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AuthorDalgaard, Peter. author
TitleIntroductory Statistics with R [electronic resource] / by Peter Dalgaard
ImprintNew York, NY : Springer New York, 2002
Connect tohttp://dx.doi.org/10.1007/b97671
Descript XV, 267 p. online resource

SUMMARY

R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix. Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He has been a member of the R Core Team since 1997


CONTENT

Basics -- Probability and distributions -- Descriptive statistics and graphics -- One- and two-sample tests -- Regression and correlation -- Analysis of variance and the Kruskal-Wallis test -- Tabular data -- Power and the computation of sample size -- Multiple regression -- Linear models -- Logistic regression -- Survival analysis


Mathematics Bioinformatics Computational biology Probabilities Statistics Mathematics Probability Theory and Stochastic Processes Statistics and Computing/Statistics Programs Bioinformatics Computer Appl. in Life Sciences



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