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Poisson Distribution

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Assessing the probability that a bookstore sells 2 rare books in a week when it typically sells 4 rare books a week.

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Compute P(X=2)=e4422!P(X=2) = \frac{e^{-4} \cdot 4^2}{2!} which gives P(X=2)=16e42P(X=2) = \frac{16e^{-4}}{2}.

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Calculating the likelihood of 0 accidents at a road crossing in a month when the average is 3 accidents per month.

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The probability is P(X=0)=e3300!P(X=0) = \frac{e^{-3} \cdot 3^0}{0!}, resulting in P(X=0)=e3P(X=0) = e^{-3}.

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The probability of a call center receiving 20 calls in an hour when they usually get 15 calls per hour.

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Use the equation P(X=20)=e15152020!P(X=20) = \frac{e^{-15} \cdot 15^{20}}{20!} to find the probability.

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The likelihood of a rare flower blooming 8 times in a season when it usually blooms 3 times.

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P(X=8)=e3388!P(X=8) = \frac{e^{-3} \cdot 3^8}{8!} computes the desired probability.

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Determining the probability of observing 12 cars passing a checkpoint within an hour when the average is 10 cars per hour.

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Using the formula P(X=k)=eλλkk!P(X=k) = \frac{e^{-\lambda} \lambda^k}{k!}, where λ=10\lambda = 10 and k=12k=12, the probability is P(X=12)=e10101212!P(X=12) = \frac{e^{-10} 10^{12}}{12!}.

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Probability of receiving 5 emails in the next hour when on average 2 emails are received per hour.

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Calculation: P(X=5)=e2255!P(X=5) = \frac{e^{-2} \cdot 2^5}{5!}, which simplifies to P(X=5)=32e2120P(X=5) = \frac{32e^{-2}}{120}.

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Evaluating the probability of observing 10 particles emitted from a radioactive source in a minute if on average 6 particles are emitted per minute.

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This is calculated by P(X=10)=e661010!P(X=10) = \frac{e^{-6} \cdot 6^{10}}{10!}.

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Finding the probability of having exactly 3 system failures in a day when the average is 1 failure per day.

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Use P(X=3)=e1133!P(X=3) = \frac{e^{-1} 1^3}{3!}, resulting in P(X=3)=e16P(X=3) = \frac{e^{-1}}{6}.

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What is the probability that a 24/7 convenience store will have exactly 1 customer in 15 minutes during nighttime where the average is 0.5 customers per 15 minutes?

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Apply P(X=1)=e0.50.511!P(X=1) = \frac{e^{-0.5} \cdot 0.5^1}{1!}, leading to P(X=1)=0.5e0.51P(X=1) = \frac{0.5e^{-0.5}}{1}.

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Figuring out the chance of no alarms in a factory over a week when historically there are 2 alarms per week.

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The probability is P(X=0)=e2200!P(X=0) = \frac{e^{-2} \cdot 2^0}{0!}, so P(X=0)=e2P(X=0) = e^{-2}.

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