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Statistical Modelling in R

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Overview

R is now the most widely used statistical package/language in university statistics departments and many research organisations. Its great advantages are that it is now the leading-edge statistical package/language and that it can be freely downloaded from the R website. Its cooperative development and open code also attract many contributors which means 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 of Statistical Modelling.
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Product Details

  • ISBN-13: 9780199219148
  • Publisher: Oxford University Press
  • Publication date: 4/29/2009
  • Pages: 568
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.40 (d)

Meet the Author

Murray Aitkin, Professorial Fellow, Department of Mathematics and Statistics, University of Melbourne, Brian Francis, Department of Mathematics and Statistics, Lancaster University, John Hinde, Professor of Statistics, School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, and Ross Darnell, CSIRO Mathematical and Information Sciences, Australia

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Table of Contents

1 Introducing R 1

2 Statistical modelling and inference 28

3 Regression and analysis of variance 97

4 Binary response data 195

5 Multinomial and Poisson response data 269

6 Survival data 347

7 Finite mixture models 433

8 Random effect models 461

9 Variance component models 508

Bibliography 554

R function and constant index 567

Dataset index 570

Subject index 571

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