Data Analysis Using Regression and Multilevel/Hierarchical Models

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, Jennifer Hill 📂 Library 📅 2006 🏛 Cambridge University Press 🌐 English

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

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

<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 🌐 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 Regression and Multilevel/Hierarchical Models

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