def y_fit(self):
        """
        Using the result of the linear least squares, the result of :math:`X_{ij}\\beta_i`
        """
        if self._y_fit is None:
            self._y_fit = _np.dot(self.X_unweighted, self.beta)
        return self._y_fit
def y_fit(self):
        """
        Using the result of the linear least squares, the result of :math:`X_{ij}\\beta_i`
        """
        if self._y_fit is None:
            self._y_fit = _np.dot(self.X_unweighted, self.beta)
        return self._y_fit
def y_fit ( self ) : if self . _y_fit is None : self . _y_fit = _np . dot ( self . X_unweighted , self . beta ) return self . _y_fit
Using the result of the linear least squares, the result of :math:`X_{ij}\\beta_i`
Using the result of the linear least squares the result of : math : X_ { ij } \\ beta_i