I got this working with minimal effort (on the package side, I had some other unrelated Julia problems though). When I take log10 of the original x and y values, and do fit = fitlinear(x_log,y_log), I get a nice match.
I can also see that the average square residue is low.
------------------- Linear Fit -------------
Equation: y = ax + b
With: a = -0.9999071242153111
b = 5.319512210970783e-5
Pearson correlation coefficient, R = 0.9999734052571524
Average square residue = 9.731441862500589e-6
Predicted Y: ypred = [2.999774567768043, 2.6987725305011243...
residues = [-0.0008626824944601985, -0.007119649365998182...
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