When there is multicollinearity in an estimated regression equation, A) the coefficients are likely to be small. B) the t-statistics are likely to be small even though the R² is large. C) the coefficient of determination is likely to be small. D) the problem of omitted variables is likely. E) the error terms will tend to have a cyclical pattern.

Question

Quizplus · Accepted Answer

Multicollinearity refers to a situation in regression analysis where independent variables are highly correlated. This does not affect the size of the coefficients directly (A and C are incorrect), nor does it inherently cause omitted variable bias (D) or cyclical error patterns (E). However, it does lead to inflated standard errors for the coefficients, which in turn results in smaller t-statistics, making it harder to find statistical significance even if the overall model (R-squared) explains a significant portion of the variance in the dependent variable.

When There Is Multicollinearity in an Estimated Regression Equation