Описание: Full of real-world case studies and practical advice, this book focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis when variables are quantitative, correspondence analysis and multiple correspondence analysis when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. All of the data sets and code are available on the book’s website.
Описание: The basic goal of a research scientist is to understand a given, unknown system. This innovative book develops a systematic approach for achieving this goal. All science is ultimately dependent upon observation which, in turn, requires a flow of information. Fisher information, in particular, is found to provide the key to understanding the system. It is developed as a new tool of exploratory data analysis, and is applied to a wide scope of systems problems. These range from molecules in a gas to biological organisms in their ecologies, to the socio-economic organization of people in their societies, to the physical constants in the universe and, ultimately, to proto-universes in the multiverse.Examples of system input-output laws discovered by the approach include the famous quarter-power laws of biology and the Tobin q-theory of optimized economic investment. System likelihood laws that can be determined include the probability density functions defining in situ cancer growth and a wide class of systems (thermodynamic, economic, cryptographic) obeying Schrodinger-like equations. Novel uncertainty principles in the fields of biology and economics are also found to hold.
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.
The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.
The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.
Описание: With an index of notations at the beginning, this book explains the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. It also features a description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques.
Описание: A review of standard algorithms provides the basis for more complex data mining techniques in this overview of exploratory data analysis. Recent reinforcement learning research is presented, as well as novel methods of parameter adaptation in machine learning.
Описание: Like its best-selling predecessor, this second edition continues to use numerous examples and applications to show how EDA methods are used in practice. This edition covers many innovative approaches for dimensionality reduction, clustering, and visualization, including nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, smoothing splines, probabilistic latent semantic analysis, spectral-based clustering, a rangefinder boxplot, scatterplots with marginal histograms, biplots, and Andrews’ images method. MATLAB® codes are available for download on the book’s website.
Описание: Praise for the First Edition . a well-written book on data analysis and data mining that provides an excellent foundation. CHOICE This is a must-read book for learning practical statistics and data analysis.
Описание: Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular.
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