Explore the evidence
Theoretical and simulated hourly ticket counts
Included in the selected eventOutside the eventSimulated proportion
Environment 05 · Event-rate decision lab
How many events may arrive next?
A Poisson model translates an average event rate into probabilities for counts within a fixed interval. Explore service demand, insurance claims, and API errors.
FrameExploreInterpretDecide
Interpret
The rate controls both centre and variance
Decide
Set service capacity
Use an upper-tail probability to judge whether current staffing can absorb a busy interval.
See the mathematics and assumptions
For X ~ Poisson(λ), P(X=x)=e⁻ˡᵃᵐᵇᵈᵃ λˣ/x!, E(X)=λ, and Var(X)=λ. The model assumes events occur independently, at a stable average rate, and counts are recorded over equal intervals.