When 
and 
have a measurement error component, we wish to write
where 
and 
are assumed to be independently distributed error components. Therefore,
So, the angle 
representing the slope 
is expressed as
In order to remove the dependence of the distribution of 
on 
, we create a new angle which we call 
where
To form an intuitive notion of this transformation, let us suppose that e = 1 and 
. If the change in x, 
is large, the error in the angle will be small. However, if 
is small, the error in the angle will be large. When we weight 
by 
, we remove the dependence of the error on 
. Of course, e is an unobserved variable, but even so, we can remove this bias from 
. The adequacy of this bias correction factor requires the independence of 
and 
.
Thus, we may estimate 
with
the standard regression equation.