Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Books
  3. A Student's Guide to Data and Error Analysis
  • Cited by 39
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2011
Online ISBN:
9780511921247
DOI:
https://doi.org/10.1017/CBO9780511921247
Series:
Student's Guides
Subjects:
Physics and Astronomy, Engineering, Mathematical Methods, General and Classical Physics, Engineering: General Interest
Information

Book description

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.

Reviews

"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

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

Contents

Contents
References
References
References
Abramowitz, M. and Stegun, I. A. (1964). Handbook of Mathematical Functions. New York, Dover Publications.
Barlow, R. (1989). Statistics – A Guide to the Use of Statistical Methods in the Physical Sciences. New York, Wiley.
Bayes, T. (1763). Phil. Trans. Roy. Soc. 53, 370–418. Reprinted in Biometrika 45, 293–315 (1958).
Bayes, T. (1764). Phil. Trans. Roy. Soc. 54, 296–325.
Berendsen, H. J. C. (1997). Goed Meten met Fouten. University of Groningen.
Berendsen, H. J. C. (2007). Simulating the Physical World. Cambridge, Cambridge University Press.
Bevington, P. R. and Robinson, D. K. (2003). Data Reduction and Error Analysis for the Physical Sciences, 3rd edn. (first edn. (1969). New York, McGraw-Hill.
Beyer, W. H. (1991). CRC Standard Probability and Statistics Tables and Formulae. Boca Raton, Fla., CRC Press.
Birkes, D. and Dodge, Y. (1993). Alternative Methods of Regression. New York, Wiley.
Box, G. E. P. and Tiao, G. C. (1973). Bayesian Inference in Statistical Analysis. Reading, Mass., Addison-Wesley.
Cox, D. R. (2006). Principles of Statistical Inference. Cambridge, Cambridge University Press.
Cramér, H. (1946). Mathematical Methods of Statistics. Princeton, NJ, Princeton University Press.
CRC Handbook (each year). Handbook of Chemistry and Physics. Boca Raton, Fla., CRC Press.
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. London, Chapman & Hall.
Frenkel, D. and Smit, B. (2002). Understanding Molecular Simulation. From Algorithms to Applications. 2nd edn., San Diego, Academic Press.
Gardner, M. (1957). Fads and Fallacies in the Name of Science. New York, Dover Publications.
Gosset, W. S. (1908). The probable error of a mean. Biometrica 6, 1.
Hald, A. (2007). A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713–1935. New York, Springer.
Hammersley, J. M. and Handscomb, D. C. (1964). Monte Carlo Methods. London, Chapman and Hall.
Hess, B. (2002). Determining the shear viscosity of model liquids from molecular dynamics simulations. J. Chem. Phys. 116, 209–217.
Huber, P. J. and Ronchetti, E. M. (2009). Robust Statistics. 2nd edn., Hoboken, NJ, Wiley.
Huff, D. (1973). How to Lie with Statistics. Harmondsworth, Penguin Books.
Jeffreys, H. (1939). Theory of Probability. Oxford, Oxford University Press.
Lee, P. M. (1989). Bayesian Statistics: An Introduction. New York, Oxford University Press.
Petruccelli, J., Nandram, B. and Chen, M. (1999). Applied Statistics for Engineers and Scientists. Upper Saddle River, NJ, Prentice Hall.
Press, W. H.Teukolsky, A. A., Vetterling, W. T. and Flannery, B. P. (1992). Numerical Recipes, The Art of Scientific Computing. 2nd edn., Cambridge, Cambridge University Press.
Price, N. C. and Dwek, R. A. (1979). Principles and Problems in Physical Chemistry for Biochemists. 2nd edn., Oxford Press, Clarendon Press.
Skyrms, B. (1966). Choice and Chance. Belmont, Cal., Wadsworth Publishing.
Straatsma, T. P., Berendsen, H. J. C. and Stam, A. J. (1986). Estimation of statistical errors in molecular simulation calculations. Mol. Phys. 57, 89.
Taylor, J. R. (1997). An Introduction to Error Analysis. The Study of Uncertainties in Physical Measurements, 2nd edn. (first edn. 1982). Sausalito, Cal., University Science Books.
Van Kampen, N. G. (1981). Stochastic Processes in Physics and Chemistry. Amsterdam, North-Holland.
Walpole, R. E., Myers, R. H., Myers, S. L. and Ye, K. (2007). Probability and Statistics for Engineers and Scientists. 8th rev. edn., Upper Saddle River, NJ, Prentice Hall.
Wolter, K. M. (2007). Introduction to Variance Estimation. New York, Springer.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.