A Concise Guide to Statistics
The text gives a concise introduction into fundamental concepts in statistics. Chapter 1: Short exposition of probability theory, using generic examples. Chapter 2: Estimation in theory and practice, using biologically motivated examples. Maximum-likeliho
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For further volumes: http://www.springer.com/series/8921
Hans-Michael Kaltenbach
A Concise Guide to Statistics
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Dr. Hans-Michael Kaltenbach ETH Zurich Schwarzwaldallee 215 4002 Basel Switzerland e-mail: [email protected]
ISSN 2191-544X ISBN 978-3-642-23501-6 DOI 10.1007/978-3-642-23502-3
e-ISSN 2191-5458 e-ISBN 978-3-642-23502-3
Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011937427 Ó Hans-Michael Kaltenbach 2012 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: eStudio Calamar, Berlin/Figueres Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
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Preface
This book owes its existence to the lecture ‘‘Statistics for Systems Biology’’, which I taught in the fall semester 2010 at the Department for Biosystems Science and Engineering of the Swiss Federal Institute of Technology (ETH Zurich). To a large part, the audience consisted of students with backgrounds in biological sciences, which explains the large proportion of biological examples in this text. Nevertheless, I hope that this text will be helpful for readers with very different backgrounds who need to quantify and analyze data to answer interesting questions. This book is not intended to be a manual, nor can it provide the answer to all questions and problems that one will encounter when analyzing data. Both the book title and the title of the book series indicate that space is limited and this book therefore concentrates more on the ideas and concepts rather than on presenting a vast array of different methods and applications. While all the standard material for an introductory course is covered, this text is very much inspired by Larry Wasserman’s excellent book All of Statistics [1] and consequently discusses several topics usually not found in introductory texts, such as the bootstrap, robust estimators, and multiple testing, which are all found in modern statistics software. Due to the space constraints, this book does not cover methods from Bayesian statistics and does not provide any exercises. Frequent reference is made to the software R (freely available from http://www.r-project.org), but the text itself is largely independen
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