IM 6.8.11 Practice: Variability and MAD

Han recorded the number of pages that he read each day for five days. The dot plot shows his data.
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[/img][br][br]Is 30 pages a good estimate of the mean number of pages that Han read each day? Explain your reasoning.
Find the mean number of pages that Han read during the five days.
Drag a triangle to mark the mean on the dot plot.
Use the dot plot and the mean to complete the table.
Calculate the mean absolute deviation (MAD) of the data. Explain or show your reasoning.
Ten sixth-grade students recorded the amounts of time each took to travel to school. The dot plot shows their travel times.
[size=150]The mean travel time for these students is approximately 9 minutes. The MAD is approximately 4.2 minutes.[/size]​[br][img]data:image/png;base64,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[/img][br][br][br]Which number of minutes—9 or 4.2—is a typical amount of time for the ten sixth-grade students to travel to school? Explain your reasoning.
Based on the mean and MAD, Jada believes that travel times between 5 and 13 minutes are common for this group. Do you agree? Explain your reasoning.
A different group of ten sixth-grade students also recorded their travel times to school. Their mean travel time was also 9 minutes, but the MAD was about 7 minutes. What could the dot plot of this second data set be? Describe or draw how it might look.
In an archery competition, scores for each round are calculated by averaging the distance of 3 arrows from the center of the target.
An archer has a mean distance of 1.6 inches and a MAD distance of 1.3 inches in the first round. In the second round, the archer's arrows are farther from the center but are more consistent. What values for the mean and MAD would fit this description for the second round? Explain your reasoning.
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Information: IM 6.8.11 Practice: Variability and MAD