{"product_id":"applied-multivariate-data-analysis-isbn-9780470711170","title":"Applied Multivariate Data Analysis","description":"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 intermediate-level 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 easy-going 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.  \u003cb\u003e1 Multivariate data and multivariate statistics.\u003c\/b\u003e  \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 Types of data.\u003c\/p\u003e \u003cp\u003e1.3 Basic multivariate statistics.\u003c\/p\u003e \u003cp\u003e1.4 The aims of multivariate analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Exploring multivariate data graphically.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 The scatterplot.\u003c\/p\u003e \u003cp\u003e2.3 The scatterplot matrix.\u003c\/p\u003e \u003cp\u003e2.4 Enhancing the scatterplot.\u003c\/p\u003e \u003cp\u003e2.5 Coplots and trellis graphics.\u003c\/p\u003e \u003cp\u003e2.6 Checking distributional assumptions using probability plots.\u003c\/p\u003e \u003cp\u003e2.7 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Principal components analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Algebraic basics of principal components.\u003c\/p\u003e \u003cp\u003e3.3 Rescaling principal components.\u003c\/p\u003e \u003cp\u003e3.4 Calculating principal component scores.\u003c\/p\u003e \u003cp\u003e3.5 Choosing the number of components.\u003c\/p\u003e \u003cp\u003e3.6 Two simple examples of principal components analysis.\u003c\/p\u003e \u003cp\u003e3.7 More complex examples of the application of principal components analysis.\u003c\/p\u003e \u003cp\u003e3.8 Using principal components analysis to select a subset of variables.\u003c\/p\u003e \u003cp\u003e3.9 Using the last few principal components.\u003c\/p\u003e \u003cp\u003e3.10 The biplot.\u003c\/p\u003e \u003cp\u003e3.11 Geometrical interpretation of principal components analysis.\u003c\/p\u003e \u003cp\u003e3.12 Projection pursuit.\u003c\/p\u003e \u003cp\u003e3.13 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Correspondence analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 A simple example of correspondence analysis.\u003c\/p\u003e \u003cp\u003e4.3 Correspondence analysis for two-dimensional contingency tables.\u003c\/p\u003e \u003cp\u003e4.4 Three applications of correspondence analysis.\u003c\/p\u003e \u003cp\u003e4.5 Multiple correspondence analysis.\u003c\/p\u003e \u003cp\u003e4.6 Summary\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multidimensional scaling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Proximity matrices and examples of multidimensional scaling.\u003c\/p\u003e \u003cp\u003e5.4 Metric least-squares multidimensional scaling.\u003c\/p\u003e \u003cp\u003e5.5 Non-metric multidimensional scaling.\u003c\/p\u003e \u003cp\u003e5.6 Non-Euclidean metrics.\u003c\/p\u003e \u003cp\u003e5.7 Three-way multidimensional scaling.\u003c\/p\u003e \u003cp\u003e5.8 Inference in multidimensional scaling.\u003c\/p\u003e \u003cp\u003e5.9 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Cluster analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Agglomerative hierarchical clustering techniques.\u003c\/p\u003e \u003cp\u003e6.3 Optimization methods.\u003c\/p\u003e \u003cp\u003e6.4 Finite mixture models for cluster analysis.\u003c\/p\u003e \u003cp\u003e6.5 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The generalized linear model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Linear models.\u003c\/p\u003e \u003cp\u003e7.2 Non-linear models.\u003c\/p\u003e \u003cp\u003e7.3 Link functions and error distributions in the generalized linear model.\u003c\/p\u003e \u003cp\u003e7.4 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Regression and the analysis of variance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Least-squares estimation for regression and analysis of variance models.\u003c\/p\u003e \u003cp\u003e8.3 Direct and indirect effects.\u003c\/p\u003e \u003cp\u003e8.4 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Log-linear and logistic models for categorical multivariate data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Maximum likelihood estimation for log-linear and linear-logistic models.\u003c\/p\u003e \u003cp\u003e9.3 Transition models for repeated binary response measures.\u003c\/p\u003e \u003cp\u003e9.4 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Models for multivariate response variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Repeated quantitative measures.\u003c\/p\u003e \u003cp\u003e10.3 Multivariate tests.\u003c\/p\u003e \u003cp\u003e10.4 Random effects models for longitudinal data.\u003c\/p\u003e \u003cp\u003e10.5 Logistic models for multivariate binary responses.\u003c\/p\u003e \u003cp\u003e10.6 Marginal models for repeated binary response measures.\u003c\/p\u003e \u003cp\u003e10.7 Marginal modelling using generalized estimating equations.\u003c\/p\u003e \u003cp\u003e10.8 Random effects models for multivariate repeated binary response measures.\u003c\/p\u003e \u003cp\u003e10.9 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Discrimination, classification and pattern recognition.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 A simple example.\u003c\/p\u003e \u003cp\u003e11.3 Some examples of allocation rules.\u003c\/p\u003e \u003cp\u003e11.4 Fisher's linear discriminant function.\u003c\/p\u003e \u003cp\u003e11.5 Assessing the performance of a discriminant function.\u003c\/p\u003e \u003cp\u003e11.6 Quadratic discriminant functions.\u003c\/p\u003e \u003cp\u003e11.7 More than two groups.\u003c\/p\u003e \u003cp\u003e11.8 Logistic discrimination.\u003c\/p\u003e \u003cp\u003e11.9 Selecting variables.\u003c\/p\u003e \u003cp\u003e11.10 Other methods for deriving classification rules.\u003c\/p\u003e \u003cp\u003e11.11 Pattern recognition and neural networks.\u003c\/p\u003e \u003cp\u003e11.12 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Exploratory factor analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 The basic factor analysis model.\u003c\/p\u003e \u003cp\u003e12.3 Estimating the parameters in the factor analysis model.\u003c\/p\u003e \u003cp\u003e12.4 Rotation of factors.\u003c\/p\u003e \u003cp\u003e12.5 Some examples of the application of factor analysis.\u003c\/p\u003e \u003cp\u003e12.6 Estimating factor scores.\u003c\/p\u003e \u003cp\u003e12.7 Factor analysis with categorical variables.\u003c\/p\u003e \u003cp\u003e12.8 Factor analysis and principal components analysis compared.\u003c\/p\u003e \u003cp\u003e12.9 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Confirmatory factor analysis and covariance structure models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Path analysis and path diagrams.\u003c\/p\u003e \u003cp\u003e13.3 Estimation of the parameters in structural equation models.\u003c\/p\u003e \u003cp\u003e13.4 A simple covariance structure model and identification.\u003c\/p\u003e \u003cp\u003e13.5 Assessing the fit of a model.\u003c\/p\u003e \u003cp\u003e13.6 Some examples of fitting confirmatory factor analysis models.\u003c\/p\u003e \u003cp\u003e13.7 Structural equation models.\u003c\/p\u003e \u003cp\u003e13.8 Causal models and latent variables: myths and realities.\u003c\/p\u003e \u003cp\u003e13.9 Summary.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Software packages.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 General-purpose packages.\u003c\/p\u003e \u003cp\u003eA.2 More specialized packages.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Missing values.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Answers to selected exercises.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e  \u003cb\u003eBrian S. Everitt\u003c\/b\u003e is Professor of Behavioural Statistics and Head of the Biostatistics and Computing Department at the Institute of Psychiatry, King’s College London, UK  \u003cp\u003e\u003cb\u003eGraham Dunn\u003c\/b\u003e is Professor of Biomedical Statistics and Head of the Biostatistics Group within the School of Epidemiology and Health Sciences, University of Manchester, UK\u003c\/p\u003e  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.  \u003cp\u003eThis intermediate-level textbook introduces the reader to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its second edition, \u003ci\u003eApplied Multivariate Data Analysis\u003c\/i\u003e 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 easy-going style of the first edition, this title provides clear explanations of each technique, supported by figures and examples, using minimal technical jargon. With extensive exercises following every chapter, the book is a valuable resource for students on applied statistics courses and for applied researchers in many disciplines.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988752646373,"sku":"NP9780470711170","price":71.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470711170.jpg?v=1761781452","url":"https:\/\/k12savings.com\/products\/applied-multivariate-data-analysis-isbn-9780470711170","provider":"K12savings","version":"1.0","type":"link"}