[A method of non-parametric evaluation of one-dimensional continuous distribution density].

A continuous density function f(x) of a one-dimensional continuous random variable X is estimated from independent measured values xi, i = 1 (1) n, by a nonparametric procedure. If the measuring precision is sufficiently high, then the density estimation f(x) will be received from the 1st derivative of the LOLINREG-approximation of the empirical distribution function which is generated by the measured values. If the measured values are granulated by a lower measuring precision, then the density estimation f(x) may be obtained from the 1st derivative of the LOLINREG-approximation of an empirical distribution function which is calculated from the natural histogram by integration. The procedure is demonstrated by examples from biometrical research.