Journal of Engineering Mathematics & Statistics

Authors : Priya Das , Dr. Anil Verma , Preeti Gupta

Abstract : Engineering data often involves multiple variables that need to be analyzed simultaneously to understand complex relationships and patterns. This paper explores various multivariate statistical techniques, such as principal component analysis (PCA), factor analysis, and cluster analysis, and their applications in engineering. The study illustrates how these techniques can be used to reduce dimensionality, identify underlying factors, and group similar observations in large datasets. Examples from fields such as materials engineering, environmental engineering, and systems engineering are provided to demonstrate the practical applications of multivariate methods. The paper also discusses the computational aspects of implementing these techniques, including the use of software tools and algorithms to handle large and complex datasets.

Keywords : Multivariate Analysis, Principal Component Analysis, Factor Analysis, Cluster Analysis, Engineering Data