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Multi- and Megavariate Data Analysis (3rd Ed. revised) Basic Principles and Applications

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Couverture de l’ouvrage Multi- and Megavariate Data Analysis (3rd Ed. revised)
To understand the world around us, as well as ourselves, we need to measure many things, many variables, many properties of the systems and processes we investigate. Hence, data collected in science, technology, and almost everywhere else are multivariate, a data table with multiple variables measured on multiple observations (cases, samples, items, process time points, experiments).

    This book describes a remarkably simple minimalistic and practical 
    approach to the analysis of data tables (multivariate data). The approach 
    is based on projection methods, which are PCA (principal components 
    analysis), and PLS (projection to latent structures) and the book shows 
    how this works in science and technology for a wide variety of 
    applications. In particular, it is shown how the great information content 
    in well collected multivariate data can be expressed in terms of simple 
    but illuminating plots, facilitating the understanding and interpretation 
    of the data. The projection approach applies to a variety of 
    data-analytical objectives, i.e., (I) summarizing and visualizing a data 
    set, (II) multivariate classification and discriminant analysis, and (III) 
    finding quantitative relationships among the variables.

    

    This works with any shape of data table, with many or few variables 
    (columns), many or few observations (rows), and complete or incomplete 
    data tables (missing data). In particular, projections handle data 
    matrices with more variables than observations very well, and the data can 
    be noisy and highly collinear.

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Thèmes de Multi- and Megavariate Data Analysis (3rd Ed. revised) :