POLYFIT Fit polynomial to data.

POLYFIT Fit polynomial to data.
    P = POLYFIT(X,Y,N) finds the coefficients of a polynomial P(X) of
    degree N that fits the data Y best in a least-squares sense. P is a
    row vector of length N+1 containing the polynomial coefficients in
    descending powers, P(1)*X^N + P(2)*X^(N-1) +...+ P(N)*X + P(N+1).

    [P,S] = POLYFIT(X,Y,N) returns the polynomial coefficients P and a
    structure S for use with POLYVAL to obtain error estimates for
    predictions.  S contains fields for the triangular factor (R) from a QR
    decomposition of the Vandermonde matrix of X, the degrees of freedom
    (df), and the norm of the residuals (normr).  If the data Y are random,
    an estimate of the covariance matrix of P is (Rinv*Rinv')*normr^2/df,
    where Rinv is the inverse of R.

    [P,S,MU] = POLYFIT(X,Y,N) finds the coefficients of a polynomial in
    XHAT = (X-MU(1))/MU(2) where MU(1) = MEAN(X) and MU(2) = STD(X). This
    centering and scaling transformation improves the numerical properties
    of both the polynomial and the fitting algorithm.

    Warning messages result if N is >= length(X), if X has repeated, or
    nearly repeated, points, or if X might need centering and scaling.

    Class support for inputs X,Y:
       float: double, single

    See also poly, polyval, roots, lscov.