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: Logistic Regression
    sub title: A Self-Learning Text
  name:
    name part: Kleinbaum, David G.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Klein, Mitchel.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 702 p. online resource.
  abstract: This very popular textbook is now in its third edition. Whether students or working professionals, readers appreciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecturer. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams. Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses. The new chapters are: • Additional Modeling Strategy Issues, including strategy with several exposures, screening variables, collinearity, influential observations and multiple-testing • Assessing Goodness to Fit for Logistic Regression • Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text. David Kleinbaum is Professor of Epidemiology at Emory University Rollins School of Public Health in Atlanta, Georgia. Dr. Kleinbaum is internationally known for his innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has taught more than 200 courses worldwide. The recipient of numerous teaching awards, he received the first Association of Schools of Public Health Pfizer Award for Distinguished Career Teaching in 2005. Mitchel Klein is Research Assistant Professor with a joint appointment in the Environmental and Occupational Health Department and the Epidemiology Department at Emory University Rollins School of Public Health. He has successfully designed and taught epidemiologic methods physicians at Emory’s Master of…
  table of contents: to Logistic Regression -- Important Special Cases of the Logistic Model -- Computing the Odds Ratio in Logistic Regression -- Maximum Likelihood Techniques: An Overview -- Statistical Inferences Using Maximum Likelihood Techniques -- Modeling Strategy Guidelines -- Modeling Strategy for Assessing Interaction and Confounding -- Additional Modeling Strategy Issues -- Assessing Goodness of Fit for Logistic Regression -- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves -- Analysis of Matched Data Using Logistic Regression -- Polytomous Logistic Regression -- Ordinal Logistic Regression -- Logistic Regression for Correlated Data: GEE -- GEE Examples -- Other Approaches for Analysis of Correlated Data.
  note: by David G. Kleinbaum, Mitchel Klein.
  subject:
    topic: Statistics
  subject:
    topic: Epidemiology
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Epidemiology
  subject:
    topic: Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Statistics for Biology and Health
  identifier: 9781441917423
  identifier: http://dx.doi.org/10.1007/978-1-4419-1742-3
  record info:
    record creation date: 100623
    record change date: 20160613064638.0
    record identifier: 978-1-4419-1742-3