Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on the multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models. Software scripts, supporting documentation, data for the examples, and some extended mathematical derivations are available on the authors’ websites (http://jeffgill.org/publications/generalized-linear-models-unified-approach-0) as well as through the \texttt{R} package \texttt{GLMpack}. Supporting material (data and code) to replicate the examples in the book can be found in the ′GLMpack′ package on CRAN or on the website https://github.com/smtorres/GLMpack. T271311
This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples.
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data.
... and Stumpf Graphical Exploratory Data Analysis Durrett: Essentials of Stochastic Processes Edwards: Introduction to Graphical Modelling Finkelstein and Levin: Statistics for Lawyers Flury: A First Course in Multivariate Statistics ...
244 MODELS FOR COUNT DATA Table 7.4 Goodness-of-Fit Tests for Loglinear Models Fitted to the Data in Table 7.3 Loglinear Associations Deviance G2 Pearson X2 df P-valuea AIC γAC γAC γAM γAC ij ij ik ij + + γAMik 497.37 443.76 2 < 0.001 ...
Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected ...
31 proc logistic data=sentence descending; title3 ' Example For Pearson Chi-Square and Deviance ' ; model prison=ibus iprior / aggregate scale=N ; weight count; output out=home . good ...
This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications.
... sandwich estimate of variance of Newey–West type with Anderson quadratic weights for the first two lags. . glm die anterior hcabg kk2 kk3 kk4 age2-age4, > nwest(anderson 2) nolog Generalized linear models family(bin cases) l(loglog) ...
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.
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded ...