Start x values at zero   Subtract minimum y value

Equation to fit:
y = R_∞ + R_oe^−t/τ y = R_∞ + R_oe^−t/τ + R_o′e^−t/τ′
A sum of two exponentials may fit data more closely but it is hard to calculate good initial guesses for the parameters. If the calculated guesses do not give good results, try entering your own below.

Fix R_∞ at (By default, τ and R_o are always > 0.)

Enter your own initial guesses for the parameters:
R_∞ = R_o = τ = R_o′ = τ′ =
Calculated initial guesses:
 

Iterate RMS error = Parameter change:
The main Fit button homes in on a fit by adjusting parameters over 10 iterations. RMS error shows how much the fit differs from the data (smaller RMS = closer fit). To iterate another 10 times, press the Fit button again; to iterate once more, press the Iterate button.
Parameter change sets the amount by which parameters change during iterations. Try changing this if you get questionable results.

Use point number as the independent variable
By default, y values (rate) are plotted and fitted against x values (time). In some cases, you may need to plot and fit rate against point number.

This page simplifies the least-squares algorithm found at http://statpages.info/nonlin.html. Go to that page to fit any arbitrary function you wish to enter.