def covar(self):
        """
        The covariance matrix for the result :math:`\\beta`
        """
        if self._covar is None:
            self._covar = _np.linalg.inv(_np.dot(_np.transpose(self.X), self.X))
        return self._covar
def covar(self):
        """
        The covariance matrix for the result :math:`\\beta`
        """
        if self._covar is None:
            self._covar = _np.linalg.inv(_np.dot(_np.transpose(self.X), self.X))
        return self._covar
def covar ( self ) : if self . _covar is None : self . _covar = _np . linalg . inv ( _np . dot ( _np . transpose ( self . X ) , self . X ) ) return self . _covar
The covariance matrix for the result :math:`\\beta`
The covariance matrix for the result : math : \\ beta