Alvin C. Rencher

Multivariate Inference

• Gebundenes Buch

Produktbeschreibung

The most accessible introduction to the theory and practice of multivariate analysis

Multivariate Statistical Inference and Applications is a user-friendly introduction to basic multivariate analysis theory and practice for statistics majors as well as nonmajors with little or no background in theoretical statistics. Among the many special features of this extremely accessible first text on multivariate analysis are:
_ Clear, step-by-step explanations of all key concepts and procedures along with original, easy-to-follow proofs
_ Numerous problems, examples, and tables of distributions
_ Many real-world data sets drawn from a wide range of disciplines
_ Reviews of univariate procedures that give rise to multivariate techniques
_ An extensive survey of the world literature on multivariate analysis
_ An in-depth review of matrix theory
_ A disk including all the data sets and SAS command files for all examples and numerical problems found in the book

These same features also make Multivariate Statistical Inference and Applications an excellent professional resource for scientists and clinicians who need to acquaint themselves with multivariate techniques. It can be used as a stand-alone introduction or in concert with its more methods-oriented sibling volume, the critically acclaimed Methods of Multivariate Analysis.

Produktdetails

• Wiley Series in Probability and Statistics 2
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 592
• Erscheinungstermin: 29. Dezember 1997
• Englisch
• Abmessung: 240mm x 161mm x 36mm
• Gewicht: 1042g
• ISBN-13: 9780471571513
• ISBN-10: 0471571512
• Artikelnr.: 14907627

Autorenporträt

ALVIN C. RENCHER, PhD, is Professor of Statistics at Brigham Young University and a Fellow of the American Statistical Association. He is the author of Methods of Multivariate Analysis and has written articles for Biometrics, Technometrics, Biometrika, Communications in Statistics, and American Statistician.

Inhaltsangabe

Introduction; Matrix Algebra; Characterizing and Displaying Multivariate Data; The Multivariate Normal Distribution; Multivariate Analysis of Variance; Discriminant Analysis: Description of Group Separation; Classification of Analysis: Allocation of Observations to Groups; Multivariate Regression: Canonical Correlation; Principal Component Analysis; Factor Analysis; Appendices.