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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:

  • Interpolation Methods — 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.

Related Examples

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