This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Hill, J. L., Brooks-Gunn, J., and Waldfogel, J. (2003). Sustained effects of high participation in an early intervention for low-birth-weight premature infants. Developmental Psychology 39,730–744. Hill, J. L., Linero, A., and Murray, ...
This term was introduced by Lindley and Smith ( 1972 ) and Smith ( 1973 ) as part of their seminal contribution on Bayesian estimation of linear models . Within this context , Lindley and Smith elaborated a general framework for nested ...
Part I of the book presents a large selection of activities for introductory statistics courses and combines chapters such as, 'First week of class', with exercises to break the ice and get students talking; then 'Descriptive statistics' , ...
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. Murray, J. S., Dunson, D. B., Carin, L., and Lucas, J. E. (2013). ... Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo ...
After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable ...
... controlling for variable X. It is analogous to the usual intraclass correlation coefficient , but now controlling for X. The formula for the ( non - residual , or raw ) intraclass correlation coefficient was just the same ...
Imai K, Ying L, Strauss A (2008) Bayesian and likelihood inference for 2×2 ecological tables: An incomplete data approach. Political Analysis, 16, 41–69. Ishwaran H, James L (2001) Gibbs sampling methods for stick-breaking priors.
A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.
Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics.