So, on this interval, the fit is good to between three and four digits. ![]() ![]() To see how good the fit is, evaluate the polynomial at the data points withĪ table showing the data, fit, and error is There are seven coefficients and the polynomial is First generate a vector of x-points, equally spaced in the interval then evaluate erf(x) at those points. This is a risky project because erf(x) is a bounded function, while polynomials are unbounded, so the fit might not be very good. If the errors in the data Y are independent normal with constant variance polyval will produce error bounds that contain at least 50% of the predictions.Įxamples This example involves fitting the error function, erf(x), by a polynomial in x. Returns the polynomial coefficients p and a structure S for use with polyval to obtain error estimates or predictions. ![]() The result p is a row vector of length n+1 containing the polynomial coefficients in descending powers: Polyfit (MATLAB Function Reference) MATLAB Function Referenceįinds the coefficients of a polynomial p(x) of degree n that fits the data, p(x(i)) to y(i), in a least squares sense.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |