Explores the statistical methods for describing and analyzing multivariate data. It's goal is to provide readers with the knowledge necessary to make proper interpretations, and select appropriate techniques for analyzing multivariate data
Coverage includes: Detecting Outliers and Data Cleaning; Multivariate Quality Control; Monitoring Quality with Principal Components; and Correspondence Analysis, Biplots, and Procrustes Analysis.
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A classic applied approach to multivariate analysis for those involved in the social, biological, and physical sciences.
This market-leading book offers a readable introduction to the statistical analysis of multivariate observations. Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Chapter topics include aspects of multivariate analysis, matrix algebra and random vectors, sample geometry and random sampling, the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, multivariate linear regression models, principal components, factor analysis and inference for structured covariance matrices, canonical correlation analysis, and discrimination and classification. For experimental scientists in a variety of disciplines.
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