in the simple linear regression equation let b

Now, if we have a number of data points now, how to draw the line that is as close as possible to each data point. In the case of two data points, it’s easy to draw a line just join them. We want to find the best regression to draw a line that is as close to every dot as possible.

How we do Regression?Ĭalculating a regression with only two data points: Son’s height regress (drift toward) the mean height.

The average population height is 1.76 meters. His sons Shaqir and Shareef O’neal are 1.96 meters and 2.06 meters tall, respectively. Shaq O’Neal is a very famous NBA player and is 2.16 meters tall. This phenomenon is nothing but regression. He observed a pattern: Either the son’s height would be as tall as his father’s height, or the son’s height would be closer to all people’s overall avg height. He studied the relationship in height between fathers and their sons.

It all started in 1800 with Francis Galton. Regression is used for predicting continuous values. Simple linear regression belongs to the family of Supervised Learning. Simple Linear Regression is one of the machine learning algorithms. Simple Linear Regression is a type of linear regression where we have only one independent variable to predict the dependent variable. The regression, in which the relationship between the input variable (independent variable) and target variable (dependent variable) is considered linear, is called Linear regression. In Statistics: A measure of the relation between the mean value of one variable and corresponding values of the other variables. Hadoop, Data Science, Statistics & others

In the simple linear regression equation let b