  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
