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<title>Distribution and Difference Significance of Preservation Methods by Bodysite</title>
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<h1>Distribution and Difference Significance of Preservation Methods by Bodysite</h1>
<p>Data imported from txt tables to R. </p>
<pre><code class="r">feces <- read.csv("~/git/preservation_stats/presv_fig1_data/feces_fig1.csv")
oral <- read.csv("~/git/preservation_stats/presv_fig1_data/oral_fig1.csv")
skin <- read.csv("~/git/preservation_stats/presv_fig1_data/skin_fig1.csv")
</code></pre>
<p>Each Preservation method will be extracted from their respective data tables<br/>
with a parser function for this porpose written in R named 'generate_vector()': </p>
<p>Example:<br/>
from BodySite 'feces'</p>
<pre><code class="r">feces_ultralow = parse_vector(data = feces, line = 1)
</code></pre>
<pre><code>## [1] 18045 6458 2959 12129 7648
</code></pre>
<p>Then analyze through graphic of distribution to determine their<br/>
distribution if any </p>
<pre><code class="r">
# distribution_test() makes a summary of information from the data set and
# tries ploting a normal,uniform and exponential distribution graph from
# this data to fit the histogram of the sample.
# The aim of the graph-fit plot is to find ( if any) a distribution like
# behavior that could fit the data for testing and comparisons.
distribution_test(feces_ultralow)
</code></pre>
<p><img 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WeeyAF9f8O/JvXVQU4NaCqwL4HxsQNpFI30bnpzoD8wovO7Wj/gYgibpBxTsRi3AYcBE2tyveWfOZnFX72dy7Z9tNP7LsGVvMRWNWk3R7a+grkPOYXTPv0x9693D5fFjqeiGj3/o3Zl+VUf45Dnl+bsra/g4VqEJoVxGDCSwLOxA5H605Uh+h+BXZNqNEVk3IfVct2GzZXsje9bxZ3rbojeYF2DJwyWjh1LFWr2/Bv0NRhmcKl5PXxpBMaxGGvEDqORaJFdeU3oLPhyZiAwvXaXC//DD6E5qHbXzL9khfxQYBdggwDPRw4pUwEmBd9NcQUw1tCLfoOYBFTzZl5qRAm+PCX47FwErNwo58YnVeeuB74AtgjwdeSQoglwN7AxsI/ByeZ/d1K/JqHSxplSgi+vD0rwGQkG7AaMBOsdO5o0JXXibwYuCDAs6EAjAnyOT5+9D9xh8PPIIUl6JqIEnykl+PLUg89UeB8frj0kdiRpMfgjcC6wfYA7Y8eTJ8GrHo0A9sXr6zfEaE4DUoLPmBJ8eUrw2RsFrAi2ZOxAaimpIT8SL1qzYYB3I4eUWwFeBf4MnGN+TK3UFyX4jCnBl6d98JkLBuwNDK+XoXqDHwM3AI8HODTA97FjyrsAn+Ln2h9jNam5IDkyES2yy5QSfHlNFKg0aCaMXlDLFfTlhPeAa6iDVfUGCwA3AUcGGB07niIJMB5ffLe3wRax45Ga0Sr6jCnBl6ch+lJ9qVWBm46di6+qXyKDtlJhsAj+RuWvAf4dO54iCjABX7ewtsHBseORmtAQfcaU4MtTgi81M5kk+GDAPsA5RRyqTxaIjca3wI2LHU+RBZiG77CYOymM00iVN+uREnzG8pzg58H3DMegBF+qLz7EloHwLnAdcEA27dWGwQDgAmDLAO/FjqceJCvsDwHeBi42nZ9RZErwGctLgh+LL0gCmA8/TvQ/wH/x87B/mnE82gdfKqMe/A9GAL8FG5Bhm91msARwPt5zV3KvsQDDgPuBa0zzuEWlOfiM5SXBb0DLf/zp+Lv12YFZ8TnM4RnHox58qYwTfJgO7A4MA+uVXbtdZ7Akvs3vL8ELtkgKksWKlwI3GswROx7pMvXgM5bH4a4VgI3wRTYAJwFvZhxDE/Btxm3mXdY9eCC8A3YrsH+27VbPYHHgPGCrAB/EjqfeBbjV/M33DeZvqHT0aHEowWcsLz14gHnxNxwvAwu2un0Avjc2S+rBl5qZzObg2xgOrAcrzBKh7Q4lc+4X4sPy70YOp2EEn9I7Ah+unzV2PFKlwDTy2amsW3lJ8A8DV+O95tWBU5Lb18Brd5+ecTxK8KUyXGTXWpgO7AbDF8nTUL3Bb/AKdZtoWD57AZ4CjgOuM18zIyLt5CXBr4kXBpkdH6LfI7l9EjAIn9/MkhJ8qQhD9M3CO3DPeGBInPbbMlgRf9P555D96JIkgncMzsXr1+fmzZ90qOEPWMpSXhJ8s++At/Bh+hmAp4FHI8ShBF8qYoIHOOZDYIPYx8oaLIMn982D7/KQiALcCjwOnBw7FpG8yUuCnxk4HHgDmAx8hSfYccAxZD8EpwRfKnKCnwbegz8dLMrvbVJ+dhQ+5/5JjBikVIAzgBksx4sxRWLIS4JP9jyzC77YrgnfF78DsFzy/SwpwZfKqlRtB8IreI/tsKxbNpiTlvKzH2bdvnTqQODXBjvFDkRE2voST+zlzAZ8XoM2Ngf2rO6udibY0jVos34YQzHWjBjBAy2f2o1gK2bVcHLk670G62fVZg490Pld4jLoZXC9wSaxY5EKjPuw3HQs615enuh3gN9V+N6GZL9KWcfFloo8RN/GbvhQfVZbpEYCVwW4O6P2pBuS2vXbAnsZrBo7HilL1ewylJc9ibsA1+PDbC8C3+D7WwfgQ6N/yDgeDdGXylGCD5+B/Q04lapHZbrH4FDgs+D73SXnAkxKjpi9xWDnAK/GjknaaC52M6GzO0rP5aUH/wx+xObewF34Yrv7gMHAz4FnM45HCb5UjhI8QBgLzAA2KK0WDLbEy9AemlYbUnvBp/S2AUYnCyMlP9SDz1BeevAA3+NJvdmO7b7OkhJ8qUiFbjq0P3AX2NMQ/lPLCxusjI8sDQzau1s4Ad4xfw250mBQgC9ixySAytVmKk8Jvr0zgIu6+JgNgD9V+N4vqH4kQAm+VM568ABhItgQ4DywPyZnyfeYwfzAP4A/hfy9qZEqBXjR4EQ8yf8hwNTYMYkSfJbynOC74wl873w5A6m+2pWOiy2VwwQPEJ71Hjy74oe+9EhS2/xKYNcAH/f0ehJXgDEGP8PLX2uffHxK8BnKyxx8OUd14zFf40fNlvv4lOqTtnrwpWYk8H3sICo4EdgYbNGeXMT8//0G4KgAL9QkMoku+Bu/vuZ1NSSuSSjBZyavCb4Xvqo+RGq/NwQN5xVGmA7sBYwEa+rBhUYAVwd4qDZxSY7sC2xlXjhL4pmIFtllJm8Jfj68oMbXwCv4H+MjwEIxg5IiCO8Al9K9kR/Mt2h+GuDimoYluZDMv28HDDfoHzueBqYefIbyluAvBl4C5sbr0T8PPAmcn3EcWjVdSOESYGGwLlXcMy+mtBLdfHMgxZCcH3AMMMrijQ42Os3BZyhvCX41/EV2cvL19/jimJWjRSRFsydwHNjs1dzZfHfFUXiN+empRibRJdUIHwOOjR1Lg1KCz1DeEvw4PMm3tiK+SE6kCuFL4HjgzM7uab5b4iJ8xbwqazWI4Mf9/soqb6mV9GgOPkN5S/D7ApfgC+zmwlc0XwwcEDEmMQKFmrYI9wKTwDo7dOTvwKUBXs4gKMmXnYEDDRaLHUiD0Rx8hvK2D/4hYFFgED7//gm+OrqmVcqqoPm5tvJYxa4zBwF3g70I4c323zTYCphNi+oaU4BvDf4KXGSwoUZwMqMefIbyluABxuO9+Hnwla9fRo1GILdFbjoSJoJtD1wCtr5/7ZJe2x7AepGCkxwIMM58FGcUsHXseBqE5uAzlJch+kXx7XFL4VWnnsB77/9Nbp8/XmiC/0EWrQdP0nMfhpedBcC893ABsGNoWcwpDSrArcB75gdbSfqU4DOUlwR/KfAc8Dq+OOpZvGTobPhQ/ch4oQmeFAvWg28WrgOmg22T3PAP4OxQuaSxNJ4jgXUNurS9UrpFc/AZyssQ/QBgY2AK3os/lJbe1Qlkv4q+QAvKMlHAIfo2hgB3X8em88L1IcA1sQOS/AgwPZmPv8Ng0wAfxY6pjmkOPkN56cE/is+BBXxIfp1W39uATHtb1hudOtVewRN8mHIwpx53IocfcRKHHR47GsmfAP8D9sMX3eWl41OPNETfgObFh+VfBW4BpuGJ/kF8Bf1vatDG5ngRlE7YLGC316C9+mGsh3FE5Cge6O4DDWYyeGR1Ht4PbHgtg2og3X7+i8RgsKkITrqsMX6X8iAv71Q/BpZPPgbgPfqJwHvAPfjQfVZ0klypAs/BA36IzFmPsMZ1YCPBtoRwVeygJH8CDDO4yWDtAPfHjkekJ/KS4MHnvZ9OPmJSgi9V0FX0YLA9MCHAdclNQ4A7wZ6CoAqJUs4u+DnyGyZD9yKFlJc5+DzpgxJ8e4WcgzdYGj9BbP+WW8Nk/AX8fLA+cSKTPAvwOXAEfo68SGEpwZdSD75U4RJ8Umf+HGC3ULJoMryJv3ifnH1kUgTBpwY/Ndgidiwi3aUEX0oJvlThEjy+UOrcyvvdw7XAzFXUq5fGdRCwj8ECsQOpMyoFnhEl+FJK8KUKVYveYG1gvgCXd3LXwcA+YAtlEJYUTPDf+V2Aywx6x46njlhygJWkTAm+VBPZrtovgsKsojc/hfBvwN6d3ztMBnbD5+Ob0o1MiijAK8C1gOon1M4kYKbYQTQCJfhS6sGXKtIQ/ZnAUQG+qu7uYRze0z8lxZik2EYA/2ewcuxA6oSK3WRECb5UE6pk114hErzBX4AvA9zXtUeGS4AAtnMKYUnBBd/CuxtwmsHsseOpA0rwGVGCL6UefKncJ3jzUwj3Bg7u5iUOBDYFq0XVRKkzAT7DF24Oix1LHZiE6tFnQgm+lPbBl8r1IjvzVbkjgcHdPwI2fI8fODIc7Ke1i07qRYC7ga+1da7H1IPPiBJ8KfXgS+W9B78X8FSAf/XsMuFTYB/gci26kwoOBvYw+HnsQApMCT4jSvCllOBLNRHy+ZyYn12wKXBiba4YngKuRovupIzgO2z2BEYZ9IodT0HpTPiMKMGX0ja5Urncs5rsTR4B7BTg+9pdOZwPzAK2fe2uKfUiwMvAHfi6Dek69eAzogRfSj344jgYuDnAWylcey9ga7DlUri2FN9wYE2DJWIHUkBaZJcRJfhSSvClLHYA7RmsBKyI73tPQZiKH1RzNlj/dNqQogowHa9yN9z8NUOqpx58RpTgSynB55zBrMBZwO4h1Tcf4RN8GPZSMM23ShsBPgKuAA6NHUvBKMFnRAm+lBJ8qbzNwZ8KnBHg4/SbCo8DdwHHp9+WFNAFwAoGy8YOpECU4DOiBF9KCb5UboboDQYC/YKvdM9IOAv4Cdhm2bUpRZCMIO0NnKkDaaqmOfiMKMGXUoJvzZiJbhePqS2DfsBQYN8Ize/pHzYgQtuSYwHeA64D9osdS0FMRAk+E0rwpZTg28pTkZuzgGMCfJ5902EysD0wCmyO7NuXnBsOrG6wZOxACkBD9BlRgi+lffBt5SLBbwf9gRBgTLwownvACfjxsvrbkR+0GqofrqH6TinBZ0QvUqXUg29rZiLXoTfod7iXBh0SMw4XxuCL7k6PHYnkSzJUfw1wUOxYck4JPiNK8KWU4NvqS/we/Emnw/sBxkeOIxEuBGYA2zN2JJI75wIrqwBOh1SqNiNK8KWU4NuKOkRvsC7Q/3w/rjNP9gfWBVsvdiCSH8lQ/T7AORqqr0iL7DKiBF9KCb6taEP0SUGbE4A9YrTfsTANP172GDD11uQHAd7FV9XvHzmUfApMwY/llpQpwZfSefBtxezBHwucHWfVfDXCN8A2wAVgP4odjeTKCGDt5LRDKZW34ll1SQm+lHrwbfUlQg/eYFVgoQCXZ91214R38Z7alWDqlQjwQ6363YFhOlZWYlGCL6UE31bmPXiDmYDT8G1HBRAeB0YBl4CpZyIABHgHuA0N1ZeTm+qY9UwJvtSMEGp4tnjhxRiiPxy4KJta87USrsWPrT08diSSK2cD6xn8OnYg0niU4EvpnWVbmSb4ZM5yJeDCrNqsoaOAAWBbxg5E8iEZqh8M/MP0eisZ0y+cdCazffDJtqKRwC7JC2PBBAN2AnYDWyl2NJIPAV4F7qMwU05SL5TgpTNZLrI7ALg2qQhWUGESsCVwFtgCsaOR3DgD2NRgodiB5MQUTFvl0qYEL53JZIg+maNcBzgn7bbSFz7Bi51cDqaKXUKA7/HfiWGxY8kJlavNgBJ8Ka2Cbiv1BJ9sIxoO7FXMoflywlP4z3QFmLZJCQH+DbxhsFXsWHJACT4DSvDSmSx68HsCYwO8kXI7GQvXAncCo7V9ThJHA4MN5okdSGSqR58BJXjpTKqlag3mBzYF/pFWG3GFUcCbwImxI5H4gr9ZPg79PqgefQaU4KUzaa+iHwHsE2Bqim1EFo4BZgPLwXG3EluAO4Amg/VjxxKRhugzoARfSvvg25qJwOQ0LmywBfBagBfSuH7ODAZWA9ssdiCSC/sDf0sOVGpEGqLPgBJ8G5onzYrBnPiq4qGRQ8lImAZsC+yhI2YlwHjgLBrm97+EevAZUIJvqzeqQ5+VU4HjQsSz5rMXJgGbAX8DWzp2NBJXcpDSIgbLx44lAs3BZ0AJvi0dNJMBg7WBvgHuih1L9sJ44M/ASLAFIwcj8Q3GT5zrHTuQjGmIPgNK8G0pwacsOSnueKCBF5yFD4GdgWt1jnxjS6o23gAcGDuWjGmIPgNK8G31QQk+bYcBFwT4PHYgcYWXgIOB68BmiR2NRDUMGGiwWOxAMqQEnwEl+LbUgy9Vs4WHyUlxywOX1OqaxRYexEvzXgk2Y+RgJJIA0/BiT2db41TSVILPgCZgOq0AACAASURBVBJ8W0rwrRlNwJTaXIoZ8NKtQ4K2IrYSrsXXIoyIHYnEk2wVfRY/jbARTEKL7FKnBN9WzRJanahlmdpdgXsDjKvR9epIGAFMADsqdiQS1bHA7gbzxQ4kA1pFnwEl+LbUg2+rJgne4MfANsBpPY6oboX9gH5gh8aOROII3qs9ksYoY6sh+gwowbelBN9WrerQnwEcFjQ60pkDgYXB9o8diMQRYCzQy2CN2LGkTAk+A0rwbSnBt9XjOvQGGwP/C/BIbUKqZ8GA3YEVwHaOHY1EMxg4KdlSWq+0Dz4DHSX4s/B3kY10lnUTdX3oSZf1aIjeYA7g8ORDqhKmAdsBg8D+EjsayV5SxvY84IjYsaRIc/AZ6CjBfwGcDXyEr/BdG6j3rTzaB99WT4foj8PL0X5To3gaRJiKH8SzI9jA2NFI9gKMBpZNtpbWo8nU9whFLnSU4I8B/g9YBT/PeijwITAKP+awHksraoi+rW734A1WBuZNjsaULguTgD8BQ8DWihyMxDEYGG71OJUaMBpnz3801fzi/A/4AHgLT4Cr4Mn+HXx+tZ5om1xb3Urw5tM6JwEH1DyihhImAJsCx4OtFjsayVbwjtXD+JRNPVKCT1lHCf4g4EG8174z8C9gOWAJPMlvA4xMOb6sqQffVl+6N0R/AHB9gPdrHE8DCl/hPfm/gy0bOxrJ3AnArgbzxg5EiqejOfXl8YV299B2DnUWYALwNF5eMS3z4AvevkyxjfaU4NuamS4+/wa/wKdw1k8looYUPgPbDLgBbHsIr8aOSLIR4DuD/YC/A1vHjqfGVNEyZeV68DMmHysBt+I9uObb+gGfJPebANxUozjG4sVQwKs4PQb8B/gvcC/w0xq10xkl+La6M0Q/DNgvwPQU4mlg4UNgK+BSsIViRyPZCfAU8K3B72PHIsVSLsFPTj4WaPV588fnwJgU4tiAli0TpwNvA7MDswL/xmuYZ0EJvq0uJXiDvwCvBngxvZAaWXgLL/l7JdiPO7u31JWDgcPNXxPrhebgU9ZRD/6eVp+3/tgi5ZhWAP6GjxB8hy/WWiflNpspwbdV9Ry8wZz4qt+haQYk4XlgH+BGsJ/EjkayEeArfMq0ns4r+A6ry91YudHRIrus51Dnxd9AvAws2Or2AcCnGcWgffBtdaWS3Qn4nvdaHU4jFYVn8IWvN2m4vnEEuBr4lcEysWOpEZWrTVm5RXbPAEfjRUoqWb7GcTyM//L+CO8xLoAfobkGcDNwSI3bq6Q3SvCtVTVEb76rYu6kjrZkIrwKtgM+XL81hLdjRySZGAJcbLBeco58kTUn+K9iB1KvyiX43fE97rtnGMeayb9NeHKfJ/l6EjAIeDSjODRE31anCd78TdEpwGaZRCSthNd8VT1XgG2TzNFLHQvwnsGdwB5ktzYpLerBp6zcEP0zeC3kZ/AtUi/gw+YrAUvhi97S8h1eUOflJLanyS65gwrdtFdND35/4Orgux4kc+F14K/A5WCLxI5GMvEPYDOD+WMH0kM6cCZlHc3BHw28hK9mPxDYFl9ENSyFOGbGDyR5A1+t/xWe7MfhJXP7pNBmOerBt9VhLfpkz/t6wLmZRSRlhDeALYHLwH4dOxpJV4Dv8dfkf8SOpYd04EzKOkrwg/Fe+3i8oM1mwCbA5inEMQL4LbALvtiuCd8XvwNePW9ECm2WowTf1kx0vIr+NOAQ7XnPg/AuvsPlQrB6PaBEEsFHN8cb/C52LD2gHnzKOkrwvfAh+hXwVezv4++4mlKI44/4MOND+BuKqcm/j+LVm7Kqea8E31ZIDoUoYV4+9b0Az2Yck1QU3sNrEZwPtkTsaCR1hwJHW3GTpObgU9ZRgr8aXxV9Fb7/ciF8Rfs9KcTxDpXfiW5IdjXNtU2urbKFKJI97wdS3+dVF1R4H+/JjwJbMnY0kp7gHbDT8LohRaQEn7KOatHvjQ/JA9yIz7deB5yXQhy7ANfjSeNFvPb9rPge+DmBP6TQZjnqwVfnRLTnPcfC+2B/Aa4B2x3CC7EjknQEuMFga4PlCjiaNgnNwaeqowT/PZ7Qm43Dy8im4RlgEXy73IJAf+AL/Oz5h5JYsqAE31bJ8LzBqsCc2vOed+EDsK3wJL8zBJUPrl9DgNEG6xRsb/xEfBG3pKSjIfp1gMeB18p8pOF74D7gQnxfdfPXWSV3UILvULLn/WT8BUVyL7yLj8KNAsuq3LNkLDmW+Va8hHGRaIg+ZR314C/C598vJ9sk2+yMJIau2ABf/FXOL+h8CKujffB/p9Hebb7CorSakjkblpoO04fAsRGimS9Cm3UgfAw2ELgerAnCnd280MKkMz0n1VkYrxFSVl+Y4Tn4/aKw3OtFmTrbkflZhXmAxWKHUoUXKWBhoY4SfG/8hbyqw0Zy4gl8KqGcgfjOgI7MAKHSlq/VqL/zmCv7BTOyAAPw0RSOg/l2gr8vCNsTZxgwje2ZDSL8D+wPwLVgs0G4thsXmYPkd0GieA4/SbCsycBrMPYJ2GEurx2Sf5uwPMuwBt6Zy7uLKGCC78iBeA34zpJiWmo93LQ5vp+/A/ZAB9/s6Hv1x5gd45aWL7nNIObWqy8itl0nrA/YdWC7dOPBev7jqur5Nxhmvu04/4xlsUIkdyjo639Hc/B/xI8m/B/wOunPwbfWC19Vr/OC4/mhTK3BVsBLwSsbSmGFKfgWupXBDoodjaTiSOBgg9liB1IFVbJLWUcJfmf81LgV8UIzf2z1kZb58HdKXwOv4FXsHsH34Eu2ZgYmmu9o2JeOTxeUwgjTgJ2AecFOjh2N1FbwLcanUYy/Vy2yS1lHCb65tz4Orw3fuheflovxXuLcSZvPA08C56fYZmsaMWjRXIf+JODoUKy1GNKhYBD2A74AGwHW0euAFEzwuiULmHeQ8kwJPmUd/WHH6E2vhk8LTE6+/h5f2LNyim1KeX2XeoH+wMwB7o4djKQhnIL/bV8K1jt2NFJTQ/D5+I4WUsemWvQp6yjBx+hNj8OTfGsrAm+n2KaUMfd45vjtA6yKHwcrdSsMB+7CC+JoPrROBPgAuIF8742fhB9oJRFMBPoln7+b/NsfmJBim2sCn+ML7L7Gf0H/C6xfg2tXs4r+wQ6+WchVlN112gGM3vhmro8dRytaxZ0q2xJsDNicFe6g5z+uLj//Br0MHjavDppPVpjX1aLE2UZHPfgYvemHgEWB24FTgTHAUmQ3RFz25LRGY7D4rN+yxG2DeDh2LJKVcBVwPDAW7Fexo5GeS8rW7ocfFpZXWvcUSZq96WqthJ/wVgvaB18F8xVYd63wTw7E2Dl2PK2oB5kJWwTsSbBV231Dz39c3X7+Dc60loPD8sV4MHYIVSrk639HPfjYvWmStufJpikLqAcP8FfgqadX4GuKUvJSaii8iW+FPRFsUOxopCaOAg41r0aYN3rNTVFnKyzHA5dkEMe3lF9s0Qt4D/8lSHs1aMMfNGMwF14Ocx1gN3zERhpO+A/YRsDlYD+DMCJ2RNJ9Ab4xPwn0KLxCqTSISj345fGh+bfxLWtvAdcCy6YUxwrAP/H9m78CfpJ8fAEsk3yetoZP8HhN6MNCy/YV9eAbVpgAbAoMABvW8WCf5F3w1++fJ8c9S4Mo91e7NvAg8AawDV5/fFs8yT+Mz83X2qvA6vjxtGOA3+Dz/9PxUrmfp9Bme31o4ARvsC4+T/FQclNflOAbXJgGYS/gbbhsZj+NTgpsH+DvVrt1TbUwDcv1Xv268ySwV4Xv7YMn4TQtjC9ouAxf3Dd/ja7bySI7mxfsyg4eX8hFFtUwmMngMfOaB803no7lqhKWFnlFtcW3YHeA5XEetxHU5PffYFfzofp8MG7FClE3v5Cv/+V68EsDt1W4/62kN0zf7C18DvgRvDefVYnURh6iPwI4L/iai2bqwUsrV08FTgBuAVsgdjTSbecDKxv8OnYgCZWrTVG5BN8H7zmX8xXZDO9MB0bhJ1+N7+S+tdIETMmordwwGAAsG2B0u29pDl7aCY8Dg4ErwBaNHY10XfAFy4OB4ZaPhRVK8CmqNPexJH4qUXtFGErprobrwZvvUhgB7F7m20rwUkb4N9hWwCVgJ0K4L3ZE0jUBxpmvtdkBuDByOKpHn6JyCf4rKg/RN3+/HjVcgse3zIwJvsixvebT5ETaCR8k2+jOBVs+ObRGiuUE4F6DuwJ8GDEOnQmfonJDNP2q+KhHDZXgDRYHBuH7Y8vRHLx0IEyBsCMwGewyHVRTLMFP6jwIOCdyKBqiT1Ee5mDyomESvHn957OAPZM/9HJmIDA9w7CkkMIw4CrgRrAs6lVIjQR4GnjLYLOIYWiIPkVK8C36AFNjB5GRXYGnArzQwX10CIRUKYwBDgauA8vT1krp3JHAEINKpwimTT34FCnBt2iIHrx5XYHt8ZPDRGokvIjXsB8K9ufIwUiVgifYo/DzRmJQDz5FSvAtGmWb3Ahgv9AYP6tkKozHTy1bC+xkML2+FECA+4E+STXLrGmRXYr0B9ii7nvw5tX83gterbCKu4t0Vfgewj74ORaXg6l3Vgz7AUMt+2SrIfoUKcG3qOsEb37s7gHAobFjkUYQRgFXAzdp8V3+JVUszwWOzrhpJfgUKcG3qOsED5wGHBNgQqf3NHqBVtBLT4Vb8VoLN4OtFDsa6ViAK4DFLP1y5K1piD5FSvAt6jbBG2wAEGBslQ/RHnipkfAiMBA4AuwQP7BQcmwwMMygd0btaZFdipTgW9Rlgjf/4zkWL2pRLZWplRoK44E/JF/cCFavxbIKL8D7wC10ePJmTakHnyIl+Bb1eh78icAZAf7bhccowUuNBUtK2p4PjAVbInZEUtEZwMCMTpzTHHyKlOBb1F0P3mBVYP4A13bxoapDLykJY/DdHMPAtogdjZQKvv5mb/zEuV4pN6cEnyIl+Ba9qaO94QYz4Qvr9unGw9WDlxSF9/F5+bXBzgNrih2RtBXgDWAMsG/KTWkOPkVK8C3qrQd/KHBRgE+68VgtspOUhckQdgUeA24Hmzd2RFLiDOB3Br9MsQ314FOkBN+ibhK8wWLAKnT/rGcN0UtGwmjgEHy//Dqxo5EWyVD9AcDZltbZFIFpKA+lRk9si7pI8Mmc2bnAkND9vewaopcMhefwVfYHg+2trXT5EeBF4HFg99ixSNcpwbeoiwSP94bGBHilB9dQgpeMhU+BjYAfAdeDzR05IGlxIvCXFIfq9YYuJUrwLQq/Tc5gSeC3wOk9vFRfNEQvmQvTIBwNnIzPy28UOyKBAN8DewAjTTmjUPSf1aLQPfik8tQIYLceDM03Uw9eIgpP49UXtwUbplX28QV4FXgQ2CuFy+tgq5QowbcodIIHDgOuD36KV08pwUtk4WsIWwLPAveALRI7IuEkYFMD/V8UhBJ8i8KeB2+wHLA6cHaNLqltcpITYTQ+PHwp2Daxo2lkyVD9PsCIGq+qn54ccCU1pgTfopA9ePO4zwR2qcHQfDNtk5McCa8A6wIrgF0KNmvsiBpVgBeAJ/A3XbUyCdWjT4USfItCJnjgCODKAO/W8JoaopecCZMgDAZuA+4FWyZ2RA3seGBLg4VrdD0dOJMSJfgWwQ/EKA6DFfCzm0fW+NIaopecCtcDOwLngu2uPfPZS2GoXtXsUqIEX1BJrfkzgd1D7VehqgcvORZeAdbC92XfBvbTuPE0ngDP4wsgd6vB5VSPPiVK8C2K1hM4FLgswEcpXFtz8JJzYTKEA4DjgFvAdo0dUQM6HtjBYP4eXkc9+JQowReQwTL48Px5KTWhHrwURPgnsCbwC7BrwfrHjqhRBO8EDAFG9XCoXnPwKVGCLxjzP4Th+Kr5tNYM9CLwfUrXFqmxMAnCocAo4E6wP8SOqFEEX1H/BH4oTXdpiD4lSvDFMxQ4N8DHsQMRyZdwL7AOMFDb6TJ1EjDQ4P+6+XgN0adECb5ADFYDFgpwecpNFW09gkgifA1hN+Aa4C6wNWJHVO+SVfU7Aecli3+7Sgk+JUrwLXK9RS4Zmj+RdGpBi9SZMAbYHDgI7EiwGWNHVM+SEtkX4gvvukpD9ClRgi+OY4GRAT6LHYhIMYSP8HPmPwfGgi0ZOaB6dwGweDLS2BVaZJcSJfgCMBgI9A9wZXZNitSDYBBGAlsDRySn080SO6p6lCz63QE402DOLjxUQ/QpUYIHwHpRuzruNWXwE3zP+z4ZNTgDSvBSd8KnELYAHgXuA1sndkT1KBlhPBI4qwsPUw8+JUrwLpd16JO9paOA/QJMyKjZvqjIjdStcB3wO2CzZKX9XLEjqjcBxgKTDLao8iGag0+JErzLZYLHT2x6JsAzGbapOvRS58IXEHbHV9qPBdskdkR1aD9gf4MFqrivhuhTogTvcpfgDZbEFwj9LeOmVcVOGkQYA6wHbAh2Ddg8sSOqF8mI457AyCqq3GmIPiVK8C5XCT7ZS3oOtT3jvVpK8NJAwlfJvvkr8Cp4f4wdUb1IRh6fofP1Q+rBp0QJ3jUBU2IH0coJwKgAH0RoWwfNSAMKtwLrAr8Hux7sZ7EjqhPHAX9ORiQrUYJPiRK8y00P3mAj4CcZVKurRHPw0qDClxB2xqfFRoMNBetOZTZJtKpyd47562w5WmSXEiV4l4sEb/Bj4Bh87ioW9eClwYXn8Zr2bwMPgm0QN55iC/AmcDV+jka5O0wFVGkwBUrwLhcJHt87eniAryLGoDl4EcJ0CKOBjYGtwW7VsH2PjASWMVgpdiCNRAneRU/wBrsDbwa4L2YcKMGLtBI+hbAdcBpwDdjgpDCWdEGyWHg7YJhB//J3kVpTgnd9iJjgDRYHNsOH52PTEL1IifAwsGbyxYNg6ol2UVLl7iDg4iq2zkkNKMG7JmBqjIaThScjgV2TBSmxaZGdSFlhKoRhwM7AULDTwWaPHFShBHgY+CdwYOxYGoESvIu5Te4E4PIAb0Vqvz0N0Yt0KLwOYUM8Ud0LthOYXkurdwKwlsGqrW7T+Rcp0C+lizIHb7AW8Cv8mMW8UIIXqUq4Bk9SswJPgG0UOaBCSObjdwVON5gjudkwDdvXmhK8yzzBG8wHnAT8NeTr3auG6EWq9sOw/UbABmBjwX4dO6q8C/ARfkrm6GQ+fjIqV1tzSvAu0wSfzLtfBuwW4Mus2q2SFtmJdFkYD2EwcBhwVnLu/BydPaqRBXgQeB4YgurRp0IJ3mXdgz8BuCrACxm2WS314EW6LTwHYV3gXv/QtrpOHAus/7MPmAVVs6s5JXiXWYI3+BMwd4Dzs2ivGzQHL9Jj4Tbgt3h1ynvBfhM5oFxK5uO3X+1Rlt3gLn4aO556k+cEPw/QL6O2MtkHb7AwsDdxS9F2RglepCbCtxAOx2uxHwB2BdhCsaPKmwCfvrAUjxxyCqdof3xt5SXBj8Xf6YIvPnsM+A/wX3yoK+13dqn34M2Hvi/G97tPTrOtHtIcvEhNhbch/AWfmjsB7Dww9VZbeXkAr384Px8Ae8WOpZ7kJcFvQMsCi9PxQx5mx7ef/BsYnnL7WeyDPwM4PTl4Ic+aCLmoyy9SZ8IrELYCrsPL3p6sQjk/mLTHuVwB/NFgudjB1Iu8JPjWVsCPa5yA96pPwk92SlNvUuzBG/wVmBzglrTaqKE8bdkTqUPhXrzs7bPAPWCHgFU6SrVRTJwwC32A7fGjZbUDoQbylODnxY8MfBlYsNXtA4BPU247tSF680I22+F7PotAc2AiqQsG4TpgdeAL4D6wzcAa9e9vIjBzgA+BU/CTNaWH8pLgH8bPC/4W/4U/Jbl9DeBmfNg+TakkePP57FHATiFeKVwRya3wHYRR+O6aNYA7wZaPHFQMk0i2yQW4CfjcYLe4IRXfjLEDSDSf0tQELICvoAf/Tx8EPJpy+2n14M8HjgvwbgrXFpG6ET4D9gFbBD/IZibgOAh5rJWRhonAj1p9fRBwg8G4APdHiqnw8pLgm32HH7ryX3x04emM2q35NjmDPYA3CvjLqTl4kWjCm8A2YAsCh4EtABzVADNnE2lV6CbAdPOpzTEG7+XoMK5CycsQ/czA4cAb+Bayr/CEOw4/I71Pyu3XtAdvsDy+M+D4Wl0zE37YgxK8SHThXQi74TUzdoF7ZgVbNnJQaWqT4AECfIMfzXuRwSxRoiq4vCT4EXjVp13wxXZN+L74HfAtEyNSbr9mCT5Z/XkaXmd+ei2umaGZ0FoBkRwJ73iiHzIJ2BfsZrClY0eVgrK16AO8DpwNnJt5RFIzX+KJvZzZgM9r0MbmVKwgZzeAzdXJ4x/orAGDXga3mr9ZKR5jbowbYodRwRexA2hwev7jSp5/GwB2Ndg1YIvFDamGjKWwyivnDf5msH+WIbXT6et/HuWlB/8O8LsK39sQeD/l9mvVgz8PuD4U9JcBHTQjknPhZQhbAGcCZ4CNqpPytyVD9O0cBaxg8PuM4qkLeVlktwtwPXAg8CI+9zIrvgd+TuAPKbff4wRvXmP+iwCjaxNSFKpDL1II4QlgI7BVgDPBvgBOhvBa5MC6q8PjYgOYeZ4Ya/B2gFeyC6248pLgnwEWwbfLLQj0x4ekRgEPAd+n3H5vYGp3H2ywEV5t7881iygO1aEXKZTwOLCxD91zONg8wLEQnowcWFd11oMnwLcG2wCXGwwMvhhbCmrHGl+vozn4aobUy97HYIDBI+ZrBYrNWAXjxNhhVKA54Lj0/MdV5fNvC4ENA7sXbFC6IdWQ0QfjruruyjoGtxv0SjusVgo57ZqXHnw5ZwAXdfExG+AVocr5BV77uZxubTI1H2m4ANgi2dJRdH1RD16kwMI7wOBk//wBYPvgq9Bv9/K4ORWYglW3HTrAfQZLA0PxuXmpIM8JvjuewPfOlzOQGr7jM5+3vxI4MMB7tbpuZJqDF6kL4X080f8crwq3D9jpwN05TvRVxxXgdIMLDTYPcG2aQRVZnhN8d96ZfZ18lPMp3uOulRHAFcHPrq8XSvAidSW8B+ydnD8/GJ+nvwi4yuvgF9peeKW7V4MvzpZ28rJNrr1e+Kr6XNZnNK+uNDHApbFjqTEleJG6FD6BcCiwPt5Tvic5j/6nkQNrrUuv98Grnu4EnGe+20rayVuCnw9fzPA1vg1iOeARIO19nlUPDSUr5gcCQ9ILJxqtohepa2EKhNF4Ma7HgPPBLgVbPHJg3RK8hsohwC0qZ1sqbwn+YuAlYG58C8TzwJP4qWzRme/LPxzYroBlaKuhQjciDSFMh3AbhN8Dw4BDwG4CWzd2ZF0VvBN4NnCV5XvaueFNBPoln7+b/NsfmFCDa/dom9y88KjBE+b79OuTcTTG2rHDqEDbtOLS8x9XBs+//RrsArAxYL8Hy7YDaD3bimawh3ntlDQUcptc3nrw44DV2t22IvB2hFh+YNDrSlgUOKLOz3ZXD16kYYVXIOxMyyFfD4ENAZsjowCmYMzU3QcHP5DmO/OKqEL+Evy+wCX4Aru5gBvwYfsDUm634uIO8++ddwt8XsCz3btKc/AiDS98CuFYvLLoW8AVyTz9/6XccKfV7KowGFjZYMsaxFN4eZuveAjvKQ/C598/wbdC/Ce9Jm1GOi6FOxx49h+wcHox5IZW0YtIIkwHbvMPWxTYE2wZvLjXlRBqXUK8w3r01QgwLUnuN5ufDTK2NqEVU94S/JzA//BefLNe+Dx8LY6MLafiQTMGRwOfJUM/m6fUfp5oiF5Eygiv44Vz5gC2Bx4GuxsYDqFWr8216METfJh+C+AOg/8G+FfPQyumvAzRDwBeBsYDb9L2SMCfAZ+l2HbZBG++DW6u4OUQG4V68CLSgfAVhGHAWviaqavBRoAtVYOLT6IGCR4g+FbrvwCjzA8ya0h5SfDn4fPtM+ELPEYCy2fUdhMwpfUNBn/FF/ftn1EMeaE5eBGpQvgOwhUQ1sXPpt8K7GGwQ8C6W3SmJj34HyKEj/HX8qsMflSr6xZJXhL8MsBpeE/6YXzefSTZnBbUpgefFLL5M/W7170jfQhMjh2EiBRJeCOpkrcevuNpdLIob9kuXmgSPZyDL4nMR4YPBK5vxEI4eUnw4/ASis1uBT4Ajsug7R8SvPkWvf3w0+G6fT68iEjjCVMgXAdhEHASsLnP09uuYLNWcYGa9uB/iMoXbw8Hrmy0Qjh5SfAH4dvhnsCHUgzYBfgdcFPKbTfhizIWA04FtgmahxYR6YHwWtKr3xzoA9wOdhrYSh08KJUED5CcOPdP/DW+YeQlwd8D/Aqfy2meA/4cWBk4mXT/U5rm4bM++Mr9rYOfOteocnm4j4gUVfgSwtkQ1gJuBnYFewrsILCftLtzagkeIMAJwHRroIXTeUnw4HverwG+aXXblOS2Q9JqdB3um3djbtkA2DY5uEBERGouPAphR2AN4DXgTLAHkyH8WajhKvqKEfh8/IwGf0+znbzIU4LPnMGcB3LaqY+y2l3B1wGIiEiqwpTkoJstgK3w+icPsfvI3Xh4jVpst+u4dTgSn5YdmnZbsTVsgjdfrXnlg6x19Wss9n7seHKi6mNzRUR6LnwM4RTgNyz+6l28uvgqYI+C7QU2V4oNHwn0V936+vHDaXIGMxuMMfgT2AZgh1Xx+EKeJlQ1ow/GXbHD6IBOM4tLz39c9f/8GytjnOj76G0PsPuTI2z/DNan9s0RDEYkRc06U8jX/4brwZsfYjMWODPAjXRQqrbBqIqdiMSUzMGHLyCcC2FtvCbK/Pgq/GvBBiXnh/RY8BHLvYCFDKrp5BVOQyX4xb3QwU3AkQHuTm5WgneqQy8iMZVZRR8+9tK4YT38bJDlgEfAzgNrf7R4lyVJfgjwU0txMXcsDZXg74CdgBOCV8trpgTv1IMXkZg6OU0uvAZhKLAqcDWwPdg9YIPB5utuo62S/C8Ndu7udfKooRL86XB7q557MyV4pwQvIjFVuQ8+TIfwAISd8YPJ3gdOBRubJPv5u9pwUpZ8V2AV89Py6kJDJfhzvE5ye31QLjL0EgAADGRJREFUggcdNCMicXWj0E2YAuEmCFsDA4FngaPAHgMbCrZY1VfyJL8TsILBHl2LI58aqi5vBU00wgrVzinBi0hMU/ATRbspTAMe9Q/rg59vcjjYIvjI7TUQXu3wCmAG+wIXG0wIMLr78cTXUD34CjRE77TITkTiCRg1K5f9QzGd7fBE/zpwYqsjbX9VOQym4ceWb1T01fVK8GXOg29QmoMXkdhSKLYVvoVwFYRN8GT/LLAX2JNgw8DWbb/1LknyWwJ9DC7pXdBzOpTg1YNvpgQvIrGlnEjDZAj3QhiMr8a/DhgEPA02Otln35QEYsHL2f7rbhhgKdfJT4MSPPRGCR40By8iDSVMSw7AGQwsD1wIrIvP4Z8HtiFYnwBn3e6nm95sMEfUkLtICV49+GaagxeRBhWmQXgoSfYrAucDqwH3gd1wOgeGe1jvXGCswQJRQ+0CJXgl+GYaoheR2HJw4FUwCM9AOBLCasBu8PX363P3JrPxTd89GfHU9ly8PVju82fuA8yA9sE79eBFJLapGE2xg2grfA6jPoOw3bfMuupzLHPst8x66hx8+RLY8OTAspofhlMLSvDqwTfTHLyIxNaNYjdZChOeZOWR17HZ4uPp/+lpHPgxsDpwD9gtyTG3C8eOspkSvBJ8Mw3Ri0hsOU/wLsD4Xkz7/QGcvrIRPoKwBrAVvt9+32QL3miw/2/v/mKkugo4jn9vgRJwod0A5Z+tW6Va26YxEVNjKNWU2DZi22gREzRaCyFGGqPhicQQ9cE05bHRQmNQm9g0qRptJIqQirZpH4i2wfoHFCoWxFL+Q6Hlz/hw7nSHZXaYLvfuOXPv95NsdmfO7O7JmZn85tzzb1E4/jYOd7JzHXyTAS8ptoscOJOODE404D7g8Qb0Q/a9DDYBm8IKO24mrLt/HBo7IPvGaNfRHrw9+CbH4CXF1hM9+KYsdA4/T5jL9UQD+vKSBmQvQfYwZAuBlTHqZ8Ab8E324CXFdpIeCngIh9RksBp4lLCM7qY2jzo72vUCAx4M+KYJwKnYlZBUaz0X8E0Z/J6wh/2jjbA7XnQGPIyD7HTsSiQgyw97kKRYemYMvp0MdgB3Aksa8O1G5Iw14Hv0EIESGO6SYuupMfh2MjiehXH5PcBvGzA9Vl0MeDX5QUdSbD0f8E0ZrCMcVvN0I2x/O+oMeElSKnp2DL6dDJ4jLKVbHOP/G/CSpFRUpgfflMHuDL4Z438b8I49S1IqenqSXWoMeEGDcbhUUFJ8BnyBDHiBB81ISkPlLtHHZMAL3MVOUhoqNckuNgPe5WHgPvSS0mAPvkAGvMAevKQ0OAZfoJoHfGMc4Da1jsFLSoM9+ALVPOA9aCZnD15SCk5hD74wBnw4z7fu7MFLii/jHOZSYerekPbgAyfZSVLFGPAGPHiJXpIqx4A34MFL9JJUOQa8AQ/24CWpcuoe8OMx4MExeEmqnLoHvD34wICXlIrTNBgbuxJVYMAb8OAleknpcD/6ghjwBjw4yU5SOtzNriAGvAEP9uAlpcP96AtiwBvw4Bi8pHTYgy+IAW/Agz14SelwDL4gBrwBD47BS0qHPfiC1D3gXQcfXJYf8iBJsdmDL0jdA94evCSlxUl2BTHgPS4WoBG7ApKU8xJ9QVIM+AyYzOjUzR58kMWugCTlDPiCpBLwE4FVwHbgFHCEELw7gNWEsfIyGPCSlBbH4AuSSsB/H/gEsAyYRQje6cD9wIfz8jIY8JKUFsfgC5LKhv73AjcAe1vuOwA8C7wE7AIeKOH/jqPuAd9gDHAmdjUkKWfAFySVHvwu4K5hyu4Edpf0f10m5xp4SWlxDL4gqfTglwFPASuBbcAxoA+4EegH7i7p/3qJ3l3sJKXFMfiCpBLwW4E5wG3AADAVOASsA7ZQ3iVkA96Al5QWe/AFSSXgIYT4ZsKSrUnAcSh9dzUD3oNmJKXFgC9IKmPwMZfJ1X2jG8fgJaXESXYFSSXgXSYXj5foJaXEMfiCpHKJPtYyOQPeHryklGSczZfv6hKl0oOPtUzOgHcMXpIqKZUefKxlcq6D9xK9pPR4AFYBUgn4WMvkxkJW913cDHhJqfEArAKkEvAwuEyu6UtDbqscEwnzHSRJFZLKGHw7D8WuQE3Yg5ekCko54DU6nGQnKTXOpC9ASpfoAW4FHst/ngb8vaXs+i5+/w7gM8OUvZcfL7iZaeNWvH3PwS/MYAlru6rZT5jLNP7a1WN7yW6u4mvcBCyJXZWLmABdPlcqg+0fV73afytzOMjLbCh9N9PuHKOfz11S++8gY01h9elSyhMZ9gEz3uHvTCZM0Gvnch788nVcPe3o4KOPvsXytf/r6i8/wkxOJveBqBgPs5f9nI1djYuYDBy96KNUFts/rnq1/1L6eT99savxtlm8wZJLmqt0iuy8fV5qb1/sCkiS1KtSHuPYT9jFTpIkVcQYYCZpDyFIkqQuzQaeAU4QNrqZC/wRuDZmpSRJ6jWpLZNbD/wFmEI4MvZF4AUGZ9ZLkqQe9AZwZf7zK/n3qYQevSRJ6lJqPfgdwLwh990C7IxQF0mSVJDbgNcJJ8sdBX4GvAZ8MmalJEnqNSnOUp8CfBp4N/Bf4Ne4Jl6SpJ6XEXZtSm34QJIkvUMTgVXAduBNoEE4PnYHsBoYH69qkiRppH4E/I4wBj8FGJd/nwf8CvhhtJpJktSDUhmDPwzcAG03458E7GL4Q2RGyz+AY5HrUGezgT2xK1Fjtn9ctn9cJwmnnWoE/gw8MEzZIuBPo1iX4TwTuwI1Z/vHZfvHZfvH1ZPtn8rxp8sIS+NWAtsIPeU+4EagH7g7XtUkSeo9qQT8VmAOYQx+gHA5/hCwDthCmHAnSZK6lErAQwjxzbErIUlSFbjWXJKkCjLgJUmqIAO+e6djV6DmbP+4bP+4bP+4bP+Kcze9uGz/uGz/uGz/uGx/SZIkSZIkSZIkSZIkSZIkSZIkSZIkSZJUurmE8+gPAeuBCXGrUwlbgEbL129ayjq190jLNGgDcP2Q+8poc5+P9tq1v++H8i0AXgROAM8SjiJv8vVfU2OBV4ClwGxgE7AqZoUqYjfwMcLRwAPA9Pz+Tu090jIFtwOPEQKkNWDKaHOfjwsN1/7g+6FsM4FjwCLgCuC7wMt5ma//Grsd+FvL7Y8D2+NUpTIuB04BY9qUdWrvkZYpWAk8QujBtAZMGW3u83Gh4drf90P5FgPPt9y+HDgH9FPx17+HzXQ2AGxrub0NeA+QRalNNVwDnAR+AfwT+CnhUy50bu+RlilYA6wgXDJsNUDxbd6prK6Ga3/fD+XbANzTcvujhB72YSr++jfgO5tKuLTTdJTw6W9SnOpUwjTg38APgIXAWeDJvKxTe4+0TJ2V0eY+H93z/VC+Y8BrhIC9h/Ah6uuE4ZJKv/7HjvY/7DGHgL6W25OBM8DxONWphOeBD7Xc/ipwhPCm6NTeIy1TZ2W0uc9H93w/jI4phDkQ1wD3Alvz+yv9+rcH39lOzp9t+UHCpZ1zUWpTDR8B5rfcfovQazlD5/YeaZk6K6PNfT665/uhfOOBjYRx8VsYDHfw9V9rY4E9wH2ET2S/BFZHrVHvm0f4hDuf8Kl6DWGWKXRu75GW6XyvcuEs+qLb3OdjeEPb3/dD+RYTlsgNDPkag6//2ptLeHEcIKxnHB+3Oj0vA5YTZpUeAX5OWMbS1Km9R1qmQUMDBsppc5+P9oa2v++H8j3E+fsMNL+m5uW+/iVJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkkboSuBwl489A4wtsS6SCnBZ7ApIkqTiGfBSfT0I/Cf/+sqQsmXALuAk8ALwgfz+jcAY4F/Auzo8TpIkRXArcBCYD8wGNjN4if5q4M28bCqwHljb8rvNS/QXe5ykiOzBS/X0WUIY/wHYA3yrpWw/cF1edhJ4Hbiizd/o9nGSInCijFRPM4BNLbd3tvx8BlgK3AUcIfTSj7X5G90+TlIE9uCletoLvK/l9rUtPy8CPgXcASwAnhjmb3T7OEkRGPBSPT0FLCeMxc8CvgM08rIpwHHCZferCJPxJgz5/b4uHydJkkbZCsIM+leB+/PvEMbRNwIHgOeAhcA+4It5+ZPAUcIHg06PkyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJAvg/zdXNADbMbyUAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>As seen in the plot, the data doesnt fit any of the distribution<br/>
to which they where tested. This left us with only one posibility<br/>
being that our samples are to small, the only viable option is<br/>
to use a student-t test. </p>
<p>Since we need to find normality to used a student-t,<br/>
Shapiro-Wilk Normality test is performed on each Presv. Method.</p>
<pre><code class="r">shapiro.test(feces_ultralow)
</code></pre>
<pre><code>##
## Shapiro-Wilk normality test
##
## data: feces_ultralow
## W = 0.9592, p-value = 0.8023
</code></pre>
<pre><code class="r">
# Test P-value should be >= .95 to be consider a normal distribution thruogh
# this test.
</code></pre>
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