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Multivariate statistics as a book category covers methods and theory for analyzing data that involve many variables measured on the same observational units, emphasizing relationships among variables and joint patterns rather than one-at-a-time analyses. Titles in this category typically treat core topics such as multivariate regression, principal component and factor analysis, MANOVA, discriminant and cluster analysis, canonical correlation, and modern dimensionality-reduction and visualization techniques, along with inference, model selection, and assessment for multivariate models. Books may range from mathematically rigorous treatments (linear algebra, probability, and statistical theory prerequisites) to applied guides that focus on computation, algorithms, and software (R, Python, MATLAB) for handling high-dimensional, correlated, or structured data in fields like psychology, ecology, genomics, finance, and social sciences. Many volumes also address extensions such as robust and Bayesian multivariate methods, sparse and high-dimensional approaches, and practical issues like interpretation, diagnostics, and data preprocessing.

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