deletions | additions       diff --git a/subsubsection_Higher_Order_Statistical_Moments__.tex b/subsubsection_Higher_Order_Statistical_Moments__.tex  index 0751993..f8b54dd 100644  --- a/subsubsection_Higher_Order_Statistical_Moments__.tex  +++ b/subsubsection_Higher_Order_Statistical_Moments__.tex 
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\subsubsection{Higher Order Statistical Moments}  We follow the analysis of Koch et al. (2015) to compute PDFs of kurtosis and skewness. For all positions in an integrated intensity map, we compute each higher-order moment within a small, confined circular region. Similar to Koch et al. (2015), we choose a circular radius of 5 pixels. The kurtosis and skewness PDFs are histograms of the moment arrays, arrays (integrated intensity maps),  and are shown in figure ??? (necessary have this elaboration? Diffucult to word without directly plagiarizing--this is what I have so far). Figure 2.  Our Kurtosis PDFs also exhibit similar behavior. They are each centered at zero, and sharply decrease with increasing kurtosis magnitude. Run W2T2t0's distribution "falls off" at a faster rateand (is more probable to have a zero Kurtosis--need this?)  than that(those)  of run W1T2t0.2's. W1T2t0.2.  The Skewness PDFs have similar shapes, but run W1T2t0.2's distribution is centered around a positive skewness, while run W2T2t0's distribution is centered around zero. (Thus,) On small scales, the integrated intensity distributions of our two runs behave similar to Gaussian distributions, though Thus,  run W1T2t0.2 has a greater tendency appears  to diverge from this behavior than that of Gaussian behaviors observed in run  W2T2t0.(Despite this difference, we find it difficult to identify clear, distinct features corresponding to feedback)