Commerce Exam  >  Commerce Notes  >  Statistics for Economics - Class XI  >  Chapter Notes - Measures of Dispersion

Measures of Dispersion Class 11 Economics

Introduction


A measure of dispersion reveals how widely the data is spread out. It provides information about the differences between data points, giving a clear picture of their distribution. This measure presents the variability and the central tendency of each item in the data set. To put it differently, dispersion refers to the degree to which values in a distribution deviate from the distribution's mean. It provides insight into how much individual items differ from each other and from the central value.

Question for Chapter Notes - Measures of Dispersion
Try yourself:Which of the following best describes a measure of dispersion?
View Solution

Question for Chapter Notes - Measures of Dispersion
Try yourself:What does dispersion indicate about a data set?
View Solution

The variation can be measured in different numerical measures, namely

  • Range: The simplest method of measuring dispersion is by calculating the difference between the largest and smallest item in a given distribution, known as the range. If Y max and Y min represent the two extreme values, the range can be calculated as follows:
    Range = Y max – Y min
  • Quartile deviation: Also known as the semi-interquartile range, quartile deviation is half of the difference between the upper and lower quartiles. The first quartile, Q1, is the value that connects the smallest number with the median of the data. The median, Q2, is the second quartile. Finally, the value that connects the largest number with the median is the third quartile, Q3. Quartile deviation can be calculated using the following formula:
    Q = ½ × (Q3 – Q1)
  • Mean deviation: Mean deviation is the arithmetic mean (average) of the absolute deviations of observations from a central value such as the mean or median. The mean deviation can be calculated using the formula:
    A = 1⁄n [∑i|xi – A|]
  • Standard deviation: The standard deviation is the square root of the arithmetic mean of the squared deviations measured from the mean. It is given as follows:
    σ = [(Σi (yi – ȳ) ⁄ n] ½ = [(Σ i yi 2 ⁄ n) – ȳ 2] ½
    Apart from a numerical value, graphics methods are also applied for estimating dispersion.

Types of Measures of Dispersion


Absolute Measures:

  • Absolute measures of dispersion are expressed in the same units as the variable being measured, such as kilograms, rupees, centimeters, or marks.

Relative Measures:

  • Relative measures of dispersion are calculated as ratios or percentages of the central tendency.
  • These measures are also referred to as coefficients of dispersion.
  • They are dimensionless numbers or percentages that do not depend on the units of measurement.

Characteristics of a Good Measure of Dispersion

  • The measure should be straightforward to compute and comprehend.
  • The measure should take into account all the data points in the series.
  • The measure should have a precise and well-defined formula.
  • The measure should not be heavily influenced by outliers.
  • The measure should not be excessively impacted by variations due to sampling.
  • The measure should be amenable to advanced mathematical and statistical analyses.

What are the objectives of computing dispersion?


Comparative Analysis:

  • Measures of dispersion provide a single value that reflects the consistency or uniformity of distribution, making it possible to compare various distributions.
  • The lower the magnitude (value) of dispersion, the higher the consistency or uniformity, and vice versa.

Reliability of Averages:

  • A small value of dispersion indicates low variation between observations and the average, making the average a reliable representative of the observations.
  • A higher value of dispersion indicates greater deviation among the observations, making the average an unreliable representative.

Controlling Variability:

  • Different measures of dispersion provide us with information on variability from different perspectives, and this information can be helpful in controlling variation.
  • In financial analysis of business and medicine, these measures of dispersion can prove very useful.

Basis for Further Statistical Analysis:

  • Measures of dispersion provide the basis for further statistical analysis, such as computing correlation, regression, and testing of hypotheses.

Question for Chapter Notes - Measures of Dispersion
Try yourself:What is the significance of a small value of dispersion?
View Solution

Relative Measures of Dispersion

  • Range
  • Inter quartile range
  • Quartile deviation or Semi-Inter-quartile range
  • Mean deviation
  • Standard Deviation
  • Lorenz curve

Range


Range is defined as the difference between two extreme observations i.e. the largest and the smallest value.
Symbolically, R = L - S
Where R = Range
L = Largest Value
S = Smallest value
Coefficient of range = Measures of Dispersion Class 11 Economics

Inter Quartile Range


Inter quartile range is the difference between upper quartile and lower quartile.
Inter-quartile range = Q3 - Q1
Where Q3 = Third quartile or upper quartile.
Q1 = First quartile or lower quartile

Quartile Deviation


Quartile deviation is known as half of difference of third quartile (Q3) and first quartile (Q1). It is also known as semi inter quartile range.
Measures of Dispersion Class 11 Economics
Where Q.D. = Quartile deviation
Q3 = Third quartile or upper quartile.
Q1 = First quartile of lower quartile.
Coefficient of quartile deviation = Measures of Dispersion Class 11 Economics

Mean Deviation


The mean deviation or average deviation is computed as the arithmetic mean of the deviations of individual items from their central value (mean, median, or mode), typically based on the median.
Calculation of mean deviation:
Individual Series
Measures of Dispersion Class 11 Economics

Discrete Series
Measures of Dispersion Class 11 Economics

Continuous Series
Measures of Dispersion Class 11 Economics

Where,
MD = Mean deviation
| D | = Deviations from mean or median ignoring + Signs
N = Number of item (Individual Series)
N = Total number of Frequencies (Discrete and continuous series)
F = Number of frequencies.
Coefficient of mean deviation

Merit of Mean deviation:

  • Considering every term in the calculation, mean deviation is a superior measure of dispersion compared to other measures such as range, percentile range, or quartile range.
  • Mean deviation finds extensive use in fields such as economics, business, commerce, and related disciplines.
  • Mean deviation is less susceptible to sampling fluctuations compared to other measures like range, percentile range, or quartile deviation.
  • When comparing two or more series, mean deviation is arguably the best measure.
  • Mean deviation is based on actual measurements, rather than estimates.
  • Mean deviation is a precisely defined measure, a critical aspect for any statistical analysis.
  • Mean deviation calculated from the median is less influenced by extreme values.
  • Because mean deviation is based on deviations from an average, it provides a better measure for comparison.

Demerits of Mean Deviation:

  • Compiling M.D. can be difficult if the average is in fractions.
  • It lacks a main property - the ability to undergo further algebraic treatment.
  • The calculation of X, M, or Z is required before other measures can be calculated, making it less straightforward.
  • When calculated from Z, it may not be very reliable as the mode (Z) may not be a true representative of the series.
  • The values of M.D. and its coefficient may differ when calculated from X, M, and Z.
  • The ignoring of positive and negative signs in M.D. is not mathematically possible, and so Standard Deviation or another measure of dispersion may be more appropriate.
  • For mean, open and series cannot be used for obtaining accurate results.
  • An increase in range with an increase in the sample may not necessarily lead to a proportional increase in average deviation.
  • M.D. is not commonly used in sociological studies.

Standard Deviation


The most widely used measure of dispersion is the standard deviation, which is calculated as the square root of the arithmetic mean of the squares of the deviations of the items from their arithmetic mean. Standard deviation can be calculated for individual series.

Question for Chapter Notes - Measures of Dispersion
Try yourself:What is the formula to calculate standard deviation in an individual series?
View Solution

Actual mean method:
Measures of Dispersion Class 11 Economics
Where σ = Standard Deviation
∑x2 = Sum total of square of Deviation taken from Mean
N = Number of items

Shortcut Method or assumed mean method:
Measures of Dispersion Class 11 Economics
Where d2 = Square of deviation taken from assumed mean.

Calculation of standard deviation in discrete series:
Actual mean method or direct method
Measures of Dispersion Class 11 Economics
Where σ = S.D.
∑x2 = Sum total of the squared deviations multiplied by frequency
N = Number of pair of observations.

Shortcut method or assumed method:
σ = S.D.
∑fd2 = Sum total of the squared deviations Multiplied by frequency
∑fd2 = Sum total of deviations multiplied by frequency.
N = Number of pair of observations.

Step deviation method:
Measures of Dispersion Class 11 EconomicsX C
σ = Standard Deviation
∑fd2 = Sum total of the squared step deviations multiplied by frequency.
∑fd2 = Sum total of step deviations multiplied by frequency
C = Common factor
N = Number of pair of observation

Individual Series:

  • Actual Mean Method
    Measures of Dispersion Class 11 Economics
    Measures of Dispersion Class 11 Economics
  • Assumed Mean Method
    Measures of Dispersion Class 11 Economics

Discrete/Continuous Series:

  • Actual Mean Method
    Measures of Dispersion Class 11 Economics
  • Assumed Mean Method
    Measures of Dispersion Class 11 Economics
  • Step Deviation Method
    Measures of Dispersion Class 11 EconomicsX C

Merits of standard donation:

  • Based on all values
  • Rigidly defined
  • Less effect of fluctuations
  • Capable of algebraic treatment

Demerits of standard donation:

  • Difficult to compute
  • More stress on extreme items
  • Dependent on unit of measurement.

Coefficient of variation:

The coefficient of variation is the most appropriate measure for comparing the stability, uniformity, consistency, or homogeneity of two or more groups of similar data. This measure is calculated by dividing the standard deviation by the mean.
Measures of Dispersion Class 11 Economics
Where C.V. = Coefficient of variation
σ = Standard deviation
X = Arithmetic mean

Lorenz Curve


Dr. Max O. Lorenz developed the Lorenz curve as a visual tool to analyze dispersion. Generally, the Lorenz curve is positioned below the line of equal distribution, except when the distribution is uniform. The space between the line of equal distribution and the plotted curve shows the degree of inequality in the items, with a greater area indicating higher inequality.

Application Lorenz Curve:

  • Distribution of income
  • Distribution of wealth
  • Distribution of wages
  • Distribution of production
  • Distribution of population
The document Measures of Dispersion Class 11 Economics is a part of the Commerce Course Statistics for Economics - Class XI.
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FAQs on Measures of Dispersion Class 11 Economics

1. What are the different types of measures of dispersion?
Ans. The different types of measures of dispersion are range, mean deviation, variance, and standard deviation. These measures help to quantify the spread or variability of data in a dataset.
2. What are the characteristics of a good measure of dispersion?
Ans. A good measure of dispersion should have the following characteristics: - It should be based on all the observations in the dataset. - It should be easy to understand and calculate. - It should be unaffected by extreme values or outliers. - It should be comparable across different datasets. - It should be based on the same units as the original data.
3. What are the objectives of computing dispersion?
Ans. The objectives of computing dispersion are: - To understand the spread or variability of data. - To compare the variability between different datasets. - To identify outliers or extreme values in the data. - To assess the reliability or consistency of measurements. - To make informed decisions based on the variability of data.
4. What are relative measures of dispersion?
Ans. Relative measures of dispersion are measures that express the dispersion of data relative to some other value. They are useful for comparing the variability of different datasets or groups. Examples of relative measures of dispersion include coefficient of variation and relative mean deviation.
5. What is the importance of measures of dispersion in statistics?
Ans. Measures of dispersion play a crucial role in statistics for several reasons: - They provide information about the spread or variability of data, which is important for understanding the characteristics of a dataset. - They help in comparing different datasets or groups, allowing us to identify patterns or differences in variability. - They assist in identifying outliers or extreme values that can significantly impact the interpretation of data. - They are used in various statistical analyses and models to assess the reliability and validity of results. - They aid in making informed decisions based on the variability of data, particularly in fields like finance, economics, and quality control.
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