deletions | additions       diff --git a/section_Statistical_Analysis_label_discuss__.tex b/section_Statistical_Analysis_label_discuss__.tex  index 3ecf61d..1eb492d 100644  --- a/section_Statistical_Analysis_label_discuss__.tex  +++ b/section_Statistical_Analysis_label_discuss__.tex 
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\section{Statistical Analysis}\label{discuss}  RB \& SO (e.g. implications for observations)  We use pseudo-distance metrics to effectively analyze differences between all synthetic observations. For each statistic, we produce a color-plot table denoting distance metric values for all simulation pairs. Section 3 identifies qualitative differences corresponding to stellar feedback; expanding upon this, we now quantify all simulation differences, and determine the sensitivities of the previously mentioned statistics (reword--Cramer wasn't mentioned, as it has no graphical output).  Figure   determining sensitivities towards our paramaters   --three categories  [Here or elsewhere?] Unlike the study carried out in Koch et al.~(2015), our simulation suite does not utilize experimental design to set the simulation parameter values. As discussed in \citet{Yeremi_2014}, comparisons between outputs in one-factor-at-a-time approaches may give a misleading signal since the statistical effects are not fully calibrated. However, we will focus our discussion on those statistics deemed by Koch et al. (2015) to be ``good", i.e., those which exhibit a response to changes in underlying physical parameters rather than to statistical fluctuations in the data.  (still have to bring color plots closer together--also have to redo PDF distance. Uploading incomplete plots now to produce rough outline of discussion)