Statistical analysis software

Abstract

Purpose. Analysis of existing software to perform statistical analysis for further use as part of the selection of the necessary software for data processing. Methodology. To conduct a software review, an analysis of scientific articles and open sources on statistical analysis software was conducted. Findings. Choosing the right statistical software is a key decision in the field of data analysis, with numerous options to meet a variety of needs. This article provides a comprehensive overview of five leading statistical software tools: IBM SPSS Statistics, RStudio, Stata, Minitab, and Python. This paper reveals key insights into the capabilities, functions, and suitability of each tool for various analytical tasks. This review concludes that the choice of statistical software should be consistent with specific project requirements, data complexity, and user experience. Researchers and analysts should consider their analytical goals and preferences when choosing the most appropriate tool. In addition, to make informed decisions in this dynamic field, it is important to stay abreast of new trends in data analysis and machine learning. Originality.  The conducted analysis revealed the possibilities and application of the most popular software for solving problems of statistical analysis. The work provides a comprehensive overview of current trends and innovations in the field of software for statistical analysis, offering readers a deeper understanding of existing tools. Practical value. The conducted analysis will allow to choose software for solving a specific task of statistical analysis based on its characteristics and existing requirements. This work helps to identify the practical benefits of statistical analysis software and promotes the implementation of these tools in various fields of activity, providing improvements in analysis and decision-making processes.