deletions | additions       diff --git a/subsection_Intensity_Statistics_We_show__.tex b/subsection_Intensity_Statistics_We_show__.tex  index b402bc7..4cdd7e5 100644  --- a/subsection_Intensity_Statistics_We_show__.tex  +++ b/subsection_Intensity_Statistics_We_show__.tex 
...
\subsection{Intensity Statistics}  We show the colorplots for all intensity statistics in Figure ???. With the exception of the Cramer statistic, we find that these statistics exhibit the strongest sensitivities to changes in stellar mass-loss rates. As seen in their colorplots, the largest distances (given by the darkest colors) appear when any strong wind model (W1) is compared to either a weak wind model (W2) or a purely turbulent model (T).The Kurtosis and Skewness statistics are our clearest examples, examples of this,  as they capture a sensitivity hierarchy. They yield the largest distances between pairs of W1 and T, followed by pairs of W1 and W2.Sensitivities towards magnetic field strength (T) and time evolution (t) mainly appear in pairings between W2's and T's, but they are not as structured as the sensitivities involving W1.  The sensitivity hierarchy among strong wind models is not seen in the PDF, SCF, and PCA. The different Sensitivities towards  magnetic field strengths strength (T)  and time  evolution times (t) mainly  appear to cause the blurring. For in pairings between W2's and T's, but they are not as structured as  the PDF, sensitivities involving W1.  The individual sensitivities are sensitivity hierarchy among strong wind models is  not as clear seen  in the PDF, SCF SCF,  and PCA.They produce similar large distances between strong wind models and weak ones as well as strong wind models and turbulent ones.  The different magnetic field strengths in our clouds and evolution times  appear to cause this the  blurring. When comparing For the PDF, this blurring is noticeable in comparisons between  weaker wind and purely turbulent models, the PDF exhibits relatively small difference, while models. In fact, some of these corresponding distances are as large as those involving W1. This does not happen in  theSCF and  PCA show even smaller ones. and SCF, since their distances not involving W1 are quite small.  We find less only note  blurring at these smaller comparisons. among the 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 set's (integrated) intensity values. The statistic exhibits a behavior different from that of the other intensity statistics. As seen in its colorplot, we find very large distances between purely turbulent runs and runs with any degree of feedback. This indicates a sensitivity towards magnetic field strength.