Probability distribution function, Expectancy value, Variance
Abstract
Nowadays, data is everywhere from social economic activities to the observation of natural phenomena, the complexity and scale of data requires researchers to master more advanced data analysis tools. As a bridge between random events and mathematical models, probability distribution function not only provides a theoretical basis for describing data characteristics, but also provides an important means for quantitative analysis of data and modeling prediction. This paper focuses on several typical probability distributions which have wide application background and deep theoretical foundation in their respective fields. This paper mainly focuses on two core issues. The first is the introduction of various probability distribution functions and their applications, while the second is how to use these distribution functions for effective data analysis and prediction. Through a series of empirical studies, this paper verifies the applicability and validity of different probability distribution functions in different fields. The results show that the correct selection and application of probability distribution function can significantly promote the accuracy and efficiency of data analysis, and provide strong support for decision making. Finally, the research in this paper not only makes the theoretical system of probability distribution function more abundant, but also offers some valuable references to practical applications. By revealing the key role of different distribution functions in data analysis and prediction, this paper provides new ideas and new methods for interdisciplinary research.
