Curve Fitting Toolbox 2.1

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 large numbers 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 a surface plot and a residual plot side by side and search for patterns in the residuals; you can simultaneously plot multiple models to compare how well they fit the data in critical regions; or you can plot the differences between two models as a new surface.

Validation Techniques

Curve Fitting Toolbox also 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.

Using the Surface Fitting Tool to search for patterns in the residuals.