## Multivariate Statistical Analysis: A Conceptual IntroductionThis classic book provides the much needed conceptual explanations of advanced computer-based multivariate data analysis techniques: correlation and regression analysis, factor analysis, discrimination analysis, cluster analysis, multi-dimensional scaling, perceptual mapping, and more. It closes the gap between spiraling technology and its intelligent application, fulfilling the potential of both. |

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This is a gentle introduction to the subject. It's OK as an introduction, but it is missing some topics such as principal component analysis.

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### Contents

FUNDAMENTAL CONCEPTS 1 Introduction | 1 |

Objects variables and scales | 8 |

Frequency distributions | 21 |

Copyright | |

96 other sections not shown

### Common terms and phrases

alternative analysis of variance application associated average beta coefficients calculate central tendency chapter characteristics cluster composite outcomes concept confidence interval consider correlation analysis correlation coefficient criterion groups criterion variable cutoff score defined degrees of freedom determine dichotomous dimensions discriminant analysis discriminant function equal example experimental variable factor analysis frequency distribution given high loading hypothesis identify individual infinite number interest interpretation matrix measured median multiple correlation multivariate normal distribution number of observations number of variables occur package color pair population mean population parameter possible prediction predictor variables procedure random sample random variables regression analysis regression equation regression line relationship relative frequency represent respective result sample mean sample space sample statistics sampling distribution scale set of objects set of scores shape shelf space shown in Figure simple outcomes spice sales standard deviation standard error statistical analysis Table technique test scores tion value on variable variation