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Nonparametric Fitting

In some cases, you are not concerned about extracting or interpreting fitted parameters. Instead, you might simply want to draw a smooth curve through your data. Fitting of this type is called nonparametric fitting. The Curve Fitting Toolbox™ software supports these nonparametric fitting methods:

  • Interpolants — Estimate values that lie between known data points.

  • Smoothing Splines — Create a smooth curve through the data. You adjust the level of smoothness by varying a parameter that changes the curve from a least-squares straight-line approximation to a cubic spline interpolant.

  • Lowess Smoothing — Create a smooth surface through the data using locally weighted linear regression to smooth data.

For details about interpolation, see 1-D Interpolation and Scattered Data Interpolation in the MATLAB® documentation.

You can also use smoothing techniques on response data. See Filtering and Smoothing Data.

To view all available model types, see List of Library Models for Curve and Surface Fitting.

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