Let us assume we are in the city of Hamburg and we want to travel to Munich. We will have to change trains in Frankfurt am Main. We know from previous train journeys that our train from Hamburg might be delayed and the we will not catch our connecting train in Frankfurt. The probability that we will not be in time for our connecting train depends on how high our possible delay will be. The connecting train will not wait for more than five minutes. Sometimes the other train is delayed as well.
The following lists 'in_time' and 'too_late' are data showing the situation over some weeks. The first component of each tuple shows the minutes the train was late and the second component shows the number of time this occurred.
# tuples are (minutes, number of times)
in_time = [(0, 22), (1, 19), (2, 17), (3, 18),
(4, 16), (5, 15), (6, 9), (7, 7),
(8, 4), (9, 3), (10, 3), (11, 2)]
too_late = [(6, 6), (7, 9), (8, 12), (9, 17),
(10, 18), (11, 15), (12,16), (13, 7),
(14, 8), (15, 5)]