For ungrouped data, i.e. discrete data, observations on the X variable are obtained as , For grouped data, that is, continuous data, observations on the variable X is obtained and tabulated with an interval of K classes in the frequency table. The middle of inertia is indicated by which occur with frequencies respectively and ,
Class Intervals  Mid Points (  Absolute Frequency ( 

  
  
  
…  …  … 
…  …  … 
  
Moments about arbitrary point A
moment of variable X about arbitrary point A on observations is defined as:
For ungrouped data
For grouped data
where
A moment about any arbitrary point in Python —
Consider these points. Below is the time (in hours) that 20 different people spend on Python.Engineering every week.
15, 25, 18, 36, 40, 28, 30, 32, 23, 22, 21, 27, 31, 20, 14, 10, 33, 11, 7, 13

moment around source A = 0 known as a raw moment and defined as:
For ungrouped data,
For grouped data,where,
Notes :
 & gt; We can find first raw moment (
) just by replacing r with 1 and second raw moment ( ) just by replacing r with 2 and so on.
 & gt; When r = 0 the momentfor both grouped and ungrouped data.
Raw moment in Python —

The moments of the X variable relative to the arithmetic mean () are known as pivot moments and are defined as:
For ungrouped data,
For grouped data,
where
and
Notes :
 & gt; We can find first raw moment (
) just by replacing r with 1 and second raw moment ( ) just by replacing r with 2 and so on.
 & gt; When r = 0 the moment, and when r = 1 the moment for both grouped and ungrouped data.
Relationship between Raw and Central Moments —
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# data points
time
=
[
15
,
25
,
18
,
36
,
40
,
28
,
30
,
32
,
23
,
22
,
21
,
27
,
31
,
20
,
14
,
10
,
33
,
11
,
7
,
13
]
# Greedy
A
=
sum
(time)
/
len
(time)
# Moment for r = 1
moment
=
(
sum
([(item

A)
for
item
in
time]))
/
len
(time)