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.
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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.