Suppose that you wish to use a linear regression to predict the dependent variable Y using the dependent variable X1. You collect a scatter plot of points (X1,i ,Yi) and notice that the plot seems to be well represented by a piecewise continuous linear function that satisfies the following conditions: For X1,i<=xo, the appropriate linear model appears to be Y1=a+B1X1,i+E1. However, when X1,1>xo the slope of the applicable linear relationship appears to change to B1+B2. Hint: First, determine the applicable form of the linear model that represents the plot of points (X1,i ,Yi) when X1,i>xo and note that both the slope and Y-intercept of the model will differ from the model applicable to the set of points (X1,i ,Yi) when X1,i<=xo. Now see if you can devise a way to combine these two linear relationships into a single multiple regression model by using the indicator variable X2,i where X2,i= +1 if X1,i >xo and X2,i=0 otherwise

a. There is no way to combine these two linear relationships into a single multiple regression. You should instead run each regression separately as simple regressions.

b. The combined model is: Y1= a + B1 X1,i + B2 X1,i X2,i + Ei

c. The combined model is: Y1= a + B1 X1,i + B2 (xo - X1,i) X2,i + Ei

d. The combined model is: Y1= a + B1 X1,i + B2 (a - xo - X1,i) X2,i + Ei

e. The combined model is: Y1= a + B1 X1,i + B2 (X1,i - xo) X2,i + Ei