Data Analysis Using Regression and Multilevel/Hierarchical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Analysis Using Regression and Multilevel/Hierarchical Models write by Andrew Gelman. This book was released on 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models available in PDF, EPUB and Kindle. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Data Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Analysis Using Regression and Multilevel/Hierarchical Models write by Andrew Gelman. This book was released on 2006-12-18. Data Analysis Using Regression and Multilevel/Hierarchical Models available in PDF, EPUB and Kindle. Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, 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.
Data Analysis Using Regression and Multilevel/hierarchical Models
Data Analysis Using Regression and Multilevel/hierarchical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Analysis Using Regression and Multilevel/hierarchical Models write by Andrew Gelman. This book was released on 2009. Data Analysis Using Regression and Multilevel/hierarchical Models available in PDF, EPUB and Kindle.
Regression and Other Stories
Regression and Other Stories - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression and Other Stories write by Andrew Gelman. This book was released on 2020-07-23. Regression and Other Stories available in PDF, EPUB and Kindle. A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
Bayesian Data Analysis, Third Edition
Bayesian Data Analysis, Third Edition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Data Analysis, Third Edition write by Andrew Gelman. This book was released on 2013-11-01. Bayesian Data Analysis, Third Edition available in PDF, EPUB and Kindle. Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
