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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. Its cooperative 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.

Produktinformation

  • Utgivningsdatum2009-01-29
  • Mått160 x 241 x 35 mm
  • Vikt985 g
  • FormatInbunden
  • SpråkEngelska
  • SerieOxford Statistical Science Series
  • Antal sidor592
  • FörlagOUP OXFORD
  • ISBN9780199219148
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Statistical modelling in GLIM4

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Smooth Tests of Goodness of Fit

J. C. W. Rayner, D. J. Best, New Zealand) Rayner, J. C. W. (Professor, Department of Mathematics and Statistics, Professor, Department of Mathematics and Statistics, University of Otago, New Zealand) Best, D. J. (CSIRO Division of Mathematics and Statistics, CSIRO Division of Mathematics and Statistics, Food Research Laboratory, New South Wales, J. C. Rayner

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Approximating Integrals via Monte Carlo and Deterministic Methods

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Models for Repeated Measurements

J. K. Lindsey, Belgium) Lindsey, J. K. (Professor, Biostatistics, Professor, Biostatistics, Limburgs Universitair Centrum, James K. Lindsey

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Likelihood Methods in Statistics

Thomas A. Severini, Illinois) Severini, Thomas A. (Department of Statistics, Department of Statistics, Northwestern University Evanston

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Numerical Methods for Nonlinear Estimating Equations

Christopher G. Small, Jinfang Wang, Ontario N2L 3G1 Canada) Small, Christopher G. (, Department of Statistics and Actuarial Science University of Waterloo Waterloo, Japan) Wang, Jinfang (, School of Agriculture Obihiro University of Agriculture and Veterinary Medicine Inada-cho, Obihiro, Hokkaido 080-8555

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Symbolic Computation for Statistical Inference

D. F. Andrews, J. E. Stafford, Canada) Andrews, D. F. (Department of Statistics, Department of Statistics, University of Toronto, Canada) Stafford, J. E. (Department of Public Health Sciences, Department of Public Health Sciences, University of Toronto, J. E. H. Stafford

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Principles of Multivariate Analysis

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Procrustes Problems

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