Difference between Correlation and Regression

Both correlation and regression can be said as the tools used in statistics that actually deals through two or more than two variables. Even though both identify with the same topic, there exist contrasts between these two methods. The main difference is correlation finds out the degree while regression explains the relationship.  Other differences between these methods are given below.

Correlation

Correlation regularly alludes to the degree to which two variables contain a relationship among one another. A positive correlation demonstrates the degree to which the variables decrease or increase in parallel whereas a negative correlation shows the degree where one variable increases when other decreases.

Regression

Regression is measure in statistics that endeavors to decide the strength of relationship exist between one dependent variable and a progression of other explanatory variables.

Correlation Vs Regression

Some contrasts related to these terms are given below

  • Definition:

Connection is tool used in statistics which decides relationship or co-relationship of any two variables.

Regression portrays how an explanatory variable is related with dependent variable numerically.

  • Use:

Correlation is utilized to speak to direct relationship exist between two variables.

Regression is utilized to fit well in line and measures one variable on premise of other variable.

  • Variation:

In correlation, there exists no distinction amongst explanatory and dependent variable that shows correlation amongst x and y is as like as y and x.

In regression, there exists the distinction amongst explanatory and dependent variable that shows the regression of y on x is not quite the same as x on y.

  • Demonstrates:

Correlation coefficient demonstrates the degree to which the variables proceed together.

Regression shows the effect of a unit change in explanatory variable (x) on the dependent variable (y).

  • Intention:

Correlation intended to locate a value that shows the relationship exists between the variables.

Regression aims to gauge estimations of dependent variable on the premise of values of independent variables.

  • Line:

Correlation never fit in a line which passes through the points of data

Regression finds the finest line which estimates the behavior of Y from X.

  • Data:

Correlation is quite often utilized when you determine both variables. It once in a while is proper when one variable is something you tentatively control.

Regression is normally utilized when X is a variable you control like time, focus, and so on.

  • Association among results:

Correlation figures the estimation of the “Pearson correlation coefficient”, that is r. the range of r is -1 to +1.

Regression evaluates goodness of fit through r2, now and then appeared in capitalized as R2.

  • Independent:

Correlation coefficient is free of decision of starting point and scale.

Regression is not independent in choosing the scale and origin.

Conclusion

Through the above mentioned explanation, it is apparent, that there is a major distinction between these tools, in spite of the fact that these two are concentrated together. Correlation is utilized when specialist needs to realize the relation between dependent and independent variables and its strength.  In regression, functional relationship is set up between two variables in order to make future prediction on events. The coefficient of correlation is r while of regression is r2 or R2.