Consider the multiple regression model with two regressors X1 and X2,where both variables are determinants of the dependent variable.You first regress Y on X1 only and find no relationship.However when regressing Y on X1 and X2,the slope coefficient changes by a large amount.This suggests that your first regression suffers from

Question

Consider the multiple regression model with two regressors X1 and X2,where both variables are determinants of the dependent variable.You first regress Y on X1 only and find no relationship.However when regressing Y on X1 and X2,the slope coefficient  changes by a large amount.This suggests that your first regression suffers from

Quizplus · Accepted Answer

The fact that the slope coefficient changes significantly when X2 is included in the regression suggests that there is omitted variable bias in the first regression, which means that X2 is a relevant determinant of Y that was not accounted for in the first regression. This can lead to incorrect inference regarding the relationship between Y and X1. Heteroskedasticity and the dummy variable trap do not seem to be relevant in this scenario, and perfect multicollinearity would have been detected in the first regression (since there was no relationship between Y and X1).

Consider the Multiple Regression Model with Two Regressors X1 and X2,where