What is freedman diaconis rule?

Freedman-Diaconis Rule is a commonly used method for determining the number of classes or bins to be used in a histogram. This rule was proposed by David Freedman and Persi Diaconis in 1981. The rule suggests that the number of classes can be determined by taking the range of the data and dividing it by the smallest meaningful difference in the data.

The formula for the Freedman-Diaconis Rule is:

Number of classes = Range / (2 * IQR * n^(-1/3))

Where Range is the difference between the maximum and minimum values in the data, IQR is the interquartile range, and n is the number of observations in the data.

The rule suggests that the number of classes should be proportional to the size of the data set, but not too small or too large. If a histogram has too few bins, then important features of the data distribution may be missed. If it has too many bins, then the histogram may be too complicated to interpret.

Overall, the Freedman-Diaconis Rule is a valuable tool for creating effective and informative histograms.