code: def chi_squareds(self, p=None):
          
          if len(self._set_xdata)==0 or len(self._set_ydata)==0: return None
          
          if p is None: p = self.results[0]
  
          # get the residuals
          rs = self.studentized_residuals(p)
  
          # Handle the none case
          if rs == None: return None
  
          # square em and sum em.
          cs = []
          for r in rs: cs.append(sum(r**2))
          return cs
docs: Returns a list of chi squared for each data set. Also uses ydata_massaged.
  
          p=None means use the fit results