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Four Types of Data Classification in Statistics

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Whenever a set of data that we have collected contains a large number of observations, the best way to examine such data is to present it in some compact and orderly form. The data set is organized and summarized in such a way that patterns are revealed and are more easily interpreted.

Statistical data are classified after taking into account the nature, scope, and purpose of an investigation. Generally, data are classified on the basis of the following four bases:

Geographical Classification of Data:

As the name suggests, in this classification the basis of classification is the geographical or locational differences between the various items in the data like States, Cities, Regions, Zones, Areas, etc. For example, the yield of agricultural output per hectare for different countries in some given period or the density of the population (per square km) in different cities of America may be different.

Geographical classifications are usually presented either in alphabetical order (which is generally the case in reference tables) or according to size or values to lay more emphasis on the important area or region (and this is generally the case in the summary table).

Chronological Classification of Data:

Chronological classification is one in which the data are classified on the basis of differences in time, e.g., the production of an industrial concern for different periods; the profits of a big business house over different years; the population of any
country for different years. The time-series data, which are quite frequent in Economic and Business Statistics are generally classified chronologically, usually starting with the first period of occurrence.

Qualitative Classification of Data:

When the data are classified according to some qualitative phenomena which are not capable of quantitative measurements like honesty, beauty, employment, intelligence, occupation, sex, literacy, etc., the classification is termed as qualitative. This is done in two ways:
(i) Simple classification: In this type of classification, each class is subdivided into two sub-classes and only one attribute is studied such as male and female; blind and not blind, educated and uneducated, and so on.
(ii) Manifold classification: In this type of classification, a class is subdivided into more than two sub-classes which may be sub-divided further. An example of this form of classification is as follows. Suppose we classify the population by sex into two classes, males and females, and each of these two classes is further divided into two classes by another attribute, say, smoking i.e., smokers and non-smokers, thus giving us four classes in all.

Quantitative Classification of Data:

In this classification, data are classified on the basis of some characteristics
which can be measured such as height, weight, income, expenditure, production, or sales. The term variable refers to any quantity or attribute whose value varies from one investigation to another. Quantitative variables can be divided into the following two types.
(i) Continuous variable is the one that can take any value within the range of numbers. Thus the height or weight of individuals can be of any value within the limits. In such a case, data are obtained by measurement.
(ii) Discrete (also called discontinuous) variable is the one whose values change by steps or jumps and can not assume a fractional value. The number of children in a family, number of workers (or employees), and number of students in a class are a few examples of discrete variables.

References:

  1. Business Statistics – SC Gupta and Indra Gupta.
  2. Fundamentals of Business Statistics – JK Sharma.

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