Consider the multiple regression model with two regressors X1 and X2, where both variables are determinants of the dependent variable Y. You first regress Y on X1 and find no relationship. However, while regressing Y on X1 and X2 the slope coefficient of the variable X1 changes by a large amount. This suggests that your first regression suffers from:
A. Perfect multicollinearity
B. Dummy variable trap
C. Omitted variable bias
D. Heteroskedasticity
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