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Regression models for categorical de...

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Long, J. Scott.

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Regression models for categorical dependent variables using Stata /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Regression models for categorical dependent variables using Stata // J. Scott Long, Jeremy Freese.
Author:	Long, J. Scott.
other author:	Freese, Jeremy.
Published:	College Station, Texas :Stata Press Publication, StataCorp LP, : 2014.,
Description:	xxiii, 589 p. :ill. ;24 cm.
Subject:	Regression analysis. -
ISBN:	9781597181112

Regression models for categorical dependent variables using Stata /
Long, J. Scott.

Regression models for categorical dependent variables using Stata /J. Scott Long, Jeremy Freese. - 3rd ed. - College Station, Texas :Stata Press Publication, StataCorp LP,2014. - xxiii, 589 p. :ill. ;24 cm.

Includes bibliographical references (pages 561-568) and indexes.

Introduction --

After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.

ISBN: 9781597181112GBP57.99

LCCN: 2014948009Subjects--Topical Terms:

529831
Regression analysis.

LC Class. No.: QA278.2 / .L66 2014

Dewey Class. No.: 519.536

Regression models for categorical dependent variables using Stata /
LDR:01182cam a2200193 a 4500 001 1979266
008 150513s2014 txua b 001 0 eng
010 $a 2014948009
020 $a 9781597181112 $q (pbk.) : $c GBP57.99
020 $a 1597181110 $q (pbk.)
040 $a BTCTA $b eng
050 # 4 $a QA278.2 $b .L66 2014
082 0 4 $a 519.536 $2 23
100 1 $a Long, J. Scott. $3 559871
245 1 0 $a Regression models for categorical dependent variables using Stata / $c J. Scott Long, Jeremy Freese.
250 $a 3rd ed.
260 # $a College Station, Texas : $b Stata Press Publication, StataCorp LP, $c 2014.
300 $a xxiii, 589 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references (pages 561-568) and indexes.
505 0 # $t Introduction -- $t Introduction to Stata -- $t Estimation, testing, and fit -- $t Methods of interpretation -- $t Models for binary outcomes : estimation, testing, and fit -- $t Models for binary outcomes : interpretation -- $t Models for ordinal outcomes -- $t Models for nominal outcomes -- $t Models for count outcomes.
520 8 $a After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.
650 # 0 $a Regression analysis. $3 529831
650 # 0 $a Social sciences $x Statistical methods $x Data processing. $3 560158
700 1 # $a Freese, Jeremy. $3 783469

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壽豐校區(SF Campus)
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last issue: 1 (2015/07/13)
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W0070893

六樓西文書區HC-Z(6F Western Language Books)

01.外借(書)_YB

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QA278.2 L66 2014

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