deletions | additions       diff --git a/subsection_Intensity_Statistics_We_show__.tex b/subsection_Intensity_Statistics_We_show__.tex  index 43a62a2..e434f44 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 TXt0.   The sensitivity hierarchy/trend with wind strength is less clear for the PDF, SCF, and PCA. For the PDF, we We  find that time evolution (randomly) randomly  impacts the magnitude of the these statistics'  strong wind distances, while distances. Weaker wind comparisons among the PDF appear to be correlated to magnetic field strength, given their structure. This does not occur in the PCA and SCF, since their distances for W2are quite small. Thus, they are only weakly sensitive to  magnetic field strengths produce a structure strength.  Weaker %Weaker  wind comparisons appear correlated to changes in magnetic field strength, given their distance trends. this %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 %and  the structure to be correlated with changes in magnetic field strengths. The correlations between weak wind models does not occur in the PCA and SCF, since their distances for W2 are quite small. Thus, they are weakly sensitive to magnetic field strength. Here, we only note blurring in our strong wind models. 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?  The Cramer Statistic is by definition 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\% of the integrated-intensity values. The statistic exhibits a behavior different from that of the other intensity statistics. As figure ??? shows, the Cramer statistic displays very large distances between purely turbulent runs and runs with any degree of feedback. Wind strength appears less important to the statistic than wind presence does, which indicates a binary sensitivity to stellar-mass loss rates. The Cramer statistic is also sensitive to magnetic field strength, because of the different distances between the purely turbulent models.  Considering these the  various degrees of sensitivities, sensitivity,  we find the PCA to be strong a candidate for constraining feedback signatures. Asseen in  Figure 3, 3 shows,  this statistic outputs displays  sharp, distinct features for a strong wind model, and its color-plot only notes shows  strong sensitivities sensitivity  to changes in stellar-mass loss rates. rate.  The other intensity statistics either output exhibit  less-distinct features, or show other sensitivities that may influence them. react to multiple physical changes.  %(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 in concert  with the PCA. Out of all of these secondary Of the remaining intensity  statistics, the SCF appears to be is  the strongest second most promising  candidate, as its color-plot behaves quite similar similarly  to the PCA.