Andrew Gelman, Jennifer Hill, Aki Vehtari

Regression and Other Stories (eBook, PDF)

• Format: PDF

Produktbeschreibung

A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 23. Juli 2020
• Englisch
• ISBN-13: 9781108907354
• Artikelnr.: 70910653

Autorenporträt

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.

Inhaltsangabe

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.