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Informationen zum Autor John C. Gower, Department of Mathematics, The Open University, Milton Keynes, UK. Over 100 papers. Books include Gower & Hand (1996) Biplots, in which the authors developed a unified theory of biplots. Sugnet Gardner, British American Tobacco, Stellenbosch, South Africa. Niel J. le Roux, Department of Statistics and Actuarial Science, University of Stellenbosch, South Africa. Klappentext Biplots are a graphical method for simultaneously displaying two kinds of information; typically, the variables and sample units described by a multivariate data matrix or the items labelling the rows and columns of a two-way table. This book aims to popularize what is now seen to be a useful and reliable method for the visualization of multidimensional data associated with, for example, principal component analysis, canonical variate analysis, multidimensional scaling, multiplicative interaction and various types of correspondence analysis.Understanding Biplots:* Introduces theory and techniques which can be applied to problems from a variety of areas, including ecology, biostatistics, finance, demography and other social sciences.* Provides novel techniques for the visualization of multidimensional data and includes data mining techniques.* Uses applications from many fields including finance, biostatistics, ecology, demography.* Looks at dealing with large data sets as well as smaller ones.* Includes colour images, illustrating the graphical capabilities of the methods.* Is supported by a Website featuring R code and datasets.Researchers, practitioners and postgraduate students of statistics and the applied sciences will find this book a useful introduction to the possibilities of presenting data in informative ways. "It is a monograph rather than a textbook, but individual chapters could very well be incorporated in a course on statistics." ( Mathematical Reviews Clippings , 1 January 2013) Zusammenfassung Statistical Graphics and Multivariate Data: Methods and Applications of Biplots popularizes what is now seen to be useful and reliable methods for the visualization of multivariate data where the researcher wants to consider principal component analysis (PCA), canonical variates analysis (CVA), or various types of correspondence analysis. Inhaltsverzeichnis Preface.1 Introduction.1.1 Types of biplots.1.2 Overview of the book.1.3 Software.1.4 Notation.2 Biplot basics.2.1 A simple example revisited.2.2 The biplot as a multidimensional scatterplot.2.3 Calibrated biplot axes.2.4 Refining the biplot display.2.5 Scaling the data.2.6 A closer look at biplot axes.2.7 Adding new variables: the regression method.2.8 Biplots and large data sets.2.9 Enclosing a configuration of sample points.2.10 Buying by mail order catalogue data set revisited.2.11 Summary.3 Principal component analysis biplots.3.1 An example: risk management.3.2 Understanding PCA and constructing its biplot.3.3 Measures of fit for PCA biplots.3.4 Predictivities of newly interpolated samples.3.5 Adding new axes to a PCA biplot and defining their predictivities.3.6 Scaling the data in a PCA biplot.3.7 Functions for constructing a PCA biplot.3.8 Some novel applications and enhancements of PCA biplots.3.9 Conclusion.4 Canonical variate analysis biplots.4.1 An example: revisiting the Ocotea data.4.2 Understanding CVA and constructing its biplot.4.3 Geometric interpretation of the transformation to the canonical space.4.4 CVA biplot axes.4.5 Adding new points and variables to a CVA biplot.4.6 Measures of fit for CVA biplots.4.7 Functions for constructing a CVA biplot.4.8 Continuing the Ocotea example.4.9 CVA biplots for two classes.4.10 A five-class CVA biplot example.4.11 Overlap in two-dimensional biplots.5 Multidimensional scaling and nonlinear biplots.5.1 Introduction.5.2 The regression method....
