The following estimated regression equation was developed for a model involving two independent variables.\

hat(y)=40.7+8.63x_(1)+2.71x_(2)

\ After

x_(2)

was dropped from the model, the least squares method was used to obtain an estimated regression equation involving only

x_(1)

\ as an independent variable.\

hat(y)=42.0+9.01x_(1)

\ a. Give an interpretation of the coefficient of

x_(1)

in both models.\ In the two independent variable case, the coefficient

x_(1)

represents the expected change in\ corresponding to a\ one unit increase in\ when\ is held constant.\ In the single independent variable case, the coefficient

x_(1)

represents the expected change in\ corresponding to\ a one unit increase in\ b. Could multicollinearity explain why the coefficient of

x_(1)

differs in the two models? If so, how? Assume that

x_(1)

and

x_(2)

are correlated.\

\\\\sqrt(-s)

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. Give an interpretation of the coefficient of x1 in both models. In the two independent variable case, the coefficient x1 represents the expected change in corresponding to a one unit increase in when is held constant. In the single independent variable case, the coefficient x1 represents the expected change in corresponding to a one unit increase in b. Could multicollinearity explain why the coefficient of x1 differs in the two models? If so, how? Assume that x1 and x2 are correlated