deletions | additions       diff --git a/subsubsection_Principal_Component_Analysis_Principle__.tex b/subsubsection_Principal_Component_Analysis_Principle__.tex  index e0b2ae5..c5ca23c 100644  --- a/subsubsection_Principal_Component_Analysis_Principle__.tex  +++ b/subsubsection_Principal_Component_Analysis_Principle__.tex 
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Because the eigenvectors provide a measure of the strength of different eigenvectors, they also serve as a proxy of the amount of power on different scales \citep{brunt08}. We find a clear difference in the eigenvalues for the cases with and without feedback. %Ask Mark about PCA and feedback -- Brunt et al. 2003 paper analyzed simualtions with point expostions? but no break  (This RB: This  appears to, so far, be the only statistic that I don't give much background on. I feel as if the PCA is more about obtaining the eigenbasis for the covariance matrix than it is about the actual covariance matrix. Here, we only look at the covariance matrix. The distance metrics table uses the eigenvalues, so I'm thinking of mentioning some of the PCA background there. I just find it kind of ironic that on this section appears to have one of the most interesting changes while being one of the shortest sections) -> sections ->SO  We may need to provide plots a plot  of the eigenvalues.