  Given data $y$ and $k$ covariates $x$ the problem is to decide which
covariates to include when approximating $y$ by a linear function of the
covariates. The decision is based on replacing subsets of the covariates by
i.i.d. normal random variables and comparing the error with that obtained by
retaining the subsets. If the two errors are not significantly different for a
particular subset it is concluded that the covariates in this subset are no
better than random noise and they are not included in the linear approximation
to $y$.
