Title: Data analysis for scientists and engineers /
Authors: Robinson, Edward L.,
Year: 2016
Subjects: Probabilities; Mathematical statistics; Engineering Data processing; Physics Data processing; Engineering Data processing; Mathematical statistics; Physics Data processing; Probabilities
Abstract: "Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering."--
Contents: Probability -- Some useful probability distribution functions -- Random numbers and Monte Carlo methods -- Elementary frequentist statistics -- Linear least squares estimation -- Nonlinear least squares estimation -- Bayesian statistics -- Introduction to Fourier analysis -- Analysis of sequences : power spectra and periodograms -- Analysis of sequences : convolution and covariance.