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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 are our clearest examples of this, as they capture a sensitivity hierarchy among pairings. These statistics explicitly yield the largest distances between pairs of W1 and T, followed by pairs of W1 and W2. 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 between involving W1, but also structured in comparisons between weaker wind models. We find the structure to be correlated to changes in magnetic field strength. This does not happen in the PCA and SCF, since their distances not involving W1 are quite small. We Here, we  only note blurring among the strong wind models. models, and find weak sensitivities to magnetic field strength.  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. strength (?).  Considering these various degrees of sensitivities, we find the PCAand SCF  to be strong a candidate for constraining feedback signatures. As seen in figure (PCA-cov_matrix), this statistic outputs sharp, distinct features for a strong wind model, and its color-plot only notes strong sensitivities for changes in stellar-mass loss rates. The other intensity statistics either output less-distinct features, or show other sensitivities that may influence their outputs. Because of this, we recommend using these statistics to support what is found with the PCA.  %Although many of  themost useful  intensity statistics for identifying features indicative produce color-plots showing a sensitivity to feedback, a few  of stellar feedback...(conclusion or here?) them yield significant differences in their graphical outputs. After applying our strong wind model (W1) to a purely turbulent one (T2t0), the behaviors in the Skewness, Kurtosis, and SCF do change, but their shapes, end behaviors, and/or slopes remain quite similar.  %so:  %Stellar mass-loss rates