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4-9 EXPONENTIAL DISTRIBUTION 125

Furthermore, the mean time until the next log-on is

1 25 0.04 hour 2.4 minutes

The standard deviation of the time until the next log-on is

1 25 hours 2.4 minutes

In the previous example, the probability that there are no log-ons in a 6-minute interval is
0.082 regardless of the starting time of the interval. A Poisson process assumes that events oc-
cur uniformly throughout the interval of observation; that is, there is no clustering of events.
If the log-ons are well modeled by a Poisson process, the probability that the first log-on after
noon occurs after 12:06 P.M. is the same as the probability that the first log-on after 3:00 P.M.
occurs after 3:06 P.M. And if someone logs on at 2:22 P.M., the probability the next log-on
occurs after 2:28 P.M. is still 0.082.
Our starting point for observing the system does not matter. However, if there are
high-use periods during the day, such as right after 8:00 A.M., followed by a period of low
use, a Poisson process is not an appropriate model for log-ons and the distribution is not
appropriate for computing probabilities. It might be reasonable to model each of the high-

and low-use periods by a separate Poisson process, employing a larger value for during
the high-use periods and a smaller value otherwise. Then, an exponential distribution with

the corresponding value of can be used to calculate log-on probabilities for the high- and
low-use periods.

Lack of Memory Property
An even more interesting property of an exponential random variable is concerned with con-
ditional probabilities.

EXAMPLE 4-22 Let X denote the time between detections of a particle with a geiger counter and assume that
X has an exponential distribution with 1.4 minutes. The probability that we detect a par-
ticle within 30 seconds of starting the counter is

P1X 0.5 minute2 F10.52 1 e 0.5 1.4 0.30

In this calculation, all units are converted to minutes. Now, suppose we turn on the geiger
counter and wait 3 minutes without detecting a particle. What is the probability that a particle
is detected in the next 30 seconds?
Because we have already been waiting for 3 minutes, we feel that we are “due.’’ That
is, the probability of a detection in the next 30 seconds should be greater than 0.3. However,
for an exponential distribution, this is not true. The requested probability can be expressed
as the conditional probability that P1X 3.5 ƒ X 32. From the definition of conditional
probability,

P1X 3.5 ƒ X 32 P13 X 3.52 P1X 32

where

P13 X 3.52 F13.52 F132 31 e 3.5 1.4 4 31 e 3 1.4 4 0.0035