Everitt B.S., Dunn G. Applied Multivariate Data Analysis

2nd ed. — John Wiley & Sons Ltd., 2010. — 324 p. — ISBN: 9780470711170, 9781118887486, 0470711175.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.Multivariate data and multivariate statisticsTypes of data
Basic multivariate statistics
The aims of multivariate analysis
Exploring multivariate data graphicallyThe scatterplot
The scatterplot matrix
Enhancing the scatterplot
Coplots and trellis graphics
Checking distributional assumptions using probability plotsExercises
Principal components analysisAlgebraic basics of principal components
Rescaling principal components
Calculating principal component scores
Choosing the number of components
Two simple examples of principal components analysis
More complex examples of the application of principal components analysis
Using principal components analysis to select a subset of variables
Using the last few principal components
The biplot
Geometrical interpretation of principal components analysis
Projection pursuitExercises
Correspondence analysisA simple example of correspondence analysis
Correspondence analysis for two-dimensional contingency tables
Three applications of correspondence analysis
Multiple correspondence analysisExercises
Multidimensional scalingProximity matrices and examples of multidimensional scaling
Metric least-squares multidimensional scaling
Non-metric multidimensional scaling
Non-Euclidean metrics
Three-way multidimensional scaling
Inference in multidimensional scalingExercises
Cluster analysisAgglomerative hierarchical clustering techniques
Optimization methods
Finite mixture models for cluster analysisExercises
The generalized linear model
Linear models
Non-linear models
Link functions and error distributions in the generalized linear modelExercises
Regression and the analysis of varianceLeast-squares estimation for regression and analysis of variance models
Direct and indirect effectsExercises
Log-linear and logistic models for categorical multivariate dataMaximum likelihood estimation for log-linear and linear-logistic models
Transition models for repeated binary response measuresExercises
Models for multivariate response variablesRepeated quantitative measures
Multivariate tests
Random effects models for longitudinal data
Logistic models for multivariate binary responses
Marginal models for repeated binary response measures
Marginal modeling using generalized estimating equations
Random effects models for multivariate repeated binary response measuresExercises
Discrimination, classification and pattern recognitionA simple example
Some examples of allocation rules
Fisher's linear discriminant function
Assessing the performance of a discriminant function
Quadratic discriminant functions
More than two groups
Logistic discrimination
Selecting variables
Other methods for deriving classification rules
Pattern recognition and neural networksExercises
Exploratory factor analysisThe basic factor analysis model
Estimating the parameters in the factor analysis model
Rotation of factors
Some examples of the application of factor analysis
Estimating factor scores
Factor analysis with categorical variables
Factor analysis and principal components analysis comparedExercises
Confirmatory factor analysis and covariance structure modelsPath analysis and path diagrams
Estimation of the parameters in structural equation models
A simple covariance structure model and identification
Assessing the fit of a model
Some examples of fitting confirmatory factor analysis models
Structural equation models
Causal models and latent variables: myths and realitiesExercisesSoftware packages
Missing values
Answers to selected exercises