Regression and Other Stories Gelman Hill Vehtari Paperback 9781107676510

Description: Regression and Other Stories A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference. Andrew Gelman (Author), Jennifer Hill (Author), Aki Vehtari (Author) 9781107676510, Cambridge University Press Paperback, published 23 July 2020 548 pages 24.5 x 18.9 x 3 cm, 1.06 kg 'Comprehensive and charming, this regression manual belongs on every regressor's shelf.' Joshua Angrist, Massachusetts Institute of Technology 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. 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. Subject Areas: Probability & statistics [PBT], Economic statistics [KCHS], Research methods: general [GPS]

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