[img]data:image/png;base64,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[/img][br][br]For this data set, is the mean or median a better measure of center? Explain your reasoning.
[br]1,235 952 457 1,486 1,759 548 1,037 1,846 1,245 976 866 1,431[br][br]Estimate the median time it will take [i]all[/i] players to finish this game.
Find the interquartile range for this sample.
[list][*]The mean height for Han’s sample is 59 inches.[/*][*]The mean height for Priya’s sample is 61 inches.[/*][/list][br]Does it surprise you that the two sample means are different?
Are the population means different? Explain your reasoning.
[list][*]Clare asked each student in her sample how much time they spend doing homework each night. The sample mean was 1.2 hours and the MAD was 0.6 hours.[/*][*]Priya asked each student in her sample how much time they spend watching TV each night. The sample mean was 2 hours and the MAD was 1.3 hours.[/*][/list][br][br]At their school, do you think there is more variability in how much time students spend doing homework or watching TV? Explain your reasoning.
Clare estimates the students at her school spend an average of 1.2 hours each night doing homework. Priya estimates the students at her school spend an average of 2 hours each night watching TV. Which of these two estimates is likely to be closer to the actual mean value for all the students at their school? Explain your reasoning.