Curve Fitting Toolbox

Fitting Data

Curve fitting refers to fitting curved lines to data. The curved line comes from regression techniques, a spline calculation, or interpolation. The data can be measured from a sensor, generated from a simulation, historical, and so on. The goal of curve fitting is to gain insight into your data. The insight will enable you to improve data acquisition techniques for future experiments, accept or refute a theoretical model, extract physical meaning from fitted coefficients, and draw conclusions about the data's parent population.
This chapter describes how to fit data and evaluate the goodness of fit with the Curve Fitting Toolbox. The sections are as follows.

The Fitting Process	The general steps you use when fitting any data set.
Parametric Fitting	Fit your data using parametric models such as polynomials and exponentials, specify fit options such as the fitting algorithm and coefficient starting points, and evaluate the goodness of fit using graphical and numerical techniques. Parametric fitting produces coefficients that describe the data globally, and often have physical meaning.
Nonparametric Fitting	Fit your data using nonparametric fit types such as splines and interpolants. Nonparametric fitting is useful when you want to fit a smooth curve through your data, and you are not interested in interpreting fitted coefficients.
Selected Bibliography	Resources for additional information.

Selected Bibliography The Fitting Process