It is used to identify relationships between two or more variables. Identification of relationships further helps to depict trends, and patterns in the sale of certain products, and much more.
What does correlation measure?
Correlation studies and measures the direction and intensity of relationships among variables. Correlation measures covariation, not causation.
Types of Correlation
Scatter Diagram In this technique, the values of the two variables are plotted as points on graph paper. In a scatter diagram, the degree of closeness of the scatter points and their overall direction enables us to examine the relationship. If all the points lie on a line, the correlation is perfect and is said to be in unity. If the scatter points are widely dispersed around the line, the correlation is low. The correlation is said to be linear if the scatter points lie near a line or on a line.
Karl Pearson’s Coefficient of Correlation
This is also known as the productmoment correlation coefficient or simple correlation coefficient. Karl Pearson’s coefficient of correlation should be used only when there is a linear relation between the variables. It tells us the direction and intensity of the relation between the variables
Step deviation method to calculate correlation coefficient
When the values of the variables are large, the burden of calculation can be considerably reduced by using a property of r. It is that r is independent of change in origin and scale. It is also known as the step deviation method. It involves the transformation of variables.Suppose, two variables are X and Y. If U= (XA)/B, V=(YC)/D, then r_{uv}= r_{xy}
It is used in the following situations:
The Spearman’s rank correlation formula is r_{a} = 1 6ΣD^{2} / n^{2}  n where n is the number of observations and D is the deviation of ranks assigned to a variable from those assigned to the other variable.
The calculation of rank correlation will be illustrated under three situations:
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