You are processing deliveries out of a warehouse using a drone delivery service. Assume that the orders are Poisson Distributed with a rate parameter (a) of 1 order every 5 minutes. What is the probability of receiving exactly 6 orders in 33 minutes? number (rtol=0.01, atol=D0.0001) What is the 92th percentile of the number of orders you could receive in 10 minutes?
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!
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Poisson Distributed with a rate parameter (a) of 1 order every 5 minutes.
What is the probability of receiving exactly 6 orders in 33 minutes?
number (rtol-D0.01, atol=D0.0001)
What is the 92th percentile of the number of orders you could receive in 10 minutes?
number (rtol-D0.01, atol=D0.0001)
If packages arrive sequentially into your warehouse's single landing area, what is the maximum amount of time
your delivery drone would have to enter the landing area, attach to a package, and fly out before the next
package arrived, assuming it only needed to do this successfully 85% of the time?
number (rtol=D0.01, atol=0.0001)"
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