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Data X:
7 0.27 0.36 20.7 0.045 45 170 1.001 3 0.45 8.8 6 6.3 0.3 0.34 1.6 0.049 14 132 0.994 3.3 0.49 9.5 6 8.1 0.28 0.4 6.9 0.05 30 97 0.9951 3.26 0.44 10.1 6 7.2 0.23 0.32 8.5 0.058 47 186 0.9956 3.19 0.4 9.9 6 7.2 0.23 0.32 8.5 0.058 47 186 0.9956 3.19 0.4 9.9 6 8.1 0.28 0.4 6.9 0.05 30 97 0.9951 3.26 0.44 10.1 6 6.2 0.32 0.16 7 0.045 30 136 0.9949 3.18 0.47 9.6 6 7 0.27 0.36 20.7 0.045 45 170 1.001 3 0.45 8.8 6 6.3 0.3 0.34 1.6 0.049 14 132 0.994 3.3 0.49 9.5 6 8.1 0.22 0.43 1.5 0.044 28 129 0.9938 3.22 0.45 11 6 8.1 0.27 0.41 1.45 0.033 11 63 0.9908 2.99 0.56 12 5 8.6 0.23 0.4 4.2 0.035 17 109 0.9947 3.14 0.53 9.7 5 7.9 0.18 0.37 1.2 0.04 16 75 0.992 3.18 0.63 10.8 5 6.6 0.16 0.4 1.5 0.044 48 143 0.9912 3.54 0.52 12.4 7 8.3 0.42 0.62 19.25 0.04 41 172 1.0002 2.98 0.67 9.7 5 6.6 0.17 0.38 1.5 0.032 28 112 0.9914 3.25 0.55 11.4 7 6.3 0.48 0.04 1.1 0.046 30 99 0.9928 3.24 0.36 9.6 6 6.2 0.66 0.48 1.2 0.029 29 75 0.9892 3.33 0.39 12.8 8 7.4 0.34 0.42 1.1 0.033 17 171 0.9917 3.12 0.53 11.3 6 6.5 0.31 0.14 7.5 0.044 34 133 0.9955 3.22 0.5 9.5 5 6.2 0.66 0.48 1.2 0.029 29 75 0.9892 3.33 0.39 12.8 8 6.4 0.31 0.38 2.9 0.038 19 102 0.9912 3.17 0.35 11 7 6.8 0.26 0.42 1.7 0.049 41 122 0.993 3.47 0.48 10.5 8 7.6 0.67 0.14 1.5 0.074 25 168 0.9937 3.05 0.51 9.3 5 6.6 0.27 0.41 1.3 0.052 16 142 0.9951 3.42 0.47 10 6 7 0.25 0.32 9 0.046 56 245 0.9955 3.25 0.5 10.4 6 6.9 0.24 0.35 1 0.052 35 146 0.993 3.45 0.44 10 6 7 0.28 0.39 8.7 0.051 32 141 0.9961 3.38 0.53 10.5 6 7.4 0.27 0.48 1.1 0.047 17 132 0.9914 3.19 0.49 11.6 6 7.2 0.32 0.36 2 0.033 37 114 0.9906 3.1 0.71 12.3 7 8.5 0.24 0.39 10.4 0.044 20 142 0.9974 3.2 0.53 10 6 8.3 0.14 0.34 1.1 0.042 7 47 0.9934 3.47 0.4 10.2 6 7.4 0.25 0.36 2.05 0.05 31 100 0.992 3.19 0.44 10.8 6 6.2 0.12 0.34 1.5 0.045 43 117 0.9939 3.42 0.51 9 6 5.8 0.27 0.2 14.95 0.044 22 179 0.9962 3.37 0.37 10.2 5 7.3 0.28 0.43 1.7 0.08 21 123 0.9905 3.19 0.42 12.8 5 6.5 0.39 0.23 5.4 0.051 25 149 0.9934 3.24 0.35 10 5 7 0.33 0.32 1.2 0.053 38 138 0.9906 3.13 0.28 11.2 6 7.3 0.24 0.39 17.95 0.057 45 149 0.9999 3.21 0.36 8.6 5 7.3 0.24 0.39 17.95 0.057 45 149 0.9999 3.21 0.36 8.6 5 6.7 0.23 0.39 2.5 0.172 63 158 0.9937 3.11 0.36 9.4 6 6.7 0.24 0.39 2.9 0.173 63 157 0.9937 3.1 0.34 9.4 6 7 0.31 0.26 7.4 0.069 28 160 0.9954 3.13 0.46 9.8 6 6.6 0.24 0.27 1.4 0.057 33 152 0.9934 3.22 0.56 9.5 6 6.7 0.23 0.26 1.4 0.06 33 154 0.9934 3.24 0.56 9.5 6 7.4 0.18 0.31 1.4 0.058 38 167 0.9931 3.16 0.53 10 7 6.2 0.45 0.26 4.4 0.063 63 206 0.994 3.27 0.52 9.8 4 6.2 0.46 0.25 4.4 0.066 62 207 0.9939 3.25 0.52 9.8 5 7 0.31 0.26 7.4 0.069 28 160 0.9954 3.13 0.46 9.8 6 6.9 0.19 0.35 5 0.067 32 150 0.995 3.36 0.48 9.8 5 7.2 0.19 0.31 1.6 0.062 31 173 0.9917 3.35 0.44 11.7 6 6.6 0.25 0.29 1.1 0.068 39 124 0.9914 3.34 0.58 11 7 6.2 0.16 0.33 1.1 0.057 21 82 0.991 3.32 0.46 10.9 7 6.4 0.18 0.35 1 0.045 39 108 0.9911 3.31 0.35 10.9 6 6.8 0.2 0.59 0.9 0.147 38 132 0.993 3.05 0.38 9.1 6 6.9 0.25 0.35 1.3 0.039 29 191 0.9908 3.13 0.52 11 6 7.2 0.21 0.34 11.9 0.043 37 213 0.9962 3.09 0.5 9.6 6 6 0.19 0.26 12.4 0.048 50 147 0.9972 3.3 0.36 8.9 6 6.6 0.38 0.15 4.6 0.044 25 78 0.9931 3.11 0.38 10.2 6 7.4 0.2 0.36 1.2 0.038 44 111 0.9926 3.36 0.34 9.9 6 6.8 0.22 0.24 4.9 0.092 30 123 0.9951 3.03 0.46 8.6 6 6 0.19 0.26 12.4 0.048 50 147 0.9972 3.3 0.36 8.9 6 7 0.47 0.07 1.1 0.035 17 151 0.991 3.02 0.34 10.5 5 6.6 0.38 0.15 4.6 0.044 25 78 0.9931 3.11 0.38 10.2 6 7.2 0.24 0.27 1.4 0.038 31 122 0.9927 3.15 0.46 10.3 6 6.2 0.35 0.03 1.2 0.064 29 120 0.9934 3.22 0.54 9.1 5 6.4 0.26 0.24 6.4 0.04 27 124 0.9903 3.22 0.49 12.6 7 6.7 0.25 0.13 1.2 0.041 81 174 0.992 3.14 0.42 9.8 5 6.7 0.23 0.31 2.1 0.046 30 96 0.9926 3.33 0.64 10.7 8 7.4 0.24 0.29 10.1 0.05 21 105 0.9962 3.13 0.35 9.5 5 6.2 0.27 0.43 7.8 0.056 48 244 0.9956 3.1 0.51 9 6 6.8 0.3 0.23 4.6 0.061 50.5 238.5 0.9958 3.32 0.6 9.5 5 6 0.27 0.28 4.8 0.063 31 201 0.9964 3.69 0.71 10 5 8.6 0.23 0.46 1 0.054 9 72 0.9941 2.95 0.49 9.1 6 6.7 0.23 0.31 2.1 0.046 30 96 0.9926 3.33 0.64 10.7 8 7.4 0.24 0.29 10.1 0.05 21 105 0.9962 3.13 0.35 9.5 5 7.1 0.18 0.36 1.4 0.043 31 87 0.9898 3.26 0.37 12.7 7 7 0.32 0.34 1.3 0.042 20 69 0.9912 3.31 0.65 12 7 7.4 0.18 0.3 8.8 0.064 26 103 0.9961 2.94 0.56 9.3 5 6.7 0.54 0.28 5.4 0.06 21 105 0.9949 3.27 0.37 9 5 6.8 0.22 0.31 1.4 0.053 34 114 0.9929 3.39 0.77 10.6 6 7.1 0.2 0.34 16 0.05 51 166 0.9985 3.21 0.6 9.2 6 7.1 0.34 0.2 6.1 0.063 47 164 0.9946 3.17 0.42 10 5 7.3 0.22 0.3 8.2 0.047 42 207 0.9966 3.33 0.46 9.5 6 7.1 0.43 0.61 11.8 0.045 54 155 0.9974 3.11 0.45 8.7 5 7.1 0.44 0.62 11.8 0.044 52 152 0.9975 3.12 0.46 8.7 6 7.2 0.39 0.63 11 0.044 55 156 0.9974 3.09 0.44 8.7 6 6.8 0.25 0.31 13.3 0.05 69 202 0.9972 3.22 0.48 9.7 6 7.1 0.43 0.61 11.8 0.045 54 155 0.9974 3.11 0.45 8.7 5 7.1 0.44 0.62 11.8 0.044 52 152 0.9975 3.12 0.46 8.7 6 7.2 0.39 0.63 11 0.044 55 156 0.9974 3.09 0.44 8.7 6 6.1 0.27 0.43 7.5 0.049 65 243 0.9957 3.12 0.47 9 5 6.9 0.24 0.33 1.7 0.035 47 136 0.99 3.26 0.4 12.6 7 6.9 0.21 0.33 1.8 0.034 48 136 0.9899 3.25 0.41 12.6 7 7.5 0.17 0.32 1.7 0.04 51 148 0.9916 3.21 0.44 11.5 7 7.1 0.26 0.29 12.4 0.044 62 240 0.9969 3.04 0.42 9.2 6 6 0.34 0.66 15.9 0.046 26 164 0.9979 3.14 0.5 8.8 6 8.6 0.265 0.36 1.2 0.034 15 80 0.9913 2.95 0.36 11.4 7 9.8 0.36 0.46 10.5 0.038 4 83 0.9956 2.89 0.3 10.1 4 6 0.34 0.66 15.9 0.046 26 164 0.9979 3.14 0.5 8.8 6 7.4 0.25 0.37 13.5 0.06 52 192 0.9975 3 0.44 9.1 5 7.1 0.12 0.32 9.6 0.054 64 162 0.9962 3.4 0.41 9.4 5 6 0.21 0.24 12.1 0.05 55 164 0.997 3.34 0.39 9.4 5 7.5 0.305 0.4 18.9 0.059 44 170 1 2.99 0.46 9 5 7.4 0.25 0.37 13.5 0.06 52 192 0.9975 3 0.44 9.1 5 7.3 0.13 0.32 14.4 0.051 34 109 0.9974 3.2 0.35 9.2 6 7.1 0.12 0.32 9.6 0.054 64 162 0.9962 3.4 0.41 9.4 5 7.1 0.23 0.35 16.5 0.04 60 171 0.999 3.16 0.59 9.1 6 7.1 0.23 0.35 16.5 0.04 60 171 0.999 3.16 0.59 9.1 6 6.9 0.33 0.28 1.3 0.051 37 187 0.9927 3.27 0.6 10.3 5 6.5 0.17 0.54 8.5 0.082 64 163 0.9959 2.89 0.39 8.8 6 7.2 0.27 0.46 18.75 0.052 45 255 1 3.04 0.52 8.9 5 7.2 0.31 0.5 13.3 0.056 68 195 0.9982 3.01 0.47 9.2 5 6.7 0.41 0.34 9.2 0.049 29 150 0.9968 3.22 0.51 9.1 5 6.7 0.41 0.34 9.2 0.049 29 150 0.9968 3.22 0.51 9.1 5 5.5 0.485 0 1.5 0.065 8 103 0.994 3.63 0.4 9.7 4 6 0.31 0.24 3.3 0.041 25 143 0.9914 3.31 0.44 11.3 6 7 0.14 0.4 1.7 0.035 16 85 0.9911 3.19 0.42 11.8 6 7.2 0.31 0.5 13.3 0.056 68 195 0.9982 3.01 0.47 9.2 5 7.3 0.32 0.48 13.3 0.06 57 196 0.9982 3.04 0.5 9.2 5 5.9 0.36 0.04 5.7 0.046 21 87 0.9934 3.22 0.51 10.2 5 7.8 0.24 0.32 12.2 0.054 42 138 0.9984 3.01 0.54 8.8 5 7.4 0.16 0.31 6.85 0.059 31 131 0.9952 3.29 0.34 9.7 5 6.9 0.19 0.28 5 0.058 14 146 0.9952 3.29 0.36 9.1 6 6.4 0.13 0.47 1.6 0.092 40 158 0.9928 3.21 0.36 9.8 6 6.7 0.19 0.36 1.1 0.026 63 143 0.9912 3.27 0.48 11 6 7.4 0.39 0.23 7 0.033 29 126 0.994 3.14 0.42 10.5 5 6.5 0.24 0.32 7.6 0.038 48 203 0.9958 3.45 0.54 9.7 7 6.1 0.3 0.56 2.8 0.044 47 179 0.9924 3.3 0.57 10.9 7 6.1 0.3 0.56 2.7 0.046 46 184 0.9924 3.31 0.57 10.9 6 5.7 0.26 0.25 10.4 0.02 7 57 0.994 3.39 0.37 10.6 5 6.5 0.24 0.32 7.6 0.038 48 203 0.9958 3.45 0.54 9.7 7 6.5 0.425 0.4 13.1 0.038 59 241 0.9979 3.23 0.57 9 5 6.6 0.24 0.27 15.8 0.035 46 188 0.9982 3.24 0.51 9.2 5 6.8 0.27 0.22 8.1 0.034 55 203 0.9961 3.19 0.52 8.9 5 6.7 0.27 0.31 15.7 0.036 44 179 0.9979 3.26 0.56 9.6 5 8.2 0.23 0.4 1.2 0.027 36 121 0.992 3.12 0.38 10.7 6 7.1 0.37 0.67 10.5 0.045 49 155 0.9975 3.16 0.44 8.7 5 6.8 0.19 0.36 1.9 0.035 30 96 0.9917 3.15 0.54 10.8 7 8.1 0.28 0.39 1.9 0.029 18 79 0.9923 3.23 0.52 11.8 6 6.3 0.31 0.34 2.2 0.045 20 77 0.9927 3.3 0.43 10.2 5 7.1 0.37 0.67 10.5 0.045 49 155 0.9975 3.16 0.44 8.7 5 7.9 0.21 0.4 1.2 0.039 38 107 0.992 3.21 0.54 10.8 6 8.5 0.21 0.41 4.3 0.036 24 99 0.9947 3.18 0.53 9.7 6 8.1 0.2 0.4 2 0.037 19 87 0.9921 3.12 0.54 11.2 6 6.3 0.255 0.37 1.1 0.04 37 114 0.9905 3 0.39 10.9 6 5.6 0.16 0.27 1.4 0.044 53 168 0.9918 3.28 0.37 10.1 6 6.4 0.595 0.14 5.2 0.058 15 97 0.9951 3.38 0.36 9 4 6.3 0.34 0.33 4.6 0.034 19 80 0.9917 3.38 0.58 12 7 6.9 0.25 0.3 4.1 0.054 23 116 0.994 2.99 0.38 9.4 6 7.9 0.22 0.38 8 0.043 46 152 0.9934 3.12 0.32 11.5 7 7.6 0.18 0.46 10.2 0.055 58 135 0.9968 3.14 0.43 9.9 6 6.9 0.25 0.3 4.1 0.054 23 116 0.994 2.99 0.38 9.4 6 7.2 0.18 0.41 1.2 0.048 41 97 0.9919 3.14 0.45 10.4 5 8.2 0.23 0.4 7.5 0.049 12 76 0.9966 3.06 0.84 9.7 6 7.4 0.24 0.42 14 0.066 48 198 0.9979 2.89 0.42 8.9 6 7.4 0.24 0.42 14 0.066 48 198 0.9979 2.89 0.42 8.9 6 6.1 0.32 0.24 1.5 0.036 38 124 0.9898 3.29 0.42 12.4 7 5.2 0.44 0.04 1.4 0.036 43 119 0.9894 3.36 0.33 12.1 8 5.2 0.44 0.04 1.4 0.036 43 119 0.9894 3.36 0.33 12.1 8 6.1 0.32 0.24 1.5 0.036 38 124 0.9898 3.29 0.42 12.4 7 6.4 0.22 0.56 14.5 0.055 27 159 0.998 2.98 0.4 9.1 5 6.3 0.36 0.3 4.8 0.049 14 85 0.9932 3.28 0.39 10.6 5 7.4 0.24 0.42 14 0.066 48 198 0.9979 2.89 0.42 8.9 6 6.7 0.24 0.35 13.1 0.05 64 205 0.997 3.15 0.5 9.5 5 7 0.23 0.36 13 0.051 72 177 0.9972 3.16 0.49 9.8 5 8.4 0.27 0.46 8.7 0.048 39 197 0.9974 3.14 0.59 9.6 6 6.7 0.46 0.18 2.4 0.034 25 98 0.9896 3.08 0.44 12.6 7 7.5 0.29 0.31 8.95 0.055 20 151 0.9968 3.08 0.54 9.3 5 9.8 0.42 0.48 9.85 0.034 5 110 0.9958 2.87 0.29 10 5 7.1 0.3 0.46 1.5 0.066 29 133 0.9906 3.12 0.54 12.7 6 7.9 0.19 0.45 1.5 0.045 17 96 0.9917 3.13 0.39 11 6 7.6 0.48 0.37 0.8 0.037 4 100 0.9902 3.03 0.39 11.4 4 6.3 0.22 0.43 4.55 0.038 31 130 0.9918 3.35 0.33 11.5 7 7.5 0.27 0.31 17.7 0.051 33 173 0.999 3.09 0.64 10.2 5 6.9 0.23 0.4 7.5 0.04 50 151 0.9927 3.11 0.27 11.4 6 7.2 0.32 0.47 5.1 0.044 19 65 0.991 3.03 0.41 12.6 4 5.9 0.23 0.3 12.9 0.054 57 170 0.9972 3.28 0.39 9.4 5 6 0.67 0.07 1.2 0.06 9 108 0.9931 3.11 0.35 8.7 4 6.4 0.25 0.32 5.5 0.049 41 176 0.995 3.19 0.68 9.2 6 6.4 0.33 0.31 5.5 0.048 42 173 0.9951 3.19 0.66 9.3 6 7.1 0.34 0.15 1.2 0.053 61 183 0.9936 3.09 0.43 9.2 5 6.8 0.28 0.4 22 0.048 48 167 1.001 2.93 0.5 8.7 5 6.9 0.27 0.4 14 0.05 64 227 0.9979 3.18 0.58 9.6 6 6.8 0.26 0.56 11.9 0.043 64 226 0.997 3.02 0.63 9.3 5 6.8 0.29 0.56 11.9 0.043 66 230 0.9972 3.02 0.63 9.3 5 6.7 0.24 0.41 9.4 0.04 49 166 0.9954 3.12 0.61 9.9 6 5.9 0.3 0.23 4.2 0.038 42 119 0.9924 3.15 0.5 11 5 6.8 0.53 0.35 3.8 0.034 26 109 0.9906 3.26 0.57 12.7 8 6.5 0.28 0.28 8.5 0.047 54 210 0.9962 3.09 0.54 8.9 4 6.6 0.28 0.28 8.5 0.052 55 211 0.9962 3.09 0.55 8.9 6 6.8 0.28 0.4 22 0.048 48 167 1.001 2.93 0.5 8.7 5 6.8 0.28 0.36 8 0.045 28 123 0.9928 3.02 0.37 11.4 6 6.6 0.15 0.34 5.1 0.055 34 125 0.9942 3.36 0.42 9.6 5 6.4 0.29 0.44 3.6 0.2 75 181 0.9942 3.02 0.41 9.1 5 6.4 0.3 0.45 3.5 0.197 76 180 0.9942 3.02 0.39 9.1 6 6.4 0.29 0.44 3.6 0.197 75 183 0.9942 3.01 0.38 9.1 5 6.8 0.26 0.24 7.8 0.052 54 214 0.9961 3.13 0.47 8.9 5 7.1 0.32 0.24 13.1 0.05 52 204 0.998 3.1 0.49 8.8 5 6.8 0.26 0.24 7.8 0.052 54 214 0.9961 3.13 0.47 8.9 5 6.8 0.27 0.26 16.1 0.049 55 196 0.9984 3.15 0.5 9.3 5 7.1 0.32 0.24 13.1 0.05 52 204 0.998 3.1 0.49 8.8 5 6.9 0.54 0.32 13.2 0.05 53 236 0.9973 3.2 0.5 9.6 5 6.8 0.26 0.34 13.9 0.034 39 134 0.9949 3.33 0.53 12 6 5.8 0.28 0.35 2.3 0.053 36 114 0.9924 3.28 0.5 10.2 4 6.4 0.21 0.5 11.6 0.042 45 153 0.9972 3.15 0.43 8.8 5 7 0.16 0.32 8.3 0.045 38 126 0.9958 3.21 0.34 9.2 5 10.2 0.44 0.88 6.2 0.049 20 124 0.9968 2.99 0.51 9.9 4 6.8 0.57 0.29 2.2 0.04 15 77 0.9938 3.32 0.74 10.2 5 6.1 0.4 0.31 0.9 0.048 23 170 0.993 3.22 0.77 9.5 6 5.6 0.245 0.25 9.7 0.032 12 68 0.994 3.31 0.34 10.5 5 6.8 0.18 0.38 1.4 0.038 35 111 0.9918 3.32 0.59 11.2 7 7 0.16 0.32 8.3 0.045 38 126 0.9958 3.21 0.34 9.2 5 6.7 0.13 0.29 5.3 0.051 31 122 0.9944 3.44 0.37 9.7 6 6.2 0.25 0.25 1.4 0.03 35 105 0.9912 3.3 0.44 11.1 7 5.8 0.26 0.24 9.2 0.044 55 152 0.9961 3.31 0.38 9.4 5 7.5 0.27 0.36 7 0.036 45 164 0.9939 3.03 0.33 11 5 5.8 0.26 0.24 9.2 0.044 55 152 0.9961 3.31 0.38 9.4 5 5.7 0.28 0.24 17.5 0.044 60 167 0.9989 3.31 0.44 9.4 5 7.5 0.23 0.36 7 0.036 43 161 0.9938 3.04 0.32 11 5 7.5 0.27 0.36 7 0.036 45 164 0.9939 3.03 0.33 11 5 7.2 0.685 0.21 9.5 0.07 33 172 0.9971 3 0.55 9.1 6 6.2 0.25 0.25 1.4 0.03 35 105 0.9912 3.3 0.44 11.1 7 6.5 0.19 0.3 0.8 0.043 33 144 0.9936 3.42 0.39 9.1 6 6.3 0.495 0.22 1.8 0.046 31 140 0.9929 3.39 0.54 10.4 6 7.1 0.24 0.41 17.8 0.046 39 145 0.9998 3.32 0.39 8.7 5 6.4 0.17 0.32 2.4 0.048 41 200 0.9938 3.5 0.5 9.7 6 7.1 0.25 0.32 10.3 0.041 66 272 0.9969 3.17 0.52 9.1 6 6.4 0.17 0.32 2.4 0.048 41 200 0.9938 3.5 0.5 9.7 6 7.1 0.24 0.41 17.8 0.046 39 145 0.9998 3.32 0.39 8.7 5 6.8 0.64 0.08 9.7 0.062 26 142 0.9972 3.37 0.46 8.9 4 8.3 0.28 0.4 7.8 0.041 38 194 0.9976 3.34 0.51 9.6 6 8.2 0.27 0.39 7.8 0.039 49 208 0.9976 3.31 0.51 9.5 6 7.2 0.23 0.38 14.3 0.058 55 194 0.9979 3.09 0.44 9 6 7.2 0.23 0.38 14.3 0.058 55 194 0.9979 3.09 0.44 9 6 7.2 0.23 0.38 14.3 0.058 55 194 0.9979 3.09 0.44 9 6 7.2 0.23 0.38 14.3 0.058 55 194 0.9979 3.09 0.44 9 6 6.8 0.52 0.32 13.2 0.044 54 221 0.9972 3.27 0.5 9.6 6 7 0.26 0.59 1.4 0.037 40 120 0.9918 3.34 0.41 11.1 7 6.2 0.25 0.21 15.55 0.039 28 159 0.9982 3.48 0.64 9.6 6 7.3 0.32 0.23 13.7 0.05 49 197 0.9985 3.2 0.46 8.7 5 7.7 0.31 0.26 7.8 0.031 23 90 0.9944 3.13 0.5 10.4 5 7.1 0.21 0.37 2.4 0.026 23 100 0.9903 3.15 0.38 11.4 7 6.8 0.24 0.34 2.7 0.047 64.5 218.5 0.9934 3.3 0.58 9.7 6 6.9 0.4 0.56 11.2 0.043 40 142 0.9975 3.14 0.46 8.7 5 6.1 0.18 0.36 2 0.038 20 249.5 0.9923 3.37 0.79 11.3 6 6.8 0.21 0.27 2.1 0.03 26 139 0.99 3.16 0.61 12.6 7 5.8 0.2 0.27 1.4 0.031 12 77 0.9905 3.25 0.36 10.9 7 5.6 0.19 0.26 1.4 0.03 12 76 0.9905 3.25 0.37 10.9 7 6.1 0.41 0.14 10.4 0.037 18 119 0.996 3.38 0.45 10 5 5.9 0.21 0.28 4.6 0.053 40 199 0.9964 3.72 0.7 10 4 8.5 0.26 0.21 16.2 0.074 41 197 0.998 3.02 0.5 9.8 3 6.9 0.4 0.56 11.2 0.043 40 142 0.9975 3.14 0.46 8.7 5 5.8 0.24 0.44 3.5 0.029 5 109 0.9913 3.53 0.43 11.7 3 5.8 0.24 0.39 1.5 0.054 37 158 0.9932 3.21 0.52 9.3 6 6.7 0.26 0.39 1.1 0.04 45 147 0.9935 3.32 0.58 9.6 8 6.3 0.35 0.3 5.7 0.035 8 97 0.9927 3.27 0.41 11 7 6.3 0.35 0.3 5.7 0.035 8 97 0.9927 3.27 0.41 11 7 6.4 0.23 0.39 1.8 0.032 23 118 0.9912 3.32 0.5 11.8 6 5.8 0.36 0.38 0.9 0.037 3 75 0.9904 3.28 0.34 11.4 4 6.9 0.115 0.35 5.4 0.048 36 108 0.9939 3.32 0.42 10.2 6 6.9 0.29 0.4 19.45 0.043 36 156 0.9996 2.93 0.47 8.9 5 6.9 0.28 0.4 8.2 0.036 15 95 0.9944 3.17 0.33 10.2 5 7.2 0.29 0.4 13.6 0.045 66 231 0.9977 3.08 0.59 9.6 6 6.2 0.24 0.35 1.2 0.038 22 167 0.9912 3.1 0.48 10.6 6 6.9 0.29 0.4 19.45 0.043 36 156 0.9996 2.93 0.47 8.9 5 6.9 0.32 0.26 8.3 0.053 32 180 0.9965 3.25 0.51 9.2 6 5.3 0.58 0.07 6.9 0.043 34 149 0.9944 3.34 0.57 9.7 5 5.3 0.585 0.07 7.1 0.044 34 145 0.9945 3.34 0.57 9.7 6 5.4 0.59 0.07 7 0.045 36 147 0.9944 3.34 0.57 9.7 6 6.9 0.32 0.26 8.3 0.053 32 180 0.9965 3.25 0.51 9.2 6 5.2 0.6 0.07 7 0.044 33 147 0.9944 3.33 0.58 9.7 5 5.8 0.25 0.26 13.1 0.051 44 148 0.9972 3.29 0.38 9.3 5 6.6 0.58 0.3 5.1 0.057 30 123 0.9949 3.24 0.38 9 5 7 0.29 0.54 10.7 0.046 59 234 0.9966 3.05 0.61 9.5 5 6.6 0.19 0.41 8.9 0.046 51 169 0.9954 3.14 0.57 9.8 6 6.7 0.2 0.41 9.1 0.044 50 166 0.9954 3.14 0.58 9.8 6 7.7 0.26 0.4 1.1 0.042 9 60 0.9915 2.89 0.5 10.6 5 6.8 0.32 0.34 1.2 0.044 14 67 0.9919 3.05 0.47 10.6 4 7 0.3 0.49 4.7 0.036 17 105 0.9916 3.26 0.68 12.4 7 7 0.24 0.36 2.8 0.034 22 112 0.99 3.19 0.38 12.6 8 6.1 0.31 0.58 5 0.039 36 114 0.9909 3.3 0.6 12.3 8 6.8 0.44 0.37 5.1 0.047 46 201 0.9938 3.08 0.65 10.5 4 6.7 0.34 0.3 15.6 0.054 51 196 0.9982 3.19 0.49 9.3 5 7.1 0.35 0.24 15.4 0.055 46 198 0.9988 3.12 0.49 8.8 5 7.3 0.32 0.25 7.2 0.056 47 180 0.9961 3.08 0.47 8.8 5 6.5 0.28 0.33 15.7 0.053 51 190 0.9978 3.22 0.51 9.7 6 7.2 0.23 0.39 14.2 0.058 49 192 0.9979 2.98 0.48 9 7 7.2 0.23 0.39 14.2 0.058 49 192 0.9979 2.98 0.48 9 7 7.2 0.23 0.39 14.2 0.058 49 192 0.9979 2.98 0.48 9 7 7.2 0.23 0.39 14.2 0.058 49 192 0.9979 2.98 0.48 9 7 5.9 0.15 0.31 5.8 0.041 53 155 0.9945 3.52 0.46 10.5 6 7.4 0.28 0.42 19.8 0.066 53 195 1 2.96 0.44 9.1 5 6.2 0.28 0.22 7.3 0.041 26 157 0.9957 3.44 0.64 9.8 7 9.1 0.59 0.38 1.6 0.066 34 182 0.9968 3.23 0.38 8.5 3 6.3 0.33 0.27 1.2 0.046 34 175 0.9934 3.37 0.54 9.4 6 8.3 0.39 0.7 10.6 0.045 33 169 0.9976 3.09 0.57 9.4 5 7.2 0.19 0.46 3.8 0.041 82 187 0.9932 3.19 0.6 11.2 7
Names of X columns:
fixedAcidity volatileAcidity citricAcid residualSugar chlorides freeSulfurDioxide totSulfurDioxide density pH sulphates alcohol quality
Endogenous Variable (Column Number)
Categorization
quantiles
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
yes
no
yes
Chart options
R Code
library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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