## 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 |

Copyright | |

13 other sections not shown

### Common terms and phrases

analysis of variance backward elimination procedure Canonical Correlation Analysis canonical functions canonical variate Chernoff faces Chi-Square CHNUP classified cluster analysis column computer printout confidence interval Consider correlation matrix corresponding covariance create credit risk data set DIAST dimensionality DISCRIMINANT ANALYSIS discriminant rule distance eigenvalues eigenvectors enclosed disk equal Error estimated experimental units Explain your answer factor analysis factor scores file labeled hypothesis JOB SATISFACTION linear combination logistic regression MANOVA Mean Square mean vectors measured variables multivariate methods multivariate normal multivariate normal distribution normal distribution number of clusters observation option original variables orthogonal outliers output Pages PELVIC perform pizzas PIZZAZZ NORMATIVE QUESTION population Posterior Probability predictor variables principal component scores principal components analysis RECVR researcher response variables SAS commands scatter plot selected SHLDR shown in Figure significant SPSS standardized tion uncorrelated univariate variance-covariance matrix WEIGHT WILD WILD WILD XXXX xxxx xxxx zero