Lambda in the Poisson distribution is the expected number of times that an event occurs. For example, say you were observing shoppers entering a store over 10 minute intervals throughout 1 day and saw had the following frequencies:
- 0 Entrances Observed 20 times
- 1 Entrances Observed 50 times
- 2 Entrances Observed 30 times
- 3 Entrances Observed 28 times
- 4 Entrances Observed 12 times
- 5 Entrances Observed 4 times
Altogether 262 people entered the store: [20 * (0) + 50 * (1) + 30 * (2) + 28 * (3) + 12 * (4) + 5 * (4)]
Over 144 time periods [20 + 50 + 30 + 28 + 12 + 4]
So lambda is the average number of entries per time period. 262/144 = 1.8194...
Poisson says p(x, lambda) = [e^(-lambda) * (lambda)^x] / x!
So the probability of 0 people entering the store would be estimated as:
p(0, 1.819) = [e^(-1.819) * (1.819)^0] / 0! = [0.162 * 1] / 1 = 0.162
We actually saw 0 people come in 20 times in 144 checks so the observed chance of 0 people entering was 20/144 = 0.138. Pretty close!
So basically you are saying you have 5 occurrences every 10 minutes so for you lambda would be 5. The average number of occurrences in a 10 minute window.
Hope this helps.