The exercise involving data in this and subsequent sections were designed to be solved using Excel.
The following estimated regression equation was developed for a model involving two independent variables.
y=40.7+ 8.63x1+ 2.71x2
After x2 was dropped from the model, the least-squares method was used to obtain an estimated regression equation involving only x1 as an independent variable. y=42.0+9.01x1
a. In the two independent variable cases, the coefficient x1 represents the expected change in (Select your y, x1, x2) corresponding to a one-unit increase in (Select your y, x1, x2) when (Select your y, x1, x2) is held constant.
In the single independent variable case, the coefficient x1 represents the expected change in (Select your y, x1, x2) corresponding to a one-unit increase in (Select your y, x1, x2).
b. Could multicollinearity explain why the coefficient of x1 differs in the two models? Assume that x1 and x2 are correlated.