IM Alg1.1.10 Practice: The Effect of Extremes

Select all the distribution shapes for which it is most often appropriate to use the mean.
For which distribution shape is it usually appropriate to use the median when summarizing the data?
The number of writing instruments in some teachers' desks is displayed in the dot plot.
[img]data:image/png;base64,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[/img][br]Which is greater, the mean or the median? 
Explain your reasoning using the shape of the distribution.
A student has these scores on their assignments.
The teacher is considering dropping a lowest score. What effect does eliminating the lowest value, 0, from the data set have on the mean and median?[br][br]0, 40, 60, 70, 75, 80, 85, 95, 95, 100
What is the five-number summary for the data 2, 2, 4, 4, 5, 5, 6, 7, 9, 15?
When the maximum, 15, is removed from the data set, what is the five-number summary?[br]
The box plot summarizes the test scores for 100 students:
[img]data:image/png;base64,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[/img][br]Which term best describes the shape of the distribution?
The histogram represents the distribution of lengths, in inches, of 25 catfish caught in a lake.
[img]data:image/png;base64,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[/img][br][br]If possible, find the mean. If not possible, explain why not.[br]
If possible, find the median. If not possible, explain why not.[br]
Were any of the fish caught 12 inches long?[br]
Were any of the fish caught 19 inches long?[br]
Close

Information: IM Alg1.1.10 Practice: The Effect of Extremes