Correlation analysis is the study of the relationship between two or more than two variables. It is the statistical tool we can use to describe the relationships between two or more than two variables. It is also defined as group of techniques to measure the association between two or more than two variables.
To explain, suppose the sales manager of Square Pharmaceuticals Ltd. wants to determine whether there is a relationship between the medicine sold in a month and advertisement.
Correlation analyses are based on the relationship between two (or more) variables. The known variable (or variables) is called the independent variable(s). The variable we are trying to predict is the dependent variable. Let’s take an example. Bankers might base their predictions of customer satisfaction on the interest rate. Thus, the interest rate is the independent variable and the customer satisfaction is the dependent variable. Bankers, for example, may add a second independent variable, environment, to improve their estimate of the customer satisfaction.
Coefficient of Correlation
The measure of correlation called the coefficient of correlation (denoted by the symbol r) summarizes in the figure the direction and degree of correlation.
Coefficient of Correlation is a measure of the strength of the linear relationship between two variables. It requires interval or ratio-scaled data.
Features of Correlation Coefficient
l It can range from -1 to 1
l Values of -1 or 1 indicate perfect and strong correlation.
l The closer to -1, the stronger the negative linear relationship
l The closer to 1, the stronger the positive linear relationship
l Values close to 0 indicate weak correlation.
l Negative values indicate an inverse relationship and positive values indicate a direct relationship.
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