Linear models are among the most common statistical tools in forestry,
and indeed in all science. It is widely known that when linear models
are applied to attributes of size (e.g., individual tree volumes), th
e conditional variance may be heterogeneous. Under these circumstances
, the usual least squares estimator remains consistent, but is no long
er efficient. This has been appreciated in forestry for some time, and
various solutions have been recommended over the years. In this paper
, we propose a Bayesian solution. Bayesians would prefer this method t
o previous solutions for many reasons. However, even non-Bayesians may
wish to consider the method as it yields (as will be shown) solutions
quite close to the maximum likelihood solution, along with the margin
al posterior distribution of each parameter.