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. |
Common terms and phrases
axes bivariate normal calculate canonical correlations canonical variates Chernoff faces classification clusters columns confidence intervals Consider Construct correlation coefficient correlation matrix corresponding density determined dimensions e₁ eigenvalues eigenvectors ellipse ellipsoid equal Equation Example Exercise factor analysis factor loadings factor model factor scores Figure function given interpretation least squares length linear combinations MANOVA maximum likelihood estimates mean vector measurements methods misclassification multivariate normal n₁ n₂ normal distribution normal population observations obtained p₁ 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 correlation sample covariance matrix sample mean sample principal components sample variance scatterplot squared distance standardized statistical sum of squares Table tion univariate V₁ values X₁ X₂ Y₁ Y₂ Z₁ Z₂ zero µ₁