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Using continuous distributions
Table A.1 Summary of basic distributions (Continued)
Shape
Name
Summary
Lognormal
(page 263)
The lognormal distribution is widely used in situations where values are
positively skewed, for example in financial analysis for security valuation or
in real estate for property valuation.
Yes-no
(page 293)
The yes-no distribution is a discrete distribution that describes a set of
observations that can have only one of two values, such as yes or no, success
or failure, true or false, or heads or tails.
Discrete
uniform
(page 283)
In the discrete uniform distribution, all integer values between the minimum
and maximum are equally likely to occur. It is the discrete equivalent of the
continuous uniform distribution.
Using continuous distributions
Continuous probability distributions describe values over a range or scale and
are shown as solid figures in the Distribution Gallery. Continuous
distributions are actually mathematical abstractions because they assume the
existence of every possible intermediate value between two numbers. That is,
a continuous distribution assumes there is an infinite number of values
between any two points in the distribution.
In many situations, you can effectively use a continuous distribution to
approximate a discrete distribution even though the continuous model does
not necessarily describe the situation exactly. For a comparison of continuous
and discrete distributions, see page 249.
The continuous distributions listed in Table A.2 are described later in this
section in alphabetical order. Page references appear below the names.
Table A.2 Summary of continuous distributions
Shape
Name
Summary
Beta
(page 255)
The beta distribution is a very flexible distribution commonly used to
represent variability over a fixed range. It can represent uncertainty in the
probability of occurrence of an event. It is also used to describe empirical
data and predict the random behavior of percentages and fractions.
Exponential
(page 257)
The exponential distribution is widely used to describe events recurring at
random points in time or space, such as the time between failures of
electronic equipment, the time between arrivals at a service booth, or
repairs needed on a certain stretch of highway. It is related to the Poisson
distribution, which describes the number of occurrences of an event in a
given interval of time or space.
Crystal Ball User Manual
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