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Guests | May 6, 2013

Benjamin Percy: IMG The Roof People

1 comment By Benjamin Percy

My sister slept with the light on until she was 27. She rightfully blames me. I would leap out of closets with my hands made into claws. I would... Continue »
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Quantitative Applications in the Social Sciences #06: Canonical Analysis and Factor Comparison

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Quantitative Applications in the Social Sciences #06: Canonical Analysis and Factor Comparison Cover

 

Synopses & Reviews

Synopsis:

An advanced study which presumes a knowledge of multiple regression and factor analysis techniques, this paper considers two techniques for comparing entire sets of data, and develops the canonical correlation model as an extension of regression analysis in which there are several dependent variables.
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Product Details

ISBN:
9780803906556
Author:
Levine, Mark S.
Publisher:
Sage Publications (CA)
Location:
Newbury Park, Calif. :
Subject:
Research
Subject:
Social sciences
Subject:
Factor analysis
Subject:
Canonical correlation (Statistics)
Subject:
Canonical correlation
Subject:
Social sciences -- Statistical methods.
Subject:
Sociology-Reference and Methodology
Edition Description:
Includes bibliographical references p. 59-62.
Series:
Quantitative Applications in the Social Sciences
Series Volume:
06
Publication Date:
19770431
Binding:
TRADE PAPER
Language:
English
Illustrations:
Yes
Pages:
64
Dimensions:
850x550
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Related Subjects

History and Social Science » Politics » General
History and Social Science » Sociology » Reference and Methodology
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Quantitative Applications in the Social Sciences #06: Canonical Analysis and Factor Comparison New Trade Paper
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Product details 64 pages Sage Publications - English 9780803906556 Reviews:
"Synopsis" by , An advanced study which presumes a knowledge of multiple regression and factor analysis techniques, this paper considers two techniques for comparing entire sets of data, and develops the canonical correlation model as an extension of regression analysis in which there are several dependent variables.
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