Identify any five differences between correlation and regression analysis.
- Correlation measures the degree of relationship between two variables while regression analysis is about the effect of one variable on another.
- Correlation indicates interrelation between two variables and does not show causality, while regression is based on causality.
- In terms of graphical representation, linear regression analysis is best fitting line through the data points while correlation is a single point.
- In correlation, there is no significant difference between the dependent and independent variables while in regression, there is difference between the two
- Correlation shows strength of association between the independent variable and dependent variable while regression shows the impact of a change in the independent variable on the dependent variable.
Correlation is a measure of co-relationship between two variables while regression is a statistical measure of how an independent variable is numerically related to dependent variable. In correlation, there is no significant difference between the dependent and independent variables while in regression, there is difference between the two. This implies that correlation between X and Y is the same as Y and X, while regression of Y on X, is different from regression of X on Y. In determining the effect of a change in independent variable Y on the dependent variable X, regression analysis shows how much is the impact. On the other hand, a correlation would show by how much is the independent variable Y related/co-related to the dependent variable X.