability of the coefficients.

Multicollinearity leads to biased estimates of regression coefficients, making hypothesis tests for individual coefficients unreliable.

Hint

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