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Data Reduction and Error Analysis for the Physical Sciences Paperback – August 1, 2002

ISBN-13: 978-0071199261 ISBN-10: 0071199268 Edition: 3rd

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Product Details

  • Paperback: 320 pages
  • Publisher: McGraw-Hill; 3rd edition (August 1, 2002)
  • Language: English
  • ISBN-10: 0071199268
  • ISBN-13: 978-0071199261
  • Product Dimensions: 6 x 0.5 x 9 inches
  • Shipping Weight: 1 pounds
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (20 customer reviews)
  • Amazon Best Sellers Rank: #157,303 in Books (See Top 100 in Books)

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Customer Reviews

I would recommend this book as the first book that any scientist in training, in any field, read.
Solanum
If Lorentzian (Cauchy) distribution had the mean, the law of large numbers would apply to it, but in fact it does not.
Peter B
This book introduced me to such concepts as variance, Chi-squared, and least squares linear regression.
Ulfilas

Most Helpful Customer Reviews

62 of 63 people found the following review helpful By A Customer on January 5, 1998
Format: Paperback
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.
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13 of 14 people found the following review helpful By A Customer on May 22, 2002
Format: Paperback
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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8 of 9 people found the following review helpful By Alex Zaharakis on December 13, 2009
Format: Paperback
Simply put, not as clear as John Taylor's error analysis book. If you want a good explanation for how and why to approach error analysis, this book does not do the job.
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7 of 8 people found the following review helpful By misterbeets on July 25, 2004
Format: 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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3 of 3 people found the following review helpful By Peter B on March 3, 2014
Format: Paperback
This book contains a lot of fundamental mistakes. I’m not talking about typos that some people complain about. I mean some fundamental mistakes, where the corrected information is written in fairly basic probability and statistics books. Here are some of them:

1. p.31: The authors claim that the Lorentzian (Cauchy) distribution has the mean mu. In fact, the mean is not defined. The parameter mu is the median, but not the mean (although the distribution is symmetric). If the importance of this fact is not clear to you, here is an example. If Lorentzian (Cauchy) distribution had the mean, the law of large numbers would apply to it, but in fact it does not. Google Cauchy distribution for more info.
2. p. 66 (both figures): The authors claim that the distribution of the number of points in each bin is Poisson. In fact, it is binomial. Although binomial converges to Poisson, the approximation is reasonable for really small p (think of variance of binomial, which is (1-p) times variance of Poisson (lambda=n*p), so with p=0.1 we still get 10% difference).
3. p. 67 formula (4.32): The authors divide by variance, which may seem intuitive, but in fact you are supposed to divide by the Expected Count. Since they incorrectly assume Poisson, they end up with the correct denominator n*p (lucky for them). If they correctly used the binomial, they would get n*p*(1-p), which is incorrect. If you correctly use binomial and the Expected Count, you get the correct denominator n*p.
4. p. 67 formula (4.33): The first part of that equation is correct, but then the authors feel the need to replace n*p with the observed count (h(x)), assuming that n*p is approximately h(x). You never want to do this for two reasons:

a.
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