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 Y_i, X_i, and E_i 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 (X_i,Y_i). 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.