An Overview of Multivariate Statistical Methods and Their Practical Applications

Abdulqader Mutlag Hamad

doi:10.47134/ppm.v3i1.2084

Authors

Abdulqader Mutlag Hamad University of Mohaghegh Ardabili

DOI:

https://doi.org/10.47134/ppm.v3i1.2084

Keywords:

Multivariate Analysis, Factor Analysis, Cluster Analysis, Discriminant Analysis, Principal Component Analysis, Multivariate Regression, Dimensionality Reduction, Statistical Modeling, Mathematical Statistics

Abstract

Multivariate data analysis is a powerful statistical approach used to analyze data involving multiple variables simultaneously. Researchers can use this method to find complicated ties, reduce the number of factors, and group data more effectively. When you need to understand data with more than one variable, you can use tools such as factor analysis, cluster analysis, discriminant analysis, principal component analysis, and multivariate regression. More and more fields, like business, engineering, health, and the social sciences, need multivariate analysis. This is because computers and other strong tools are getting better all the time. You will learn about some important multivariate methods and how they are used in the real world in this study. It also talks about the ideas that make them work. It talks about how these ways can help people make better decisions based on facts

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