Statistical Data Analysis (häftad)
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illustrated ed
Clarendon Press
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233 x 157 x 12 mm
300 g
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Statistical Data Analysis

Häftad, Engelska, 1998-03-01
370 kr
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The book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and researchers who work with experimental data. The topics include probability, random variables, Monte Carlo techniques, statistical tests, parameter estimation, and unfolding methods.
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Recensioner i media

"Glen Cowan is a particle physicist who seems to have got everything right. Results are stated clearly, without mathematical proof but with enough explanation to satisfy the physicist's need to understand not only how, but also why...Those teaching an advanced undergraduate or graduate course in statistics or physicists will find this a good textbook...Do not be fooled by the fact that it does not have the "textbook look" - the exercises have been made available
separately on a Web site. " CERN Courier

"The material presented in this book is dense.In less than two hundred pages, it takes the reader from the basic notions of probability, through neural networks, Monte Carlo methods, and regularization techniques." Short Book Reviews

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Övrig information

Dr Glen D. Cowan, CERN, PPE Division, CH-1211 Geneva 23, Switzerland, Tel: +41 22 767 6539, Fax: +41 22 767 9425, Email: Glen Cowan received his Ph.D. in physics in 1988 from the University of California, Berkeley, after completing his thesis work on particle production in high energy electron-positron collisions. From there he moved to Europe, and has held positions at the Max Planck Institute in Munich and the University of Siegen, Germany. For most of this time, he has been based at the European Organisation for Nuclear Research (CERN) in Geneva. He is a member of the ALEPH experiment at the electron positron collider LEP.


Preface; Notation; 1. Fundamental Concepts; 2. Examples of Probability Functions; 3. The Monte Carlo Method; 4. Statistical Tests; 5. General Concepts of Parameter Estimation; 6. The Method of Maximum Likelihood; 7. The Method of Least Squares; 8. The Method of Moments; 9. Statistical Errors, Confidence Intervals and Limits; 10. Characteristic Functions and Related Examples; 11. Unfolding; Bibliography; Index