FindFit[data, expr, pars, vars] finds numerical values of the parameters pars that make expr give a best fit to data as a function of vars.
The data can have the form , , ... , , , , ... , , ... , where the number of coordinates x, y, ... is equal to the number of variables in the list vars.
The data can also be of the form , , ... , with a single coordinate assumed to take values 1, 2, ... .
FindFit returns a list of replacements for , , ... .
The expression expr must yield a numerical value when pars and vars are all numerical.
The expression expr can depend either linearly or nonlinearly on the .
In the linear case, FindFit finds a globally optimal fit.
In the nonlinear case, it finds in general only a locally optimal fit.
FindFit[data, expr, , , , , ... , vars] starts the search for a fit with -> , -> , ... .
FindFit by default finds a least-squares fit.
The option NormFunction -> f specifies that the norm f[residual] should be minimized.
The following options can be given:
The default settings for AccuracyGoal and PrecisionGoal are WorkingPrecision/2.
The settings for AccuracyGoal and PrecisionGoal specify the number of digits to seek in both the values of the parameters returned, and the value of the NormFunction.
FindFit continues until either of the goals specified by AccuracyGoal or PrecisionGoal is achieved.
Possible settings for Method are as for FindMinimum.
See Section 1.6.6 and Section 3.8.2.
Implementation Notes: see Section A.9.4.
See also: FindMinimum, Fit, NMinimize, Interpolation.
Related packages: Statistics`NonlinearFit`, Statistics`LinearRegression`.
New in Version 5.0.