Suppose we are counting events that occur according to a Poisson distribution, such as the number of data-processing jobs submitted to a computer center. If it is known that exactly one such event has occurred in a given interval of time, say (0,t), then the actual time of occurrence is uniformly distributed over this interval. Suppose that during a given 30-minute period, one data-processing job was submitted. Find the probability that the job was submitted during the last 5 minutes of the 30-minute period.
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!
Suppose we are counting events that occur according to a Poisson distribution, such as the number of data-processing jobs submitted to a computer center. If it is known that exactly one such
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