## Applied Multivariate Statistical AnalysisWith a wealth of examples and exercises, this is a brand new edition of a classic work on multivariate data analysis. A key advantage of the work is its accessibility. This is because, in its focus on applications, the 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. In this second edition a wider scope of methods and applications of multivariate statistical analysis is introduced. All quantlets have been translated into the R and Matlab language and are made available online. |

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### Contents

A Short Excursion into Matrix Algebra | 41 |

Moving to Higher Dimensions | 61 |

Multivariate Distributions 93 | 92 |

Theory of the Multinormal | 147 |

Theory of Estimation | 161 |

Hypothesis Testing | 171 |

I | 197 |

Decomposition of Data Matrices by Factors | 203 |

Exercises | 418 |

Appendix | 421 |

A Symbols and Notations 423 | 422 |

B Data | 427 |

Car Data | 430 |

Classic Blue Pullovers Data | 432 |

French Food Data | 434 |

French Baccalauréat Frequencies | 435 |

Principal Components Analysis | 215 |

Factor Analysis 251 | 250 |

Cluster Analysis | 271 |

Discriminant Analysis | 289 |

Correspondence Analysis | 305 |

Canonical Correlation Analysis 321 | 320 |

Multidimensional Scaling | 331 |

Conjoint Measurement Analysis | 347 |

Applications in Finance | 359 |

Computationally Intensive Techniques | 371 |

Sliced Inverse Regression | 379 |

Support Vector Machines | 385 |

Classification and Regression Trees | 401 |

II | 404 |

Boston Housing | 417 |

U S Crime Data | 436 |

Plasma Data | 437 |

WAIS Data | 438 |

ANOVA Data | 439 |

Timebudget Data | 440 |

Geopol Data | 441 |

U S Health Data | 443 |

Vocabulary Data | 444 |

Athletic Records Data | 445 |

Unemployment Data | 447 |

Bankruptcy Data | 448 |

450 | |

455 | |

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### Common terms and phrases

algorithm approximation assets asymptotic axis baccalauréat Boston housing data canonical correlation Cauchy distribution Chapter classification clusters column Compute Consider coordinates copula correlation matrix correspondence analysis covariance matrix d-dimensional data matrix data set decomposition deﬁned denotes density diagonal dimension dimensional discriminant rule distance matrix distribution function eigenvalues eigenvectors estimated EXAMPLE EXERCISE factor analysis Figure ﬁrst Gaussian given groups hypothesis independent interpretation L2-norm Laplace distribution linear combination marginal maximizes mean measure multinormal multivariate multivariate random variable node normal distribution observations obtain orthogonal parameters plot point cloud population portfolio principal component analysis principal components projection Projection Pursuit random variable random vector regression representation sample scatterplot Section solution Summary Swiss bank notes Table technique Test Problem test statistic Theorem transformation variance weights Wishart distribution X1 and X2 zero