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Analytical Methods for Social Research Ser.: Regression and Other Stories by Aki Vehtari, Jennifer Hill and Andrew Gelman (2020, Trade Paperback)
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Regression and Other Stories, Paperback by Gelman, Andrew; Hill, Jennifer; Vehtari, Aki, ISBN 1107676517, ISBN-13 9781107676510, Brand New, Free shipping in the US
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-101107676517
ISBN-139781107676510
eBay Product ID (ePID)16038756742
Product Key Features
Number of Pages548 Pages
Publication NameRegression and Other Stories
LanguageEnglish
Publication Year2020
SubjectProbability & Statistics / General, General
TypeTextbook
AuthorAki Vehtari, Jennifer Hill, Andrew Gelman
Subject AreaMathematics
SeriesAnalytical Methods for Social Research Ser.
FormatTrade Paperback
Dimensions
Item Height1.2 in
Item Weight37.5 Oz
Item Length9.6 in
Item Width7.4 in
Additional Product Features
Intended AudienceCollege Audience
Reviews'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
Dewey Edition23
IllustratedYes
Dewey Decimal519.536
Table Of ContentPreface; 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.
SynopsisMost 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., 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.
LC Classification NumberQA278.2
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