Statistical Data Analysis

• Concise guide to statistical data analysis in the Physical Sciences
• Outstanding clarity of exposition, based on graduate course work, opens the subject for a wide audience
• Includes several hot topics usually not treated in texts on data analysis: neural networks, unfolding (i.e. inverseproblems, image restoration), comparison of classical and Bayesian statistics
• Examples and exercises provided on the World Wide Web (see author's page below)
• Competing texts: either highly theoretical or written at a much simpler level

Readership: physics, astrophysics, and nuclear physics; professionals and students of the Physical Sciences involved in data analysis.

Glen Cowan, Royal Holloway College, University of London

""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"

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