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Quantitative Applications in the Social Sciences #22: Applied Regression: An Introduction

Quantitative Applications in the Social Sciences #22: Applied Regression: An Introduction Cover

 

Synopses & Reviews

Synopsis:

Applied regression allows social scientists who are not specialists in quantitative techniques to arrive at clear verbal explanations of their numerical results. Provides a lucid discussion of more specialized subjects: analysis of residuals, interaction effects, specification error, multicollinearity, standardized coefficients, and dummy variables.

Product Details

ISBN:
9780803914940
Author:
Lewis-Beck, Michael S.
Publisher:
Sage Publications, Inc
Author:
Lewis-Beck, Michael S., Professor
Location:
Beverly Hills, Calif. :
Subject:
General
Subject:
Research
Subject:
Regression analysis
Subject:
Probability & Statistics - General
Subject:
Mathematics | Probability and Statistics
Edition Description:
Bibliography: p. 77
Series:
Quantitative Applications in the Social Sciences
Series Volume:
22
Publication Date:
19800831
Binding:
TRADE PAPER
Language:
English
Illustrations:
Yes
Pages:
80
Dimensions:
8.62x5.60x.18 in. .21 lbs.

Related Subjects

Health and Self-Help » Health and Medicine » Nursing
History and Social Science » Politics » General
History and Social Science » Sociology » Reference and Methodology
History and Social Science » World History » General
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Quantitative Applications in the Social Sciences #22: Applied Regression: An Introduction
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Product details 80 pages Sage Publications - English 9780803914940 Reviews:
"Synopsis" by , Applied regression allows social scientists who are not specialists in quantitative techniques to arrive at clear verbal explanations of their numerical results. Provides a lucid discussion of more specialized subjects: analysis of residuals, interaction effects, specification error, multicollinearity, standardized coefficients, and dummy variables.
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