Applied Multivariate Statistical Analysis
This market leading text provides experimental scientists in a wide variety of disciplines with 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. The Fourth Edition has been revised to take greater advantage of graphical displays of multivariate data and of statistical software programs that facilitate the analysis of complex data. *NEW - Graphical displays of multivariate data moved from Chapter 12 to chapter 1 and many new illustrations and graphics have been added to provide a more visual approach to the subject. *NEW - discussions of important topics including: - Detecting Outliers and Data Cleaning in Chapter 4.- Multivariate Quality Control in Chapter 5. - Monitoring Quality with Principal Components in Chapter 8.- Correspondence Analysis, Biplots, and Procrustes Analysis in Chapter 12. *NEW - Expanded coverage of the following topics: Generalized variance, Assessing normality and transformations to normality, Repeated measures designs, Model checking and other aspects of regre
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SAMPLE GEOMETRY AND RANDOM SAMPLING
THE MULTIVARIATE NORMAL DISTRIBUTION
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approximation axes bivariate normal Bonferroni calculate canonical correlations canonical variates Chernoff faces chi-square cluster columns confidence intervals Consider Construct coordinates correlation coefficient corresponding cross products data matrix determined dimensions eigenvalues eigenvectors ellipse ellipsoid equal equation error rate Example Exercise factor analysis factor loadings factor model factor scores Figure function given independent interpretation large sample least squares length linear combinations linkage MANOVA maximum likelihood estimates measurements methods misclassification multivariate normal normal distribution normal populations observations obtained orthogonal outliers pairs parameters points positive definite prediction prior probabilities procedure profiles Q-Q plot random sample random variables random vector regression model reject H0 residual response Result ri ri ri rotated sample canonical sample covariance matrix sample mean sample principal components sample variance scatter plot simultaneous confidence intervals squared distance standardized statistical distance sum of squares tion treatment univariate values zero