code: def cov(self, ddof=None, bias=0):
          '''The covariance matrix from the aggregate sample. It accepts an
          optional parameter for the degree of freedoms.
  
          :parameter ddof: If not ``None`` normalization is by (N - ddof), where N is
              the number of observations; this overrides the value implied by bias.
              The default value is None.
          '''
          N = self.n
          M = N if bias else N-1
          M = M if ddof is None else N-ddof
          return (self.sxx - outer(self.sx,self.sx)/N)/f cov(self, ddof=None, bias=0):
          '''The covariance matrix from the aggregate sample. It accepts an
          optional parameter for the degree of freedoms.
  
          :parameter ddof: If not ``None`` normalization is by (N - ddof), where N is
              the number of observations; this overrides the value implied by bias.
              The default value is None.
          '''
          N = self.n
          M = N if bias else N-1
          M = M if ddof is None else N-ddof
          return (self.sxx - outer(self.sx,self.sx)/N)/M
docs: The covariance matrix from the aggregate sample. It accepts an
          optional parameter for the degree of freedoms.
  
          :parameter ddof: If not ``None`` normalization is by (N - ddof), where N is
              the number of observations; this overrides the value implied by bias.
              The default value is None.