Applied multivariate statistical analysis
Explores 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 dataCoverage 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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f Aspects of Multivariate Analysis
Matrix Algebra and Random Vectors
Sample Geometry and Random
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approximately axes bivariate normal calculate canonical correlations canonical variates Chernoff faces clusters columns confidence intervals Consider Construct coordinates correlation coefficient corresponding determined diagonal element diagram dimensions eigenvalues eigenvectors ellipse ellipsoid equal Equation error rate Example Exercise F-distribution factor analysis factor loadings factor model factor scores Figure function given independent interpretation 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 parameters points positive definite predictor variables prior probabilities procedure 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 scatterplot squared distance standardized statistical statistical distance sum of squares tion univariate values zero