A Student's Guide to Data and Error Analysis
Part of Student's Guides
- Author: Herman J. C. Berendsen, Rijksuniversiteit Groningen, The Netherlands
- Date Published: April 2011
- availability: Available
- format: Paperback
- isbn: 9780521134927
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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.
Read more- A concise guide to bring students up-to-speed quickly
- Provides the essential equations and methods that students need to handle and present scientific data
- Includes data sets, units and additional background information in the appendices
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×Product details
- Date Published: April 2011
- format: Paperback
- isbn: 9780521134927
- length: 238 pages
- dimensions: 226 x 152 x 13 mm
- weight: 0.38kg
- contains: 47 b/w illus. 12 tables 49 exercises
- availability: Available
Table of Contents
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.Instructors have used or reviewed this title for the following courses
- Chemical Engineering Lab
- Comparative Biomedical Imaging
- Data Analysis and model Estimation
- Introductory physics sequence with lab
- Life at low Reynolds number
- Materials Laboratory 2: Processing
- Measurements Laboratory ll
- Measurements and Instrumentation
- Microclimatology Instrumentation
- Physical Chemistry I and ll lab
- Research Methods for Sciences
- Statistics and Data Analysis in Geology
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