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CFA Level 2 - Quantitative Methods - Evaluating Regression Model Fit and Interpreting Model Results
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An analyst is evaluating a regression model that predicts the sales of a product based on its price, advertising expenditure, and competition. The model has an adjusted R-squared of 0.85, and all independent variables are significant at the 1% level. However, when the analyst examines the variance inflation factors (VIF), they find that the VIF for advertising expenditure is 9.2. What does this finding suggest about the model?
a.
The high VIF for advertising expenditure suggests the presence of multicollinearity between advertising expenditure and one or more of the other independent variables, which may affect the reliability of the coefficient estimates.
b.
The high VIF for advertising expenditure suggests that the model is well-specified and the coefficient estimates for all independent variables are reliable and unbiased, indicating a strong predictive power of the model.
c.
The high VIF for advertising expenditure suggests that advertising expenditure is the most important predictor of product sales, and the model should focus primarily on this variable while considering removing price and competition from the model.
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