Data Analysis Using Regression and Multilevel/Hierarchical ModelsData 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 missingdata imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/ 
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LibraryThing Review
User Review  Harlan879  LibraryThingA good comprehensive survey of the topics. But, different sections assume different levels of background knowledge, from nearly nothing to gradlevel statistics theory. I like their views on the ... Read full review
Review: Data Analysis Using Regression and Multilevel/Hierarchical Models
User Review  Scott  Goodreadsgood R walkthroughs Read full review
Contents
Why?  1 
Concepts and methods from basic probability and statistics  13 
Singlelevel regression  29 
before and after fitting the model  53 
Logistic regression  79 
Generalized linear models  109 
Vorking with regression inferences  135 
Simulation for checking statistical procedures and model fits  155 
Fitting multilevel linear and generalized linear models in Bugs  375 
Likelihood and Bayesian inference and computation  387 
Debugging and speeding convergence  415 
From data collection to model understanding to model  435 
Understanding and summarizing the fitted models  457 
Analysis of variance  487 
Causal inference using multilevel models  503 
Model checking arid comparison  513 
Causal inference using regression on the treatment variable  167 
Causal inference using more advanced models  199 
Multilevel regression  235 
the basics  251 
varying slopes nonnested models  279 
Multilevel logistic regression  301 
Multilevel generalized linear models  325 
Fitting multilevel models  343 
Missingdata imputation  529 
A Six quick tips to improve your regression modeling  547 
Software  565 
575  
Author index  601 
607  
Other editions  View all
Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman,Jennifer Hill Limited preview  2006 
Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman,Jennifer Hill No preview available  2007 
Common terms and phrases
1mer analysis ANOVA arsenic level assumptions Bayesian Bayesian inference Bugs code Bugs model causal effect causal inference Chapter classical regression coef.est coef.se Intercept compared complete pooling compute consider constant term corresponding countylevel data points dataset defined deviance display dnorm dunif earnings election estimate ethnicity example Figure fit the model fitted model function Gelman Gibbs sampler graph grouplevel predictors height illustrate imputation indicators individuallevel instrumental variables interactions interpret interval likelihood linear models linear regression logistic regression matrix mean measurements model fit multilevel model n.sims nopooling normal distribution observed outcome overdispersion plot Poisson regression population posterior pretest precincts prior distribution probability propensity score radon levels random regression coefficients regression line regression model replicated rnorm sample scale sigma simple simulation slope standard deviation standard error statistically significant switching tau.y test scores topcoding treatment effect uncertainty values variance parameters variation varyingintercept vector vote y.hat[i zero
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Page 592  On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.