Applied multivariate statistical analysis

Most of the observable phenomena in the empirical sciences are of a multivariate nature.¡ In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices.¡ In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication.¡ In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior.¡ The underlying data structure of these and many other quantitative studies of applied sciences is multivariate.¡ Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data.¡ The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics.¡ All chapters have exercises that highlight applications in different fields. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been added All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.

Applied Multivariate Statistical Analysis; Preface to the 3rd Edition; Preface to the 2nd Edition; Preface to the 1st Edition; Contents; Part I: Descriptive Techniques; Chapter 1: Comparison of Batches; 1.1 Boxplots; 1.2 Histograms; 1.3 Kernel Densities; 1.4 Scatterplots; 1.5 Chernoff-Flury Faces; 1.6 Andrews' Curves; 1.7 Parallel Coordinate Plots; 1.8 Hexagon Plots; 1.9 Boston Housing; 1.10 Exercises; Part II: Multivariate Random Variables; Chapter 2: A Short Excursion into Matrix Algebra; 2.1 Elementary Operations; 2.2 Spectral Decompositions; 2.3 Quadratic Forms; 2.4 Derivatives.

2.5 Partitioned Matrices2.6 Geometrical Aspects; 2.7 Exercises; Chapter 3: Moving to Higher Dimensions; 3.1 Covariance; 3.2 Correlation; 3.3 Summary Statistics; 3.4 Linear Model for Two Variables; 3.5 Simple Analysis of Variance; 3.6 Multiple Linear Model; 3.7 Boston Housing; 3.8 Exercises; Chapter 4: Multivariate Distributions; 4.1 Distribution and Density Function; 4.2 Moments and Characteristic Functions; 4.3 Transformations; 4.4 The Multinormal Distribution; 4.5 Sampling Distributions and Limit Theorems; 4.6 Heavy-Tailed Distributions; 4.7 Copulae; 4.8 Bootstrap; 4.9 Exercises.

Chapter 5: Theory …