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Multivariate Statistical Methods: A Primer, Third Edition(English)

Multivariate Statistical Methods: A Primer, Third Edition(English)

          
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About the Book

Multivariate methods are now widely used by scientists in quantitative sciences and statistics due to the ready availability of computer packages for performing the calculations. Access to suitable computer software is essential to using these methods, however a working knowledge of how multivariate statistical methods can be used is a prerequisite to the use of any brand of software. Multivariate Statistical Methods: A Primer introduces the methods to non-mathematicians and provides a general overview without overwhelming the novice with comprehensive details.This third edition is a thoroughly revised, updated edition of a best-selling introductory textbook and primer. It retains the author's trademark clear, concise style and focuses on examples in the biological and environmental sciences. Topics new to this edition include confirmatory factor analysis, the use of mixture models for cluster analysis, and the emerging techniques of data mining and neural networks. While not linked to any specific software package, the book now includes an appendix comparing and contrasting various statistical software packages, such as Stata, Statistica, SAS, and Genstat. The author also notes which software was used for a particular example, when appropriate.In his efforts to produce a book that is as short as possible and that enables you to begin to use multivariate methods in an intelligent manner, the author has produced a succinct and handy reference. With updated information on multivariate analyses, new examples using the latest software, and updated references, this book will bring you up to speed with useful tools for statistical analysis.

Table of Contents:
THE MATERIAL OF MULTIVARIATE ANALYSIS
Examples of Multivariate Data
Preview of Multivariate Methods
The Multivariate Normal Distribution
Computer Programs
Graphical Methods
Chapter Summary
References

MATRIX ALGEBRA
The Need for Matrix Algebra
Matrices and Vectors
Operations on Matrices
Matrix Inversion
Quadratic Forms
Eigenvalues and Eigenvectors
Vectors of Means and Covariance Matrices
Further Reading
Chapter Summary
References

DISPLAYING MULTIVARIATE DATA
The Problem of Displaying Many Variables in Two Dimensions
Plotting index Variables
The Draftsman's Plot
The Representation of Individual Data P:oints
Profiles of Variables
Discussion and Further Reading
Chapter Summary
References

TESTS OF SIGNIFICANCE WITH MULTIVARIATE DATA
Simultaneous Tests on Several Varables
Comparison of Mean Values for Two Samples: The Single Variable Case
Comparison of Mean Values for Two Samples: The Multivariate Case
Multivariate Versus Univariate Tests
Comparison of Variation for Two Samples: The Single Variable Case
Comparison of Variation for Two Samples: The Multivariate Case
Comparison of Means for Several Samples
Comparison of Variation for Several Samples
Discussion
Chapter Summary
Exercises
References

MEASURING AND TESTING MULTIVARIATE DISTANCES
Multivariate Distances
Distances Between Individual Observations
Distances Between Populations and Samples
Distances Based on Proportions
Presence-Absence data
The Mantel Randomization Test
Computer Programs
Discussion and Further Reading
Chapter Summary
Exercise
References

PRINCIPAL COMPONENTS ANALYSIS
Definition of Principal Components
Procedure for a Principal Components Analysis
Computer Programs
Further Reading
Chapter Summary
Exercises
References

FACTOR ANALYSIS
The Factor Analysis Model
Procedure for a Factor Analysis
Principal Components Factor Analysis
Using a Factor Analysis Program to do Principal Components Analysis
Options in Analyses
The Value of Factor Analysis
Computer Programs
Discussion and Further Reading
Chapter Summary
Exercise
References

DISCRIMINANT FUNCTION ANALYSIS
The Problem of Separating Groups
Discrimination Using Mahalanobis Distances
Canonical Discriminant Functions
Tests of Significance
Assumptions
Allowing for Prior Probabilities of Group Membership
Stepwise Discriminant Function Analysis
Jackknife Classification of Individuals
Assigning of Ungrouped Individuals to Groups
Logistic Regression
Computer Programs
Discussion and Further Reading
Chapter Summary
Exercises
References

CLUSTER ANALYSIS
Uses of Cluster analysis
Types of Cluster Analysis
Hierarchic Methods
Problems of Cluster Analysis
Measures of Distance
Principal Components Analysis with Cluster Analysis
Computer Programs
Discussion and Further Reading
Chapter Summary
Exercises
References

CANONICAL CORRELATION ANALYSIS
Generalizing a Multiple Regression Analysis
Procedure for a Canonical Correlation Analysis
Tests of Significance
Interpreting Canonical Variates
Computer Programs
Further Reading
Chapter Summary
Exercise
References

MULTIDIMENSIONAL SCALING
Constructing a Map from a Distance Matrix
Procedure for Multidimensional Scaling
Computer Programs
Further Reading
Chapter Summary
Exercise
References

ORDINATION
The Ordination Problem
Principal Components Analysis
Principal Coordinates Analysis
Multidimensional Scaling
Correspondence Analysis
Comparison of Ordination Methods
Computer Programs
Further Reading
Chapter Summary
Exercise
References

EPILOGUE
The Next Step
Some General Reminders
Missing Values
References

APPENDIX
Computer Packages for Multivariate Analyses
References


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Product Details
  • ISBN-13: 9781584884149
  • Publisher: Taylor & Francis Inc
  • Publisher Imprint: Chapman & Hall/CRC
  • Depth: 13
  • Height: 234 mm
  • No of Pages: 224
  • Series Title: English
  • Sub Title: A Primer, Third Edition
  • Width: 156 mm
  • ISBN-10: 1584884142
  • Publisher Date: 06 Jul 2004
  • Binding: Paperback
  • Edition: New edition
  • Language: English
  • Returnable: N
  • Spine Width: 13 mm
  • Weight: 318 gr


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