IM Alg1.1.11 Lesson: Comparing and Contrasting Data Distributions

Evaluate the mean of each data set mentally.
27, 30, 33
61, 71, 81, 91, 101
0, 100, 100, 100, 100
0, 5, 6, 7, 12
Use the applet below. Take turns with your partner to match a data display with a written statement.
[list=1][*]For each match that you find, explain to your partner how you know it’s a match.[/*][*]For each match that your partner finds, listen carefully to their explanation. If you disagree, discuss your thinking and work to reach an agreement.[/*][/list][br]After matching, determine if the mean or median is more appropriate for describing the center of the data set based on the distribution shape. Discuss your reasoning with your partner. If it is not given, calculate (if possible) or estimate the appropriate measure of center. Be prepared to explain your reasoning.[br]
Each box plot summarizes the number of miles driven each day for 30 days in each month. The box plots represent, in order, the months of August, September, October, November, and December.
The five box plots have the same median. Explain why the median is more appropriate for describing the center of the data set than the mean for these distributions.
Arrange the box plots in order of least variability to greatest variability. Check with another group to see if they agree.
[*]The five dot plots have the same mean. Explain why the mean is more appropriate for describing the center of the data set than the median.[/*]
Arrange the dot plots in order of least variability to greatest variability. Check with another group to see if they agree.
These two box plots have the same median and the same IQR.
[img]data:image/png;base64,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[/img][br]How could we compare the variability of the two distributions?[br]
These two dot plots have the same mean and the same MAD.
[table][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][/table][br][br]How could we compare the variability of the two distributions?

IM Alg1.1.11 Practice: Comparing and Contrasting Data Distributions

In science class, Clare and Lin estimate the mass of eight different objects that actually weigh 2,000 grams each.
Some summary statistics:[br][br][table][tr][td]Clare[br][list][*]mean: 2,000 grams[/*][*]MAD: 275 grams[/*][*]median: 2,000 grams[/*][*]IQR: 500 grams[/*][/list][/td][td]Lin[br][list][*]mean: 2,000 grams[/*][*]MAD: 225 grams[/*][*]median: 1,950 grams[/*][*]IQR: 350 grams[/*][/list][/td][/tr][/table]Which student was better at estimating the mass of the objects? Explain your reasoning.
A reporter counts the number of times a politician talks about jobs in their campaign speeches.
What is the MAD of the data represented in the dot plot?[br][br][img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeMAAABmCAYAAAD4ffnnAAAU2UlEQVR4Ae2dTagdRfOHA74kKKLxCxM/gyIRFfwKGD/Qi5FoQBNRg4Jooqi4ibmi4ELR4EbExVVXAVEjoi4MmoVuosGgICKo4MKAGAQxQY0gfqAgaL88h3+9/7p1Zm7unXNuzsypX8MwMz3d1VVP91RNz8w5s6AoiYAIiIAIiIAIjJTAgpG2rsZFQAREQAREQASKgrEGgQiIgAiIgAiMmICC8Yg7QM2LgAiIgAiIgIKxxoAIiIAIiIAIjJiAgvGIO0DNi4AIiIAIiICCscaACIiACIiACIyYgILxiDtAzYuACIiACIiAgrHGgAiIgAiIgAiMmICC8Yg7QM2LgAiIgAiIgIKxxoAIiIAIiIAIjJiAgvGIO0DNd5PAN998U9auXVsWLlxYli9fXrZu3dpNQxpqvX///rJ+/fqyaNGicsYZZ5SpqamGklRNBEQAAgrGGgci0IDApZdeWhYsWDBt2blzZwNJ3ayyevXqabbDYvv27d00RlqLQAsIKBi3oBOkQrcI7Nmzpy8QEYw2bdrULUMaartv375K+++8886GElVNBERAwVhjQATmSGDv3r2Vwejhhx+eo6RuFj9w4ECl/ffee283DZLWItACAgrGLegEqdA9Atdff31fQPr000+7Z0hDjW+77bY++99///2G0lRNBERAwVhjQAQaEPjrr7/K5s2by9lnn13WrFlT3n333QZSul2FOwHnnHNOueaaa/S8uNtdKe1bQEDBuAWdIBVEQAREQARyE1Awzt3/sl4EREAERKAFBBSMW9AJUkEEREAERCA3AQXj3P0v6xsS+Omnn8qGDRvKkiVLysqVK8sbb7zRUFI3q/3++++Ft6eXLl1aVqxYUV566aVuGiKtRaAlBBSMW9IRUqNbBFatWtX3NvGHH37YLSMG0HbdunV99r/zzjsDSFRVEchNQME4d//L+gYEvv76675AxJ9+PPjggw2kda/KDz/8UGn/XXfd1T1jpLEItISAgnFLOkJqdIdAXTCenJzsjhEDaKpgPAA8VRWBGgIKxjVglC0CMxG4+uqr+2aHuk2t29QzjRkdE4GZCCgYz0RHx0SghsCPP/5Y+C/mE088sVxyySXl9ddfryk5ntn2AhcvsF188cXlxRdfHE9DZZUIHCICCsaHCLSaEQEREAEREIE6AgrGdWSULwIiIAIiIAKHiICC8SECrWZEQAREQAREoI6AgnEdmYPkf/fdd+Xuu+8uZ511VrnpppvKxx9/fJAa43X4559/Lvfff39Zvnx5ueGGG8quXbvGy8CDWPPnn3+WBx54oPehiOuuu65k/I1t9g9FPProo+Xcc88tExMT5dVXXz3IiBm/w08++WS58MILyxVXXFFeeOGF8TPwEFukYNwQ+EUXXTTtbdojjzyyfP/99w2lda/aVVddNc1+fmf71Vdfdc+QhhrrE4q5P6HIv48x5v3y1ltvNRxN3av20EMPTbMdDtu2beueIS3SWMG4QWfs3r27byAyGJ9//vkG0rpX5fPPP6+0nyvlDGnv3r2V9jNTzJAOHDhQaT8BKkP6+++/K+1fv359BvN7Nh577LF9DFavXp3G/vkwVMG4AdWdO3f2DUSC8TPPPNNAWveqfPLJJ5X2P/bYY90zpoHGe/bsqbR/06ZNDaR1r8q+ffsq7eenXhnSH3/8UWn/2rVrM5jfs3HhwoV9DK688so09s+HoQrGDamefvrpfYMx023a8847r89+gnSWdNlll/XZ/95772UxvzAL8rdo2d6+fXsa+2+55ZY++zN9LIP3ZWL/P/fcc2n6fz4MVTBuSJXAw78wHXbYYb2XGN58882GkrpZ7csvvyxr1qwp//nPfwqB+ZVXXummIQ215lY1H0tghsBLbFu3bm0oqZvV9u/fX7gtu2jRonLmmWeWqampbhrSUOvffvut3HHHHeXwww8vp512WnnqqacaSupmtX/++afcc8895YgjjignnXRSefzxx7tpSIu0VjAesDMYlJnTv//+m9l82S4CIiACQyEwcDDmT+N5oSFrwv5ff/01q/kF+1myJtmv/tf41/k/DP83cDDmt5aZg/EjjzySOhhv2bIldTB++umny7fffjuMc7GTMvgFQaZ3JWIn8fvazz77LGan2X/ttdfKRx99lMbeaCg/Z+OF3mEkBeMBKSoYKxgrGOf5fXl0FwrGCsYKxvGsGNG+grGCsYKxgvGI3M/Im9XMWDPjkQ9CU0DBWMFYwVjB2PxBtrWCcYuCMb+3vO+++3r/U8x/FWdbLrjggt5/VGez2+xdsWJF77u+tp9tvXLlynL77benG/fWz5dffnm59dZb09rP38LefPPNae1ftWpV7yd+Nh6yra+99tr2PDMmGPM7O/4EI+Ny1FFHlVNPPTWl7fT34sWLyymnnJLW/mOOOaacfPLJae3nbxH5nWnGcx+bjzvuuLJ06dK09h9//PFlyZIlae0/4YQT2hOM9Ta13qbO/NMOvU2tt6n1NrXeph7G4wm9TT0gRT0z1jNjPTPWM+MB3Uhnq+uZcYueGWtmrJmxZsb6nXFno8mAiuunTfppk37aNOBJNKzqmhlrZqyZsWbGw/InXZOjmXGLZsa7du1K/Q9c2J/57zCxP/PMGPszB2Psz/wPXNif+Zkx9mf+By7sZxlGGviZ8TCUkAwREAEREAERyExAwThz78t2ERABERCBVhBQMG5FN0gJERABERCBzAQUjDP3vmwXAREQARFoBQEF41Z0g5QQAREQARHITGDgYPzBBx+k5cdbtLt3705rvwwXAQh88cUXOg+SDgX6PvOvCYZpf6NgDPyJiYne/5HyR+lHH310efnll9MMx7fffrssW7Zsmv2Tk5Np7I+GMgZYMiXsXbBgwbQl24XpE0880ftv8vPPP7/X/zimDAm7Y9/bfgb7sREfyP/Scx7Q//jDTEGZsY7N2M5/lBMPf/nll4G6v1EwRhE6wxL7DMYsnYG93vHSCVyQ+DxjM+5rHFPGYMwJmLG/bTxPTU31HBHnglLpTUYyXZBGf4cfWLduXZqhwPnvY+CGDRvK5s2bB7K/UTCuajG7c+JEzHR3gDGAI7ZBmckRYTsXn5lTdMaZWWA750Gm8z9OvrA9iw/gIpzx7xMT0Zjnj89meygehZlh7JzZND4uZbhCoiOy3Bmg3+hzvuXMwGTJciLamLXxzjsDg96eMpldWdPfdjHCBRkMMo392E/wIBhnSswCb7zxxt7Yp+/xBX6mOM4s6G9uUcfEOTGILxhKMB7GFD0a1vZ9nBDPCRiEdEy223WcjNyaImUMxlx8cQHCwrY5praP22HoR38z7rGdccD5DwPyMyY4ZJoVWx9zW5rnxgShTPYTcBnv/uLD3iMY5KJ04GBMJ/AQe5ArAuvcLq2xF+fDQkfQOVkCMjbT55bYxyFlTYwFC0wZGHDOx/FOXtVsYdx5MPZhkc3/cRGGDyAg4f+y3aa3u6FclMKBdyjsblHTMT9QMM4aiKtg2wyh6ti45XHiMRPcsmVLb9m4cWPPEbM/yJVhlzlxcma5IKm6+CIYDeqMutj/3BWwO0Rd1L+Jzkw68AH+AsQuSprIG4c6w7C/cTBWIJ4+hDghOTEzJGz1C3ZzcpKXNRhzZZwlGNPHzAZ9GoYz8vK6sA0HLkCyjfmqizH6K+PFmI3TYVyUNQrGBGKeFezYsaP38gYvcLBkuU2L/d5WBic8/DME66QM67qTc1xtx/ky9i1l7H+cD3dEmB2x8P4EF2OZEgxYsiW7GPP+zm5bZ2TBHcF4p6AJh0bBGPDMAuJCfoZkv7HkSpAZAhz8wMzAwNvIhUmWvsdunBEvr9D/LBn7nwBMn+OEWNj2ty39+BjXbcaAvygfVzur7OIC1M4B+p+Lkkz9b8+IeV48LNsbBeOqzlGeCIiACIiACIhAMwIKxs24qZYIiIAIiIAIDI2AgvHQUEqQCIiACIiACDQjoGDcjJtqiYAIiIAIiMDQCCgYDw2lBImACIiACIhAMwIKxs24qZYIiIAIiIAIDI2AgvHQUEqQCIiACIiACDQjoGDcjJtqiYAIiIAIiMDQCCgYDw2lBImACIiACIhAMwIKxs24qZYIiIAIiIAIDI2AgvHQUEqQCIiACIiACDQjoGDcjJtqzQMB/ue3K1/AQVc+jqI0WgL8R/Kh/H/oLo3R0faMWp8rAQXjuRJT+XkjwAcX2v7lH/4Mnw+K20dCuvrVHoIK36TuarIvx/GhFvqi6iKOsUSwJvlt9pt+5GFYY9Tr47e72h/Se3ACCsaDM5SEIREYlqMbkjqVYvhayzA+l1YpfJ4y+aJYDFYEMwJZV7+0g+4EsZkSQdrKsE3fkbCZ+jCYaxrWGI26mZ5z1Uflx4eAgvH49GXnLRmWo5tPEOjIrKpLiYsHmyF2Se86XZnVE8wOZpO3m+B7sPJ17fn8YY1RdLPPrno9fVvazkVAwThXf0+zlpkBDoqZwrPPPlv4SDZLnEVRpmoWEfOphxwS5aM8O06+lfMKmaPz5Xbs2OGLTNvGmcU2rAAyTGf0pJztW5m49u1u27atb9ZIEFi2bFmZmJiYUR5yaI9UpaPnDYe62akxpAwyffLsrRxtkm8JudhB4Nq4ceM0nb2OVp71wRhQxmZxxpV2aScmb6eVqbPV1/XMYO4T9ZHlbfI2+7KMJzvmtymDDZGp8UI+S2ybenMZo96O2IdeH7/t9dd2LgIKxrn6e5q1OAGeGy5evLjnZOxj8ex7R4XjomxMMR/HZ06Sj257eQQM5DKr5DkrMxWCmk+mDzMFX45ntN6Js01dytEGZWnXB1vThTzaQjZl6xLlkGHl0B99zSGz5hiyaNfKVcmztgmAxoE18rlVahzQB3nY5xP2kWf2RV0oC3suDCgX2yAIkLAJPWmXMl5n09G3S3l08/I8AyuLvMnJyZ5+9CX8scM/g8YGk4WulMMe2qhLvl+9XDhaghn6eZvqZGIvdpL8NvvUt2PsM97hiY60bdzs1nZPyP/JwU7K2RhlO45Rsxd9TWdkWvL6+G07rnU+AgrG+fr8fxabw/FOiYM4FxyIpRh06/LNweOkfEJedH5W1rdt+lgARAYOGkePDpbYRibHLJGH87c8k4/jtjwrG9c4YupGx4sdOGif0NHr4o/ZtrUdOVgQ8TbTdmRjwcDksSbP64IO1Iu6UC4G9ygfeaajtQGjKgaxXcojDw6eKxcA5FvfwZI+mkvCltivyKP/fcA13T3HubRjNvj62FNnUyzn7UQWHPwYrerTueqn8vkIKBjn6/P/WYzzIUDEFIMBTpKyMcX8OieJvKp2cGreyZpDjO1wYeDbx2HHIBQdoOlis8Qo0+9bEPB5bEeZ5KFHbDvWs7a9E6cMduC0Y4KDvxBgP+ptMtGJhA6U8wGRfHiS7xP7UReTZ+VoP9bjGPJjffa9vibDlzM9oh1WtmpdJzeOR9M92lQlsy7P62r9XKUr49ZfmM40Ru0iyIKzn9HX6aF8ETAC089ay9U6BYE6x4Kj98Ev7hucmG9OMgaIWM7q4xA5Zok2veOzfMpQ1hLbOD5uVfuFfAsSdbqYDL+Owd4f8zLJR0evsy9r29Z2DBaz4cBMsMo+7CXfZEYmsW3bZ+3rWb7paPt1ull9b3OVPCtn+rFvOjKjr3oGb22zrgr6dhw5zNotme6+LTs227W3weTZhY6XES8EZjtGCexcNKI3t/SrZPt2tC0C/+/hxCIdARwLS0zRMcd9Kx/zzanZcVvHcpaPQ+SYpdk6OqtHe3Exp1eni7Xl19Hh+mO0ZQGefHT0Ovuytm1ts/ZpNhwsGNNmtI19u9BBFrrFZG37fMpFXWK5Ot2QY7xNZpU8KxfbQV9kW2Cy29gmy9YHC8beVtM9tmWyZrP2Npg8Gzu+fhwbsx2jJoM7BNShPX8XyI5rLQJGoP9stiNajz0BnARLTNExx30rHx2VOTU7buu6+jgojlmq04eZq7/Nza3eqhm0yWFdp4svY9vo4J/HWj7O2Ttt8tHR62xl/draZu3TbDnQZtUt0yiLcjFZ2z4/2sCxWI7gXyWvKkhWyUNmXT7HkAO7+Bzd60m/+gsfOxbvXJjuka+Vn83a62r9XMWcccc4tzTbMWrlbU3fV/G141qLQP/ZLCZpCNQ5lhg0zFHjUC2xzS04ZFgyJ2n7to7yLB/nxDFLyCLPz56sHV+ObdqumsmYrDpd7LhfmzOOgYAgwIzOJ3T0uvhjtm1tx2BBPc/LykcOOH8uDjxvK2trZFU5d2vbyrGmXAw0sVwdA9qBgdcFedE2a8fyfXnTJQZVy7d1ld3oRV/7vjHdrS2rP5d1tIF+iW/3V7VTN0bpLxsXVbbDv6q/5qKzyo43AQXj8e7fGa3DsVQFhxg0cC7MWnA4/P6SZ2AWiH19c16x0SjPjuOczIGRZ/rQFm2w0CazE+/g2KYsOtjvZ3Gk7Fuq08WOx7W9cMTPVrAReegRHT7tep2jHPat7Vh3thwIQNgc7fPOHFl+3/Swtm2ftbGy3896HX25OgYxkNNutA05Ph/9eM5tbdJPHI+yfPv0K3bT59b/Ns58ObOxSgdfbqZtryvluAD0Y9z0jXdgZjNG0cts8OeLn2HPpJuO5SSgYJyz33tW43yrnmPhTGI+jhIHi0NhTcBg8eXY51hMVfIoQ1nvUJFlcq0tZkQ+EHvZlKcctz5Ze0dfp4uvH7dxyMjBAbNGRky06XWOx9m3tmP92XIwmbSFLsbc3zFAFjrGZG37fPjB0ThxrKoc+bNhUMfG51ub5Jn+kYfX0bapZ3ZTl+2YTPfZyIt1bT8GY/JNZ+v/qn5GH9o1HbCtaowyFr3tVbJMF61FAAIKxhoHIiAC6QhUBeN0EGRwqwgoGLeqO6SMCIjAfBNgBqxgPN+UJX+uBBSM50pM5UVABDpJgFvwvAvA89z4Yl4nDZLSY0VAwXisulPGiIAI1BFgRmzPodlWEoE2EVAwblNvSBcREAEREIGUBBSMU3a7jBYBERABEWgTAQXjNvWGdBEBERABEUhJQME4ZbfLaBEQAREQgTYRUDBuU29IFxEQAREQgZQEFIxTdruMFgEREAERaBMBBeM29YZ0EQEREAERSElAwThlt8toERABERCBNhFQMG5Tb0gXERABERCBlAT+C5fj3e7OAPy9AAAAAElFTkSuQmCC[/img]
Four amateur miniature golfers attempt to finish 100 holes under par several times.
Each round of 100, the number of holes they successfully complete under par is recorded. Due to the presence of extreme values, box plots were determined to be the best representation for the data. List the four box plots in order of variability from least to greatest.[br][br][img]data:image/png;base64,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[/img][br][img]data:image/png;base64,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[/img]
Select [b]all[/b] the distribution shapes for which the median could be much less than the mean.
What is the five-number summary for the data 0, 2, 2, 4, 5, 5, 5, 5, 7, 11?
When the minimum, 0, is removed from the data set, what is the five-number summary?[br]
What effect does eliminating the highest value, 180, from the data set have on the mean and median?[br][br]25, 50, 50, 60, 70, 85, 85, 90, 90, 180
The histogram represents the distribution of the number of seconds it took for each of 50 students to find the answer to a trivia question using the internet.
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[/img][br]Which interval contains the median?

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