  The parameter (SNR) is obtained for distinguishing the Gaussian function, the
distribution of random variables in the absence of cross correlation, from
other functions, which makes it possible to describe collective states with
strong cross-correlation of data. The signal-to-noise ratio (SNR) in
one-dimensional space is determined and a calculation algorithm based on the
fractal variety of the Cantor dust in a closed loop is given. The algorithm is
invariant for linear transformations of the initial data set, has
renormalization-group invariance, and determines the intensity of
cross-correlation (collective effect) of the data. The description of the
collective state is universal and does not depend on the nature of the
correlation of data, nor is the universality of the distribution of random
variables in the absence of data correlation. The method is applicable for
large sets of non-Gaussian or strange data obtained in information technology.
In confirming the hypothesis of Koshland, the application of the method to the
intensity data of digital X-ray diffraction spectra with the calculation of the
collective effect makes it possible to identify a conformer exhibiting
biological activity.
