The measure of dispersion indicates the scattering of data. It explains the disparity of data from one another delivering a precise view of the distribution of data. The measure of dispersion displays and gives us an idea about the variation and central value of an individual item.
The variation can be measured in different numerical measures namely:
(i) Range – It is the simplest method of measurement of dispersion and defines the difference between the largest and the smallest item in a given distribution. Suppose, If Y max and Y min are the two ultimate items then
Range = Y max – Y min
(ii) Quartile Deviation – It is known as SemiInterQuartile Range i.e. half of the difference between the upper quartile and lower quartile. The first quartile is derived as (Q), the middle digit (Q1) connects the least number with the median of the data. The median of a data set is the (Q2) second quartile. Lastly, the number connecting the largest number and the median is the third quartile (Q3). Quartile deviation can be calculated by
Q = ½ × (Q3 – Q1)
(iii) Mean DeviationMean deviation is the arithmetic mean (average) of deviations ⎜D⎜of observations from a central value {Mean or Median}.
Mean deviation can be evaluated by using the formula: A = 1⁄n [∑ixi – A]
(iv) Standard Deviation Standard deviation is the Square Root of the Arithmetic Average of the squared of the deviations measured from the mean. The standard deviation is given as
σ = [(Σi (yi – ȳ) ⁄ n] ½ = [(Σ i yi 2 ⁄ n) – ȳ 2] ½
Apart from a numerical value, graphics method are also applied for estimating dispersion.
Q.1DEFINE DISPERSION. WHAT ARE THE TWO MAIN TYPES OF MEASURES OF
DISPERSION? 
(A) DISPERSION 
 Dispersion is the extent to which values in a distribution differ from the average of the distribution.
 It gives us an idea about the extent to which individual items vary from one another and from the central value.

(B) FOLLOWING ARE THE TWO MAIN TYPES OF MEASURES OF DISPERSION: 
(1) ABSOLUTE MEASURES 
 Absolute measures of dispersion are expressed in the unit of Variable itself. Like, Kilograms, Rupees, Centimeters, Marks etc.

(2) RELATIVE MEASURES 
 Relative measures of dispersion are obtained as ratios or percentages of the average.
 These are also known as ‘Coefficient of dispersion’
 These are pure numbers or percentages totally independent of the units of measurements.

Q.2WHAT ARE THE OBJECTIVES OF COMPUTING DISPERSION?
OR
WHAT IS THE NEED OF COMPUTING DISPERSION? 
ANSWER: 
(1) COMPARATIVE STUDY 
 Measures of dispersion give a single value indicating the degree of consistency or uniformity of distribution. This single value helps us in making comparisons of various distributions.
 The smaller the magnitude (value) of dispersion, higher is the consistency or uniformity and viceversa.

(2) RELIABILITY OF AN AVERAGE 
 A small value of dispersion means low variation between observations and average. It means the average is a good representative of observation and very reliable.
 A higher value of dispersion means greater deviation among the observations. In this case, the average is not a good representative and it cannot be considered reliable.

(3) CONTROL THE VARIABILITY 
 Different measures of dispersion provide us data of variability from different angles and this knowledge can prove helpful in controlling the variation.
 Especially, in the financial analysis of business and Medical, these measures of dispersion can prove very useful.

(4) BASIS FOR FURTHER STATISTICAL ANALYSIS 
 Measures of dispersions provide the basis further statistical analysis like, computing Correlation, Regression, Test of hypothesis, etc.

Q.3LIST CHARACTERISTICS OF A GOOD MEASURE OF DISPERSION. 
ANSWER: 
CHARACTERISTICS OF A GOOD MEASURE OF DISPERSION 
 It should be easy to calculate & simple to understand.
 It should be based on all the observations of the series.
 It should be rigidly defined.
 It should not be affected by extreme values
 It should not be unduly affected by sampling fluctuations.
 It should be capable of further mathematical treatment and statistical analysis.

Q.4WHAT ARE THE VARIOUS ‘ABSOLUTE MEASURES’ OF DISPERSION? 
ANSWER:
FOLLOWING ARE THE DIFFERENT ‘ABSOLUTE MEASURES’ OF DISPERSION: 
(1) RANGE 
 It is the simplest method of measurement of dispersion.
 It is defined as the difference between the largest and the smallest item in a given distribution.
 Range = Largest item (L) – Smallest item (S)

(2) INTERQUARTILE RANGE 
 It is defined as the difference between the Upper Quartile and Lower Quartile of a given distribution.
Interquartile Range = Upper Quartile (Q_{3})–Lower Quartile(Q_{1}) 
(3) QUARTILE DEVIATION 
 It is known as SemiInterQuartile Range i.e. half of the difference between the upper quartile and lower quartile.
 Quartile Deviation = _{\(\frac{Upper\, Quartile\left ( _{Q3} \right )\, \, Lower\, Quartile\left ( _{Q1} \right )}{2}\)}

(4) MEAN DEVIATION 
 Mean deviation is the arithmetic mean (average) of deviations ⎜D⎜of observations from a central value {Mean or Median}.

(5) STANDARD DEVIATION 
 Standard deviation is the Square Root of the Arithmetic Average of the squared of the deviations measured from the mean.

(6) LORENZ CURVE 
 The Lorenz Curve is a graphic method of measuring estimated dispersion.
 This curve is often used to measure the inequalities of income or wealth in a society.

Q.5WHAT ARE THE VARIOUS ‘RELATIVE MEASURES’ OF DISPERSION? 
ANSWER:
FOLLOWING ARE THE RELATIVE MEASURE OF DISPERSION: 
(1) COEFFICIENT OF RANGE 
It refers to the ratio of the difference between two extreme items of the distribution to their sum.
Coefficient of Range_{ \(\frac{Largest\, Item\left ( _{L} \right )\, \,Smallest\, Item\left ( _{S} \right )}{Largest\, Item\left ( _{L} \right )\, \,Smallest\, Item\left ( _{S} \right )}\)} 
(2) COEFFICIENT OF QUARTILE DEVIATION 
It refers to the ratio of the difference between Upper Quartile and Lower Quartile of a distribution to their sum.
Coefficient of Quartile Deviation = _{\(\frac{_{Q3}\, \, _{Q1}}{_{Q3}\, +\, {Q1}}\)} 
(3) COEFFICIENT OF MEAN DEVIATION 
 Mean deviation is an absolute measure of dispersion.
 In order to transform it into a relative measure, it is divided by the particular average, from which it has been calculated.
 It is then known as the Coefficient of Mean Deviation.
 Coefficient of Mean Deviation from Mean _{\(\left ( \bar{X} \right )\, =\,\frac{MD_{\bar{X}}}{\bar{X}}\)}
 Coefficient of Mean Deviation from Median _{\(\left ( ME \right )\, =\,\frac{MD_{Me}}{Me}\)}

(4) COEFFICIENT OF STANDARD DEVIATION 
 It is calculated by dividing the standard deviation _{\(\left ( \sigma \right )\)} by the mean \(\left ( \bar{X} \right )\) of the data.
 Coefficient of Standard Deviation _{\(=\, \frac{\sigma }{X}\)}

(5) COEFFICIENT OF VARIATION 
 It is used to compare two data with respect to stability (or uniformity or consistency or homogeneity).
 It indicates the relationship between the standard deviation and the arithmetic mean expressed in terms of percentage.
 Coefficient of Variation (C.V.) _{\(=\, \frac{\sigma }{X}\, X\, 100\)}
 Where, C.V. = Coefficient of Variation; _{\(\sigma\)}= Standard Deviation;
_{\(\bar{X}\)} = Arithmetic Mean 
Q.6WHAT ARE THE MERITS AND DEMERITS OF RANGE? 
ANSWER: 
MERITS 
 It is very easy to calculate and simple to understand.
 No special knowledge is needed while calculating range.
 It takes least time for computation.
 It provides the broad picture of the data at a glance.

DEMERITS 
 It is a crude measure because it is only based on two extreme values (highest and lowest).
 It cannot be calculated in case of openended series.
 Range is significantly affected by fluctuations of sampling i.e. it varies widely from sample to sample.

Q.7WHAT ARE THE MERITS AND DEMERITS OF QUARTILE DEVIATION? 
ANSWER: 
MERITS 
 It is also quite easy to calculate and simple to understand.
 It can be used even in case of openend distribution.
 It is less affected by extreme values so, it a superior to ‘Range’.
 It is more useful when dispersion of middle 50% is to be computed.

DEMERITS 
 It is not based on all the observations.
 It is not capable of further algebraic treatment or statistical analysis.
 It is affected considerably by fluctuations of sampling.
 It is not regarded as very reliable measure of dispersion because it ignores 50% observations.

Q.8WHAT ARE THE MERITS AND DEMERITS OF MEAN DEVIATION? 
ANSWER: 
MERITS 
 It is based on all the observations of the series and not only on the limits like Range and QD.
 It is simple to calculate and easy to understand.
 It is not much affected by extreme values.
 For calculating mean deviation, deviations can be taken from any average.

DEMERITS 
 Ignoring + and – signs is bad from the mathematical viewpoint.
 It is not capable of further mathematical treatment.
 It is difficult to compute when mean or median are in fraction.
 It may not be possible to use this method in case of Open ended series.
