Statistics and Analysis of Scientific Data covers
the foundations of probability theory and statistics, and a number of
numerical and analytical methods that are essential for the present-day
analyst of scientific data. Topics covered include probability theory,
distribution functions of statistics, fits to two-dimensional datasheets
and parameter estimation, Monte Carlo methods and Markov chains. Equal
attention is paid to the theory and its practical application, and
results from classic experiments in various fields are used to
illustrate the importance of statistics in the analysis of scientific
data. The main pedagogical method is a theory-then-application
approach, where emphasis is placed first on a sound understanding of the
underlying theory of a topic, which becomes the basis for an efficient
and proactive use of the material for practical applications. The level
is appropriate for undergraduates and beginning graduate students, and
as a reference for the experienced researcher. Basic calculus is used in
some of the derivations, and no previous background in probability and
statistics is required. The book includes many numerical tables of data,
as well as exercises and examples to aid the students' understanding of
the topic.