ANALYSIS THE SIGNIFICANCE OF RELIABLE EXPERIMENTALLY DETERMINED DISTRIBUTION LAWS

Abstract

Experimental data collected drift (random size) of the law affecting real experimental data set, so it is not the only manifestation of the set of theoretical parameters. The same law theoretically true distribution may materialize through an infinity of sets of experimental data due random factors influence. The paper presents practical ways of determining confidence intervals using Monte Carlo method. The central issue of how the analysis is to determine the parameters found empirical distribution so as accurately to model the system state