- Paperback: 336 pages
- Publisher: McGraw-Hill Education; 3rd edition (July 23, 2002)
- Language: English
- ISBN-10: 0072472278
- ISBN-13: 978-0072472271
- Product Dimensions: 6.3 x 0.6 x 9.1 inches
- Shipping Weight: 15.5 ounces (View shipping rates and policies)
- Average Customer Review: 4.2 out of 5 stars See all reviews (25 customer reviews)
- Amazon Best Sellers Rank: #74,937 in Books (See Top 100 in Books)
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Data Reduction and Error Analysis for the Physical Sciences 3rd Edition
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Top Customer Reviews
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.Read more ›
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.Read more ›
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.
In My opinion, McGraw Hill has done a terrible disservice to the authors and should be embarrassed at doing such a poor job of publishing an otherwise excellent book.
Most Recent Customer Reviews
The revised addition is still the gold standard if you want a concise overview of standard data and error analysis techniques. A great reference.Published 3 months ago by S. Doney
Any older but still reliable book. Another perspective on data/uncertainty analysis.Published 12 months ago by Mario Valdez
This is the standard text for data analysis in physics at an advanced undergraduate or graduate level. Read morePublished 19 months ago by S. Dorsher
This is a must for experimental science. Also the online avaible codes are great. I wrote a good nonlinear least squares program that can fit almost any function to data. Read morePublished on February 6, 2014 by dgs
Book arrived in a timely manner and the used material was just as the summary described. I found it a good buy and satisfactory to my needs.Published on December 31, 2013 by Anthony
I still have the issue I came to use extensively back in the 70's - and I still do. This new version keeps on being useful for both the undergrad and grad student and for the... Read morePublished on October 24, 2013 by Mariangela de Oliveira-Abans
I'm a grad student and this seems like a very useful book.... I learned about it in a meeting few weeks ago when one of my adviser's friends recommended me to buy it. Read morePublished on January 2, 2013 by S. S. H.