Regression and Other Stories by Andrew Gelman (English) Hardcover Book

Description: Regression and Other Stories by Andrew Gelman, Jennifer Hill, Aki Vehtari Real statistical problems are complex and subtle. This text is about using regression to solve real problems of comparison, estimation, prediction, and causal inference, based on real stories from the authors experience. It offers practical advice for understanding assumptions and implementing methods through graphics and computing in R and Stan. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Author Biography 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 is Professor of Applied Statistics at New York University. Aki Vehtari is Associate Professor in Computational Probabilistic Modeling at Aalto University, Finland. 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. Review 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 UniversityRegression 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. Its an essential resource for students and practitioners across the statistical and social sciences. Sharad Goel, Department of Management Science and Engineering, Stanford UniversityWith 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 CambridgeGelman and Hill, have done it again, this time with Aki Vehtari. They have written a textbook that should be on every applied quantitative researchers 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, MassachusettsComprehensive and charming, this regression manual belongs on every regressors shelf. Joshua Angrist, Massachusetts Institute of Technology Promotional A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference. Review Quote 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 Promotional "Headline" A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference. Description for Bookstore Real statistical problems are complex and subtle. This text is about using regression to solve real problems of comparison, estimation, prediction, and causal inference, based on real stories from the authors experience. It offers practical advice for understanding assumptions and implementing methods through graphics and computing in R and Stan. Description for Library Real statistical problems are complex and subtle. This text is about using regression to solve real problems of comparison, estimation, prediction, and causal inference, based on real stories from the authors experience. It offers practical advice for understanding assumptions and implementing methods through graphics and computing in R and Stan. Details ISBN110702398X Year 2020 ISBN-10 110702398X ISBN-13 9781107023987 Format Hardcover Series Analytical Methods for Social Research Language English Author Aki Vehtari Publisher Cambridge University Press Publication Date 2020-07-23 DEWEY 519.536 UK Release Date 2020-07-23 Imprint Cambridge University Press Place of Publication Cambridge Country of Publication United Kingdom AU Release Date 2020-07-23 NZ Release Date 2020-07-23 Illustrations Worked examples or Exercises; 38 Halftones, black and white; 145 Line drawings, black and white Pages 548 Alternative 9781107676510 Audience Tertiary & Higher Education We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:168648020;

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