Applied Multivariate Statistical AnalysisAspects of mulyivariate analysis; Matrix algebra and random vectors; Sampling geometry and random sampling; The multivariate normal distribution; Inferences about a mean vector; Comparisons of several multivariate means; Multivariate linear regression models; Analysis of covariance structure: principal components; Factor analysis and inference structured covarience matrices; Canonical correlation analysis; Classification and grouping techniques; Discrimination and classification; Clustering. |
From inside the book
Results 1-3 of 52
Page 234
... residuals , because SSres = SSobs - SSmean SS . However , this is false economy because plots of the residuals provide checks on the model assumptions . ... , X2n2 ... The vector representations of the arrays involved in the ...
... residuals , because SSres = SSobs - SSmean SS . However , this is false economy because plots of the residuals provide checks on the model assumptions . ... , X2n2 ... The vector representations of the arrays involved in the ...
Page 295
... Residuals should be plotted in various ways to detect possible anomalies . For general diagnostic purposes these are the best graphs . 1. Plot the residuals , ê ,, against the predicted values , ŷ , = Bo + B1Z ; 1 + + B , z ...
... Residuals should be plotted in various ways to detect possible anomalies . For general diagnostic purposes these are the best graphs . 1. Plot the residuals , ê ,, against the predicted values , ŷ , = Bo + B1Z ; 1 + + B , z ...
Page 296
... Residual plots . ( d ) residuals versus time may reveal a systematic pattern ( a plot of the positions of the residuals in space may also reveal associations among the errors ) . For instance , residuals that increase over time indicate ...
... Residual plots . ( d ) residuals versus time may reveal a systematic pattern ( a plot of the positions of the residuals in space may also reveal associations among the errors ) . For instance , residuals that increase over time indicate ...
Contents
Matrix Algebra and Random Vectors | 35 |
Sample Geometry and Random Sampling | 88 |
35335 | 95 |
Copyright | |
13 other sections not shown
Common terms and phrases
approximately axes calculate canonical correlations canonical variates chi-square chi-square distribution classification clusters confidence intervals confidence region correlation coefficient correlation matrix corresponding cross-products density determined discriminant eigenvalues eigenvectors ellipse ellipsoid Equation error Example Exercise F-distribution factor analysis factor loadings Figure function given H₁ large sample length likelihood ratio likelihood ratio test linear combinations MANOVA maximum likelihood estimates mean vector measurements multivariate normal n₁ n₂ normal distribution normal population observations obtained P₁ pairs parameters population mean Q-Q plots random sample random variables random vector regression model reject residual response Result rotated S₁ sample correlation sample covariance matrix sample mean sample variance scatterplot simultaneous confidence intervals Spooled squared distance statistical sum of squares Table treatment univariate V₁ values X₁ X₂ Y₁ Y₂ Z₁ zero μ₁ μ₂ μι Σ Σ