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.

Sun Feb 12 18:20:50 EST 1995