  The use of regression analysis for processing experimental data is fraught
with certain difficulties, which, when models are constructed, are associated
with assumptions, and there is a normal law of error distribution and variables
are statistically independent. In practice , these conditions do not always
take place . This may cause the constructed economic and mathematical model to
have no practical value. As an alternative approach to the study of numerical
series, according to the author, smoothing of numerical series using
Fermat-Torricelli points with subsequent interpolation of these points by
series of exponents could be used. The use of exponential series for
interpolating numerical series makes it possible to achieve the accuracy of
model construction no worse than regression analysis . At the same time, the
interpolation by series of exponents does not require the statistical material
that the errors of the numerical series obey the normal distribution law, and
statistical independence of variables is also not required. Interpolation of
numerical series by exponential series represents a "black box" type model,
that is, only input parameters and output parameters matter.
