Basically linear regression is used in several algorithms to find out a bunch of things. that can be height and weight or length and price. which means that linear regression can be implemented in different different case and algorithm will be different also.

We often wish to look at the relationship between two things (e.g. between A person's height and weight) by comparing data for each of these things. A good way of doing this is by drawing a scatter diagram."Regression" is the PROCESS of finding the function satisfied by the points on the scatter diagram. Of course, the points might not fit the function exactly but the aim is to get as close as possible. "Linear" means that the function we are looking for is a straight line (so our function f will be of the form f(x) = mx + c for constants m and c).

Here, 'd' is the distance from the line to the data points. the smallest the distance is the accurate the line will be and the value of the error will be less.

If I draw a red line and suppose that the liner model here and the value of 'D' will be Higher in this case.

Least Squares Regression Lines: This is a method of finding a regression line without estimating where the line should go by eye .If the equation of the regression line is y = ax + b, we need to find what a and b are.

->Regression Lines can be found by least square method or 3 median method.