# Simple Linear Regression

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:

 Yi = A Xi + B + Ei,     i=1,Ã‚Â¼,N

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.

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