Sir Francis Galton invented the simple linear regression model near the end of the nineteenth century. The example used looked at how a parent's height influences the height of their child. This study used data and laid the basis of regression analysis. The correlation between the height of parents and children is well known, and using data on 928 pairs of height measurements, a linear regression was developed by Galton. In an equivalent form, however, the method might have been in informal use before Galton officially invented it. The simple linear regression model consists of a single input (independent) variable and the output is also a single output.

In this supervised learning method, the target variable/output/dependent variable is a continuous variable, and it can also take values in intervals, including non-negative and real numbers. The input/independent variable has no restrictions and as such it can be numeric, categorical, or in any other form we used earlier...