Understanding the Effects of Multicollinearity

Easy Multiple Regression Analysis Multicollinearity

Consider a multiple regression analysis conducted to determine the impact of several independent variables on a dependent variable. In this analysis, a researcher has identified potential multicollinearity issues among several independent variables.

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, making it difficult to ascertain the individual contribution of each variable. This condition can inflate the variance of the coefficient estimates and affect the precision of the predictions.

Which of the following statements about multicollinearity is true?

Multicollinearity can lead to biased estimates of the regression coefficients.

Multicollinearity increases the standard errors of the coefficients, but does not bias them.

Highly collinear variables should be included in the model to ensure robustness.

Hint

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