Why is multicollinearity a problem in multiple regression analysis?

A. It increases the model's goodness-of-fit. B. It leads to biased estimates of the regression coefficients. C. It causes the standard errors of the coefficients to be inflated. D. It violates the normality assumption of the error terms.

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Explanation

Multicollinearity is a problem because it causes the standard errors of the regression coefficients to be inflated, which can lead to less precise estimates and make it difficult to determine the individual effect of each explanatory variable. This reduces the reliability of hypothesis tests.

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Why is multicollinearity a problem in multiple regression analysis?