This Exam P sample reference tests Poisson Distribution. This is a lower tail of an aggregate Poisson count. Three independent daily counts with mean 4 combine to a Poisson mean of 12; adding the zero-count and one-count masses gives (1+12)e to the minus 12, so choice A.
These notes identify the calculation error associated with each wrong letter when that error is reproducible.
BThe expression 72e to the minus 12 is the probability of exactly two events, since 12 squared divided by 2 factorial is 72. The requested count is at most one.
CThe expression 85e to the minus 12 adds the exactly-two mass 72e to the minus 12 to the correct zero-and-one total. It calculates at most two events.
DThe expression 5e to the minus 4 is the zero-and-one probability for a single Poisson mean of 4. It fails to aggregate all three days.
EThe expression 13e to the minus 4 mixes the aggregate coefficient 13 with the one-day exponential factor. The full three-day mean is 12 in both parts of the Poisson mass.
Original practice · fully worked
Original variant: unresolved alerts after limited service
Two independent radio channels generate Poisson alert counts during a shift, with means 0.8 and 1.2. A duty operator can resolve one alert during the shift; any additional alerts remain unresolved. Calculate the expected number of unresolved alerts.
A 0.1353
B 0.8647
C 1.0000
D 1.1353
E 2.0000
Variant answer in brief
The combined alert count is Poisson with mean 2. The number resolved is one whenever at least one alert arrives, so its expectation is 1-exp(−2). Subtracting this from the expected total gives 2-(1-exp(−2))=1+exp(−2)=1.1353, choice D.
Setup
Setup
Let X and Y be the independent channel counts and N their combined total.
N=X+Y∼Poisson(0.8+1.2)=Poisson(2)
Model
Model
The operator resolves one alert if N is positive and resolves none if N is zero.
U=(N−1)+=N−1{N≥1}
Compute
Compute
Take expectations and use the zero-count probability of a Poisson variable.
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