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Noisy data presented in tabular form are given. Fit and build a model curve

Noisy data presented in tabular form are given. Fit and build a model curve. Choose a functional dependence of the form:

enter image description here

Solution

  • Here is the Scilab code for your first example, you will be able to adapt it to other cases. To evaluate the obtained polynomial F use Scilab function horner (see the associated help page by typing help horner).

    gd = [1.2 1.4 1.6 1.8 2.1 2.2 2.4 2.6 2.8 3.1]';
Fd = [1.55 2.73 3.91 5.51 7.11 9.12 11.12 12.91 15.45 17.91]';
c = [ones(gd), gd, gd.^2, gd.^3]\Fd;
F = poly(c,"g","coeff");
disp(F)

    the above script displays

      7.6508968 -14.91761g +9.8440484g² -1.2735795g³

    You can plot the graph of the polynomial and the original noisy data like this:

    g = linspace(1.2,3.1,100);
plot(g, horner(F,g),gd,Fd,'o')

    enter image description here