Gasoline                package:nlme                R Documentation

_R_e_f_i_n_e_r_y _y_i_e_l_d _o_f _g_a_s_o_l_i_n_e

_D_e_s_c_r_i_p_t_i_o_n:

     The 'Gasoline' data frame has 32 rows and 6 columns.

_F_o_r_m_a_t:

     This data frame contains the following columns:

     _y_i_e_l_d a numeric vector giving the percentage of crude oil
          converted to gasoline after distillation and fractionation

     _e_n_d_p_o_i_n_t a numeric vector giving the temperature (degrees F) at
          which all the gasoline is vaporized

     _S_a_m_p_l_e an ordered factor giving the inferred crude oil sample
          number

     _A_P_I a numeric vector giving the crude oil gravity (degrees API)

     _v_a_p_o_r a numeric vector giving the vapor pressure of the crude oil
          (lbf/in^2)

     _A_S_T_M a numeric vector giving the crude oil 10% point ASTM-the
          temperature at which 10% of the crude oil has become vapor. 


_D_e_t_a_i_l_s:

     Prater (1955) provides data on crude oil properties and gasoline
     yields.  Atkinson (1985) uses these data to illustrate the use of
     diagnostics in multiple regression analysis.  Three of the
     covariates-'API', 'vapor', and 'ASTM'-measure characteristics of
     the crude oil used to produce the gasoline.  The other covariate -
     'endpoint'-is a characteristic of the refining process. Daniel and
     Wood (1980) notice that the covariates characterizing the crude
     oil occur in only ten distinct groups and conclude that the data
     represent responses measured on ten different crude oil samples.

_S_o_u_r_c_e:

     Prater, N. H. (1955), Estimate gasoline yields from crudes,
     _Petroleum Refiner_, *35* (5).

     Atkinson, A. C. (1985), _Plots, Transformations, and Regression_,
     Oxford Press, New York.

     Daniel, C. and Wood, F. S. (1980), _Fitting Equations to Data_,
     Wiley, New York

     Venables, W. N. and Ripley, B. D. (1999) _Modern Applied
     Statistics with S-PLUS (3rd ed)_, Springer, New York.