|Curve Fitting||Fit curves and surfaces to data|
|Constructor for cfit object|
|Constructor for sfit object|
|Coefficient names of cfit, sfit, or fittype object|
|Coefficient values of cfit or sfit object|
|Confidence intervals for fit coefficients of cfit or sfit object|
|Differentiate cfit or sfit object|
|Evaluate cfit, sfit, or fittype object|
|Integrate cfit object|
|Plot cfit or sfit object|
|Prediction intervals for cfit or sfit object|
|Problem-dependent parameter values of cfit or sfit object|
|Numerically integrate sfit object|
Workflow for refining your fit, comparing multiple fits, and using statistics to determine the best fit.
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 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.
Compare your fit with validation data or test set in Curve Fitting app.
Generate MATLAB code from an interactive session in the Curve Fitting app, recreate fits and plots, and analyze fits in the workspace.
This example shows how to work with a curve fit.
This example shows how to work with a surface fit.
After fitting data with one or more models, evaluate the goodness of fit using plots, statistics, residuals, and confidence and prediction bounds.
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.
This example shows how to fit and compare polynomials up to sixth degree using Curve Fitting Toolbox, fitting some census data.
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.
Curve Fitting Toolbox™ software lets you calculate confidence bounds for the fitted coefficients, and prediction bounds for new observations or for the fitted function.
This example shows how to find the first and second derivatives of a fit, and the integral of the fit, at the predictor values.