On 26/02/2019 09:57, N_Cook wrote:
y=2.483612 + 0.053029*x^1.575023

Just because you can obtain a model fit with 6 significant figures shown
in each coefficient doesn't mean that they are right.

I'd be very surprised if anything more than the first two leading digits
were even in the right ballpark.

Instead of using R^2 as a measure of goodness of fit why don't you try
and do it using the actual residuals of the data and an estimate of the
noise to compute chi-squared. Then you can do a proper analysis and see
just how uncertain each of your coefficients are. IOW how well defined
each of them is with the others sat on their respective peak value.

--
Regards,
Martin Brown