module for performing statistical calculations.
  This module (as you may notice) provides very few statistical 
  routines. It does, however, provide biweight (robust) estimators of 
  location and scale, as described in Beers et al. 1990 (AJ, 100, 32), in 
  addition to a robust least squares fitting routine that uses the biweight
  transform.
  Some routines may fail if they are passed lists with few items and 
  encounter a `divide by zero' error. Where this occurs, the function will 
  return None. An error message will be printed to the console when this 
  happens if astStats.REPORT_ERRORS=True (the default). Testing if an 
  astStats function returns None can be used to handle errors in 
  scripts.
  
    | float | 
        
          | mean(dataList) Calculates the mean average of a list of numbers.
 | source code |  | 
    | float | 
        
          | weightedMean(dataList) Calculates the weighted mean average of a two dimensional list 
      (value, weight) of numbers.
 | source code |  | 
    | float | 
        
          | stdev(dataList) Calculates the (sample) standard deviation of a list of numbers.
 | source code |  | 
    | float | 
        
          | rms(dataList) Calculates the root mean square of a list of numbers.
 | source code |  | 
    | float |  | 
    | float |  | 
    | float |  | 
    | float | 
        
          | MAD(dataList) Calculates the Median Absolute Deviation of a list of numbers.
 | source code |  | 
    | float | 
        
          | biweightLocation(dataList,
        tuningConstant) Calculates the biweight location estimator (like a robust average) of
      a list of numbers.
 | source code |  | 
    | float | 
        
          | biweightScale(dataList,
        tuningConstant) Calculates the biweight scale estimator (like a robust standard 
      deviation) of a list of numbers.
 | source code |  | 
    | dictionary | 
        
          | biweightClipped(dataList,
        tuningConstant,
        sigmaCut) Iteratively calculates biweight location and scale, using sigma 
      clipping, for a list of values.
 | source code |  | 
    | list |  | 
    | dictionary | 
        
          | OLSFit(dataList) Performs an ordinary least squares fit on a two dimensional list of 
      numbers.
 | source code |  | 
    | dictionary |  | 
    | dictionary |  | 
    | dictionary | 
        
          | weightedLSFit(dataList,
        weightType) Performs a weighted least squares fit on a three dimensional list of 
      numbers [x, y, y error].
 | source code |  | 
    | dictionary | 
        
          | biweightLSFit(dataList,
        tuningConstant,
        sigmaCut=None) Performs a weighted least squares fit, where the weights used are the
      biweight transforms of the residuals to the previous best fit .i.e.
 | source code |  | 
    | list |  | 
    | list |  | 
    | list | 
        
          | weightedBinner(data,
        weights,
        binMin,
        binMax,
        binTotal) Bins the input data, recorded frequency is sum of weights in bin.
 | source code |  |