= AIC deletion threshold in a normal linear model
{c}
{title2=$\operatorname{RSS}_{\mathrm{reduced}}/\operatorname{RSS}_{\mathrm{full}}<e^{2q/n}$}
Deleting $q$ fitted mean parameters improves the <Akaike information criterion> exactly when the displayed condition holds, since the criterion difference is $n\log(\operatorname{RSS}_{\mathrm{reduced}}/\operatorname{RSS}_{\mathrm{full}})-2q$. For one parameter, the equivalent <partial F-test> statistic is below $(n-p)(e^{2/n}-1)$, where $p$ is the full mean-model dimension. This is not the usual fixed-level significance-test cutoff. Fits must use the same observations and response transformation.
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