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### V6 Resource File                             ###
### generated on: Mon May 16 10:05:08 EDT 2011   ###
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### This file is designed to be delivered to the ###
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4844=Failed to restore orthogonal polynomial approximation from internal data
6065=Failed to read orthogonal polynomial coefficient file
6848=Chebyshev initialization cancelled
7716=Unexpected EOF while reading inputs matrix in coefficients data file {0}
8689=Could not calculate recommended number of designs
16714=Calculating Orthogonal polynomial coefficients
17536=Could not get approx property in Orthogonal Polynomial Approximation
18510=Successive Orthogonal
19452=Orthogonal polynomial approximation technique initialization failed
21457=Type of fit-polynomial:
29669=Unexpected EOF while reading outputs matrix in coefficients data file {0}
29741=Could not use quadrature method for initializing approximation
29831=Unrecognized option "{0}" in orthogonal polynomial approximation technique.
30654=Unrecognized value for "{0}" in orthogonal polynomial approximation technique: {1}
31919=Orthogonal polynomial initialization failed
33216=Orthogonal Polynomial Approximation Technique Options
36941=Reading orthogonal polynoimal coefficients from coefficients data file
37161=Could not calculate min number of designs
46217=Degree of fit-polynomial:
49733=RuntimeEnv is not set, Orthogonal polynomial approximation technique object is not properly configured
54005=Chebyshev
56650=Orthogonal polynomial approximation was not initialized properly.
62087=Coefficients data file {0} does not contain the following approximation parms: {1}
67962=Include cross-terms
68397=Could not get parameter names.
69212=Orthogonal polynomial configuration problem, \ncoefficients data file copy was not created, \ninitialization aborted
69818=Could not store orthogonal polynomial configuration in plug-in
74525=Approximation is not configured, no input parameters
75553=Insufficient number of sampling points for orthogonal polynomial initialization: {0}
84000=Approximation is not configured, no output parameters
85346=Could not reset coeff data in approx properties
87524=Failed to initialize orthogonal polynomial approximation
92743=Could not evaluate orthogonal polynomial approximation
96199=Orthogonal polynomial approximation technique initialization cancelled
96473=Orthogonal polynomial initialization cancelled
97235=Could not use quadrature method for fitting; switching to regression.
98317=Could not get orthogonal polynomial configuration from plugin
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desc=Orthogonal polynomial approximation technique plugin
dispname=Orthogonal Polynomial Model
techniqueoptionsdesc=<b>Type of fit-polynomial:</b> <ul><li> Chebyshev - When data points are generated using an equally spaced orthogonal array, Isight uses the quadrature method to calculate 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. </li><li>Successive Orthogonal Polynomial - A series of polynomials that are orthogonal with respect to the data provided 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. </li></ul><b>Degree of fit-polynomial:</b> The degree of the polynomial model is limited to this value.<br><b>Include cross terms</b> creates a model that also includes cross (or interaction) terms. Including cross-terms when there are several input variables is generally not recommended because this can cause the number of model terms to increase enormously.
longdesc=Orthogonal polynomial approximation is a type of regression technique. Orthogonal polynomials minimize the autocorrelation between the response values that exists because of the sampling location. Another advantage of using functions that are orthogonal w.r.t. the data is that the inputs can be decoupled in the analysis of variance (ANOVA). <p> Chebyshev orthogonal polynomials are a common type of orthogonal polynomials that are particularly useful for equally spaced sample points. They are used when the sampling strategy is an orthogonal array. Isight allows the use of Chebyshev polynomials even when other sampling strategies are used; in this case however ANOVA cannot be computed. <p> Isight also provides the ability to generate orthogonal polynomial approximations for other kinds of sampling. The successive orthogonal polynomial technique generates a series of polynomials that are orthogonal with respect to the data provided. These polynomials are then used as basis function to obtain an approximation for the responses. Note that the basis functions only depend on the sample locations and not the response values. <p> Initialization of the orthogonal polynomial approximation requires at least 2d+1 design points, where d is the degree of the expected polynomial.  The data file must contain the required number of data points.
