With the correlation coefficient, the variables X and Y are interchangeable. The slope of the line is b, and a is the intercept (the value of y when x = 0). The linear model yields an R 2 of 53.2% thereby meaningfully outperforming the standard medical model. So, this regression technique finds out a linear relationship between x (input) and y (output). Linear regression is one of the most commonly used predictive modelling techniques.It is represented by an equation = + +, where a is the intercept, b is the slope of the line and e. Scatter plots are a great way to see data visually. Let's now input the values in the formula to arrive at the figure. Formula =FORECAST.LINEAR (x, known_y's, known_x's) The FORECAST.LINEAR function uses the following arguments: X (required argument) - This is a numeric x-value for which we want to forecast a new y-value. By following the steps in this tutorial, you can implement Linear Regression on a valid dataset and make estimations on future values. Typically, for linear regression, it is written as: Ordinary least squares Linear Regression.