rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Generalized linear models
  name:
    name part: Gill, Jeff
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Torres, Michelle
    name part: (Statistician)
    role:
      role term: author.
  name:
    name part: Atkinson, Paul
    name part: 1947-
    role:
      role term: editor.
  name:
    name part: Delamont, Sara
    name part: 1947-
    role:
      role term: editor.
  name:
    name part: Cernat, Alexandru
    role:
      role term: editor.
  name:
    name part: Sakshaug, Joseph W.
    role:
      role term: editor.
  name:
    name part: Williams, Richard A.
    name part: active 2020
    role:
      role term: editor.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: To understand the relationship between variables of social and political nature, social scientists have a strong interest in regression-style specifications such as Poisson, logistic, multinomial, and more. Although these specifications are unnecessarily treated as distinct and particular, they are all part of the generalized linear models (GLMs), a class of regression models that share a common theoretical basis and structure. This unified framework facilitates the understanding and derivation of the properties of these models, as well as a more principled application of them to actual social science data. This entry introduces and explains the underlying structure of GLMs, demonstrates the theoretical basis that their various forms share, and illustrates their application with two running examples. A proper understanding of the GLM framework can increase a researcher's flexibility with regard to new data types.
  target audience: specialized
  note: by Jeff Gill & Michelle Torres ; edited by Paul Atkinson, Sara Delamont, Alexandru Cernat, Joseph W. Sakshaug & Richard A. Williams.
  note: Includes bibliographical references and index.
  subject:
    topic: Political science
  subject:
    topic: Psychology
  subject:
    topic: Science
  subject:
    topic: Sciences
  subject:
    topic: science (modern discipline)
  subject:
    topic: Political science
  subject:
    topic: Psychology
  subject:
    topic: Science
  classification: HD57.7 P37 2020
  classification: 658.409223
  identifier: 9781529749434
  identifier: 1529749433
  identifier: 9781526421036
  identifier: 1526421038
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 201001
    record change date: 20260205205649.9
    record identifier: in00005681248
    language of cataloging:
      language term: eng