The Palace Hotel believes its customers may be waiting too long for room service. The hotel operations manager knows that the time for room service orders is normally distributed, and he sampled 10 room service orders during a 3-day period and timed each (in minutes), as follows: 23 23 15 12 26 16 19 18 30 25 The operations manager believes that only 10% of the room service orders should take longer than 25 minutes if the hotel has good customer service. Does the hotel room service meet this goal?
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!
The Palace Hotel believes its customers may be waiting too long for room service. The hotel operations
manager knows that the time for room service orders is
sampled 10 room service orders during a 3-day period and timed each (in minutes), as follows:
23 23
15 12
26 16
19 18
30 25
The operations manager believes that only 10% of the room service orders should take longer than
25 minutes if the hotel has good customer service. Does the hotel room service meet this goal?
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