Expressions
Build the formulas, distributions, and conditions that drive timing and routing.
- ExpressionsHow to write expressions in ProcessModel: arithmetic, comparisons, functions, and where formulas can be used.
- The Expression BuilderBuild the time and distribution expressions that drive your model: fit real data, build a distribution by hand, or use a time table.
- Reserved WordsThe words built into ProcessModel that you cannot use as names, and how to work around a clash.
- Stat::FitObsoleteStat::Fit was a separate distribution-fitting tool; ProcessModel now has its own built-in fitting in the Expression Builder.
- DistributionsProbability distributions in ProcessModel: pick the right one, enter it in any numeric field, with a page for each.
- Triangular DistributionThe triangular distribution in ProcessModel: T(min, mode, max) from three estimates, the everyday choice for a new model.
- Uniform DistributionThe uniform distribution in ProcessModel: U(min, max), every value in a range equally likely.
- Normal DistributionThe normal distribution in ProcessModel: N(mean, sd), values clustered around an average.
- Exponential DistributionThe exponential distribution in ProcessModel: E(mean), the gap between independent random arrivals.
- Lognormal DistributionThe lognormal distribution in ProcessModel: a positive, right-skewed shape for task times that occasionally run long, and how to enter it.
- Beta DistributionThe beta distribution in ProcessModel: Beta(min, max, p, q), a flexible shape on a fixed range.
- Gamma DistributionThe gamma distribution in ProcessModel: Gamma(shape, rate), a positive, right-skewed duration.
- Weibull DistributionThe Weibull distribution in ProcessModel: Weibull(shape, scale), the standard choice for time to failure.
- Erlang DistributionThe Erlang distribution in ProcessModel: Erlang(stages, rate), a sum of exponential stages.
- Poisson DistributionThe Poisson distribution in ProcessModel: Poisson(mean), a count of events in a period.
- Binomial DistributionThe binomial distribution in ProcessModel: Binomial(trials, p), successes out of a number of tries.
- Geometric DistributionThe geometric distribution in ProcessModel: Geometric(p), tries until the first success.
- Negative Binomial DistributionThe negative binomial distribution in ProcessModel: NegBinomial(successes, p), tries to reach several successes.
- Rayleigh DistributionThe Rayleigh distribution in ProcessModel: Rayleigh(scale), a skewed positive value from one parameter.
- Discrete DistributionThe discrete distribution in ProcessModel: D2 to D5, pick from a few set values by percentage.
- User-Defined DistributionUser-defined distributions in ProcessModel: UD by name, built from your own data or value and percent pairs.