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      Foulsham (Iremonger Series #2)

      Edward Carey 9781468309546

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Understanding Regression Assumptions

by

Understanding Regression Assumptions Cover

 

Synopses & Reviews

Synopsis:

Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.

Product Details

ISBN:
9780803942639
Introduction:
Lewis-Beck, Michael S.
Publisher:
Sage Publications (CA)
Introduction by:
Lewis-Beck, Michael S.
Introduction:
Lewis-Beck, Michael S.
Author:
Berry, William D.
Location:
Newbury Park :
Subject:
Methodology
Subject:
Research
Subject:
Social sciences
Subject:
Regression analysis
Subject:
Error analysis (Mathematics)
Subject:
Error analysis
Subject:
Probability & Statistics - General
Subject:
Social sciences -- Statistical methods.
Subject:
Sociology-Reference and Methodology
Copyright:
Edition Description:
Includes bibliographical references p. 89-90.
Series:
Quantitative Applications in the Social Sciences
Series Volume:
92
Publication Date:
19930231
Binding:
TRADE PAPER
Language:
English
Illustrations:
Yes
Pages:
104
Dimensions:
8.70x5.36x.23 in. .28 lbs.

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Related Subjects

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

Understanding Regression Assumptions New Trade Paper
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$21.25 In Stock
Product details 104 pages Sage Publications - English 9780803942639 Reviews:
"Synopsis" by , Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.
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