This book surveys probability theory and statistics, plus a number of essential numerical and analytical methods. Covers distribution functions of statistics, fits to two-dimensional datasheets and parameter estimation, Monte Carlo methods and Markov chains.

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.

Includes numerical tables of data for critical distribution functions, making the textbook a self-contained guide for students Covers the theory and practice of Monte Carlo Markov chains, a leading tool for the analysis of complex data sets, and a topic virtually absent in other textbooks 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

Autorentext

Massimiliano Bonamente is a professor of physics and astronomy at the University of Alabama in Huntsville (UAH), USA. He received his laurea degree cum laude in electrical engineering from the Universita' di Perugia, Italy in 1996, and a Ph.D. degree in physics from UAH in 2000. After postdoctoral work at the Osservatorio Astrofisico di Catania, Italy, and the NASA Marshall Space Flight Center, NASA, and as an assistant research professor at UAH, he began a tenure-track appointment at UAH as an assistant professor in 2007, and has been a full professor of physics and astronomy since 2014. He was selected as an outstanding faculty member in the College of Science at UAH in 2011, where he has taught a variety of courses for undergraduate and graduate students in the areas of general physics, mathematics and statistics, thermodynamics, and astrophysics. His research interests are primarily in high-energy astrophysics, cosmology and applied statistics, and he has published over 80refereed journal articles.

Klappentext

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.

Zusammenfassung
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.

Inhalt

Theory of Probability.- Random Variables and Their Distribution.- Sum and Functions of Random Variables.- Estimate of Mean and Variance and Confidence Intervals.- Distribution Function of Statistics and Hypothesis Testing.- Maximum Likelihood Fit to a Two-Variable Dataset.- Goodness of Fit and Parameter Uncertainty.- Comparison Between Models.- Monte Carlo Methods.- Markov Chains and Monte Carlo Markov Chains.- A: Numerical Tables.- B: Solutions.