IM 6.8.8 Practice: Describing Distributions on Histograms

The histogram summarizes the data on the body lengths of 143 wild bears. Describe the distribution of body lengths. Be sure to comment on shape, center, and spread.
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[/img]
Which data set is more likely to produce a histogram with a symmetric distribution? Explain your reasoning.
[list][*]Data on the number of seconds on a track of music in a pop album.[/*][*]Data on the number of seconds spent talking on the phone yesterday by everyone in the school.[br][/*][/list]
Evaluate the expression 4x³ for each value of x.
[math]x=1[/math]
[math]x=2[/math]
[math]x=\frac{1}{2}[/math]
Decide if each data set might produce one or more gaps when represented by a histogram. For each data set that you think might produce gaps, briefly describe or give an example of how the values in the data set might do so.
The ages of students in a sixth-grade class.
The ages of people in an elementary school.
The ages of people eating in a family restaurant.
The ages of people who watch football.
The ages of runners in a marathon.
Jada drank 12 ounces of water from her bottle. This is 60% of the water the bottle holds.
Write an equation to represent this situation. Explain the meaning of any variables you use.
How much water does the bottle hold?
Close

Information: IM 6.8.8 Practice: Describing Distributions on Histograms