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Before proceeding, it is worthwhile reading about the Central Limit Theorem. because it explains the Normal distribution approximation to most of the distributions offered here.

ModelAssist for Crystal Ball provides you with the following Here are a few examples of approximations of distributions by other distributions:

• Approximations to the Binomial Distribution

• Approximations to the Negative Binomial distribution

• Approximations to the Hypergeometric distribution

• Approximations to the Inverse Hypergeometric distribution

• Normal approximation to the Beta distribution

• Normal approximation to the Chi Squared distribution

• Normal approximation to the Gamma distribution

• Normal approximation to the Lognormal distribution

• Normal approximation to the Poisson distribution

• Normal approximation to the Student-t distribution

• Recursive formulae formulas for discrete distributions

The section on recursive distributions shows you how you can create precise discrete distributions where the parameters have become unmanageably large to deal with in the normal way.

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Once you have reviewed the material in this section, you might like to test how much you have learned by taking the self-test a quiz:

A quiz on Approximating distributions: 

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