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MATLAB Curve Fitting Toolbox

  • NG # 01864-SW-MATHLAB-004

Curve Fitting Toolbox Fit curves and surfaces to data using regression, interpolation, and smoothing

Manufactured By :
MathWorks
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Need Help Ordering?
Call +88 01714243446

Stock
Limited

Custom Pricing

Get A Quote

Overview

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.

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.

Specification

 

  • Curve Fitting app 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

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