IM Alg1.1.15 Practice: Comparing Data Sets

Twenty students participated in a psychology experiment which measured their heart rates in two different situations.
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[/img][br]What are the appropriate measures of center and variability to use with the data? Explain your reasoning.[br]
Which situation shows a greater typical heart rate?[br]
Which situation shows greater variability?[br]
[size=150]Invent two situations that you think would result in distributions with similar measures of variability. Explain your reasoning.[/size][br]
[size=150]Invent two situations that you think would result in distributions with different measures of variability. Explain your reasoning.[/size][br]
The data set and some summary statistics are listed.
[size=150]11.5, 12.3, 13.5, 15.6, 16.7, 17.2, 18.4, 19, 19.5, 21.5[/size][br][br][list][*]mean: 16.52[/*][*]median: 16.95[/*][*]standard deviation: 3.11[/*][*]IQR: 5.5[br][/*][/list][br]How does adding 5 to each of the values in the data set impact the shape of the distribution?
How does adding 5 to each of the values in the data set impact the measures of center?[br]
How does adding 5 to each of the values in the data set impact the measures of variability?[br]
Here are two box plots:
[img]data:image/png;base64,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[/img][br]Which box plot has a greater median?[br]
Which box plot has a greater measure of variability?[br]
The depth of two lakes is measured at multiple spots. For the first lake, the mean depth is about 45 feet with a standard deviation of 8 feet. For the second lake, the mean depth is about 60 feet with a standard deviation of 27 feet.
Noah says the second lake is generally deeper than the first lake. Do you agree with Noah?
The dot plots display the height, rounded to the nearest foot, of maple trees from two different tree farms.
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[/img][br]Compare the mean and standard deviation of the two data sets.[br]
What does the standard deviation tell you about the trees at these farms?[br]
Which box plot has an IQR of 10?
What effect does eliminating the lowest value, -6, from the data set have on the mean and median?
[size=150]-6, 3, 3, 3, 3, 5, 6, 6, 8, 10[/size]
Close

Information: IM Alg1.1.15 Practice: Comparing Data Sets