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Data Reduction and Error Analysis for the Physical Sciences
 
 
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Data Reduction and Error Analysis for the Physical Sciences [Paperback]

Philip Bevington (Author), D. Keith Robinson (Author)
4.0 out of 5 stars  See all reviews (14 customer reviews)

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Book Description

0072472278 978-0072472271 July 23, 2002 3rd
The purpose of this book is to provide an introduction to the concepts of statistical analysis of data for students at the undergraduate and graduate level, and to provide tools for data reduction and error analysis commonly required in the physical sciences. The presentation is developed from a practical point of view, including enough derivation to justify the results, but emphasizing methods of handling data more than theory. The text provides a variety of numerical and graphical techniques. Computer programs that support these techniques will be available on an accompanying website in both Fortran and C++.

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Data Reduction and Error Analysis for the Physical Sciences + An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements + Experiments in Modern Physics, Second Edition
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Product Details

  • Paperback: 336 pages
  • Publisher: McGraw-Hill Science/Engineering/Math; 3rd edition (July 23, 2002)
  • Language: English
  • ISBN-10: 0072472278
  • ISBN-13: 978-0072472271
  • Product Dimensions: 9.2 x 6.5 x 0.5 inches
  • Shipping Weight: 15.5 ounces (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #61,699 in Books (See Top 100 in Books)

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Customer Reviews

14 Reviews
5 star:
 (7)
4 star:
 (1)
3 star:
 (5)
2 star:
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Average Customer Review
4.0 out of 5 stars (14 customer reviews)
 
 
 
 
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59 of 60 people found the following review helpful:
5.0 out of 5 stars "Updated" classic, but still vintage '92, January 5, 1998
By A Customer
Robinson's second edition continues the late Bevington's tradition of clear and concise writing, making this book a priceless reference for scientists. Robinson has added discussions of modern problems such as resolving closely-spaced peaks in a spectrum. The new version also adds chapters on Monte Carlo techniques and maximum-likelihood analysis, both powerful tools for data analysis made possible by better computers.

The chapter structure has been modified considerably, so those who have grown comfortable with the first edition over the past decades may not be able to find things as easily. Other than that, most of the weaknesses are computer-related. Much has changed even since 1992.

Robinson added an appendix on graphical presentation. This sounds promising but is a pretty trivial discussion of when to use linear or logarithmic axes and the advantages of a historgram. Might be useful for a very young student, but these days playing with such things is easy in any graphing program.

Many of the computer code snippets have been removed. Most of them were only a few lines of code with lots of comment lines anyway. The codes that remain have been moved from the main text to a densely-packed appendix, which makes them more difficult to study while reading the text.

The codes themselves have been updated from old FORTRAN to a structured language, but I would have preferred C or FORTRAN 90 over the chosen PASCAL. The latter may be useful for undergraduate students, but I've never seen a PASCAL compiler in a working physics lab.

The included disk is a now-obsolete 5.25" floppy. I had to hunt for a machine that could read it and copy over to a 3.5" disc. The text claims repeatedly that the disc has both FORTRAN 77 and PASCAL routines on it, but my copy only has the PASCAL.

In the end, it's the textual content that is important, and this book is a fantastic basic discussion of data analysis and statistics for students and a great reference for the practicing scientist.

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13 of 14 people found the following review helpful:
5.0 out of 5 stars Great Book, May 22, 2002
By A Customer
I make measurements frequently and this book is great for providing the background to analyze your data.

I took undergraduate level statistics and it never really gave the practical applied background in how to analyze data. It merely presented concepts and presumed you knew how and why to apply them. This book is very good at helping you to understand the how and why.

I have read a number of other statistics book in search of the practical applied information provided in this book and did not find it in the other books.

The writing is clear and consice. There is enough background provided for even those unexposed to statistics.

I have not tried the software. Most of the formulas are easy to apply and can be implemented in simple programs or spreadsheets in very little time.

In short, I recommend this book to anyone making measurements of any kind.

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7 of 8 people found the following review helpful:
5.0 out of 5 stars All new but just as good, July 25, 2004
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This review is from: Data Reduction and Error Analysis for the Physical Sciences (Paperback)
This book seems to have been completely rewritten by the new author, only keeping the outline of the original, and it's for the better. The writing is as careful as the original, and as economical, so you have to master the early chapters or the rest is hopeless, as things start off slowly but quickly become difficult. It begins by considering the error in a single measurement, and proceeds to estimating errors derived from curve fitting. A few nuclear decay experiments provide examples throughout, and the author insists on calculating many quantities manually, even though in practice it would never be done that way. Some background topics like matrix algebra appear in the appendix too.
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Inside This Book (learn more)
First Sentence:
It is a well-established fact of scientific investigation that the first time an experiment is performed the results often bear all too little resemblance to the "truth" being sought. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
full error matrix, fiducial region, parent distribution, production vertex, instrumental uncertainties, same parent population, curvature matrix, fiducial volume, parameter increments, separate fits, uniform deviates, fitting function, random deviates, vertical error bars, analytic uncertainties, unity matrix, measuring uncertainties
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo
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