4.7 In a simple linear regression model suppose we know that the intercept parameter is zero, so the model is y; : B2x;+ er.T he least squarese stimatoro f P2i s developedi n Exercise 2.4.

(a) What is the least squares predictor of y in this case?^

(b) When an intercept is not present in a model, R' is often defined to be R?,: | - SSE1 2y7, where SSEi s the usual sum of squaredr esiduals.C ompute Rl for the data in Exercise 2.4.

(c) Compare the value of Rl in part

(b) to the generalized Rz : fi., where ! is the predictor based on the restricted model in part (a).^

(d) Compute SSZ : I(y' - y)2 and SSR : I(y, - y)2, where ! is the predictor based on the restricted model in part (a). Does the sum of squares decomposition SSI: SSR + SSE hold in this case?

4.5.2 CovrpursR. ExnncrsEs