IM Alg1.1.12 Lesson: Standard Deviation
What do you notice? What do you wonder?
[table][tr][td]mean: 10, MAD: 1.56, standard deviation: 2[/td][td]mean: 10, MAD: 2.22, standard deviation: 2.58[/td][/tr][tr][td][img]data:image/png;base64,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[/img][/td][td][img]data:image/png;base64,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[/img][/td][/tr][tr][td][/td][td][/td][/tr][tr][td]mean: 10, MAD: 2.68, standard deviation: 2.92[/td][td]mean: 10, MAD: 1.12, standard deviation: 1.61[/td][/tr][tr][td][img]data:image/png;base64,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[/img][/td][td][img]data:image/png;base64,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[/img][/td][/tr][tr][td][/td][td][/td][/tr][tr][td]mean: 10, MAD: 2.06, standard deviation: 2.34[/td][td]mean: 10, MAD: 0, standard deviation: 0[/td][/tr][tr][td][img]data:image/png;base64,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[/img][/td][td][img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAEiCAYAAADXvYSyAAAbA0lEQVR4Ae3da6hU1RvH8dT856WsrHfqyTxpvtMiylRSDOxkmlIiEVHpsTQkpHrRxcg0y3dZWSEYHbETgVDSzcoSi0y7vDCU1FIsM6KbeaEyKl1/nhn27JnlmTN79mXtvdb6DsjMPrP3Wuv5PHvmdzx7PJ6muCGAAAIIIBBD4LQYx3AIAggggAACigDhJEAAAQQQiCVAgMRi4yAEEEAAAQKEcwCBOgIHDx5U7e3tqk+fPmrTpk119uLLCPgrQID423sqbyBw2mmnqeo/V111VYMjeBoBvwQIEL/6TbVNCFSHR/C4icPZFQHnBQgQ51tMgXEFgtAI7idPnhx3KI5DwEkBAsTJtlJUGgJ//vmneuCBB1RLS4vavn17GkMyBgJOCRAgTrWTYhBAAAFzAgSIOWtmQgABBJwSIECcaifFpC3w4osvqilTpqhjx46lPTTjIWC9AAFifQspICuBGTNmVD7G29raqvbt25fVVIyLgJUCBIiVbWPRJgSCT18F97169TIxLXMgYI0AAWJNq1ioaYEgOIL7UaNGmV4C8yFQaAECpNDtYXF5CsivMAnCQ+7XrFmT53KYG4HCCRAghWsJCyqawP79+4u2JNaDQCEECJBCtIFFIIAAAvYJECD29YwVI4AAAoUQIEAK0QYWUUSBxYsXV66BzJw5s4hLZE0I5CpAgOTKz+RFFqi+gC6Pzz///CIvl7UhYFyAADFOzoS2COgBItvcEEAgFOAVEVrwCIEaAT1A5Eda3BBAIBQgQEILHiFwisChQ4dUR0fHKV/nCwggoBQBwlmAAAIIIBBLgACJxcZBCCCAAAIECOcAAnUE1q9fr0aOHFn6KO/y5cvr7MWXEfBXgADxt/dU3kBAv4i+cuXKBkfwNAJ+CRAgfvWbapsQ0AOEj/E2gceuXggQIF60mSLjCOgBMnv27DjDcAwCzgoQIM62lsKSCkhgBCFy2WWXqV9++SXpkByPgFMCBIhT7aQYBBBAwJwAAWLOmpkQQAABpwQIEKfaSTEIIICAOQECxJw1M1km0NraWrkGItdCNmzYYFkFLBeBbAUIkGx9Gd1igeACevW9xeWwdARSFyBAUidlQFcEqoMjeOxKbdSBQBoCBEgaiozhpIB8dDcIjt69e6stW7Y4WSdFIRBXgACJK8dxCCCAgOcCBIjnJwDlI4AAAnEFCJC4chyHAAIIeC5AgHh+AlB+fYHp06dXroFceOGFau/evfV35hkEPBQgQDxsOiVHEwguoAf3s2bNinYgeyHgiQAB4kmjKbN5gSA4qu+bH4UjEHBXgABxt7dUllCgb9++lR9hSYisWbMm4YgcjoBbAgSIW/2kmhQFPv74YzVhwoRSiKxevTrFkRkKATcECBA3+kgVCCCAgHEBAsQ4ORMigAACbggQIG70kSoyEti9e7davnx5RqMzLAJ2CxAgdveP1WcoUP3pK3l83333ZTgbQyNgnwABYl/PWLEhAT1AZJsbAgiEArwiQgseIVAjoAfIoEGDap5nAwHfBQgQ388A6q8r8Mwzz6j//e9/pY/xzp8/v+5+PIGArwIEiK+dp24EEEAgoQABkhCQwxFAAAFfBQgQXztP3ZEFtm7dGnlfdkTAJwECxKduU2tTAvpFdLkmwg0BBEIBAiS04BECNQJ6gIwYMaLmeTYQ8F2AAPH9DKD+ugJ6gMhv5+WGAAKhAAESWvAIgRqBOXPmVH6d++WXX65+/fXXmufZQMB3AQLE9zOA+rsVePvtt1V7e3u3+/AkAr4KECC+dp66EUAAgYQCBEhCQA5HAAEEfBUgQHztPHU3FDhw4ICaPXu26t27t9q4cWPD/dkBAd8ECBDfOk69kQX0T2GNHz8+8rHsiIAPAgSID12mxlgCeoDINjcEEAgFeEWEFjxCoEZAD5Brrrmm5nk2EPBdgADx/Qyg/roCx48fV4sWLVLDhg1TO3bsqLsfTyDgqwAB4mvnqRsBBBBIKECAJATkcAQQQMBXAQLE185TdySBF154QbW1takjR45E2p+dEPBJgADxqdvU2pSAfhF91qxZTR3Pzgi4LkCAuN5h6ostoAcIH+ONTcmBjgoQII42lrKSC+gBMmrUqOSDMgICDgkQIA41k1LSFdiyZYuaOHFi6Ve6y7UQbgggUCtAgNR6sIUAAgggEFGAAIkIxW4IIIAAArUCBEitB1sI1Ajs2rVLPfHEEzVfYwMBBMoCBAhnAgJ1BPSL6Pfee2+dPfkyAn4KECB+9p2qIwjoAXLeeedFOIpdEPBHgADxp9dU2qSAHiCDBg1qcgR2R8BtAQLE7f5SXQKBlStXqjPOOKP0Md677rorwUgcioCbAgSIm32lqpQEjh49qjo7O1MajWEQcEuAAHGrn1SDAAIIGBMgQIxRM5GtAp988omtS2fdCGQqQIBkysvgNgu89tprasSIEaVrII8//rjNpbB2BDIRIEAyYWVQFwT0T2FJmHBDAIFQgAAJLXiEQI2AHiD9+vWreZ4NBHwXIEB8PwOov66AHiBz5sypuy9PIOCjAAHiY9epObLAhg0bVHt7e+T92REBnwQIEJ+6Ta0IIIBAigIESIqYDIUAAgj4JECA+NRtam1K4LvvvlO33367Ov3009XGjRubOpadEfBBgADxocvUGEtAv4g+fvz4WONwEAKuChAgrnaWuhIL6AEi29wQQCAU4BURWvAIgRoBPUDa2tpqnmcDAd8FCBDfzwDqryvw999/q4cffli1traqnTt31t2PJxDwVYAA8bXz1I0AAggkFCBAEgJyOAIIIOCrAAHia+epO5LA6tWr1eTJk9Xhw4cj7c9OCPgkQID41G1qbUpg2rRppV/lLhfThw4dqr755pumjmdnBFwXIEBc7zD1xRbQP4XVq1ev2GNxIAIuChAgLnaVmlIR0ANk9OjRqYzLIAi4IkCAuNJJ6khdoH///pUfYUmYrF27NvU5GBABmwUIEJu7x9qNCBw4cMDIPEyCgG0CBIhtHWO9CCCAQEEECJCCNIJlIIAAArYJECC2dYz1GhNYtGhR5RrIjBkzjM3LRAjYIkCA2NIp1mlcQP8U1sCBA42vgQkRKLIAAVLk7rC2XAX0AJFtbgggEArwiggteIRAjYAeIEuWLKl5ng0EfBcgQHw/A6i/W4Fjx46pzs7ObvfhSQR8FSBAfO08dSOAAAIJBQiQhIAcjgACCPgqQID42nnqbijw6quvquHDh5c+yrts2bKG+7MDAr4JECC+dZx6IwvoF9GffvrpyMeyIwI+CBAgPnSZGmMJ6AHCx3hjMXKQwwIEiMPNpbRkAnqAtLe3JxuQoxFwTIAAcayhlJOewNy5cyu/ymTMmDHq0KFD6Q3OSAg4IECAONBESkAAAQTyECBA8lBnTgQQQMABAQLEgSZSAgIIIJCHAAGShzpzWiEwdOjQyjUQuaD+5ptvWrFuFomAKQECxJQ081gnoH8Ki4/xWtdCFpyxAAGSMTDD2ytAgNjbO1ZuRoAAMePMLBYKXHHFFZUfYfXp00dt27bNwipYMgLZCRAg2dkyMgIIIOC0AAHidHspDgEEEMhOgADJzpaREUAAAacFCBCn20txSQSmTp1auQbS0tKivv766yTDcSwCzgkQIM61lILSEtA/hTVz5sy0hmYcBJwQIECcaCNFZCGgBwj/DiQLZca0WYAAsbl7rD1TgTPPPLPyIywJj7Vr12Y6H4MjYJsAAWJbx1ivMYGtW7eqSZMmlUKko6PD2LxMhIAtAgSILZ1inQgggEDBBAiQgjWE5SCAAAK2CBAgtnSKdeYisHPnTrVs2bJc5mZSBIouQIAUvUOsLzcB/VNY99xzT25rYWIEiihAgBSxK6ypEAJ6gPAx3kK0hUUUSIAAKVAzWEqxBPQAGTx4cLEWyGoQyFmAAMm5AUxfXIHnnntO9e3bt/Qx3gULFhR3oawMgZwECJCc4JkWAQQQsF2AALG9g6wfAQQQyEmAAMkJnmntEfjoo4/sWSwrRcCgAAFiEJup7BLQL6I/9dRTdhXAahHIWIAAyRiY4e0V0ANk+PDh9hbDyhHIQIAAyQCVId0Q0AOkX79+bhRGFQikJECApATJMO4J3HHHHZVf537llVeq33//3b0iqQiBBAIESAI8DnVf4N1331V33nmn+4VSIQIxBAiQGGgcggACCCCgFAHCWYBAA4GTJ0822IOnEfBTgADxs+9UHUFg//796tZbb1U9evRQ77zzToQj2AUBvwQIEL/6TbVNCOifwho3blwTR7MrAu4LECDu95gKYwroASLb3BBAIBTgFRFa8AiBGgE9QNra2mqeZwMB3wUIEN/PAOqvK/DPP/+oRx55RMm/QP/qq6/q7scTCPgqQID42nnqRgABBBIKECAJATkcAQQQ8FWAAPG189QdSWDVqlVq0qRJ6rfffou0Pzsh4JMAAeJTt6m1KQH9IvrMmTObOp6dEXBdgABxvcPUF1tAD5CePXvGHosDEXBRgABxsavUlIqAHiCXXHJJKuMyCAKuCBAgrnSSOlIX2LZtm7r66qtLv9K9o6Mj9fEZEAHbBQgQ2zvI+jMXOHjwYOZzMAECNgoQIDZ2jTUjgAACBRAgQArQBJZQXIEdO3aoxx57rLgLZGUI5ChAgOSIz9TFFnjooYcq/6Xt9OnTi71YVodADgIESA7oTGmHgP4prIEDB9qxcFaJgCEBAsQQNNPYJ6AHyODBg+0rghUjkKEAAZIhLkPbLaAHyNKlS+0uiNUjkLIAAZIyKMO5JfDHH3+oV155xa2iqAaBlAQIkJQgGQYBBBDwTYAA8a3j1IsAAgikJECApATJMO4JrFu3Tg0bNqz0UV6uf7jXXypKLkCAJDdkBEcF9IvoF110kaOVUhYC8QQIkHhuHOWBgB4gss0NAQRCAV4RoQWPEKgR0ANk7ty5Nc+zgYDvAgSI72cA9Xcr8N5776l58+Z1uw9PIuCrAAHia+epGwEEEEgoQIAkBORwBBBAwFcBAsTXzlN3Q4F9+/apW265pfQx3g0bNjTcnx0Q8E2AAPGt49QbWaClpaXy69zlgvobb7wR+Vh2RMAHAQLEhy5TYywB/VNYfIw3FiMHOSxAgDjcXEpLJqAHyLXXXptsQI5GwDEBAsSxhlJOegJjxoyp/Airb9++6tNPP01vcEZCwAEBAsSBJlICAgggkIcAAZKHOnMigAACDggQIA40kRIQQACBPAQIkDzUmdMKgeuuu65yDWTIkCFq9+7dVqybRSJgSoAAMSXNPNYJ6J/C6tmzp3U1sGAEshQgQLLUZWyrBfQA4d+BWN1OFp+BAAGSASpDuiEwYMCAyo+wJDw6OzvdKIwqEEhJgABJCZJh3BX48ccf3S2OyhBIIECAJMDjUAQQQMBnAQLE5+5TOwIIIJBAgABJgMehbgs8+OCDlWsg119/vdvFUh0CMQQIkBhoHOKHgP4prIULF/pROFUiEFGAAIkIxW7+CegBwsd4/TsHqLh7AQKkex+e9VhAD5ClS5d6rEHpCJwqQICcasJXECgJPP/886p///6l6yB33303KgggoAkQIBoImwgggAAC0QQIkGhO7IUAAgggoAkQIBoImwjoAps3b9a/xDYCCCilCBBOAwTqCOgX0VesWFFnT76MgJ8CBIiffafqCAJ6gPAx3gho7OKVAAHiVbspthkBPUDkE1ncEEAgFCBAQgseIVAjMG/evMqvMhk3bpw6cuRIzfNsIOC7AAHi+xlA/QgggEBMAQIkJhyHIYAAAr4LECC+nwHU31Dgv//+a7gPOyDgowAB4mPXqTmSQEtLS+UaiFxQf/311yMdx04I+CJAgPjSaepsWkD/FNbYsWObHoMDEHBZgABxubvUlkhADxDZ5oYAAqEAr4jQgkcI1AjI3ziCEJF/A/LZZ5/VPM8GAr4LECC+nwHUX1fgxIkTasmSJeriiy9We/bsqbsfTyDgqwAB4mvnqRsBBBBIKECAJATkcAQQQMBXAQLE185Td0OBKVOmVK6BDB48WO3atavhMeyAgE8CBIhP3abWpgSCC+jB/Y033tjU8eyMgOsCBIjrHaa+2AJBcFTfxx6MAxFwUIAAcbCplJSOwIABAyo/wpIQ6ezsTGdgRkHAEQECxJFGUkb6AvLvPiZPnlwKkbVr16Y/ASMiYLkAAWJ5A1k+AgggkJcAAZKXPPMigAAClgsQIJY3kOVnK/Dll1+W/jV6trMwOgJ2ChAgdvaNVRsQqP70lTxeuHChgVmZAgF7BAgQe3rFSg0L6AEi29wQQCAU4BURWvAIgRoBPUCGDBlS8zwbCPguQID4fgZQf12BVatWqbPOOqv0MV5+fFWXiSc8FiBAPG4+pSOAAAJJBAiQJHociwACCHgsQIB43HxKjyawadOmaDuyFwKeCRAgnjWccqML6BfRn3zyyegHsycCHggQIB40mRLjCegB0traGm8gjkLAUQECxNHGUlZyAT1A+vfvn3xQRkDAIQECxKFmUkq6AvPnz6/8Ovfx48ero0ePpjsBoyFguQABYnkDWX62Ah988IFasGBBtpMwOgKWChAgljaOZSOAAAJ5CxAgeXeA+Qsv8O+//xZ+jSwQgTwECJA81JnTCoG9e/eqm2++uXQd5K233rJizSwSAZMCBIhJbeaySkD/FNbYsWOtWj+LRSBrAQIka2HGt1ZADxDZ5oYAAqEAr4jQgkcI1AjoATJlypSa59lAwHcBAsT3M4D66wqcOHFCLV26VI0cOVLt2bOn7n48gYCvAgSIr52nbgQQQCChAAGSEJDDEUAAAV8FCBBfO0/dkQSeffZZNWHCBPXzzz9H2p+dEPBJgADxqdvU2pSAfhH9hhtuaOp4dkbAdQECxPUOU19sAT1AevToEXssDkTARQECxMWuUlMqAnqAXHrppamMyyAIuCJAgLjSSepIXeCLL75QbW1tpV9l8tJLL6U+PgMiYLsAAWJ7B1l/5gI//fRT5nMwAQI2ChAgNnaNNSOAAAIFECBACtAEllBcge3bt6tHH320uAtkZQjkKECA5IjP1MUWuP/++yv/pe3UqVPVyZMni71gVoeAYQECxDA409kjoH8K69xzz7Vn8awUAQMCBIgBZKawU0APkCFDhthZCKtGICMBAiQjWIa1X0APkGXLltlfFBUgkKIAAZIiJkO5J3D8+HG1bt069wqjIgRSEChUgPzwww8plJRsCNYQ+v3111/hRk6P6EcZHofQIW8LmT/vNYhGEdZQqAD5/vvvc3qbCqd9+eWXw42cHn3++ec5zVw7bREC5P33369dVA5bixcvzmHW2iknTZpU+4UctmbPnp3DrLVTdnR0KPkNyXne9u7dq2666aY8l1Cae9q0abmvgQDRWkCAhCAESNmCACk7ECBlBwIkfI8gQEKL0iMCJAQhQMoWBEjZgQApOxAg4XsEARJalB4RICEIAVK2IEDKDgRI2YEACd8jUg0Q/WOPbJ9W+ZfMWGDBOcA5ULRzIIyCeI9SDZB4SwiP4iJ62YKL6OE5wUX0sgUX0csOXEQPXxtcRA8tSo8IkDIIARKeGARI2YIAKTsQIOFrgwAJLUqPCJAyCAESnhgESNmCACk7ECDha4MACS1Kj4oQIHKBLO9bEdYgBkW4iF4EC9ZQfkUUxSHvdcj8ea9BOlKENRTqGkjeb9zMjwACCCAQXYAAiW7FnggggAACVQIESBUGDxFAAAEEogsQINGt2BMBBBBAoEqAAKnCkIfyf2B/+OGH2lf92zx8+LD69ttvcy+8CGuoRuDcKGsUwUHWUIR1VJ8fvj0uRIBMmDBBbd68ufInrybIvxIN3rDkDdTkTearNpDHsh7Tv0ZD6h81alTJQR6fc845FROTHmeffbZav359KdBNrkE+JirziX31TUxkTXKT+4kTJ1Y/nepj+SZm6NChp6xBJpH1BetIdVJtMDkfZ8yYccoaZG55vcrttttuO+V5bZjEmytWrCidj/pAsgZZo/zJ2kNeixdccEHdWuV1kvUagjr181LeH6rfN2Q/k7faV4nJmavmyhq/aqq6D+XNSl6cRbqJi+kTQgyCEBULOWFNu8gbqPQjuMmLV96sTNykXnnT0l+osi0vVLnJ2vTn01ybBFhXb87yZiHzmvimQgJMQlOvs/rckJqlN1ndxEBeA9OnT+92ioULF9acs93uHOPJoPe6RTCUhFkQqsHX0r6Xc0LeC/Q1mDgXuqulEAGio3S34KyeK8IaqmuTNzH9xVr9fFaP5SSV7zyDWx4hpr8ounpDD9aX1b1+PuhvEPp2FuvQ1xDMofsEX8/ivt4agrmyDJBgjkbWEnRZ34KfCOjziI98Q9Fojfpxcbf1fpiat956cw8QecOS7zTkRRF812X6u+7g5JDvdOSxrEW+q8nzpp8oJtcitcv8ea1B+j969OhSgMrjPNai166/aZt44eprCM4BfS3B17O4r7cGmUu+K270t4M01lTPOjgv1qxZk8Y03Y4RvEdU7ySvk+C9qt4aq/dP47HeD/npgJwP8ke+2ZN1mrzlHiB6sQJg+s27q5NDb5S+zqy3TZ2Qeh3ygpATMTgx5Y08eJHo+2a5HfzMWfoQBFqW8+lj6/2Xb26qbyb6o68hmL8IASLnhHznb+LcqGctc8tr18Tfkhu9R9RbY9CztO7rnRMyvnhIqJu8FS5ApHgTfy2uRpafueuN0ber98/6samTsas69B8HyHeYEiJ53uR80N/As16P3n+5JlB9kzetrG/6GoL58g6QIDyC9WR9H+X1kPU3nXqABNvSo+o/UdaaxKveORGMKc/L2kzdcg8QKVbewOUmJ6b8/N0kQAAtP2cPLhbLerI+EYJ59Xt5ozTx5qTPG2zLm1P1BWw5IU2+YQXrCO6lJ6bDQ+bWX6jSk+CTVxKoJnqkryEwMdkPfQ1Su/5NRrCurO67ei3Ke0VwkzVlfb0wCIxgTv2+qzXq+6Sxrfej+sd38h6WdZDqNeQeIMEFUnlxCo6JF6aOEGzL/HIyyn3WJ2Qwp34vc5t8g9Dnl+3AQSxMn5DBeuR8CM6J4Gsm78Wg+ibfVAQhIvfBNz3V+6T9WF9DML7J80Nfg2zrf7JeT1dvzrIGOT/kb4YmfmJR1AARB3md5vVaqX2VBGco9wgggAACCDQQIEAaAPE0AggggEDXAgRI1y58FQEEEECggQAB0gCIpxFAAAEEuhYgQLp24asIIIAAAg0ECJAGQDyNAAIIINC1AAHStQtfRQABBBBoIECANADiaQQQQACBrgX+D68U6QPC0teLAAAAAElFTkSuQmCC[/img][/td][/tr][/table]
Use the applet below to find the mean and the standard deviation for the data in the dot plots.
[size=100][br][table][tr][td][i]Partner 1[/i][br]Dot plots:[/td][td][i]Partner 2[br][/i]Dot 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I3EVtvw4cPNkydPGh6DDhBwQQDRdUEdm6kQWLlypZkzZ465evVqKuMxCASyIIDoZkEZGxCAAAT+TwDRJRUgAAEIZEgA0c0QNqbSI9Dd3W3mzp1r3nrrLdPV1ZXewIwEAcsEEF3LgBneDgG9lNL7Ik37e/fu2THEqBBImQCimzJQhsuGwKBBg3qJ7vHjx7MxjBUItEgA0W0RIN3dEFi/fr0vurNmzXLjBFYh0AQBRLcJaHTJB4FTp06Z3bt358MZvIBAnQQQ3TpB0QwCEIBAGgQQ3TQoMoYTAvpfaNevX3diG6MQaJYAotssOfo5JdDZ2Wmeeuqpyn3dzz//3KkvGIdAIwQQ3UZo0TY3BF588UX/izQ9Mnb27Nnc+IYjEEgigOgm0eFcbglMnDixl+jq9T0UCBSBAKJbhCjhYx8CZ86cMaNGjaoI73fffdfnPBUQyCsBRDevkcGvugg8ePCgrnY0gkBeCCC6eYkEfkAAAm1BANFtizCXc5JHjhwxGzduLOfkmFVpCSC6pQ1tuSf25Zdf+l+kTZ8+vdyTZXalIoDoliqc7TGZ//77zzzzzDO+6OqRsYMHD7bH5Jll4QkguoUPYXtOYPbs2b7oPv/88+bff/9tTxDMunAEEN3ChQyHPQJLliwxH3zwgTl//rxXxR4CuSeA6OY+RDgIAQiUiQCiW6ZoMhcIQCD3BBDd3IcIB+MILFy40Lz//vvm3LlzcU2oh0DuCCC6uQsJDtVDQG+L8N6R1tHRYR4+fFhPN9pAwDkBRNd5CHCgGQIDBgzwRVfie/jw4WaGoQ8EMieA6GaOHINpEPjqq6980f3www/TGJIxIJAJAUQ3E8wYsUFAbwDevHmzjaEZEwLWCNQU3bt37xptrktefHDtR57iAYueVSEO3d3dTpdIHvLin3/+yY1W5Dk3a4rub7/9Zn755RenCSXjX3/9tXMfxEE8XJbbt2+bnTt3unShYltv4f3rr7+c+nH58mWjH71xXfQ6eNeie/LkSXP69GmnKCS6W7ZsceqDjO/fv9/88ccfTv24evWq2bdvX6QPiG4kluhKRLfKBdGtskB0e1ggutWcQHSrLFo6QnSr+BDdKgtEt4cFolvNiZZF97XXXjNvvPGG0+2FF15wal/zF4fRo0c79WP8+PHm5ZdfduqDWLzyyitm3LhxTv14/fXXzfDhw536IBZ6Sabe2eZyjYwYMcKMHDnSqQ8TJkwwL730klMfFINXX33VjB071qkfsr969eqqCgeO6rq9sGrVqsp9Et0rcbV99NFHzmx7cxaHb775xqkfO3bsMIsXL3bqg3h8+umnZuvWrU790LvRVqxY4dQHsZg/f77Zs2ePUz+++OILs2bNGqc+/PDDD2bBggVOfVA8li1bZjZt2uTUj++//7410eWLtJ7LFLcXqpdrbi9UWXB7oYcFtxeqOdHy7QVEtwcmoltNKkS3ygLR7WGB6FZzohSim4c3A+RFdPPAQj7wyFjPIhMLHhkzRqKbl9zMwyNjcSxq3tOtajdHEIAABCDQKgFEt1WC9IcABCDQAAFEtwFYNIUABCDQKgFEt1WC9IcABCDQAIGaonv27NnK85gNjNlyU33zp2dA44rOffLJJ5XfY7D5wxbr1q0z8iWp1PI1qW8952rx1/z1uxRz5swxnZ2d9QzZcBvPRlxH+ah4aLPlQ9i27KxcuTJTm5pnLZvKTcVC7WzkpheLevJfbeWHjSL+8kHjJ60RrSHx2Lt3b+puePOTH9u2bYscX77VillkxxYqPTZx6yFRdJVAetX15MmTW3Chsa4KjuzF2dR5+SXg2k+bNq0xA3W01tgaVz7UEhGvXR3DNtxECav/dRXHQgPqnJfQ8jvtIqGRDb3mPK7IRyW37KudDT/CtvWfImRTm+xnYVOvBwra1HGwiJViJl+UNzZyU7H24i0G2uKK7ItN2kXz9vhrnnG5obzx1mraPmg8sZZ98dZcZStc5Jv8VRv5oxjZLLIjv4J5EraZKLqe4MjZrEqjNm0klcAJlBLL8ydq/jqvBWCLj2xrixtfSZa06KJ8brROHLyEjesbjIGSP5xkcf3SqhefpDilZSc4Tq3cUNtnn3022CX146Tc0MLXFpc7aToTZUO5qYtUVkXrMGotBH3TB8isc1M2w7mZKLoesKDjXp3tfT0240Cn5VvSwlLwBFSlHl+b9SlpYSmpvYU1ffp0qwmVNEctMJ0Xj6hPG83OvZ5+uiAERb+ePq228WxqH1cUNy8/4tq0Wq/8jOKdVW7Kf32ii/pEL9+Um2Kg3LAtdvIhLG7yT3x0Trc4okS51RhE9Vde6HaHbi9EXXgKK7oKooKZlPhRQBqpixNd2Qza1rGtkiS6sqvEVpFPNsUnaY5aWGKlBAtyscXEG9eLg+0F7dnz9pqv9098ry64zyI3JSZxoh6MQVLcgj43c5zEX755gmM7N8XCsxWeh+qVm9qCXMLt0vxb89W6lU2tSV2YgqWQoptFUguSoEVdPSV0+tWzd955p7INGjSosg+CTes4SXSV2EH/bC6wuLFlP5jwYuZdCNJiEDVO0oKPap9WnZhHfbr0xs8iN2UjyNyzrX1WuVmLf3jt2BK8pItPmJPaZpGbwXjIplgES2FEd+3atRW/s0hqD1A4cXbt2mW6urq80/4+TpD8Bi0chEX35s2b5ttvv62MGBQ8XU2z/KS7YcMG8+eff1b+2Rj856VEKelTYAso/K61FrzfMOWDOMHVSzJVssjNsJB4U9Sva504ccL709/byM04/g8fPqz8Cp+MKze9vFB7G7kZJ7h6e4XeKiJWng/ySetZfWwWzVWbV2Q/bLMu0Y27qnoD29gHbV67ds0MGTLE/P3335UrvBIpuAmujSJYwbFnzJhh9BN64RL0NXyu1b/Di+znn3+u/IasN66u3BIDBTfoq3c+rX14jmPGjDEHDhyoDK9klg/awlf1tOwHx9E8g/HXcTixg+3TOI6zeefOncpvyF65cqXiQ9ivtGOiOIRtaH7z5s0zS5cu7TPVcNz6NGiiQqzDPqhOrwsaNmyYefLkSWVU1Skn1DZtDjIQ9sGb66RJk/xXWukDgNZGVrnp3eP24hT1ybou0W0iLnSBAAQgAIEIAohuBBSqIAABCNgigOjaIsu4EIAABCIIILoRUKiCAAQgYIsAomuLLONCAAIQiCCA6EZAoQoCEICALQKIri2yjAsBCEAgggCiGwGFKghAAAK2CCC6tsgyLgQgAIEIAohuBBSqIAABCNgigOjaIsu4EIAABCIIILoRUKiCAAQgYIsAomuLLONCAAIQiCCA6EZAoQoCEICALQKIri2yjAsBCEAgggCiGwGFKghAAAK2CCC6tsgyLgQgAIEIAohuBBSqIAABCNgi8D/AlAf2TBLYCgAAAABJRU5ErkJggg==[/img][/td][td][img]data:image/png;base64,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[/img][/td][/tr][tr][td][/td][td][/td][/tr][tr][td][/td][td][/td][/tr][tr][td]Conditions:[/td][td]Conditions:[/td][/tr][tr][td][list][*]10 numbers with a standard deviation equal to the standard deviation of your first dot plot with a mean of 6.[/*][*]10 numbers with a standard deviation three times greater than the data in the first row.[/*][*]10 different numbers with a standard deviation as close to 2 as you can get in 1 minute.[/*][/list][/td][td][list][*]10 numbers with a standard deviation equal to the standard deviation of your first dot plot with a mean of 12.[/*][*]10 numbers with a standard deviation four times greater than the data in the first row.[/*][*]10 different numbers with a standard deviation as close to 2 as you can get in 1 minute.[/*][/list][/td][/tr][/table][/size]
What do you notice about the mean and standard deviation you and your partner found for the three dot plots?
Invent some data that fits the conditions. Be prepared to share your data set and reasoning for choice of values.
Begin with the data: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
Use technology to find the mean, standard deviation, median, and interquartile range.[br]
How do the standard deviation and mean change when you remove the greatest value from the data set?
How do they change if you add a value to the data set that is twice the greatest value?
What do you predict will happen to the standard deviation and mean when you remove the least value from the data set? Check to see if your prediction was correct.
What happens to the standard deviation and mean when you add a value to the data set equal to the mean?
Add a second value equal to the mean. What happens?
Add, change, and remove values from the data set to answer the question: What appears to change more easily, the standard deviation or the interquartile range? Explain your reasoning.[br]
How is the standard deviation calculated? We have seen that the standard deviation behaves a lot like the mean absolute deviation and that is because the key idea behind both is the same.
Using the original data set, calculate the deviation of each point from the mean by subtracting the mean from each data point.
If we just tried to take a mean of those deviations what would we get?
There are two common ways to turn negative values into more useful positive values: take the absolute value or square the value. To find the MAD we find the absolute value of each deviation, then find the mean of those numbers. To find the standard deviation we square each of the deviations, then find the mean of those numbers. Then finally take the square root of that mean. Compute the MAD and the standard deviation of the original data set.
IM Alg1.1.12 Practice: Standard Deviation
The shoe size for all the pairs of shoes in a person's closet are recorded.
7 7 7 7 7 7 7 7 7 7[br][br]What is the mean?
What is the standard deviation?[br]
Here is a data set:
[size=150]1 2 3 3 4 4 4 4 5 5 6 7[/size][br][br]What happens to the mean and standard deviation of the data set when the 7 is changed to a 70?[br]
[size=100]For the data set with the value of 70, why would the median be a better choice for the measure of center than the mean?[br][/size]
Which of these best estimates the standard deviation of points in a card game?
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[/img]
The mean of data set A is 43.5 and the MAD is 3.7. The mean of data set B is 12.8 and the MAD is 4.1.
Which data set shows greater variability? Explain your reasoning.[br]
What differences would you expect to see when comparing the dot plots of the two data sets?[br]
[size=150]Select [b]all [/b]the distribution shapes for which the mean and median [i]must[/i] be about the same. [/size]
What is the IQR?
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[/img]
The data represent the number of cans collected by different classes for a service project.
[size=150]12 14 22 14 18 23 42 13 9 19 22 14[br][/size][br]Find the mean.[br][br]
Find the median.[br]
Eliminate the greatest value, 42, from the data set. Explain how the measures of center change.[br]