A Student's Guide to Data and Error Analysis

A concise, practical guide in statistical methods for experimental data handling; ideal for course use and a handy reference for researchers.

Herman J. C. Berendsen (Author)

9780521134927, Cambridge University Press

Paperback, published 7 April 2011

238 pages, 47 b/w illus. 12 tables 49 exercises
22.6 x 15.2 x 1.3 cm, 0.38 kg

"Overall, this would be a nice text or reference to accompany a short course in statistics for undergraduate science or engineering..also useful for researchers desiring a primer or review...Recommended." - CHOICE

All students taking laboratory courses within the physical sciences and engineering will benefit from this book, whilst researchers will find it an invaluable reference. This concise, practical guide brings the reader up-to-speed on the proper handling and presentation of scientific data and its inaccuracies. It covers all the vital topics with practical guidelines, computer programs (in Python), and recipes for handling experimental errors and reporting experimental data. In addition to the essentials, it also provides further background material for advanced readers who want to understand how the methods work. Plenty of examples, exercises and solutions are provided to aid and test understanding, whilst useful data, tables and formulas are compiled in a handy section for easy reference.

Part I. Data and Error Analysis: 1. Introduction
2. The presentation of physical quantities with their inaccuracies
3. Errors: classification and propagation
4. Probability distributions
5. Processing of experimental data
6. Graphical handling of data with errors
7. Fitting functions to data
8. Back to Bayes: knowledge as a probability distribution
Answers to exercises
Part II. Appendices: A1. Combining uncertainties
A2. Systematic deviations due to random errors
A3. Characteristic function
A4. From binomial to normal distributions
A5. Central limit theorem
A6. Estimation of the varience
A7. Standard deviation of the mean
A8. Weight factors when variances are not equal
A9. Least squares fitting
Part III. Python Codes
Part IV. Scientific Data: Chi-squared distribution
F-distribution
Normal distribution
Physical constants
Probability distributions
Student's t-distribution
Units.

Subject Areas: Maths for engineers [TBJ], Maths for scientists [PDE], Probability & statistics [PBT]