R ist als statistische Standard-Programmiersprache fur viele zu einem unverzichtbaren Werkzeug fur zahlreiche Techniken der statistischen Modellierung und Datenanalyse geworden. Sie ermoglicht eine breite Auswahl statistischer Methoden von der einfachen Regression bis hin zu Generalisierten Linearen Modellen. Statistik mit R ist eine anschauliche und leicht nachvollziehbare Einfuhrung in die statistische Analyse unter Verwendung dieser leistungsstarken und frei erhaltlichen Software. Michael J. Crawley bietet dem Leser Schritt-fur-Schritt-Anleitungen, die keine Programmiervorkenntnisse voraussetzen und die dem Nicht-Statistiker helfen, die Methodik vollstandig zu verstehen. Mit Hilfe von Praxisbeispielen wird der Leser von den einfachst en Fallen, bei denen vor allem die Richtigkeit der getroffenen Annahmen uberpruft wird (beispielsweise Konstanz einer Varianz, Normalverteilung von Fehlern), uber Falle, bei denen die Angemessenheit des gewahlten Modells beurteilt wird, bis hin zu Fallen, die die Anpassung an die Daten behandeln, gefuhrt. Ausserdem deckt das Buch die volle Breite der statistischen Techniken ab, die ublicherweise fur die Datenanalyse bei Forschungsarbeiten benotigt werden, zum Beispiel t-Tests, Chi-Quadrat-Tests, weiterfuhrende Methoden wie Regression und Varianzanalyse und fortgeschrittene Techniken wie Generalisierte Lineare Modelle. Statistik mit R ist eine Einfuhrung in ein breites Spektrum von statistischen Methoden, die in zahlreichen wissenschaftlichen Dis ziplinen Anwendung fi nden.

Plant ecology is the scientific study of the factors influencing the distribution and abundance of plants. This benchmark text, extremely well received in its first edition, shows how pattern and structure at different levels of plant organization--from ecophysiology through population dynamics to community structure and ecosystem function--are influenced by abiotic factors (eg, climate and soils) and by biotic factors (eg, competition and herbivory). Adopting a dynamic approach, this book combines descriptive text with theoretical models and experimental data. It will be invaluable reading for both student and practising ecologist alike.

In this second edition, the structure of the book has been completely revised, moving from the small scale to the large scale, in keeping with contemporary teaching methods. This fresh approach allows consideration of several new and important topics such as plant secondary chemistry, herbivory, sex, and breeding systems. Additional chapters address topical applied issues in plant ecology including global warming, pollution and biodiversity.The latest edition of a very widely adopted textbookWritten by a team of leading experts and edited by an international authority in the field

.".".I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)"

A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R

This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.

Includes numerous worked examples and exercises within each chapter.

S-Plus is a first-rate graphical environment, used by thousands worldwide to perform basic, intermediate and advanced statistical analysis. It is remarkably powerful, yet relatively simple to use, once you have the basics at your fingertips. Statistical Computing: An Introduction to Data Analysis using S-Plus provides a pragmatic introduction to analysing data using S-Plus, whilst covering a huge breadth of topics, and assuming minimal statistical knowledge.

• Provides an accessible yet comprehensive introduction to statistical computing, and can be used as a reference volume for S-Plus.

• Covers a breadth of topics, including the basics, such as sampling and measures of central tendency and variation; the intermediate, such as analysis of variance and regression; and the most advanced modern methods, such as nonlinear mixed effects modelling and tree models.

• Develops each concept from first principles in small steps, with worked examples and implementation advice throughout.

• Assumes minimal experience of statistics and computing.

• Emphasises graphical data inspection, parameter estimation and model criticism.

• Supported by a Web site featuring all the data-frames, along with problems and worked examples.

This is very much an introductory statistics book for all scientists. It is based on the premise that effective data analysis requires the mastery of a core of central ideas and methods, and that these cut across the boundaries of academic disciplines. It is suitable for advanced undergraduate, graduate students, researchers, and industry professionals from science, medicine, engineering, economics, the social sciences, and many other disciplines that have a need for statistical data analysis.