Hugendubel.info - Die B2B Online-Buchhandlung 

Merkliste
Die Merkliste ist leer.
Bitte warten - die Druckansicht der Seite wird vorbereitet.
Der Druckdialog öffnet sich, sobald die Seite vollständig geladen wurde.
Sollte die Druckvorschau unvollständig sein, bitte schliessen und "Erneut drucken" wählen.

Applied Multivariate Data Analysis

BuchKartoniert, Paperback
354 Seiten
Englisch
Wileyerschienen am02.03.2001Revised edition
Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure.mehr

Produkt

KlappentextMultivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure.
Details
ISBN/GTIN978-0-470-71117-0
ProduktartBuch
EinbandartKartoniert, Paperback
FormatTrade Paperback (USA)
Verlag
Erscheinungsjahr2001
Erscheinungsdatum02.03.2001
AuflageRevised edition
Seiten354 Seiten
SpracheEnglisch
MasseBreite 156 mm, Höhe 234 mm, Dicke 19 mm
Gewicht538 g
Artikel-Nr.10008707

Inhalt/Kritik

Inhaltsverzeichnis
1 Multivariate data and multivariate statistics. 1.1 Introduction. 1.2 Types of data. 1.3 Basic multivariate statistics. 1.4 The aims of multivariate analysis. 2 Exploring multivariate data graphically. 2.1 Introduction. 2.2 The scatterplot. 2.3 The scatterplot matrix. 2.4 Enhancing the scatterplot. 2.5 Coplots and trellis graphics. 2.6 Checking distributional assumptions using probability plots. 2.7 Summary. Exercises. 3 Principal components analysis. 3.1 Introduction. 3.2 Algebraic basics of principal components. 3.3 Rescaling principal components. 3.4 Calculating principal component scores. 3.5 Choosing the number of components. 3.6 Two simple examples of principal components analysis. 3.7 More complex examples of the application of principal components analysis. 3.8 Using principal components analysis to select a subset of variables. 3.9 Using the last few principal components. 3.10 The biplot. 3.11 Geometrical interpretation of principal components analysis. 3.12 Projection pursuit. 3.13 Summary. Exercises. 4 Correspondence analysis. 4.1 Introduction. 4.2 A simple example of correspondence analysis. 4.3 Correspondence analysis for two-dimensional contingency tables. 4.4 Three applications of correspondence analysis. 4.5 Multiple correspondence analysis. 4.6 Summary Exercises. 5 Multidimensional scaling. 5.1 Introduction. 5.2 Proximity matrices and examples of multidimensional scaling. 5.4 Metric least-squares multidimensional scaling. 5.5 Non-metric multidimensional scaling. 5.6 Non-Euclidean metrics. 5.7 Three-way multidimensional scaling. 5.8 Inference in multidimensional scaling. 5.9 Summary. Exercises. 6 Cluster analysis. 6.1 Introduction. 6.2 Agglomerative hierarchical clustering techniques. 6.3 Optimization methods. 6.4 Finite mixture models for cluster analysis. 6.5 Summary. Exercises. 7 The generalized linear model. 7.1 Linear models. 7.2 Non-linear models. 7.3 Link functions and error distributions in the generalized linear model. 7.4 Summary. Exercises. 8 Regression and the analysis of variance. 8.1 Introduction. 8.2 Least-squares estimation for regression and analysis of variance models. 8.3 Direct and indirect effects. 8.4 Summary. Exercises. 9 Log-linear and logistic models for categorical multivariate data. 9.1 Introduction. 9.2 Maximum likelihood estimation for log-linear and linear-logistic models. 9.3 Transition models for repeated binary response measures. 9.4 Summary. Exercises. 10 Models for multivariate response variables. 10.1 Introduction. 10.2 Repeated quantitative measures. 10.3 Multivariate tests. 10.4 Random effects models for longitudinal data. 10.5 Logistic models for multivariate binary responses. 10.6 Marginal models for repeated binary response measures. 10.7 Marginal modelling using generalized estimating equations. 10.8 Random effects models for multivariate repeated binary response measures. 10.9 Summary. Exercises. 11 Discrimination, classification and pattern recognition. 11.1 Introduction. 11.2 A simple example. 11.3 Some examples of allocation rules. 11.4 Fisher's linear discriminant function. 11.5 Assessing the performance of a discriminant function. 11.6 Quadratic discriminant functions. 11.7 More than two groups. 11.8 Logistic discrimination. 11.9 Selecting variables. 11.10 Other methods for deriving classification rules. 11.11 Pattern recognition and neural networks. 11.12 Summary. Exercises. 12 Exploratory factor analysis. 12.1 Introduction. 12.2 The basic factor analysis model. 12.3 Estimating the parameters in the factor analysis model. 12.4 Rotation of factors. 12.5 Some examples of the application of factor analysis. 12.6 Estimating factor scores. 12.7 Factor analysis with categorical variables. 12.8 Factor analysis and principal components analysis compared. 12.9 Summary. Exercises. 13 Confirmatory factor analysis and covariance structure models. 13.1 Introduction. 13.2 Path analysis and path diagrams. 13.3 Estimation of the parameters in structural equation models. 13.4 A simple covariance structure model and identification. 13.5 Assessing the fit of a model. 13.6 Some examples of fitting confirmatory factor analysis models. 13.7 Structural equation models. 13.8 Causal models and latent variables: myths and realities. 13.9 Summary. Exercises. Appendices. A Software packages. A.1 General-purpose packages. A.2 More specialized packages. B Missing values. C Answers to selected exercises. References. Index.mehr

Autor

Brian S. Everitt is Professor of Behavioural Statistics and Head of the Biostatistics and Computing Department at the Institute of Psychiatry, King's College London, UK
Graham Dunn is Professor of Biomedical Statistics and Head of the Biostatistics Group within the School of Epidemiology and Health Sciences, University of Manchester, UK