finds a leastsquares fit to a list of data as a linear combination of the functions funs of variables vars.


  • The data can have the form {{x_(1),y_(1),... ,f_(1)},{x_(2),y_(2),... ,f_(2)},...}, 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 {f_(1),f_(2),...}, with a single coordinate assumed to take values 1, 2, .
  • The argument funs can be any list of functions that depend only on the objects vars.
  • Fit[{f1,f2,},{1,x,x^2},x] gives a quadratic fit to a sequence of values fi. The result is of the form a0+a1x+a2x^2, where the ai are real numbers. The successive values of x needed to obtain the fi are assumed to be 1, 2, . »
  • Fit[{{x1,f1},{x2,f2},},{1,x,x^2},x] does a quadratic fit, assuming a sequence of x values xi. »
  • Fit[{{x1,y1,f1},},{1,x,y},{x,y}] finds a fit of the form a0+a1x+a2y. »
  • Fit always finds the linear combination of the functions in the list funs that minimizes the sum of the squares of deviations from the values fi. »
  • Exact numbers given as input to Fit are converted to approximate numbers with machine precision. »


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Basic Examples  (1)

Here is some data:

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Find the line that best fits the data:

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Find the quadratic that best fits the data:

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Show the data with the two curves:

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Scope  (2)

Generalizations & Extensions  (1)

Properties & Relations  (5)

Possible Issues  (1)

See Also

FindFit  LeastSquares  PseudoInverse  Interpolation  InterpolatingPolynomial  Solve  ListPlot  LinearModelFit  NonlinearModelFit


Introduced in 1988