The strength of the relationship of two measurable variables is evaluated through a statistical method called the correlation analysis. The strength of a variable is directly proportional to its correlation, meaning that the stronger the relationship between two variables, the higher the correlation. Therefore, if one is to draw a graph of the correlation between two variables, it would be that of a straight line. The value of coefficients for correlation lies between negative 1 and positive 1. With positive 1 stipulating the perfect positive linear relation between two variables whereas, the coefficient, negative 1 stipulates the opposite. The coefficient 0 indicates that two variables, the dependent and independent variables, have no linear relationship.
Regression analysis entails the identification of the relation of one or numerous independent variables to the dependent variables. The variable outcome is called the response variable, whereas the element at risk or rather cofounders are called predictors. In regression, there is a difference between the dependent and the independent variables. It helps in the estimation of the value of a given variable concerning another. Regression uses an estimation of the perfect straight line in summary of the association of the two variables. Both correlation and regression analysis are similarly based on how they both share out in their relationship with variables.
In the case of two variables, X and Y, regression brings to light how X can cause changes to Y and how interchanging X and Y could change the analysis results. However, in correlation, two variables are irreplaceable. Whereas in regression, an equation is produced, correlation is simply an exclusive statistic. The establishment of the cause of the relationship or even effect can neither be interpreted by the correlation nor the regression analysis. However, they can establish to what extent or how exactly each of the variables is associated.