## Applied Multivariate Statistical Analysis, Volume 1This market-leading book offers a readable introduction to the statistical analysis of multivariate observations. Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Chapter topics include aspects of multivariate analysis, matrix algebra and random vectors, sample geometry and random sampling, the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, multivariate linear regression models, principal components, factor analysis and inference for structured covariance matrices, canonical correlation analysis, and discrimination and classification. For experimental scientists in a variety of disciplines. |

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a very comphrehensive and in-dept monograph, good multivariate statistical handbook for research or grad studies.

### Contents

ASPECTS OF MULTIVARIATE ANALYSIS | 1 |

MATRIX ALGEBRA AND RANDOM VECTORS | 50 |

SAMPLE GEOMETRY AND RANDOM SAMPLING | 112 |

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### Common terms and phrases

approximation axes bivariate normal Bonferroni calculate canonical correlations canonical variates Chernoff faces chi-square clusters columns confidence intervals Consider Construct coordinate correlation coefficient corresponding data matrix data set determined dimensions eigenvalues eigenvectors ellipse ellipsoid equal equation Example Exercise F-distribution factor analysis factor loadings factor model factor scores function given independent interpretation large sample least squares length likelihood ratio linear combinations linear regression linkage MANOVA maximum likelihood estimates measurements method misclassification multivariate normal normal distribution normal population observations obtained orthogonal outliers parameters points prediction prior probabilities procedure Q-Q plot random sample random variables random vector regression model reject H0 residual response Result rotated sample canonical sample covariance matrix sample mean sample principal components sample variance scatter plot simultaneous confidence intervals specific variances squared distance standardized statistical statistical distance sum of squares tion treatment univariate values zero