Statistical Modelling in R by Murray AitkinStatistical Modelling in R by Murray Aitkin

Statistical Modelling in R

byMurray Aitkin, Brian Francis, John Hinde

Paperback | February 28, 2009

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R is now the most widely used statistical package/language in university statistics departments and many research organisations. Its great advantages are that for many years it has been the leading-edge statistical package/language and that it can be freely downloaded from the R web site. Itscooperative development and open code also attracts many contributors meaning that the modelling and data analysis possibilities in R are much richer than in GLIM4, and so the R edition can be substantially more comprehensive than the GLIM4 edition.This text provides a comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma,exponential and Weibull distributions, making this book ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines.
Murray Aitkin is a Professorial Fellow at the Department of Mathematics and Statistics, University of Melbourne. In 1992 he was awarded an ARC Senior Research Fellowship, initially at the Australian National University and then at the University of Western Australia, where he worked on foundational issues in statistics. At the conclu...
Title:Statistical Modelling in RFormat:PaperbackDimensions:568 pages, 9.21 × 6.14 × 0.03 inPublished:February 28, 2009Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199219133

ISBN - 13:9780199219131


Table of Contents

Preface1. Introducing R2. Statistical modelling and inference3. Regression and analysis of variance4. Binary response data5. Multinomial and Poisson response data6. Survival data7. Finite mixture models8. Random effects models9. Variance component modelsBibliographyR function and constant indexDataset indexSubject index