Glossary

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:

Y_i = A X_i + B + E_i, i=1,Ã‚Â¼,N

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.

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