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