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.

翻译：我们提出了一个新的通用的加权最小平方单向回归法,根据这种方法,从属变量作为独立变量一系列适当选择的功能加以扩展。该系列的每个术语都是通过迭接过程获得的,这种迭接过程减少了残余物的平方之和。因此,通过将回归序列评价到足够多的术语,我们原则上可以减少剩余物的平方之和,并提高适配性的准确性。