Скачать или смотреть Lesson 28 - Exercise 15 Using a Johnson Moments Distribution

In mathematics, a moment is a specific quantitative measure of the shape of a function. It is used in both mechanics and statistics. If the function is a probability distribution, then the zeroth moment is the total probability, that is, one, the first moment is the mean, the second central moment is the variance, the third standardized moment is the skewness, and the fourth standardized moment is the kurtosis. The mathematical concept is closely related to the concept of moment in physics .
@RISK comes with three Johnson distributions. Two of them are designed for bounded and unbounded parameters. We will not use these. @RISK contains a third distribution that uses these four moments as parameters to build a certain distribution. It is called a Johnson Moments. In other words, if you can calculate out of a certain dataset these four moments: the mean, the standard deviation, the skewness and the kurtosis, you could construct a distribution function that exactly mimics these for moments or parameters using the Johnson Moments distribution. We will use here the theoretical parameters calculated from the previous compound distribution.