Main Title |
Data analysis using regression and multilevel/hierarchical models / |
Author |
Gelman, Andrew.
|
Other Authors |
|
Publisher |
Cambridge University Press, |
Year Published |
2007 |
OCLC Number |
299769112 |
ISBN |
052168689X; 0521867061; 9780521686891; 9780521867061 |
Subjects |
Regression analysis ;
Multilevel models (Statistics)
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
ELBM |
HA31.3.G45 2007b |
|
AWBERC Library/Cincinnati,OH |
03/13/2024 |
|
Edition |
Reprinted with corrections 2007. |
Collation |
xxii, 625 pages : illustrations ; 26 cm |
Notes |
Includes bibliographical references (pages 575-600) and indexes. |
Contents Notes |
Why? -- Concepts and methods from basic probability and statistics -- Linear regression: the basics -- Linear regression: before and after fitting the model -- Logistic regression -- Generalized linear models -- Simulation for checking statistical procedures and model fits -- Causal inference using regression on the treatment variable -- Causal inference using more advanced models -- Multilevel structures -- Multilevel linear models: the basics -- Multilevel linear models: varying slopes, non-nested models, and other complexities Multilevel logistic regression -- Multilevel generalized linear models -- Multilevel modeling Bugs and R: the basics -- Fitting multilevel linear and generalized linear models in Bugs and R -- Likelihood and Bayesian inference and computation -- Debugging and speeding convergence -- Sample size and power calculations -- Understanding and summarizing the fitted models -- Analysis of variance -- Causal inference using multilevel models -- Model checking and comparison -- Missing-data imputation -- Six quick tips to improve your regression modeling -- Statistical graphics for research and presentation -- Software. |