Most times, the relationship between variables goes a long way in helping to understand the event or situation at hand. In the modern business world, we tend to investigate the relationship between variables or events to further understand how to deal with situations or make decisions. In explaining the relationship between variables, knowing the direction and strength of the direction helps in analysing the events which is where the concept of correlation comes into play.
Correlation denotes the association between two random variables. It helps to determine the direction of the relationship between two variables. This means if the variables have a direct or positive and an indirect or negative relationship. A direct relationship means both variables are going in the same direction while an indirect relationship means both variables are going in opposite or inverse directions. This concept also goes further to inform us of the strength of this relationship between the variables which will help to complement the direction relationship earlier established. Since the relationship between the variables ranges from a negative relationship to a positive relationship it is important to note that the correlation coefficient that explains the direction and strength of the relationship ranges between -1 and +1. By implication, an extreme case will be a perfectly strong negative relationship while the other extreme will be a perfectly strong positive relationship.
It is however worth noting that correlation does not mean causation. This simply means that defining the direction of the relationship and strength of the same of the variables does not imply that one variable has a causative effect on the other, that is, both variables cannot explain one another. Hence, we need to further analyse the variable to understand the causative effect between them which is where regression analysis sets in.
Regression analysis goes beyond identifying the relationship between variables, as it tends to establish the causative factor between a variable and one or more variables. It simply makes use of past data to develop a model that shows relationships and can predict or forecast the future concerning the variables in the model. Since the regression model establishes causation, it makes a distinction between the dependent and the independent variable. An independent variable is a variable that is changed or controlled to test the effect on the dependent variable, It is also known as the explanatory variable while the dependent variable is the variable that changes as a result of manipulation of the independent variable, the dependent variable is also called the response or explained variable.
The concept of correlation and regression analysis is a very useful tool for forecasting and predicting the behaviour of variables. Other real-life applications of regression analysis include but are not limited to financial forecasting, sales and promotion forecasting, testing automobiles weather analysis and prediction and also time series forecasting. It is important to understand when to use correlation and regression analysis as the application of the former is quite limited when compared to the scope of the latter.
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