Descriptive Statistics
Curve Fitting Toolbox renders a broad array of descriptive statistics, comprising:
Degrees of freedom
Sum of squares due to root error and mean squared error
Adjusted R-square and R-square
The Table of Fits names all of the candidate models in a separate table, permitting developer to cursorily contrast and equate various models.
Visual Inspection of Data
The toolbox permits developer with respect to vision inspect candidate models to bring out issues with fit that are not evident in sum-up statistics. For illustration, developer can:
Bring into existence residual plots and side-by-side surface to search for blueprints in the residuals
At the same instant plot various models to equate by what means they fit the data in vital parts
Plot the variations among two models as a new surface.
Validation Techniques
Curve Fitting Toolbox affirms validation techniques that assists protect against overfitting. Developer can bring forth a prognostic model employing a coaching data set, enforce the model to a validation data set and then assess goodness of fit.
Postprocessing Analysis
On one occasion developer have picked out the curve or surface that best depicts the data serial developer can execute post processing investigation. Curve Fitting Toolbox permits developer to:
Bring into existence plots
Take the model to approximate values
Judge to be probable confidence intervals
Make prediction bounds
Establish after a calculation the area beneath the curve
Predict in advance derivatives
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