Librería Portfolio

Features

Concise and accessible approach requiring minimal mathematical background.
Appeals to a broad range of quantitative scientists and applied statisticians.
Presents all the key topics for a course in multivariate statistics.
Updated with chapter appendices of R code for all analyses in the book.
Provides all the data used in the book on a companion website.
Summary

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used.

The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.



Table of Contents

The Material of Multivariate Analysis.

Matrix Algebra.

Displaying Multivariate Data.

Tests of Significance with Multivariate Data.

Measuring and Testing Multivariate Distances.

Principal Components Analysis.

Factor Analysis.

Discriminant Function Analysis.

Cluster Analysis.

Canonical Correlation Analysis.

Multidimensional Scaling.

Ordination.

Epilogue.