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Fit Postprocessing

Plotting, outliers, residuals, confidence intervals, validation data, integrals and derivatives, generate MATLAB® code

Apps

Curve Fitting Fit curves and surfaces to data

Functions

cfit Constructor for cfit object
sfit Constructor for sfit object
coeffnames Coefficient names of cfit, sfit, or fittype object
coeffvalues Coefficient values of cfit or sfit object
confint Confidence intervals for fit coefficients of cfit or sfit object
differentiate Differentiate cfit or sfit object
feval Evaluate cfit, sfit, or fittype object
integrate Integrate cfit object
plot Plot cfit or sfit object
predint Prediction intervals for cfit or sfit object
probvalues Problem-dependent parameter values of cfit or sfit object
quad2d Numerically integrate sfit object

Examples and How To

Create Multiple Fits in Curve Fitting App

Workflow for refining your fit, comparing multiple fits, and using statistics to determine the best fit.

Explore and Customize Plots

In Curve Fitting app, display fit, residual, surface, or contour plots; display prediction bounds and multiple plots, use zoom, pan, data cursor, and outliers modes; change axes limits and print plots.

Remove Outliers

Remove points or exclude by rule in Curve Fitting app or using the fit function, including excluding outliers by distance from the model, using standard deviations.

Select Validation Data

Compare your fit with validation data or test set in Curve Fitting app.

Generate Code and Export Fits to the Workspace

Generate MATLAB code from an interactive session in the Curve Fitting app, recreate fits and plots, and analyze fits in the workspace.

Evaluate a Curve Fit

This example shows how to work with a curve fit.

Evaluate a Surface Fit

This example shows how to work with a surface fit.

Compare Fits in Curve Fitting App

Search for the best fit by creating multiple fits, comparing graphical and numerical results including fitted coefficients and goodness-of-fit statistics, and analyzing your best fit in the workspace.

Compare Fits Programmatically

This example shows how to fit and compare polynomials up to sixth degree using Curve Fitting Toolbox, fitting some census data.

Residual Analysis

The residuals from a fitted model are defined as the differences between the response data and the fit to the response data at each predictor value.

Confidence and Prediction Bounds

Curve Fitting Toolbox™ software lets you calculate confidence bounds for the fitted coefficients, and prediction bounds for new observations or for the fitted function.

Differentiating and Integrating a Fit

This example shows how to find the first and second derivatives of a fit, and the integral of the fit, at the predictor values.

Concepts

Evaluating Goodness of Fit

After fitting data with one or more models, evaluate the goodness of fit using plots, statistics, residuals, and confidence and prediction bounds.

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