Rules of Thumb to choose an initial number of class intervals and refine that choice (potentially automatically) I was wondering if there are established rules of thumb (or algorithms) that, given a set of observations can help:


*

*choose an initial number of class intervals.

*refine that choice to a better number.


I could find talk of using square-root(N), where N is the number of observations as an initial guess of the number of class intervals.
Thanks in advance.
 A: The help of the R command hist http://stat.ethz.ch/R-manual/R-patched/library/grDevices/html/nclass.html has some references to algorithms for computing the number of the bins:
Sturges, H. A. (1926) The choice of a class interval. Journal of the American Statistical Association 21, 65–66.
Scott, D. W. (1979) On optimal and data-based histograms. Biometrika 66, 605–610.
Freedman, D. and Diaconis, P. (1981) On the histogram as a density estimator: L_2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 57, 453–476.
A: See also
HOGG, David W. Data analysis recipes: Choosing the binning for a histogram. arXiv preprint arXiv:0807.4820, 2008.
The abstract:

Data points are placed in bins when a histogram is created, but there
  is always a decision to be made about the number or width of the bins.
  This decision is often made arbitrarily or subjectively, but it need
  not be. A jackknife or leave-one-out cross-validation likelihood is
  defined and employed as a scalar objective function for optimization
  of the locations and widths of the bins. The objective is justified as
  being related to the histogram’s usefulness for predicting future
  data. The method works for data or histograms of any dimensionality.

