  The "spectral correlation function" analysis we introduce in this paper is a
new tool for analyzing spectral-line data cubes. Our initial tests, carried out
on a suite of observed and simulated data cubes, indicate that the spectral
correlation function [SCF] is likely to be a more discriminating statistic than
other statistical methods normally applied. The SCF is a measure of similarity
between neighboring spectra in the data cube. When the SCF is used to compare a
data cube consisting of spectral-line observations of the ISM with a data cube
derived from MHD simulations of molecular clouds, it can find differences that
are not found by other analyses. The initial results presented here suggest
that the inclusion of self-gravity in numerical simulations is critical for
reproducing the correlation behavior of spectra in star-forming molecular
clouds.
