These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discuss how to determine error bars on those estimates, and how to proceed for combinations of measured values. Techniques for fitting data to a given set of models will be described in the second part of these notes. This primer equips readers to properly derive the results covered, presenting the content in a style suitable for a physics audience. It also includes scripts in python, perl and gnuplot for performing a number of tasks in data analysis and fitting, thereby providing readers with a useful reference guide.

Autorentext
Research Professor of Physics. D. Phil., Oxford University 1973
Honor and Awards:
2014 Martin-Gutzwiller-Fellowship at the Max Planck Institute for the Physics of Complex Systems, Dresden
2012 Member of the American Academy of Arts and Sciences
2012 Humboldt Research Award (Humboldt Prize) of the Alexander von Humboldt Foundation
2009 Aneesur Rahman Prize of the American Physical Society for Computational Physics
2008 Outstanding Referee of the American Physical Society
2004-2005 Outstanding Faculty Award of the Division of Physical and Biological Sciences.
2003-2004 Excellence in Teaching Award of the University of California, Santa Cruz
1989, Fellow of the American Physical Society
1985 Maxwell Medal and Prize from the Institute of Physics (UK)

Inhalt
Introduction.- Averages and Error Bars.- Fitting Data to a Model.- Appendices.- References.