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1U6FYQSvv7664MgjKQbF2wkVSpZsqTqz4q2i5jYn3zyiWr2ueeeU9BH6sNeZwzY9v/11157LT3xxBP263TdjgVsxMVGnGeEEs7OznbtwzLwE9j79u0jxKTGYGPloMGnnFWmTJmijiFOtlPhgo3QwWjrrrvuCkrVYbUN8JOTk63dqGtjwMYV4iMPoF122WVBH3tRr952IBawkdXg4MGD1K5dOyWmrTnHTT+BDSGQYc0ONuowiiJ1RsGCBROeNQztlyhRIiLYSBDw5JNPOvoHB40CG5m7EIwdIlpBxl3v4LSBLtjPPPMMzZ8/X53doUMH+v3337ld+S7we6QcNPCP5auGDRu6ascdsdFQpBw0qMevYho0aEChOSAjdW4U2NYFIscI57nNssdaB2y8lBQqVEjlMkHewCuuuEJ9vGEk5hS/jdiRwLZ0+uqrr9RA9NFHH1lVEdeJABufEMgxySlGgI3na6R/SElJoT59+iihkD5Dp+iAHdou8tFYLyqhxyLt+w1s5OrBp+iyZcuUHMjo1rlzZxo+fDj94x//UMe2bdsWSapAnQ7Y0PfGG29Uj4lWA/irjE5mNyPAxkcPMuTiZrAgA6tuiQfsIUOGUE5ODrtLv4GNdM945rUy4iLJKBKM3nTTTSo3DOfRQBfsli1bBgabBQsWqMceZFCuUKEC4S9o9j8FRnKcEWBHujDdunjA1u3Lb2Dr6hPJXgfsSOfr1gnYuor5MGtYDBKFnSJgh0nCq5ARm6eTZSVfqVtKOK/j/krduXn3owK2u0Z2CwHbrkb0bQE7ujZGHhGweW4RsHk6GWMlYPNcIWDzdDLGSsDmuULA5ulkjJWAzXOFgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8V4SBPW7MKN6ZCbKqI6k6tJQE2Lt37dI6Jx7jvXuzczUHjSeB3zdv2kg1Kleg6S9Moecmjvd8QT81KldUup84cYK8XiAa+sjLZfTwYdSvZ3ea8uwkz/0z+ZlJNKBPLxo6MCXXfIR0KgnPQbPh5/XUoc0jtGrlSlq6ZLHny5rVq6jWP6so0TC32+vF+o+Tl8EenjqY3po1i5Yv+95z/6CPd2e/SYP7980VH2H2JX7pnnCw8Sv1sSNzN2tYnapJucZZfpm2unPHjlzTbDeyhuViOjxPHkViCb8Qr8IyCUpPQXl55OkV9vIoyZV4wp0tKwGbp7yAzdPJGCsBm+cKAZunkzFWAjbPFQI2TydjrARsnisEbJ5OxlgJ2DxXCNg8nYyxErB5rhCweToZYyVg81whYPN0MsZKwOa5QsDm6WSMlYDNc4WAzdPJGCsBm+cKAZunkzFWAjbPFQkDG8HYEdnz3Xff5fV82oo7V+S3336jm2++mcaPHx9oHwExy5YtS2XKlCG3cLg4Kb9MguLkoEGgdQSkLFCgQCCjgSXcqlWrlK8qVapkVUVd64Q4mzVrlgr+b0XARRqVWrVq0f3336/y0GDmnlsxKnbfZ599pnKbnHfeefTGG2+4XXvQcS7YSMtRv359mjBhQuB8JHayCqJ5uuWj8RPYyDDRo0cPBfDy5cstmdQaAwFy0dxxxx1B9ZF2uGCvWLGCEEK6SJEigWirSN9hQf7666+r64nUh73OKLAvv/xywv/WwoULE25Ap3DBRpuvvPIKjRs3Lqx5xF+GI92Kn8CGFjt37lRgWzloUJeWlkalSpWiYcOGqRHdTTMu2FY7iLv966+/WruBNeKocz7NjQEb6c+slA9/+ctfaM6cOYGb4WzogP3CCy8EgY3RoEWLFmokR+xnt+I3sEMzGiApVfHixenQoUMq+HsiR2xoHylVx7x581Rs7NTUVNYPCIwA2xoRkCmqdu3aanTA/1g833FLPGBbfeDnRElJSa6JnfwONh7l8B6EgahYsWKElHhIo+FUdEbsSGBbbb/88suEoPBuxQiws7KyVOoHJDBFBi8IhfyCOqkZEgE2xEK6kG+//dZRN7+DjcRUSEiFlIUYgC666CL1aOIkWqLAxotjo0aNnLpSx4wAO/QqMRq89dZbodWO+zpgT5w4UX2EokFkokIavqVLl9LMmTOVwwCuU/Eb2GvWrFEjNDQKLV27dlWjdmh96L4O2NAXL48HDhxQzaBf5L1ZsmSJyt48d+7c0ObD9o0DGzeFl4P09PSwi3Wq0AF7y5YtKnch2gPYyAKMv5IAcE7xG9h4lsYLN/46EVrwuMgBTQds9IE2rUgA6AMv+1gAN6cYBzbnoiPZ6IAd6XydOr+BraNNNFtdsKO1w60XsLlK2ewEbJsYzE0BmylUqJmM2KGKOO/LV+rO+lhHE/aVutWg7lrA1lNMwObpJWDzdDLGSsDmuULA5ulkjJWAzXOFgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8VwjYPJ2MsRKwea4QsHk6GWMlYPNcEQb2pPFpvDMTZFW3erUEteTeTH75Sj1n7173m02QxcGDByilt/Oc7QR1pZrxJPD79m1b6borL6P6NatT7aQqni91qiVRUvn71A1hvq7XC36QYM08S6QzcrOtCWPHUNk7bydo57WP0Ee5UnfQiCGpueYjzNr0JFWHjNi5ial+X3gUkRHbXbewRxHJaOAu2tm0kGdsnvoCNk8nY6wEbJ4rBGyeTsZYCdg8VwjYPJ2MsRKwea4QsHk6GWMlYPNcIWDzdDLGSsDmuULA5ulkjJWAzXOFgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8VwjYPJ2MsRKwea6IG+yTJ0/S6tWrad26dWqdnZ3N6/m0VSxxRdavX09ff/01ffPNN6TTX36ZtspJ1QF5MzMzCYEp7SkyMjIyVB38BR3/+OMPR3/FEwkKMcvBhk4xJsTZ9u3bVURPRFrF0qpVK537IF2wEWCxWrVqNHnyZBWQEs7hFj+B3b1794Bf7AEha9asGaiHvwC6U4kV7G7dutHTTz+tnQjAGLAXLFig8pk4ieN0TBdsgIw4z7EUP4GNTAIYlQGvlaoD85wRpH/Tpk1s+WIB+8knnyRkT4ilGAP2pEmTlHgIIv7AAw+ozFw6N6QLNh4/OnXqRMh8hccgneInsKELALaDjeRT2Mdyww03qEc5N/10wcajYXJysgrEjyROzz//vFsXQceNARujAdIwDB48WAlWr169oAt129EFe/fu3UqsPn36qDR4OTk5bl0EjvsN7NAcNAcPHqR///vfNHv2bLr66quVv9xGb12wFy1aRIUKFQpo3qBBA/ruu+8C+24bxoBtv9DWrVvTlVdeqYKy2+udtnXBtrf10ksvqUj59jqnbb+DbdcGIytG7tdee81eHbatCzZS7yENn1XwLhQp05t1PHRtBNh4ZsNbN0ZRPPtCKE5aOvvNxAM28s5MmTLF3pzjtt/B3rNnDyErBNLVtWnTRvlrw4YNjprpgo3HncqVKwfafOyxx9jZDHCSEWDjIpBQyXpuq1OnjrqwwF0xNnTBRiKnqlWrUunSpenxxx/Xeqb3G9j4cxt8s3jxYuWJfv36BXyF+s8//9zVQ7pgo0GkRKxRo4ZKtGXPpOzamSlgcy7UzUYXbLf2nI77DWwnLbjHYgGb23YkOyNG7EgXplsnYOspJt888vSK+5tHXjfRrQTs6NpEOiJgR1IlvE7ADtfE6BoBm+ceAZunkzFWAjbPFQI2TydjrARsnisEbJ5OxlgJ2DxXCNg8nYyxErB5rhCweToZYyVg81whYPN0MsZKwOa5QsDm6WSMlYDNc4WAzdPJGCsBm+eKMLBHDR/COzNBVslVkxLUknsz+WWuyLYtW9xvNkEWO3emU0rvM9NQE9Rs1GY8mSuybt1aSh3QX3V69MgR8npBR3VOg40psF4v+OFqXk/VMWRACq1ZtUrdh9f+OXH8OP28fh0N6NtLMeG1f9CJJ2Bv3bqFShT/P3q8w7+ofatHPF86/astVa1QXomGG/J6wa+18zrY40aPoga1alDHtq0998+jbVtTozq1aXjqoFzzkWc5aCaOG6NuIrf+qVutam51peZu53Ww8YydnZWVa5rt3/9L3s8atnbNapIcNLnGTEwdycsjT7awl0cBmyfc2bISsHnKC9g8nYyxErB5rhCweToZYyVg81whYPN0MsZKwOa5QsDm6WSMlYDNc4WAzdPJGCsBm+cKAZunkzFWAjbPFQI2TydjrARsnisEbJ5OxlgJ2DxXCNg8nYyxErB5rhCweToZYyVg81yRMLC//PJLQhqI77//ntfzaSudSFDPPvsszZ8/P9B+VlYWtWzZUqXuaNKkCWHfqeSX+djcHDQTJ06kdu3aRcwugACigwadmrXnpFmssfsQoH/MGP0JdZ5MW411EtTf/vY3FcWzVq1agcieTmLZj3HBRhaDCy+8kMaOHRs4/d1336UZM2YE9t02/AR22bJl6aabblJ+sQ8206dPV3UI9du7d283ySgWsBG3/KqrrlKZDVw7CDEwBuy+fftS0aJFae3atSGXyNvlgo0Y3DNnziR7WFqAvWzZMl5HRPlm2ipnxD58+DBZia+sHDTbtm2jggULqsHBLVuYJWosYCMON7KFIRGAbjECbOSAKVy4sMpnYsXI7t//1C9wuDfEBRvtTZ06NSg6PrJSlSxZktq3b8/qzk8jNgRBUHf4xQI7NTVV7V933XVqXaRIEUKgdqcSC9hoDwkB8JioW4wA20r3gNRrKBi9ISRyn3CLDtgvvPBCENhWHxCxWLFi8oxtCXJ6HZqDplmzZso/v//+Ox04cEBtu8EXK9hIfuXWdsjlql0jwIZAeO610tMtXLhQibVixYpI1xyxLhFgo+FevXoR+ncqfhuxQ8Hu3LkzFShQICDRFVdcQQ0bNgzsR9rwJdgQomfPngpmpM3AqFmhQoVI+kSt0wH7ueeeo9GjR6u2AKn1nIgMtOXKlVOjUNSOfPaMDR2sPI/WewhyL+ITtWLFivTwww+r7W+//dZJspheHtHgypUrqWnTpo5tRzpoxIhtXdi0adOoVKlS6k9LVh13rQP23Llz6f3331dNIw0e8pyUL1+emjdvTkePHnXt0m8j9s6dO5U+9pR3eATBn/qQyHTp0qWumsU6YuNFNS0tzbX9UAOjwA69OJ19HbB12o1k6zewI2mgWxcr2Lr9WPYCtqWExlrA1hDrtKmAra+ZOkNGbD3h5Ct1nl4J+0qd1124lYAdrolTjYDtpM6ZYwL2GS3yxJaAzXOTgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8VwjYPJ2MsRKwea4QsHk6GWMlYPNcIWDzdDLGSsDmuSIM7PFjT82e450ev5UEftfTEGDv2b1b76Q4rPft25v3A79v2byJyt97N40ZPoyGDEzxfBk9fChVr3Rqmuvx48fJ6wUTbPJ6RoOxI4arVCpIn+G1j4YPHkRPPfYoDR00QP3X8No/8A1maSLXTbwlaMRGIp0ujz9GGTvTacPP6z1fdmdmUu2kf6p7wAQlrxf8lC2vgz1s0EBa8MkntGXzZs/9g4Hu808X0KB+fXLNR57N7hszYli8/1m0zreyhmmdFKNxfpndl759e4wK6J+WuSsj76fDizX8gr5cZ86QSVBntOBsycsjRyWioEcRAZsn2tm0ErB56gvYPJ2MsRKwea4QsHk6GWMlYPNcIWDzdDLGSsDmuULA5ulkjJWAzXOFgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8VwjYPJ2MsRKwea4QsHk6GWMlYPNcERfYmFR09dVXq6idVuD3yy+/nPbt28frnYi4c0UQhBLpH0aOHBnWNnKpuIXDxUn5ZRIUJ6MBcsBce+21yjdWqg5kg7D8ZK3tqU/ChCWKKdrq4sWLVT6i0qVL06uvvhqp2ah1ns3uGzk0NWqnkQ78+OOPKu8MoubfeuuthDw0OoULdocOHVQgcSQMshcAf9mGiiMAABC8SURBVOmll1Lbtm3t1RG3/QR2x44dadiwYQrk5cuXKz2QfAphgxG/HImPALcFfUTBYgAbc7aTk5MDzTVq1ChicqeAQciGMWBb17V161YlFCc0rXUO1lywYfvyyy+HZTR46KGHCBnLnnjiCXuzEbf9BDYESE9PVz6xUnXYRSlTpgwlJSXZqyJu6walhP8feOCBQFujRo3SSoBlHNiIuXzjjTcGboi7oQN2aKoOJFuaMmWKCvj+6KOPunbpN7BDMxpYAlkB4T///HOrKupaF2zE5LYH/0fw9xdffDFq+6EHjALbipT/xhtvhF6n636sYCOLAUYGpAtBDppWrVq5/jpGwD7ljhYtWtBtt93m6hsY6IKNc1577TW68847CY+P9erVoyVLlrD6gpFRYAOqiy66SL2cse/gtGGsYG/evJmQNQziIWkQ0oRMnjzZsXsBmwjZDfBs/d577zlqZR2MBWzrXKzbtGlj33XdNgZs62MNWXNjKTpg48URL0ShBS9F8vIYqgqpXIuA+LvvvgscrFKlCl188cWBfbeNWMDGyyk+vevWrav14ohrMQZsjJzz5s1TvzB3EynScR2w8fyGpJyh5ciRI+pxJLQ+dN9vIzYe0z788EM6dOiQkgLQzJkzRyVdCtUm2n4sYOMvL7E8luIajAE7miDceh2wuW1Gs/Mb2NF00KmPBWyd9kNtBexQRRj7AjZDpBATATtEEO6ujNhcpU7ZyVfqPL3i+kqd14WzlYDtrE/oUQE7VJHI+wJ2ZF2MrRWwea4RsHk6GWMlYPNcIWDzdDLGSsDmuULA5ulkjJWAzXOFgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8V4SBPWl8Gu/MBFnVrV4tQS25N5NfvlLP2bvX/WYTZHHw4IG8n4Nm+7atdN2Vl1H9mtWpdlIVz5c61ZIoqfx9ygXHjh0jrxfkN8nrqTomjB1DZe+8naCd1z5CH+VK3UEjhpz6HazX/kH7mKmZ8Bw0CPwuI3aChjqPmsGjiIzY7uKGPYro/krdvQtnC5kr4qxP6FF5xg5VJPK+gB1ZF2NrBWyeawRsnk7GWAnYPFcI2DydjLESsHmuELB5OhljJWDzXCFg83QyxkrA5rlCwObpZIyVgM1zhYDN08kYKwGb5woBm6eTMVYCNs8VAjZPJ2OsBGyeKwRsnk7GWAnYPFcI2DydjLESsHmuSAjYiKf31VdfqSCIvG7PWMUyVwQx/GbPnk0//fTTmYYYW/ll2ionVQfk2LBhg8pggBh+9oKwvgiWj9l0biWeSFDZ2dkRYy069WlMiLOVK1eqsLRWTpNXXnnF6brDjumC/cMPP6jQwQiDi/DBH330UVib0Sr8BPbjjz8e8Is92mqNGjUC9YhhHQp9qHaxgg2tkbqla9euoU067hsD9h133EFIl4HSrVs3QnIlnTnPumAjkZIViR/5b+BAbvET2PgEXb9+vYLYStUxd+5ctY/5zsgVw4mTHSvYSOT0yCOPUN++fbnuUXbGgI0UGddccw0tWrSIypUrR/369dO6EV2wt2zZQojID7j79++vPm65HfoJbGiycePGILDXrl2rElENGTKEkPbk5ptvpoyMDEf5YgF727ZtKjY2kjp16dLFsf3Qg8aAbYlnPYq88847odfquK8LNp4be/ToQVOnTiV8rNo/Zh078lk6PGgRKQdN9erVFezwF9LVuZVYwMZgh08FxE6Hr3SKEWDjkaNgwYLUqVMnde0YCSDYrl272PeiCzbyOSLdBApeTho0aMDuy28jdijYAwYMUP6BYHhxhK/cwNMF+/XXX6eiRYvS8OHDVV6gEiVK5L0cNAcPHqTzzz+fkMsPHz9PPfWU52C3b99e5ZWEc1atWqWS93DJ9jvYSBkImBcuXEhIcIrtnj17OsqnCzZ+s4g0fPjLFV7wW7Zsyfrri3URRozYuBgkwIRA1rJgwQLrGllr3REbOW9KlixJlStXVlnD8ILELX4De/Xq1cov9sc1fOJZvsK7ysmTJx3l0wXb3hjYQPIrnWIM2DoXHclWF+xIbXDr/AY2Vxcnu3jAdmo32jEBO5oyDvUCtoM4UQ4J2FGEcauWEdtNoeDj8pV6sB7R9hLylXq0xjn1AjZHpTM2AvYZLZy2BGwndQw8JmDznCJg83QyxkrA5rlCwObpZIyVgM1zhYDN08kYKwGb5woBm6eTMVYCNs8VAjZPJ2OsBGyeKwRsnk7GWAnYPFcI2DydjLESsHmuCAN79PChvDMTZJVcNSlBLbk3k1++Ut+xbZv7zSbIYlfGTkrprTenOp6uPZkrsn79OurdvSthOmrmrl2eL4cPHybrm0dA5+WCie9//PGH1s/W4nGQV+cOHTiAFn/zNe3NzvbcP9lZWbT0u8U0sF9vdTte+gdtw0eegL1ly2a69x8lKKV3L+rZtYvnS/9ePahaxfJKNPwGz+sFoun8HtMrOONpN23kCGrdojn16f605/5BH21bPkjDBg/MNR8hTxAAj7eEPYpMSBsdb5ta59etVlXLPh5jjAp5HWw8Y2ft2ROPDFrn5uTsy/tZw5BcSXLQaPk9143l5ZEnediILWDzhDtbVgI2T3kBm6eTMVYCNs8VAjZPJ2OsBGyeKwRsnk7GWAnYPFcI2DydjLESsHmuELB5OhljJWDzXCFg83QyxkrA5rlCwObpZIyVgM1zhYDN08kYKwGb5woBm6eTMVYCNs8VAjZPJ2OsBGyeKxIC9nPPPaeCRA4ceGoWGK/rU1bWtFXOOUOHDqUPPvggYIo8KojxjAVxs91KfpkExc1BM3jwYGrcuDFt3bo1IM0vv/xC9evXV+GXd+/eHaiPthFriDNkMyhfvjxlZmZGazpivSfTVmOZBNWxY0cVvbN169Zq3apVq4gXHK2SC3bbtm3pyiuvpLS0tEBTb775ZmD7nnvuob179wb2I234CWz8Z7/rrruUT5YtW6bkwH9+RFpNSkqi++67T23vcZkpGAvY999/f8AXVapUod9++y2SOyLWGQE2YiFffPHFhHwjKC+++KIKBL9///6IFx2pkgv2oUOHaObMmYG+QttCEp/FixeHVgft+wlshAfesWOHgtfKQYMcQeedd57SBFpccsklNG7cuCCNQnd0wUaY54cffjjQTO/evVWc7ECFy4YRYB84cEAJl5KSoi4XH30FChQI+uhzuY/AL2jc7HAceVOiOaJp06Yqw4FTO34CGzpYI7QFNoDDiI0CgLDtFr9aF2zknUE6EKuMGDGCJk2aZO26ro0AG1c5bdo0JRBEKlSoEF111VWUlZXlegOWAXfEhn00sJFJbPr06VaTUdd+Azs0VQcGIqTOgK+sBelVnIou2GgLmdywzJgxQ6XE+/TTT526CDpmDNi4KlwMPvbwXIfcizolXrAhWmpqKqtLv4NtiYRnXiSnuvDCCwP5fKxjoetYwEYbSC6L9C1ID5LnnrFxA/iYw0fc9ddfr0aBnJycUG0c93XAfvbZZ2nUqFGB9pC30C2HSsDYh1nDkP4OI7P18ogfMyNPEFJ0oN6upV0n+3asYKMNPB4i2ZJOMWbERp4RvPkOGzZM5/oDtjpgf/HFFwSYUSAAkjolJyer/itVqkTz5s0LtBtpw28jNv7UVrduXTVyQg+8UOKvVxUrVnTNyGvpFwvY+LNszZo1SeePCFZ/xoBtXVCsax2wY+3DOs9vYFv3Hc86FrDj6U/AjkE9AVtfNAFbXzN1hozYesLJV+o8vRLylTqvq8hWAnZkXaLVCtjRlAmuF7CD9TB+T8DmuUjA5ulkjJWAzXOFgM3TyRgrAZvnCgGbp5MxVgI2zxUCNk8nY6wEbJ4rBGyeTsZYCdg8VwjYPJ2MsRKwea4QsHk6GWMlYPNcEQb2hLQxvDMTZCWB3/WEBNi5Gfj9l5ycvB/4feuWzVTqtltUGoinn3ycvF56d+uaq6k6jh07luczGqSNGkktmzahHl2e8tw/PTo/RY80b5qrqTrwc8OEp+pYv24t9e3ZnX4/fJj2Zmd5vvz32DGysobhZrxe8ktypaWLF9P+X37x3D/4RfvyZd/TwL6nkit57R+079nsPkmHp/dokNvWeBTZbgun4HX/GfkhHV4s4RfiFVYmQekpKC+PPL3CXh4lBw1PuLNlJWDzlBeweToZYyVg81whYPN0MsZKwOa5QsDm6WSMlYDNc4WAzdPJGCsBm+cKAZunkzFWAjbPFQI2TydjrARsnisEbJ5OxlgJ2DxXCNg8nYyxErB5rhCweToZYyVg81whYPN0MsZKwOa5IiawkdMEeUwQsdMeNvjEiROBqKs33ngjHT9+3PUqdOeKLFy4UAVVRKoJRPPEVFRu8UuIs19//ZXKlCmj/AMfIduDVZDeBHVYXnnlFas66lonxBmCvKNde9jgJk2a0L333ktI34FglW7Fs9l9nLkiH374obrIa665hpAbxio1atSgG264QcVcxjEk2HErumDv27cv0OTEiRMJC7f4Bezs7GyVqycjI0PFwQZsGBA2btyowEMAfQRlR/0PP/zgKB8X7B9//JGQCuSmm24KArtz586O7YcePKtgWxdz7bXXUps2bdQucsRAKAQUR0GWgYIFC9LBgwct84hrXbDtjcyaNYt69eplr3Lc9gvYdhEQyhd+QX6gsWPHqm18smJuOgK/u8UX54Jt9VmyZEnCJwYKINXxj3UO5mXHW2J6FEGn6ByjsgU2UqtBwLfffltd00svvUSXXXYZYbK6U4kHbDwGIR0Ft/gRbDweXHDBBcoPCPpuJVeCZpdeeikh65tT0QEbTCAViAU2fg3TvHlzKleunMpL48YCruOsj9ihYOPjD2C/8847Siekz0BWKrebiRXs0aNH05gxer/P9BvYS5YsUT5BtjUUO9iA7vzzz6dOnTqpY9H+iQdse5vz588nBOd3K8aBjQsuUqQIPfTQQ+ray5Ytq5Kaut1ILGDPmTOHJk+e7NZ02HE/gQ2NMNAsWrQooAPejVCHZ+/09HS17ZYFIlFg490ImS/cylkHG89o5557LuGt1yrI8QfhrMWeEdayCV3rgo1n+GLFihGchExhn3/+eWiTUff9AjZgtXxgrdu1a6d0waht1bVv3z6qVtYBHbDBxHXXXUfIToaCF9ZnnnlG+QkpQ5Aqz62cdbBxgUisFAovRoNvvvmGOOmM0YYu2EgYBMGQi+ajjz5iiWWJ6Rew8fi3dOlSlVQJOYKQ2BUjtFUAmNtfQyxbHbBxDrKFAXCU7du3Ez45sOzatctq0nFtBNiOV8g8qAs2s9mIZn4BO+LNx1ipC3aM3QROE7ADUvA3BGy+VpalgG0pobmWEVtPMPlKnadXzH/H5jXvbiVgu2tktxCw7WpE3xawo2tj5BEBm+cWAZunkzFWAjbPFQI2TydjrARsnisEbJ5OxlgJ2DxXCNg8nYyxErB5rhCweToZYyVg81whYPN0MsZKwOa5QsDm6WSMlYDNc0UQ2GtWr6LxY0fzzkyQVa0qlRPUknsz+eEr9QF9etHe7Gz3m02QxYED+6lvj6cT1Jp7M/idbMJ/QbM7M5PuuOXvVKdaEtVOquL5UrNKJWr3cEt1t/hRrtfL0aNH6eTJk+7qGmwxa+arVKH03WpWpNc+wrfCFcvcQzOmnfq5n9f+QfvwUcLBNtifcmmigJYCQY8iWmeKsShgsAICtsHOkUuLXQEBO3bt5EyDFfh/SVcXwRfPvMoAAAAASUVORK5CYII=[/img][br][br]How many values are in the spreadsheet? Explain your reasoning.[br]

[list][*]If you entered the data in the order that the values are listed, the number 7 is in the cell at position A1 and the number 5 is in cell A5. List all of the cells that contain the number 13.[/*][/list][list][*]In cell C1 type the word “Sum”, in C2 type “Mean”, and in C3 type “Median”. You may wish to double-click or drag the vertical line between columns C and D to allow the entire words to be seen.[br][/*][/list]

[list][size=150][*]Next to the word Sum, in cell D1, type =Sum(A1:A20)[/*][*]Next to the word Mean, in cell D2, type =Mean(A1:A20)[/*][*]Next to the word Median, in cell D3, type =Median(A1:A20)[/*][/size][/list][br]What are the values for each of the statistics?[br]

Change the value in A1 to 8. How does that change the statistics?[br]

What value can be put into A1 to change the mean to 10.05 and the median to 9?

[list][size=150][*]Click on the letter A for the first column so that the entire column is highlighted.[/*][*]Click on the button that looks like a histogram to get a new window labeled One Variable Analysis .[/*][*]Click Analyze to see a histogram of the data.[/*][/size][/list][list][size=150][*]Click the button [math]\Sigma x[/math] to see many of the statistics.[br][/*][/size][/list][size=100]What does the value for n represent?[/size]

What does the value for [math]\Sigma x[/math]  represent?

What other statistics do you recognize?[br]

[size=150]Adjust the slider next to the word Histogram.[/size][br]What changes?

[size=150]Click on the button to the right of the slider to bring in another window with more options. Then, click the box next to Set Classes Manually and set the Width to 5. [/size][br]What does this do to the histogram?[br]

[size=150]Click the word Histogram and look at a box plot and dot plot of the data. When looking at the box plot, notice there is an x on the right side of box plot. This represents a data point that is considered an outlier. Click on the button to the right of the slider and uncheck the box labeled Show Outliers to include this point in the box plot.[/size] [br]What changes?

Why might you want to show outliers?

Why might you want to include or exclude outliers?

[size=150]Each stem is written only once and shared by all data points with the same first digit(s). For example, the values 31, 32, and 45 might be represented like:[br][img]data:image/png;base64,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[/img][br][/size][size=150]A class took an exam and earned the scores:[/size][size=150][br]86, 73, 85, 86, 72, 94, 88, 98, 87, 86, 85, 93, 75, 64, 82, 95, 99, 76, 84, 68[br][/size][br]Use technology to create a stem and leaf plot for this data set.[br]How can we see the shape of the distribution from this plot?

What information can we see from a stem and leaf plot that we cannot see from a histogram?

What do we have more control of in a histogram than in a stem and leaf plot?

0.5 1 1.2 1.3 2.1 2.1 2.1 2.3 2.5 2.6 3.5 3.6 3.7 4 4.1 4.1 4.2 4.2 4.5 4.7 4.8[br][br]Use technology to create a histogram for the data with intervals 0–1, 1–2, and so on. Describe the shape of the distribution.[br][br]

Which interval has the highest frequency?[br]

[size=150]133, 133, 134, 134, 134, 135, 135, 135, 135, 135, 135, 136, 136, 136, 137, 137, 138, 138, 139, 140[/size][br][br]Use technology to create a dot plot and a box plot. What is the shape of the distribution?[br][br]

Compare the information displayed by the dot plot and box plot.[br]

[img]data:image/png;base64,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[/img][br]

How many values are represented by the histogram?[br]

Write a statistical question that could have produced the data set summarized in the histogram.[br]

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[/img][br]On average, what was the satisfaction rating of the landscaping company?