1.

Record Nr.

UNINA9910826557703321

Autore

Brown Bruce (Bruce L.)

Titolo

Multivariate analysis for the biobehavioral and social sciences / / Bruce L. Brown, ... [et al.]

Pubbl/distr/stampa

Hoboken, N.J., : Wiley, c2012

ISBN

9786613332257

9781283332255

1283332256

9781118131626

1118131622

9781118131619

1118131614

9781118131596

1118131592

Edizione

[1st ed.]

Descrizione fisica

1 online resource (xiv, 475 pages) : illustrations

Classificazione

MAT029020

Disciplina

300.1/519535

Soggetti

Social sciences - Statistical methods

Multivariate analysis

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Overview of Multivariate and Regression Methods -- The Seven Habits of Highly Effective Quants: A Review of Elementary Statistics Using Matrix Algebra -- Fundamentals of Matrix Algebra -- Factor Analysis and Related Methods: Quintessentially Multivariate -- Multivariate Graphics -- Canonical Correlation: The Underused Method -- Hotelling's as the Simplest Case of Multivariate Inference -- Multivariate Analysis of Variance -- Multiple Regression and the General Linear Model -- Appendices: Statistical Tables -- Name Index -- Subject Index.

Sommario/riassunto

Multivariate Analysis for the Social Sciences provides clear guidelines combined with the insight needed to understand the methods and applications of multivariate statistics. This easy-to-follow book provides students in social, behavioral, and health science-whose focus



is not primarily in mathematics-with an abundance of chapter-ending questions and answers, including: conceptual questions about the meaning of each method; questions that test the reader's ability to carry out the computational procedures on simple datasets; and data analysis questions for using analytical packages to analyze both simplest case data and also to practice with more realistic datasets.