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Regression and Other Stories

£34.99

textbook

Part of Analytical Methods for Social Research

  • Date Published: July 2020
  • availability: In stock
  • format: Paperback
  • isbn: 9781107676510

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Description
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About the Authors
  • Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, 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 immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

    • Emphasis on practice rather than theory sets this apart from other texts
    • Three chapters on causal inference
    • Code and data for all examples in the book are available on the web site in the popular open-source programs R and Stan
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    Reviews & endorsements

    'Gelman, Hill and Vehtari provide an introductory regression book that hits an amazing trifecta: it motivates regression using real data examples, provides the necessary (but not superfluous) theory, and gives readers tools to implement these methods in their own work. The scope is ambitious - including introductions to causal inference and measurement - and the result is a book that I not only look forward to teaching from, but also keeping around as a reference for my own work.' Elizabeth Tipton, Northwestern University

    'Regression and Other Stories is simply the best introduction to applied statistics out there. Filled with compelling real-world examples, intuitive explanations, and practical advice, the authors offer a delightfully modern perspective on the subject. It's an essential resource for students and practitioners across the statistical and social sciences.' Sharad Goel, Department of Management Science and Engineering, Stanford University

    'With modern software it is very easy to fit complex regression models, and even easier to get their interpretation completely wrong. This wonderful book, summarising the authors' years of experience, stays away from mathematical proofs, and instead focuses on the insights to be gained by careful plotting and modelling of data. In particular the chapters on causal modelling, and the challenges of working with selected samples, provide some desperately needed lessons.' David Spiegelhalter, University of Cambridge

    'Gelman and Hill, have done it again, this time with Aki Vehtari. They have written a textbook that should be on every applied quantitative researcher's bookshelf. Most importantly they explain how to do and interpret regression with real world, complicated examples. Practicing academics in addition to students will benefit from giving this book a close read.' Christopher Winship, Harvard University, Massachusetts

    'Comprehensive and charming, this regression manual belongs on every regressor's shelf.' Joshua Angrist, Massachusetts Institute of Technology

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    Product details

    • Date Published: July 2020
    • format: Paperback
    • isbn: 9781107676510
    • length: 548 pages
    • dimensions: 245 x 189 x 30 mm
    • weight: 1.06kg
    • contains: 183 b/w illus. 215 exercises
    • availability: In stock
  • Table of Contents

    Preface
    Part I. Fundamentals:
    1. Overview
    2. Data and measurement
    3. Some basic methods in mathematics and probability
    4. Statistical inference
    5. Simulation
    Part II. Linear Regression:
    6. Background on regression modeling
    7. Linear regression with a single predictor
    8. Fitting regression models
    9. Prediction and Bayesian inference
    10. Linear regression with multiple predictors
    11. Assumptions, diagnostics, and model evaluation
    12. Transformations and regression
    Part III. Generalized Linear Models:
    13. Logistic regression
    14. Working with logistic regression
    15. Other generalized linear models
    Part IV. Before and After Fitting a Regression:
    16. Design and sample size decisions
    17. Poststratification and missing-data imputation
    Part V. Causal Inference:
    18. Causal inference and randomized experiments
    19. Causal inference using regression on the treatment variable
    20. Observational studies with all confounders assumed to be measured
    21. Additional topics in causal inference
    Part VI. What Comes Next?:
    22. Advanced regression and multilevel models
    Appendices: A. Computing in R
    B. 10 quick tips to improve your regression modelling
    References
    Author index
    Subject index.

  • Resources for

    Regression and Other Stories

    Andrew Gelman, Jennifer Hill, Aki Vehtari

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  • Authors

    Andrew Gelman, Columbia University, New York
    The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, New York Times, Slate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, and Data Analysis and Regression Using Multilevel/Hierarchical Models. Andrew Gelman is Higgins Professor of Statistics and Professor of Political Science at Columbia University.

    Jennifer Hill, New York University
    Jennifer Hill is Professor of Applied Statistics at New York University.

    Aki Vehtari, Aalto University, Finland
    Aki Vehtari is Associate Professor in Computational Probabilistic Modeling at Aalto University, Finland.

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