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

ŷ = 42.0 + 9.01x1

(a)

Give an interpretation of the coefficient of x1 in both models.

In the two independent variable case, the coefficient of x1 represents the expected change in y corresponding to a ---Select--- unit ---Select--- in x1 when x2 is held constant. In the single independent variable case, the coefficient of x1 represents the expected change in y corresponding to a ---Select--- unit increase in x1.

(b)

Could multicollinearity explain why the coefficient of x1 differs in the two models? If so, how?

---Select---. If x1 and x2 are correlated, one would expect a change in x1 to be ---Select--- a change in x2.

---Select--- 1 Options

A) one

B) two

C) three

D) four

E) five

---Select--- 2 options

A) increase

B) decrease

---Select--- 3 options

A) one

B) two

C) three

D) four

E) five

---Select--- 4 options

A) Yes

B) No

---Select--- 5 options

A) accompanied by

B) independent of