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| Multivariate data and multivariate statistics | |
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| Introduction | |
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| Types of data | |
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| Basic multivariate statistics | |
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| The aims of multivariate analysis | |
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| Exploring multivariate data graphically | |
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| Introduction | |
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| The scatterplot | |
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| The scatterplot matrix | |
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| Enhancing the scatterplot | |
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| Coplots and trellis graphics | |
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| Checking distributional assumptions using probability plots | |
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| Summary | |
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| Exercises | |
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| Principal components analysis | |
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| Introduction | |
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| Algebraic basics of principal components | |
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| Rescaling principal components | |
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| Calculating principal component scores | |
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| Choosing the number of components | |
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| Two simple examples of principal components analysis | |
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| More complex examples of the application of principal components analysis | |
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| Using principal components analysis to select a subset of variables | |
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| Using the last few principal components | |
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| The biplot | |
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| Geometrical interpretation of principal components analysis | |
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| Projection pursuit | |
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| Summary | |
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| Exercises | |
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| Correspondence analysis | |
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| Introduction | |
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| A simple example of correspondence analysis | |
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| Correspondence analysis for two-dimensional contingency tables | |
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| Three applications of correspondence analysis | |
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| Multiple correspondence analysis | |
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| Summary | |
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| Exercises | |
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| Multidimensional scaling | |
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| Introduction | |
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| Proximity matrices and examples of multidimensional scaling | |
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| Metric least-squares multidimensional scaling | |
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| Non-metric multidimensional scaling | |
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| Non-Euclidean metrics | |
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| Three-way multidimensional scaling | |
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| Inference in multidimensional scaling | |
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| Summary | |
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| Exercises | |
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| Cluster analysis | |
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| Introduction | |
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| Agglomerative hierarchical clustering techniques | |
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| Optimization methods | |
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| Finite mixture models for cluster analysis | |
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| Summary | |
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| Exercises | |
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| The generalized linear model | |
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| Linear models | |
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| Non-linear models | |
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| Link functions and error distributions in the generalized linear model | |
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| Summary | |
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| Exercises | |
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| Regression and the analysis of variance | |
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| Introduction | |
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| Least-squares estimation for regression and analysis of variance models | |
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| Direct and indirect effects | |
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| Summary | |
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| Exercises | |
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| Log-linear and logistic models for categorical multivariate data | |
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| Introduction | |
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| Maximum likelihood estimation for log-linear and linear-logistic models | |
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| Transition models for repeated binary response measures | |
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| Summary | |
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| Exercises | |
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| Models for multivariate response variables | |
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| Introduction | |
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| Repeated quantitative measures | |
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| Multivariate tests | |
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| Random effects models for longitudinal data | |
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| Logistic models for multivariate binary responses | |
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| Marginal models for repeated binary response measures | |
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| Marginal modelling using generalized estimating equations | |
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| Random effects models for multivariate repeated binary response measures | |
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| Summary | |
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| Exercises | |
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| Discrimination, classification and pattern recognition | |
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| Introduction | |
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| A simple example | |
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| Some examples of allocation rules | |
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| Fisher's linear discriminant function | |
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| Assessing the performance of a discriminant function | |
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| Quadratic discriminant functions | |
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| More than two groups | |
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| Logistic discrimination | |
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| Selecting variables | |
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| Other methods for deriving classification rules | |
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| Pattern recognition and neural networks | |
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| Summary | |
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| Exercises | |
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| Exploratory factor analysis | |
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| Introduction | |
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| The basic factor analysis model | |
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| Estimating the parameters in the factor analysis model | |
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| Rotation of factors | |
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| Some examples of the application of factor analysis | |
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| Estimating factor scores | |
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| Factor analysis with categorical variables | |
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| Factor analysis and principal components analysis compared | |
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| Summary | |
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| Exercises | |
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| Confirmatory factor analysis and covariance structure models | |
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| Introduction | |
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| Path analysis and path diagrams | |
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| Estimation of the parameters in structural equation models | |
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| A simple covariance structure model and identification | |
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| Assessing the fit of a model | |
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| Some examples of fitting confirmatory factor analysis models | |
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| Structural equation models | |
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| Causal models and latent variables: myths and realities | |
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| Summary | |
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| Exercises | |
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| Appendices | |
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| Software packages | |
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| General-purpose packages | |
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| More specialized packages | |
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| Missing values | |
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| Answers to selected exercises | |
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| References | |
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| Index | |