deletions | additions       diff --git a/subsection_Intensity_Statistics_We_show__.tex b/subsection_Intensity_Statistics_We_show__.tex  index 69fd6dc..7a139bb 100644  --- a/subsection_Intensity_Statistics_We_show__.tex  +++ b/subsection_Intensity_Statistics_We_show__.tex 
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%(should I include: This sort of ordering confirms the notion that a cloud with strong winds is most different from a purely turbulent cloud, followed by a weaker winded one.--Not the best wording but I'm wondering if this sort of blunt statement is necessary, especially since it also slightly addresses time sensitivities.)  And, they capture similar, weaker sensitivities between pairs of W2 and T.   The sensitivity hierarchy with strong wind models is not as clear in the PDF, SCF, and PCA. For the PDF, this blurring is not only noticeable in comparisons involving W1, but also structured in those between weaker wind models. We find the blurring to be associated with time evolution, %(and potentially magnetic field strength),   and the structure to be correlated with changes in magnetic field strengths. The structure among weak wind models does not occur in the PCA and SCF, since their distances not involving W1 are quite small. Thus, they are weakly sensitive towards magnetic field strength. Here, we only note blurring in our strong wind models.  The Cramer Statistic is defined as a distance metric, so it we include its discussion and analysis here. As described in Yeremi et. al (2014), this statistic compares the interpoint differences between two data sets with the point differences between each individual data set. Following K16, we compute the Cramer Statistic using only the top 20(percent) of our data sets' integrated-intensity values. The statistic exhibits a behavior different from that of the other intensity statistics. As seen in its colorplot, we only find very large distances between purely turbulent runs and runs with any degree of feedback. This indicates a sensitivity towards magnetic field strength. %I saw that this was in the conclusion, but I'm not sure how to interpret it. Is this becuase the distances between them and wind models vary with respect to "t"? Is also it not sensitive at all to winds?  Considering these various degrees of sensitivities, we find the PCA to be strong a candidate for constraining feedback signatures. As seen in Figure 3, this statistic outputs sharp, distinct features for a strong wind model, and its color-plot only notes strong sensitivities to changes in stellar-mass loss rates. The other intensity statistics either output less-distinct features, or show other sensitivities that may influence them.   %(may have to address the PCA pairs of W1T1 and W2T2's).   Because of this, we recommend using these statistics to support what is found with the PCA. Out of all of these secondary statistics, the SCF appears to be the strongest candidate, as its color-plot behaves quite similar to the PCA.