IM Alg1.1.5 Practice: Calculating Measures of Center and Variability

The data set represents the number of errors on a typing test.
5 6 8 8 9 9 10 10 10 12[br][br]What is the median? Interpret this value in the situation.[br]
What is the IQR?[br]
The data set represents the heights, in centimeters, of ten model bridges made for an engineering competition.
13 14 14 16 16 16 16 18 18 19[br][br]What is the mean?[br]
What is the MAD?[br]
Describe the shape of the distribution shown in the dot plot. The dot plot displays the golf scores from a golf tournament.
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[/img]
The dot plot shows the weight, in grams, of several different rocks. Select all the terms that describe the shape of the distribution.
[img]data:image/png;base64,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[/img]
The dot plot represents the distribution of wages earned during a one-week period by 12 college students.
[img]data:image/png;base64,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[/img][br]What is the mean? Interpret this value based on the situation.[br]
What is the median? Interpret this value based on the situation.[br]
Would a box plot of the same data have allowed you to find both the mean and the median?[br]
The box plot displays the temperature of saunas in degrees Fahrenheit.
[img]data:image/png;base64,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[/img][br]What is the median?
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Information: IM Alg1.1.5 Practice: Calculating Measures of Center and Variability