TY  - JOUR
T1  - Kinetic Mean and Kinetic Standard Deviation: New Statistical Concepts with New Research
Implications
AU - J. Alkhatib, Ahed 
JO  - Research Journal of Applied Sciences
VL  - 15
IS  - 8
SP  - 249
EP  - 252
PY  - 2020
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2020.249.252
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2020.249.252
KW  - significance
KW  -Kinetic standard deviation (Ksd)
KW  -Kinetic mean (Km)
KW  -t test
KW  -implications
AB  - Both Mean (M) and Standard Deviation (SD)
are considered basic parameters in statistics and
mathematics in measuring central tendencies. Both
parameters are widely used in statistical software
programs such as Excel spreading sheets, SPSS and
others. The main objectives of this study were to
introduce new statistical concepts: Kinetic mean (Km)
and Kinetic Standard Deviation (KSD) to demonstrate
hidden variations and to understand in depth their impact
on our understanding of biomedical data. In classic
statistics, the M value gives a value for the whole sample
and also the SD gives the deviation of all numbers from
their M. This study is based on giving the idea of
integrating portioned data. The data are further subdivided
into short intervals and the M and SD are computed
accordingly. Other statistical parameters such as the T test
are computed accordingly. At the end of performing many
mathematical calculations, a table summarizing the whole
parameters is presented. The results of the study showed
that the mean of the sample and the kinetic mean were
close to each other, although, the kinetic mean was
slightly less than that of the sample mean. The standard
deviation of the sample was higher than that of the kinetic
standard deviation. The main idea of this study was to
check the internal variations in both kinetic mean and
kinetic standard deviation for each two adjacent figures.
In this case, both kinetic mean and standard deviations
were shown to be in kinetic mode. We were also able to
compute the significance between these values.
ER  - 