Interval-valued Fermatean fuzzy sets approach for the Spearman rank correlation coefficient

Abstract

Correlation is an important concept that can be used to analyze data sets and assist business leaders in gaining valuable insights into the relationships between business outcomes. When conducting a correlation analysis, the more data points available, the more accurate the analysis results will be. Having sufficient data before relying on correlations is essential to make business decisions. Methods for calculating correlation coefficients between sets have been developed and used in application areas. Interval-valued Fermatean fuzzy-based Spearman rank correlation coefficients are given in this study, and their basic features are examined. In order to demonstrate the application of Interval-valued Fermatean fuzzy-based Spearman rank correlation coefficients to real-world problems, the Hospital Disaster Preparedness application is studied according to information from a university hospital. The disaster preparedness of four hospital departments is evaluated using information obtained from the Hospital Disaster Management unit. The preparedness status of the departments is determined using the Interval-valued Fermatean fuzzy-Spearman rank correlation coefficients method. The Interval-valued Fermatean fuzzy-Spearman rank correlation coefficients method is compared with the previously known Fermatean fuzzy-based correlation coefficients methods. In this comparison process, it is seen that the results of the new method are similar to some previously known methods. This new method of Fermatean fuzzy-based correlation coefficients could be used to discuss techniques for order preference that are similar to ideal solutions and multi-criteria decision-making.