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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 This is a risky project because erf(x) is a bounded function, while polynomials are unbounded, so the fit might not be very good.įirst generate a vector of x points, equally spaced in the interval then evaluate erf(x) at those points. This example involves fitting the error function, erf(x), by a polynomial in x. This centering and scaling transformation improves the numerical properties of both the polynomial and the fitting algorithm. If the errors in the data y are independent normal with constant variance, polyval produces error bounds that contain at least 50% of the predictions.įinds the coefficients of a polynomial in
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![polyfit matlab polyfit matlab](https://i.ytimg.com/vi/2yGsXtcvSrs/maxresdefault.jpg)
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 Functions) 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.