**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.

See also: Line of regression, Regression analysis