## Applied Multivariate Statistical AnalysisExplores the statistical methods for describing and analyzing multivariate data. It's goal is to provide readers with the knowledge necessary to make proper interpretations, and select appropriate techniques for analyzing multivariate data Coverage includes: Detecting Outliers and Data Cleaning; Multivariate Quality Control; Monitoring Quality with Principal Components; and Correspondence Analysis, Biplots, and Procrustes Analysis. |

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User Review - bluetyson - LibraryThingApplied Multivariate Statistical Analysis by Richard A. Johnson is your basic beyond basic if you will, garden variety big honking textbook deal. A somewhat useful tome, but again, not that ... Read full review

### Contents

Matrix Algebra and Random Vectors | 37 |

Sample Geometry and Random | 92 |

The Multivariate Normal Distribution | 126 |

Copyright | |

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### Common terms and phrases

approximately axes bivariate normal calculate canonical correlations canonical variates Chernoff faces clusters columns confidence intervals Consider Construct coordinates correlation coefficient corresponding Cov(X density determined dimensions eigenvalues eigenvectors ellipse ellipsoid equal Equation Example Exercise F-distribution factor analysis factor loadings factor model factor scores Figure function given independent interpretation large sample least squares length likelihood ratio linear combinations linear discriminant linear regression MANOVA maximizes maximum likelihood estimates measurements methods misclassification multivariate normal normal distribution normal population observations obtained p x p pairs parameters positive definite prior probabilities procedure Q-Q plot random sample random variables random vector regression model residual response Result rotated sample canonical sample covariance matrix sample mean sample principal components sample variance scatterplot ſº squared distance standardized statistical sum of squares tion univariate values X1 and X2 Z(ZZ zero