Data Analysis Using Regression and Multilevel/Hierarchical Models

✍ Scribed by Andrew Gelman, Jennifer Hill

Publisher: Cambridge University Press
Year: 2006
Tongue: English
Leaves: 652
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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. Author resource page: http://www.stat.columbia.edu/~gelman/arm

✦ Subjects

Probability & Statistics;Applied;Mathematics;Science & Math;Politics & Government;Elections & Political Process;Ideologies & Doctrines;International & World Politics;Political Science;Public Affairs & Policy;Specific Topics;United States;Politics & Social Sciences;Statistics;Mathematics;Science & Mathematics;New, Used & Rental Textbooks;Specialty Boutique;Political Science;Civil Rights;Government;International Relations;Political History;Political Ideologies;Public Affairs;Public Policy;Social S

📜 SIMILAR VOLUMES

Data Analysis Using Regression and Multi

📁 Data Analysis Using Regression and Multilevel/Hierarchical Models

✍ Andrew Gelman, Jennifer Hill 📂 Library 📅 2007 🏛 Cambridge University Press 🌐 English

I got this book while working on an article that involved a hierarchical model with a binary dependent variable - after poking through Radenbush/Bryk and a variety of other texts that left me frustrated. Not only did this book teach me how to properly specify and estimate the model in R, I also lear

Data Analysis Using Regression and Multi

📁 Data Analysis Using Regression and Multilevel-Hierarchical Models

✍ Andrew Gelman; Jennifer Hill 📂 Library 📅 2006 🏛 Cambridge University Press 🌐 English

John Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit "Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multi

📁 Data Analysis Using Regression and Multilevel/Hierarchical Models

✍ Andrew Gelman 📂 Library 📅 2008 🏛 Cambridge University Press 🌐 English

Data analysis using hierarchical general

📁 Data analysis using hierarchical generalized linear models with R

✍ Youngjo Lee, Lars Ronnegard, Maengseok Noh 📂 Library 📅 2017 🏛 Chapman and Hall/;CRC Press 🌐 English

<P>Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the

Data analysis using hierarchical general

📁 Data analysis using hierarchical generalized linear models with R

✍ Youngjo Lee, Lars Ronnegard, Maengseok Noh 📂 Library 📅 2017 🏛 Chapman and Hall/;CRC Press 🌐 English

Data Analysis Using Hierarchical General

📁 Data Analysis Using Hierarchical Generalized Linear Models with R

✍ Youngjo Lee, Lars Ronnegard, Maengseok Noh 📂 Library 📅 2017 🏛 Chapman and Hall/CRC 🌐 English