Terms related to variability metrics:
 & gt; Deviation  & gt; Variance  & gt; Standard Deviation  & gt; Mean Absolute Deviation  & gt; Meadian Absolute Deviation  & gt; Order Statistics  & gt; Range  & gt; Percentile  & gt; Interquartile Range
Sequence: [2, 3, 5, 6, 7, 9] Suppose, Central / Observed Value = 7 Deviation = [5, 4, 2 , 1, 0, 2]
Example:
Sequence: [2, 3, 5, 6, 7, 9] Mean = 5.33 Total Terms, n = 6 Squared Deviation = (2  5.33) ^{ 2 + (3  5.33) 2 + (5  5.33) 2 (6  5.33) 2 + (7  5.33) 2 + (9  5.33) 2 Variance = Squared Deviation / n }
Code —

Output:
Variance: 5.5555555555555545
Example:
Sequence: [2, 3, 5, 6, 7, 9] Mean = 5.33 Total Terms, n = 6 Squared Deviation = (2  5.33) ^{ 2 + (3  5.33) 2 + (5  5.33) 2 (6  5.33) 2 + (7  5.33) 2 + (9  5.33) 2 Variance = Squared Deviation / n Standard Deviation = (Variance) 1/2 }
Code —

Output:
Standard Deviation: 2.357022603955158
Example:
Sequence: [2, 4, 6, 8] Mean = 5 Deviation around mean = [3, 1, 1, 3] Mean Absolute Deviation = (3 + 1 + 1 + 3) / 4
# Average Absolute Deviation
import
numpy as np
def
mad (data):
return
np.mean (np.absolute (
data

np.mean (data)))
Sequence
=
[
2
,
4
,
6
,
8
]
print
(
"Mean Absolute Deviation: "
, mad (Sequence))
Output:
Mean Absolute Deviation: 2.0
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