Definition: The Regression Analysis is a statistical tool used to determine the probable change in one variable for the given amount of change in another. This means, the value of the unknown variable can be estimated from the known value of another variable.
The degree to which the variables are correlated to each other depends on the Regression Line. The regression line is a single line that best fits the data, i.e. all the points plotted are connected via a line in the manner that the distance from the line to the points is the smallest.
The regression also tells about the relationship between the two or more variables, then what is the difference between regression and correlation? Well, there are two important points of differences between Correlation and Regression. These are:
- The Correlation Coefficient measures the “degree of relationship” between variables, say X and Y whereas the Regression Analysis studies the “nature of relationship” between the variables.
- Correlation coefficient does not clearly indicate the cause-and-effect relationship between the variables, i.e. it cannot be said with certainty that one variable is the cause, and the other is the effect. Whereas, the Regression Analysis clearly indicates the cause-and-effect relationship between the variables.
The regression analysis is widely used in all the scientific disciplines. In economics, it plays a significant role in measuring or estimating the relationship among the economic variables. For example, the two variables – price (X) and demand (Y) are closely related to each other, so we can find out the probable value of X from the given value of Y and similarly the probable value of Y can be found out from the given value of X.