An introduction to generalized linear models first edition - Annette J. Dobson

Annette J. Dobson

ISBN 10: 0412311100 / ISBN 13: 9780412311109

Editore: 1990, 1990

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Dati bibliografici

• Titolo: An introduction to generalized linear models first edition - Annette J. Dobson
• Editore: 1990
• Data di pubblicazione: 1990
• Lingua: Inglese
• ISBN 10: 0412311100
• ISBN 13: 9780412311109
• Rilegatura: Rilegato
• Edizione: prima edizione
• Condizione: Used: Good
• Cataloghi dei venditori:
  • Grand Format

Descrizione articolo

Riassunto

An undergraduate-level introduction to the topic of generalized linear models An Introduction to Generalized Linear Models-a new edition of An Introduction to Statistical Modelling-demonstrates how generalized linear models provide a unifying framework for many commonly used multivariate statistical methods, including multiple regression and analysis of variance or covariance for continuous response data, logistic regression for binary responses, and log-linear models for counted responses. The theory for these models is developed using the exponential family of distributions, maximum likelihood estimation, and likelihood ration tests. Chapters on each of the main types of generalized linear models are included. The statistical computing program GLIM , developed to fit these models to data, is used extensively. Other programs, particularly MINITAB, are used to illustrate particular issues. The student is assumed to have a working knowledge of basic statistical concepts and methods, at the level of most introductory statistics courses, and some acquaintance with calculus and matrix algebra. Methods described in this text are widely applicable for analyzing data from the fields of medicine, agriculture, biology, engineering, industrial experimentation, and the social sciences.

Contenuti

Preface. Introduction. Model Fitting. Exponential Family of Distributions and Generalized Linear Models. Estimation. Inference. Multiple Regression. Analysis of Variance and Covariance. Binary Variables and Logistic Regression. Contingency Tables and Log-Linear Models. Appendix A. Appendices. Outline of Solutions for Selected Exercises. References. Index.