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:

     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 pounds per square inch. (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 (eds) (1994) _A Handbook of Small Data
     Sets._ Chapman & Hall.

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_E_x_a_m_p_l_e_s:

     library(nlme)
     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)

