

petrol(MASS)                                 R Documentation

_N_. _L_. _P_r_a_t_e_r_'_s _P_e_t_r_o_l _R_e_f_i_n_e_r_y _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n_:

     The yield of a petroleum refining process with four
     covariates.  The crude oil appears to come from only 10
     distinct samples.

     These data were originally used by Prater (1956) to
     build an estimation equation for the yield of the
     refining process of crude oil to gasoline.

_A_r_g_u_m_e_n_t_s_:

      No: Crude oil sample identification label. (factor)

      SG: Specific gravity, degrees API.  (Constant within
          sample.)

      VP: Vapour pressure in psi. (Constant within sample.)

     V10: Volatility of crude; ASTM 10% point. (Constant
          within sample.)

      EP: Desired volatility of gasoline. (The end point.
          Varies within sample.)

       Y: Yield as a percentage of crude.

_F_o_r_m_a_t_:

     The variables are as follows

_S_o_u_r_c_e_:

     N. H. Prater (1956) Estimate gasoline yields from
     crudes.  Petroleum Refiner 35, 236-238.

     This dataset is also given in D. J. Hand, F. Daly, K.
     McConway, D.  Lunn, and E. Ostrowski E. (eds) (1993) A
     Handbook of Small Data Sets.  Chapman  Hall.

_E_x_a_m_p_l_e_s_:

     library(nlme)
     data(petrol)
     Petrol <- petrol
     Petrol[, 2:5] <- scale(as.matrix(Petrol[, 2:5]), scale = FALSE)
     pet3.lme <- lme(Y ~ SG + VP + V10 + EP,
                     random = ~ 1 | No, data = Petrol)
     pet3.lme <- update(pet3.lme, method = "ML")
     pet4.lme <- update(pet3.lme, fixed = Y ~ V10 + EP)
     anova(pet4.lme, pet3.lme)

