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One fundamental puzzle in star formation is why the efficiency at which dense gas forms stars is only a few percent per free fall time \citep[][]{krumholz14review}. Early three-dimensional hydrodynamic simulations discovered that supersonic turbulence decays rapidly and predicted that without additional energy input turbulence should decay significantly within a dynamical time \citep{stone98,maclow99}. This implies that gravity should be able to efficiently form stars after a dynamical time. However, turbulence observed within molecular clouds does not appear to weaken and star formation efficiencies are small after several dynamical times \citep{KandT07} One explanation for the longevity of observed turbulence is that motions are driven internally via feedback from forming or evolved stars \citep[][and references therein]{krumholz14ppvi}. In principle this should introduce a characteristic energy input scale \citep{carroll09,hansen12,Offner_2015}, which should impact turbulent statistics. However, from an observational prospective, stellar feedback is {\it messy} and identifying clear feedback signatures is complex for the reasons mentioned above. %Analysis is typically restricted by the assumption that the velocities and extent along perpendicular directions are s  Disentangling feedback signatures from the turbulent background and assessing their role is challenging since any low-velocity motions excited by feedback are lost in the general cloud turbulence \citep{swift08,arce10,arce11}.   Few prior numerical or observational studies have examined the response of turbulent statistics to stellar feedback. Several studies of the most commonly computed turbulent statistic, the velocity power spectrum, find that it may be sensitive to feedback. In numerical simulations, turbulence shaped by both isolated and clustered outflows exhibits a steepened velocity power spectrum \citep{nakamura07,cunningham09,carroll09}. In observations of NGC1333, \citet{swift08} identified a break in the power spectrum of the $^{13}$CO intensity moment map, which they attribute to a characteristic scale associated with the embedded protostellar outflows (the break is absent in the $^{12}$CO data). \citet{brunt09} reexamine the NGC1333 spectral cubes with principle component analysis but see no direct evidence of outflow driving and conclude that the turbulence instead predominently driven on large scales.  Numerical simulations of point-source (supernovae) driving also discovered changes in the spectral slope but found no obvious critical injection scale \citep{joung06}. Probability distribution functions (PDFs) of densities, intensities or velocities are also commonly computed \citep[e.g.,][]{nordlund99,lombardi06,federrath08}. Both observations and simulations suggest that gravity shapes the distribution at high densities \citep{kainulainen09,collins12}, but the impact of feedback on PDFs is less clear. \citet{beaumont13} showed that observed CO velocity distributions extend to higher velocities than synthetic observations of simulations containing pure large-scale turbulence and gravity; they attribute this difference to expanding shells associated with stellar winds. \citet{Offner15} confirm that when winds are included in simulations a high-velocity tail appears. In contrast, the column density probability distribution does not appear sensitive to the inclusion of stellar feedback \citep{beaumont13}. The impact of feedback on higher order statistics, such as principle component analysis (PCA), the spectral correlation function (SCF), dendrograms, the bispectrum and many others, is even less well explored \citep{rosolowsky99,heyer97,rosolowsky08,burkhart09}. \citet{burkhart10}, in analyzing HI maps of the Small Magellenic Cloud, noted the possible signature of supernovae on the bispectrum, which appears as break around $\sim$160 pc. It seems likely that stellar feedback influences other higher order statistics as well.   %hansen: ocity and ke power spectrum, 2-pt correlation function  %hansen 2-point correlation function - outflows+compare to rho Oph, little dependence on feedback