Correlation is used to measure the degree of association that is there between two variables. Whenever the researcher is dealing with two variables, the talk is about simple correlation and when the involvement is of more than two variables. On the other hand, regression is used to explain the variations that happen in one variable. This is usually referred to as the changes made in the dependant variable by the independent variable. It helps to identify the nature of the relationship. It is not necessary that the independent variable in regression would be only one. They can be more than one also. When there is only one independent variable, we call it simple regression and when there is more than one variable, we call it multiple regression analysis.
Correlation helps in measuring the degree of association that is there between two or more variables. When the research is dealing with two variables, then the correlation applicable is simple correlation. When the involvement is of more than two variables then the subject matter moves towards multiple correlation. Correlation is largely of three types:
- Positive Correlation: When the two variables X and Y move in the same direction, it is said that the correlation between the two is positive. If one variable increases the other variable also increases and likewise in the situation of one variable decreasing, the other one also decreases.
- Negative Correlation: In the situation when the two variables X and Y move in the opposite direction, it is called as negative correlation. In the case when one variable increases and the other variable decreases or vice versa.
- Zero Correlation: When the correlation between two variables X and Y is completely null or zero. The increase or decrease in Y is not dependent upon an increase or decrease in X.
If the correlation coefficient is equal to 1, the two variables are said to be positively correlated. If the coefficient of correlation is -1 the variables are said to be lying on a negatively sloped straight line
