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Mathematical and Statistical Background
Exploring Multivariate Data
Principal Components Analysis
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analysis of variance applied assessment assumption behavioural Brand x Prev Chapter chi-square cluster analysis co-ordinates coefficients consider contingency table correlation matrix corresponding covariance matrix data in Table data set degrees of freedom dendrogram described diagram dummy variables effect eigenvalues eigenvectors Euclidean distances examination example explanatory variables factor analysis Female Figure function goodness-of-fit groups i-th indicating individuals interaction investigator Kruskal latent variables least squares estimation linear model linear-logistic models log odds log true odds log-linear models Male mean measure methods multidimensional scaling multivariate data number of visits observed variables obtained original variables orthogonal package Parameter Estimate plot principal components analysis principal factor analysis procedure regression model relationship residual response variable rotation sample scores Section self-assessment of health shown in Table significance similar similarity matrix single linkage social class statistical sum of squares techniques Temp Two-dimensional solution values variation vector weighted least squares zero