Descriptive Statistics using MATLAB Assignment Help

Matlab - Descriptive Statistics using MATLAB

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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