Suppose we have a random sample of 50 people and their weight, W and height, H are recorded to the nearest pound and inch respectively. A regression of W on H and an intercept gives:

Wi = 99.41(6.45) + 3.94(1.86)Hi

R^2 = 0.81, SER = \(\sqrt{Su^2}\) = 10.1

(Standard errors are given in parentheses)

(g). Suppose we measured weight in kilos. What will the new intercept be?

(h). Prove that if Wi* = \(\lambda1+\lambda2Wi\) then the new intercept b1* = \(\lambda1+\lambda2b1\) where b1 is the intercept of the original regression of W on H.

(i). Explain how the standard error of the slope coefficient in (h) is related to the standard error of the slope in the original regression