Mathematics
Grade10
Easy
Question
75 71 73 74 73 70 74 76 76 76 73 72 72 73 72 72 72 74 79 75 73
What are the frequencies for each interval of 5 points?
Choose whether a dot plot, histogram, or box plot is the most appropriate data display to answer each question about a data set. ?
- By using Dot plot
- By using Histogram
- By using Box plot
- None of the above
The correct answer is: By using Histogram
To choose a plot for the given data set.
A histogram would be most appropriate since it displays the intervals and the frequencies of the data.
Therefore, histogram can be used for the given dataset.
Related Questions to study
Mathematics
40 47 43 35 42 33 40 47 49 46 52 42 48 43 34 45
What is the median value of the data set?
40 47 43 35 42 33 40 47 49 46 52 42 48 43 34 45
What is the median value of the data set?
MathematicsGrade10
Mathematics
The median can determine from……….
The median can determine from……….
MathematicsGrade10
Mathematics
We can use all or our summary statistics to make comparisons :
Measures of spread: ……………..and ………………..
We can use all or our summary statistics to make comparisons :
Measures of spread: ……………..and ………………..
MathematicsGrade10
Mathematics
The data collected two different groups or data collected ………….and ……………an event.
The data collected two different groups or data collected ………….and ……………an event.
MathematicsGrade10
Mathematics
The data collected two different groups or data collected ………….and ……………an event.
The data collected two different groups or data collected ………….and ……………an event.
MathematicsGrade10
Mathematics
The dot plot above shows the volume of juice squeezed from mangos.
What is the median volume of juice squeezed, in fluid ounces?
Volume of juice(fluid ounces )
![](data:image/png;base64,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)
The median volume of juice squeezed is 3 fluid ounces.
The dot plot above shows the volume of juice squeezed from mangos.
What is the median volume of juice squeezed, in fluid ounces?
Volume of juice(fluid ounces )
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXQAAACsCAYAAABreVelAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFxEAABcRAcom8z8AACUxSURBVHhe7Z0HeBXV1obtHRULF0HBC1L0qqD+XFBUmkgREelFulQpoRepAUKTGoqUhNBDpIQmoXcIIZQAJpRQkxBKSIMAqd9/vn3nIMKElDNJhmG9z10P1ykr0843a6+99uzHIAiCIFgCEXRBEASLIIIuCIJgEUTQBUEQLIIIuiAIgkUQQRcEQbAIIuiCIAgWQQRdEATBIoigC4IgWAQRdEEQBIsggi4IgmARRNAFQRAsggi6IAiCRRBBFwRBsAgi6IIgCBZBBF0QBMEiiKALgiBYBBF0QRAEiyCCLgiCYBFE0AVDSUpK0v6fIAjZjQi64BAU8JiYGJw5cwbbt2/HqlWrsG3bNpw+fVotF4EXhOxDBF3INCkpKVi7di2aNGmCYsWKoWDBgsifPz8KFCiAokWLomHDhkrgExMTtT0EQchKRNCFTHHx4kWMHj0an3zyCZ566ik89thj99mTTz6JEiVKYPjw4QgNDdX2FAQhqxBBFzJMWFgYXF1dVSSuJ+T3Wr58+TBhwgSEhIRoHgRByApE0IUM4+3tjTJlyuiKd2pWqlQpeHl5aR4EQcgKRNCFDOPi4oIXX3xRV7hTsxdeeAGDBw/WPAiCkBWIoAsZpk+fPrqinZZ17dpV8yAIQlYggi5kmCFDhuCJJ57QFe3U7PHHH0e/fv00D4IgZAUi6EKGmTdvHj788EMl0nrirWfvv/8+3NzcNA+CIGQFIuhChuEgop49e+Lpp5/WFe97jdE80y2nTp3SPAiCkBWIoAsZhgOKDhw4gObNmyNXrly6Im43doY2btwY+/btU/sJgpB1iKALmSYwMFANGqpWrZoaKfraa68pAc+dO7caKVqlShUMHToUR48eFTEXhGxABF1wCAr1iRMnsGDBAiXurIBxdnbG/PnzleCLkAtC9iGCLgiCYBFE0AVBECyCCLogCIJFEEEXBEGwCCLogiAIFkEEXRAEwSKIoAuCIFgEEXRBEASLIIIuCIJgEUTQBUEQLIIIuiAIgkUQQRcEQbAIIuiCIAgWQQT9ESMuLg7nzp1TX0LkZ235pcRLly4hOTlZ2yLjxMfHIzQ0FEFBQThy5Ij6l/99+/ZtbQtBELIDEfRHhBs3bqhJKaZNm4ZmzZqhQoUKKFOmDKpWrYru3btj6dKlSugTExO1PdKGQn78+HH1qdwOHTqgcuXKKF26NCpVqoR27drBw8NDvThE2AUhexBBfwS4ePEiJk+ejJIlS+KVV17BM888c2c+UE4P9/zzz6vJKerUqYP169fj1q1b2p6pExMTgyVLlijx5oQWzz333J2Jo/nvs88+i1dffRXlypVT30qPiorS9hQEIasQQbc4ERERGDt2LPLmzfuPqeH0jHOEli9fHhs2bEBCQoLm4X6Ytlm0aJGaKFrPz71WvHhxNbE0WwmCIGQdIugWhqK8fPlyfP7557pCq2eMrNu2bYtjx45pXu5n165dqF27tu7+qVnNmjWxbds2zYMgCFmBCLqFuX79uppt/6WXXtIVWT1juqRw4cLw8vLSvNzPhAkT8MYbb+jun5oxpTN69GjNgyAIWYEIuoWJjo7OcCRNo6hPnz5d83I/ffv21d0vLXNyctI8CIKQFYigWxgKet26dXXF9UHGXPqMGTM0L/fTv3//O52q6TVuz2oaQRCyDhF0C8NOyN69eyNXrly6IpuaffDBB/D29ta83A9LH/Pnz6+7b2r21ltvYeLEiZoHQRCyAhF0C8NO0ZUrV6rSQj2R1bPXX38dXbp0UfXjqbF37140adJElSrq+bjXWCbZoEED7Ny5U/MgCEJWIIJuYVJSUhAbG4u5c+eqqFtPbO82VrjUq1cPhw4dUoOGUuPmzZvYtGmTKnG0156nZlxftmxZVd/O/QRByDpE0B8BOKhnzZo1SqxTq3gpUqSIyo0HBARoez0Yjv5kpN6xY0eVTtHzmSdPHlUCuWPHjnQNVhIEwTFE0B8hKNasXmHnZPPmzdGoUSMluAMHDlRD/69cuaJtmX74uQAOGurTpw9at26Nhg0bolWrVip3P2fOHJw+fVrbUhCErEYE/RGF4n3hwgVVq24UkZGROH/+vPpXEITsRwRdEATBIoigC4IgWAQRdEEQBIsggi4IgmARRNAFQRAsggi6IAiCRRBBFwRBsAgi6IIgCBZBBF0QBMEiiKALgiBYBBF0QRAEiyCCLgiCYBEeWUFPTk5WU7Q9qp915Xnz/HkdjIITatDng76lnlH4TXf6fBi+pR4XF4eYmBjtv8xLYmKiuqb8BLJgLR4pQeeXBfft24fFixdj3Lhx6rOxLi4ucHd3x5YtWxAeHq5taU1CQ0OxefNmuLm5YcSIEer8x48fD09PT+zfv18JUkbhlxV3796N+fPnY8yYMRgwYICa3Z+f1OUMRREREdqW6YcvmwMHDsDLywsTJkxQxzl8+HDMnj1bTawREhKibZnznD17Vk3eMXPmTAwbNgyDBg3CpEmTsGzZMhw+fFi95MwAXzT8fv2iRYvw22+/qfs0cuRI9Ynjbdu24fLly9qWwsPMIyPoFy9eVHNhli5dWncyhnfffVd90/vYsWOGRphmgJEYxYXfQS9QoMB9526fVYiidOnSJW2vB8PInp/KHTVqFD766KP7fNI4SxJfHPwmelJSkrbng6Gw8AX79ddf46mnnrrP59tvv42uXbvi4MGDOdq64nyte/bsQZs2bfCvf/3rvuPk7E/ffvutCh6uXr2q7ZX9sIUTFhamXjKfffbZfcdJ4+QmFPjjx4+b5gUkZA7LCzqFJzg4GO3bt8ebb76p+0Db7YUXXlA/QkaBbJZaAZ7Hn3/+iYoVK+L555/XPW+7UZg4nygnrXhQKobiTEHlBBmvvvqqri+7vfLKK6hbt65qATxI1Pn32ILo1q0b8uXLp+vLbpzLtFy5cli1alWOCBD/JiPd//73v3j66ad1j5H2+OOPo2DBgqqFQVGnuGYnvKZBQUFqwhHOFat3jHbjTFY1atTA9u3bLfPsP4pYXtAp5oMHD0bevHl1H+R7jVFh06ZNVRrhQaL2MEAB5Q+0fv36ac79aTdGwExvMJWQGkePHoWTk5MSaz0f9xrFolOnTqqVkBqM9pmqoQDq+bjXKJa1a9dWqbLsFCC2CviCrFq1qu5x6VmxYsXg6uqa7Sk9Rtz9+vXDG2+8oXtc9xpbFS1btlRpSeHhxPKCzlnvP/nkE90HODWj+DO3/rCnXpgTHzp0aLp/0HYrVaoU1q1bp3m5H+Zd2UzX2zc1K1SoEGbMmKF5uJ+NGzfiiy++UEKtt7+e5c6dW6UKjJx1KS040xNbMbly5dI9Jj3jy7RSpUoqh52dLFmyBB9++KHuMaVm+fPnV/0q6U2RCebC8oLOyIiRh97D+yDjnJsP+yz1rGTgHJ965/cgY+pp1qxZmpf7YQpBL7/9IHvyySfVPKOpMXfuXLz88su6+z7I6tSpg2vXrmlesh5O20dx1juWBxnTWUwRZSdjx47FM888o3s8qRlfqExPSi794cTygs5oQ+/BTcsaNGiQqaoPM0FB/+GHH3TPLy1jB3JqUJj19knLGNmmBitYMvqSoFWvXj1TlTSZhakotiT0juVBxoielS/ZCVNneseSlrVo0UIE/SHF8oLOiok8efLoPripGSP6zp07P/Q16rGxsWpW/4xEaYzQ2Cm5cOFCzcv9sLLlxRdf1N0/NWPU7+zsrHm4H5YosgInIykXdkiywy87J6Vmxy1z9xl5+fCcmEdneWN2MnXqVLz22mu6x5SascO5V69e0jH6kGJ5Qd+1axdq1aqlmvx6D7Ceffrpp6qO+mF/qFmuyJrz1MoK9YziX69evQd2jLFTMKNpB3tVSmr4+/ujcePGGUqPsSySpZbZmRpjq4djGNgnoHdMesZOYZY3BgYGal6yh61bt6rKlfR2iNNYucPc+8NeEPCoYnlBZ4cZB70wQtJ7gO81Rp7MEbN21wow58uIi5GX3vnea+xE40CjB6WbGBGzbyKt8kK7sYXE1NeDUiMU5eXLl6NEiRLpitIp/CxxZAokO8sB+bdYPdK6det0tXwopkzR+Pj4ZPvITLbQ2EL997//rXts9xqrljg4Kr1jEQTzYXlBJxQSdvKlNrDCbuzhZ5nXyZMntT0ffihAjAwp6noDYO62MmXKwMPDI10pDL7wGKkWL15c15fdWA3DFA1fLGnB0Yx8+X755Ze6vuzG8QQUc5ZPZndtN2H0yhZFu3bt0uzI/eabb9SLKqf6Y1iVw9RLyZIldY/Pbkx3sbyXg8CEh5dHQtAJI3UORedwZ1ZGMBJk5EJBqly5shol6u3tbdnohCNl2SlHYWe6hOfN8+d1YIqFNeCsvecIyPQSFRWFDRs2qNLI77//XkX3HHH7n//8B999950aBs/INCNVKBQ+X19fVaHBjmkKkf0+VahQAT169FD59pxuQfFFwgFY7Gtgf8tXX32FokWLqlQMU3Y//fST+mwBP2GQ030xfFFyeD87SZl+ZAqO94kpKw6k69+/P1avXq3EX3i4eWQE3Q4Fg5Ed656Z02W9tZ+fX44Oz85O+KOlYPK8ef68DvzcgSOiQ8E4dOiQ6vRj3T9FnCNJmW/OLExPsGXBUbs8TubtWcdtxhcuO0r5Mly7dq0SRn4v58SJE6brg+HLOiAgQL2E7feJI3izs+xTyFoeOUEXBEGwKiLogiAIFkEEXRAEwSKIoAuCIFgEEXRBEASLIIIuCIJgEUTQBUEQLIIIuiAIgkUQQRcEQbAIIuiCIAgWQQRdEATBIoigC4IgWAQRdEEQBIsggi4IgmARRNAFQRAsggi6IAiCRRBBFwRBsAgi6IIgCBZBBF0QBMEiGCrop06dwpgxY9C9e3c1B6QRM7Jzrs+ZM2eiS5cuWLBggZrs2VE4X+XSpUuVT1dXV8MmHOZckr1798aIESPUPJ1GcPjwYTUJc9++fbFjxw5tqWOcP38eEydOhJOTE1asWGHI3JecV3Tu3LlqwmQ3NzdERkZqazJPUlKSmk+UM/yPGzcOZ8+e1dY4Buf/7Nevn5rlnnOfGkFQUJCagLxnz55qzk4j4Pyp06ZNU8+pp6enIZNN37x5E4sXL1Y+p0+fjsuXL2trHIPnzAm8R40ahePHj2tLhezGUEHnJMH58uXD448/rn6ERgg6H44yZcrgscceUzPLGzFJMMWnSZMmyidnqOfM7EZAgXjqqafw6quv4o8//tCWOsb8+fORK1cuPP3000owjICCVqRIEXX+rVu3Vj9yRwkJCVEzyNNnuXLlDBHfhIQEtG/fXvnkzP+cud4I+HJ45pln8OKLL8Ld3V1b6hje3t5444038MQTT6iXhREcOXIEn376qTr/unXrGvKS5ITQderUUT4/++wzNWG6EfTp00ed+5tvvok1a9ZoS4XsxlBB543kD4UPS9u2bQ0RdEa6xYsXVz6//vprXLx4UVuTeSjoNWvWVD75Atq3b5+2xjEYndEnja0JI5g9e/Ydn4MGDdKWOgaFkeJDn/Xr10dcXJy2JvOcO3cOpUuXVj5LlCiB4OBgbU3moaD/9NNPyufLL7+MjRs3amscY/jw4XeuKSNgI1iyZMkdn4x+jYCtB77I6LNKlSqGzM4fERGBb775RvksXLgwDh06pK1xjE6dOimfFPVly5ZpS4XsxlBBX7t2rYp6eGM7dOhgiKD/9ddf+PDDD5XPihUrIjw8XFuTeSjoP/74o/JZoEAB+Pn5aWscg1EKfbKFsmjRIm2pYzCCpE/akCFDtKWOsX37duTNm1f5bNSokSGCzjRO2bJllU9GfqdPn9bWZB4KevPmzZXP119/HZs2bdLWOIaLi8uda/r7779rSx2DLTK2ouiTrVMjoNjaW1LVq1c3TNCrVq2qfBYrVkyl9Iyga9euyuezzz6r0nhCziCCLoIugm4AIugi6GYgywS9Y8eO2lLHCAwM/IegG5FDj42N/Yeg79+/X1vjGOy4pE8KOjuejGDOnDnKJ42do0bAztW7Bd2IHPqFCxf+IehnzpzR1mQedtbeLejsdDYC9kXYr+mMGTO0pY7BTna7oLMowAgotncLulE59LsFPSAgQFvjGOxgp08R9JzF9ILOTtGPPvpI+axUqRKuXLmirck8N27cQO3atZVPCrpRnaLsDKNPCjqrEozAw8ND+aQ5OztrSx1j165ddwS9cePGqurHUUJDQ/Hll18qn//3f/+ncuqOkpycjBYtWiifFPStW7dqaxyDlRj2a8oKKiNg3tgu6Kz2MAJ2itoF/bvvvkN0dLS2JvNERUWhWrVqyif7pozqFBVBNweGC/pLL72kbmzTpk1V1MayQ/7YM2OMSLZs2aIiCfpkBMiOIi7X2z49xiYnS8zsD3X+/PlVZy4fdL3t02Ms/WJnLV9i9ElBnzJlijpOLtfbJy3jfoymWJFBnzRGfmyh8O/p7ZOWsTyT50nxYTUCfdaqVUuVmzp6n9ixXKpUKeWTL2BW0jhyn3g8rJSpV6+e8pk7d271kuTx8zz09knL7NfO3pKiUdz5TDCVp7dPWsb7xPNkpG8X9J9//lkdI4MPvX3SYzxPVo3ZO0XZOmWBAJ8Jve3TY9yXAl6+fHnlk52iLDd05Nm3nyOrpejzueeeE0HPQQwXdJbs8cYWKlRIlVoxAuS/GTX+kFnhwN79V155RfmkCNWoUUMt19snPcZyxR9++EEJOX2+8MIL6sfCFxD/pt4+aVnDhg1VtYj9xUNjxQePk8v19knLuB+PldGu3ecHH3yABg0aqL+nt09axvPjefIHzR8efbKFwvRTZu8TjefJlACjaPqk+PKF6ch94vGwFVWwYEHlk9VTrHJy5D7Zr529xUdjWSCvM9fp7ZOW8T7xPFlaywoP+mRUzWNkOktvn7TMfp9YjWIPkNiiYmUWj1Vvn/QY96UPe+uM5bCVK1d26JryHLmvvSXB4xVBzzkMF/TXXntN3VgxMbFHz1heKoKecxgu6PZomlEQRw2y95v/ZsZYLcCowh75vfPOO6ppx+V626fHmOtjjTxbEPTJKIVRliM+eY6sPWZnoP3BZsuCPrlcb5+0jPvxWO01wzRG/VzuiE8eEyNftkzos2jRoqoiiX9Lb5/0GH0y1/3WW28pn3ny5EGzZs0cvk+//PIL3n//feWTLQqmhxy9prxXn3/++Z1rWqFCBfW3HL2mbJHYI/SSJUve+Vt6+6TH6JOtCXuLly0VPreO3Cfu26ZNmzutHrakGGE7cp/s58ixB/TJPjQR9JwjyzpF27Vrp8rOWKngiLEXnqM56ZM/Po5I1NsuI8ZcIsWBPply2Lt3r+52GbVevXopn8yhc4Sn3jYZtVmzZimfNA4s0tsmo8Z+CXuzm6kGlnHqbZcRY5niF198oXwyjXHixAnd7TJirL5hOoA++VJnTllvu4za3QOLpk6dqrtNRo35fXsOnSKnt01GjdVX7733nvLJFwbz/3rbZcTYj8Bggz75Mvf399fdLqNGUadP6RTNWbJM0DlyzAjYYWfPeTJapRg7Cqs67MOfGa0YVYvbv39/5ZOCbtTQ/3nz5imfNAqREezZs+dONM0WEL+Z4ijsVPzqq6+UT+b9+eI1glatWimfHNnK+nkj4PeG7NeUI3GNgEP/7YLOEcNGwDEYFF36ZN8Rq7Mchd9CYn8HfbLKhWXBRsAOe/oUQc9ZskzQZWCRDCxyFBlYJAOLhIwhgi6CLoJuACLoIuhmQARdBF0E3QBE0EXQzYChgr5y5Up1U2ktW7Y0RNA5EOLdd99VPlnra8TXFjnizt4xxDJLdooaAasI7OfPb4MbAQes2H0a9VlWdoraX7zsHDYiN8tBQKzuoE/mZtn34SgUdFYg0Sfr0H18fLQ1jsHPHNuv6eTJk7WljsEXuN0ngxkjYIelva+DYwcoxo7CAVus56dPjsUwapQ0iyDs529U/5GQcQwVdA7N/vjjj1VJICsyjBB0CgOjE/pkxYMRQ//ZMcRRnfTJh5tDrI2AnW0cfceoavXq1dpSx+A3QljpQL9GiQ9bJCzd4/kzmjRi4gSOjGTFDH3yJcFRwo7C6glOGEKfHIXKzlwj4MQOvJ68rkZ9c2fdunWqxJJ+OcGJEbDDkoUAPH+OPjVq6D87mumT36/nqGkjYIc9fbIizagJPoSMY6igM4Jgc54PN8vWjIDRI4eVM53DJmh8fLy2JvNQKBj5c1YligQ/1mUETDMwimQEbNRMMEwx8aNULNkzasYe/qj5PRfeJ6a0+M0UR+FLgdEe7xPL7Yz44BcDAooaj3Pnzp1qiL0RMD1E0eF1NWq2KgYaDGh4/41onRAGHmw98jll+S6fW0dhq4dpFvr09fU1ZAYwwnPmfeK39tkKEHIGQwWd8EfIMjgjonM7D5NPiqORPklW+KWvR/U+Efv1NPpY5T4Ze5+EjGG4oAuCIAg5gwi6IAiCRRBBFwRBsAgi6IIgCBZBBF0QBMEiiKALgiBYBBF0QRAEiyCCLgiCYBFE0AVBECyCCLogCIJFEEEXBEGwCCLogiAIFkEEXRAEwSKIoAuCIFgEEXRBEASLIIIuCIJgEUTQBUEQLIJxgh5/BdEXAuDvfwB+vntxMOgMgqOBJG21+UjA7dgoXAq/jlu3TXqUcWG4dHIPtm/agFWrt2Lb0RCEJmjrzER8DG6cD8BfRwKwb48v/A8cRlDEbcRpq81JMpByHZfPheN8cARuJjo+DZ8xpNj+l8Sju4dEJESE4EpkLKIcn4nOcJIizyIkYAf8/A9i74EzOBMWA8dnqhUyigGCbhPDxOu44LcMi6cOxeDhYzBs4AAMGzMZk739ERwRb5NOk5AUh7hr4Th76gROBSzDomWL4LwgAKcumu3R4zWNxnnfP+D5Wzd0bdUY31X7Ed81d8GIxX/hvO0XbY7ftE184qNx+egGrJnpgnHjXDGkf28M7NoRPcaugk+ATeipm9rWpiI5CvHnlmL8EFf0GbENwbGOz1VrDLZ7Hx+GkKMHsdtnL3x992O/3z5sW7UES93cscIvGEG2N6VprmnSbcRfPYaAFeMxa6QTBgwbhm4d+mKk6xJsCk9ArHkjOkviuKAnhCPxL3eMGDsFHVz34NTlGMRER+PEWnd4De2M2VuO4aBZQrXrQQha54FBTj3g1KICSndsj/cn+2HPRZOFvUmhiDuzHL97rMbvy88g8loYrpxbg9lNG6FuydYYsPwUjppBf1JsB3Fike1F7oyGw9dj9eGriIq2Hdvioehd/Ft07LwYa6+wLWQ2kpFy9RAOzmqEr+q0QVnn3Qi8bhblsR1H3B5sGNYS3/8rPwrmfxvvFCiIQiW/Rd1fV2DlyeuIM42aJyPunC+2jGiN0eOmYcGJSIRePA7/ibXR36kFev55CSdjtE2FbMFhQU++ehDB86rBafhg9PKJww1tOY4vxY6pzdDIdSV+P2wSRWeEHnERZ4ICcNyzM1oOaI+Ck/ywxWR5jOSLvji7vDNcl/jA86S2EHG4MK8FutUsgvcGLsfMQDMc823g0BiMaFQV+Uu4YJjPZS5B9K6JmPROPtSsMgCjA2w/etOF6BcQtW8aprSqiX993gmfj9pri3rNIui21mL0Rqyb8xs6tBqKgYNHw3X2Qnj6HMSxC7ZgySwNCXL7mC1wm4xWjZwxwM0f4WphBE7NboORfTtjgM8VnIpVC4VswmFBjzvzJzb0fw9t+/bAkD3JiLEn/y6sxLYZTfFxh8nosuI8zPQcMrLAgZH4dXRXFLFF6FtCzHV0UQHbsXNEbyzduAcH7xLDm7u6YmznPMjdchIGbI0yQbM7Ebi2Bzu9PDB69EZsDoyx3edbuLTxN4yxRZUNag7D1MAU3NS2NguRQftwfO4gLJnQDR81H42yI3Yh6LpJEtMptpDo2nps2rwR09dcQUiECRPmGslBM/HH2Koo7DQPQ3bcCeWQcOkkQoJP4vS1BNyUlEu24qCgJ+PakXlwr1UA9Zp2twl6ImLtKnN+FTa6NsZ79Ueg+fwgXNcWm4LE60je7YzeI7uYUtAjDnhiSfvyaPPrWEw8eE3rXEzG1VU/26LhPCjWYQbG+90wgaDzCBKQkBCPOHs3xO2D8J/bHdXLtUb7sVtwzBahmec3bWs/3DyK/Vt3YOXc1QjcNBF1B4xHaecd+CvWJK20FNvrL3Ijtni7wXn0H5g3fzW8F82D1yof+Jy6YaIO0SRc+bM3JnUoiDLDJqDf4p3wXbYQaxcvwPyt5xFwVdtMyFYcFPRbuLx3Gib/5x18X7UXhvraBF1bg7DV2DSmAYp90hcNxx5CmLbYFJhc0G+fWopNgz/Bl426oMm8MNvL0CaciX9h7a8N0fC/3+Jnd1/sjch5Of+bRKTEnsTR7avhObonfu3VGy3mHobPZXNFl0nx13Ft53z4bNyBTUFhiPCdgea9RqKUmQSdKZeozdiwdDr6D3bHzFle8JwzE1PHDsewYR74c18oLpviDXkbZ1Z0wahaefBNsz7oPmUNNnvNw6p5rhg19FdM8FgB//Ak3DBvA8OSOCjoN3HFdwqmlngbP1S/R9DDbTd4ZH0UK9QDdYf645y22BSYXNARew5X/RZizvLN8DoSb4uAQ3FpzRD0q90Wddt6Y8PpOJOlMa7jVugO+MweiT6duqPnAFe4e/oh4Mw1RJulGjDZdozhx7B+7hqs3X4cUQjH7SO/o2E3swm67Tjiz+DEqVPY/dcN3NLEO9pvHha3+hKdxizEvHPJJmidxePCqq4YWC0PClQfgt5a/wkQgwse1TGkaxVbyzwYB66a5QF4NHBQ0BMRcdQN7rULoG6zHhh6d8rlwipsmtQYRWo6o5l7oO0HZCLMLuh3Ex+JqwEL4N6+BX4d4I6lp/F3x7PpSEZS8lWE7JyN36rVQMtOMzAr6DZumKAxkXj1CC7vWYQlu85h52UuuQocm4EGPceitMtenPyfGuU8KbZreDNaVYpFxv1dj54U7In1Lm+jdOeeaL3yGm7l+DVNQMiKXzC4wdso3McdYw/bf0OxiFrdDB1blce7TqvgddLcoxGshsOdorGnlsO7SyG0cOqOIbuT/u4UPb8Sm6f9hOLNx6LtkmBzRZQPjaAnIfrQCqx37YUJS7Zj44V4k9Sf20lC0o3LiIy4jLDoJC1CszXGg5fCs3UBFC9XBxWnHsPJHC/zv43zfjvh4zoHWw8ex7mYONyIOomYnePxY6ch+PTX9dgfHoPbiXoDerKZmxEIWz8a06b8hhFrziNEa/Imn/WyCXpeFK/TFDV+P4krOS7oKQhf1w3TOxVGnYnLsehONdYNJO0eiJ5dG+DtjrbnIFDqFrMThwU9IWwXDk4sBadh/TBwR+Lfgh68AptntsA3zgvhsj3SBE3Eu0i6gZQ9w9BnVFcUcd2PrWYcfpliO6bjXtjgOQ7DFu2F78Xk/1UKJd1CTHgkbsWaYDBUQgiCF7VAj59roKLLFvx54X/XMf6sN1b8kh+FS3+LUi5+CMzRukXb3048hUOrZmJQyy7o4dQLA4c7w3mgEwa2qYQPPv8aecq2Rhdnd6wOCMPFHH5Qk6MDcXjiF2jatAqq2l6GRyL/tzzhxHysHpQPn7btjpbeV00xCjPh4FQsHlUNH4/wwih/e8gWi+g1LdCh3Tco2HcdVgSbrcbJ2jgs6Ig7g9gd/dFz2G9o63Ee9vdxzM5ZWNavNnrN34k1YWarXbLFuf4j0X9cDxSdcQS7TdcjH4vkCH9smjsdEycswtLdwThxNgThFw5g7/4dmO15GIcCo7Vtc46U22E4OqkKfmlUFtWmHsZeTXxunVwIj1qFULHmL/hlbQSu5nSzIjkKkaEncGivH3bv2IXdvvuwb9tS+Iyvh7K1GuH9du74Y3cgzkXE5fjnClKuhyJ8aW+4zRqP8QEJTAzZuIVLW2zPQq3yaDvSE55hJslL39gDvxUjUK21G0atCNEWRuHElLro/0t9/Lz8AgJM04nyaOC4oDOZEnMSW1ewFnkCpnssxjx3N0wfNxLjJtmauIERJumVt3HzEq4F+2Lz2gWY378yypf7GE9W6oEuk7zg7XsCAeEJiDfBsd4O34g948qjSomS+HfhL1C5QjlUrPC17Xg/QamqNVHDZSc2nsr5pG9K0m1EBi7HxnmDMGrcJEyfsxBuv0/FpCFd0bvTAEzy2ofDNpE33U/6+mWc2+KOpX1Ko/DHZfHm92Mwa6M/Aq9eR0xONyUTE5FyKRhnfFdi5TI3eCz2wPSJEzGuzzAMc/4DGw6G45q2ac4Tg9jz/tgwcQLGDJ+EcdNtxztrAsYNd8GsxRtxNMIW75mqaW59DBB0jcijOLnD9oN288DMqVMx13sTNpy+jZtm+jXHXcTVEzuxbuV8zJ4wAAO7d0XHbiPgMn0RPHcG4kBoAszwna5bYbuxd2YbDGjXCE2aNET9+vU1a4gWXfrDdds5nDFNSzbB9qPeh11e0zHXfTamjJ8A18luWOwbhhCzdDT+gwTEhJ7E4dUeWDyxF3p27w2nAdMwZ8UW+IVG4ZpJBOjG2X3wXzoRbu7TMc5lKma7+WBHWIL5PniWYrvJoduxeeU8jBk3BTOmTcP8LYE4YhNzIfsxTtAFQRCEHEUEXRAEwSKIoAuCIFgEEXRBEASLIIIuCIJgEUTQBUEQLIIIuiAIgkUQQRcEQbAIIuiCIAgWQQRdEATBIoigC4IgWAQRdEEQBIsggi4IgmARRNAFQRAsggi6IAiCRRBBFwRBsATA/wNReubTX5P42wAAAABJRU5ErkJggg==)
MathematicsGrade10
The median volume of juice squeezed is 3 fluid ounces.
Mathematics
The range is the difference between the …………………….. values.
The range is the difference between the …………………….. values.
MathematicsGrade10
Mathematics
The median is …………… when the data are ordered from least to greatest.
The median is …………… when the data are ordered from least to greatest.
MathematicsGrade10
Mathematics
The median is useful to describe the center of data with …………………
The median is useful to describe the center of data with …………………
MathematicsGrade10
Mathematics
The mean is the …………………..value
The mean is the …………………..value
MathematicsGrade10
Mathematics
The measure of distance which is greatly influenced by extreme values in data Is considered as
The measure of distance which is greatly influenced by extreme values in data Is considered as
MathematicsGrade10
Mathematics
The population variance is also called
population variance can be defined as the average of the distances from each data point in a particular population to the mean squared, and it indicates how data points are spread out in the population.
The population variance is also called
MathematicsGrade10
population variance can be defined as the average of the distances from each data point in a particular population to the mean squared, and it indicates how data points are spread out in the population.
Mathematics
The number of values is 11 and the value of the arithmetic mean is 19 then the sum of all values is
The number of values is 11 and the value of the arithmetic mean is 19 then the sum of all values is
MathematicsGrade10
Mathematics
The sum of values of data is divided by the total number of values is used to calculate
The sum of values of data is divided by the total number of values is used to calculate
MathematicsGrade10
Mathematics
The standard deviation of the population is denoted by
The standard deviation of the population is denoted by
MathematicsGrade10