Curve Fitting Toolbox

Developing, Comparing, and Managing Models

Curve Fitting Toolbox lets you fit multiple candidate models to a data set. You can then evaluate goodness of fit using a combination of descriptive statistics, visual inspection, and validation.

Descriptive Statistics

Curve Fitting Toolbox provides a wide range of descriptive statistics, including:

  • R-square and adjusted R-square
  • Sum of squares due to errors and root mean squared error
  • Degrees of freedom

The Table of Fits lists all of the candidate models in a sortable table, enabling you to quickly compare and contrast multiple models.

The Surface Fitting Tool, which provides a sortable table of candidate models.

The Curve Fitting app, which provides a sortable table of candidate models.

Visual Inspection of Data

The toolbox enables you to visually inspect candidate models to reveal problems with fit that are not apparent in summary statistics. For example, you can:

  • Generate side-by-side surface and residual plots to search for patterns in the residuals
  • Simultaneously plot multiple models to compare how well they fit the data in critical regions
  • Plot the differences between two models as a new surface
Surface generated with the Surface Fitting Tool.

Surface generated with the Surface Fitting Tool. The color of the heat map corresponds to the deviation between the fitted surface and a reference model.

Validation Techniques

Curve Fitting Toolbox supports validation techniques that help protect against overfitting. You can generate a predictive model using a training data set, apply your model to a validation data set, and then evaluate goodness of fit.

Next: Postprocessing Analysis

Try Curve Fitting Toolbox

Get trial software

Battery Data Acquisition and Analysis Using MATLAB

View webinar