def reduced_chi_squared(self, p=None):
if len(self._set_xdata)==<NUM_LIT:0> or len(self._set_ydata)==<NUM_LIT:0>: return None<EOL>if p is None: p = self.results[<NUM_LIT:0>]<EOL>chi2 = self.chi_squared(p)<EOL>dof  = self.degrees_of_freedom()<EOL>if not _s.fun.is_a_number(chi2) or not _s.fun.is_a_number(dof):<EOL><INDENT>return None<EOL><DEDENT>return _n.divide(self.chi_squared(p), self.degrees_of_freedom())<EOL>
Returns the reduced chi squared for all massaged data sets. 

p=None means use the fit results.