Linear Regression Equation Explained. So our y intercept is literally just 2 minus 1. Each point of data is of the the form x y and each point of the line of best fit using least squares linear regression has the form x y x y.
Each point of data is of the the form x y and each point of the line of best fit using least squares linear regression has the form x y x y. So it equals 1. As the simple linear regression equation explains a correlation between 2 variables one independent and one dependent variable it is a basis for many analyses and predictions.
Apart from business and data driven marketing lr is used in many other areas such as analyzing data sets in statistics biology or machine learning projects and etc.
To perform a simple linear regression analysis and check the results you need to run two lines of code. R square also known as the coefficient of determination is a commonly used statistic to evaluate the model. These just are the reciprocal of each other so they cancel out. It is the value of y obtained using the regression line.
