Freedman-Diaconis rule
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In statistics, the Freedman-Diaconis rule is used to specify the size of the bins to be used in a histogram; which will tend to smooth the data. The general equation for the rule is:
where
-
- is the data
- is the interquartile range of the data
- is the number of observations in the sample
[edit] Sturges' rule
Another approach is the use Sturges' rule: use a bin so large that there are about 1 + log2n non-empty bins.
For a thousand items, the Freedman-Diaconis rule would suggest about 40 bars; Sturges, 11.
[edit] Reference
- Freedman D and Diaconis P (1981). On the histogram as a density estimator:L2 theory. Probability Theory and Related Fields. 57(4): 453-476