Usually we can make a line of best fit by the judgment of eyes which may vary from person to person. But a more accurate way of finding the line of best fit for a particular data is to make use of the least square method to determine the line of best fit.

In this method we first calculate the mean of all the x- values and the mean of all the y- values separately. Let the means be denoted by X and Y. Then we use the formula below to find the slope of the line of best fit.

$m$ = $\frac{[\sum_{(i = 1)}^{n}\ {(x_{i}\ –\ X)\ (y_{i}\ –\ Y)}]}{[\sum_{(i = 1)}^{n}\ {(x_{i}\ –\ X)^{2}}]}$

Next we determine the y- intercept of the line of best fit using the formula given below.

$b\ =\ Y\ –\ m\ X$

Now we make use of the slope and the y- intercept to find the equation of the required line of best fit using the formula $y\ =\ mx\ +\ b$.

This is a more accurate method and does not change the equation of the line of best fit which does when we do it be judgment. With this method we can have a common line of best fit for a given set of data values.