TABLE OF CONTENTS Mathcad provides powerful capabilities that work with the problems of mathematical statistics. First, there are several built-in special functions that allow you to calculate probability densities and other basic characteristics of the distribution laws of random values ( see Section 13.1). In addition, Mathcad implements an impressive number of pseudo-random value generators for each distribution law ( see Section 13.1), which enable you to perform efficient modeling using Monte-Carlo methods. You can create bar charts and calculate statistic characteristics of random value samples and random processes such as averages, dispersions, correlations, etc. ( see Section 13.2). Notice that random sequences can be created using random number generators or imported from files.
The concluding sections of this chapter go over some methods of solving several typical problems related to random process analysis ( see Section 13.3) and mathematical statistics ( see Section 13.4).
The methods known as Monte-Carlo methods are those most widely used for modeling various physical, economical, and other effects. The main idea of these methods lies in the creation of specific sequences of random values modeling specific effects, such as noise in a physical experiment, random dynamics of stokes indices, etc. For this purpose, Mathcad provides a range of pseudo-random number generators.
According to the definition, a random variable can take some value, but the specific value it takes depends on random circumstances of the experiment and can't be predicted for certain. It is only possible to speak about the
