The sufficient closeness of the medians of the ordered samples of random data to the normal distribution is used in computer systems for control, monitoring and diagnosing electric power equipment. However, it remains what other probability density function (pdf) of elements (sample statistics) have such similarities. This paper presents various methods for statistical testing hypotheses for pdf-converter channels as statistics of given sizes odd numbered and ordered samples of bounded and uniformly distributed random numbers. The use of various different criteria and the results of estimates studied under the same conditions showed a sufficient conformity of the results of tests for three statistical criteria. It made possible to draw a reasonable conclusion about the preferable use of the adapted chi-square test for assessing the congruence of analytical pdf channels of the converter with normal distribution. We also suggested using the "statistical closeness window" to define those channels of the converter that do not significantly differ from the normal distribution. In addition, we presented an empirical formula determining the dependence of the size of the window of the statistical closeness window on the sample size. The results of the research are summarized in a statistical model of a multichannel uncorrelated data converter. References 27, figures 7.

The concept of multichannel parallel converting of probability density function (pdf) of random data was previously used for single-element pdf-converters. In development of this concept, here we investigate converting properties of spdf-converters channels formed by the sum of the ​​pairs of ordered sample elements (order statistics). The characteristics of the conversion results as dependencies on the size of the samples and the displacement of the channels relative to the median of the samples were obtained for data with a uniform distribution density. Also where excluded the areas of mutual dependence of the density functions of the summed elements, which further where normalized together with approximating them functions. Despite the apparent structural differences, the goal of this study still was to determine the closeness of the converted data with some standard functions of the probability distribution density, in particular, with the normal distribution law. As before, the estimates of the closeness of the spdf-converter channels were obtained using the chi-square criteria. The results of the research were used to determine the size and location of the statistical closeness windows, and to construct spdf-converters statistical model. References 20, figures 14.

The coherence function for an arbitrary pair the input sequence-a given element of ordered samples random data's is defined. The essentially nonlinear character of this data processing method is established. Parametric and statistical estimates of the proximity of median transformations to the normal Gaussian distribution law are obtained. A technique for applying the Pearson criterion for estimating the statistical proximity of analytically defined functions is presented. References 15, figures 7.