Curve Fitting Toolbox
Description
- Introduction and Key Features
- Working with Curve Fitting Toolbox
- Regression
- Splines and Interpolation
- Smoothing
- Previewing and Preprocessing Data
- Developing, Comparing, and Managing Models
- Postprocessing Analysis
Introduction
Curve Fitting Toolbox™ provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and postprocess 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 postprocessing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.
Key Features
- Graphical tools for curve and surface fitting
- Linear and nonlinear regression with custom equations
- Library of regression models with optimized starting points and solver parameters
- Interpolation methods, including B-splines, thin plate splines, and tensor-product splines
- Smoothing techniques, including smoothing splines, localized regression, Savitzky-Golay filters, and moving averages
- Preprocessing routines, including outlier removal and sectioning, scaling, and weighting data
- Postprocessing routines, including interpolation, extrapolation, confidence intervals, integrals and derivatives
Surface generated using the Surface Fitting Tool. The tool supports a variety of fitting methods, including linear regression, nonlinear regression, interpolation, and smoothing.
