For a set of empirical measurements sampled from some probability distribution, the Freedman-Diaconis rule is designed to minimize the difference between the area under the empirical probability distribution and the area under the theoretical probability distribution.[clarification needed]
The general equation for the rule is:
where is the interquartile range of the data and is the number of observations in the sample
Another approach is to use Sturges' rule: use a bin so large that there are about non-empty bins (Scott, 2009). This works well for n under 200, but was found to be inaccurate for large n. For a discussion and an alternative approach, see Birgé and Rozenholc.
- Freedman, David; Diaconis, Persi (December 1981). "On the histogram as a density estimator: L2 theory". Probability Theory and Related Fields. 57 (4): 453–476. CiteSeerX 10.1.1.650.2473. doi:10.1007/BF01025868. ISSN 0178-8051.
- Scott, D.W. (2009). "Sturges' rule". WIREs Computational Statistics. 1 (3): 303–306. doi:10.1002/wics.35.
- Hyndman, R.J. (1995). "The problem with Sturges' rule for constructing histograms" (PDF).
- Birgé, L.; Rozenholc, Y. (2006). "How many bins should be put in a regular histogram". ESAIM: Probability and Statistics. 10: 24–45. CiteSeerX 10.1.1.3.220. doi:10.1051/ps:2006001.
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