Data analysis for scientists and engineers (Book, 2016) [WorldCat.org]
skip to content
Data analysis for scientists and engineers
Checking...

Data analysis for scientists and engineers

Author: Edward L Robinson, (Professor of astronomy)
Publisher: Princeton : Princeton University Press, [2016] ©2016
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"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  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Document Type: Book
All Authors / Contributors: Edward L Robinson, (Professor of astronomy)
ISBN: 9780691169927 0691169926
OCLC Number: 944469135
Description: xiii, 393 pages : illustrations ; 25 cm
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.
Responsibility: Edward L. Robinson.

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."--

Reviews

Editorial reviews

Publisher Synopsis

"Robinson's text is an excellent overview of modern statistical techniques and is sure to become a definitive reference. He ably and concisely presents all of the necessary foundational mathematics Read more...

 
User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.