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Subject Area
Mathematics
ISBN
9781107676510
Publication Name
Regression and Other Stories
Item Length
9.6 in
Publisher
Cambridge University Press
Subject
Probability & Statistics / General, General
Series
Analytical Methods for Social Research Ser.
Publication Year
2020
Type
Textbook
Format
Trade Paperback
Language
English
Item Height
1.2 in
Author
Aki Vehtari, Jennifer Hill, Andrew Gelman
Item Width
7.4 in
Item Weight
37.5 Oz
Number of Pages
548 Pages

關於產品

Product Information

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.

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1107676517
ISBN-13
9781107676510
eBay Product ID (ePID)
16038756742

Product Key Features

Author
Aki Vehtari, Jennifer Hill, Andrew Gelman
Publication Name
Regression and Other Stories
Format
Trade Paperback
Language
English
Subject
Probability & Statistics / General, General
Series
Analytical Methods for Social Research Ser.
Publication Year
2020
Type
Textbook
Subject Area
Mathematics
Number of Pages
548 Pages

Dimensions

Item Length
9.6 in
Item Height
1.2 in
Item Width
7.4 in
Item Weight
37.5 Oz

Additional Product Features

Intended Audience
College Audience
Lc Classification Number
Qa278.2
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
Table of Content
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.
Dewey Decimal
519.536
Dewey Edition
23
Illustrated
Yes

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