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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

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Titel: An Introduction to Generalized Linear Models, First Edition (Chapman & Hall/CRC Texts in Statistical Science)
Verlag: Taylor & Francis Ltd, United Kingdom
Erscheinungsjahr: 1990
Sprache: Englisch
ISBN-10: 0412311100
ISBN-13: 9780412311109
Einband: Paperback
Zustand: Very Good

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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.

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