Pseudorandom number generator‘s statistical features were analyzed in this work. Pseudorandom numbers are applied in many fields, that‘s why it‘s important for them to satisfy following requirements: • to have uniform distribution, • to be uncorrelated. Hypothesis that random numbers are distributed uniformly is checked by Pearson test. Hypothesis that autocorrelation function is equal to zero is checked by Box Ljung test. During investigation it was noticed, that in all ways generated random numbers didn’t have uniform distribution, except linear congriuential generator. Applying different transformations was set, that for combinations v1-v8 when using parabola, transformed random numbers had uniform distribution. Arcsine was the best transformation for nonlinear congriuential generator. While testing hypothesis about autocorrelation function’s equality to zero was noticed, that zero hypothesis rejection or not depends on: • random numbers generation algorithm, • generated sample size, The best generator, which satisfied requirements, is linear congriuential generator, but it is not suitable, because it is too predictable. Nonlinear congruential generator is chosen as the best one, because its statistical features are closest to the linear generator.