Example:
Consider the problem of scheduling an operating room. From
past experience we have observed that the expected time required
for a single operation is 2.5 hours with a standard deviation
of 1 hour. We decide to schedule the time for an operation
so that there is a 90% chance that the operation will be finished
in the allotted time. The question is how much time to allow?
We assume the time required is a random variable with a normal
distribution.
The figure shows the definition of the distribution created
with the add-in. The moments are computed with user-defined
functions. The entry in cell H9 is the solution to the
problem. It holds the function:
RV_Inverse(RV14, 0.9)
The function returns the value of the random variable where
the cumulative distribution is 0.9. Allowing 3.78 hours for
an
operation provides
a 90%
chance that the operation will be complete.
When numerical procedures are not available, probabilities
are computed using tables that appear in many text books.
|
and
,
the mean and standard deviation. The Normal distribution has
applications in many practical contexts.
It is often used, with theoretical justification, as the experimental
variability associated with physical measurements. Many other
distributions can be approximated by the normal distribution
using suitable parameters. This distribution reaches its maximum
value at 
