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Population coefficient of variation Example for ungrouped data ( Enter your problem )
  1. Formula & Example
  2. Population Variance Example
  3. Population Standard deviation Example
  4. Population coefficient of variation Example

4. Population coefficient of variation Example





1. Calculate Population Coefficient of Variation from the following data
`3,13,11,15,5,4,2`


Solution:
`x``x^2`
39
13169
11121
15225
525
416
24
------
`sum x=53``sum x^2=569`


Mean `bar x=(sum x)/n`

`=(3+13+11+15+5+4+2)/7`

`=53/7`

`=7.5714`



Population Standard deviation `sigma = sqrt((sum x^2 - (sum x)^2/n)/n)`

`=sqrt((569 - (53)^2/7)/7)`

`=sqrt((569 - 401.2857)/7)`

`=sqrt(167.7143/7)`

`=sqrt(23.9592)`

`=4.8948`



Coefficient of Variation (Population) `=sigma / bar x * 100 %`

`=4.8948/7.5714 * 100 %`

`=64.65 %`


2. Calculate Population Coefficient of Variation from the following data
`10,50,30,20,10,20,70,30`


Solution:
`x``x - bar x = x - 30``(x - bar x)^2`
10-20400
5020400
3000
20-10100
10-20400
20-10100
70401600
3000
---------
`sum x=240``sum (x - bar x)=0``sum (x - bar x)^2=3000`


Mean `bar x=(sum x)/n`

`=(10+50+30+20+10+20+70+30)/8`

`=240/8`

`=30`



Population Standard deviation `sigma = sqrt((sum (x - bar x)^2)/n)`

`=sqrt(3000/8)`

`=sqrt(375)`

`=19.3649`



Coefficient of Variation (Population) `=sigma / bar x * 100 %`

`=19.3649/30 * 100 %`

`=64.55 %`


3. Calculate Population Coefficient of Variation from the following data
`85,96,76,108,85,80,100,85,70,95`


Solution:
`x``x - bar x = x - 88``(x - bar x)^2`
85-39
96864
76-12144
10820400
85-39
80-864
10012144
85-39
70-18324
95749
---------
`sum x=880``sum (x - bar x)=0``sum (x - bar x)^2=1216`


Mean `bar x=(sum x)/n`

`=(85+96+76+108+85+80+100+85+70+95)/10`

`=880/10`

`=88`



Population Standard deviation `sigma = sqrt((sum (x - bar x)^2)/n)`

`=sqrt(1216/10)`

`=sqrt(121.6)`

`=11.0272`



Coefficient of Variation (Population) `=sigma / bar x * 100 %`

`=11.0272/88 * 100 %`

`=12.53 %`




This material is intended as a summary. Use your textbook for detail explanation.
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