High airline occupancy rates on scheduled flights are essential to profitability. Suppose that a scheduled flight must average at least 60% occupancy in order to be profitable. An examination of the occupancy rates for 120 10:00 a.m. flights from Washington, DC to Phoenix showed a mean occupancy rate per flight of 58% and a standard deviation of 11%. Do the occupancy data for the 120 flights suggest that this scheduled flight is unprofitable? Test using level of significance = .10.
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
1. High airline occupancy rates on scheduled flights are essential to profitability. Suppose that a scheduled flight must average at least 60% occupancy in order to be profitable. An examination of the occupancy rates for 120 10:00 a.m. flights from Washington, DC to Phoenix showed a mean occupancy rate per flight of 58% and a standard deviation of 11%. Do the occupancy data for the 120 flights suggest that this scheduled flight is unprofitable? Test using level of significance = .10.
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