Applied Multivariate Methods for Data AnalystsStatisticians and nonstatisticians alike will appreciate this modern and comprehensive new text. Dallas Johnson uses real-life examples and explains the when to, why to, and how to of numerous multivariate methods, stressing the importance and practical application of each. He keeps technical details to a minimum for greater student understanding. Students will be able to DO multivariate analyses when they complete this book. Drawing on nearly 20 years of experience teaching public seminars and college courses in applied multivariate methods. Johnson emphasizes those aspects that have been most useful to practitioners trying to solve real problems using real data. |
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Contents
APPLIED MULTIVARIATE METHODS | 1 |
SAMPLE CORRELATIONS | 35 |
MULTIVARIATE DATA PLOTS | 55 |
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Common terms and phrases
analysis of variance Canonical Correlation Analysis canonical functions canonical variate Chernoff faces Chi-Square CHNUP classified cluster analysis Coefficients computer printout Consider correlation matrix covariance create credit risk data set DIAST dimensionality discriminant analysis discriminant rule distance eigenvalues eigenvectors enclosed disk equal Error estimates Example 5.1 experimental units Explain your answer factor analysis factor scores file labeled HHHH hypothesis Linear Models Procedure logistic regression MANOVA Mean Square mean vectors measured variables multivariate normal multivariate normal distribution normal distribution number of clusters observation option original variables orthogonal outliers Pages PELVIC pizzas PIZZAZZ NORMATIVE QUESTION population Posterior Probability PRIN2 principal component scores principal components analysis RECVR researcher response variables rotation SAS commands scatter plot SHLDR shown in Figure significant SPSS standardized statistical Sum of Squares TEXTURE1 tion uncorrelated univariate variance-covariance matrix WILD X X XXXX XXXX XXXX XXXX zero