index unit ground_max ground_min ground_avg air_max air_min air_avg evap 01 1 84 65 147 85 59 151 30 02 1 84 65 149 86 61 159 34 03 1 79 66 142 83 64 152 33 04 1 81 67 147 83 65 158 26 05 1 84 68 167 88 69 180 41 06 1 74 66 131 77 67 147 4 07 1 73 66 131 78 69 159 5 08 1 75 67 134 84 68 159 20 09 1 84 68 161 89 71 195 31 10 1 86 72 169 91 76 206 38 11 1 88 73 178 91 76 208 43 12 1 90 74 187 94 76 211 47 13 1 88 72 171 94 75 211 45 14 1 88 72 171 92 70 201 45 15 1 81 69 154 87 68 167 11 16 1 79 68 149 83 68 162 10 17 1 84 69 160 87 66 173 30 18 1 84 70 160 87 68 177 29 19 1 84 70 168 88 70 169 23 20 1 77 67 147 83 66 170 16 21 1 87 67 166 92 67 196 37 22 1 89 69 171 92 72 199 50 23 1 89 72 180 94 72 204 36 24 1 93 72 186 92 73 201 54 25 1 93 74 188 93 72 206 44 # x25.txt # # Reference: # # S Chatterjee, B Price, # Statistical Theory and Methodology, # Wiley, 1977, page 61. # # Helmut Spaeth, # Mathematical Algorithms for Linear Regression, # Academic Press, 1991, # ISBN 0-12-656460-4. # # Discussion: # # The daily amount of evaporation from the ground can be represented # as a function of ground and air temperatures. # # There are 25 rows of data. Data includes: # # I, the index; # A0, 1; # A1, the maximum ground temperature, # A2, the minimum ground temperature, # A3, an index for the daily average ground temperature, # A4, the maximum air temperature, # A5, the minimum air temperature, # A6, an index for the daily average air temperature, # B, the daily quantity of evaporated water. # # We seek a model of the form: # # B = A0 * X0 + A1 * X1 + A2 * X2 + A3 * X3 + A4 * X4 + A5 * X5 + A6 * X6 #