Page 9 - DMGT209_QUANTITATIVE_TECHNIQUES_II

Quantitative Techniques-II

Notes As an illustration, let us suppose that we are interested in knowing the income level of the
people living in a certain city. For this we may adopt the following procedures:
(i) Data collection: The following data is required for the given purpose:
(a) Population of the city

(b) Number of individuals who are getting income
(c) Daily income of each earning individual
(ii) Organise (or condense) the data: The data so obtained should now be organised in
different income groups. This will reduce the bulk of the data.
(iii) Presentation: The organised data may now be presented by means of various types of
graphs or other visual aids. Data presented in an orderly manner facilitates statistical
analysis.
(iv) Analysis: On the basis of systematic presentation (tabular form or graphical form),
determine the average income of an individual and extent of disparities that exist.
This information will help to get an understanding of the phenomenon (i.e. income
of ‘individuals).
(v) Interpretation: All the above steps may now lead to drawing conclusions which will
aid in decision-making-a policy decision for improvement of the existing situation.

Characteristics of Data
It is probably more common to refer to data in quantitative form as statistical data. But not all
numerical data is statistical. In order that numerical description may be called statistics they
must possess the following characteristics:
 They must be aggregate of facts, for example, single unconnected figures cannot be- used
to study the characteristics of the phenomenon.

 They should be affected to a marked extent by multiplicity of causes, for example, in social
services the observations recorded are affected by a number of factors (controllable and
uncontrollable).

 They must be enumerated or estimated according to reasonable standard of accuracy, for
example, in the measurement of height one may measure correct up to 0.01 of a cm; the
quality of the product is estimated by certain tests on small samples drawn from a big lot
of products.

 They must have been collected in a systematic manner for a pre-determined purpose. Facts
collected in a haphazard manner and without a complete awareness of the object, will be
confusing and cannot be made the basis of valid conclusions.

Example: Collected data on price serve no purpose unless one knows whether he wants
to collect data on wholesale or retail prices and what are the relevant commodities in view.
’
 They must be placed in relation to each other. That is, data collected should be comparable;
otherwise these cannot be placed in relation to each other, e.g. statistics on the yield of
crop and quality of soil are related but these yields cannot have any relation with the
statistics on the health of the people.
 They must be numerically expressed. That is, any facts to be called statistics must be
numerically or quantitatively expressed. Qualitative characteristics such as beauty,
intelligence, etc. cannot be included in statistics unless they are quantified.

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