Provides an accessible but thorough introduction to the most up-to-date, commonly used statistical methods

Emphasizes graphical methods for exploratory data analysis, visualizing numerical optimization, and plotting residuals

Assumes a working knowledge of basic statistical concepts and methods and an acquaintance with calculus and matrix algebra

Includes numerous examples from a wider range of application areas, including business, medicine, agriculture, biology, engineering, and the social sciences

Provides online data sets and outline solutions to the exercises on the Internet at www.crcpress.com/us/ElectronicProducts/downandup.asp

Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models.

Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application.

Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.

INTRODUCTION
Background
Scope
Notation
Distributions Related to the Normal Distribution
Quadratic Forms
Estimation
Exercises
MODEL FITTING
Introduction
Examples
Some Principles of Statistical Modelling
Notation and Coding for Explanatory Variables
Exercises
EXPONENTIAL FAMILY AND GENERALIZED LINEAR
MODELS
Introduction
Exponential Family of Distributions
Properties of Distributions in the Exponential Family
Generalized Linear Models
Examples
Exercises
ESTIMATION
Introduction
Example: Failure Times for Pressure Vessels
Maximum Likelihood Estimation
Poisson Regression Example
Exercises
INFERENCE
Introduction
Sampling Distribution for Score Statistics
Taylor Series Approximations
Sampling Distribution for Maximum Likelihood Estimators
Log-Likelihood Ratio Statistic
Sampling Distribution for the Deviance
Hypothesis Testing
Exercises
NORMAL LINEAR MODELS
Introduction
Basic Results
Multiple Linear Regression
Analysis of Variance
Analysis of Covariance
General Linear Models
Exercises
BINARY VARIABLES AND LOGISTIC REGRESSION
Probability Distributions
Generalized Linear Models
Dose Response Models
General Logistic Regression Model
Goodness of Fit Statistics
Residuals
Other Diagnostics
Example: Senility and WAIS
Exercises
NOMINAL AND ORDINAL LOGISTIC REGRESSION
Introduction
Multinominal Distribution
Nominal Logistic Regression
Ordinal Logistic Regression
General Comments
Exercises
COUNT DATA, POISSON REGRESSION, AND LOG-LINEAR MODELS
Introduction
Poisson Regression
Examples of Contingency Tables
Probability Models for Contingency Tables
Log-Linear Models
Inference for Log-Linear Models
Numerical Examples
Remarks
Exercises
SURVIVAL ANALYSIS
Introduction
Survivor Functions and Hazard Functions
Empirical Survivor Function
Estimation
Inference
Model checking
Example: Remission Times
Exercises
clustered and longitudinal data
Introduction
Example: Recovery from Stroke
Repeated Measures Models for Normal Data
Repeated Measures Models for NON-NORMAL DATA
Multilevel Models
Stroke Example Continued
Comments
Exercises
SOFTWARE
REFERENCES
INDEX

Editorial Reviews

" The second edition … is successful in, filling a void in the otherwise sparse literature on the subject of generalized linear models at the introductory level … a wide range of research applications are covered and ample workings are also provided to aid the reader in statistical calculations … I would highly recommend this text for a reader interested in finding out at an introductory level what the subject area of generalized linear models is all about, including the non-statistician, undergraduate and graduate-level student."
-Kerrie Nelson, Department of Statistics, LeConte College, University of South Carolina, Columbia, USA, in Statistics in Medicine, Vol. 23, 2004

"... a unique and useful text for intermediate undergraduate teaching."
-Times Higher Education Supplement

"…I liked Dobson's basic and relatively brief presentation…Thanks go to the publisher for the softcover edition and attendant modest price, another of the book's virtues besides its brevity. These attributes make this book a recommended purchase for those who need a book on logistic regression. It is a good place to start."
-Technometrics, November 2002

Resource	OS Platform	Updated	Description	Instructions
C1658.zip	All Windows Version	January 06, 2003	Introduction to Generalized Linear Models 2nd Edition