This article presents a study that uses multiple linear regression analysis to examine the factors influencing the number of people affiliated with different insurance plans within the Comprehensive Health Insurance (SIS) system in Peru.The study highlights the importance of multiple linear regression analysis in understanding the factors that affect SIS Comprehensive Health Insurance affiliates. It also showcases the value of utilizing interactive tools like RShiny to enhance data analysis, providing a dynamic and participatory experience for researchers and users interested in the subject.To facilitate the analysis and visualization of SIS-related data, the researchers developed an interactive application using RShiny. This tool allows for the easy loading, visualization, and analysis of data in a user-friendly and practical manner. By providing an interactive platform, users can effectively explore and understand the factors that impact SIS affiliates.The results of the analysis indicate that the selected variables have a significant positive influence on the total number of affiliates. This suggests that the specific insurance plan examined in this study has a favorable effect on the enrollment of individuals in SIS. Additionally, the data shows a linear trend, supporting the use of a linear regression model to describe this relationship. Active affiliates,Comprehensive health insurance SIS,Data Visualization,Multiple Linear Regression Analysis,RShiny

翻译：本文采用多元线性回归分析，探讨秘鲁全面健康保险（SIS）体系内不同保险计划参保人数的影响因素。研究凸显了多元线性回归分析在理解SIS全面健康保险参保影响因素方面的重要性，同时展示了利用R Shiny等交互式工具增强数据分析的价值，为关注该课题的研究人员和用户提供动态参与式的分析体验。为便于SIS相关数据的分析与可视化，研究团队基于R Shiny开发了交互式应用程序。该工具支持用户以直观便捷的方式完成数据加载、可视化及分析，通过交互式平台有效探究影响SIS参保的关键因素。分析结果表明，所选变量对参保总人数具有显著正向影响，说明本研究关注的特定保险计划对SIS个人参保具有积极作用。此外，数据呈现线性趋势，支持采用线性回归模型描述该关系。活跃参保者，全面健康保险SIS，数据可视化，多元线性回归分析，R Shiny