Applied Multivariate Data Analysis 2e
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. Les mer
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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. This intermediatelevel textbook introduces the reader
to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its 2nd edition, 'Applied Multivariate
Data Analysis' has been fully expanded and updated, including major chapter revisions as well as new sections on neural networks
and random effects models for longitudinal data. Maintaining the easygoing style of the first edition, the authors provide
clear explanations of each technique, as well as supporting figures and examples, and minimal technical jargon. With extensive
exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied statistics
courses and applied researchers in many disciplines.
 FAKTA

Utgitt:
2001
Forlag: John Wiley & Sons Inc
Innbinding: Paperback
Språk: Engelsk
ISBN: 9780470711170
Format: 23 x 16 cm
 KATEGORIER:
 VURDERING

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Les vurderinger
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 twodimensional 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 leastsquares multidimensional scaling. 5.5 Nonmetric multidimensional scaling. 5.6 NonEuclidean metrics.
5.7 Threeway 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 Nonlinear 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 Leastsquares estimation for regression and analysis of variance models. 8.3 Direct and
indirect effects. 8.4 Summary. Exercises. 9 Loglinear and logistic models for categorical multivariate data. 9.1 Introduction.
9.2 Maximum likelihood estimation for loglinear and linearlogistic 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 Generalpurpose packages. A.2 More specialized packages. B Missing values. C Answers to selected exercises. References.
Index.
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