This example illustrates how to generate a random value from the famous bell-shaped normal distribution with a given mean m and standard deviation s.
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This example illustrates how to generate a random value from the famous bell-shaped normal distribution with a given mean m and standard deviation s.
This example explains how to use the RiskLogistic and RiskPareto function.
This example explains how to use the RiskPearson5 and RiskPearson6 functions.
The RiskSplice function lets you splice two distributions together at some point. This example shows how to use it.
The RiskStudent, RiskChiSq and RiskFfunctions can be used to simulate values from the three corresponding distributions commonly used in statistical inference: the Student's t, chi-square, and F distributions. You could simulate values from any of these distributions, just like you can simulate values from any other distribution. This example shows how to use them.
The RiskTriang, RiskTrigen and RiskDoubleTriang functions are used for modeling a continuous uncertain quantity that must be between specified minimum and maximum values. This example explains the main differences between them.
The RiskUniform function generates a value that is equally likely to be anywhere between specified minimum and maximum values. This example shows how to use it.
The RiskVary function is intended primarily for sensitivity analysis is @RISK's companion product, TopRank, but it is included in @RISK for compatibility with TopRank. You can use the RiskVary function in @RISK, but it provides no advantage over the "regular" @RISK distribution functions.
