  Statistical parameters are used in finance, weather, industrial, science,
among other vast number of different fields to draw conclusions. New more
efficient selection methods are mandatory to analyses the huge amount of
astronomical data. The standard and new data-mining parameters to analyses
non-correlated data are used to set the best way to discriminate stochastic and
non-stochastic variations. We introduce 16 modified statistical parameters
covering different features of statistical distribution, like; average,
dispersion, and shape parameters. Many of dispersion and shape parameters are
unbound parameters, i.e. equations which do not require the calculation of the
average. Moreover, the majority of them have lower error than previous ones
that is mainly observed for distributions having few measurements. A set of
non-correlated variability indices, sample size corrections, and a new noise
model as well as tests of different apertures and cutoffs on the data (BAS
approach) are introduced. The number of misselections is reduced by about 520%
using a single waveband and 1200% combining all wavebands. On the other hand,
the even mean also improves the correlated indices introduced in Paper 1
Ferreira Lopes & Cross (2016). The misselection rate is reduced by about 18% if
the even mean is used instead of the mean to compute the correlated indices in
the WFCAM database. Even statistics allows us to improve the effectiveness of
both correlated and non-correlated indices. The correlated variability indices,
proposed in the first paper of this series, are also improved if the even mean
is used. The even parameters will also be useful for classifying light curves
in the last step of this project. We consider that the first step of this
project, where we set new techniques and methods that provide a huge improve on
the efficiency of selection of variable stars, is now complete.
