water                  package:alr3                  R Documentation

_C_a_l_i_f_o_r_n_i_a _w_a_t_e_r

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

     Can Southern California's water supply in future years be
     predicted from past data? One factor affecting water availability
     is stream runoff.  If runoff could be predicted, engineers,
     planners and policy makers could do their jobs more efficiently.
     Multiple linear regression models have been used in this regard.
     This dataset contains 43 years worth of precipitation measurements
     taken at six sites in the Owens Valley ( labeled APMAM, APSAB,
     APSLAKE, OPBPC, OPRC, and OPSLAKE), and stream runoff volume at a
     site near Bishop, California.

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

     This data frame contains the following columns:

     _Y_e_a_r collection year

     _A_P_M_A_M Snowfall in inches measurement site

     _A_P_S_A_B Snowfall in inches measurement site

     _A_P_S_L_A_K_E Snowfall in inches measurement site

     _O_P_B_P_C Snowfall in inches measurement site

     _O_P_R_C Snowfall in inches measurement site

     _O_P_S_L_A_K_E Snowfall in inches measurement site

     _B_S_A_A_M Stream runoff near Bishop, CA, in acre-feet

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

     Source:  http://www.stat.ucla.edu.

_R_e_f_e_r_e_n_c_e_s:

     Weisberg, S. (2005). _Applied Linear Regression_, 3rd edition. New
     York: Wiley, Problem 1.5.

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

     data(dwp)

