What is the importance of Correlation?

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What is the importance of Correlation?

1. Most of the variables show some kind of relationship. For instance, there is relationship between price and supply, income and expenditure etc. With the help of correlation analysis we can measure in one figure the degree of relationship.

2. Once we know that two variables are closely related, we can estimate the value of one variable given the value of another. This is known with the help of regression.

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3. Correlation analysis contributes to the understanding of economic behavior, aids in locating the critically important variables on which others depend.

4. Progressive development in the methods of science and philosophy has been characterized by increase in the knowledge of relationship. In nature also one finds multiplicity of interrelated forces.

5. The effect of correlation is to reduce the range of uncertainty. The prediction based on correlation analysis is likely to be more variable and near to reality.

Correlation

X :

5

10

15

20

25

30

Y :

10

13

18

17

21

29

Thus, from the above example it is clear that the ratio of change between two variables is not same. Now, if we plot all these variables on a graph, they would not fall on a straight line.

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C. Number of Variables

According to the number of variables, correlation is said to be of the following three types viz;

(i) Simple Correlation.

(ii) Partial Correlation.

(iii) Multiple Correlations.

(i) Simple Correlation:

In simple correlation, we study the relationship between two variables. Of these two variables one is principal and the other is secondary? For instance’, income and expenditure, price_ and demand etc. Here income and price are principal variables while expenditure and demand are secondary variables.

(ii) Partial Correlation:

If in a given problem, more than two variables are involved and of these variables we study the relationship between only two variables keeping the other variables constant, correlation is said to be partial. It is so because the effect of other variables is assumed” to be constant

(iii) Multiple Correlations:

Under multiple correlations, the relationship between two and more variables is studied jointly. For instance, relationship between rainfall, use of fertilizer, manure on per hectare productivity of maize crop.

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