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: Regression and other stories
  name:
    name part: Gelman, Andrew
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Hill, Jennifer
    name part: 1969-
    role:
      role term: author.
  name:
    name part: Vehtari, Aki
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    date issued: 2021
    copyright date: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xv, 534 pages : illustrations ; 26 cm.
  abstract: Many textbooks on regression focus on theory and the simplest examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is a book about how to use regression to solve real problems of comparison, estimation, prediction and causal inference. It focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use fresh out of the box.
  table of contents: Preface -- Part 1: Fundamentals. Overview -- Data and measurement -- Some basic methods in mathematics and probability -- Statistical inference -- Simulation -- Part 2: Linear regression -- Background on regression modeling -- Linear regression with a single predictor -- Fitting regression models -- Prediction and Bayesian inference -- Linear regression with multiple predictors -- Assumptions, diagnostics, and model evaluation -- Transformations and regression -- Part 3: Generalized linear models. Logistic regression -- Working with logistic regression -- Other generalized linear models -- Part 4: Before and after fitting a regression. Design and sample size decisions -- Poststratification and missing-data imputation -- Part 5: Causal inference -- Causal inference and randomized experiments -- Causal inference using regression on the treatment variable -- Observational studies with all confounders assumed to be measured -- Additional topics in causal inference -- Part 6: What comes next? Advanced regression and multilevel models -- Appendixes. Computing in R -- 10 quick tips to improve your regression modeling.
  note: Andrew Gelman, Columbia University, New York, Jennifer Hill, New York University, Aki Vehtari, Aalto University, Finland.
  note: Includes bibliographical references (pages [497]-515) and indexes.
  subject:
    topic: Regression analysis
  classification: 519.5/36
  related item:
    title info:
      title: Analytical methods for social research
  identifier: 9781107676510
  identifier: 1107676517
  identifier: 9781107023987
  identifier: 110702398X
  record info:
    record content source: YDX
    record creation date: 200419
    record change date: 20210422112723.0
    record identifier: in00004408149