Coverart for item
The Resource Regression : best practices and modern methods, Jason W. Osborne, (electronic resource & linear modeling)

Regression : best practices and modern methods, Jason W. Osborne, (electronic resource & linear modeling)

Label
Regression : best practices and modern methods
Title
Regression
Title remainder
best practices and modern methods
Statement of responsibility
Jason W. Osborne
Title variation
Regression and linear modeling
Creator
Subject
Summary
In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.--
Assigning source
Provided by publisher
http://library.link/vocab/creatorName
Osborne, Jason W
Illustrations
  • photographs
  • maps
Nature of contents
  • dictionaries
  • encyclopedias
Series statement
SAGE research methods
http://library.link/vocab/subjectName
  • Regression analysis
  • Linear models (Statistics)
Label
Regression : best practices and modern methods, Jason W. Osborne, (electronic resource & linear modeling)
Link
https://eui.idm.oclc.org/login?url=https://methods.sagepub.com/book/regression-linear-modeling
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
SAGE000007584
Extent
1 online resource (462 pages)
Form of item
online
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9781071802724
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)1136122004
Label
Regression : best practices and modern methods, Jason W. Osborne, (electronic resource & linear modeling)
Link
https://eui.idm.oclc.org/login?url=https://methods.sagepub.com/book/regression-linear-modeling
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
SAGE000007584
Extent
1 online resource (462 pages)
Form of item
online
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9781071802724
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)1136122004

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