Multivariate analysis for the biobehavioral and social sciences:

By:

Brown, Bruce

Contributor(s):

Material type: Text

TextPublication details: Hoboken: John Wiley & Sons, 2012Description: xiv, 475pISBN:

9780470537565

Subject(s):

DDC classification:

300.1519535 BRO

Summary: 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.

Item type:

Book

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Item type	Current library	Call number	Status	Date due	Barcode
Book	IIM Kashipur	300.1519535 BRO (Browse shelf(Opens below))	Available		141

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Previous	300.15113223 Fuzzy set theory Applications in the social sciences	300.15195 Data analytics for the social sciences : applications in R /	300.15195 MET Methods and application of statistics in the social and behavioral sciences/	300.1519535 BRO Multivariate analysis for the biobehavioral and social sciences: a graphical approach/	300.1519536 COR Correlation and regression analysis	300.1519536 COR Correlation and regression analysis	300.1519536 COR Correlation and regression analysis	Next

Includes bibliographical references and indexes.

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.

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