This Exam P sample reference tests Independent Poisson Sum. A one-year zero-count probability of 0.90 implies a Poisson mean of -ln(0.90). Adding 15 independent yearly counts gives a Poisson total whose variance is 15[-ln(0.90)]=1.5804, so choice E is correct.
These notes identify the calculation error associated with each wrong letter when that error is reproducible.
AThis is approximately the one-year Poisson mean -ln(0.90), so it omits aggregation over all 15 years.
BThis scales the one-year parameter by the square root of 15, mixing a standard-deviation scaling rule with the Poisson variance calculation.
CThis treats each year as a Bernoulli indicator with variance 0.10(0.90) and adds 15 such variances, discarding the possibility of multiple counts.
DThis replaces the Poisson mean -ln(0.90) by the positive-count probability 0.10 and reports 15(0.10).
Original practice · fully worked
Original variant: quiet monitoring window
Independent daily alert counts for a monitoring system follow Poisson distributions with a common mean. The variance of the total alert count over eight days is 12. Calculate the probability of receiving no alerts during a separate three-day period.
A 0.000006
B 0.011109
C 0.125000
D 0.223130
E 0.988891
Variant answer in brief
The eight-day Poisson total has mean and variance 12, so the daily mean is 1.5. A three-day total therefore has mean 4.5, and its zero-count probability is exp(-4.5)=0.011109, which is choice B.
Setup
Setup
Let λ denote the common mean of one daily Poisson count.
Nd∼Poisson(λ)
Model
Model
The eight-day total is Poisson with parameter 8lambda, and its variance is therefore 8lambda.
Var(d=1∑8Nd)=8λ=12
Compute
Compute
Recover the daily parameter, form the three-day parameter, and evaluate the Poisson zero probability.
λ=1.5
d=1∑3Nd∼Poisson(4.5)
Pr(d=1∑3Nd=0)=e−4.5=0.0111089965…
Answer
Answer
The probability that the three-day period is quiet is approximately 0.011109.
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