Buch, Englisch, 542 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 975 g
Buch, Englisch, 542 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 975 g
Reihe: Chapman & Hall/CRC Texts in Statistical Science
ISBN: 978-1-58488-701-0
Verlag: Chapman and Hall/CRC
While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.
Reorganized and with additional material, this edition begins with a brief summary of some relevant general statistical concepts and an introduction to basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The text also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments, such as large sample methods and computer-intensive applications.
Keeping mathematics to a minimum, this text introduces nonparametric methods to undergraduate students who are taking either mainstream statistics courses or statistics courses within other disciplines. By giving the proper attention to data collection and the interpretation of analyses, it provides a full introduction to nonparametric methods.
Zielgruppe
Undergraduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
Weitere Infos & Material
Some basic concepts. Fundamentals of nonparametric methods. Location inference for single samples. Other single-sample inferences. Methods for paired samples. Methods for two independent samples. Basic tests for three or more samples. Analysis of structured data. Analysis of survival data. Correlation and concordance. Bivariate linear regression. Categorical data. Association in categorical data. Robust estimation. Modern nonparametrics.
