Data analysis

Description

This book bridges the gap between statistical theory and physcal experiment. It provides a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasizes concise but rigorous mathematics but always retains its focus on applications. The reader is presumed to have a sound basic knowledge of differential and integral calulus and some knowledge of vectors and matrices (an appendix develops the vector and matrix methods used and provides a collection of related computer routines). After an introduction of probability, random variables, computer generation of random numbers (Monte Carlo methods) and impotrtant distributions (such as the biomial, Poisson, and normal distributions), the book turns to a discussion of statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with the discussion of several important stistical methods: least squares, analysis of variance, polynomial regression, and analysis of tiem series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae. The book is intended for graduate students setting out on experimental research, but it should also provide a useful reference and programming guide for experienced experimenters. A large number of problems (many with hints or solutions) serve to help the reader test.

Subjects

Links

• SpringerLink

Other Editions

• Data analysis: statistical and computational methods for scientists and engineers[electronic resource] :Springer1999-01-01
• Data AnalysisPaperbackSpringer2011-10-06
• Data AnalysisSpringer International Publishing AG2014-01-01
• Data AnalysishardcoverSpringer2014-02-15
• Data AnalysisPaperbackSpringer2017-04-30