stackloss                package:base                R Documentation

_B_r_o_w_n_l_e_e'_s _S_t_a_c_k _L_o_s_s _P_l_a_n_t _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Operational data of a plant for the oxidation of ammonia to nitric
     acid.

_U_s_a_g_e:

     data(stackloss)

_F_o_r_m_a_t:

     `stackloss' is a data frame with 21 observations on 4 variables.

       [,1]  `Air Flow'    Flow of cooling air
       [,2]  `Water Temp'  Cooling Water Inlet Temperature
       [,3]  `Acid Conc.'  Concentration of acid [per 1000, minus 500]
       [,4]  `stack.loss'  Stack loss

     For compatibility with S-PLUS, the data sets `stack.x', a matrix
     with the first three (independent) variables of the data frame,
     and `stack.loss', the numeric vector giving the fourth (dependent)
     variable, are provided as well.

_D_e_t_a_i_l_s:

     ``Obtained from 21 days of operation of a plant for the oxidation
     of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced
     are absorbed in a countercurrent absorption tower.''  (Brownlee,
     cited by Dodge, slightly reformatted by MM.)

     `Air Flow' represents the rate of operation of the plant. `Water
     Temp' is the temperature of cooling water circulated through coils
     in the absorption tower. `Acid Conc.' is the concentration of the
     acid circulating, minus 50, times 10: that is, 89 corresponds to
     58.9 per cent acid. `stack.loss' (the dependent variable) is 10
     times the percentage of the ingoing ammonia to the plant that
     escapes from the absorption column unabsorbed; that is, an
     (inverse) measure of the over-all efficiency of the plant.

_S_o_u_r_c_e:

     Brownlee, K. A. (1960, 2nd ed. 1965) Statistical Theory and
     Methodology in Science and Engineering. New York: Wiley. pp.
     491-500.

_R_e_f_e_r_e_n_c_e_s:

     Dodge, Y. (1996) The guinea pig of multiple regression. In: Robust
     Statistics, Data Analysis, and Computer Intensive Methods; In
     Honor of Peter Huber's 60th Birthday, 1996, Lecture Notes in
     Statistics 109, Springer-Verlag, New York.

_E_x_a_m_p_l_e_s:

     data(stackloss)
     summary(lm.stack <- lm(stack.loss ~ stack.x))

