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# Crystal Ball features

A list of all the public Crystal ball features, which are grouped into three types:

### 1. Distributions

Functions that generate random values from probability distributions.

Beta

Returns values from a Beta distribution.

Yes/No

Returns values from a Bernouilli distribution (or use Binomial(p, 1).

Binomial

Returns values from a Binomial distribution.

Custom

With Crystal Ball's Custom Distribution you can represent six different distributions, all of which are described here:

2. Discrete

3. General

6. Combination of distributions

Discrete Uniform

Returns integer values between a specified minimum and maximum, all with the same probability (the Integer Uniform distribution). Discrete Uniform distribution can be constructed with the Custom Distribution in Crystal Ball.

Exponential

Returns values from an Exponential distribution.

ExtremeValue

Returns values from a Gumbel distribution for maximum observations. This is one of three Extreme Value distributions.

Gamma

Returns values from a Gamma distribution.

Geometric

Returns values from a Geometric distribution.

Hypergeometric

Returns values from a hypergeometric distribution.

Logistic

Returns values from a Logistic distribution.

Lognormal

Returns values from a Lognormal distribution.

MaximumExtreme

Returns values from a Gumbel distribution for maximum observations. This is one of three Extreme Value distributions.

MinimumExtreme

Returns values from a Gumbel distribution for minimum observations. This is one of three Extreme Value distributions.

Negbinomial

Returns values from a Negative Binomial distribution.

Normal

Returns values from a Normal distribution.

Pareto

Returns values from a Pareto distribution.

Poisson

Returns values from a Poisson distribution.

Student

Returns values from a Student, or t- distribution (can also construct it in two other ways).

Triangular

Returns values from a Triangle distribution.

Uniform

Returns values from a Uniform distribution.

Weibull

Returns values from a Weibull distribution.

### 2. Crystal Ball Tools

Crystal Ball Tools are programs that extend the functionality of Crystal Ball. They are ordered in two categories:

Setup Tools

Batch Fit

The Batch Fit tool lets you "automatically" fit (continuous) probability distributions to multiple data series. At Epix Analytics, we don't use this tool often because fitting distributions to data should be done carefully (e.g. often one by one) and not always be based on just one of the available Goodness of Fit Statistics

Correlation Matrix

The Correlation Matrix lets you enter a matrix of correlations between assumptions in one step. In the section about Rand Order Correlation, you can see how to use this tool.

The Tornado Chart Tool allows you to determine the impact of each model variable (one at a time) on one specific forecast. In the section about Spider Charts and the Tornado Chart tool, you can read how to use this tool.

Analysis Tools

Bootstrap

The Bootstrap Tool of Crystal Ball is a special case of the more general Bootstrap method discussed in ModelAssist Advanced. Crystal Ball's Bootstrap tool looks at  how robust your  simulation forecast statistics are. For example, given that you have done 10,000  iterations, the Bootstrap Tool determines how precisely the forecast statistics have been determined, meaning by how much would those statistics change if one were to run an essentially infinite number of iterations. Therefore, the main use of the  Crystal Ball's Bootstrap Tool is to determine if you have run sufficient iterations. In contrast, the Bootstrap method discussed in ModelAssist Advanced helps you quantify the uncertainty you have about input parameters in your risk analysis model that have been estimated from data .

Decision Table

The Decision Table tool allows you to run multiple simulations to test different values of one or two decision variables. Here, you can see how this tool can be used, with an example model. The model Market growth model provides another example.

Scenario Analysis

The Scenario Analysis Tool allows you to examine which combination of assumption values gives you a certain forecast result. The tool runs a simulation after which it matches all the forecasts with their corresponding assumption values. In the resulting table, it shows all the forecast values in the range you specified (e.g. between 95% and 100% percentile) sorted, along with all the corresponding assumption (input) values. This tool can therefore be used as one of the methods to get a better understanding of an output of a simulation.

Two dimensional Simulation

The Two-Dimensional Simulation Tool in Crystal Ball lets you separate the effect of uncertainty (lack of knowledge) and variability and randomness in a forecast. For a more detailed descriptions, see here.

### 3. Statistics functions

Functions that report the simulation results within Excel cells. Although not always fully supported by Crystal Ball, these functions are very useful, in combination with on-screen recalculation, to monitor some aspect of the simulation. They are also useful for automatically producing reports in Excel, though this can slow down your models.

CB.GetForeStatFN(x,2) - mean

CB.GetForeStatFN(source,2) reports the mean of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,3) - median

CB.GetForeStatFN(source,3) reports the median of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,4) - mode

CB.GetForeStatFN(source,4) reports the mode of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,5) - standard deviation

CB.GetForeStatFN(source,5) reports the standard deviation of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,6) - variance

CB.GetForeStatFN(source,6) reports the Variance of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,7) - skewness

CB.GetForeStatFN(source,7) reports the Skewness of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,8) - kurtosis

CB.GetForeStatFN(source,8) reports the kurtosis of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,9) - coefficient of variability

CB.GetForeStatFN(source,9) reports the coefficient of variability of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,10) - minimum

CB.GetForeStatFN(source,10) reports the minimum of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,11) - maximum

CB.GetForeStatFN(source,11) reports the maximum of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,12) - range width

CB.GetForeStatFN(source,12) reports the range width of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForeStatFN(x,13) - standard error

CB.GetForeStatFN(source,13) reports the standard error of the values generated for the source (cell reference, cell name or output name) so far in the simulation.

CB.GetForePercentFN - percentile

CB.GetForePercentileFN(source,P) reports for which of the values generated for the source (cell reference, cell name or output name) so far in the simulation, the fraction P are lower.

CB.GetCertaintyFN - Target

CB.GetCertaintlyFN(Data source, target X value)/100 returns the cumulative probability for target value in the simulated distribution for the cell, output, or input.

CB.IterationsFN - Iteration

CB.IterationsFN() reports the current iteration of the simulation when running.

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