Correlation and Regression
Regression describes a statistical technique that helps to measure the association between a dependent variable and independent variable (s). Given dependent and independent variables, regression measures the direction and rate of change of one variable to the other. Therefore, through regression, it is possible to predict the value of dependent variable using the independent variables; the predictor and criterion variables help in determining the cause and effect association. On the other hand, correlation measures the closeness of the association between variables. It indicates the degree of association between variables (Jain, 2009).
The two statistical methods are similar in that they measure relationship aspects amid variables. Regression measures the direction of an association between variables while correlation measures the degree of association between variables (Jain, 2009). Besides, the two are similar in that they make use of available variables; without the use of these variables, the statistical methods cannot come up with the relationship aspects between variables.
On the other hand, there are some differences between the two statistical approaches. In regression, there must exist an independent variable and dependent variable (s) while in determining correlation, there is no need of having dependent and independent variables; the analysis does not involve the use of fixed and dependent variables. Correlation does not assume cause and effect association between two variables always. Although variables may high correlation, it does not imply one variable is the effect while the other is the effect. However, regression always expresses cause and effect association amid two variables (Jain, 2009). Besides, regression helps in making prediction while correlation does not aid in making any prediction between variables. Therefore, regression analysis will be preferred to correlation in researches involving cause and effect relationships.
Jain, T.R. (2009). Quantitative Method for MBA. New York: FK Publications.