Correlation indicates only the nature and extent of linear relationship.
Regression is the study about the impact of the independent variable on the dependent variable. It is used for predictions.

**DIFFERENCE BETWEEN CORRELATION AND REGRESSION**

1. It indicates only the nature and extent of linear relationship

2. If the linear correlation is coefficient is positive / negative
, then the two variables are positively / or negatively correlated

3. One of the variables can be taken as x and the other one can be
taken as the variable y.

4. It is symmetric in x and y,

ie., r_{XY }= r_{YX}

1. It is the study about the impact of the independent variable on
the dependent variable. It is used for predictions.

2. The regression coefficient is positive, then for every unit
increase in x, the corresponding average increase in y is b_{YX}.
Similarly, if the regression coefficient is negative , then for every unit
increase in x, the corresponding average decrease in y is b_{YX}.

3. Care must be taken for the choice of independent variable and
dependent variable. We can not assign arbitrarily x as independent variable and *y* as dependent variable.

4. It is not symmetric in *x*
and *y*, that is, b_{XY} and b_{YX} have
different meaning and interpretations.

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

12th Statistics : Chapter 5 : Regression Analysis : Difference Between Correlation and Regression |

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