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?