Understanding Multicollinearity in Regression Analysis

Very Easy Multiple Regression Analysis Multicollinearity

A financial analyst is examining a multiple regression model that aims to predict stock returns based on various economic indicators. The regression output suggests that some of the independent variables exhibit high levels of correlation with each other. The analyst is concerned about the implications of this correlation.

Which of the following statements best describes multicollinearity in the context of multiple regression analysis?

Multicollinearity occurs when independent variables in a regression model are not correlated with each other.

Multicollinearity can lead to inflated standard errors of the estimated coefficients.

Multicollinearity makes the regression model less useful for predicting the dependent variable.

Hint

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