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Data X:
7.5 26 50 4 25 1.00 0.50 0.67 0.67 0.50 149 149 0 6.0 57 62 4 16 0.89 0.50 0.83 0.33 1.00 139 139 139 6.5 37 54 5 25 0.89 0.40 1.00 0.67 1.00 148 148 0 1.0 67 71 4 29 0.89 0.50 0.83 0.00 0.00 158 158 158 1.0 43 54 4 25 0.89 0.70 0.67 0.00 1.00 128 128 128 5.5 52 65 9 23 0.78 0.30 0.00 0.00 0.50 224 224 224 8.5 52 73 8 22 0.89 0.40 0.83 0.67 0.00 159 159 0 6.5 43 52 11 23 1.00 0.40 0.50 0.67 1.00 105 105 105 4.5 84 84 4 35 0.89 0.70 0.83 0.00 0.00 159 159 159 2.0 67 42 4 29 0.78 0.60 0.33 0.67 0.50 167 167 167 5.0 49 66 6 27 1.00 0.60 0.50 1.00 0.50 165 165 165 0.5 70 65 4 26 0.78 0.20 0.67 0.00 0.50 159 159 159 5.0 52 78 8 22 0.89 0.40 1.00 0.00 0.50 119 119 119 5.0 58 73 4 31 0.89 0.40 0.50 0.67 1.00 176 176 0 2.5 68 75 4 17 0.89 0.50 0.67 0.33 0.00 54 54 0 5.0 62 72 11 28 0.89 0.30 0.17 0.67 0.50 91 0 0 5.5 43 66 4 30 0.89 0.40 0.83 0.33 0.50 163 163 163 3.5 56 70 4 26 0.67 0.70 0.67 0.33 1.00 124 124 0 3.0 56 61 6 21 1.00 0.50 0.67 0.33 1.00 137 0 137 4.0 74 81 6 26 0.78 0.20 0.67 0.00 1.00 121 121 0 0.5 65 71 4 34 0.78 0.30 0.50 0.67 0.50 153 153 153 6.5 63 69 8 29 0.89 0.60 1.00 0.33 1.00 148 148 148 4.5 58 71 5 28 0.78 0.60 0.83 0.33 1.00 221 221 0 7.5 57 72 4 22 0.89 0.20 0.83 0.33 1.00 188 188 188 5.5 63 68 9 22 0.89 0.70 1.00 0.67 0.00 149 149 149 4.0 53 70 4 25 0.33 0.20 0.67 0.00 0.00 244 244 244 7.5 57 68 7 21 1.00 1.00 1.00 0.33 1.00 148 0 148 7.0 51 61 10 28 0.89 0.40 0.83 0.67 0.50 92 0 0 4.0 64 67 4 27 0.89 0.40 1.00 1.00 1.00 150 150 150 5.5 53 76 4 30 0.67 0.20 0.83 0.67 0.50 153 153 0 2.5 29 70 7 23 0.56 0.40 0.67 0.33 1.00 94 94 0 5.5 54 60 12 27 0.89 0.40 0.67 0.00 1.00 156 156 0 3.5 58 72 7 27 0.89 0.70 1.00 0.67 0.50 132 132 132 2.5 43 69 5 31 1.00 0.20 0.67 0.67 0.50 161 161 161 4.5 51 71 8 28 0.78 0.60 1.00 1.00 0.50 105 105 105 4.5 53 62 5 16 0.78 0.30 1.00 1.00 0.50 97 97 97 4.5 54 70 4 13 0.33 0.30 0.50 0.33 0.00 151 151 0 6.0 56 64 9 30 0.78 0.20 0.67 0.00 0.00 131 0 131 2.5 61 58 7 25 0.89 0.50 0.83 0.67 0.50 166 166 166 5.0 47 76 4 16 0.89 0.70 1.00 0.67 1.00 157 157 0 0.0 39 52 4 26 0.78 0.60 1.00 0.67 0.50 111 111 111 5.0 48 59 4 22 0.89 0.40 1.00 0.67 1.00 145 145 145 6.5 50 68 4 22 0.89 0.60 1.00 0.33 1.00 162 162 162 5.0 35 76 4 28 1.00 0.40 1.00 1.00 1.00 163 163 163 6.0 30 65 7 27 0.67 0.30 0.83 0.67 1.00 59 0 59 4.5 68 67 4 14 1.00 0.50 0.83 0.67 0.50 187 187 0 5.5 49 59 7 28 0.89 0.20 0.50 0.00 1.00 109 109 109 1.0 61 69 4 27 0.89 0.30 0.83 0.00 1.00 90 0 90 7.5 67 76 4 16 0.89 0.50 0.17 0.00 1.00 105 105 0 6.0 47 63 4 23 0.78 0.70 0.83 1.00 1.00 83 0 83 5.0 56 75 4 25 0.89 0.40 1.00 0.67 0.50 116 0 116 1.0 50 63 8 20 0.78 0.30 1.00 0.00 0.50 42 0 42 5.0 43 60 4 28 0.78 0.20 0.67 0.67 1.00 148 148 148 6.5 67 73 4 32 1.00 0.50 1.00 0.00 0.50 155 0 155 7.0 62 63 4 33 0.78 0.40 1.00 0.00 0.00 125 125 125 4.5 57 70 4 26 1.00 0.60 1.00 0.67 1.00 116 116 116 0.0 41 75 7 21 0.78 0.40 0.83 1.00 1.00 128 0 0 8.5 54 66 12 27 0.67 0.40 0.33 0.00 0.50 138 138 138 3.5 45 63 4 20 0.33 0.20 0.33 0.33 0.00 49 0 0 7.5 48 63 4 28 1.00 0.90 1.00 0.67 1.00 96 0 96 3.5 61 64 4 26 1.00 0.80 1.00 0.67 0.50 164 164 164 6.0 56 70 5 21 0.78 0.80 0.83 0.00 1.00 162 162 0 1.5 41 75 15 16 0.67 0.30 1.00 1.00 1.00 99 99 0 9.0 43 61 5 28 1.00 0.20 0.83 0.67 0.50 202 202 202 3.5 53 60 10 19 0.89 0.40 0.67 0.00 1.00 186 186 0 3.5 44 62 9 24 0.89 0.20 0.83 1.00 1.00 66 0 66 4.0 66 73 8 26 0.78 0.20 0.67 0.67 1.00 183 183 0 6.5 58 61 4 24 1.00 0.10 0.83 0.67 1.00 214 214 214 7.5 46 66 5 23 0.56 0.40 0.67 1.00 0.00 188 188 188 6.0 37 64 4 27 0.67 0.50 1.00 0.00 0.50 104 0 0 5.0 51 59 9 29 0.89 0.80 0.83 0.33 1.00 177 177 0 5.5 51 64 4 26 0.89 0.40 0.67 0.67 0.50 126 126 0 3.5 56 60 10 19 0.89 0.60 0.83 0.33 0.50 76 0 0 7.5 66 56 4 19 0.89 0.50 0.83 0.67 1.00 99 0 99 6.5 37 78 4 12 0.78 0.30 0.67 0.00 0.00 139 139 0 NA 59 53 6 23 0.89 0.80 1.00 1.00 1.00 78 78 78 6.5 42 67 7 25 1.00 0.40 0.33 0.00 0.00 162 162 0 6.5 38 59 5 24 1.00 0.60 0.83 0.67 0.50 108 0 108 7.0 66 66 4 26 0.89 0.40 1.00 0.33 0.50 159 159 0 3.5 34 68 4 23 0.44 0.30 0.83 0.00 0.00 74 0 0 1.5 53 71 4 28 0.78 0.80 0.83 0.00 1.00 110 110 110 4.0 49 66 4 25 0.89 0.60 0.50 0.33 1.00 96 0 0 7.5 55 73 4 23 0.67 0.30 0.50 0.00 0.00 116 0 0 4.5 49 72 4 28 0.78 0.50 0.83 0.67 1.00 87 0 0 0.0 59 71 6 23 0.78 0.40 1.00 0.33 1.00 97 0 97 3.5 40 59 10 25 0.33 0.30 0.33 0.67 0.00 127 0 0 5.5 58 64 7 27 0.89 0.70 1.00 0.33 0.50 106 0 106 5.0 60 66 4 28 0.89 0.20 0.67 0.33 0.50 80 0 80 4.5 63 78 4 17 0.89 0.40 0.83 1.00 1.00 74 0 0 2.5 56 68 7 23 0.89 0.60 1.00 0.67 0.50 91 0 0 7.5 54 73 4 24 0.56 0.60 0.83 0.00 1.00 133 0 0 7.0 52 62 8 18 0.67 0.60 0.83 0.67 0.50 74 0 74 0.0 34 65 11 21 0.67 0.40 1.00 0.33 1.00 114 0 114 4.5 69 68 6 34 0.78 0.60 0.83 0.00 1.00 140 0 140 3.0 32 65 14 24 0.78 0.50 1.00 0.33 1.00 95 0 0 1.5 48 60 5 24 0.78 0.50 0.83 0.00 1.00 98 0 98 3.5 67 71 4 28 0.89 0.60 0.67 0.00 1.00 121 0 0 2.5 58 65 8 27 1.00 0.80 0.83 0.33 1.00 126 0 126 5.5 57 68 9 24 0.89 0.50 0.83 0.67 0.50 98 0 98 8.0 42 64 4 19 0.89 0.60 0.83 0.67 1.00 95 0 95 1.0 64 74 4 19 0.78 0.40 0.83 0.67 1.00 110 0 110 5.0 58 69 5 27 1.00 0.30 0.67 0.67 1.00 70 0 70 4.5 66 76 4 28 0.78 0.30 0.83 1.00 0.50 102 0 0 3.0 26 68 5 22 0.67 0.20 0.00 0.00 0.00 86 0 86 3.0 61 72 4 32 0.78 0.40 0.83 0.00 0.50 130 0 130 8.0 52 67 4 20 0.89 0.50 1.00 0.00 0.50 96 0 96 2.5 51 63 7 26 0.67 0.30 0.17 0.00 0.00 102 0 0 7.0 55 59 10 19 0.22 0.40 0.17 0.00 0.00 100 0 0 0.0 50 73 4 24 0.44 0.50 0.50 1.00 0.00 94 0 0 1.0 60 66 5 21 0.89 0.30 0.50 0.67 1.00 52 0 0 3.5 56 62 4 21 0.67 0.50 1.00 0.00 0.50 98 0 0 5.5 63 69 4 27 0.89 0.40 0.67 0.67 0.50 118 0 0 5.5 61 66 4 18 0.67 0.40 0.83 0.67 1.00 99 0 99 0.5 52 51 6 25 0.78 0.60 1.00 0.00 1.00 0 48 48 7.5 16 56 4 27 0.78 0.30 1.00 0.67 1.00 0 50 50 9 46 67 8 28 0.78 0.40 1.00 0.33 0.50 0 150 150 9.5 56 69 5 28 1.00 0.30 1.00 1.00 1.00 0 154 154 8.5 52 57 4 19 0.78 1.00 1.00 1.00 1.00 0 0 0 7 55 56 17 27 0.67 0.40 1.00 0.00 0.50 0 0 68 8 50 55 4 26 0.89 0.80 0.83 1.00 1.00 0 194 194 10 59 63 4 27 0.89 0.30 1.00 0.67 1.00 0 158 0 7 60 67 8 26 1.00 0.50 0.83 0.67 1.00 0 159 159 8.5 52 65 4 28 0.78 0.40 1.00 0.00 0.50 0 67 0 9 44 47 7 20 0.67 0.30 0.83 0.67 1.00 0 147 0 9.5 67 76 4 32 0.89 0.50 0.83 1.00 1.00 0 39 39 4 52 64 4 25 0.67 0.30 1.00 0.67 1.00 0 100 100 6 55 68 5 33 0.67 0.30 0.67 0.00 1.00 0 111 111 8 37 64 7 25 1.00 0.40 0.83 0.00 1.00 0 138 138 5.5 54 65 4 35 0.67 0.30 1.00 0.00 0.50 0 101 101 9.5 72 71 4 28 1.00 0.60 1.00 0.33 0.50 0 0 131 7.5 51 63 7 30 0.89 0.60 0.83 0.67 1.00 0 101 101 7 48 60 11 28 0.89 0.40 1.00 1.00 1.00 0 114 114 7.5 60 68 7 27 1.00 0.40 1.00 0.00 0.00 0 165 0 8 50 72 4 21 0.67 0.40 1.00 0.67 0.50 0 114 114 7 63 70 4 25 0.44 0.30 0.67 0.67 1.00 0 111 111 7 33 61 4 31 0.89 0.20 1.00 0.33 0.00 0 75 75 6 67 61 4 28 0.56 0.50 0.83 0.67 1.00 0 82 82 10 46 62 4 29 0.78 0.40 1.00 0.67 1.00 0 121 121 2.5 54 71 4 35 1.00 0.40 1.00 0.67 0.00 0 32 32 9 59 71 6 25 1.00 0.40 0.83 0.67 1.00 0 150 0 8 61 51 8 29 0.89 0.30 0.67 0.67 0.50 0 117 117 6 33 56 23 12 0.67 0.40 0.83 0.67 0.50 0 0 71 8.5 47 70 4 30 0.89 0.20 1.00 0.33 1.00 0 165 165 6 69 73 8 27 0.33 0.00 0.00 0.00 0.00 0 154 154 9 52 76 6 28 0.89 0.40 1.00 0.67 1.00 0 126 126 8 55 68 4 28 0.78 0.60 1.00 0.00 1.00 0 149 0 9 41 48 7 25 1.00 0.40 0.67 0.67 0.50 0 145 0 5.5 73 52 4 28 0.44 0.40 1.00 0.00 0.50 0 120 120 7 52 60 4 28 0.67 0.40 0.83 0.00 0.00 0 109 0 5.5 50 59 4 28 0.33 0.20 0.17 0.00 0.00 0 132 0 9 51 57 10 26 0.89 0.40 0.83 1.00 1.00 0 172 172 2 60 79 6 22 0.89 0.30 0.83 0.00 0.50 0 169 0 8.5 56 60 5 24 1.00 0.60 0.83 0.67 0.00 0 114 114 9 56 60 5 28 0.89 0.60 0.83 1.00 1.00 0 156 156 8.5 29 59 4 27 0.89 0.40 0.83 0.00 1.00 0 172 0 9 66 62 4 27 1.00 0.50 1.00 0.67 0.50 0 0 68 7.5 66 59 5 26 0.89 0.40 0.83 0.00 1.00 0 0 89 10 73 61 5 29 1.00 0.60 1.00 1.00 1.00 0 167 167 9 55 71 5 27 0.78 0.60 0.83 0.67 1.00 0 113 0 7.5 64 57 5 29 0.78 0.90 1.00 0.67 1.00 0 0 0 6 40 66 4 28 0.67 0.40 0.83 0.67 0.00 0 0 0 10.5 46 63 6 28 0.89 0.80 1.00 1.00 1.00 0 0 0 8.5 58 69 4 27 0.67 0.50 0.83 1.00 1.00 0 0 87 8 43 58 4 24 0.78 0.40 0.83 1.00 0.00 0 173 0 10 61 59 4 29 0.89 0.40 1.00 0.67 0.50 0 2 2 10.5 51 48 9 17 0.89 0.70 1.00 1.00 0.50 0 0 0 6.5 50 66 18 27 0.78 0.40 1.00 0.33 1.00 0 0 49 9.5 52 73 6 23 1.00 0.80 1.00 0.67 1.00 0 0 0 8.5 54 67 5 27 1.00 0.40 1.00 1.00 0.50 0 0 96 7.5 66 61 4 22 1.00 0.30 1.00 0.67 0.50 0 0 0 5 61 68 11 27 0.67 0.50 1.00 0.67 1.00 0 0 0 8 80 75 4 35 0.89 0.80 1.00 0.67 1.00 0 0 100 10 51 62 10 22 1.00 0.40 0.83 0.33 0.50 0 0 0 7 56 69 6 20 1.00 1.00 1.00 1.00 0.00 0 0 141 7.5 56 58 8 26 0.89 0.50 1.00 0.67 1.00 0 165 165 7.5 56 60 8 26 0.89 0.50 1.00 0.67 1.00 0 165 165 9.5 53 74 6 26 0.89 0.30 1.00 0.33 1.00 0 0 110 6 47 55 8 29 0.89 0.30 0.83 0.33 1.00 0 118 118 10 25 62 4 18 0.89 0.30 0.50 0.00 1.00 0 158 0 7 47 63 4 28 1.00 0.40 0.67 0.33 0.50 0 0 146 3 46 69 9 15 0.67 0.50 1.00 0.33 1.00 0 49 0 6 50 58 9 19 1.00 0.50 0.67 0.67 1.00 0 0 0 7 39 58 5 13 0.89 0.40 1.00 0.00 0.00 0 0 0 10 51 68 4 26 0.89 0.70 1.00 1.00 0.00 0 155 155 7 58 72 4 26 0.89 0.50 0.50 0.33 0.50 0 0 0 3.5 35 62 15 21 0.89 0.40 0.67 0.33 0.00 0 0 147 8 58 62 10 27 1.00 0.70 0.67 1.00 1.00 0 0 0 10 60 65 9 24 1.00 0.70 0.67 1.00 1.00 0 0 0 5.5 62 69 7 22 1.00 0.70 0.67 1.00 1.00 0 0 0 6 63 66 9 22 0.89 0.70 0.67 1.00 1.00 0 0 0 6.5 53 72 6 24 0.89 0.70 0.67 0.00 0.00 0 0 61 6.5 46 62 4 22 0.89 0.70 1.00 0.67 1.00 0 0 60 8.5 67 75 7 27 0.33 0.10 0.67 0.33 0.00 0 0 109 4 59 58 4 26 0.67 0.20 0.67 0.67 1.00 0 0 68 9.5 64 66 7 25 0.56 0.30 0.33 0.33 1.00 0 0 0 8 38 55 4 20 0.44 0.60 0.83 0.33 0.50 0 0 0 8.5 50 47 15 28 1.00 0.80 1.00 1.00 1.00 0 0 73 5.5 48 72 4 22 0.89 0.80 1.00 0.33 0.50 0 151 0 7 48 62 9 27 0.33 0.00 0.17 0.00 0.00 0 0 0 9 47 64 4 20 0.67 0.30 0.67 0.33 1.00 0 0 0 8 66 64 4 26 0.67 0.60 0.83 0.33 1.00 0 0 0 10 47 19 28 30 1.00 0.50 0.83 0.67 1.00 0 220 220 8 63 50 4 24 0.78 0.70 1.00 0.33 0.50 0 0 65 6 58 68 4 23 0.67 0.30 0.83 0.00 1.00 0 141 0 8 44 70 4 26 1.00 0.30 1.00 0.67 0.00 0 0 0 5 51 79 5 24 0.78 0.40 1.00 0.67 0.50 0 122 122 9 43 69 4 27 0.89 0.40 0.83 1.00 1.00 0 0 0 4.5 55 71 4 31 0.89 0.10 0.83 0.00 1.00 0 44 44 8.5 38 48 12 21 0.89 0.50 1.00 0.67 1.00 0 0 52 9.5 45 73 4 10 0.00 0.00 0.00 0.00 0.00 0 0 0 8.5 50 74 6 15 0.67 0.40 1.00 0.33 0.00 0 0 101 7.5 54 66 6 29 1.00 0.60 0.83 0.67 0.50 0 0 42 7.5 57 71 5 29 1.00 0.40 1.00 0.33 1.00 0 152 152 5 60 74 4 24 0.67 0.10 0.33 0.00 1.00 0 107 0 7 55 78 4 20 0.89 0.30 0.83 0.00 1.00 0 0 0 8 56 75 4 25 0.89 0.70 0.83 0.67 1.00 0 154 0 5.5 49 53 10 25 0.56 0.30 0.17 0.00 1.00 0 103 103 8.5 37 60 7 25 0.67 0.50 0.83 0.33 0.00 0 0 96 9.5 59 70 4 31 1.00 0.30 0.83 0.67 1.00 0 175 175 7 46 69 7 25 1.00 0.60 0.67 0.67 1.00 0 0 57 8 51 65 4 26 1.00 0.90 1.00 1.00 1.00 0 0 0 8.5 58 78 4 26 0.67 0.40 0.83 0.00 1.00 0 143 0 3.5 64 78 12 33 0.44 0.30 1.00 0.00 0.50 0 0 0 6.5 53 59 5 27 0.89 0.90 1.00 0.67 1.00 0 110 110 6.5 48 72 8 21 0.44 0.50 1.00 0.00 0.00 0 131 131 10.5 51 70 6 28 0.56 0.30 1.00 1.00 0.50 0 167 0 8.5 47 63 17 19 0.89 0.60 0.83 0.67 0.50 0 0 0 8 59 63 4 31 0.67 0.20 1.00 0.33 0.50 0 137 0 10 62 71 5 27 0.89 0.40 0.83 1.00 1.00 0 0 86 10 62 74 4 33 1.00 0.50 0.83 0.67 0.50 0 121 121 9.5 51 67 5 27 0.78 0.40 0.83 0.67 0.50 0 149 0 9 64 66 5 23 0.44 0.00 0.00 0.00 0.00 0 168 0 10 52 62 6 23 0.89 0.20 1.00 0.33 1.00 0 140 0 7.5 67 80 4 30 0.89 0.50 1.00 0.67 1.00 0 0 88 4.5 50 73 4 29 0.89 0.30 1.00 0.67 0.50 0 168 168 4.5 54 67 4 35 0.44 0.00 0.00 0.00 0.00 0 94 94 0.5 58 61 6 27 1.00 0.50 0.83 1.00 1.00 0 51 51 6.5 56 73 8 16 0.89 0.60 0.83 0.33 1.00 0 0 0 4.5 63 74 10 23 0.67 0.30 0.83 0.00 0.50 0 145 145 5.5 31 32 4 26 0.33 0.00 0.00 0.00 0.00 0 66 66 5 65 69 5 33 0.78 0.30 0.67 0.00 0.00 0 0 85 6 71 69 4 30 0.89 0.50 1.00 0.67 1.00 0 109 0 4 50 84 4 30 0.78 0.40 0.67 0.00 1.00 0 0 0 8 57 64 4 28 0.78 0.50 0.83 0.67 0.50 0 0 102 10.5 47 58 16 17 0.89 0.70 1.00 1.00 0.50 0 0 0 6.5 47 59 7 24 0.78 0.80 1.00 0.67 1.00 0 0 86 8 57 78 4 23 0.78 0.60 1.00 0.33 1.00 0 0 114 8.5 43 57 4 31 0.67 0.40 0.83 0.33 0.50 0 164 0 5.5 41 60 14 26 0.89 0.50 0.83 0.33 0.00 0 119 119 7 63 68 5 25 0.89 0.50 1.00 0.00 1.00 0 126 0 5 63 68 5 26 0.78 0.30 1.00 0.33 1.00 0 132 132 3.5 56 73 5 28 1.00 0.60 1.00 0.00 1.00 0 142 142 5 51 69 5 26 1.00 0.30 0.67 0.67 0.50 0 83 0 9 50 67 7 28 0.78 0.60 0.83 1.00 0.50 0 0 94 8.5 22 60 19 27 0.78 0.30 0.33 0.33 1.00 0 0 0 5 41 65 16 21 0.89 0.70 1.00 0.67 1.00 0 166 166 9.5 59 66 4 25 0.89 0.70 1.00 1.00 1.00 0 0 0 3 56 74 4 30 0.67 0.60 0.67 1.00 1.00 0 0 64 1.5 66 81 7 28 1.00 0.50 1.00 0.33 0.00 0 93 0 6 53 72 9 19 0.67 0.50 0.83 0.33 0.50 0 0 0 0.5 42 55 5 27 0.56 0.40 0.67 0.00 1.00 0 0 105 6.5 52 49 14 27 0.78 0.40 1.00 0.33 1.00 0 0 49 7.5 54 74 4 19 1.00 0.70 1.00 1.00 1.00 0 0 0 4.5 44 53 16 28 0.67 0.20 0.17 0.00 0.00 0 0 95 8 62 64 10 28 0.78 0.50 0.83 0.67 0.50 0 0 102 9 53 65 5 28 0.56 0.40 0.83 0.67 0.00 0 0 0 7.5 50 57 6 23 1.00 0.20 1.00 0.67 1.00 0 0 63 8.5 36 51 4 26 0.89 0.50 0.67 0.67 0.00 0 0 0 7 76 80 4 28 0.44 0.40 0.50 0.00 1.00 0 0 0 9.5 66 67 4 27 1.00 0.70 0.67 1.00 1.00 0 0 117 6.5 62 70 5 30 0.89 0.60 0.83 0.67 0.00 0 0 57 9.5 59 74 4 20 0.78 0.40 0.83 0.00 0.00 0 0 0 6 47 75 4 27 0.89 0.50 1.00 0.67 1.00 0 0 73 8 55 70 5 25 0.11 0.00 0.17 0.00 0.00 0 0 0 9.5 58 69 4 28 0.89 0.70 1.00 0.67 1.00 0 0 0 8 60 65 4 26 0.89 0.40 0.67 0.67 1.00 0 0 105 8 44 55 5 28 1.00 0.50 0.67 1.00 1.00 0 117 0 9 57 71 8 21 0.89 0.60 0.83 0.67 0.50 0 0 0 5 45 65 15 11 1.00 0.80 0.50 0.67 0.50 0 0 31
Names of X columns:
Ex AMS.I AMS.E AMS.A SOFTSTATTOT Calculation Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio Estimation lfm_year lfm_course lfm_gender
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
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