Configuring Orthogonal Polynomial Technique Options

Orthogonal polynomial approximation is a regression technique. Orthogonal polynomials minimize the autocorrelation between the response values that exist because of the sampling location. An advantage of using orthogonal polynomials as the basis for fitting is that the inputs can be decoupled in the analysis of variance (ANOVA).

For more information, see Chebyshev/Orthogonal Polynomial Model.

  1. Double-click the Approximation component icon .

    The Approximation Component Editor appears.

  2. From the Approximation Component Editor, click the Technique Options tab.

  3. In the Type of fit-polynomial list, select Chebyshev or Successive orthogonal polynomial.

    Option Description
    Chebyshev If the data points are generated using an equally spaced orthogonal array, Isight uses the quadrature method to calculate the coefficients of the model. If the levels are not equally spaced or, if the data are not from an orthogonal array, Isight processes Chebyshev polynomials as transformations and uses a linear regression approach to compute the coefficients.
    Successive Orthogonal Polynomial The successive orthogonal polynomial technique generates a series of polynomials that are orthogonal with respect to the data provided. These polynomials are used as basis functions to obtain an approximation for the responses. Basis functions depend only on the sample locations and not the response values.

  4. In the Degree of fit-polynomial text box, type the value to which this model is limited.

  5. Click Include cross terms to use cross-terms in the model.

  6. Click OK to save your changes and to close the Approximation Component Editor.