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Data Analysis Using Regression and Multilevel/Hierarchical Models
monograph
Author(s):
Andrew Gelman
,
Jennifer Hill
Publication date
(Online):
2009
Publisher:
Cambridge University Press
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Author and book information
Book
ISBN:
9780511790942
Publication date (Print):
2006
Publication date (Online):
2009
DOI:
10.1017/CBO9780511790942
SO-VID:
d5216856-5ad0-4844-913a-fbf12a3f6ebb
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Book chapters
pp. xix
Preface
pp. 1
Why?
pp. 13
Concepts and methods from basic probability and statistics
pp. 29
Single-level regression
pp. 31
Linear regression: the basics
pp. 53
Linear regression: before and after fitting the model
pp. 79
Logistic regression
pp. 109
Generalized linear models
pp. 135
Working with regression inferences
pp. 137
Simulation of probability models and statistical inferences
pp. 155
Simulation for checking statistical procedures and model fits
pp. 167
Causal inference using regression on the treatment variable
pp. 199
Causal inference using more advanced models
pp. 235
Multilevel regression
pp. 237
Multilevel structures
pp. 251
Multilevel linear models: the basics
pp. 279
Multilevel linear models: varying slopes, non-nested models, and other complexities
pp. 301
Multilevel logistic regression
pp. 325
Multilevel generalized linear models
pp. 343
Fitting multilevel models
pp. 345
Multilevel modeling in Bugs and R: the basics
pp. 375
Fitting multilevel linear and generalized linear models in Bugs and R
pp. 387
Likelihood and Bayesian inference and computation
pp. 415
Debugging and speeding convergence
pp. 435
From data collection to model understanding to model checking
pp. 437
Sample size and power calculations
pp. 457
Understanding and summarizing the fitted models
pp. 487
Analysis of variance
pp. 503
Causal inference using multilevel models
pp. 513
Model checking and comparison
pp. 529
Missing-data imputation
pp. 547
Six quick tips to improve your regression modeling
pp. 551
Statistical graphics for research and presentation
pp. 565
Software
pp. 575
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