Curve Fitting Toolbox

Regression

Curve Fitting Toolbox supports linear and nonlinear regression.

Linear Regression

The toolbox supports over 100 regression models, including:

  • Lines and planes
  • High order polynomials (up to ninth degree for curves and fifth degree for surfaces)
  • Fourier and power series
  • Gaussians
  • Weibull functions
  • Exponentials
  • Rational functions
  • Sum of sines

All of these standard regression models include optimized solver parameters and starting conditions to improve fit quality. Alternatively, you can use the Custom Equation option to specify your own regression model.

In the Curve Fitting app you can generate fits based on complicated parametric models by using a drop-down menu. At the command line you can access the same models using intuitive names.

Nonlinear regression using a second-order Fourier series.

Nonlinear regression using a second-order Fourier series. You can pass the argument “fourier2” to the fit command (top, left) or select a second-order Fourier series in the Fit Editor (top, right).

Surface generated using the Custom Equation option of the Surface Fitting Tool.

Surface generated using the Custom Equation option of the Surface Fitting Tool. You can specify a custom equation or input a MATLAB function.

The regression analysis options in Curve Fitting Toolbox enable you to:

  • Choose between two types of robust regression: bisquare or least absolute residual
  • Specify starting conditions for the solvers
  • Constrain regression coefficients
  • Choose Trust-Region or Levenberg-Marquardt algorithms
Fit Options GUI in the Surface Fitting Tool.

Fit Options GUI in the Surface Fitting Tool. You can control the type of robust regression, the choice of optimization solver, and the behavior of the optimization solver with respect to starting conditions and constraints.

Next: Splines and Interpolation

Try Curve Fitting Toolbox

Get trial software

Battery Data Acquisition and Analysis Using MATLAB

View webinar