Applied Multivariate Statistical Analysis
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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ASPECTS OF MULTIVARIATE ANALYSIS
MATRIX ALGEBRA AND RANDOM VECTORS
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analysis appear approximately associated axes Calculate called canonical Chapter choice coefficient columns confidence Consequently constant Construct contains correlation corresponding covariance matrix defined definite density dependent determined deviation diagonal dimensions direction discussed distance eigenvalues eigenvectors elements ellipse equal Equation error estimates Example Exercise expression factor Figure function given gives independent indicated interpretation length linear combinations loadings mean vector measurements methods multivariate normal normal distribution Note observations obtained origin orthogonal matrix pairs plot points population positive principal components probability Proof proportion random random variables random vector regression represent residual respectively response Result sample covariance sample mean sample variance scatter plot Show simultaneous square standardized statistical symmetric matrix Table transformation unit univariate values variables variation vector X₁ zero μι