Data Analysis Using Regression and Multilevel/Hierarchical Models
| Author | : | |
| Rating | : | 4.59 (536 Votes) |
| Asin | : | 052168689X |
| Format Type | : | paperback |
| Number of Pages | : | 648 Pages |
| Publish Date | : | 2014-10-14 |
| Language | : | English |
DESCRIPTION:
"Nice addition to the library" according to D. Lamb. Overall, I really like this book. I think there are certainly flaws, but the vast majority is quite useful. I'm not a statistician, but have taken a couple of classes in statistics, through multivariate linear models and some logistic regression. This book was definitely at my level, and didn't require a background in mathematical statistics. I found the parts where the authors went through building l. An excellent contribution but . John S Pros:They tackle a complex topic from many different angles. They present enough code and theory to get people up and running with the techniques, assuming some prior familiarity with likelihood based inference and R. Otherwise you might need to dig through some of the references to understand everything. Regardless, this book is a valuable reference to keep in your library.They use matrix notation sp. The content of the book is good and useful The content of the book is good and useful. The R examples are a disaster. Nothing is clearly explained and examples just appear in the book out of thin air, with no context on how to conduct them. All of the data is at the author's website; however, the examples in the book are not clear as to which data sets to use or which code to run. The code fails often. If you want the book for information on M
Instructors considering textbooks for courses on the practice of statistical modeling should move this book to the top of their list." Daniel B. Gelman and Hill have written a much needed book that is sophisticated about research design without being technical. It appears destined to adorn the shelves of a great many applied statisticians and social scientists for years to come." Brad Carlin, University of Minnesota"Gelman and Hill have written what may be the first truly modern book on modeling. I recommend it very warmly." Journal of Applied Statistics"Gelman and Hill's book is an excellent intermediate text that would be very useful for researchers interested in multilevel modeling This book gives a wealth of information for anyone interested in multilevel modeling and seems destined to be a classic." Brandon K. This hands-on textbook is sure to become a popular choice in applied regression courses." Donald Green, Yale University"Simply put, Data Analysis Usin
Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. 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. Practical tips regarding building, fitting, and understanding are provided throughout. 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. Author resource page: statlumbia/gelman/arm/. 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
. Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. She has co-authored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal and the Journal of Policy Analysis and Management, among others. His other books are Bayesian Data Analysis (1995, second edition 2003)