petrol                 package: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.

_U_s_a_g_e:

     data(petrol)

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

     The variables are as follows

     `_N_o' Crude oil sample identification label. (factor)

     `_S_G' Specific gravity, degrees API.  (Constant within sample.)

     `_V_P' Vapour pressure in psi. (Constant within sample.)

     `_V_1_0' Volatility of crude; ASTM 10% point. (Constant within
          sample.)

     `_E_P' Desired volatility of gasoline. (The end point.  Varies
          within sample.)

     `_Y' Yield as a percentage of crude.

_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)

