Regression
Once a positive correlation between two variables has been established, we can proceed to graph this statistical relationship in the form of a linear equation. This equation is valuable in predicting the value of one variable given the value of its counterpart. The equation is known as a regression line, or line of least-squares.
To construct this line, we must interpret our data in terms of that familiar equation, y=m x+b . This Equation represents the line of least-squares of our sample data. (The true regression line for the Population, the line we are attempting to approximate, is represented by the equation y= Mx+ B.) Let's begin by solving for the slope of the line. Here is the equation for m :
As you can see, many of the values used to solve for r are used to find the slope. Once the slope is calculated there are several methods for calculating the y-intercept.
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