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Correlation Analysis

Definition: The Correlation Analysis is the statistical tool used to study the closeness of the relationship between two or more variables. The variables are said to be correlated when the movement of one variable  is accompanied by the movement of another variable.

The correlation analysis is used when the researcher wants to determine the possible association between the variables and to begin with; the following steps are to be followed:

  • Determining whether the relation exists and then measuring it (The measure of correlation is called as the Coefficient of Correlation).
  • Testing its significance
  • Establishing the cause-and-effect relation, if any.

In the correlation analysis, there are two types of variables- Dependent and Independent. The purpose of such analysis is to find out if any change in the independent variable results in the change in the dependent variable or not. Now the question arises that what is the need to study the correlation? The study of correlation is very useful in the practical life due to the following reasons:

  1. Several variables show some kind of relationship, such as income and expenditure, demand and sales, etc. and hence, with the help of correlation analysis the degree of relationship between these variables can be measured in one figure.
  2. Once the closeness of variables is determined, we can estimate the value of unknown variable provided the value of another variable is given. This can be done using the regression analysis.
  3. The correlation analysis helps the manufacturing firm in estimating the price, cost, sales of its product on the basis of the other variables that are functionally related to it.
  4. It contributes towards the economic behavior as it helps an economist in identifying the critically important variables on which several other economic variables depend on.

The correlation analysis is the most widely used method and is often the most abused statistical measures. This is because the researcher may overlook the fact that the correlation only measures the strength of linear relationships and does not necessarily imply a relationship between the variables.

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