Ordinary least squares regression is a special (and the most common) kind of ordinary linear regression. It is based on the least squares method of finding regression parameters.
Technically, the aim of ordinary least squares regression is to find out those values [^a] and [^b] of parameters a (“intercept”) and b (“slope”) in the simple linear regression model
| yi = a + b xi; i = 1,…,N |
that minimize the sum of squares of residuals:
| N � i=1 | (yi – | ^ a | – | ^ b | xi)2 |