the coefficients are returned, when True
    diagnostic information from the singular value decomposition is
    also returned.
w : array_like, shape (M,), optional
    Weights. If not None, the weight ``w[i]`` applies to the unsquared
    residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
    chosen so that the errors of the products ``w[i]*y[i]`` all have
    the same variance.  When using inverse-variance weighting, use
    ``w[i] = 1/sigma(y[i])``.  The default value is None.
window : {[beg, end]}, optional
    Window to use for the returned series. The default
    value is the default class domain
symbol : str, optional
    Symbol representing the independent variable. Default is 'x'.

Returns
-------
new_series : series
    A series that represents the least squares fit to the data and
    has the domain and window specified in the call. If the
    coefficients for the unscaled and unshifted basis polynomials are
    of interest, do ``new_series.convert().coef``.

[resid, rank, sv, rcond] : list
    These values are only returned if ``full == True``

    - resid -- sum of squared residuals of the least squares fit
    - rank -- the numerical rank of the scaled Vandermonde matrix
    - sv -- singular values of the scaled Vandermonde matrix
    - rcond -- value of `rcond`.

    For more details, see `linalg.lstsq`.

r