This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Curve Fitting Toolbox

Fit curves and surfaces to data using regression, interpolation, and smoothing

Curve Fitting Toolbox™ provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.

After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.

Getting Started

Learn the basics of Curve Fitting Toolbox

Linear and Nonlinear Regression

Fit curves or surfaces with linear and nonlinear library models and custom models


Fit interpolating curves or surfaces, estimate values between known data points


Fit using smoothing splines and localized regression, smooth data with moving average and other filters

Fit Postprocessing

Plotting, outliers, residuals, confidence intervals, validation data, integrals and derivatives, generate MATLAB® code


Construct splines with or without data; ppform, B-form, tensor-product, rational, and stform thin-plate splines