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"Curve Fitting Solutions"
Polynomial Fit Method

x-array = { , , , , , }
y-array = { , , , , , }

[ Initial x-array: { 0, 1, 2, 3, 4, 5 } ]
[ Initial y-array: { 2, 1, 4, 4, 3, 2 } ]

Polynomial Fit Method

As we mentioned before, please refer to ("Straight Line Fit Method") the polynomial fit is a special case of the linear least squares methods.

Algorithm Creation

In this case, the basis function becomes:

fj(x) = xj,    j = 0,1,...,m

where the matrix and vector in the normal equation become,

Αjk = ∑ni=0  xjj + k,     βk = ∑ni=0  xik yi

Testing the Polynomial Fit Method

In order to test the Polynomial Fit Method as defined above, a new TestPolynomialFit() static method has been added and executed. Supporting code and methods are not shown.

           static void TestPolynomialFit();
                 double[] xarray = new double[] { t1, t2, t3, t4, t5, t6 };
                 double[] yarray = new double[] { t7, t8, t9, t10, t11, t12 };
                    double sigma = 0.0;
                    VectorR results = CurveFitting.PolynomialFit(xarray, yarray, m, out sigma);
                    ListBox1.Items.Add("\nOrder of polynomial m = " + m.ToString() + ",
                       Standard deviation = " + sigma.ToString());
                    ListBox1.Items.Add("Coefficients = " + results.ToString());
                    ListBox1.Items.Add(" ");

As a sample we provide the input data points using two double arrays x-array and y-array. We used the same input function as in ("Straight Line Fit Method"). Running this example generates results shown above. Note the order of the polynomial is given by simple iteration from 1 to 3.

From these results we see that the quadratic polynomial with m = 2:

f(x) = 1.2857 + 1.6x - 0.2857x2

produces the smallest deviation, which can be considered as the best fit to the data.

The user can manipulate all values and try variations on the arrays themselves by specifying new estimate values.

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