Data analysis and graphics using R. An example-based approach. 2nd ed.

Autor/inn/en	Maindonald, John; Braun, W. John
Titel	Data analysis and graphics using R. An example-based approach. 2nd ed. Gefälligkeitsübersetzung: Datenanalyse und Grafiken unter Verwendung von R. Ein beispielgestützter Ansatz.
Quelle	Cambridge u.a.: Cambridge University Press (2007), 502 S.Verfügbarkeit
Reihe	Cambridge series in statistical and probabilistic mathematics. 10
Sprache	englisch
Dokumenttyp	gedruckt; Monographie
ISBN	978-0-511-24794-1; 978-0-521-86116-8
Schlagwörter	Mehrebenenanalyse; Zeitreihenanalyse; Lehrbuch; Datenanalyse; Regressionsanalyse; Mathematik; Statistik; Analyse; Statistische Methode
Abstract	"Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data. Forschungsmethode: Dokumentation. (author's abstract, IAB-Doku). Contents: Preface; 1. A brief introduction to R; 2. Styles of data analysis; 3. Statistical models; 4. An introduction to formal inference; 5. Regression with a single predictor; 6. Multiple linear regression; 7. Exploiting the linear model framework; 8. Generalized linear models and survival analysis; 9. Time series models; 10. Multi-level models and repeated measures; 11. Tree-based classification and regression; 12. Multivariate data exploration and discrimination; 13. Regression on principal component or discriminant scores; 14. The R system - additional topics; Epilogue - models; References; Index of R symbols and functions; Index of terms; Index of names."
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2008/3