Page 209 - DECO504_STATISTICAL_METHODS_IN_ECONOMICS_ENGLISH

Unit 14: Correlation Analysis Vs. Regression Analysis

However, it should be noted that coefficient of correlation is one of the most widely used and Notes
also one of the most widely abused of statistical measures. It is abused in the sense that one
sometimes overlooks the fact that r measures nothing but the strength of linear relationships
and that it does not necessarily imply a cause-effect relationship.
4. Progressive development in the methods of science and philosophy has been characterised by
increase in the knowledge of relationships or correlations. Nature has been found to be a
multiplicity of interrelated forces.

Correlation and Causation
Correlation analysis helps in determining the degree of relationship between two or more variables—
it does not tell us anything about cause and effect relationship. Even a high degree of correlation does
not necessarily mean that a relationship of cause and effect exists between the variables or, simply
stated, correlation does not necessarily imply causation of functional relationship though the existence
of causation always implies correlation. By itself it establishes only covariation. The explanation of a
significant degree of correlation may be due to any one or a combination of the following reasons:
1. The correction may be due to pure chance, especially in a small sample: We may get a high
degree of correlation between two variables in a sample but in the universe there may not be
any relationship between the variables at all. This is especially so in case of small samples. Such
a correlation may arise either because of pure random sampling variation or because of the bias
of the investigator in selecting the sample. The following example shall illustrate the point:
Income Weight
(Rs.) (lb.)

10,000 120
20,000 140
30,000 160
40,000 180
50,000 200

The above data show a perfect positive relationship between income and weight, i.e., as the income
is increasing the weight is increasing and the ratio of change between two variables is same.
2. Both the correlated variables may be influenced by one or more other variables: It is just
possible that a high degree of correlation between variables may be due to the same causes
affecting each variable or different causes affecting each with the same effect. For example, a
high degree of correlation between the yield per acre of rice and tea may be due to the fact that
both are related to the amount of rainfall. But none of the two variables is the cause of the other.
3. Both the variables may be mutually influencing each other so that neither can be designated
as cause and the other the effect: There may be a high degree of correlation between the variables
but it may be difficult to pin point as to which is the cause and which is the effect. This is
especially likely to be so in case of economic variables. For example, such variables as demand
and supply, price and production, etc., mutually interact. To take a specific case, it is a well
known principle of economics that as the price of a commodity increases its demand goes
down and so price is the cause, and demand the effect. But it is also possible that increased
demand of a commodity due to growth of population or other reasons may force its price up.
Now the cause is the increased demand, the effect the price. Thus at times it may become
difficult to explain from the two correlated variables which is the cause and which is the effect
because both may be reacting on each other.
The above points clearly bring out the fact that correlation does not manifest causation of functional
relationship. By itself, it establishes only covariation. Correlation observed between variables that
could not conceivably be causally related are called spurious or non-sense correlation. More appropriately
we should remember that it is the interpretation of the degree of correlation that is spurious, not the

LOVELY PROFESSIONAL UNIVERSITY 203