Statistical Modelling in GLIM

Aitkin, Murray; Anderson, Dorothy; Francis, Brian; Hinde, John

ISBN 10: 0198522045 / ISBN 13: 9780198522041

Published by Clarendon Press, Oxford, 1989

About this Item

First edition, first printing. Octavo hardback. xi, 374 pp. "Damaged" stamp from published on verso of title page, however there does not seem to be much wrong with the book. Very slight fading toward to of boards, Very Good+ condition. No dust jacket.

Seller Inventory # 52431

Bibliographic Details

• Title: Statistical Modelling in GLIM
• Publisher: Clarendon Press, Oxford
• Publication Date: 1989
• Language: English
• ISBN 10: 0198522045
• ISBN 13: 9780198522041
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Very Good+
• Dust Jacket Condition: No Jacket
• Seller catalogs:
  • Mathematics and Business Studies

About this title

Synopsis

An introduction to both the theory of statistical models and the practical implementation of these techniques in the analysis of data using the package GLIM 3, the statistical package for Generalized Linear Interactive Modelling, developed by the Working Party on Statistical Computing of the Royal Statistical Society. The authors have aimed to integrate both the theoretical and practical aspects, thus all the statistical principles which are discussed are illustrated by worked examples using GLIM's interactive facilities. A full description of the use of GLIM 3 for model fitting is given with detailed discussions of many examples. This book was written from 1982-1987 as part of an Economic and Social Research Council research programme at the Centre for Applied Statistics in the analysis of complex social data, which supported Dorothy Anderson and John Hinde. There are several way this book can be used. It is written in sequence which is intended to be appropriate for intensive courses. Chapter 1 gives a gentle introduction to GLIM 3 for novices and chapter 2 a general introduction to the principles of statistical modelling, with two simple examples. This chapter also develops the necessary theory of maximum likelihood estimation and likelihood ratio testing. Chapter 3 discusses the normal model, chapter 4 binomial data, chapter 5 multinomial and Poisson data and chapter 6 survival data. This work should be of value to statisticians working in a wide range of fields including biomedical research and the social sciences as well as providing a "hands on" guide for students in these areas using these techniques for the first time.