API documentation of the 'jhplot.math.polysolve.PolySolve' Java class

Class PolySolve


Class PolySolve

  • All Implemented Interfaces:
    ImageObserver, MenuContainer, Serializable, Accessible, RootPaneContainer

    public final class PolySolveextends JApplet
    Polynomial Regression Data Fit. For a given data set of x,y pairs, a polynomial regression of this kind can be generated:


    In which c0,c1,c2... represent coefficients created by a mathematical procedure described in detail here. In this regression method, the choice of degree and the evaluation of the fit\'s quality depend on judgments that are left up to the user. It is well known about this class of regression method that an effort to squeeze more correlation out of the algorithm than the data can support will sometimes produce an out-of-control function that, although it matches the data points, wanders wherever it pleases between those points. Therefore, a "good" (approaching 1.0) correlation coefficient is not enough to assure a well-behaved or meaningful function. Decisions about a result\'s appropriateness is more a matter of judgment than mathematics. A "perfect" fit (one in which all the data points are matched) can be gotten by setting the degree of the regression to the number of data pairs minus one. But, depending on the nature of the data set, this can also sometimes produce the pathological result described above in which the function wanders freely between data points in order to match the data exactly.

    For those seeking a standard two-element linear regression, select polynomial degree 1 below, and for the standard form \xe2\x80\x94


    b corresponds to be the first parameter listed in the results window below, and m to the second.

    See Also:
    Serialized Form
    • Field Detail

      • zeroColor

        public Color zeroColor
      • lineColor

        public Color lineColor
      • dataColor

        public Color dataColor
    • Constructor Detail

      • PolySolve

        public PolySolve()
        Main method to start the regression fit.
      • PolySolve

        public PolySolve(P1D input)
        Main method to start the regression fit.
      • PolySolve

        public PolySolve(H1D input)
        Main method to start the regression fit.
      • PolySolve

        public PolySolve(double[] xx,                 double[] yy)
        Main class to start the regression fit.
    • Method Detail

      • close

        public void close()
      • BuildPolySolve

        public void BuildPolySolve(double[] xx,                           double[] yy)
        Main method to start the regression fit.
      • init

        public void init()
        Initializes the applet PolySolve
        init in class Applet
      • process

        public void process(boolean update)
      • generateTable

        public void generateTable()

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