I was putzing around with Excel and the LOGEST function. This fits your data to $y=ab^x$. It does this by linearizing the data, and doing a least-squares fit to $\log(y) = \log(a) + \log(b) x.$ It then transforms the parameters back. However, I noticed that when the "Additional regression parameters" are returned, they are exactly the same as if I had done a linear fit on $\log(y)$ vs $x$, including the Sum Of Squares and standard error of the parameters. The list is stand_err_a, stand_err_b, standard_err_y, r², degree_freedom, F, SSreg, SSres.
The only values that are any different are the $a$ and $b$ parameters.
Is this the expected result? Should the standard errors be transformed back to the log form or not? What about the sum of squares?
Additionally, maybe the bigger question is...is there a better procedure (numeric, or matrix) to fit log data?