Simple Linear Regression:
The simple linear regression is aimed at finding the “best-fit” values of two parameters – A and B in the following regression equation:
where Yi, Xi, and Ei are the values of the dependent variable, of the independent variable, and of the random error, respectively. Parameter A is called “the slope of the regression line”, B – “the y-intercept of the regression line”.
The initial data (sample) are N pairs (Xi,Yi). The regression equation describes a line in the X-Y plane, and the data are N points on that plane.
A popular method for finding the “best-fit” values of A and B (i.e. the slope and intercept of the line that best fits the data) is the Least Squares Method.
If the symbols do not display properly, try
the graphic version of this page