Applied Multivariate Statistical Analysis& This market leader offers a readable introduction to the statistical analysis of multivariate observations. Gives readers the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Starts with a formulation of the population models, delineates the corresponding sample results, and liberally illustrates everything with examples. & Offers an abundance of examples and exercises based on real data.& Appropriate for experimental scientists in a variety of disciplines. |
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Applied Multivariate Statistical Analysis Richard Arnold Johnson,Dean W. Wichern No preview available - 2002 |
Common terms and phrases
A₁ analysis approximately axes bivariate normal Calculate chi-square chi-square distribution columns components confidence ellipse confidence intervals confidence region Construct corresponding covariance matrix Data courtesy data matrix decomposition determined deviation vectors dimensions eigenvalues eigenvectors ellipse ellipsoid Equation Example Exercise factor Figure given H₁ independent length Let X1 linear combinations linearly MANOVA maximum likelihood estimator mean vector measurements multivariate normal multivariate normal distribution n₁ n₂ normal density normal distribution normal population observations obtained outliers pairs percentile population mean positive definite Q-Q plot random sample random variables random vector regression model residual response Result S₁ sample covariance sample covariance matrix sample mean sample variance scatter plot simultaneous confidence intervals statistical statistical distance sum of squares T2-intervals transformations treatment univariate V₁ values variance-covariance matrix X₁ X₂ Y₁ Z₁ zero μ₁ μ₂ μι Σ Σ ΣΣ