deletions | additions       diff --git a/section_Statistical_Analysis_label_distance__.tex b/section_Statistical_Analysis_label_distance__.tex  index 3815d3a..e646712 100644  --- a/section_Statistical_Analysis_label_distance__.tex  +++ b/section_Statistical_Analysis_label_distance__.tex 
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\section{Statistical Analysis}\label{distance}  RB \& SO (e.g. implications for observations)  We use pseudo-distance metrics to efficiently study differences between all synthetic observations. As described in (sec. 2/tab. 1), a pseudo distance is a single value that encapsulates the degree of difference between two metrics, and they can be used to compare the statistical outputs of various spectral cubes.  Section 3 identifies qualitative differences between a run with strong feedback and one that is purely turbulent. Expanding upon this, we now quantify all simulation differences and determine the sensitivities of the statistics to stellar mass-loss rates, magnetic field strength, and evolutionary time. This allows us to check if the previously identified features actually pinpoint signatures of feedback, rather than specifically corresponding to any combination of simulation parameters. For each statistic, we produce a color-plot showing distances between all simulation pairs. Figures ???, ???, and ??? display all of the color-plots. Here, we provide a brief method for analyzing one. Each colored square represents the distance between one simulation pair, denoted by the horizontal and vertical indices. The colorbar denotes the distance values, whose range depends on the statistic.For a complete description of the distance metrics, we refer the reader to Koch et al. (2016).  We arrange the simulations in order to easily compare strong wind models (W1) with weaker wind models (W2) or purely-turbulent models (T). Table ??? provides a summary of our findings, which we discuss in\S\ref{Intensity Statistics}, \S\ref{conclude}  sec 4.1, 4.2, and 4.3. %For a complete description of the distance metrics, we refer the reader to Koch et al. (2016).