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linear and nonlinear regression -凯发k8网页登录

fit curves or surfaces with linear or nonlinear library models or custom models

regression is a method of estimating the relationship between a response (output) variable and one or more predictor (input) variables. you can use linear and nonlinear regression to predict, forecast, and estimate values between observed data points. curve fitting toolbox™ functions allow you to perform regression by fitting a curve or surface to data using the library of linear and nonlinear models, or custom equations.

use the curve fitter app to fit curves and surfaces to data interactively. for more information, see . you can also use the fit function to fit a curve or surface to a set of data at the command line. for a simple example, see polynomial curve fitting.

apps

curve fitterfit curves and surfaces to data

functions

exclude data from fit
fitfit curve or surface to data
fit type for curve and surface fitting
create or modify fit options object
prepare data inputs for curve fitting
prepare data inputs for surface fitting
input argument names of cfit, sfit, or fittype object
category of fit of cfit, sfit, or fittype object
coefficient names of cfit, sfit, or fittype object
coefficient values of cfit or sfit object
dependent variable of cfit, sfit, or fittype object
evaluate cfit, sfit, or fittype object
formula of cfit, sfit, or fittype object
get fit options structure property names and values
independent variable of cfit, sfit, or fittype object
determine if cfit, sfit, or fittype object is linear
number of input arguments of cfit, sfit, or fittype object
number of coefficients of cfit, sfit, or fittype object
problem-dependent parameter names of cfit, sfit, or fittype object
assign values in fit options structure
set model fit options
name of cfit, sfit, or fittype object

topics

tutorials


  • find all library model types for the curve fitter app and the fit function, set fit options, and optimize starting points.
  • introduction to least-squares fitting
    perform least-squares fitting by using error distributions and linear, weighted, robust, and nonlinear least squares.

  • fit polynomials in the curve fitter app or with the fit function.
  • exponential models
    fit exponential models in the curve fitter app or with the fit function.

  • fit fourier series models in the curve fitter app or with the fit function.

  • fit gaussian models in the curve fitter app or with the fit function.

  • fit power series models in the curve fitter app or with the fit function.

  • fit rational polynomial models in the curve fitter app or with the fit function.

  • fit sum of sines models in the curve fitter app or with the fit function.

  • fit weibull distribution models in the curve fitter app or with the fit function.

  • if the curve fitting toolbox library does not contain a desired parametric equation, you can create your own custom equation.
  • custom linear fitting
    in the curve fitter app, you can use the custom equation fit to define your own linear or nonlinear equations.

tools workflow

  • interactive curve and surface fitting
    select data and model types to fit curves and surfaces by using the curve fitter app and then save your session.

  • select data to fit curves and surfaces in curve fitter app, identify compatible size data and troubleshoot data problems.
  • compare fits in curve fitter app
    find the best fit by comparing visual and numeric results, including fitted coefficients and goodness-of-fit statistics.

  • create and compare surface fits in curve fitter app using example data.
  • surface fitting to biopharmaceutical data
    curve fitting toolbox software provides some example data for an anesthesia drug interaction study.

  • this example fits the enso data using several custom nonlinear equations.

  • this example fits two poorly resolved gaussian peaks on a decaying exponential background using a general (nonlinear) custom model.

programmatic workflow


  • workflow for programmatic curve and surface fitting in curve fitting toolbox.

  • find all curve fitting toolbox library model names for programmatic data fitting with the fit function.

  • learn how to create, access, and modify curve and surface fit objects.
  • polynomial curve fitting
    this example shows how to fit polynomials up to sixth degree to some census data using curve fitting toolbox.

  • this example shows how to fit a polynomial model to data using the linear least-squares method.

  • this example shows how to fit a polynomial model to data using the bisquare weights, least absolute residuals (lar), and linear least-squares methods.

  • this example shows how to fit a custom equation to census data, specifying bounds, coefficients, and a problem-dependent parameter.

  • this example shows how to fit an exponential model to data using the trust-region and levenberg-marquardt nonlinear least-squares algorithms.

  • this example shows how to fit a polynomial model to data using both the linear least-squares method and the weighted least-squares method for comparison.

  • this example shows how to use curve fitting toolbox™ to fit response surfaces to some anesthesia data to analyze drug interaction effects.
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