In this work, the distributional properties of the goodness-of-fit term in
likelihood-based information criteria are explored. These properties are then
leveraged to construct a novel goodness-of-fit test for normal linear
regression models that relies on a non-parametric bootstrap. Several simulation
studies are performed to investigate the properties and efficacy of the
developed procedure, with these studies demonstrating that the bootstrap test
offers distinct advantages as compared to other methods of assessing the
goodness-of-fit of a normal linear regression model