In Exercise 8.83, we presented some data collected in a study by Susan Beckham and her colleagues. In this study, measurements were made of anterior compartment pressure (in millimeters of mercury) for ten healthy runners and ten healthy cyclists. The data summary is repeated here for your convenience. Runners Cyclists Condition Mean s Mean s Rest 14.5 3.92 11.1 3.98 80% maximal O2 12.2 3.49 11.5 4.95 consumption a Is there sufficient evidence to justify claiming that a difference exists in mean compartment pressures for runners and cyclists who are resting? Use = .05. Bound or determine the associated p-value. b Does sufficient evidence exist to permit us to identify a difference in mean compartment pressures for runners and cyclists at 80% maximal O2 consumption? Use = .05. Bound or determine the associated p-value.
Econ 225: Ex. You have 12 shirts in your closet. 9 White shirts, 3 Black shirts. Suppose you pick a shirt at random. Put it on, when you get home you take it off and wash it. Suppose you do the same thing the next day, without replacing the first shirt. What is the probability that both shirts you picked out are white Day 1: P(Picking a white shirt)= 9/12 (9 white shirts/ 12 total shirts) Day 2: P(Picking white shirt) = 8/11 (8 White shirts/ 11 total shirts; given that you picked a white shirt on the first day) (Probability of day 1) * (Probability of day 2; Given that day 1’s shirt was white) = (9/12) * (8/11) = .55 Whatever happens on the first day will affect what happens on the second day. It is a dependent probability because day 2 depe
