Measures of Dispersion
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Quartiles
If we divide a cumulative frequency curve into quarters, the value at the lower quarter is referred to as the lower quartile, the value at the middle gives the median and the value at the upper quarter is the upper quartile.
A set of numbers may be as follows: 8, 14, 15, 16, 17, 18, 19, 50. The mean of these numbers is 19.625 . However, the extremes in this set (8 and 50) distort the range. The interquartile range is a method of measuring the spread of the numbers by finding the middle 50% of the values. It is useful since it ignore the extreme values. It is a method of measuring the spread of the data.

The lower quartile is (n+1)/4 th value (n is the cumulative frequency, ie 157 in this case) and the upper quartile is the 3(n+1)/4 the value. The difference between these two is the interquartile range (IQR).
In the above example, the upper quartile is the 118.5th value and the lower quartile is the 39.5th value. If we draw a cumulative frequency curve, we see that the lower quartile, therefore, is about 17 and the upper quartile is about 37. Therefore the IQR is 20 (bear in mind that this is a rough sketch- if you plot the values on graph paper you will get a more accurate value).

Variance and Standard Deviation
These measures of dispersion are very important. Like the interquartile range, they measure the spread of the data.

What the formula means:
(1)   xr - m  means take each value in turn and subtract the mean from each value.
(2)  (xr - m)²  means square each of the results obtained from step (1). This is to get rid of any minus signs.
(3)  S(xr - m)²  means add up all of the results obtained from step (2).
(4) Divide step (3) by n, which is the sum of the numbers
(5) For the standard deviation, square root the answer to step (4).

Example:
Find the variance and standard deviation of the following numbers: 1, 3, 5, 5, 6, 7, 9, 10 .

The mean = 46/ 8 = 5.75

(Step 1): (1 - 5.75), (3 - 5.75), (5 - 5.75), (5 - 5.75), (6 - 5.75), (7 - 5.75), (9 - 5.75), (10 - 5.75)
= -4.75, -2.75, -0.75, -0.75, 0.25, 1.25, 3.25, 4.25

(Step 2): 22.563, 7.563, 0.563, 0.563, 0.063, 1.563, 10.563, 18.063

(Step 3): 22.563 + 7.563 + 0.563 + ...
= 61.504

(Step 4): n = 46, therefore variance = 61.504/ 46 = 1.34 (3sf)
(Step 5): standard deviation = 1.16 (3sf)

Grouped Data
There are many ways of writing the formula for the standard deviation. The one above is for a population of numbers. The formula for the standard deviation when the data is grouped is:

Example:
The table shows marks (out of 10) obtained by 20 people in a test
Mark (x)   Frequency (f)
  1                 0
  2                 1
  3                 1
  4                 3
  5                 2
  6                 5
  7                 5
  8                 2
  9                 0
  10               1

Work out the variance of this data.
In such questions, it is often easiest to set your working out in a table:
    fx             fx²
    0              0
    2              4
    3              9
   12             48
   10             50
   30             180
   35             245
   16             128
    0               0
   10             100

Sf = 20
Sfx = 118
Sfx² = 764

variance =  Sfx²  - ( Sfx )²
                  Sf      (  Sf  )
 =  764  -  (118)²
      20       ( 20 )
 =  38.2 - 34.81 = 3.39

© Matthew Pinkey

Other Notes in this Category

1. Averages
2. Histograms & Cumulative Freq.
3. Measures of Dispersion
4. Probability

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