Microsoft Office Tutorials and References
In Depth Information
Using NORM. DIST and POISSON. DIST to Determine Probabilities
POISSON.DIST
Poisson is another kind of distribution used in many areas of statistics. Its
most common use is to model the number of events taking place in a
specified time period. Suppose you were modeling the number of employees
calling in sick each day, or the number of defective items produced at your
factory each week. In these cases, the Poisson distribution is appropriate.
The Poisson distribution is useful for analyzing rare events. What exactly
does rare mean? People calling in sick at work is hardly rare, but a specific
number calling in sick is rare, at least statistically speaking. Situations where
Poisson is applicable include numbers of car accidents, counts of customers
arriving, manufacturing defects, and the like. One way to express it is that the
events are individually rare, but there are many opportunities for them to
happen.
The Poisson distribution is a discrete distribution. This means that the X
values in the distribution can only take on specified, discrete values such as
X = 1, 2, 3, 4, 5 and so on. This is different from the normal distribution, which
is a continuous distribution in which X values can take any value (X = 0.034,
1.2365, and so on). The discrete nature of the Poisson distribution is suited to
the kinds of data you use it with. For example, with employees calling in sick,
you may have 1, 5, or 8 on a given day, but certainly not 1.45, 7.2, or 9.15!
Figure 11-9 shows a Poisson distribution that has a mean of 20. Values on the
X axis are number of occurrences (of whatever youâ€™re studying), and values
on the Y axis are probabilities. You can use this distribution to determine
the probability of a specific number of occurrences happening. For example,
this chart tells you that the probability of having exactly 15 occurrences is
approximately 0.05 (5 percent).
Figure 11-9:
A Poisson
distribution
with a
mean of 20.
Search JabSto ::

Custom Search