_R_e_g_r_e_s_s_i_o_n _D_i_a_g_n_o_s_t_i_c_s
	
	     lm.influence(z)
	     rstudent(z)
	     dfbetas(z)
	     dffits(z)
	     covratio(z)
	
	_A_r_g_u_m_e_n_t_s:
	
	           z : the results returned by lm.
	
	_D_e_s_c_r_i_p_t_i_o_n:
	
	     This suite of functions can be used to compute some of
	     the regression diagnostics discussed in Belsley, Kuh
	     and Welsch (1980).
	
	     The primary function is lm.influence which is used by
	     the other functions to compute the basic statistics of
	     interest.  The other functions produce particular diag-
	     nostic quantities from the fundamental quantities pro-
	     duced by lm.influence.
	
	_V_a_l_u_e_s:
	
	     A list containing the following components:
	
	    hat : a vector containing the diagonal of the ``hat''
	          matrix.
	
	   coef : the change in the estimated coefficients which
	          results when the i-th case is dropped from the
	          regression is contained in the i-th row of this
	          matrix.
	
	  sigma : a vector whose i-th element contains the estimate
	          of the residual standard deviation obtained when
	          the i-th case is dropped from the regression.
	
	_R_e_f_e_r_e_n_c_e_s:
	
	     Belsley, D. A., E. Kuh and R. E. Welsch (1980).
	     Regression Diagnostics.  New York: Wiley.
	
	_S_e_e _A_l_s_o:
	
	     lm.
	
	_E_x_a_m_p_l_e_s:
	
	     # Analysis of the life-cycle savings data
	     # given in Belsley, Kuh and Welsch.
	     data(savings)
	     z <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data=savings)
	     rstudent(z)
	     dfbetas(z)
	     dffits(z)
	     covratio(z)