Structured regression for categorical data

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Structured regression for categorical data
Gerhard Tutz
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Last edited by ImportBot
August 2, 2020 | History

Structured regression for categorical data

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"Categorical data play an important role in many statistical analyses. They appear whenever the outcomes of one or more categorical variables are observed. A categorical variable can be seen as a variable for which the possible values form a set of categories, which can be finite or, in the case of count data, infinite. These categories can be records of answers (yes/no) in a questionnaire, diagnoses like normal/abnormal resulting from a medical examination or choices of brands in consumer behaviour. Data of this type are common in all sciences that use quantitative research tools, for example social sciences, economics, biology, genetics and medicine, but also engineering and agriculture. In some applications all of the observed variables are categorical and the resulting data can be summarized in contingency tables which contain the counts for combinations of possible outcomes. In other applications categorical data are collected together with continuous variables and one wants to investigate the dependence of one or more categorical variables on continuous and/or categorical variables"--

Publish Date
Language
English

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Edition Availability
Cover of: Structured regression for categorical data
Structured regression for categorical data
2011, Cambridge University Press
in English

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Book Details


Edition Notes

Published in
Cambridge, New York
Series
Cambridge series in statistical and probabilistic mathematics

Classifications

Dewey Decimal Class
519.5/36
Library of Congress
QA278.2 .T88 2011, QA278.2 .T88 2012

The Physical Object

Pagination
p. cm.

ID Numbers

Open Library
OL25076742M
ISBN 13
9781107009653
LCCN
2011000390
OCLC/WorldCat
700468349

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Download catalog record: RDF / JSON
August 2, 2020 Edited by ImportBot import existing book
October 27, 2011 Created by LC Bot import new book