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Difference between correlation and regression

Math is not as horrifying a subject as many of the students feel. One can excel in math by grabbing good concepts of it. Difference between correlation and regression The better practice of math questions and optimum knowledge of the math terminologies and concepts help you relish the fruit. One way of grabbing the concept of math terminologies is to make a comparison of these. 

Correlation VS Regression

Correlation is the most commonly used term for math and stats. Its uses for linear relationships and reflect the extent and nature of such relationships. It is a source of defining the linear relationship with ease. However, regression is the term that uses for prediction purposes. It is a vital tool for predicting the dependent variable. Regression is a way to determine the impact on the dependent variable when any change is made in the independent variable.

Data Representation

The data representation for the regression and correlation is different. Correlation ensures the presentation of the data in the form of a single point. However, in the case of regression, the situation differs. Regression deals with the data and variables and represents it in terms of line.


X and y are the variables that are not possible to interchange for the regression. In the case of regression, the variables remain the same, and there is no modification to them. However, for the correlation, the variables can interchange when required. It does not cause any interruption or hassle to change one variable into the other. 

Scale and Origin

The change of scale and origin does not cause any impact on the coefficient correlation. Similarly, the change of origin also does not affect the regression coefficient at all. But, the change in scale leads to significant changes in the regression coefficient. The range of -1 and d1 has the coefficient relation lying anywhere in between. However, the value is found to be great than one for one of the regression coefficients. 

Variable Value: Difference between correlation and regression

When the functional relationships use then it is possible to determine the value of other variables. If the value for one variable is papular, then it is possible to determine the value for another variable. It can occur only in the case of regression. While for the correlation, the value of other variables cannot determine even if the value of one variable is papular.

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In a Nutshell

The formula that is used for the calculation of regression is Y= a + bX. Here, x is papular as the independent variable, while Y is the dependent variable. However, a is representing the y-intercept while b is papular to the slope. One must know the values for x and y coefficients to determine the correlation. One can determine the value for the regression and correlation from the online calculators. These help to calculate the values for regression and correlation. Separate the values for x and y in the online calculator with the assistance of commas. 



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