I read that a continuous random variable having an exponential distribution can be used to model inter-event arrival times in a Poisson process. Examples included the times when asteroids hit the earth, when earthquakes happen, when calls are received at a call center, etc. In all these examples, the expected value of the number of events per unit of time, lambda, is known and is constant over time. Moreover, each event's occurrence is independent of previous events' occurrences. And the exponential variable that models inter-event arrivals has the same lambda parameter as the Poisson variable that models the number of events.
And now, my problem. It don't get the connection between the intuition behind the exp distrib and its pdf. It seems obvious that the more time it passes by without an earthquake happening, the more likely it is than an earthquake will happen. Assume my understanding of lambda is correct, i.e., lambda = the rate at which the event happens, e.g., 5 earthquakes per minute on some remote, angry planet. On the pdf graphic, the prob of generating a value between 0 and 1 is greater than the prob of choosing a value between 4 and 5 for instance. How is this graphic related to the fact that on average we need to have 5 earthquakes per minute?