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关键词：
Data Analysis Using Regression and Multilevel/Hierarchical Models
书目信息
ISBN：
9780521686891（13位）
中图分类号：
O1
杜威分类号：
中文译名：
应用回归和多级/等级模型分析数据
作者：
Andrew Gelman
编者：
语种：
English
出版信息
出版社：
Cambridge University Press
出版地：
出版年：
2007
版本：
版本类型：
原版
丛书题名：
卷期：
文献信息
关键词：
Politics (general)
前言：
摘要：
内容简介：
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
目次：
1. Why?; 2. Concepts and methods from basic probability and statistics; Part IA. Single-level Regression: 3. Linear regression: the basics; 4. Linear regression: before and after fitting the model; 5. Logistic regression; 6. Generalized linear models; Part IB. Working with Regression Inferences: 7. Simulation of probability models and statistical inferences; 8. Simulation for checking statistical procedures and model fits; 9. Causal inference using regression on the treatment variable; 10. Causal inference using more advanced models; Part IIA. Multilevel Regression: 11. Multilevel structures; 12. Multilevel linear models: the basics; 13. Multilevel linear models: varying slopes, non-nested models, and other complexities; 14. Multilevel logistic regression; 15. Multilevel generalized linear models; Part IIB. Fitting Multilevel Models: 16. Multilevel modeling in bugs and R: the basics; 17. Fitting multilevel linear and generalized linear models in bugs and R; 18. Likelihood and Bayesian inference and computation; 19. Debugging and speeding convergence; Part III. From Data Collection to Model Understanding to Model Checking: 20. Sample size and power calculations; 21. Understanding and summarizing the fitted models; 22. Analysis of variance; 23. Causal inference using multilevel models; 24. Model checking and comparison; 25. Missing data imputation; Appendixes: A. Six quick tips to improve your regression modeling; B. Statistical graphics for research and presentation; C. Software; References.
附录：
全文链接：
读者对象：
political methodology, quantitative methods, educational psychology, statistics
实体信息
页码：
500
装帧：
Paperback
尺寸：
其它形态细节：
其它信息
原价：
USD
39.9900
原版ISBN：
其它ISBN：
图书特色：
书评：
扩展信息
Isbn：
052168689X
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