Development of software for applied statistical analysis of random variables

Abstract

Classical applied mathematical statistics is a branch of mathematical science that deals with the development and application of mathematical methods and models for the collection, analysis, interpretation and presentation of empirical data. It includes a wide range of methods used to study patterns in data, estimate distribution parameters, test hypotheses, and make predictions. Classical applied mathematical statistics is used in various fields, such as: economics; medicine; social sciences; engineering As such, the industry provides important tools and techniques for data-driven decision-making, which is critical in many areas of today's society. When conducting applied research, there is often a need to analyze a group of processes or objects that have certain qualitative or quantitative characteristics. Then the question arises regarding the choice of generator of pseudorandom variables. When choosing a generator, the following criteria should be taken into account: quality of randomness; speed of action; ease of implementation; security; the possibility of customization; application compliance. The generator should be fast enough for specific applications, especially if large amounts of random numbers are to be generated, but should be easy to implement in the desired programming environment. For example, for cryptographic applications, the generator must be resistant to attacks, that is, satisfy the requirements of cryptographic security. The generator should allow parameter settings to meet specific task requirements. The choice of generator should meet the specific needs of the project, be it simulation, cryptography, scientific computing, or other tasks. Therefore, the development of software with the ability to choose a generator of random variables is relevant. The developed software includes modern software generators that make it possible to use them for applied tasks of automation, computer-integrated technologies, robotics, non-destructive testing and cyber security, and information protection.