This can always be done in closed form since this is a case of simple linear regression.

The values of y-intercept and slope can be determined from the experimental points using simple linear regression with a spreadsheet.

Gradient boosting, where a succession of simple regressions are used to weight data points to sequentially reduce error.

Interval mapping is originally based on the maximum likelihood but there are also very good approximations possible with simple regression.

This rules out regression techniques - even simple regression produces two different equations.

It has similar statistical efficiency properties to simple linear regression but is much less sensitive to outliers.

A simple linear regression is then fitted to the log-log plot.

For simple linear regression the effect is an underestimate of the coefficient, known as the attenuation bias.

The Deming regression is only slightly more difficult to compute compared to the simple linear regression.

Interestingly, studies have found an opposite relationship, by running simple regressions.