Page 172 - DMGT404 RESEARCH_METHODOLOGY

Research Methodology

Notes have some additional information apart from the study of correlation. For example if, on
the basis of some additional information, we say that the price of tea affects its demand,
then price will be the cause and quantity will be the effect. The causal variable is
also termed as independent variable while the other variable is termed as dependent
variable.
2. The two variables may act upon each other: Cause and effect relation exists in this case
also but it may be very difficult to find out which of the two variables is independent.

Example: If we have data on price of wheat and its cost of production, the correlation
between them may be very high because higher price of wheat may attract farmers to produce
more wheat and more production of wheat may mean higher cost of production, assuming that
it is an increasing cost industry. Further, the higher cost of production may in turn raise the price
of wheat.
For the purpose of determining a relationship between the two variables in such situations,
we can take any one of them as independent variable.
3. The two variables may be acted upon by the outside influences: In this case we might get a
high value of correlation between the two variables, however, apparently no cause and
effect type relation seems to exist between them.

Example: The demands of the two commodities, say X and Y, may be positively correlated
because the incomes of the consumers are rising. Coefficient of correlation obtained in such a
situation is called a spurious or nonsense correlation.
4. A high value of the correlation coefficient may be obtained due to sheer coincidence (or
pure chance): This is another situation of spurious correlation. Given the data on any two
variables, one may obtain a high value of correlation coefficient when in fact they do not
have any relationship.

Example: A high value of correlation coefficient may be obtained between the size of shoe
and the income of persons of a locality.

9.1.1 Scatter Diagram

Let the bivariate data be denoted by (X , Y ), where i = 1, 2 ...... n. In order to have some idea about
i i
the extent of association between variables X and Y, each pair (X , Y ), i = 1, 2......n, is plotted on
i i
a graph. The diagram, thus obtained, is called a Scatter Diagram.
Each pair of values (X , Y ) is denoted by a point on the graph. The set of such points may cluster
i i
around a straight line or a curve or may not show any tendency of association. Various possible
situations are shown with the help of following diagrams:

Did u know? What the sets of point in generally known?
The sets of points in scatter diagram are known as dots of the diagram

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