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Interaction Effects in Multiple Regression

by

Interaction Effects in Multiple Regression Cover

 

Synopses & Reviews

Publisher Comments:

Includes bibliographical references (p. 89-91) and index.

Book News Annotation:

Jaccard (U. at Albany, State U. of New York) and Turrisi (Boise State U.) introduce readers to the basics of interaction analysis using multiple regression methods with one or more continuous predictor variables. The text is designed for researchers with a rudimentary background in multiple regression. This revision of the 1990 first edition incorporates references to 30 publications appearing in the past ten-plus years, and features new topics, including interaction models with clustered data and random coefficient models. No subject index. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.

Product Details

ISBN:
9780761927426
Author:
Jaccard, James
Publisher:
Sage Publications, Inc
Author:
Teitel, Lee
Author:
Turrisi, Robert
Location:
Thousand Oaks, Calif.
Subject:
Statistics
Subject:
Social sciences
Subject:
Regression analysis
Subject:
Probability & Statistics - General
Subject:
Probability & Statistics - Regression Analysis
Subject:
Social sciences -- Statistical methods.
Subject:
Sociology - General
Copyright:
Edition Number:
2
Edition Description:
Second
Series:
Quantitative Applications in the Social Sciences Series
Series Volume:
no. 16-18
Publication Date:
20030331
Binding:
TRADE PAPER
Language:
English
Illustrations:
Yes
Pages:
104
Dimensions:
8.46x5.60x.22 in. .28 lbs.

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Interaction Effects in Multiple Regression New Trade Paper
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Product details 104 pages Sage Publications - English 9780761927426 Reviews:
"Synopsis" by , Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.
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