## 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 missing-data 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 grad-level statistics theory. I like their views on the ... Read full review

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Useful.

### 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 county-level data points dataset defined deviance display dnorm dunif earnings election estimate ethnicity example Figure fit the model fitted model function Gelman Gibbs sampler graph group-level predictors height illustrate imputation indicators individual-level instrumental variables interactions interpret interval likelihood linear models linear regression logistic regression matrix mean measurements model fit multilevel model n.sims no-pooling normal distribution observed outcome overdispersion plot Poisson regression population posterior pre-test 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 varying-intercept vector vote y.hat[i zero

### Popular passages

Page 592 - On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.