Scale an event-count model to its observation window
Your goal: Convert a rate per base interval to a window mean, then report exact, cumulative, interval and upper-tail event probabilities.
Full task requirements
Enter separate formulas in E2:E6. B2 is the assumed mean event count per base interval and B3 is the selected window divided by that base interval. E2 is their product. Under the stated constant-rate independent-event Poisson model, E3 is P(X=B4), E4 is P(X<=B4), E5 is P(B5<=X<=B6), and E6 is P(X>=B7). Keep the upper tail nonzero when it is very small; all probabilities are unrounded proportions.
Three events per base hour over two hoursMatch the event rate to the observation window
Read each probability-model input beside its original A label. Count, event proportion and exposure units have different roles; probabilities retain full precision.
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Step 1 of 4
Match the event rate to the observation window
Rules for this step
Select mean events per base interval and the selected/base window ratio. The Poisson mean scales with exposure under the stated constant-rate independent-event model.