MTU Cork Library Catalogue

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Methods of multivariate analysis / Alvin C. Rencher.

By: Rencher, Alvin C, 1934-.
Material type: materialTypeLabelBookSeries: Wiley series in probability and mathematical statistics.Publisher: New York ; Chichester : Wiley, 1995Description: xvi, 627 p. ; 25 cm. + hbk.ISBN: 0471571520.Subject(s): Multivariate analysisDDC classification: 519.535
Contents:
Introduction -- Matrix algebra -- Characterizing and displaying multivariate data -- The multivaraiate normal distribution -- Tests on one or two mean vectors -- Multivariate analysis of variance -- Tests on covariance matrices -- Discriminant analysis:description of group separation -- Classification analysis: allocation of observations to groups -- Multivariate regression -- Canonical correlation -- Principal component analysis -- Factor analysis.
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
General Lending MTU Bishopstown Library Lending 519.535 (Browse shelf(Opens below)) 1 Available 00014960
Total holds: 0

Enhanced descriptions from Syndetics:

Researchers involved in the collection of scientific data often end up with multivariate systems. When several variables are simultaneously measured on the same experimental unity, they are usually correlated, and the pattern formed is often too difficult for the human mind to grasp. This text discusses in detail many proven techniques for finding the dimensionality of the pattern and unravelling the information contained in the complexity of variables. The book includes exercises and solutions as well as 41 data sets taken from various areas of application, such as engineering, manufacturing, medicine, social science and economics.

Includes bibliographical references (pages 606-617) and index.

Introduction -- Matrix algebra -- Characterizing and displaying multivariate data -- The multivaraiate normal distribution -- Tests on one or two mean vectors -- Multivariate analysis of variance -- Tests on covariance matrices -- Discriminant analysis:description of group separation -- Classification analysis: allocation of observations to groups -- Multivariate regression -- Canonical correlation -- Principal component analysis -- Factor analysis.

Table of contents provided by Syndetics

  • Introduction
  • Matrix Algebra
  • Characterizing and Displaying Multivariate Data
  • The Multivariate Normal Distribution
  • Tests on One or Two Mean Vectors
  • Multivariate Analysis of Variance
  • Tests on Covariance Matrices
  • Discriminant Analysis: Description of Group Separation
  • Classification Analysis: Allocation of Observations to Groups
  • Multivariate Regression
  • Canonical Correlation
  • Principal Component Analysis
  • Factor Analysis
  • Cluster Analysis
  • Graphical Procedures
  • Tables
  • Answers and Hints to Problems
  • Data Sets and SAS Files
  • References
  • Index

Reviews provided by Syndetics

CHOICE Review

In this well-written and interesting book, Rencher has done a great job in presenting intuitive and innovative explanations of some of the otherwise difficult concepts, and has successfully avoided overdoing proofs. The first volume of a two-volume set, the second, "to appear in 1996," will be theoretically oriented. The first two chapters (of 13) provide an overview of multivariate analysis and the fundamentals of matrix algebra; others treat multivariate normal distribution and display of data from multivariate populations. Univariate techniques (e.g., t-tests, analysis of variance, testing of the variances of populations, multiple linear regression, multiple correlation) with one dependent variable are extended to analogous multivariate methods involving several dependent variables in five other chapters. The remaining four chapters discuss multivariate techniques such as discriminate analysis, classification analysis, principal component analysis, and factor analysis; for these no corresponding univariate procedures exist. Necessary prerequisites are a course on matrix algebra and two courses on statistical methods. Appendixes provide useful statistical tables, and answers and hints to chapter problem sets. Highly recommended for all academic libraries, graduate as well as undergraduate. D. V. Chopra; Wichita State University

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