Curve Fitting Toolbox 2.1
Product Description
- Introduction and Key Features
- Working with Curve Fitting Toolbox
- Fitting Models and Methods
- Previewing and Preprocessing Data
- Developing, Comparing, and Managing Models
- Post-Processing Analysis
Fitting Models and Methods
Curve Fitting Toolbox provides a variety of fitting methods, including:
- Linear regression
- Nonlinear regression (trust-region, Levenberg-Marquardt, and Gauss-Newton algorithms)
- Interpolation (linear, biharmonic, nearest neighbor, cubic spline, shape-preserving spline)
- Smoothing (moving average, loess, lowess, smoothing spline, and Savitzky-Golay)
You can specify your own custom equations or choose from a library of linear, nonlinear, and nonparametric fitting models, including:
- Polynomials (to ninth degree for curves and fifth degree for surfaces)
- Exponential functions
- Rational (to degree 5/5)
- Peak (Gaussian)
- Distribution (Weibull)
- Fourier and power series
- Sum of sines
You can assign both weights and bounds on your coefficients. You can also choose from two forms of robust fitting: bisquare or least absolute residual.
The Surface Fitting Tool (left) and the Curve Fitting Tool (below, left). You can evaluate goodness of fit using a combination of descriptive statistics, visual inspection, and validation. | |
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