  We present a new and general method of weighted least square univariate
regression where the dependent variable is expanded as a series of suitably
chosen functions of the independent variables. Each term of the series is
obtained by an iterative process which reduces the sum of the square of the
residuals. Thus by evaluating the regression series to a sufficiently large
number of terms we can, in principle, reduce the sum of the square of residuals
and improve the accuracy of the fit.
