256 pages in excellent, clean condition. Many figures and tables. An offprint from the same author, titled Some Statistical Heresies, is loose in the book. Red, glossy covers with illustration and white titles. Light wear on corners. VG+.

Bestandsnummer des Verk�ufers 205612

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Bibliografische Details

Titel

APPLYING GENERALIZED LINEAR MODELS

Verlag

Springer - Verlag, New York

Erscheinungsjahr

1997

ISBN-10

0387982183

ISBN-13

9780387982182

Einband

Hardcover

Zustand

VG+

Buchtyp

Book

Verk�uferkataloge

Science

�ber diesen Titel

Inhaltsangabe

This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

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Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression, and ANOVA.

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