Functions > Data Analysis > Curve Fitting > Exponential Regression
Exponential Regression
expfit(vx, vy, [vg])—Returns a vector containing three coefficients for an exponential curve of the form A · exp(b·x) + C that best approximates the data in vx and vy.
The expfit function employs the Levenberg-Marquardt method for minimization. For an exponential fit that differs from the form above, use genfit.
Arguments
vx, vy are vectors of real data values of the same length, corresponding to the x and y values in the data set. The x values must be ≥ 0. There must be at least three data points.
 For performing an exponential fit on data that has negative x values, you must shift your data to the positive axis. Exponential fits on negative-valued x data can produce an imaginary-valued fitting function.
vg (optional) is a three-element vector of real guess values for the parameters A, b, and C in the exponential equation. If this argument is not used, then expfit generates a guess from a line fitted to the logs of vy.