Simple linear regression

Simple Linear Regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X.
The scenario where we have one independent variable is called simple linear regression.
Machine learning, more specifically the field of predictive modelling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible. In applied machine learning we will borrow, reuse and steal algorithms from many different fields, including statistics.
Actually, linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning.
Linear regression has many practical use:
If the goal is prediction, or forecasting, or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y.
Linear Regression equation:

Linear regression is a linear model
y = dependent variable
x1 or x = independent variable [x because simple linear regression is a case of one independent variable]