The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes his Web address where additional resources can be found.
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.
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 ...
... 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 ...
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 ...
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data.
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 ...
This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and...
This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications.
With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework.