Abstract: Multivariate data is a term used for data containing several random variables. Dimension reduction methods can reduce data dimensionality to a lower-dimensional space while retaining as much information as possible. These methods overcome several issues, such as the complexity of the model, thus, improving the performance of learning algorithms and making it easier to visualize the data. Principal component analysis is one of the most common statistical approaches to summarize the information content of large data matrices using a small set of variables, being used across various fields, including machine learning, development indexes, and among others. The current work is inspired by the Saudi Vision 2030 programs, which aim to improve the quality of life of individuals and families in the Kingdom. Quality of life is defined as the degree to which certain standards are met by an individual’s objectively verifiable conditions, activities, and activity outcomes. To determine the key factors that affect the quality of life, data can be analyzed using dimension reduction techniques such as principal component analysis. This study assessed the impact of various health, economic, natural, and other factors on quality of life in Asia, using United Nations Development Program data covering 14 variables.
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Keywords and phrases: principal component analysis, quality of life, human development index.
Received: September 17, 2023; Accepted: October 26, 2023; Published: December 5, 2023
How to cite this article: Zakiah I. Kalantan, Ghadi Abdulrahman Alamri, Reem Ayesh Albogami, Wesal Musaad Aljadani, Atheer Juman Almalki and Sulafah M. Saleh Binhimd, Quality of life assessment: leveraging principal component analysis for effective evaluation, Advances and Applications in Statistics 91(1) (2024), 27-46. http://dx.doi.org/10.17654/0972361724003
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
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