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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 21 6.0 57 62 4 16 0.89 0.50 0.83 0.33 1.00 139 139 139 22 6.5 37 54 5 25 0.89 0.40 1.00 0.67 1.00 148 148 0 22 1.0 67 71 4 29 0.89 0.50 0.83 0.00 0.00 158 158 158 18 1.0 43 54 4 25 0.89 0.70 0.67 0.00 1.00 128 128 128 23 5.5 52 65 9 23 0.78 0.30 0.00 0.00 0.50 224 224 224 12 8.5 52 73 8 22 0.89 0.40 0.83 0.67 0.00 159 159 0 20 6.5 43 52 11 23 1.00 0.40 0.50 0.67 1.00 105 105 105 22 4.5 84 84 4 35 0.89 0.70 0.83 0.00 0.00 159 159 159 21 2.0 67 42 4 29 0.78 0.60 0.33 0.67 0.50 167 167 167 19 5.0 49 66 6 27 1.00 0.60 0.50 1.00 0.50 165 165 165 22 0.5 70 65 4 26 0.78 0.20 0.67 0.00 0.50 159 159 159 15 5.0 52 78 8 22 0.89 0.40 1.00 0.00 0.50 119 119 119 20 5.0 58 73 4 31 0.89 0.40 0.50 0.67 1.00 176 176 0 19 2.5 68 75 4 17 0.89 0.50 0.67 0.33 0.00 54 54 0 18 5.0 62 72 11 28 0.89 0.30 0.17 0.67 0.50 91 0 0 15 5.5 43 66 4 30 0.89 0.40 0.83 0.33 0.50 163 163 163 20 3.5 56 70 4 26 0.67 0.70 0.67 0.33 1.00 124 124 0 21 3.0 56 61 6 21 1.00 0.50 0.67 0.33 1.00 137 0 137 21 4.0 74 81 6 26 0.78 0.20 0.67 0.00 1.00 121 121 0 15 0.5 65 71 4 34 0.78 0.30 0.50 0.67 0.50 153 153 153 16 6.5 63 69 8 29 0.89 0.60 1.00 0.33 1.00 148 148 148 23 4.5 58 71 5 28 0.78 0.60 0.83 0.33 1.00 221 221 0 21 7.5 57 72 4 22 0.89 0.20 0.83 0.33 1.00 188 188 188 18 5.5 63 68 9 22 0.89 0.70 1.00 0.67 0.00 149 149 149 25 4.0 53 70 4 25 0.33 0.20 0.67 0.00 0.00 244 244 244 9 7.5 57 68 7 21 1.00 1.00 1.00 0.33 1.00 148 0 148 30 7.0 51 61 10 28 0.89 0.40 0.83 0.67 0.50 92 0 0 20 4.0 64 67 4 27 0.89 0.40 1.00 1.00 1.00 150 150 150 23 5.5 53 76 4 30 0.67 0.20 0.83 0.67 0.50 153 153 0 16 2.5 29 70 7 23 0.56 0.40 0.67 0.33 1.00 94 94 0 16 5.5 54 60 12 27 0.89 0.40 0.67 0.00 1.00 156 156 0 19 3.5 58 72 7 27 0.89 0.70 1.00 0.67 0.50 132 132 132 25 2.5 43 69 5 31 1.00 0.20 0.67 0.67 0.50 161 161 161 18 4.5 51 71 8 28 0.78 0.60 1.00 1.00 0.50 105 105 105 23 4.5 53 62 5 16 0.78 0.30 1.00 1.00 0.50 97 97 97 21 4.5 54 70 4 13 0.33 0.30 0.50 0.33 0.00 151 151 0 10 6.0 56 64 9 30 0.78 0.20 0.67 0.00 0.00 131 0 131 14 2.5 61 58 7 25 0.89 0.50 0.83 0.67 0.50 166 166 166 22 5.0 47 76 4 16 0.89 0.70 1.00 0.67 1.00 157 157 0 26 0.0 39 52 4 26 0.78 0.60 1.00 0.67 0.50 111 111 111 23 5.0 48 59 4 22 0.89 0.40 1.00 0.67 1.00 145 145 145 23 6.5 50 68 4 22 0.89 0.60 1.00 0.33 1.00 162 162 162 24 5.0 35 76 4 28 1.00 0.40 1.00 1.00 1.00 163 163 163 24 6.0 30 65 7 27 0.67 0.30 0.83 0.67 1.00 59 0 59 18 4.5 68 67 4 14 1.00 0.50 0.83 0.67 0.50 187 187 0 23 5.5 49 59 7 28 0.89 0.20 0.50 0.00 1.00 109 109 109 15 1.0 61 69 4 27 0.89 0.30 0.83 0.00 1.00 90 0 90 19 7.5 67 76 4 16 0.89 0.50 0.17 0.00 1.00 105 105 0 16 6.0 47 63 4 23 0.78 0.70 0.83 1.00 1.00 83 0 83 25 5.0 56 75 4 25 0.89 0.40 1.00 0.67 0.50 116 0 116 23 1.0 50 63 8 20 0.78 0.30 1.00 0.00 0.50 42 0 42 17 5.0 43 60 4 28 0.78 0.20 0.67 0.67 1.00 148 148 148 19 6.5 67 73 4 32 1.00 0.50 1.00 0.00 0.50 155 0 155 21 7.0 62 63 4 33 0.78 0.40 1.00 0.00 0.00 125 125 125 18 4.5 57 70 4 26 1.00 0.60 1.00 0.67 1.00 116 116 116 27 0.0 41 75 7 21 0.78 0.40 0.83 1.00 1.00 128 0 0 21 8.5 54 66 12 27 0.67 0.40 0.33 0.00 0.50 138 138 138 13 3.5 45 63 4 20 0.33 0.20 0.33 0.33 0.00 49 0 0 8 7.5 48 63 4 28 1.00 0.90 1.00 0.67 1.00 96 0 96 29 3.5 61 64 4 26 1.00 0.80 1.00 0.67 0.50 164 164 164 28 6.0 56 70 5 21 0.78 0.80 0.83 0.00 1.00 162 162 0 23 1.5 41 75 15 16 0.67 0.30 1.00 1.00 1.00 99 99 0 21 9.0 43 61 5 28 1.00 0.20 0.83 0.67 0.50 202 202 202 19 3.5 53 60 10 19 0.89 0.40 0.67 0.00 1.00 186 186 0 19 3.5 44 62 9 24 0.89 0.20 0.83 1.00 1.00 66 0 66 20 4.0 66 73 8 26 0.78 0.20 0.67 0.67 1.00 183 183 0 18 6.5 58 61 4 24 1.00 0.10 0.83 0.67 1.00 214 214 214 19 7.5 46 66 5 23 0.56 0.40 0.67 1.00 0.00 188 188 188 17 6.0 37 64 4 27 0.67 0.50 1.00 0.00 0.50 104 0 0 19 5.0 51 59 9 29 0.89 0.80 0.83 0.33 1.00 177 177 0 25 5.5 51 64 4 26 0.89 0.40 0.67 0.67 0.50 126 126 0 19 3.5 56 60 10 19 0.89 0.60 0.83 0.33 0.50 76 0 0 22 7.5 66 56 4 19 0.89 0.50 0.83 0.67 1.00 99 0 99 23 6.5 37 78 4 12 0.78 0.30 0.67 0.00 0.00 139 139 0 14 NA 59 53 6 23 0.89 0.80 1.00 1.00 1.00 78 78 78 28 6.5 42 67 7 25 1.00 0.40 0.33 0.00 0.00 162 162 0 16 6.5 38 59 5 24 1.00 0.60 0.83 0.67 0.50 108 0 108 24 7.0 66 66 4 26 0.89 0.40 1.00 0.33 0.50 159 159 0 20 3.5 34 68 4 23 0.44 0.30 0.83 0.00 0.00 74 0 0 12 1.5 53 71 4 28 0.78 0.80 0.83 0.00 1.00 110 110 110 24 4.0 49 66 4 25 0.89 0.60 0.50 0.33 1.00 96 0 0 22 7.5 55 73 4 23 0.67 0.30 0.50 0.00 0.00 116 0 0 12 4.5 49 72 4 28 0.78 0.50 0.83 0.67 1.00 87 0 0 22 0.0 59 71 6 23 0.78 0.40 1.00 0.33 1.00 97 0 97 20 3.5 40 59 10 25 0.33 0.30 0.33 0.67 0.00 127 0 0 10 5.5 58 64 7 27 0.89 0.70 1.00 0.33 0.50 106 0 106 23 5.0 60 66 4 28 0.89 0.20 0.67 0.33 0.50 80 0 80 17 4.5 63 78 4 17 0.89 0.40 0.83 1.00 1.00 74 0 0 22 2.5 56 68 7 23 0.89 0.60 1.00 0.67 0.50 91 0 0 24 7.5 54 73 4 24 0.56 0.60 0.83 0.00 1.00 133 0 0 18 7.0 52 62 8 18 0.67 0.60 0.83 0.67 0.50 74 0 74 21 0.0 34 65 11 21 0.67 0.40 1.00 0.33 1.00 114 0 114 20 4.5 69 68 6 34 0.78 0.60 0.83 0.00 1.00 140 0 140 20 3.0 32 65 14 24 0.78 0.50 1.00 0.33 1.00 95 0 0 22 1.5 48 60 5 24 0.78 0.50 0.83 0.00 1.00 98 0 98 19 3.5 67 71 4 28 0.89 0.60 0.67 0.00 1.00 121 0 0 20 2.5 58 65 8 27 1.00 0.80 0.83 0.33 1.00 126 0 126 26 5.5 57 68 9 24 0.89 0.50 0.83 0.67 0.50 98 0 98 23 8.0 42 64 4 19 0.89 0.60 0.83 0.67 1.00 95 0 95 24 1.0 64 74 4 19 0.78 0.40 0.83 0.67 1.00 110 0 110 21 5.0 58 69 5 27 1.00 0.30 0.67 0.67 1.00 70 0 70 21 4.5 66 76 4 28 0.78 0.30 0.83 1.00 0.50 102 0 0 19 3.0 26 68 5 22 0.67 0.20 0.00 0.00 0.00 86 0 86 8 3.0 61 72 4 32 0.78 0.40 0.83 0.00 0.50 130 0 130 17 8.0 52 67 4 20 0.89 0.50 1.00 0.00 0.50 96 0 96 20 2.5 51 63 7 26 0.67 0.30 0.17 0.00 0.00 102 0 0 11 7.0 55 59 10 19 0.22 0.40 0.17 0.00 0.00 100 0 0 8 0.0 50 73 4 24 0.44 0.50 0.50 1.00 0.00 94 0 0 15 1.0 60 66 5 21 0.89 0.30 0.50 0.67 1.00 52 0 0 18 3.5 56 62 4 21 0.67 0.50 1.00 0.00 0.50 98 0 0 18 5.5 63 69 4 27 0.89 0.40 0.67 0.67 0.50 118 0 0 19 5.5 61 66 4 18 0.67 0.40 0.83 0.67 1.00 99 0 99 19 0.5 52 51 6 25 0.78 0.60 1.00 0.00 1.00 0 48 48 23 7.5 16 56 4 27 0.78 0.30 1.00 0.67 1.00 0 50 50 22 9 46 67 8 28 0.78 0.40 1.00 0.33 0.50 0 150 150 21 9.5 56 69 5 28 1.00 0.30 1.00 1.00 1.00 0 154 154 25 8.5 52 57 4 19 0.78 1.00 1.00 1.00 1.00 0 0 0 30 7 55 56 17 27 0.67 0.40 1.00 0.00 0.50 0 0 68 17 8 50 55 4 26 0.89 0.80 0.83 1.00 1.00 0 194 194 27 10 59 63 4 27 0.89 0.30 1.00 0.67 1.00 0 158 0 23 7 60 67 8 26 1.00 0.50 0.83 0.67 1.00 0 159 159 23 8.5 52 65 4 28 0.78 0.40 1.00 0.00 0.50 0 67 0 18 9 44 47 7 20 0.67 0.30 0.83 0.67 1.00 0 147 0 18 9.5 67 76 4 32 0.89 0.50 0.83 1.00 1.00 0 39 39 23 4 52 64 4 25 0.67 0.30 1.00 0.67 1.00 0 100 100 19 6 55 68 5 33 0.67 0.30 0.67 0.00 1.00 0 111 111 15 8 37 64 7 25 1.00 0.40 0.83 0.00 1.00 0 138 138 20 5.5 54 65 4 35 0.67 0.30 1.00 0.00 0.50 0 101 101 16 9.5 72 71 4 28 1.00 0.60 1.00 0.33 0.50 0 0 131 24 7.5 51 63 7 30 0.89 0.60 0.83 0.67 1.00 0 101 101 25 7 48 60 11 28 0.89 0.40 1.00 1.00 1.00 0 114 114 25 7.5 60 68 7 27 1.00 0.40 1.00 0.00 0.00 0 165 0 19 8 50 72 4 21 0.67 0.40 1.00 0.67 0.50 0 114 114 19 7 63 70 4 25 0.44 0.30 0.67 0.67 1.00 0 111 111 16 7 33 61 4 31 0.89 0.20 1.00 0.33 0.00 0 75 75 19 6 67 61 4 28 0.56 0.50 0.83 0.67 1.00 0 82 82 19 10 46 62 4 29 0.78 0.40 1.00 0.67 1.00 0 121 121 23 2.5 54 71 4 35 1.00 0.40 1.00 0.67 0.00 0 32 32 21 9 59 71 6 25 1.00 0.40 0.83 0.67 1.00 0 150 0 22 8 61 51 8 29 0.89 0.30 0.67 0.67 0.50 0 117 117 19 6 33 56 23 12 0.67 0.40 0.83 0.67 0.50 0 0 71 20 8.5 47 70 4 30 0.89 0.20 1.00 0.33 1.00 0 165 165 20 6 69 73 8 27 0.33 0.00 0.00 0.00 0.00 0 154 154 3 9 52 76 6 28 0.89 0.40 1.00 0.67 1.00 0 126 126 23 8 55 68 4 28 0.78 0.60 1.00 0.00 1.00 0 149 0 23 9 41 48 7 25 1.00 0.40 0.67 0.67 0.50 0 145 0 20 5.5 73 52 4 28 0.44 0.40 1.00 0.00 0.50 0 120 120 15 7 52 60 4 28 0.67 0.40 0.83 0.00 0.00 0 109 0 16 5.5 50 59 4 28 0.33 0.20 0.17 0.00 0.00 0 132 0 7 9 51 57 10 26 0.89 0.40 0.83 1.00 1.00 0 172 172 24 2 60 79 6 22 0.89 0.30 0.83 0.00 0.50 0 169 0 17 8.5 56 60 5 24 1.00 0.60 0.83 0.67 0.00 0 114 114 24 9 56 60 5 28 0.89 0.60 0.83 1.00 1.00 0 156 156 24 8.5 29 59 4 27 0.89 0.40 0.83 0.00 1.00 0 172 0 19 9 66 62 4 27 1.00 0.50 1.00 0.67 0.50 0 0 68 25 7.5 66 59 5 26 0.89 0.40 0.83 0.00 1.00 0 0 89 20 10 73 61 5 29 1.00 0.60 1.00 1.00 1.00 0 167 167 28 9 55 71 5 27 0.78 0.60 0.83 0.67 1.00 0 113 0 23 7.5 64 57 5 29 0.78 0.90 1.00 0.67 1.00 0 0 0 27 6 40 66 4 28 0.67 0.40 0.83 0.67 0.00 0 0 0 18 10.5 46 63 6 28 0.89 0.80 1.00 1.00 1.00 0 0 0 28 8.5 58 69 4 27 0.67 0.50 0.83 1.00 1.00 0 0 87 21 8 43 58 4 24 0.78 0.40 0.83 1.00 0.00 0 173 0 19 10 61 59 4 29 0.89 0.40 1.00 0.67 0.50 0 2 2 23 10.5 51 48 9 17 0.89 0.70 1.00 1.00 0.50 0 0 0 27 6.5 50 66 18 27 0.78 0.40 1.00 0.33 1.00 0 0 49 22 9.5 52 73 6 23 1.00 0.80 1.00 0.67 1.00 0 0 0 28 8.5 54 67 5 27 1.00 0.40 1.00 1.00 0.50 0 0 96 25 7.5 66 61 4 22 1.00 0.30 1.00 0.67 0.50 0 0 0 21 5 61 68 11 27 0.67 0.50 1.00 0.67 1.00 0 0 0 22 8 80 75 4 35 0.89 0.80 1.00 0.67 1.00 0 0 100 28 10 51 62 10 22 1.00 0.40 0.83 0.33 0.50 0 0 0 20 7 56 69 6 20 1.00 1.00 1.00 1.00 0.00 0 0 141 29 7.5 56 58 8 26 0.89 0.50 1.00 0.67 1.00 0 165 165 25 7.5 56 60 8 26 0.89 0.50 1.00 0.67 1.00 0 165 165 25 9.5 53 74 6 26 0.89 0.30 1.00 0.33 1.00 0 0 110 20 6 47 55 8 29 0.89 0.30 0.83 0.33 1.00 0 118 118 20 10 25 62 4 18 0.89 0.30 0.50 0.00 1.00 0 158 0 16 7 47 63 4 28 1.00 0.40 0.67 0.33 0.50 0 0 146 20 3 46 69 9 15 0.67 0.50 1.00 0.33 1.00 0 49 0 20 6 50 58 9 19 1.00 0.50 0.67 0.67 1.00 0 0 0 23 7 39 58 5 13 0.89 0.40 1.00 0.00 0.00 0 0 0 18 10 51 68 4 26 0.89 0.70 1.00 1.00 0.00 0 155 155 25 7 58 72 4 26 0.89 0.50 0.50 0.33 0.50 0 0 0 18 3.5 35 62 15 21 0.89 0.40 0.67 0.33 0.00 0 0 147 19 8 58 62 10 27 1.00 0.70 0.67 1.00 1.00 0 0 0 25 10 60 65 9 24 1.00 0.70 0.67 1.00 1.00 0 0 0 25 5.5 62 69 7 22 1.00 0.70 0.67 1.00 1.00 0 0 0 25 6 63 66 9 22 0.89 0.70 0.67 1.00 1.00 0 0 0 24 6.5 53 72 6 24 0.89 0.70 0.67 0.00 0.00 0 0 61 19 6.5 46 62 4 22 0.89 0.70 1.00 0.67 1.00 0 0 60 26 8.5 67 75 7 27 0.33 0.10 0.67 0.33 0.00 0 0 109 10 4 59 58 4 26 0.67 0.20 0.67 0.67 1.00 0 0 68 17 9.5 64 66 7 25 0.56 0.30 0.33 0.33 1.00 0 0 0 13 8 38 55 4 20 0.44 0.60 0.83 0.33 0.50 0 0 0 17 8.5 50 47 15 28 1.00 0.80 1.00 1.00 1.00 0 0 73 30 5.5 48 72 4 22 0.89 0.80 1.00 0.33 0.50 0 151 0 25 7 48 62 9 27 0.33 0.00 0.17 0.00 0.00 0 0 0 4 9 47 64 4 20 0.67 0.30 0.67 0.33 1.00 0 0 0 16 8 66 64 4 26 0.67 0.60 0.83 0.33 1.00 0 0 0 21 10 47 19 28 30 1.00 0.50 0.83 0.67 1.00 0 220 220 23 8 63 50 4 24 0.78 0.70 1.00 0.33 0.50 0 0 65 22 6 58 68 4 23 0.67 0.30 0.83 0.00 1.00 0 141 0 17 8 44 70 4 26 1.00 0.30 1.00 0.67 0.00 0 0 0 20 5 51 79 5 24 0.78 0.40 1.00 0.67 0.50 0 122 122 20 9 43 69 4 27 0.89 0.40 0.83 1.00 1.00 0 0 0 22 4.5 55 71 4 31 0.89 0.10 0.83 0.00 1.00 0 44 44 16 8.5 38 48 12 21 0.89 0.50 1.00 0.67 1.00 0 0 52 23 9.5 45 73 4 10 0.00 0.00 0.00 0.00 0.00 0 0 0 0 8.5 50 74 6 15 0.67 0.40 1.00 0.33 0.00 0 0 101 18 7.5 54 66 6 29 1.00 0.60 0.83 0.67 0.50 0 0 42 25 7.5 57 71 5 29 1.00 0.40 1.00 0.33 1.00 0 152 152 23 5 60 74 4 24 0.67 0.10 0.33 0.00 1.00 0 107 0 12 7 55 78 4 20 0.89 0.30 0.83 0.00 1.00 0 0 0 18 8 56 75 4 25 0.89 0.70 0.83 0.67 1.00 0 154 0 24 5.5 49 53 10 25 0.56 0.30 0.17 0.00 1.00 0 103 103 11 8.5 37 60 7 25 0.67 0.50 0.83 0.33 0.00 0 0 96 18 9.5 59 70 4 31 1.00 0.30 0.83 0.67 1.00 0 175 175 23 7 46 69 7 25 1.00 0.60 0.67 0.67 1.00 0 0 57 24 8 51 65 4 26 1.00 0.90 1.00 1.00 1.00 0 0 0 29 8.5 58 78 4 26 0.67 0.40 0.83 0.00 1.00 0 143 0 18 3.5 64 78 12 33 0.44 0.30 1.00 0.00 0.50 0 0 0 15 6.5 53 59 5 27 0.89 0.90 1.00 0.67 1.00 0 110 110 29 6.5 48 72 8 21 0.44 0.50 1.00 0.00 0.00 0 131 131 16 10.5 51 70 6 28 0.56 0.30 1.00 1.00 0.50 0 167 0 19 8.5 47 63 17 19 0.89 0.60 0.83 0.67 0.50 0 0 0 22 8 59 63 4 31 0.67 0.20 1.00 0.33 0.50 0 137 0 16 10 62 71 5 27 0.89 0.40 0.83 1.00 1.00 0 0 86 23 10 62 74 4 33 1.00 0.50 0.83 0.67 0.50 0 121 121 23 9.5 51 67 5 27 0.78 0.40 0.83 0.67 0.50 0 149 0 19 9 64 66 5 23 0.44 0.00 0.00 0.00 0.00 0 168 0 4 10 52 62 6 23 0.89 0.20 1.00 0.33 1.00 0 140 0 20 7.5 67 80 4 30 0.89 0.50 1.00 0.67 1.00 0 0 88 24 4.5 50 73 4 29 0.89 0.30 1.00 0.67 0.50 0 168 168 20 4.5 54 67 4 35 0.44 0.00 0.00 0.00 0.00 0 94 94 4 0.5 58 61 6 27 1.00 0.50 0.83 1.00 1.00 0 51 51 24 6.5 56 73 8 16 0.89 0.60 0.83 0.33 1.00 0 0 0 22 4.5 63 74 10 23 0.67 0.30 0.83 0.00 0.50 0 145 145 16 5.5 31 32 4 26 0.33 0.00 0.00 0.00 0.00 0 66 66 3 5 65 69 5 33 0.78 0.30 0.67 0.00 0.00 0 0 85 15 6 71 69 4 30 0.89 0.50 1.00 0.67 1.00 0 109 0 24 4 50 84 4 30 0.78 0.40 0.67 0.00 1.00 0 0 0 17 8 57 64 4 28 0.78 0.50 0.83 0.67 0.50 0 0 102 20 10.5 47 58 16 17 0.89 0.70 1.00 1.00 0.50 0 0 0 27 6.5 47 59 7 24 0.78 0.80 1.00 0.67 1.00 0 0 86 26 8 57 78 4 23 0.78 0.60 1.00 0.33 1.00 0 0 114 23 8.5 43 57 4 31 0.67 0.40 0.83 0.33 0.50 0 164 0 17 5.5 41 60 14 26 0.89 0.50 0.83 0.33 0.00 0 119 119 20 7 63 68 5 25 0.89 0.50 1.00 0.00 1.00 0 126 0 22 5 63 68 5 26 0.78 0.30 1.00 0.33 1.00 0 132 132 19 3.5 56 73 5 28 1.00 0.60 1.00 0.00 1.00 0 142 142 24 5 51 69 5 26 1.00 0.30 0.67 0.67 0.50 0 83 0 19 9 50 67 7 28 0.78 0.60 0.83 1.00 0.50 0 0 94 23 8.5 22 60 19 27 0.78 0.30 0.33 0.33 1.00 0 0 0 15 5 41 65 16 21 0.89 0.70 1.00 0.67 1.00 0 166 166 27 9.5 59 66 4 25 0.89 0.70 1.00 1.00 1.00 0 0 0 26 3 56 74 4 30 0.67 0.60 0.67 1.00 1.00 0 0 64 22 1.5 66 81 7 28 1.00 0.50 1.00 0.33 0.00 0 93 0 22 6 53 72 9 19 0.67 0.50 0.83 0.33 0.50 0 0 0 18 0.5 42 55 5 27 0.56 0.40 0.67 0.00 1.00 0 0 105 15 6.5 52 49 14 27 0.78 0.40 1.00 0.33 1.00 0 0 49 22 7.5 54 74 4 19 1.00 0.70 1.00 1.00 1.00 0 0 0 27 4.5 44 53 16 28 0.67 0.20 0.17 0.00 0.00 0 0 95 10 8 62 64 10 28 0.78 0.50 0.83 0.67 0.50 0 0 102 20 9 53 65 5 28 0.56 0.40 0.83 0.67 0.00 0 0 0 17 7.5 50 57 6 23 1.00 0.20 1.00 0.67 1.00 0 0 63 23 8.5 36 51 4 26 0.89 0.50 0.67 0.67 0.00 0 0 0 19 7 76 80 4 28 0.44 0.40 0.50 0.00 1.00 0 0 0 13 9.5 66 67 4 27 1.00 0.70 0.67 1.00 1.00 0 0 117 27 6.5 62 70 5 30 0.89 0.60 0.83 0.67 0.00 0 0 57 23 9.5 59 74 4 20 0.78 0.40 0.83 0.00 0.00 0 0 0 16 6 47 75 4 27 0.89 0.50 1.00 0.67 1.00 0 0 73 25 8 55 70 5 25 0.11 0.00 0.17 0.00 0.00 0 0 0 2 9.5 58 69 4 28 0.89 0.70 1.00 0.67 1.00 0 0 0 26 8 60 65 4 26 0.89 0.40 0.67 0.67 1.00 0 0 105 20 8 44 55 5 28 1.00 0.50 0.67 1.00 1.00 0 117 0 23 9 57 71 8 21 0.89 0.60 0.83 0.67 0.50 0 0 0 22 5 45 65 15 11 1.00 0.80 0.50 0.67 0.50 0 0 31 24
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 NUMERACYTOT

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


Chart options



R Code
par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '1' 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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