Customer Reviews

An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements

5 star:		(25)
4 star:		(6)
3 star:		(0)
2 star:		(0)
1 star:		(0)

 

 

Many undergraduate students in sciences and engineering must have encountered this experience: You conduct an experiment and collect the relevant data. You are asked to fit your data into a straight line by performing one or multiple linear regression. You are also to present any uncertainty and error in your data as well as calculation. You panic and scratch your head and don't know what's the appropriate procedure to carry out these analysis.

Here comes John Taylor's "An Introduction to Error Analysis", which introduces the study of uncertainties to students. The book assumes no prior knowledge and uses a plethora of pertinent examples (drawn from chemistry, physics, and engineering) to illustrate topics like propagation of uncertainties, random uncertainties, rejection of data, least-squares fitting, and distribution.

This book will save hours of studying and researching on error analysis method. It is very well-written and reader-friendly that lower division students will find it useful.

I bought the first edition of this book as an engineering graduate student in the early '80s, and it sparked my enduring fascination with statistical methods. I'm a software engineer with a major statistical software firm now, and I still refer to my copy regularly.

When I purchased the second edition this year for my son, who is a junior in college, I doubted that it could improve on the original.

I was wrong. John Taylor has outdone himself. The new examples are superb enhancements of an already outstanding text.

Even if you have achieved a high level of mathematical sophistication, this book is a great read. I find myself gaining great new insights to basic principles due to Taylor's logical developments. This is simply the best available introductory text on error analysis.

I had to get this book because it was one of the texts required for an undergradute physics course in data analysis. It turns out to have been a very useful book. I've used it as reference, for among other things, analyzing data collected for a port development project in Pusan, South Korea, developing software used for medical diagnostics equipment, and, most recently, in developing financial software.

As a professional engineer with a recurring need to crunch large amounts of statistical data, I find that this book is the perfect quick guide to things that forget and don't use that often. If has easy to follow language, and the best part about it is that I don't need to reread the whole thing to get a good explanation of a topic in the last chapter.

I had to knock it down a star because it is a touch out of date. The math is fine, but I wish that there was a companion that explained how to do some of the more uncommon operations using common spreadsheeting or data analysis software. Sometimes, figuring out how to get MS Excel to do what Taylor recommends that I do can be more cumbersome than anything else.

If nothing else, it has a great picture on the cover.

Taylor's book is simply amazing.

In little more than three hundred pages it manages to explain in a crystal clear manner concepts such as the propagation of errors (starting from simple cases and moving to the general treatment), the meaning of the standard deviation of the population, of the sample and of the mean, the maximum likelihood principle, hypothesis test and confidence levels, the chi squared test and the meaning of correlation.
True, this is not a textbook on mathematical statistic, so you won't find elaborate proofs here: much is left to the reader's intuition. But as the saying goes, 'is not a bug, it's a feature!'. This text makes you understand what all those books on statistics and probability are about (or at least some of their most important applications) and it does it so well that you will reach the end of each chapter asking yourself "oh, that was it?".

Part of the book is devoted to application of error analysis and you will find chapters on weighted means, on the rejection of data, plus linear and nonlinear regression. The exercises are intriguing and all in all this is a very well written book.

Even if you plan to study the matter deeper, on tougher textbooks, please consider preparing yourself to the tougher mathematical stuff by reading this wonderful book. You won't regret it. And possibly, you will come back to it from time to time.

Students in our cell biology laboratory use this book frequently to help us analyze our data. For example there is an excellent discussion about the difference between standard error and standard error of the mean. While it is aimed at undergraduate physicists and engineers, it will be practical for (and easily digested by) biologists as well.

Everyone who does and will design, execute and/or post-analyze measurements, regardless of where - classroom or real -, should read this book. I've studied statistics and probabilities and took a number of engineering and physical science classes, but none gave me such a coherent view of the problem and how to approach it. This book gives you the first clear & definite step forward with the subject.

This is a must have book for any one looking for introductory level. Its language is very easy, contents are very rich, full of solved example and illustrations. what i like the most is that you can read it quick and learn the subject yourself. I bought this one along with Robinson's book but for some reason I was slow reading Robinson's so I decided to switch to this one and found it very easy to read and grasp. each chapter end with summary so you can review what you learned quick. Chapters also has check points to ensure you really understand the key ideas.

This book does an excellent job of starting from the basics and then building on them at a comfortable pace until the reader has been taken to the more advanced and useful points of error analysis. This book is clearly written, with appropriate and helpful examples throughout. The practice problems at the end of the chapters are relevant and straightforward, further aiding the learning process. The author not only understands the subject of error analysis inside and out, he also knows the right way to teach it. Though the book approaches error analysis from a physics point of view, I found it very applicable to all of my scientific coursework, including biochemistry and (especially) analytical chemistry.