IM 7.8.15 Lesson: Estimating Population Measures of Center
Would you use the median or mean to describe the center of each data set? Explain your reasoning.
Heights of 50 basketball players 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[/img]
Ages of 30 people at a family dinner party 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[/img]
Backpack weights of sixth-grade students 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[/img]
How many books students read over summer break 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[/img]
Here are the ages (in years) of a random sample of 10 viewers for 3 different television shows. The shows are titled, “Science Experiments YOU Can Do,” “Learning to Read,” and “Trivia the Game Show.”
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[/img][br][br]Calculate the mean for [i]one[/i] of the samples. Make sure each person in your group works with a different sample. Record the answers for all three samples.
Which show do you think each sample represents? Explain your reasoning.
Here are three more samples of viewer ages collected for these same 3 television shows.
[img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAowAAABECAYAAAALF2QmAAAgAElEQVR4Ae2dC/RPVRbHKUKUZ/IskTTejylU8liJopFXkVEpMQitoZAi715eIeWRaZIwaqI8i/Ga5C2kl0RCGGmGqZjGnvU5a85v3d/9398jfyv33P/ea/3W7977O/f+zv6e1/fsvc+52URFEVAEFAFFQBFQBBQBRUARSIJAtiS/6U+KgCKgCCgCioAioAgoAoqAKGHUSqAIKAKKgCKgCCgCioAikBQBJYxJ4dEfFQFFQBFQBBQBRUARUASUMGodUAQUAUVAEVAEFAFFQBFIioASxqTw6I+KgCKgCCgCioAioAgoAk4Rxo8++kj69Okj//73v7XkFAFFQBFQBBQBRUARUAR+JQScIox/+ctfJFu2bPKPf/wj0/DMnDlTHnjgAfn2228z/aywP2DgwIFGV/S1n/3795tsv/baa7Fr9je+u3TpEna1TP7+9a9/BeYfHRYuXBinw6pVq6Rz586yZs2auOsunGzYsCGDnpMmTXIh62nnMVk99T/kySeflD/+8Y/+y6E+D6qrPXv2jOX5xx9/lLFjx0rr1q3l1ltvFXQ8ceJE7HdXDr788kvp3bu3TJ06NS7LZ86ckbfeekvuu+8+qV+/vvz+97+X9evXx6Vx4SRVPT1+/LgMGDBAbrnlFunUqZOTOlIO6PHKK6/IPffcI0899VRc0aTCIC5xSE8S1VOb3VdffVXuvPNOufvuu+Wdd96xl534DiofxsQxY8bE5T8VBnGJRdxaJX2uCOOuXbvk0ksvNeTzs88+82MSuXN0bdeunfTo0SP2OXDggNETTOncvZ8qVapIuXLlnMCBAdWbd44feughU7aQYeQ///mPNG/eXAoWLCjZs2eX6dOnO6GbN5M09LJly8bKj7L0D8je9C4eJ6unXn0YxHLmzCnFixf3Xg798ZYtWyRXrlxxZfjYY4/F8s0k7frrr5dRo0bJkCFDpFChQlK3bt3Y7y4ckO+LL75Y8ufPLx07dozLMm312muvNeRj9OjRUq1aNbnooouE/tglSVZP//nPf0qZMmWkSZMmMnv2bDPxpsxdm6QuX75cSpQoIRUqVDDltXLlyrgiSoZBXMKQniSrp2R56NChhiO88MIL0q9fP7nwwgtl7ty5IdUmY7boQ/zjIv3Jgw8+GEucCoNYQs9BlrMwQh6uu+46AxzWyqgTxmPHjhnydPToUU+xJz7EylGsWDFx2Xo1fvx4KVmypJw6dSqmKDMuMGCAcpEwYomCJEZV0q2nX3/9tSEjdHyuEcY333xTKlWqlLAIDx06JP/9739jv2Mhp4/CuuyKTJgwQbZv324spH7CiA5e/b755hu54IIL5LnnnnNFPUlVTwcPHiylS5eO63uaNm0qfFyRffv2GdIPUcIq7JdUGPjTh/E8WT3FsponTx5jXbV5x1JcuXJle+rc97Zt20xb27lzZyzvyTCIJfIdJCWMq1evlpo1axrwmGnYjuv999+XG264wTBwGkevXr3iGgizKzpHZszMRHgG9+zdu9d0JMxAa9WqJTt27Ihl5+2335bGjRvL7t27pVmzZmZ2TQHNmTMnlibIwrhgwQKpXbu2+Z969eoJbsdkMmzYMClfvrzRJSsQxo0bN0qBAgWSQRL324svvihFixaVH374Ie66KyeQxFKlSgkWjCBxlTDSJsaNGxekUiSupVNPGbxw8+HKpLNzjTBCjHBxpSuEy9BHzZ8/P91bQpMOl3oQYfRmEGscluJ3333XeznUx6nqaYMGDUzoiFeJKVOmmImqlyx7fw/bcYsWLaRly5YJs5UKg4Q3hvCHoHo6bdo0yZ07t5w8eTKW46VLl5q26KqB6a677pI77rgjpo/3IAgD7+/e44SE8fTp01K4cGHp1q2b7NmzRxYvXixHjhwx90LKFi1aJMyIMV0XKVJEIGJWIJdYqSCSpIPIkaZGjRqmo3/jjTeMyxOXhJVZs2aZWQ3uUKxbkFP+mw6TwkL8hJGOFDcjpJG84bZjZkB+g2Tr1q3mP3g2s+CsQBgxo+MewiWNlQrinkiwvuJOGT58eKIkob+Om5Y6kSj2y1XCWLFiRbnxxhvl3nvvleeff95YS0NfGL8gg+nUU/qFK664Qr7//nvBVeQaYezevbtcc8010r59e3n88cflk08+SYrQihUrTB/1+eefJ00Xxh+TDUIYCiCJ1Oc2bdrEGRvCqIs3T6nqKToxqfHKX//6V1OONgzI+1vYjrGuMS7+6U9/krZt25pxGgOQNRaR31QYhE2nZPkJqqf9+/cX+luv4NkAl2XLlnkvO3H86aefGuvi2rVrA/MbhEFgwmQxjFScHDlyyIwZMxLdG7tOR1inTp3YOWAT1Gxl8+bNBuy+ffvaS/Lyyy+ba4cPHzbXqIQUyOTJk2NpOKABNmzY0FzzE0bI5TPPPBOXvnr16jJo0KC4a5xgeSK9JUNZhTB+8MEHxuVD0DJuETB+4oknMuDDBTqJSy65xAQ7ByYI+cWff/7ZdHCJ9CP7rhJGrFMQRSZhWIAhTokmRiEvpsDspaqneB6om3g9EBcJIzFtWL4ZkOiLiG3zL8zygtOoUSMTC+e95spxokEIKxtE38agumRdBPtU9ZS4MOLd3nvvPVNULNDEG8c1wn3CLrQvxgiMO9RXFinddNNNcYaYVBiEXUdv/oLqKZZxjFxewQABLq+//rr3shPH999/vynDRJkNwiBR2oQWRm54+OGHDWkkXsgSO65DviZOnGgygUuaxo+b1wpxOmx/Y4VOglgVb5A+1j4KwK6Ss2SQ+AivYLnEOonYNDRCttZhAQPEFHOr/RCoy6omv7BqjUpg3QJZhTD6cXj66acN7gcPHoz7CVwISPeWW1wCB05ozFiYrSU8KMuuEkavLtR/YjS7du3qvRypY289pW4yccQqZ8VFwmjzzjfu9dtvv90sKvBet8dMbAkl+eKLL+wlp75TDUJ4MxgPMErgAnRVvPUUHQjlwfXH2MZYhJeOVe943VyQl156yeTd63rFMspYm2gi7sfABT1tHoPqKf0qi8+8wphCmbq2WpowQNpYsnwHYeDV3XuclDCScMmSJcY8i5uPfRAR3Ag0ANzU3333nbCg4Oqrr449F8LotSbyA5n2EkYqJAXAbAWxZBB3k1dY7YNFxZuGAdPG90AE2SLH+/GvSGPAYYZHHohN4ANx4P+Z5WMhzSoCUURvYky9goUXTFxwm3jzbY8ZgIl5TbUwJAqEEZ2JD8ZKFVXx1lNCYGxbte2Xtsw1zglxcVEIw0EH/yQZrw79kovuL1sO6Q5CTPSrVq1qb3Pu21tPvZnH+o8b96effjIx+YQEuSBYFWlbeGu8Qt9K7HCQJMIgKG3YrgXVUyZrGMK8Yg1MmzZt8l4O/THchrILWrxkMx+Egf3N/52SMHIDlQfrE8u0LdOeN29e7FmQw3NFGNetWxd7LgcE4BJIjFhSCWFEmIGnu8IOgur9EPdIZw0hzgp7MRrARIxFF70JXPYKrnzvknvvby4cs2iKju6rr75Kmt2oEMbbbrvNDERJlXX4RzwPtp7iTfC2XY6xdlx22WXmOvsbuiiE0+Bmt14PdLBk0cWFLt4ySHcQgoQwoLkq3noapAMTcyY1qeJVg+49H9c+/vhj0+4+/PDD2N/Tvug3WQEeJKkwCLonLNeC6ilbCNH3EPtnhZ03sBYzAXBFWGNC3bPbyyXKdxAGidImJIx00pZN445m5SmrvSCMuJftknvSQNzYI85KZiyMxE7A5ll0w/YnmMKtOdVPGNkriQUdLKyh04VFU+H9syObL++3nTEw+ERZIPZs0g0+7APHbP7mm2+OG6SIo6JMvQ3ENUzYKokNZlOJi4SRwQYrG+4uYmlwAdEuXLZA+cspnXrqvcc1lzRlhxUfjwx9GxNW+i68M1ZwzeIJgUiy/YX9uGj1DxqE6HOZ2BEfDx5Ys2iPI0aMsBCE/jvdekrMLbH0WIrZjN0lwaWOS5Z6x1ZkWKkI9WG7HSRdDFzQOaiekm/GSWL+IcssOiPu1rtnqgu6Pfroo3LllVeafYiT5TcRBkH3JCSMEEE6NLbFgVl36NAhFrQ7cuRIQzD4jZXOBPqyKaSVzBBGFsPwv8RF5suXzwS32+f6CSMdL42SdHQ83IP72lZse1/Qd1YgjJBEApaZLUEIwYdy9Mf4EZTdqlWrIJicuAZxQkcbMpEs0y4SRrsqEZLIh06AthAVSbeeevV1jTAyMWUHAtsWsSzSbxLLhxAXTtnyu//jouU/aBDCanr55ZfHMGD8wGqVzgTfW/bn6zjdepo3b17j7SBm3sVJHZMaxgM8NowZLLBjNxQkXQzOVxn90v8Nqqc8g1XReN3QnzGDNmjb6i/9j/ORnjAXP39KlI9EGASlT0gYSQxAABcEFHtonUtXriWDxDBSKQnWTLcjwbJIPpkNJfPVBwGQFa6xnxQzXgYlFTcRoF6z0fG5bHNhQyIr1FPCaejbsnI/hceK/tpVDFLVUyxSpHFd8DLS5wRJKgyC7nHxGm5dF1a3/1rYJiWMv1Ym+B8vYfw1/1f/SxFQBBQBRUARUAQUAUUgOQJKGJPjo78qAoqAIqAIKAKKgCKQ5REIDWHExc2CFdzRKoqAIqAIKAKKgCKgCCgC4UEgNIQxPJBoThQBRUARUAQUAUVAEVAEvAgoYfSioceKgCKgCCgCioAioAgoAhkQUMKYARK9oAgoAoqAIqAIKAKKgCLgRUAJoxcNPVYEFAFFQBFQBBQBRUARyICAEsYMkOgFRUARUAQUAUVAEVAEFAEvAkoYvWjosSKgCCgCioAioAgoAopABgScIoy8+q1Pnz7CDvQqioAioAgoAoqAIqAIKAK/DgJOEUb7Nhher3U2smHDBnnggQfiPpMmTTqbRzl3z6pVq6Rz586yZs2aDHl/7bXX4jABo/fffz9DurBe4AXx/nLt2bNnWLOb6XzNnDnT6Bu11wQOHDgwQznu378/03iF8QEbN240uq5cuTKM2TurPDGR5x3fbdu2lYYNGwpt8MCBA2f1rLDexGvixo4dK61btxbewfvkk0/KiRMn4rLren968OBBGTBggDRt2lTuvPNOmTJlirOvcYwrGM8Jrzzs3bu3qad33XWXLFq0yPOr+4e0uyeeeEKaNWsmTZo0kZEjR56TVxxmKcI4ZswYKVu2rPTo0SP2mTp1qvu1I4kGvAe8efPmUrBgQcmePbtMnz49Q+rf/e53Uq9evRgm4OPSQLZlyxbJlStXXP4fe+yxDHpG4cKuXbvk0ksvlWzZsslnn30WBZViOqBXu3bt4soxaoQDZX/44Qe59tprJWfOnPLyyy/H9Hf9YPny5XLDDTfI6NGjBfJfvHhxufrqq+Wnn35yXbVY/rt06SLXX3+9jBo1SoYMGSKFChWSunXrxn7nwPX+tEKFCtKqVSsZN26caYsXXnihIZBxSjp8gsGpZMmSZlycP3++IVMXXHCBvPPOOw5rFZ/1wYMHC0T4xRdfNIai3LlzS8eOHeMTncVZliKMzHghQ1lN6LyPHj0qF110USBhrFKlinmXt6u4vPnmm1KpUiVXs592viH/1113nTz44IORI4zHjh0zOlFPoy6PPPKI3H777YZQRYkwnjlzJq7oZs2aZcoUa2pU5NChQ3FvI1u4cKHREe+VFdf7U/8k7dFHH5W8efNGxsqI0eTiiy+OswxjhWOyGhXxvzGPiQ5lmFlJShhXr14tNWvWlDx58gizDtsocFcyk8QiULp0aenVq5ecOnUqlhfAZxAnk6ThGdyzd+9eY8ansGrVqiU7duyI3fP2229L48aNZffu3caMysytcuXKMmfOnFiaIJf0ggULpHbt2uZ/sJLhek0kmGeZNWVVSUQY8+XLJ9u2bXMWlueee864TpxVIM2MDxs2TMqXL2/aYdQsjJCKAgUKpImEu8non+hPd+7cKYULF46UhdFfKrj5sKIeP37c/1NkzgkLoS1iqbLien9q9bDfs2fPNjry+t4oyLRp0wxh/O6772LqwCFwUUdV8Lj99re/zbR6CQnj6dOnTYfWrVs32bNnjyxevFiOHDli/pBOj86A2RZuiCJFigiDmRXIZbFixQyRJB1EjjQ1atSQCRMmyBtvvCHlypWTatWq2VuE2ShEktkZcYWQU/6bxrh06VKTzk8YaaS4WiGN5A2XM50x+Q2SihUryo033ij33nuvPP/888bqFpQuqteCCCO4gXHLli3loYcekldffTWO/LuARffu3eWaa66R9u3by+OPPy6ffPKJC9n+RXncunWraR+0i+3bt5syi5JLeu7cuZI/f34zy8cT4FIMbboFSazbVVddZWLguIdJcZQsjOh08uRJM/mkHylTpow888wz6cLjZLoVK1aYtvj555+b/EehP/UXxKBBg6REiRL+y86e48UgNI0YTfrTESNGmLpqy9BZxXwZP3z4sDARJ3yC8rM8ypfsF50mJIzMCnPkyCEzZsxI+UAG7Dp16sTSQczq168fO9+8ebNpVH379o1do6OEqKAUwoDB+eTJk2NpOIDgEUCN+Akj5NLfIVWvXl2o4EGCJQqiiEW0aNGicsUVVyQkl0H3u34tiDBC+qlQfCDSxAJi6SW42xVhBkzcVP/+/c2EAx1wFUVFsN5T14cPH25UiiJh/OCDD4T2+dRTT5mOnL6AoO0oSdeuXY2Hxbpuo0gY165da/pW4qXxQnm9SFEqS6tLo0aNzKICex6F/tTqwjdWOCzhjA9Rko8//tiEaNHP8MGIFTV59tlnjUEN/Tp16iRnu1jYi0tCwkiihx9+2JBGYqYsseM6A9jEiRPlpptuMi5p3A64yqwQT8b2N1bwpxNU6l1ggsUERdavX2+SWTJILJNXsFxinURsGhRnRR6dEsSU4E77gUnffffd3kcEHtvAVzrxrCJBhNGv+9///ndTVq42IAZj4sOwckdFWLGIld7GpUSRMPrL6umnnzb9Ays2oyBLliyRyy67TLz6RJEw2rLC4EC8OB6fqJJGJnCEUXzxxRdW7QzfLvenP//8s5m84cp0yYCQoRB8F1gkCU/AwLBv3z7jlcLIwOr3KMqXX34pN998s/HCseAuM5KUMPJgOjoshrh+2QcRadOmjRmQcVMzA2FRBavhrEAYvdZErmOt9BJG3GkQRiwLiCWD33//vX2M+WZmgzXQmwayZ2NHGEzZZsT7Cdo6Ju6h/z8hxhLLTVaRdAgjWODedXlbGhts7598uFjOkERWKdJ+WOnGh3Kk7dDJYd2PokCs0JFY6CgIXhImzbYM+UY/JtvEakdVLr/8cuPRiZp+eN5of8uWLUupmov9KRNvtipjjULUtrZitTCeS69wjZXT1vrv/S0Kx+yuQX/z1ltvZUqdlISRpzPTYBsIgkJtjMa8efNifww5PFeEcd26dbHnctCiRQtp0KCBuWZJpTWtMrvDjXW2ctttt5kFNmd7v2v3pUMYsR4TS5YZXM83LoQpXHLJJTGL3PnOT2b/n8mV90PMLo2fyVzU9mK0WOF5QMeorLD9+uuv48qQ8qSdDR06VL766iurdqS+GXwhHHiqoiSWLHoXuiTSz8X+lHLDq4gVLpn1NJHOYb8Ol/F7FsePH28m5sTgRlFYTEx/6uVtZ6NnQsKIy3fTpk3mmbijS5UqZTbwhDAyU+7Xr59h46SBuBFEaiUzFkYWxuByY9ENy99xO9v9kfyEkc6WTpeFNVhiqOjEJkBw/cJCCBbrYJIl+ByXF89OZ4bof5ar50GEkaBtVmyyZQubmeLaJwSAWBwXhPIk/hVLN3UGMkWdwOodVYmiS5qODEsG7RiXUdWqVY0bxbrho1iWUXNJv/vuu8ZjhPuS9sgYwViRrsfHhTJmhS0Wfyal9Jv2Y7eicb0/pb0R78buJhBiqx/ffu+fC+UVlEe8Z3hM6UcRJm9wFkKZoiJwJzgPXIgJKZvMsxA5s163hIQRIsjAS8Uh6LVDhw6xOAZ2Dacj4DdWOtsNTC3YmSGMLIbhf3HVsD0Bbw6w4ieMEAQWuJAOMsQ9uK+JS/CLXYUJSeRz5ZVXOr33oF+/dM6DCCN7bOHupDzBhU1oiS91RWjsrMZk9oQOWBapjxDgqErUCCODFPHQtgxpx/Q3TE6jLFEjjGwWTMwi7ZCypF3S70ZFsBbSR6Kb/4NFDnG9P8WA4tfNnvMGmygIRgbKi36GkAnKlLcTRWUPWPpTvKe0QzuuswDtXIzrCQkjFYNBF1dK0ODLnkzn0h1mySCzGBRmz8YgS2FQhcWySD4pcI4TCb9hRTuX+U70Xy5dh3gTGMsr9lwVwhSoM8nK31Xdskq+cQfhOmFgVnETAfpuJuxRGXzPphSi0J+ejd6u3UM/w7iHNzWKwhuW6E/PpZs9KWH8NUH0EsZf83/1vxQBRUARUAQUAUVAEVAEkiOghDE5PvqrIqAIKAKKgCKgCCgCWR6B0BBGXNwsWMGloaIIKAKKgCKgCCgCioAiEB4EQkMYwwOJ5kQRUAQUAUVAEVAEFAFFwIuAEkYvGnqsCCgCioAioAgoAoqAIpABASWMGSDRC4qAIqAIKAKKgCKgCCgCXgSUMHrR0GNFQBFQBBQBRUARUAQUgQwIKGHMAIleUAQUAUVAEVAEFAFFQBHwIqCE0YuGHisCioAioAgoAoqAIqAIZEDAKcL40UcfSZ8+fSK7M3uG0tELioAioAgoAoqAIqAIhAABpwijfRsMr4HLjPA6oOeff17uuOMOefvttzPzKGfu5TWL6NqtWzdp2bKlM/lON6Ps3zljxgx54IEHEr6DeNWqVdK5c2dZs2ZNuo8NTTr0mzBhgqmzrVu3llmzZoUmb+cqIwMHDjTlRxnaz/79+zM8ft26dULaW265JfC98RluCNGFdOppiLL7i7LCK9ZeeOEF817ehg0bSs+ePeXAgQO/6BmuJE7VlzDG9O7dW6ZOneqKSnH5TFVPeRUv+tEG+/XrJ8ePH4+734WTVGXE3tBDhw6Vvn37uqBOXB5//PFHGTt2rDBW3HrrrfLkk0/KiRMn4tKczUmWI4zjxo2T3Llzy80332wA/fTTT88GN6fu2bNnj9SpU0eKFCliOvGokY0vvvhCKleuLIULF5Zs2bKZ94N6C4h3oTdv3lwKFixoXjQ/ffp0789OHLdq1UquvvpqefXVV+Xpp5+WXLlyyXPPPedE3tPN5KWXXirt2rWTHj16xD5ewgEhgUjSfu+66y556aWXhHfPuyKp6qkreiTK5/Lly+WGG26Q0aNHG0JfvHhxU2d5p21UJJ2+ZMiQIXLxxRdL/vz5pWPHjs6pnqqefvPNN1K6dGkzeZ02bZpUq1ZNatWq5dQ74FOV0TvvvGPGS8YUdHVNunTpItdff72MGjVK0LVQoUJSt27dTKuRpQjjvHnzJE+ePPLmm29mGjhXHoBlkQbduHFjYcYURdm7d68hT8z6gwgjOmOROnr0qFx00UXiGmH829/+Zogub0KyAmmkE4BERUGOHTtmyo4ySiQPP/ywlClTRnbt2pUoSaivp1NPQ61AisydOXMmLgUTU9rjxo0b4667fpKqL8ETsH37dmPZcZEwpqqnAwYMkPLly4udCGCQuPDCC4Xx1RVJVUYLFy6U+fPny8iRI50kjIcOHYp7ax760BY3bNiQqSJKShhXr14tNWvWNCSrQoUKsT97//33zUwSiwDsu1evXnGziyZNmhhSBsslDc/gHioi5lFmX8xIduzYEcs87lJIze7du6VZs2ZmMMRqNGfOnFiaIJf0ggULpHbt2uZ/6tWrJ5CGIMEcW7JkScHCmJUEszRldC7M0WHH7YMPPkhIGG3eXSSMTz31lJQtW9aqYL4///xzo+vatWvjrrt6AqkoUKBAwuxv2rTJkP3NmzcnTOPKD+nUU1d0SZbPRYsWSc6cOZ10VybTy/6Wqi9hrHORMFr9EtXTUqVKGRenTcc31qv27dt7LzlxnKqMXCWMfvC//fZbM15AgjMjCQnj6dOnjYuPmDdmEIsXL47FhkHK6AxgsbghcHUOGzYslg/IZbFixQyRJB1EjjQ1atQwcVhvvPGGlCtXzli+7E3MRiGSVapUkUmTJhlyyn/DipcuXWqS+QkjyuNmhDQeOXJExowZY8gt+fULhBTyyiyISoJ7j8aMDlGW6tWrG+ta9+7d5Te/+Y1xTf/5z3+OpMqJOjivsqk6eW/asBxj0bjkkkuEuBQrxAzRNl5//XV7yenvuXPnGhceLmli35hgeuWRRx6Rpk2byrPPPmtcLUwmn3jiibiJqjd9mI/Tqadhzn+yvJ08eVK2bdtmQiewBj/zzDPJkjv9W6q+JBUZCbvyQfWUPoh+h/bqFUJFCEdwTVKVUVQI44oVK0y5YWjIjCQkjAxIOXLkMAsJUv0BZIQYOSsVK1aU+vXr21PBKkAl8waPvvzyy+ba4cOHTToqIGkmT54cu4+DG2+8UQigRvyEEXLp75AgSIMGDYp7BicEr+bLl88MOsuWLTOk9KqrrjKxb8SlRFHQi1i3yy+/XMaPH2/I/X333Wdwnj17duRUDurg/Eqm6uT96cNwvnLlSlNmI0aMMATp1KlThlTRXt57770wZDHTeaDsiMnEmgoxRDcIoZVGjRqZCR9kkgkrJJr2/Ic//MEmceY7nXrqjDK+jGLxLlq0qAmhgEB4vUi+pM6fpupLUpGRsAMQVE9ZKELbxFDkFXYvwU3tmqQqo6gQRvpPPL+ZlYSEkQcTMwRpfPDBB8USO64zYE2cOFFuuukm4+7E7eCtLJUqVTLb39jMseLqggsuiFsxtnXrVlPx1q9fb5JZMkgsk1ewXGKdRGwaVkkTu5U9e3ZDTAmAt58SJUrI3Xff7X2EOcZygVvPa6XB0knl91szMtzs6AUW9KDflClT4jTAAkzZRU2COji/jqk6eX/6sJxDpGhDBGFDlDp16mTKNqoWcmI0qbsHDx40RYDH4p577okrjq5du5rYKZ9YckwAAASgSURBVBtLFfdjiE/Sqachzn5aWcPgwOIlYsajShpT9SWpyEhaQJ7HREH1lP6Gdonn0CsYjQgzc01SlVEUCOPw4cNNuA+LmTIrSQkjD1+yZIlgMcT1yz6ISJs2bQTSgZv6u+++M7N9XLxWIIxeayLXIZ7eLQY+++wzU/GolIglg/5Vj6zyYcbqTQNhtD55AnBnzpwZ9wnaNgVLhLVUmoeJCM+h8rPSK4piMfLHdTIRIA4lahLUwfl1TNXJ+9OH6ZywC3RkMMYaB4mKqkAUaZt2gdq1115rVvt59SW8hDTEPbsk6dRTl/RJlle8G8S4R1FS9SWpyEjYMQmqpyxswkDkXzjILg7sROGapCoj1wkjW83hZcSrei4kJWHkT1hpS4fNvksMWnTS3hVRkMNzRRjZY80rLVq0kAYNGphLllTafRgJkk93axHiIiG9P/zwQ+zxkGF0YRVqVAWyjbXGK4QPeEMGvL+5fBzUwfn1SdXJ+9OH8ZwYXbbsoDOIquB5oG3aFbZt27Y1rmqvvv379zeDFx4PlySdeuqSPonyCrlgwR0T1ChKqr4kFRkJOyaJ6injMR47K3gQ8QKmOxbb+8LwnaqMXCaMlixmdqGLt5wSEkZcvqxMRHBHY5HCtQlhxDXGZp10CKSBuHlXcWbGwsjCGLYkYNENsxjczuyJhPgJI3GJDJyYx6m05IetRyC4fkEfLDKs3OaYdJCmqlWrBqb33+/qOXui0Zg//PBDsw0CZQimYBk1SdTBefVM1cl704btmC1nXnnlFWNxv/POO8OWvUzlhwkom3TTjrds2WLaJXulco7Qz9DvoD/tm4Vw9Em4PV2TdOqpazqR33fffddYwAn7wfPEGEGZBXl8XNTPn+dUfUkqMuJ/XtjOE9XTt956y3gMGXeZrOHly5s3r9m2LGw6pMpPqjJylTDiNWWrI9Z47Ny5M/bx7mubCpug3xMSRjpoyBgri4mb6tChQyz+DxDpCPiNPf7sxpD2DzJDGFkMw/9i9iZWizcHWPETRkglC1xIR+PlHixq+/bts7fEfQMci2JIR/5ZUPPVV1/FpYnaCQMuu7yzAh2MwIqtdqIoiTo4r66pOnlv2jAds9UUFjdWB9MmmBxFRaijxNSiH+2S9kl/w+TUK7incXHatn7//fc7uQ9lOvXUq7crx4MHDzYxi5QhZckqaf9qWld0SSefqfqSVGQknf84n2mS1VO8VsSngsEVV1xhJgrnM69n+9+pyshFwgiJxyhEG/R/WI+SGUlIGHkoq2x5BVDQKmI2gSZG7lyJJYPEMDKAsGdjkKUw6P8YPMknFph0BlLSMQPOSgKWYGotNllJ9yjoivXNhmJEQZ8gHdiShXjEVC5m2rp38VrQs/Ta+UGA/oUJe7IN2M9PzvRfzzUCtFPe+qKSdRBIShh/TRi8hPHX/F/9L0VAEVAEFAFFQBFQBBSB5AgoYUyOj/6qCCgCioAioAgoAopAlkcgNIQRFzcLUdRlmuXrpAKgCCgCioAioAgoAiFDIDSEMWS4aHYUAUVAEVAEFAFFQBFQBP6PgBJGrQqKgCKgCCgCioAioAgoAkkR+B8dT3bj1G8x/AAAAABJRU5ErkJggg==[/img][br][br]Calculate the mean for [i]one[/i] of these samples. Record all three answers.
Which show do you think each of these samples represents? Explain your reasoning.
For each show, estimate the mean age for all the show's viewers.
Calculate the mean absolute deviation for one of the shows' samples. Make sure each person in your group works with a different sample. Record all three answers.
What do the different values for the MAD tell you about each group?
An advertiser has a commercial that appeals to 15- to 16-year-olds. Based on these samples, are any of these shows a good fit for this commercial? Explain or show your reasoning.
A movie rating website has many people rate a new movie on a scale of 0 to 100. Here is a dot plot showing a random sample of 20 of these reviews.
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[/img][br][br]Would the mean or median be a better measure for the center of this data? Explain your reasoning.
Use the sample to estimate the measure of center that you chose for [i]all [/i]the reviews.
For this sample, the mean absolute deviation is 19.6, and the interquartile range is 15. Which of these values is associated with the measure of center that you chose?
Movies must have an average rating of 75 or more from all the reviews on the website to be considered for an award. Do you think this movie will be considered for the award? Use the measure of center and measure of variability that you chose to justify your answer.
Estimate typical temperatures in the United States today by looking up current temperatures in several places across the country. Use the data you collect to decide on the appropriate measure of center for the country, and calculate the related measure of variation for your sample.
IM 7.8.15 Practice: Estimating Population Measures of Center
A random sample of 15 items were selected.
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[/img][br][br]For this data set, is the mean or median a better measure of center? Explain your reasoning.
A video game developer wants to know how long it takes people to finish playing their new game. They surveyed a random sample of 13 players and asked how long it took them (in minutes).
[br]1,235 952 457 1,486 1,759 548 1,037 1,846 1,245 976 866 1,431[br][br]Estimate the median time it will take [i]all[/i] players to finish this game.
Find the interquartile range for this sample.
Han and Priya want to know the mean height of the 30 students in their dance class. They each select a random sample of 5 students.
[list][*]The mean height for Han’s sample is 59 inches.[/*][*]The mean height for Priya’s sample is 61 inches.[/*][/list][br]Does it surprise you that the two sample means are different?
Are the population means different? Explain your reasoning.
Clare and Priya each took a random sample of 25 students at their school.
[list][*]Clare asked each student in her sample how much time they spend doing homework each night. The sample mean was 1.2 hours and the MAD was 0.6 hours.[/*][*]Priya asked each student in her sample how much time they spend watching TV each night. The sample mean was 2 hours and the MAD was 1.3 hours.[/*][/list][br][br]At their school, do you think there is more variability in how much time students spend doing homework or watching TV? Explain your reasoning.
Clare estimates the students at her school spend an average of 1.2 hours each night doing homework. Priya estimates the students at her school spend an average of 2 hours each night watching TV. Which of these two estimates is likely to be closer to the actual mean value for all the students at their school? Explain your reasoning.