I had a different interpretation of (b). we have log(y) = a + b*log(x)$\log(y) = a + b\log(x)$ from the regression. Hence, sd(log(y)) = |b|sd(log(x)) = (0.8)(0.05)=0.04, $$sd(\log(y)) = |b|sd(\log(x)) = (0.8)\times(0.05)=0.04,$$
since sd(log(x))$sd(\log(x))$ is given. Squaring this we get the regression sum of squares SSR = 0.0016$SSR = 0.0016$. From (a) we have the residual standard deviation (0.049$0.049$). Squaring this yields the error sum of squares SSE = 0.0024.$SSE = 0.0024.$ Thus, the total sum of squares on the log scale equals 0.004, giving an R-squared of 1 - 0.0016/0.004 = 0.6$1 - 0.0016/0.004 = 0.6$
