Principal Components Analysis, 69. sējumsSAGE, 1989 - 96 lappuses For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use. |
Saturs
Series Editors Introduction | 11 |
Basic Concepts of Principal Components | 15 |
Geometrical Properties of Principal | 23 |
Decomposition Properties of Principal | 42 |
Rotation of Principal Components | 48 |
Principal Components Versus Factor | 55 |
Uses of Principal Components in Regression | 65 |
Using Principal Components to Detect Outlying | 75 |
Other Techniques Related to Principal | 87 |
| 94 | |
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Bieži izmantoti vārdi un frāzes
associated latent vectors axis burglary canonical correlation analysis cipal components cluster column common factors compo components account contingency table coordinate axes correlation matrix correspondence analysis covariance matrix crime data cross products data set decompose decomposition defined dependent dimensional space discarded discriminant analysis elements equal example factor analysis factor loading matrix GNP/POP group variation independent variables indicates interpret largest latent root largest principal component latent vector associated linear composite linear discriminant function motor vehicle theft multicollinearity multiple correlations multivariate nents observations original variables orthogonal plot ponents presented in Table principal component loadings principal component scores principal components analysis represent robbery rotated components satisfaction variables second principal component set of variables seven crime variables squared multiple correlations standard errors standardized variables subset of variables sum of squares swarm of points tion total variance transformation uncorrelated unique vari variable set variable space variance explained varimax weight vector x₁ zero
Populāri fragmenti
95. lappuse - MC (1972). World handbook of social and political indicators (2nd ed.). New Haven, CT: Yale University Press.
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