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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 15 Dec 2014 23:02:43 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/15/t1418684738xvsv0kqp6bh67cm.htm/, Retrieved Thu, 31 Oct 2024 23:35:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269098, Retrieved Thu, 31 Oct 2024 23:35:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-15 23:02:43] [37e054ac358b2aa7c2a1d0b751dfa890] [Current]
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Dataseries X:
12.9 0 1 0 11 8 7 18 12 20 4 21 149 68 0 149 0
12.2 0 1 1 19 18 20 23 20 19 4 22 139 39 139 139 0
12.8 0 1 0 16 12 9 22 14 18 5 22 148 32 0 148 0
7.4 0 1 1 24 24 19 22 25 24 4 18 158 62 158 158 0
6.7 0 1 1 15 16 12 19 15 20 4 23 128 33 128 128 0
12.6 0 1 1 17 19 16 25 20 20 9 12 224 52 224 224 0
14.8 0 1 0 19 16 17 28 21 24 8 20 159 62 0 159 0
13.3 0 1 1 19 15 9 16 15 21 11 22 105 77 105 105 0
11.1 0 1 1 28 28 28 28 28 28 4 21 159 76 159 159 0
8.2 0 1 1 26 21 20 21 11 10 4 19 167 41 167 167 0
11.4 0 1 1 15 18 16 22 22 22 6 22 165 48 165 165 0
6.4 0 1 1 26 22 22 24 22 19 4 15 159 63 159 159 0
10.6 0 1 1 16 19 17 24 27 27 8 20 119 30 119 119 0
12 0 1 0 24 22 12 26 24 23 4 19 176 78 0 176 0
6.3 0 1 0 25 25 18 28 23 24 4 18 54 19 0 54 0
11.3 0 0 0 22 20 20 24 24 24 11 15 91 31 0 0 0
11.9 0 1 1 15 16 12 20 21 25 4 20 163 66 163 163 0
9.3 0 1 0 21 19 16 26 20 24 4 21 124 35 0 124 0
9.6 0 0 1 22 18 16 21 19 21 6 21 137 42 137 0 0
10 0 1 0 27 26 21 28 25 28 6 15 121 45 0 121 0
6.4 0 1 1 26 24 15 27 16 28 4 16 153 21 153 153 0
13.8 0 1 1 26 20 17 23 24 22 8 23 148 25 148 148 0
10.8 0 1 0 22 19 17 24 21 26 5 21 221 44 0 221 0
13.8 0 1 1 21 19 17 24 22 26 4 18 188 69 188 188 0
11.7 0 1 1 22 23 18 22 25 21 9 25 149 54 149 149 0
10.9 0 1 1 20 18 15 21 23 26 4 9 244 74 244 244 0
16.1 0 0 1 21 16 20 25 20 23 7 30 148 80 148 0 0
13.4 0 0 0 20 18 13 20 21 20 10 20 92 42 0 0 0
9.9 0 1 1 22 21 21 21 22 24 4 23 150 61 150 150 0
11.5 0 1 0 21 20 12 26 25 25 4 16 153 41 0 153 0
8.3 0 1 0 8 15 6 23 23 24 7 16 94 46 0 94 0
11.7 0 1 0 22 19 13 21 19 20 12 19 156 39 0 156 0
9 0 1 1 20 19 19 27 21 24 7 25 132 34 132 132 0
9.7 0 1 1 24 7 12 25 19 25 5 18 161 51 161 161 0
10.8 0 1 1 17 20 14 23 25 23 8 23 105 42 105 105 0
10.3 0 1 1 20 20 13 25 16 21 5 21 97 31 97 97 0
10.4 0 1 0 23 19 12 23 24 23 4 10 151 39 0 151 0
12.7 0 0 1 20 19 17 19 24 21 9 14 131 20 131 0 0
9.3 0 1 1 22 20 19 22 18 18 7 22 166 49 166 166 0
11.8 0 1 0 19 18 10 24 28 24 4 26 157 53 0 157 0
5.9 0 1 1 15 14 10 19 15 18 4 23 111 31 111 111 0
11.4 0 1 1 20 17 11 21 17 21 4 23 145 39 145 145 0
13 0 1 1 22 17 11 27 18 23 4 24 162 54 162 162 0
10.8 0 1 1 17 8 10 25 26 25 4 24 163 49 163 163 0
12.3 0 0 1 14 9 7 25 18 22 7 18 59 34 59 0 0
11.3 0 1 0 24 22 22 23 22 22 4 23 187 46 0 187 0
11.8 0 1 1 17 20 12 17 19 23 7 15 109 55 109 109 0
7.9 0 0 1 23 20 18 28 17 24 4 19 90 42 90 0 0
12.7 0 1 0 25 22 20 25 26 25 4 16 105 50 0 105 0
12.3 0 0 1 16 22 9 20 21 22 4 25 83 13 83 0 0
11.6 0 0 1 18 22 16 25 26 24 4 23 116 37 116 0 0
6.7 0 0 1 20 16 14 21 21 21 8 17 42 25 42 0 0
10.9 0 1 1 18 14 11 24 12 24 4 19 148 30 148 148 0
12.1 0 0 1 23 24 20 28 20 25 4 21 155 28 155 0 0
13.3 0 1 1 24 21 17 20 20 23 4 18 125 45 125 125 0
10.1 0 1 1 23 20 14 19 24 27 4 27 116 35 116 116 0
5.7 0 0 0 13 20 8 24 24 27 7 21 128 28 0 0 0
14.3 0 1 1 20 18 16 21 22 23 12 13 138 41 138 138 0
8 0 0 0 20 14 11 24 21 18 4 8 49 6 0 0 0
13.3 0 0 1 19 19 10 23 20 20 4 29 96 45 96 0 0
9.3 0 1 1 22 24 15 18 23 23 4 28 164 73 164 164 0
12.5 0 1 0 22 19 15 27 19 24 5 23 162 17 0 162 0
7.6 0 1 0 15 16 10 25 24 26 15 21 99 40 0 99 0
15.9 0 1 1 17 16 10 20 21 20 5 19 202 64 202 202 0
9.2 0 1 0 19 16 18 21 16 23 10 19 186 37 0 186 0
9.1 0 0 1 20 14 10 23 17 22 9 20 66 25 66 0 0
11.1 0 1 0 22 22 22 27 23 23 8 18 183 65 0 183 0
13 0 1 1 21 21 16 24 20 17 4 19 214 100 214 214 0
14.5 0 1 1 21 15 10 27 19 20 5 17 188 28 188 188 0
12.2 0 0 0 16 14 7 24 18 22 4 19 104 35 0 0 0
12.3 0 1 0 20 15 16 23 18 18 9 25 177 56 0 177 0
11.4 0 1 0 21 14 16 24 21 19 4 19 126 29 0 126 0
8.8 0 0 0 20 20 16 21 20 19 10 22 76 43 0 0 0
14.6 0 0 1 23 21 22 23 17 16 4 23 99 59 99 0 0
12.6 0 1 0 18 14 5 27 25 26 4 14 139 50 0 139 0
13 0 1 0 16 16 10 25 17 25 7 16 162 59 0 162 0
12.6 0 0 1 17 13 8 19 17 23 5 24 108 27 108 0 0
13.2 0 1 0 24 26 16 24 24 18 4 20 159 61 0 159 0
9.9 0 0 0 13 13 8 25 21 22 4 12 74 28 0 0 0
7.7 0 1 1 19 18 16 23 22 26 4 24 110 51 110 110 0
10.5 0 0 0 20 15 14 23 18 25 4 22 96 35 0 0 0
13.4 0 0 0 22 18 15 25 22 26 4 12 116 29 0 0 0
10.9 0 0 0 19 21 9 26 20 26 4 22 87 48 0 0 0
4.3 0 0 1 21 17 21 26 21 24 6 20 97 25 97 0 0
10.3 0 0 0 15 18 7 16 21 22 10 10 127 44 0 0 0
11.8 0 0 1 21 20 17 23 20 21 7 23 106 64 106 0 0
11.2 0 0 1 24 18 18 26 18 22 4 17 80 32 80 0 0
11.4 0 0 0 22 25 16 25 25 28 4 22 74 20 0 0 0
8.6 0 0 0 20 20 16 23 23 22 7 24 91 28 0 0 0
13.2 0 0 0 21 19 14 26 21 26 4 18 133 34 0 0 0
12.6 0 0 1 19 18 15 22 20 20 8 21 74 31 74 0 0
5.6 0 0 1 14 12 8 20 21 24 11 20 114 26 114 0 0
9.9 0 0 1 25 22 22 27 20 21 6 20 140 58 140 0 0
8.8 0 0 0 11 16 5 20 22 23 14 22 95 23 0 0 0
7.7 0 0 1 17 18 13 22 15 23 5 19 98 21 98 0 0
9 0 0 0 22 23 22 24 24 23 4 20 121 21 0 0 0
7.3 0 0 1 20 20 18 21 22 22 8 26 126 33 126 0 0
11.4 0 0 1 22 20 15 24 21 23 9 23 98 16 98 0 0
13.6 0 0 1 15 16 11 26 17 21 4 24 95 20 95 0 0
7.9 0 0 1 23 22 19 24 23 27 4 21 110 37 110 0 0
10.7 0 0 1 20 19 19 24 22 23 5 21 70 35 70 0 0
10.3 0 0 0 22 23 21 27 23 26 4 19 102 33 0 0 0
8.3 0 0 1 16 6 4 25 16 27 5 8 86 27 86 0 0
9.6 0 0 1 25 19 17 27 18 27 4 17 130 41 130 0 0
14.2 0 0 1 18 24 10 19 25 23 4 20 96 40 96 0 0
8.5 0 0 0 19 19 13 22 18 23 7 11 102 35 0 0 0
13.5 0 0 0 25 15 15 22 14 23 10 8 100 28 0 0 0
4.9 0 0 0 21 18 11 25 20 28 4 15 94 32 0 0 0
6.4 0 0 0 22 18 20 23 19 24 5 18 52 22 0 0 0
9.6 0 0 0 21 22 13 24 18 20 4 18 98 44 0 0 0
11.6 0 0 0 22 23 18 24 22 23 4 19 118 27 0 0 0
11.1 0 0 1 23 18 20 23 21 22 4 19 99 17 99 0 0
4.35 1 1 1 20 17 15 22 14 15 6 23 48 12 48 48 48
12.7 1 1 1 6 6 4 24 5 27 4 22 50 45 50 50 50
18.1 1 1 1 15 22 9 19 25 23 8 21 150 37 150 150 150
17.85 1 1 1 18 20 18 25 21 23 5 25 154 37 154 154 154
16.6 1 0 0 24 16 12 26 11 20 4 30 109 108 0 0 109
12.6 1 0 1 22 16 17 18 20 18 17 17 68 10 68 0 68
17.1 1 1 1 21 17 12 24 9 22 4 27 194 68 194 194 194
19.1 1 1 0 23 20 16 28 15 20 4 23 158 72 0 158 158
16.1 1 1 1 20 23 17 23 23 21 8 23 159 143 159 159 159
13.35 1 1 0 20 18 14 19 21 25 4 18 67 9 0 67 67
18.4 1 1 0 18 13 13 19 9 19 7 18 147 55 0 147 147
14.7 1 1 1 25 22 20 27 24 25 4 23 39 17 39 39 39
10.6 1 1 1 16 20 16 24 16 24 4 19 100 37 100 100 100
12.6 1 1 1 20 20 15 26 20 22 5 15 111 27 111 111 111
16.2 1 1 1 14 13 10 21 15 28 7 20 138 37 138 138 138
13.6 1 1 1 22 16 16 25 18 22 4 16 101 58 101 101 101
18.9 1 0 1 26 25 21 28 22 21 4 24 131 66 131 0 131
14.1 1 1 1 20 16 15 19 21 23 7 25 101 21 101 101 101
14.5 1 1 1 17 15 16 20 21 19 11 25 114 19 114 114 114
16.15 1 1 0 22 19 19 26 21 21 7 19 165 78 0 165 165
14.75 1 1 1 22 19 9 27 20 25 4 19 114 35 114 114 114
14.8 1 1 1 20 24 19 23 24 23 4 16 111 48 111 111 111
12.45 1 1 1 17 9 7 18 15 28 4 19 75 27 75 75 75
12.65 1 1 1 22 22 23 23 24 14 4 19 82 43 82 82 82
17.35 1 1 1 17 15 14 21 18 23 4 23 121 30 121 121 121
8.6 1 1 1 22 22 10 23 24 24 4 21 32 25 32 32 32
18.4 1 1 0 21 22 16 22 24 25 6 22 150 69 0 150 150
16.1 1 1 1 25 24 12 21 15 15 8 19 117 72 117 117 117
11.6 1 0 1 11 12 10 14 19 23 23 20 71 23 71 0 71
17.75 1 1 1 19 21 7 24 20 26 4 20 165 13 165 165 165
15.25 1 1 1 24 25 20 26 26 21 8 3 154 61 154 154 154
17.65 1 1 1 17 26 9 24 26 26 6 23 126 43 126 126 126
16.35 1 1 0 22 21 12 22 23 23 4 23 149 51 0 149 149
17.65 1 1 0 17 14 10 20 13 15 7 20 145 67 0 145 145
13.6 1 1 1 26 28 19 20 16 16 4 15 120 36 120 120 120
14.35 1 1 0 20 21 11 18 22 20 4 16 109 44 0 109 109
14.75 1 1 0 19 16 15 18 21 20 4 7 132 45 0 132 132
18.25 1 1 1 21 16 14 25 11 21 10 24 172 34 172 172 172
9.9 1 1 0 24 25 11 28 23 28 6 17 169 36 0 169 169
16 1 1 1 21 21 14 23 18 19 5 24 114 72 114 114 114
18.25 1 1 1 19 22 15 20 19 21 5 24 156 39 156 156 156
16.85 1 1 0 13 9 7 22 15 22 4 19 172 43 0 172 172
14.6 1 0 1 24 20 22 27 8 27 4 25 68 25 68 0 68
13.85 1 0 1 28 19 19 24 15 20 5 20 89 56 89 0 89
18.95 1 1 1 27 24 22 23 21 17 5 28 167 80 167 167 167
15.6 1 1 0 22 22 11 20 25 26 5 23 113 40 0 113 113
14.85 1 0 0 23 22 19 22 14 21 5 27 115 73 0 0 115
11.75 1 0 0 19 12 9 21 21 24 4 18 78 34 0 0 78
18.45 1 0 0 18 17 11 24 18 21 6 28 118 72 0 0 118
15.9 1 0 1 23 18 17 26 18 25 4 21 87 42 87 0 87
17.1 1 1 0 21 10 12 24 12 22 4 19 173 61 0 173 173
16.1 1 1 1 22 22 17 18 24 17 4 23 2 23 2 2 2
19.9 1 0 0 17 24 10 17 17 14 9 27 162 74 0 0 162
10.95 1 0 1 15 18 17 23 20 23 18 22 49 16 49 0 49
18.45 1 0 0 21 18 13 21 24 28 6 28 122 66 0 0 122
15.1 1 0 1 20 23 11 21 22 24 5 25 96 9 96 0 96
15 1 0 0 26 21 19 24 15 22 4 21 100 41 0 0 100
11.35 1 0 0 19 21 21 22 22 24 11 22 82 57 0 0 82
15.95 1 0 1 28 28 24 24 26 25 4 28 100 48 100 0 100
18.1 1 0 0 21 17 13 24 17 21 10 20 115 51 0 0 115
14.6 1 0 1 19 21 16 24 23 22 6 29 141 53 141 0 141
15.4 1 1 1 22 21 13 23 19 16 8 25 165 29 165 165 165
15.4 1 1 1 21 20 15 21 21 18 8 25 165 29 165 165 165
17.6 1 0 1 20 18 15 24 23 27 6 20 110 55 110 0 110
13.35 1 1 1 19 17 11 19 19 17 8 20 118 54 118 118 118
19.1 1 1 0 11 7 7 19 18 25 4 16 158 43 0 158 158
15.35 1 0 1 17 17 13 23 16 24 4 20 146 51 146 0 146
7.6 1 1 0 19 14 13 25 23 21 9 20 49 20 0 49 49
13.4 1 0 0 20 18 12 24 13 21 9 23 90 79 0 0 90
13.9 1 0 0 17 14 8 21 18 19 5 18 121 39 0 0 121
19.1 1 1 1 21 23 7 18 23 27 4 25 155 61 155 155 155
15.25 1 0 0 21 20 17 23 21 28 4 18 104 55 0 0 104
12.9 1 0 1 12 14 9 20 23 19 15 19 147 30 147 0 147
16.1 1 0 0 23 17 18 23 16 23 10 25 110 55 0 0 110
17.35 1 0 0 22 21 17 23 17 25 9 25 108 22 0 0 108
13.15 1 0 0 22 23 17 23 20 26 7 25 113 37 0 0 113
12.15 1 0 0 21 24 18 23 18 25 9 24 115 2 0 0 115
12.6 1 0 1 20 21 12 27 20 25 6 19 61 38 61 0 61
10.35 1 0 1 18 14 14 19 19 24 4 26 60 27 60 0 60
15.4 1 0 1 21 24 22 25 26 24 7 10 109 56 109 0 109
9.6 1 0 1 24 16 19 25 9 24 4 17 68 25 68 0 68
18.2 1 0 0 22 21 21 21 23 22 7 13 111 39 0 0 111
13.6 1 0 0 20 8 10 25 9 21 4 17 77 33 0 0 77
14.85 1 0 1 17 17 16 17 13 17 15 30 73 43 73 0 73
14.75 1 1 0 19 18 11 22 27 23 4 25 151 57 0 151 151
14.1 1 0 0 16 17 15 23 22 17 9 4 89 43 0 0 89
14.9 1 0 0 19 16 12 27 12 25 4 16 78 23 0 0 78
16.25 1 0 0 23 22 21 27 18 19 4 21 110 44 0 0 110
19.25 1 1 1 8 17 22 5 6 8 28 23 220 54 220 220 220
13.6 1 0 1 22 21 20 19 17 14 4 22 65 28 65 0 65
13.6 1 1 0 23 20 15 24 22 22 4 17 141 36 0 141 141
15.65 1 0 0 15 20 9 23 22 25 4 20 117 39 0 0 117
12.75 1 1 1 17 19 15 28 23 28 5 20 122 16 122 122 122
14.6 1 0 0 21 8 14 25 19 25 4 22 63 23 0 0 63
9.85 1 1 1 25 19 11 27 20 24 4 16 44 40 44 44 44
12.65 1 0 1 18 11 9 16 17 15 12 23 52 24 52 0 52
19.2 1 0 0 20 13 12 25 24 24 4 0 131 78 0 0 131
16.6 1 0 1 21 18 11 26 20 28 6 18 101 57 101 0 101
11.2 1 0 1 21 19 14 24 18 24 6 25 42 37 42 0 42
15.25 1 1 1 24 23 10 23 23 25 5 23 152 27 152 152 152
11.9 1 1 0 22 20 18 24 27 23 4 12 107 61 0 107 107
13.2 1 0 0 22 22 11 27 25 26 4 18 77 27 0 0 77
16.35 1 1 0 23 19 14 25 24 26 4 24 154 69 0 154 154
12.4 1 1 1 17 16 16 19 12 22 10 11 103 34 103 103 103
15.85 1 0 1 15 11 11 19 16 25 7 18 96 44 96 0 96
18.15 1 1 1 22 21 16 24 24 22 4 23 175 34 175 175 175
11.15 1 0 1 19 14 13 20 23 26 7 24 57 39 57 0 57
15.65 1 0 0 18 21 12 21 24 20 4 29 112 51 0 0 112
17.75 1 1 0 21 20 17 28 24 26 4 18 143 34 0 143 143
7.65 1 0 0 20 21 23 26 26 26 12 15 49 31 0 0 49
12.35 1 1 1 19 20 14 19 19 21 5 29 110 13 110 110 110
15.6 1 1 1 19 19 10 23 28 21 8 16 131 12 131 131 131
19.3 1 1 0 16 19 16 23 23 24 6 19 167 51 0 167 167
15.2 1 0 0 18 18 11 21 21 21 17 22 56 24 0 0 56
17.1 1 1 0 23 20 16 26 19 18 4 16 137 19 0 137 137
15.6 1 0 1 22 21 19 25 23 23 5 23 86 30 86 0 86
18.4 1 1 1 23 22 17 25 23 26 4 23 121 81 121 121 121
19.05 1 1 0 20 19 12 24 20 23 5 19 149 42 0 149 149
18.55 1 1 0 24 23 17 23 18 25 5 4 168 22 0 168 168
19.1 1 1 0 25 16 11 22 20 20 6 20 140 85 0 140 140
13.1 1 0 1 25 23 19 27 28 25 4 24 88 27 88 0 88
12.85 1 1 1 20 18 12 26 21 26 4 20 168 25 168 168 168
9.5 1 1 1 23 23 8 23 25 19 4 4 94 22 94 94 94
4.5 1 1 1 21 20 17 22 18 21 6 24 51 19 51 51 51
11.85 1 0 0 23 20 13 26 24 23 8 22 48 14 0 0 48
13.6 1 1 1 23 23 17 22 28 24 10 16 145 45 145 145 145
11.7 1 1 1 11 13 7 17 9 6 4 3 66 45 66 66 66
12.4 1 0 1 21 21 23 25 22 22 5 15 85 28 85 0 85
13.35 1 1 0 27 26 18 22 26 21 4 24 109 51 0 109 109
11.4 1 0 0 19 18 13 28 28 28 4 17 63 41 0 0 63
14.9 1 0 1 21 19 17 22 18 24 4 20 102 31 102 0 102
19.9 1 0 0 16 18 13 21 23 14 16 27 162 74 0 0 162
11.2 1 0 1 21 18 8 24 15 20 7 26 86 19 86 0 86
14.6 1 0 1 22 19 16 26 24 28 4 23 114 51 114 0 114
17.6 1 1 0 16 13 14 26 12 19 4 17 164 73 0 164 164
14.05 1 1 1 18 10 13 24 12 24 14 20 119 24 119 119 119
16.1 1 1 0 23 21 19 27 20 21 5 22 126 61 0 126 126
13.35 1 1 1 24 24 15 22 25 21 5 19 132 23 132 132 132
11.85 1 1 1 20 21 15 23 24 26 5 24 142 14 142 142 142
11.95 1 1 0 20 23 8 22 23 24 5 19 83 54 0 83 83
14.75 1 0 1 18 18 14 23 18 26 7 23 94 51 94 0 94
15.15 1 0 0 4 11 7 15 20 25 19 15 81 62 0 0 81
13.2 1 1 1 14 16 11 20 22 23 16 27 166 36 166 166 166
16.85 1 0 0 22 20 17 22 20 24 4 26 110 59 0 0 110
7.85 1 0 1 17 20 19 25 25 24 4 22 64 24 64 0 64
7.7 1 1 0 23 26 17 27 28 26 7 22 93 26 0 93 93
12.6 1 0 0 20 21 12 24 25 23 9 18 104 54 0 0 104
7.85 1 0 1 18 12 12 21 14 20 5 15 105 39 105 0 105
10.95 1 0 1 19 15 18 17 16 16 14 22 49 16 49 0 49
12.35 1 0 0 20 18 16 26 24 24 4 27 88 36 0 0 88
9.95 1 0 1 15 14 15 20 13 20 16 10 95 31 95 0 95
14.9 1 0 1 24 18 20 22 19 23 10 20 102 31 102 0 102
16.65 1 0 0 21 16 16 24 18 23 5 17 99 42 0 0 99
13.4 1 0 1 19 19 12 23 16 18 6 23 63 39 63 0 63
13.95 1 0 0 19 7 10 22 8 21 4 19 76 25 0 0 76
15.7 1 0 0 27 21 28 28 27 25 4 13 109 31 0 0 109
16.85 1 0 1 23 24 19 21 23 23 4 27 117 38 117 0 117
10.95 1 0 1 23 21 18 24 20 26 5 23 57 31 57 0 57
15.35 1 0 0 20 20 19 28 20 26 4 16 120 17 0 0 120
12.2 1 0 1 17 22 8 25 26 24 4 25 73 22 73 0 73
15.1 1 0 0 21 17 17 24 23 23 5 2 91 55 0 0 91
17.75 1 0 0 23 19 16 24 24 21 4 26 108 62 0 0 108
15.2 1 0 1 22 20 18 21 21 23 4 20 105 51 105 0 105
14.6 1 1 0 16 16 12 20 15 20 5 23 117 30 0 117 117
16.65 1 0 0 20 20 17 26 22 23 8 22 119 49 0 0 119
8.1 1 0 1 16 16 13 16 25 24 15 24 31 16 31 0 31




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.25881 + 0.70396YearN[t] -0.681224GroupN[t] + 0.672392gender[t] + 0.0588095AMS.I1[t] -0.0577708AMS.I2[t] -0.0257743AMS.I3[t] -0.0590307AMS.E1[t] + 0.0242987AMS.E2[t] -0.0354835AMS.E3[t] -0.0582734AMS.A[t] + 0.041479NUMERACYTOT[t] + 0.027742LFM[t] + 0.0327447CH[t] -0.00914513Gender_LFM[t] -0.00369178Group_LFM[t] + 0.0306051Year_LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.25881 +  0.70396YearN[t] -0.681224GroupN[t] +  0.672392gender[t] +  0.0588095AMS.I1[t] -0.0577708AMS.I2[t] -0.0257743AMS.I3[t] -0.0590307AMS.E1[t] +  0.0242987AMS.E2[t] -0.0354835AMS.E3[t] -0.0582734AMS.A[t] +  0.041479NUMERACYTOT[t] +  0.027742LFM[t] +  0.0327447CH[t] -0.00914513Gender_LFM[t] -0.00369178Group_LFM[t] +  0.0306051Year_LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.25881 +  0.70396YearN[t] -0.681224GroupN[t] +  0.672392gender[t] +  0.0588095AMS.I1[t] -0.0577708AMS.I2[t] -0.0257743AMS.I3[t] -0.0590307AMS.E1[t] +  0.0242987AMS.E2[t] -0.0354835AMS.E3[t] -0.0582734AMS.A[t] +  0.041479NUMERACYTOT[t] +  0.027742LFM[t] +  0.0327447CH[t] -0.00914513Gender_LFM[t] -0.00369178Group_LFM[t] +  0.0306051Year_LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.25881 + 0.70396YearN[t] -0.681224GroupN[t] + 0.672392gender[t] + 0.0588095AMS.I1[t] -0.0577708AMS.I2[t] -0.0257743AMS.I3[t] -0.0590307AMS.E1[t] + 0.0242987AMS.E2[t] -0.0354835AMS.E3[t] -0.0582734AMS.A[t] + 0.041479NUMERACYTOT[t] + 0.027742LFM[t] + 0.0327447CH[t] -0.00914513Gender_LFM[t] -0.00369178Group_LFM[t] + 0.0306051Year_LFM[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.258812.021844.0855.87315e-052.93658e-05
YearN0.703960.9120440.77180.4409020.220451
GroupN-0.6812241.02434-0.6650.5066140.253307
gender0.6723920.9048080.74310.458070.229035
AMS.I10.05880950.05579881.0540.2928770.146439
AMS.I2-0.05777080.0495729-1.1650.2449330.122466
AMS.I3-0.02577430.0419285-0.61470.5392760.269638
AMS.E1-0.05903070.057773-1.0220.3078360.153918
AMS.E20.02429870.03953210.61470.5393160.269658
AMS.E3-0.03548350.0476136-0.74520.4567980.228399
AMS.A-0.05827340.0481585-1.210.227360.11368
NUMERACYTOT0.0414790.02737711.5150.1309570.0654787
LFM0.0277420.01026792.7020.007348110.00367406
CH0.03274470.008118714.0337.22807e-053.61404e-05
Gender_LFM-0.009145130.00722934-1.2650.2069990.103499
Group_LFM-0.003691780.00894344-0.41280.6800980.340049
Year_LFM0.03060510.007250584.2213.35956e-051.67978e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.25881 & 2.02184 & 4.085 & 5.87315e-05 & 2.93658e-05 \tabularnewline
YearN & 0.70396 & 0.912044 & 0.7718 & 0.440902 & 0.220451 \tabularnewline
GroupN & -0.681224 & 1.02434 & -0.665 & 0.506614 & 0.253307 \tabularnewline
gender & 0.672392 & 0.904808 & 0.7431 & 0.45807 & 0.229035 \tabularnewline
AMS.I1 & 0.0588095 & 0.0557988 & 1.054 & 0.292877 & 0.146439 \tabularnewline
AMS.I2 & -0.0577708 & 0.0495729 & -1.165 & 0.244933 & 0.122466 \tabularnewline
AMS.I3 & -0.0257743 & 0.0419285 & -0.6147 & 0.539276 & 0.269638 \tabularnewline
AMS.E1 & -0.0590307 & 0.057773 & -1.022 & 0.307836 & 0.153918 \tabularnewline
AMS.E2 & 0.0242987 & 0.0395321 & 0.6147 & 0.539316 & 0.269658 \tabularnewline
AMS.E3 & -0.0354835 & 0.0476136 & -0.7452 & 0.456798 & 0.228399 \tabularnewline
AMS.A & -0.0582734 & 0.0481585 & -1.21 & 0.22736 & 0.11368 \tabularnewline
NUMERACYTOT & 0.041479 & 0.0273771 & 1.515 & 0.130957 & 0.0654787 \tabularnewline
LFM & 0.027742 & 0.0102679 & 2.702 & 0.00734811 & 0.00367406 \tabularnewline
CH & 0.0327447 & 0.00811871 & 4.033 & 7.22807e-05 & 3.61404e-05 \tabularnewline
Gender_LFM & -0.00914513 & 0.00722934 & -1.265 & 0.206999 & 0.103499 \tabularnewline
Group_LFM & -0.00369178 & 0.00894344 & -0.4128 & 0.680098 & 0.340049 \tabularnewline
Year_LFM & 0.0306051 & 0.00725058 & 4.221 & 3.35956e-05 & 1.67978e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.25881[/C][C]2.02184[/C][C]4.085[/C][C]5.87315e-05[/C][C]2.93658e-05[/C][/ROW]
[ROW][C]YearN[/C][C]0.70396[/C][C]0.912044[/C][C]0.7718[/C][C]0.440902[/C][C]0.220451[/C][/ROW]
[ROW][C]GroupN[/C][C]-0.681224[/C][C]1.02434[/C][C]-0.665[/C][C]0.506614[/C][C]0.253307[/C][/ROW]
[ROW][C]gender[/C][C]0.672392[/C][C]0.904808[/C][C]0.7431[/C][C]0.45807[/C][C]0.229035[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0588095[/C][C]0.0557988[/C][C]1.054[/C][C]0.292877[/C][C]0.146439[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0577708[/C][C]0.0495729[/C][C]-1.165[/C][C]0.244933[/C][C]0.122466[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0257743[/C][C]0.0419285[/C][C]-0.6147[/C][C]0.539276[/C][C]0.269638[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0590307[/C][C]0.057773[/C][C]-1.022[/C][C]0.307836[/C][C]0.153918[/C][/ROW]
[ROW][C]AMS.E2[/C][C]0.0242987[/C][C]0.0395321[/C][C]0.6147[/C][C]0.539316[/C][C]0.269658[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0354835[/C][C]0.0476136[/C][C]-0.7452[/C][C]0.456798[/C][C]0.228399[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0582734[/C][C]0.0481585[/C][C]-1.21[/C][C]0.22736[/C][C]0.11368[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.041479[/C][C]0.0273771[/C][C]1.515[/C][C]0.130957[/C][C]0.0654787[/C][/ROW]
[ROW][C]LFM[/C][C]0.027742[/C][C]0.0102679[/C][C]2.702[/C][C]0.00734811[/C][C]0.00367406[/C][/ROW]
[ROW][C]CH[/C][C]0.0327447[/C][C]0.00811871[/C][C]4.033[/C][C]7.22807e-05[/C][C]3.61404e-05[/C][/ROW]
[ROW][C]Gender_LFM[/C][C]-0.00914513[/C][C]0.00722934[/C][C]-1.265[/C][C]0.206999[/C][C]0.103499[/C][/ROW]
[ROW][C]Group_LFM[/C][C]-0.00369178[/C][C]0.00894344[/C][C]-0.4128[/C][C]0.680098[/C][C]0.340049[/C][/ROW]
[ROW][C]Year_LFM[/C][C]0.0306051[/C][C]0.00725058[/C][C]4.221[/C][C]3.35956e-05[/C][C]1.67978e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.258812.021844.0855.87315e-052.93658e-05
YearN0.703960.9120440.77180.4409020.220451
GroupN-0.6812241.02434-0.6650.5066140.253307
gender0.6723920.9048080.74310.458070.229035
AMS.I10.05880950.05579881.0540.2928770.146439
AMS.I2-0.05777080.0495729-1.1650.2449330.122466
AMS.I3-0.02577430.0419285-0.61470.5392760.269638
AMS.E1-0.05903070.057773-1.0220.3078360.153918
AMS.E20.02429870.03953210.61470.5393160.269658
AMS.E3-0.03548350.0476136-0.74520.4567980.228399
AMS.A-0.05827340.0481585-1.210.227360.11368
NUMERACYTOT0.0414790.02737711.5150.1309570.0654787
LFM0.0277420.01026792.7020.007348110.00367406
CH0.03274470.008118714.0337.22807e-053.61404e-05
Gender_LFM-0.009145130.00722934-1.2650.2069990.103499
Group_LFM-0.003691780.00894344-0.41280.6800980.340049
Year_LFM0.03060510.007250584.2213.35956e-051.67978e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.771273
R-squared0.594863
Adjusted R-squared0.570027
F-TEST (value)23.9516
F-TEST (DF numerator)16
F-TEST (DF denominator)261
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22576
Sum Squared Residuals1293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.771273 \tabularnewline
R-squared & 0.594863 \tabularnewline
Adjusted R-squared & 0.570027 \tabularnewline
F-TEST (value) & 23.9516 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 261 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22576 \tabularnewline
Sum Squared Residuals & 1293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.771273[/C][/ROW]
[ROW][C]R-squared[/C][C]0.594863[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.570027[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.9516[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]261[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.22576[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.771273
R-squared0.594863
Adjusted R-squared0.570027
F-TEST (value)23.9516
F-TEST (DF numerator)16
F-TEST (DF denominator)261
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22576
Sum Squared Residuals1293







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.54940.350642
212.210.29441.90562
312.811.22461.57544
47.411.1411-3.74109
56.710.1411-3.44108
612.611.0551.545
714.811.55583.24417
813.311.30161.99843
911.111.08780.0121734
108.211.1099-2.90985
1111.410.72910.670895
126.411.2066-4.8066
1310.69.055841.54416
141212.983-0.982955
156.37.62841-1.32841
1611.39.717331.58267
1711.911.52820.37176
189.310.1684-0.868385
199.611.6941-2.0941
20109.739350.260651
216.49.20602-2.80602
2213.810.14273.65732
2310.812.8421-2.04212
2413.811.71952.08049
2511.710.81640.883648
2610.912.5965-1.69651
2716.113.12882.97116
2813.410.85442.54556
299.911.1868-1.28678
3011.510.98630.513748
318.39.39914-1.09914
3211.711.06870.631252
3399.61351-0.613509
349.711.5715-1.8715
3510.89.595271.20473
3610.39.144121.15588
3710.411.0229-0.622938
3812.710.43532.26465
399.310.982-1.68202
4011.812.1661-0.366092
415.910.0603-4.16026
4211.410.7480.651963
431311.25081.74916
4410.811.5952-0.79517
4512.39.784392.51561
4611.312.2718-0.971828
4711.810.0671.73305
487.910.1771-2.27709
4912.710.12342.57663
5012.39.691352.60865
5111.610.70050.89946
526.79.18634-2.48634
5310.99.982790.917209
5412.110.76521.3348
5513.310.34952.95052
5610.110.3518-0.25177
575.710.8011-5.10108
5814.39.69214.6079
5988.45209-0.452089
6013.311.34041.9596
619.312.214-2.91399
6212.510.51881.98117
637.69.1502-1.5502
6415.912.29093.60912
659.211.53-2.32998
669.19.66672-0.566724
6711.111.9924-0.892362
681313.3446-0.344595
6914.510.65163.84836
7012.211.03681.1632
7112.312.5189-0.218906
7211.410.54570.85431
738.810.2856-1.48556
7414.611.48523.11481
7512.611.11741.48261
761311.47051.52949
7712.611.04581.55418
7813.212.02021.17978
799.99.55440.345598
807.710.2413-2.54134
8110.510.8889-0.388925
8213.410.69472.70531
8310.910.62440.275613
844.39.86916-5.56916
8510.311.5019-1.20186
8611.811.56880.231227
8711.29.968391.23161
8811.49.221372.17863
898.610.3167-1.71668
9013.211.40481.79523
9112.69.895852.70415
925.610.4931-4.89313
939.911.6931-1.79314
948.89.87568-1.07568
957.79.81258-2.11258
96910.6481-1.64808
977.311.0384-3.73838
9811.49.736361.66364
9913.69.922623.67738
1007.910.6024-2.70241
10110.79.849290.850711
10210.310.19040.109629
1038.39.90129-1.60129
1049.610.9833-1.3833
10514.210.70693.49313
1068.510.2902-1.79017
10713.510.14143.35859
1084.910.2318-5.33176
1096.48.86785-2.46785
1109.610.8718-1.27177
11111.610.77430.825658
11211.110.05321.04685
1134.3510.4525-6.10247
11412.711.03221.66776
11518.115.44592.65412
11617.8515.57732.27272
11716.618.0709-1.47094
11812.611.73890.861128
11917.118.8613-1.76132
12019.117.78241.3176
12116.119.2257-3.1257
12213.3511.02882.32124
12318.416.8511.54904
12414.79.792474.90753
12510.612.7651-2.16511
12612.613.0252-0.425197
12716.214.81341.38656
12813.614.0183-0.418295
12918.916.68432.21574
13014.113.3050.795017
13114.513.53650.963495
13216.1518.1707-2.02073
13314.7513.81240.93757
13414.813.71721.08279
13512.4512.41410.0358868
13612.6512.8075-0.157543
13717.3514.31793.03209
1388.610.0058-1.40581
13918.417.25121.14881
14016.115.32520.774801
14111.611.886-0.285961
14217.7515.35572.39427
14315.2515.4217-0.171679
14417.6514.25883.39116
14516.3517.0315-0.681505
14617.6517.35840.29162
14713.614.0452-0.44525
14814.3514.5522-0.202153
14914.7515.5713-0.821309
15018.2516.3041.94603
1519.916.6487-6.74873
1521615.27030.72969
15318.2516.03042.21958
15416.8517.9946-1.14463
15514.611.93492.66512
15613.8514.6834-0.833441
15718.9518.22740.722559
15815.614.67370.926343
15914.8516.7799-1.92992
16011.7513.7519-2.00188
16118.4517.08561.36444
16215.913.81912.08095
16317.118.7316-1.63157
16416.19.021097.07891
16519.919.70190.198104
16610.9510.14980.800156
16718.4517.26411.18591
16815.113.40611.69387
1691514.77120.228761
17011.3513.6324-2.28244
17115.9514.79391.15613
17218.115.75852.34146
17314.617.0147-2.41467
17415.416.2653-0.865302
17515.416.3084-0.908406
17617.615.26212.33794
17713.3515.0444-1.69438
17819.117.24651.85352
17915.3516.9472-1.59718
1807.610.2311-2.63114
18113.415.2114-1.81142
18213.916.2634-2.36339
18319.117.07342.02655
18415.2515.14580.10419
18512.916.1332-3.23322
18616.115.75770.342314
18717.3514.30793.0421
18813.1515.1292-1.97923
18912.1513.7864-1.63636
19012.611.9780.621987
19110.3512.6942-2.34418
19215.414.42460.975377
1939.612.1603-2.56027
19418.214.92563.27439
19513.613.46220.137828
19614.8513.31961.53039
19714.7517.5401-2.79009
19814.113.3510.748977
19914.912.3922.50803
20016.2515.16951.08055
20119.2518.54490.705125
20213.612.78950.810549
20313.615.7866-2.1866
20415.6515.44750.202548
20512.7512.9963-0.246289
20614.612.5822.01797
2079.8510.8264-0.976362
20812.6512.36170.288268
20919.217.29891.90108
21016.614.73721.86276
21111.211.5461-0.346085
21215.2515.5573-0.307285
21311.914.4894-2.58942
21413.212.68350.516463
21516.3517.7992-1.44922
21612.412.6808-0.280751
21715.8514.48131.36871
21818.1516.80651.3435
21911.1512.7335-1.58351
22015.6516.3074-0.657375
22117.7515.37332.37673
2227.6510.3044-2.65436
22312.3513.4933-1.14333
22415.613.84571.75431
22519.317.29792.00209
22615.211.19864.00137
22717.114.8952.20496
22815.613.36942.23058
22918.415.6382.76205
23019.0516.31962.73042
23118.5515.89572.6543
23219.117.93661.16345
23313.113.4627-0.362694
23412.8515.8947-3.04469
2359.512.2784-2.77838
2364.510.5779-6.07793
23711.8510.76261.0874
23813.615.2232-1.62318
23911.712.0402-0.340196
24012.412.7722-0.372174
24113.3514.8812-1.53118
24211.412.2296-0.829628
24314.914.25160.648371
24419.919.41410.485857
24511.213.3862-2.18623
24614.615.4737-0.873704
24717.618.0191-0.419131
24814.0513.33830.711678
24916.115.58340.516568
25013.3514.4131-1.0631
25111.8514.4582-2.60823
25211.9513.1327-1.18275
25314.7514.29120.458809
25415.1513.36681.78319
25513.216.0283-2.82833
25616.8516.19420.655821
2577.8511.8841-4.03414
2587.712.2969-4.59686
25912.615.0496-2.44957
2607.8514.8561-7.00613
26110.9511.2711-0.321088
26212.3514.0837-1.73366
2639.9512.9192-2.96919
26414.914.11860.781353
26516.6514.6312.01901
26613.412.71910.680877
26713.9513.37660.57342
26815.714.41291.28706
26916.8515.50241.34755
27010.9511.9396-0.989573
27115.3514.39330.956736
27212.212.5782-0.378178
27315.114.00571.09434
27417.7516.40371.34634
27515.215.19680.00319622
27614.614.50270.0972613
27716.6515.72320.926814
2788.110.2987-2.19866

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.5494 & 0.350642 \tabularnewline
2 & 12.2 & 10.2944 & 1.90562 \tabularnewline
3 & 12.8 & 11.2246 & 1.57544 \tabularnewline
4 & 7.4 & 11.1411 & -3.74109 \tabularnewline
5 & 6.7 & 10.1411 & -3.44108 \tabularnewline
6 & 12.6 & 11.055 & 1.545 \tabularnewline
7 & 14.8 & 11.5558 & 3.24417 \tabularnewline
8 & 13.3 & 11.3016 & 1.99843 \tabularnewline
9 & 11.1 & 11.0878 & 0.0121734 \tabularnewline
10 & 8.2 & 11.1099 & -2.90985 \tabularnewline
11 & 11.4 & 10.7291 & 0.670895 \tabularnewline
12 & 6.4 & 11.2066 & -4.8066 \tabularnewline
13 & 10.6 & 9.05584 & 1.54416 \tabularnewline
14 & 12 & 12.983 & -0.982955 \tabularnewline
15 & 6.3 & 7.62841 & -1.32841 \tabularnewline
16 & 11.3 & 9.71733 & 1.58267 \tabularnewline
17 & 11.9 & 11.5282 & 0.37176 \tabularnewline
18 & 9.3 & 10.1684 & -0.868385 \tabularnewline
19 & 9.6 & 11.6941 & -2.0941 \tabularnewline
20 & 10 & 9.73935 & 0.260651 \tabularnewline
21 & 6.4 & 9.20602 & -2.80602 \tabularnewline
22 & 13.8 & 10.1427 & 3.65732 \tabularnewline
23 & 10.8 & 12.8421 & -2.04212 \tabularnewline
24 & 13.8 & 11.7195 & 2.08049 \tabularnewline
25 & 11.7 & 10.8164 & 0.883648 \tabularnewline
26 & 10.9 & 12.5965 & -1.69651 \tabularnewline
27 & 16.1 & 13.1288 & 2.97116 \tabularnewline
28 & 13.4 & 10.8544 & 2.54556 \tabularnewline
29 & 9.9 & 11.1868 & -1.28678 \tabularnewline
30 & 11.5 & 10.9863 & 0.513748 \tabularnewline
31 & 8.3 & 9.39914 & -1.09914 \tabularnewline
32 & 11.7 & 11.0687 & 0.631252 \tabularnewline
33 & 9 & 9.61351 & -0.613509 \tabularnewline
34 & 9.7 & 11.5715 & -1.8715 \tabularnewline
35 & 10.8 & 9.59527 & 1.20473 \tabularnewline
36 & 10.3 & 9.14412 & 1.15588 \tabularnewline
37 & 10.4 & 11.0229 & -0.622938 \tabularnewline
38 & 12.7 & 10.4353 & 2.26465 \tabularnewline
39 & 9.3 & 10.982 & -1.68202 \tabularnewline
40 & 11.8 & 12.1661 & -0.366092 \tabularnewline
41 & 5.9 & 10.0603 & -4.16026 \tabularnewline
42 & 11.4 & 10.748 & 0.651963 \tabularnewline
43 & 13 & 11.2508 & 1.74916 \tabularnewline
44 & 10.8 & 11.5952 & -0.79517 \tabularnewline
45 & 12.3 & 9.78439 & 2.51561 \tabularnewline
46 & 11.3 & 12.2718 & -0.971828 \tabularnewline
47 & 11.8 & 10.067 & 1.73305 \tabularnewline
48 & 7.9 & 10.1771 & -2.27709 \tabularnewline
49 & 12.7 & 10.1234 & 2.57663 \tabularnewline
50 & 12.3 & 9.69135 & 2.60865 \tabularnewline
51 & 11.6 & 10.7005 & 0.89946 \tabularnewline
52 & 6.7 & 9.18634 & -2.48634 \tabularnewline
53 & 10.9 & 9.98279 & 0.917209 \tabularnewline
54 & 12.1 & 10.7652 & 1.3348 \tabularnewline
55 & 13.3 & 10.3495 & 2.95052 \tabularnewline
56 & 10.1 & 10.3518 & -0.25177 \tabularnewline
57 & 5.7 & 10.8011 & -5.10108 \tabularnewline
58 & 14.3 & 9.6921 & 4.6079 \tabularnewline
59 & 8 & 8.45209 & -0.452089 \tabularnewline
60 & 13.3 & 11.3404 & 1.9596 \tabularnewline
61 & 9.3 & 12.214 & -2.91399 \tabularnewline
62 & 12.5 & 10.5188 & 1.98117 \tabularnewline
63 & 7.6 & 9.1502 & -1.5502 \tabularnewline
64 & 15.9 & 12.2909 & 3.60912 \tabularnewline
65 & 9.2 & 11.53 & -2.32998 \tabularnewline
66 & 9.1 & 9.66672 & -0.566724 \tabularnewline
67 & 11.1 & 11.9924 & -0.892362 \tabularnewline
68 & 13 & 13.3446 & -0.344595 \tabularnewline
69 & 14.5 & 10.6516 & 3.84836 \tabularnewline
70 & 12.2 & 11.0368 & 1.1632 \tabularnewline
71 & 12.3 & 12.5189 & -0.218906 \tabularnewline
72 & 11.4 & 10.5457 & 0.85431 \tabularnewline
73 & 8.8 & 10.2856 & -1.48556 \tabularnewline
74 & 14.6 & 11.4852 & 3.11481 \tabularnewline
75 & 12.6 & 11.1174 & 1.48261 \tabularnewline
76 & 13 & 11.4705 & 1.52949 \tabularnewline
77 & 12.6 & 11.0458 & 1.55418 \tabularnewline
78 & 13.2 & 12.0202 & 1.17978 \tabularnewline
79 & 9.9 & 9.5544 & 0.345598 \tabularnewline
80 & 7.7 & 10.2413 & -2.54134 \tabularnewline
81 & 10.5 & 10.8889 & -0.388925 \tabularnewline
82 & 13.4 & 10.6947 & 2.70531 \tabularnewline
83 & 10.9 & 10.6244 & 0.275613 \tabularnewline
84 & 4.3 & 9.86916 & -5.56916 \tabularnewline
85 & 10.3 & 11.5019 & -1.20186 \tabularnewline
86 & 11.8 & 11.5688 & 0.231227 \tabularnewline
87 & 11.2 & 9.96839 & 1.23161 \tabularnewline
88 & 11.4 & 9.22137 & 2.17863 \tabularnewline
89 & 8.6 & 10.3167 & -1.71668 \tabularnewline
90 & 13.2 & 11.4048 & 1.79523 \tabularnewline
91 & 12.6 & 9.89585 & 2.70415 \tabularnewline
92 & 5.6 & 10.4931 & -4.89313 \tabularnewline
93 & 9.9 & 11.6931 & -1.79314 \tabularnewline
94 & 8.8 & 9.87568 & -1.07568 \tabularnewline
95 & 7.7 & 9.81258 & -2.11258 \tabularnewline
96 & 9 & 10.6481 & -1.64808 \tabularnewline
97 & 7.3 & 11.0384 & -3.73838 \tabularnewline
98 & 11.4 & 9.73636 & 1.66364 \tabularnewline
99 & 13.6 & 9.92262 & 3.67738 \tabularnewline
100 & 7.9 & 10.6024 & -2.70241 \tabularnewline
101 & 10.7 & 9.84929 & 0.850711 \tabularnewline
102 & 10.3 & 10.1904 & 0.109629 \tabularnewline
103 & 8.3 & 9.90129 & -1.60129 \tabularnewline
104 & 9.6 & 10.9833 & -1.3833 \tabularnewline
105 & 14.2 & 10.7069 & 3.49313 \tabularnewline
106 & 8.5 & 10.2902 & -1.79017 \tabularnewline
107 & 13.5 & 10.1414 & 3.35859 \tabularnewline
108 & 4.9 & 10.2318 & -5.33176 \tabularnewline
109 & 6.4 & 8.86785 & -2.46785 \tabularnewline
110 & 9.6 & 10.8718 & -1.27177 \tabularnewline
111 & 11.6 & 10.7743 & 0.825658 \tabularnewline
112 & 11.1 & 10.0532 & 1.04685 \tabularnewline
113 & 4.35 & 10.4525 & -6.10247 \tabularnewline
114 & 12.7 & 11.0322 & 1.66776 \tabularnewline
115 & 18.1 & 15.4459 & 2.65412 \tabularnewline
116 & 17.85 & 15.5773 & 2.27272 \tabularnewline
117 & 16.6 & 18.0709 & -1.47094 \tabularnewline
118 & 12.6 & 11.7389 & 0.861128 \tabularnewline
119 & 17.1 & 18.8613 & -1.76132 \tabularnewline
120 & 19.1 & 17.7824 & 1.3176 \tabularnewline
121 & 16.1 & 19.2257 & -3.1257 \tabularnewline
122 & 13.35 & 11.0288 & 2.32124 \tabularnewline
123 & 18.4 & 16.851 & 1.54904 \tabularnewline
124 & 14.7 & 9.79247 & 4.90753 \tabularnewline
125 & 10.6 & 12.7651 & -2.16511 \tabularnewline
126 & 12.6 & 13.0252 & -0.425197 \tabularnewline
127 & 16.2 & 14.8134 & 1.38656 \tabularnewline
128 & 13.6 & 14.0183 & -0.418295 \tabularnewline
129 & 18.9 & 16.6843 & 2.21574 \tabularnewline
130 & 14.1 & 13.305 & 0.795017 \tabularnewline
131 & 14.5 & 13.5365 & 0.963495 \tabularnewline
132 & 16.15 & 18.1707 & -2.02073 \tabularnewline
133 & 14.75 & 13.8124 & 0.93757 \tabularnewline
134 & 14.8 & 13.7172 & 1.08279 \tabularnewline
135 & 12.45 & 12.4141 & 0.0358868 \tabularnewline
136 & 12.65 & 12.8075 & -0.157543 \tabularnewline
137 & 17.35 & 14.3179 & 3.03209 \tabularnewline
138 & 8.6 & 10.0058 & -1.40581 \tabularnewline
139 & 18.4 & 17.2512 & 1.14881 \tabularnewline
140 & 16.1 & 15.3252 & 0.774801 \tabularnewline
141 & 11.6 & 11.886 & -0.285961 \tabularnewline
142 & 17.75 & 15.3557 & 2.39427 \tabularnewline
143 & 15.25 & 15.4217 & -0.171679 \tabularnewline
144 & 17.65 & 14.2588 & 3.39116 \tabularnewline
145 & 16.35 & 17.0315 & -0.681505 \tabularnewline
146 & 17.65 & 17.3584 & 0.29162 \tabularnewline
147 & 13.6 & 14.0452 & -0.44525 \tabularnewline
148 & 14.35 & 14.5522 & -0.202153 \tabularnewline
149 & 14.75 & 15.5713 & -0.821309 \tabularnewline
150 & 18.25 & 16.304 & 1.94603 \tabularnewline
151 & 9.9 & 16.6487 & -6.74873 \tabularnewline
152 & 16 & 15.2703 & 0.72969 \tabularnewline
153 & 18.25 & 16.0304 & 2.21958 \tabularnewline
154 & 16.85 & 17.9946 & -1.14463 \tabularnewline
155 & 14.6 & 11.9349 & 2.66512 \tabularnewline
156 & 13.85 & 14.6834 & -0.833441 \tabularnewline
157 & 18.95 & 18.2274 & 0.722559 \tabularnewline
158 & 15.6 & 14.6737 & 0.926343 \tabularnewline
159 & 14.85 & 16.7799 & -1.92992 \tabularnewline
160 & 11.75 & 13.7519 & -2.00188 \tabularnewline
161 & 18.45 & 17.0856 & 1.36444 \tabularnewline
162 & 15.9 & 13.8191 & 2.08095 \tabularnewline
163 & 17.1 & 18.7316 & -1.63157 \tabularnewline
164 & 16.1 & 9.02109 & 7.07891 \tabularnewline
165 & 19.9 & 19.7019 & 0.198104 \tabularnewline
166 & 10.95 & 10.1498 & 0.800156 \tabularnewline
167 & 18.45 & 17.2641 & 1.18591 \tabularnewline
168 & 15.1 & 13.4061 & 1.69387 \tabularnewline
169 & 15 & 14.7712 & 0.228761 \tabularnewline
170 & 11.35 & 13.6324 & -2.28244 \tabularnewline
171 & 15.95 & 14.7939 & 1.15613 \tabularnewline
172 & 18.1 & 15.7585 & 2.34146 \tabularnewline
173 & 14.6 & 17.0147 & -2.41467 \tabularnewline
174 & 15.4 & 16.2653 & -0.865302 \tabularnewline
175 & 15.4 & 16.3084 & -0.908406 \tabularnewline
176 & 17.6 & 15.2621 & 2.33794 \tabularnewline
177 & 13.35 & 15.0444 & -1.69438 \tabularnewline
178 & 19.1 & 17.2465 & 1.85352 \tabularnewline
179 & 15.35 & 16.9472 & -1.59718 \tabularnewline
180 & 7.6 & 10.2311 & -2.63114 \tabularnewline
181 & 13.4 & 15.2114 & -1.81142 \tabularnewline
182 & 13.9 & 16.2634 & -2.36339 \tabularnewline
183 & 19.1 & 17.0734 & 2.02655 \tabularnewline
184 & 15.25 & 15.1458 & 0.10419 \tabularnewline
185 & 12.9 & 16.1332 & -3.23322 \tabularnewline
186 & 16.1 & 15.7577 & 0.342314 \tabularnewline
187 & 17.35 & 14.3079 & 3.0421 \tabularnewline
188 & 13.15 & 15.1292 & -1.97923 \tabularnewline
189 & 12.15 & 13.7864 & -1.63636 \tabularnewline
190 & 12.6 & 11.978 & 0.621987 \tabularnewline
191 & 10.35 & 12.6942 & -2.34418 \tabularnewline
192 & 15.4 & 14.4246 & 0.975377 \tabularnewline
193 & 9.6 & 12.1603 & -2.56027 \tabularnewline
194 & 18.2 & 14.9256 & 3.27439 \tabularnewline
195 & 13.6 & 13.4622 & 0.137828 \tabularnewline
196 & 14.85 & 13.3196 & 1.53039 \tabularnewline
197 & 14.75 & 17.5401 & -2.79009 \tabularnewline
198 & 14.1 & 13.351 & 0.748977 \tabularnewline
199 & 14.9 & 12.392 & 2.50803 \tabularnewline
200 & 16.25 & 15.1695 & 1.08055 \tabularnewline
201 & 19.25 & 18.5449 & 0.705125 \tabularnewline
202 & 13.6 & 12.7895 & 0.810549 \tabularnewline
203 & 13.6 & 15.7866 & -2.1866 \tabularnewline
204 & 15.65 & 15.4475 & 0.202548 \tabularnewline
205 & 12.75 & 12.9963 & -0.246289 \tabularnewline
206 & 14.6 & 12.582 & 2.01797 \tabularnewline
207 & 9.85 & 10.8264 & -0.976362 \tabularnewline
208 & 12.65 & 12.3617 & 0.288268 \tabularnewline
209 & 19.2 & 17.2989 & 1.90108 \tabularnewline
210 & 16.6 & 14.7372 & 1.86276 \tabularnewline
211 & 11.2 & 11.5461 & -0.346085 \tabularnewline
212 & 15.25 & 15.5573 & -0.307285 \tabularnewline
213 & 11.9 & 14.4894 & -2.58942 \tabularnewline
214 & 13.2 & 12.6835 & 0.516463 \tabularnewline
215 & 16.35 & 17.7992 & -1.44922 \tabularnewline
216 & 12.4 & 12.6808 & -0.280751 \tabularnewline
217 & 15.85 & 14.4813 & 1.36871 \tabularnewline
218 & 18.15 & 16.8065 & 1.3435 \tabularnewline
219 & 11.15 & 12.7335 & -1.58351 \tabularnewline
220 & 15.65 & 16.3074 & -0.657375 \tabularnewline
221 & 17.75 & 15.3733 & 2.37673 \tabularnewline
222 & 7.65 & 10.3044 & -2.65436 \tabularnewline
223 & 12.35 & 13.4933 & -1.14333 \tabularnewline
224 & 15.6 & 13.8457 & 1.75431 \tabularnewline
225 & 19.3 & 17.2979 & 2.00209 \tabularnewline
226 & 15.2 & 11.1986 & 4.00137 \tabularnewline
227 & 17.1 & 14.895 & 2.20496 \tabularnewline
228 & 15.6 & 13.3694 & 2.23058 \tabularnewline
229 & 18.4 & 15.638 & 2.76205 \tabularnewline
230 & 19.05 & 16.3196 & 2.73042 \tabularnewline
231 & 18.55 & 15.8957 & 2.6543 \tabularnewline
232 & 19.1 & 17.9366 & 1.16345 \tabularnewline
233 & 13.1 & 13.4627 & -0.362694 \tabularnewline
234 & 12.85 & 15.8947 & -3.04469 \tabularnewline
235 & 9.5 & 12.2784 & -2.77838 \tabularnewline
236 & 4.5 & 10.5779 & -6.07793 \tabularnewline
237 & 11.85 & 10.7626 & 1.0874 \tabularnewline
238 & 13.6 & 15.2232 & -1.62318 \tabularnewline
239 & 11.7 & 12.0402 & -0.340196 \tabularnewline
240 & 12.4 & 12.7722 & -0.372174 \tabularnewline
241 & 13.35 & 14.8812 & -1.53118 \tabularnewline
242 & 11.4 & 12.2296 & -0.829628 \tabularnewline
243 & 14.9 & 14.2516 & 0.648371 \tabularnewline
244 & 19.9 & 19.4141 & 0.485857 \tabularnewline
245 & 11.2 & 13.3862 & -2.18623 \tabularnewline
246 & 14.6 & 15.4737 & -0.873704 \tabularnewline
247 & 17.6 & 18.0191 & -0.419131 \tabularnewline
248 & 14.05 & 13.3383 & 0.711678 \tabularnewline
249 & 16.1 & 15.5834 & 0.516568 \tabularnewline
250 & 13.35 & 14.4131 & -1.0631 \tabularnewline
251 & 11.85 & 14.4582 & -2.60823 \tabularnewline
252 & 11.95 & 13.1327 & -1.18275 \tabularnewline
253 & 14.75 & 14.2912 & 0.458809 \tabularnewline
254 & 15.15 & 13.3668 & 1.78319 \tabularnewline
255 & 13.2 & 16.0283 & -2.82833 \tabularnewline
256 & 16.85 & 16.1942 & 0.655821 \tabularnewline
257 & 7.85 & 11.8841 & -4.03414 \tabularnewline
258 & 7.7 & 12.2969 & -4.59686 \tabularnewline
259 & 12.6 & 15.0496 & -2.44957 \tabularnewline
260 & 7.85 & 14.8561 & -7.00613 \tabularnewline
261 & 10.95 & 11.2711 & -0.321088 \tabularnewline
262 & 12.35 & 14.0837 & -1.73366 \tabularnewline
263 & 9.95 & 12.9192 & -2.96919 \tabularnewline
264 & 14.9 & 14.1186 & 0.781353 \tabularnewline
265 & 16.65 & 14.631 & 2.01901 \tabularnewline
266 & 13.4 & 12.7191 & 0.680877 \tabularnewline
267 & 13.95 & 13.3766 & 0.57342 \tabularnewline
268 & 15.7 & 14.4129 & 1.28706 \tabularnewline
269 & 16.85 & 15.5024 & 1.34755 \tabularnewline
270 & 10.95 & 11.9396 & -0.989573 \tabularnewline
271 & 15.35 & 14.3933 & 0.956736 \tabularnewline
272 & 12.2 & 12.5782 & -0.378178 \tabularnewline
273 & 15.1 & 14.0057 & 1.09434 \tabularnewline
274 & 17.75 & 16.4037 & 1.34634 \tabularnewline
275 & 15.2 & 15.1968 & 0.00319622 \tabularnewline
276 & 14.6 & 14.5027 & 0.0972613 \tabularnewline
277 & 16.65 & 15.7232 & 0.926814 \tabularnewline
278 & 8.1 & 10.2987 & -2.19866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12.9[/C][C]12.5494[/C][C]0.350642[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.2944[/C][C]1.90562[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.2246[/C][C]1.57544[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.1411[/C][C]-3.74109[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.1411[/C][C]-3.44108[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.055[/C][C]1.545[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.5558[/C][C]3.24417[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.3016[/C][C]1.99843[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]11.0878[/C][C]0.0121734[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.1099[/C][C]-2.90985[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.7291[/C][C]0.670895[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.2066[/C][C]-4.8066[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.05584[/C][C]1.54416[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]12.983[/C][C]-0.982955[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]7.62841[/C][C]-1.32841[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]9.71733[/C][C]1.58267[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.5282[/C][C]0.37176[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.1684[/C][C]-0.868385[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.6941[/C][C]-2.0941[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.73935[/C][C]0.260651[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]9.20602[/C][C]-2.80602[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]10.1427[/C][C]3.65732[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]12.8421[/C][C]-2.04212[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]11.7195[/C][C]2.08049[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]10.8164[/C][C]0.883648[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]12.5965[/C][C]-1.69651[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]13.1288[/C][C]2.97116[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]10.8544[/C][C]2.54556[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]11.1868[/C][C]-1.28678[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]10.9863[/C][C]0.513748[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]9.39914[/C][C]-1.09914[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]11.0687[/C][C]0.631252[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]9.61351[/C][C]-0.613509[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]11.5715[/C][C]-1.8715[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]9.59527[/C][C]1.20473[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]9.14412[/C][C]1.15588[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]11.0229[/C][C]-0.622938[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.4353[/C][C]2.26465[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]10.982[/C][C]-1.68202[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]12.1661[/C][C]-0.366092[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.0603[/C][C]-4.16026[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.748[/C][C]0.651963[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.2508[/C][C]1.74916[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]11.5952[/C][C]-0.79517[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]9.78439[/C][C]2.51561[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]12.2718[/C][C]-0.971828[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.067[/C][C]1.73305[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.1771[/C][C]-2.27709[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.1234[/C][C]2.57663[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]9.69135[/C][C]2.60865[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.7005[/C][C]0.89946[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]9.18634[/C][C]-2.48634[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.98279[/C][C]0.917209[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.7652[/C][C]1.3348[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.3495[/C][C]2.95052[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.3518[/C][C]-0.25177[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]10.8011[/C][C]-5.10108[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]9.6921[/C][C]4.6079[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]8.45209[/C][C]-0.452089[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]11.3404[/C][C]1.9596[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]12.214[/C][C]-2.91399[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.5188[/C][C]1.98117[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]9.1502[/C][C]-1.5502[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]12.2909[/C][C]3.60912[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]11.53[/C][C]-2.32998[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]9.66672[/C][C]-0.566724[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]11.9924[/C][C]-0.892362[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.3446[/C][C]-0.344595[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]10.6516[/C][C]3.84836[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]11.0368[/C][C]1.1632[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]12.5189[/C][C]-0.218906[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.5457[/C][C]0.85431[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.2856[/C][C]-1.48556[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]11.4852[/C][C]3.11481[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]11.1174[/C][C]1.48261[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.4705[/C][C]1.52949[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]11.0458[/C][C]1.55418[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]12.0202[/C][C]1.17978[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]9.5544[/C][C]0.345598[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]10.2413[/C][C]-2.54134[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.8889[/C][C]-0.388925[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]10.6947[/C][C]2.70531[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]10.6244[/C][C]0.275613[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]9.86916[/C][C]-5.56916[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]11.5019[/C][C]-1.20186[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]11.5688[/C][C]0.231227[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]9.96839[/C][C]1.23161[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]9.22137[/C][C]2.17863[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]10.3167[/C][C]-1.71668[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]11.4048[/C][C]1.79523[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]9.89585[/C][C]2.70415[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]10.4931[/C][C]-4.89313[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]11.6931[/C][C]-1.79314[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]9.87568[/C][C]-1.07568[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]9.81258[/C][C]-2.11258[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]10.6481[/C][C]-1.64808[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]11.0384[/C][C]-3.73838[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]9.73636[/C][C]1.66364[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]9.92262[/C][C]3.67738[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]10.6024[/C][C]-2.70241[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]9.84929[/C][C]0.850711[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]10.1904[/C][C]0.109629[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]9.90129[/C][C]-1.60129[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]10.9833[/C][C]-1.3833[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]10.7069[/C][C]3.49313[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]10.2902[/C][C]-1.79017[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]10.1414[/C][C]3.35859[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]10.2318[/C][C]-5.33176[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]8.86785[/C][C]-2.46785[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]10.8718[/C][C]-1.27177[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]10.7743[/C][C]0.825658[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]10.0532[/C][C]1.04685[/C][/ROW]
[ROW][C]113[/C][C]4.35[/C][C]10.4525[/C][C]-6.10247[/C][/ROW]
[ROW][C]114[/C][C]12.7[/C][C]11.0322[/C][C]1.66776[/C][/ROW]
[ROW][C]115[/C][C]18.1[/C][C]15.4459[/C][C]2.65412[/C][/ROW]
[ROW][C]116[/C][C]17.85[/C][C]15.5773[/C][C]2.27272[/C][/ROW]
[ROW][C]117[/C][C]16.6[/C][C]18.0709[/C][C]-1.47094[/C][/ROW]
[ROW][C]118[/C][C]12.6[/C][C]11.7389[/C][C]0.861128[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]18.8613[/C][C]-1.76132[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]17.7824[/C][C]1.3176[/C][/ROW]
[ROW][C]121[/C][C]16.1[/C][C]19.2257[/C][C]-3.1257[/C][/ROW]
[ROW][C]122[/C][C]13.35[/C][C]11.0288[/C][C]2.32124[/C][/ROW]
[ROW][C]123[/C][C]18.4[/C][C]16.851[/C][C]1.54904[/C][/ROW]
[ROW][C]124[/C][C]14.7[/C][C]9.79247[/C][C]4.90753[/C][/ROW]
[ROW][C]125[/C][C]10.6[/C][C]12.7651[/C][C]-2.16511[/C][/ROW]
[ROW][C]126[/C][C]12.6[/C][C]13.0252[/C][C]-0.425197[/C][/ROW]
[ROW][C]127[/C][C]16.2[/C][C]14.8134[/C][C]1.38656[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]14.0183[/C][C]-0.418295[/C][/ROW]
[ROW][C]129[/C][C]18.9[/C][C]16.6843[/C][C]2.21574[/C][/ROW]
[ROW][C]130[/C][C]14.1[/C][C]13.305[/C][C]0.795017[/C][/ROW]
[ROW][C]131[/C][C]14.5[/C][C]13.5365[/C][C]0.963495[/C][/ROW]
[ROW][C]132[/C][C]16.15[/C][C]18.1707[/C][C]-2.02073[/C][/ROW]
[ROW][C]133[/C][C]14.75[/C][C]13.8124[/C][C]0.93757[/C][/ROW]
[ROW][C]134[/C][C]14.8[/C][C]13.7172[/C][C]1.08279[/C][/ROW]
[ROW][C]135[/C][C]12.45[/C][C]12.4141[/C][C]0.0358868[/C][/ROW]
[ROW][C]136[/C][C]12.65[/C][C]12.8075[/C][C]-0.157543[/C][/ROW]
[ROW][C]137[/C][C]17.35[/C][C]14.3179[/C][C]3.03209[/C][/ROW]
[ROW][C]138[/C][C]8.6[/C][C]10.0058[/C][C]-1.40581[/C][/ROW]
[ROW][C]139[/C][C]18.4[/C][C]17.2512[/C][C]1.14881[/C][/ROW]
[ROW][C]140[/C][C]16.1[/C][C]15.3252[/C][C]0.774801[/C][/ROW]
[ROW][C]141[/C][C]11.6[/C][C]11.886[/C][C]-0.285961[/C][/ROW]
[ROW][C]142[/C][C]17.75[/C][C]15.3557[/C][C]2.39427[/C][/ROW]
[ROW][C]143[/C][C]15.25[/C][C]15.4217[/C][C]-0.171679[/C][/ROW]
[ROW][C]144[/C][C]17.65[/C][C]14.2588[/C][C]3.39116[/C][/ROW]
[ROW][C]145[/C][C]16.35[/C][C]17.0315[/C][C]-0.681505[/C][/ROW]
[ROW][C]146[/C][C]17.65[/C][C]17.3584[/C][C]0.29162[/C][/ROW]
[ROW][C]147[/C][C]13.6[/C][C]14.0452[/C][C]-0.44525[/C][/ROW]
[ROW][C]148[/C][C]14.35[/C][C]14.5522[/C][C]-0.202153[/C][/ROW]
[ROW][C]149[/C][C]14.75[/C][C]15.5713[/C][C]-0.821309[/C][/ROW]
[ROW][C]150[/C][C]18.25[/C][C]16.304[/C][C]1.94603[/C][/ROW]
[ROW][C]151[/C][C]9.9[/C][C]16.6487[/C][C]-6.74873[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.2703[/C][C]0.72969[/C][/ROW]
[ROW][C]153[/C][C]18.25[/C][C]16.0304[/C][C]2.21958[/C][/ROW]
[ROW][C]154[/C][C]16.85[/C][C]17.9946[/C][C]-1.14463[/C][/ROW]
[ROW][C]155[/C][C]14.6[/C][C]11.9349[/C][C]2.66512[/C][/ROW]
[ROW][C]156[/C][C]13.85[/C][C]14.6834[/C][C]-0.833441[/C][/ROW]
[ROW][C]157[/C][C]18.95[/C][C]18.2274[/C][C]0.722559[/C][/ROW]
[ROW][C]158[/C][C]15.6[/C][C]14.6737[/C][C]0.926343[/C][/ROW]
[ROW][C]159[/C][C]14.85[/C][C]16.7799[/C][C]-1.92992[/C][/ROW]
[ROW][C]160[/C][C]11.75[/C][C]13.7519[/C][C]-2.00188[/C][/ROW]
[ROW][C]161[/C][C]18.45[/C][C]17.0856[/C][C]1.36444[/C][/ROW]
[ROW][C]162[/C][C]15.9[/C][C]13.8191[/C][C]2.08095[/C][/ROW]
[ROW][C]163[/C][C]17.1[/C][C]18.7316[/C][C]-1.63157[/C][/ROW]
[ROW][C]164[/C][C]16.1[/C][C]9.02109[/C][C]7.07891[/C][/ROW]
[ROW][C]165[/C][C]19.9[/C][C]19.7019[/C][C]0.198104[/C][/ROW]
[ROW][C]166[/C][C]10.95[/C][C]10.1498[/C][C]0.800156[/C][/ROW]
[ROW][C]167[/C][C]18.45[/C][C]17.2641[/C][C]1.18591[/C][/ROW]
[ROW][C]168[/C][C]15.1[/C][C]13.4061[/C][C]1.69387[/C][/ROW]
[ROW][C]169[/C][C]15[/C][C]14.7712[/C][C]0.228761[/C][/ROW]
[ROW][C]170[/C][C]11.35[/C][C]13.6324[/C][C]-2.28244[/C][/ROW]
[ROW][C]171[/C][C]15.95[/C][C]14.7939[/C][C]1.15613[/C][/ROW]
[ROW][C]172[/C][C]18.1[/C][C]15.7585[/C][C]2.34146[/C][/ROW]
[ROW][C]173[/C][C]14.6[/C][C]17.0147[/C][C]-2.41467[/C][/ROW]
[ROW][C]174[/C][C]15.4[/C][C]16.2653[/C][C]-0.865302[/C][/ROW]
[ROW][C]175[/C][C]15.4[/C][C]16.3084[/C][C]-0.908406[/C][/ROW]
[ROW][C]176[/C][C]17.6[/C][C]15.2621[/C][C]2.33794[/C][/ROW]
[ROW][C]177[/C][C]13.35[/C][C]15.0444[/C][C]-1.69438[/C][/ROW]
[ROW][C]178[/C][C]19.1[/C][C]17.2465[/C][C]1.85352[/C][/ROW]
[ROW][C]179[/C][C]15.35[/C][C]16.9472[/C][C]-1.59718[/C][/ROW]
[ROW][C]180[/C][C]7.6[/C][C]10.2311[/C][C]-2.63114[/C][/ROW]
[ROW][C]181[/C][C]13.4[/C][C]15.2114[/C][C]-1.81142[/C][/ROW]
[ROW][C]182[/C][C]13.9[/C][C]16.2634[/C][C]-2.36339[/C][/ROW]
[ROW][C]183[/C][C]19.1[/C][C]17.0734[/C][C]2.02655[/C][/ROW]
[ROW][C]184[/C][C]15.25[/C][C]15.1458[/C][C]0.10419[/C][/ROW]
[ROW][C]185[/C][C]12.9[/C][C]16.1332[/C][C]-3.23322[/C][/ROW]
[ROW][C]186[/C][C]16.1[/C][C]15.7577[/C][C]0.342314[/C][/ROW]
[ROW][C]187[/C][C]17.35[/C][C]14.3079[/C][C]3.0421[/C][/ROW]
[ROW][C]188[/C][C]13.15[/C][C]15.1292[/C][C]-1.97923[/C][/ROW]
[ROW][C]189[/C][C]12.15[/C][C]13.7864[/C][C]-1.63636[/C][/ROW]
[ROW][C]190[/C][C]12.6[/C][C]11.978[/C][C]0.621987[/C][/ROW]
[ROW][C]191[/C][C]10.35[/C][C]12.6942[/C][C]-2.34418[/C][/ROW]
[ROW][C]192[/C][C]15.4[/C][C]14.4246[/C][C]0.975377[/C][/ROW]
[ROW][C]193[/C][C]9.6[/C][C]12.1603[/C][C]-2.56027[/C][/ROW]
[ROW][C]194[/C][C]18.2[/C][C]14.9256[/C][C]3.27439[/C][/ROW]
[ROW][C]195[/C][C]13.6[/C][C]13.4622[/C][C]0.137828[/C][/ROW]
[ROW][C]196[/C][C]14.85[/C][C]13.3196[/C][C]1.53039[/C][/ROW]
[ROW][C]197[/C][C]14.75[/C][C]17.5401[/C][C]-2.79009[/C][/ROW]
[ROW][C]198[/C][C]14.1[/C][C]13.351[/C][C]0.748977[/C][/ROW]
[ROW][C]199[/C][C]14.9[/C][C]12.392[/C][C]2.50803[/C][/ROW]
[ROW][C]200[/C][C]16.25[/C][C]15.1695[/C][C]1.08055[/C][/ROW]
[ROW][C]201[/C][C]19.25[/C][C]18.5449[/C][C]0.705125[/C][/ROW]
[ROW][C]202[/C][C]13.6[/C][C]12.7895[/C][C]0.810549[/C][/ROW]
[ROW][C]203[/C][C]13.6[/C][C]15.7866[/C][C]-2.1866[/C][/ROW]
[ROW][C]204[/C][C]15.65[/C][C]15.4475[/C][C]0.202548[/C][/ROW]
[ROW][C]205[/C][C]12.75[/C][C]12.9963[/C][C]-0.246289[/C][/ROW]
[ROW][C]206[/C][C]14.6[/C][C]12.582[/C][C]2.01797[/C][/ROW]
[ROW][C]207[/C][C]9.85[/C][C]10.8264[/C][C]-0.976362[/C][/ROW]
[ROW][C]208[/C][C]12.65[/C][C]12.3617[/C][C]0.288268[/C][/ROW]
[ROW][C]209[/C][C]19.2[/C][C]17.2989[/C][C]1.90108[/C][/ROW]
[ROW][C]210[/C][C]16.6[/C][C]14.7372[/C][C]1.86276[/C][/ROW]
[ROW][C]211[/C][C]11.2[/C][C]11.5461[/C][C]-0.346085[/C][/ROW]
[ROW][C]212[/C][C]15.25[/C][C]15.5573[/C][C]-0.307285[/C][/ROW]
[ROW][C]213[/C][C]11.9[/C][C]14.4894[/C][C]-2.58942[/C][/ROW]
[ROW][C]214[/C][C]13.2[/C][C]12.6835[/C][C]0.516463[/C][/ROW]
[ROW][C]215[/C][C]16.35[/C][C]17.7992[/C][C]-1.44922[/C][/ROW]
[ROW][C]216[/C][C]12.4[/C][C]12.6808[/C][C]-0.280751[/C][/ROW]
[ROW][C]217[/C][C]15.85[/C][C]14.4813[/C][C]1.36871[/C][/ROW]
[ROW][C]218[/C][C]18.15[/C][C]16.8065[/C][C]1.3435[/C][/ROW]
[ROW][C]219[/C][C]11.15[/C][C]12.7335[/C][C]-1.58351[/C][/ROW]
[ROW][C]220[/C][C]15.65[/C][C]16.3074[/C][C]-0.657375[/C][/ROW]
[ROW][C]221[/C][C]17.75[/C][C]15.3733[/C][C]2.37673[/C][/ROW]
[ROW][C]222[/C][C]7.65[/C][C]10.3044[/C][C]-2.65436[/C][/ROW]
[ROW][C]223[/C][C]12.35[/C][C]13.4933[/C][C]-1.14333[/C][/ROW]
[ROW][C]224[/C][C]15.6[/C][C]13.8457[/C][C]1.75431[/C][/ROW]
[ROW][C]225[/C][C]19.3[/C][C]17.2979[/C][C]2.00209[/C][/ROW]
[ROW][C]226[/C][C]15.2[/C][C]11.1986[/C][C]4.00137[/C][/ROW]
[ROW][C]227[/C][C]17.1[/C][C]14.895[/C][C]2.20496[/C][/ROW]
[ROW][C]228[/C][C]15.6[/C][C]13.3694[/C][C]2.23058[/C][/ROW]
[ROW][C]229[/C][C]18.4[/C][C]15.638[/C][C]2.76205[/C][/ROW]
[ROW][C]230[/C][C]19.05[/C][C]16.3196[/C][C]2.73042[/C][/ROW]
[ROW][C]231[/C][C]18.55[/C][C]15.8957[/C][C]2.6543[/C][/ROW]
[ROW][C]232[/C][C]19.1[/C][C]17.9366[/C][C]1.16345[/C][/ROW]
[ROW][C]233[/C][C]13.1[/C][C]13.4627[/C][C]-0.362694[/C][/ROW]
[ROW][C]234[/C][C]12.85[/C][C]15.8947[/C][C]-3.04469[/C][/ROW]
[ROW][C]235[/C][C]9.5[/C][C]12.2784[/C][C]-2.77838[/C][/ROW]
[ROW][C]236[/C][C]4.5[/C][C]10.5779[/C][C]-6.07793[/C][/ROW]
[ROW][C]237[/C][C]11.85[/C][C]10.7626[/C][C]1.0874[/C][/ROW]
[ROW][C]238[/C][C]13.6[/C][C]15.2232[/C][C]-1.62318[/C][/ROW]
[ROW][C]239[/C][C]11.7[/C][C]12.0402[/C][C]-0.340196[/C][/ROW]
[ROW][C]240[/C][C]12.4[/C][C]12.7722[/C][C]-0.372174[/C][/ROW]
[ROW][C]241[/C][C]13.35[/C][C]14.8812[/C][C]-1.53118[/C][/ROW]
[ROW][C]242[/C][C]11.4[/C][C]12.2296[/C][C]-0.829628[/C][/ROW]
[ROW][C]243[/C][C]14.9[/C][C]14.2516[/C][C]0.648371[/C][/ROW]
[ROW][C]244[/C][C]19.9[/C][C]19.4141[/C][C]0.485857[/C][/ROW]
[ROW][C]245[/C][C]11.2[/C][C]13.3862[/C][C]-2.18623[/C][/ROW]
[ROW][C]246[/C][C]14.6[/C][C]15.4737[/C][C]-0.873704[/C][/ROW]
[ROW][C]247[/C][C]17.6[/C][C]18.0191[/C][C]-0.419131[/C][/ROW]
[ROW][C]248[/C][C]14.05[/C][C]13.3383[/C][C]0.711678[/C][/ROW]
[ROW][C]249[/C][C]16.1[/C][C]15.5834[/C][C]0.516568[/C][/ROW]
[ROW][C]250[/C][C]13.35[/C][C]14.4131[/C][C]-1.0631[/C][/ROW]
[ROW][C]251[/C][C]11.85[/C][C]14.4582[/C][C]-2.60823[/C][/ROW]
[ROW][C]252[/C][C]11.95[/C][C]13.1327[/C][C]-1.18275[/C][/ROW]
[ROW][C]253[/C][C]14.75[/C][C]14.2912[/C][C]0.458809[/C][/ROW]
[ROW][C]254[/C][C]15.15[/C][C]13.3668[/C][C]1.78319[/C][/ROW]
[ROW][C]255[/C][C]13.2[/C][C]16.0283[/C][C]-2.82833[/C][/ROW]
[ROW][C]256[/C][C]16.85[/C][C]16.1942[/C][C]0.655821[/C][/ROW]
[ROW][C]257[/C][C]7.85[/C][C]11.8841[/C][C]-4.03414[/C][/ROW]
[ROW][C]258[/C][C]7.7[/C][C]12.2969[/C][C]-4.59686[/C][/ROW]
[ROW][C]259[/C][C]12.6[/C][C]15.0496[/C][C]-2.44957[/C][/ROW]
[ROW][C]260[/C][C]7.85[/C][C]14.8561[/C][C]-7.00613[/C][/ROW]
[ROW][C]261[/C][C]10.95[/C][C]11.2711[/C][C]-0.321088[/C][/ROW]
[ROW][C]262[/C][C]12.35[/C][C]14.0837[/C][C]-1.73366[/C][/ROW]
[ROW][C]263[/C][C]9.95[/C][C]12.9192[/C][C]-2.96919[/C][/ROW]
[ROW][C]264[/C][C]14.9[/C][C]14.1186[/C][C]0.781353[/C][/ROW]
[ROW][C]265[/C][C]16.65[/C][C]14.631[/C][C]2.01901[/C][/ROW]
[ROW][C]266[/C][C]13.4[/C][C]12.7191[/C][C]0.680877[/C][/ROW]
[ROW][C]267[/C][C]13.95[/C][C]13.3766[/C][C]0.57342[/C][/ROW]
[ROW][C]268[/C][C]15.7[/C][C]14.4129[/C][C]1.28706[/C][/ROW]
[ROW][C]269[/C][C]16.85[/C][C]15.5024[/C][C]1.34755[/C][/ROW]
[ROW][C]270[/C][C]10.95[/C][C]11.9396[/C][C]-0.989573[/C][/ROW]
[ROW][C]271[/C][C]15.35[/C][C]14.3933[/C][C]0.956736[/C][/ROW]
[ROW][C]272[/C][C]12.2[/C][C]12.5782[/C][C]-0.378178[/C][/ROW]
[ROW][C]273[/C][C]15.1[/C][C]14.0057[/C][C]1.09434[/C][/ROW]
[ROW][C]274[/C][C]17.75[/C][C]16.4037[/C][C]1.34634[/C][/ROW]
[ROW][C]275[/C][C]15.2[/C][C]15.1968[/C][C]0.00319622[/C][/ROW]
[ROW][C]276[/C][C]14.6[/C][C]14.5027[/C][C]0.0972613[/C][/ROW]
[ROW][C]277[/C][C]16.65[/C][C]15.7232[/C][C]0.926814[/C][/ROW]
[ROW][C]278[/C][C]8.1[/C][C]10.2987[/C][C]-2.19866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.54940.350642
212.210.29441.90562
312.811.22461.57544
47.411.1411-3.74109
56.710.1411-3.44108
612.611.0551.545
714.811.55583.24417
813.311.30161.99843
911.111.08780.0121734
108.211.1099-2.90985
1111.410.72910.670895
126.411.2066-4.8066
1310.69.055841.54416
141212.983-0.982955
156.37.62841-1.32841
1611.39.717331.58267
1711.911.52820.37176
189.310.1684-0.868385
199.611.6941-2.0941
20109.739350.260651
216.49.20602-2.80602
2213.810.14273.65732
2310.812.8421-2.04212
2413.811.71952.08049
2511.710.81640.883648
2610.912.5965-1.69651
2716.113.12882.97116
2813.410.85442.54556
299.911.1868-1.28678
3011.510.98630.513748
318.39.39914-1.09914
3211.711.06870.631252
3399.61351-0.613509
349.711.5715-1.8715
3510.89.595271.20473
3610.39.144121.15588
3710.411.0229-0.622938
3812.710.43532.26465
399.310.982-1.68202
4011.812.1661-0.366092
415.910.0603-4.16026
4211.410.7480.651963
431311.25081.74916
4410.811.5952-0.79517
4512.39.784392.51561
4611.312.2718-0.971828
4711.810.0671.73305
487.910.1771-2.27709
4912.710.12342.57663
5012.39.691352.60865
5111.610.70050.89946
526.79.18634-2.48634
5310.99.982790.917209
5412.110.76521.3348
5513.310.34952.95052
5610.110.3518-0.25177
575.710.8011-5.10108
5814.39.69214.6079
5988.45209-0.452089
6013.311.34041.9596
619.312.214-2.91399
6212.510.51881.98117
637.69.1502-1.5502
6415.912.29093.60912
659.211.53-2.32998
669.19.66672-0.566724
6711.111.9924-0.892362
681313.3446-0.344595
6914.510.65163.84836
7012.211.03681.1632
7112.312.5189-0.218906
7211.410.54570.85431
738.810.2856-1.48556
7414.611.48523.11481
7512.611.11741.48261
761311.47051.52949
7712.611.04581.55418
7813.212.02021.17978
799.99.55440.345598
807.710.2413-2.54134
8110.510.8889-0.388925
8213.410.69472.70531
8310.910.62440.275613
844.39.86916-5.56916
8510.311.5019-1.20186
8611.811.56880.231227
8711.29.968391.23161
8811.49.221372.17863
898.610.3167-1.71668
9013.211.40481.79523
9112.69.895852.70415
925.610.4931-4.89313
939.911.6931-1.79314
948.89.87568-1.07568
957.79.81258-2.11258
96910.6481-1.64808
977.311.0384-3.73838
9811.49.736361.66364
9913.69.922623.67738
1007.910.6024-2.70241
10110.79.849290.850711
10210.310.19040.109629
1038.39.90129-1.60129
1049.610.9833-1.3833
10514.210.70693.49313
1068.510.2902-1.79017
10713.510.14143.35859
1084.910.2318-5.33176
1096.48.86785-2.46785
1109.610.8718-1.27177
11111.610.77430.825658
11211.110.05321.04685
1134.3510.4525-6.10247
11412.711.03221.66776
11518.115.44592.65412
11617.8515.57732.27272
11716.618.0709-1.47094
11812.611.73890.861128
11917.118.8613-1.76132
12019.117.78241.3176
12116.119.2257-3.1257
12213.3511.02882.32124
12318.416.8511.54904
12414.79.792474.90753
12510.612.7651-2.16511
12612.613.0252-0.425197
12716.214.81341.38656
12813.614.0183-0.418295
12918.916.68432.21574
13014.113.3050.795017
13114.513.53650.963495
13216.1518.1707-2.02073
13314.7513.81240.93757
13414.813.71721.08279
13512.4512.41410.0358868
13612.6512.8075-0.157543
13717.3514.31793.03209
1388.610.0058-1.40581
13918.417.25121.14881
14016.115.32520.774801
14111.611.886-0.285961
14217.7515.35572.39427
14315.2515.4217-0.171679
14417.6514.25883.39116
14516.3517.0315-0.681505
14617.6517.35840.29162
14713.614.0452-0.44525
14814.3514.5522-0.202153
14914.7515.5713-0.821309
15018.2516.3041.94603
1519.916.6487-6.74873
1521615.27030.72969
15318.2516.03042.21958
15416.8517.9946-1.14463
15514.611.93492.66512
15613.8514.6834-0.833441
15718.9518.22740.722559
15815.614.67370.926343
15914.8516.7799-1.92992
16011.7513.7519-2.00188
16118.4517.08561.36444
16215.913.81912.08095
16317.118.7316-1.63157
16416.19.021097.07891
16519.919.70190.198104
16610.9510.14980.800156
16718.4517.26411.18591
16815.113.40611.69387
1691514.77120.228761
17011.3513.6324-2.28244
17115.9514.79391.15613
17218.115.75852.34146
17314.617.0147-2.41467
17415.416.2653-0.865302
17515.416.3084-0.908406
17617.615.26212.33794
17713.3515.0444-1.69438
17819.117.24651.85352
17915.3516.9472-1.59718
1807.610.2311-2.63114
18113.415.2114-1.81142
18213.916.2634-2.36339
18319.117.07342.02655
18415.2515.14580.10419
18512.916.1332-3.23322
18616.115.75770.342314
18717.3514.30793.0421
18813.1515.1292-1.97923
18912.1513.7864-1.63636
19012.611.9780.621987
19110.3512.6942-2.34418
19215.414.42460.975377
1939.612.1603-2.56027
19418.214.92563.27439
19513.613.46220.137828
19614.8513.31961.53039
19714.7517.5401-2.79009
19814.113.3510.748977
19914.912.3922.50803
20016.2515.16951.08055
20119.2518.54490.705125
20213.612.78950.810549
20313.615.7866-2.1866
20415.6515.44750.202548
20512.7512.9963-0.246289
20614.612.5822.01797
2079.8510.8264-0.976362
20812.6512.36170.288268
20919.217.29891.90108
21016.614.73721.86276
21111.211.5461-0.346085
21215.2515.5573-0.307285
21311.914.4894-2.58942
21413.212.68350.516463
21516.3517.7992-1.44922
21612.412.6808-0.280751
21715.8514.48131.36871
21818.1516.80651.3435
21911.1512.7335-1.58351
22015.6516.3074-0.657375
22117.7515.37332.37673
2227.6510.3044-2.65436
22312.3513.4933-1.14333
22415.613.84571.75431
22519.317.29792.00209
22615.211.19864.00137
22717.114.8952.20496
22815.613.36942.23058
22918.415.6382.76205
23019.0516.31962.73042
23118.5515.89572.6543
23219.117.93661.16345
23313.113.4627-0.362694
23412.8515.8947-3.04469
2359.512.2784-2.77838
2364.510.5779-6.07793
23711.8510.76261.0874
23813.615.2232-1.62318
23911.712.0402-0.340196
24012.412.7722-0.372174
24113.3514.8812-1.53118
24211.412.2296-0.829628
24314.914.25160.648371
24419.919.41410.485857
24511.213.3862-2.18623
24614.615.4737-0.873704
24717.618.0191-0.419131
24814.0513.33830.711678
24916.115.58340.516568
25013.3514.4131-1.0631
25111.8514.4582-2.60823
25211.9513.1327-1.18275
25314.7514.29120.458809
25415.1513.36681.78319
25513.216.0283-2.82833
25616.8516.19420.655821
2577.8511.8841-4.03414
2587.712.2969-4.59686
25912.615.0496-2.44957
2607.8514.8561-7.00613
26110.9511.2711-0.321088
26212.3514.0837-1.73366
2639.9512.9192-2.96919
26414.914.11860.781353
26516.6514.6312.01901
26613.412.71910.680877
26713.9513.37660.57342
26815.714.41291.28706
26916.8515.50241.34755
27010.9511.9396-0.989573
27115.3514.39330.956736
27212.212.5782-0.378178
27315.114.00571.09434
27417.7516.40371.34634
27515.215.19680.00319622
27614.614.50270.0972613
27716.6515.72320.926814
2788.110.2987-2.19866







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.7164370.5671270.283563
210.5814980.8370040.418502
220.4434920.8869840.556508
230.3695470.7390930.630453
240.299350.59870.70065
250.2438730.4877450.756127
260.1723810.3447610.827619
270.1348460.2696920.865154
280.0958130.1916260.904187
290.06157410.1231480.938426
300.05656640.1131330.943434
310.04644380.09288750.953556
320.02907950.0581590.97092
330.09333690.1866740.906663
340.2746770.5493540.725323
350.2225520.4451040.777448
360.2536790.5073590.746321
370.2725210.5450420.727479
380.290640.5812790.70936
390.2701890.5403780.729811
400.2314120.4628250.768588
410.2723320.5446630.727668
420.2671340.5342670.732866
430.2410420.4820830.758958
440.2277090.4554170.772291
450.1952230.3904460.804777
460.1597770.3195540.840223
470.173210.346420.82679
480.1717250.3434490.828275
490.2346250.469250.765375
500.2611610.5223220.738839
510.2204020.4408040.779598
520.2489690.4979370.751031
530.2326940.4653870.767306
540.1974770.3949540.802523
550.31260.62520.6874
560.2709080.5418160.729092
570.626580.7468410.37342
580.659240.681520.34076
590.6244910.7510180.375509
600.6191620.7616760.380838
610.6277610.7444770.372239
620.6206610.7586790.379339
630.7253090.5493820.274691
640.785270.429460.21473
650.790790.4184210.20921
660.7680360.4639270.231964
670.7399960.5200080.260004
680.7062910.5874190.293709
690.7379270.5241470.262073
700.7134490.5731020.286551
710.6773230.6453550.322677
720.6446250.7107510.355375
730.6166010.7667970.383399
740.6614070.6771870.338593
750.6338530.7322950.366147
760.6207640.7584710.379236
770.5979450.8041110.402055
780.581260.837480.41874
790.5416470.9167070.458353
800.5381240.9237520.461876
810.4989090.9978180.501091
820.51990.9602010.4801
830.4826250.965250.517375
840.6774220.6451560.322578
850.6704910.6590170.329509
860.6358090.7283820.364191
870.6108220.7783560.389178
880.6297640.7404710.370236
890.6126630.7746740.387337
900.6078960.7842080.392104
910.6163170.7673660.383683
920.7632760.4734480.236724
930.7564870.4870260.243513
940.7355860.5288270.264414
950.7198210.5603590.280179
960.6931770.6136460.306823
970.7329630.5340750.267037
980.7181030.5637940.281897
990.7720590.4558830.227941
1000.7738790.4522420.226121
1010.7570960.4858080.242904
1020.728890.542220.27111
1030.7143430.5713140.285657
1040.7002890.5994210.299711
1050.7473640.5052730.252636
1060.7276760.5446490.272324
1070.7859030.4281950.214097
1080.8643360.2713270.135664
1090.8458110.3083780.154189
1100.8319820.3360350.168018
1110.8133930.3732140.186607
1120.7935650.4128690.206435
1130.8458930.3082140.154107
1140.8270740.3458520.172926
1150.9013710.1972570.0986286
1160.8935850.2128310.106415
1170.8850390.2299220.114961
1180.8767090.2465810.123291
1190.8845420.2309170.115458
1200.8704160.2591670.129584
1210.8950240.2099510.104976
1220.9046110.1907780.0953892
1230.8973570.2052850.102643
1240.9405950.118810.0594049
1250.9414260.1171480.0585741
1260.9322960.1354070.0677037
1270.9239120.1521770.0760885
1280.9111940.1776110.0888056
1290.9069840.1860320.0930161
1300.8925740.2148510.107426
1310.8795430.2409130.120457
1320.8770890.2458230.122911
1330.8623240.2753520.137676
1340.8443010.3113980.155699
1350.8218050.356390.178195
1360.798840.402320.20116
1370.8181860.3636280.181814
1380.8089310.3821390.191069
1390.7874590.4250820.212541
1400.7615410.4769170.238459
1410.7341050.5317910.265895
1420.7342290.5315420.265771
1430.7068010.5863980.293199
1440.7447350.510530.255265
1450.7221840.5556330.277816
1460.6911330.6177340.308867
1470.6613040.6773910.338696
1480.6283370.7433260.371663
1490.600620.7987610.39938
1500.5965940.8068130.403406
1510.8556450.288710.144355
1520.8378920.3242160.162108
1530.8411720.3176570.158828
1540.8238080.3523830.176192
1550.8341770.3316460.165823
1560.8161990.3676020.183801
1570.7951110.4097780.204889
1580.7705080.4589850.229492
1590.7720210.4559570.227979
1600.7697070.4605850.230293
1610.7522250.4955510.247775
1620.7497370.5005260.250263
1630.7424320.5151350.257568
1640.9715990.05680190.0284009
1650.9650830.06983470.0349173
1660.9602540.07949180.0397459
1670.9533750.09324990.046625
1680.9521540.09569260.0478463
1690.943020.113960.0569798
1700.9473530.1052950.0526474
1710.9402060.1195880.0597938
1720.937540.1249190.0624597
1730.9361530.1276940.0638472
1740.9265840.1468320.073416
1750.9147940.1704120.0852059
1760.9210090.1579820.0789911
1770.9122910.1754180.087709
1780.9101670.1796650.0898325
1790.8987630.2024730.101237
1800.8956950.2086090.104305
1810.9110430.1779150.0889575
1820.9167990.1664010.0832006
1830.9136580.1726840.0863418
1840.898080.2038410.10192
1850.9047540.1904920.0952459
1860.8890980.2218040.110902
1870.901230.1975390.0987697
1880.9034360.1931270.0965637
1890.9011990.1976010.0988007
1900.8843260.2313490.115674
1910.880710.238580.11929
1920.8640980.2718040.135902
1930.8886380.2227240.111362
1940.9036890.1926220.0963108
1950.8926570.2146850.107343
1960.8832940.2334120.116706
1970.8932940.2134110.106706
1980.8735610.2528780.126439
1990.8634520.2730950.136548
2000.8407380.3185230.159262
2010.8185220.3629570.181478
2020.7961290.4077410.203871
2030.8096680.3806650.190332
2040.7787090.4425820.221291
2050.7627140.4745720.237286
2060.7573170.4853660.242683
2070.7504940.4990110.249506
2080.7189290.5621410.281071
2090.6959590.6080820.304041
2100.6923480.6153040.307652
2110.6539350.692130.346065
2120.6121070.7757860.387893
2130.6043970.7912060.395603
2140.560280.8794390.43972
2150.5639050.8721910.436095
2160.524430.9511390.47557
2170.5214510.9570990.478549
2180.4855350.9710690.514465
2190.4475520.8951040.552448
2200.4124420.8248840.587558
2210.4159870.8319740.584013
2220.4635260.9270530.536474
2230.4284880.8569760.571512
2240.5773890.8452230.422611
2250.5522190.8955620.447781
2260.6037330.7925330.396267
2270.612340.7753210.38766
2280.662860.674280.33714
2290.7379350.524130.262065
2300.7623110.4753780.237689
2310.7376590.5246810.262341
2320.7025730.5948540.297427
2330.6763160.6473680.323684
2340.6516160.6967670.348384
2350.620080.759840.37992
2360.693030.6139390.30697
2370.6639210.6721590.336079
2380.6112130.7775740.388787
2390.6191750.761650.380825
2400.5640690.8718620.435931
2410.5258040.9483920.474196
2420.463520.9270390.53648
2430.4086670.8173340.591333
2440.3518280.7036570.648172
2450.3008560.6017110.699144
2460.2418350.483670.758165
2470.1991050.3982110.800895
2480.4207480.8414960.579252
2490.3584830.7169650.641517
2500.2911560.5823120.708844
2510.2254650.450930.774535
2520.1851580.3703160.814842
2530.1815120.3630240.818488
2540.3072720.6145430.692728
2550.2729570.5459130.727043
2560.1974780.3949570.802522
2570.1266810.2533630.873319
2580.06960050.1392010.930399

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.716437 & 0.567127 & 0.283563 \tabularnewline
21 & 0.581498 & 0.837004 & 0.418502 \tabularnewline
22 & 0.443492 & 0.886984 & 0.556508 \tabularnewline
23 & 0.369547 & 0.739093 & 0.630453 \tabularnewline
24 & 0.29935 & 0.5987 & 0.70065 \tabularnewline
25 & 0.243873 & 0.487745 & 0.756127 \tabularnewline
26 & 0.172381 & 0.344761 & 0.827619 \tabularnewline
27 & 0.134846 & 0.269692 & 0.865154 \tabularnewline
28 & 0.095813 & 0.191626 & 0.904187 \tabularnewline
29 & 0.0615741 & 0.123148 & 0.938426 \tabularnewline
30 & 0.0565664 & 0.113133 & 0.943434 \tabularnewline
31 & 0.0464438 & 0.0928875 & 0.953556 \tabularnewline
32 & 0.0290795 & 0.058159 & 0.97092 \tabularnewline
33 & 0.0933369 & 0.186674 & 0.906663 \tabularnewline
34 & 0.274677 & 0.549354 & 0.725323 \tabularnewline
35 & 0.222552 & 0.445104 & 0.777448 \tabularnewline
36 & 0.253679 & 0.507359 & 0.746321 \tabularnewline
37 & 0.272521 & 0.545042 & 0.727479 \tabularnewline
38 & 0.29064 & 0.581279 & 0.70936 \tabularnewline
39 & 0.270189 & 0.540378 & 0.729811 \tabularnewline
40 & 0.231412 & 0.462825 & 0.768588 \tabularnewline
41 & 0.272332 & 0.544663 & 0.727668 \tabularnewline
42 & 0.267134 & 0.534267 & 0.732866 \tabularnewline
43 & 0.241042 & 0.482083 & 0.758958 \tabularnewline
44 & 0.227709 & 0.455417 & 0.772291 \tabularnewline
45 & 0.195223 & 0.390446 & 0.804777 \tabularnewline
46 & 0.159777 & 0.319554 & 0.840223 \tabularnewline
47 & 0.17321 & 0.34642 & 0.82679 \tabularnewline
48 & 0.171725 & 0.343449 & 0.828275 \tabularnewline
49 & 0.234625 & 0.46925 & 0.765375 \tabularnewline
50 & 0.261161 & 0.522322 & 0.738839 \tabularnewline
51 & 0.220402 & 0.440804 & 0.779598 \tabularnewline
52 & 0.248969 & 0.497937 & 0.751031 \tabularnewline
53 & 0.232694 & 0.465387 & 0.767306 \tabularnewline
54 & 0.197477 & 0.394954 & 0.802523 \tabularnewline
55 & 0.3126 & 0.6252 & 0.6874 \tabularnewline
56 & 0.270908 & 0.541816 & 0.729092 \tabularnewline
57 & 0.62658 & 0.746841 & 0.37342 \tabularnewline
58 & 0.65924 & 0.68152 & 0.34076 \tabularnewline
59 & 0.624491 & 0.751018 & 0.375509 \tabularnewline
60 & 0.619162 & 0.761676 & 0.380838 \tabularnewline
61 & 0.627761 & 0.744477 & 0.372239 \tabularnewline
62 & 0.620661 & 0.758679 & 0.379339 \tabularnewline
63 & 0.725309 & 0.549382 & 0.274691 \tabularnewline
64 & 0.78527 & 0.42946 & 0.21473 \tabularnewline
65 & 0.79079 & 0.418421 & 0.20921 \tabularnewline
66 & 0.768036 & 0.463927 & 0.231964 \tabularnewline
67 & 0.739996 & 0.520008 & 0.260004 \tabularnewline
68 & 0.706291 & 0.587419 & 0.293709 \tabularnewline
69 & 0.737927 & 0.524147 & 0.262073 \tabularnewline
70 & 0.713449 & 0.573102 & 0.286551 \tabularnewline
71 & 0.677323 & 0.645355 & 0.322677 \tabularnewline
72 & 0.644625 & 0.710751 & 0.355375 \tabularnewline
73 & 0.616601 & 0.766797 & 0.383399 \tabularnewline
74 & 0.661407 & 0.677187 & 0.338593 \tabularnewline
75 & 0.633853 & 0.732295 & 0.366147 \tabularnewline
76 & 0.620764 & 0.758471 & 0.379236 \tabularnewline
77 & 0.597945 & 0.804111 & 0.402055 \tabularnewline
78 & 0.58126 & 0.83748 & 0.41874 \tabularnewline
79 & 0.541647 & 0.916707 & 0.458353 \tabularnewline
80 & 0.538124 & 0.923752 & 0.461876 \tabularnewline
81 & 0.498909 & 0.997818 & 0.501091 \tabularnewline
82 & 0.5199 & 0.960201 & 0.4801 \tabularnewline
83 & 0.482625 & 0.96525 & 0.517375 \tabularnewline
84 & 0.677422 & 0.645156 & 0.322578 \tabularnewline
85 & 0.670491 & 0.659017 & 0.329509 \tabularnewline
86 & 0.635809 & 0.728382 & 0.364191 \tabularnewline
87 & 0.610822 & 0.778356 & 0.389178 \tabularnewline
88 & 0.629764 & 0.740471 & 0.370236 \tabularnewline
89 & 0.612663 & 0.774674 & 0.387337 \tabularnewline
90 & 0.607896 & 0.784208 & 0.392104 \tabularnewline
91 & 0.616317 & 0.767366 & 0.383683 \tabularnewline
92 & 0.763276 & 0.473448 & 0.236724 \tabularnewline
93 & 0.756487 & 0.487026 & 0.243513 \tabularnewline
94 & 0.735586 & 0.528827 & 0.264414 \tabularnewline
95 & 0.719821 & 0.560359 & 0.280179 \tabularnewline
96 & 0.693177 & 0.613646 & 0.306823 \tabularnewline
97 & 0.732963 & 0.534075 & 0.267037 \tabularnewline
98 & 0.718103 & 0.563794 & 0.281897 \tabularnewline
99 & 0.772059 & 0.455883 & 0.227941 \tabularnewline
100 & 0.773879 & 0.452242 & 0.226121 \tabularnewline
101 & 0.757096 & 0.485808 & 0.242904 \tabularnewline
102 & 0.72889 & 0.54222 & 0.27111 \tabularnewline
103 & 0.714343 & 0.571314 & 0.285657 \tabularnewline
104 & 0.700289 & 0.599421 & 0.299711 \tabularnewline
105 & 0.747364 & 0.505273 & 0.252636 \tabularnewline
106 & 0.727676 & 0.544649 & 0.272324 \tabularnewline
107 & 0.785903 & 0.428195 & 0.214097 \tabularnewline
108 & 0.864336 & 0.271327 & 0.135664 \tabularnewline
109 & 0.845811 & 0.308378 & 0.154189 \tabularnewline
110 & 0.831982 & 0.336035 & 0.168018 \tabularnewline
111 & 0.813393 & 0.373214 & 0.186607 \tabularnewline
112 & 0.793565 & 0.412869 & 0.206435 \tabularnewline
113 & 0.845893 & 0.308214 & 0.154107 \tabularnewline
114 & 0.827074 & 0.345852 & 0.172926 \tabularnewline
115 & 0.901371 & 0.197257 & 0.0986286 \tabularnewline
116 & 0.893585 & 0.212831 & 0.106415 \tabularnewline
117 & 0.885039 & 0.229922 & 0.114961 \tabularnewline
118 & 0.876709 & 0.246581 & 0.123291 \tabularnewline
119 & 0.884542 & 0.230917 & 0.115458 \tabularnewline
120 & 0.870416 & 0.259167 & 0.129584 \tabularnewline
121 & 0.895024 & 0.209951 & 0.104976 \tabularnewline
122 & 0.904611 & 0.190778 & 0.0953892 \tabularnewline
123 & 0.897357 & 0.205285 & 0.102643 \tabularnewline
124 & 0.940595 & 0.11881 & 0.0594049 \tabularnewline
125 & 0.941426 & 0.117148 & 0.0585741 \tabularnewline
126 & 0.932296 & 0.135407 & 0.0677037 \tabularnewline
127 & 0.923912 & 0.152177 & 0.0760885 \tabularnewline
128 & 0.911194 & 0.177611 & 0.0888056 \tabularnewline
129 & 0.906984 & 0.186032 & 0.0930161 \tabularnewline
130 & 0.892574 & 0.214851 & 0.107426 \tabularnewline
131 & 0.879543 & 0.240913 & 0.120457 \tabularnewline
132 & 0.877089 & 0.245823 & 0.122911 \tabularnewline
133 & 0.862324 & 0.275352 & 0.137676 \tabularnewline
134 & 0.844301 & 0.311398 & 0.155699 \tabularnewline
135 & 0.821805 & 0.35639 & 0.178195 \tabularnewline
136 & 0.79884 & 0.40232 & 0.20116 \tabularnewline
137 & 0.818186 & 0.363628 & 0.181814 \tabularnewline
138 & 0.808931 & 0.382139 & 0.191069 \tabularnewline
139 & 0.787459 & 0.425082 & 0.212541 \tabularnewline
140 & 0.761541 & 0.476917 & 0.238459 \tabularnewline
141 & 0.734105 & 0.531791 & 0.265895 \tabularnewline
142 & 0.734229 & 0.531542 & 0.265771 \tabularnewline
143 & 0.706801 & 0.586398 & 0.293199 \tabularnewline
144 & 0.744735 & 0.51053 & 0.255265 \tabularnewline
145 & 0.722184 & 0.555633 & 0.277816 \tabularnewline
146 & 0.691133 & 0.617734 & 0.308867 \tabularnewline
147 & 0.661304 & 0.677391 & 0.338696 \tabularnewline
148 & 0.628337 & 0.743326 & 0.371663 \tabularnewline
149 & 0.60062 & 0.798761 & 0.39938 \tabularnewline
150 & 0.596594 & 0.806813 & 0.403406 \tabularnewline
151 & 0.855645 & 0.28871 & 0.144355 \tabularnewline
152 & 0.837892 & 0.324216 & 0.162108 \tabularnewline
153 & 0.841172 & 0.317657 & 0.158828 \tabularnewline
154 & 0.823808 & 0.352383 & 0.176192 \tabularnewline
155 & 0.834177 & 0.331646 & 0.165823 \tabularnewline
156 & 0.816199 & 0.367602 & 0.183801 \tabularnewline
157 & 0.795111 & 0.409778 & 0.204889 \tabularnewline
158 & 0.770508 & 0.458985 & 0.229492 \tabularnewline
159 & 0.772021 & 0.455957 & 0.227979 \tabularnewline
160 & 0.769707 & 0.460585 & 0.230293 \tabularnewline
161 & 0.752225 & 0.495551 & 0.247775 \tabularnewline
162 & 0.749737 & 0.500526 & 0.250263 \tabularnewline
163 & 0.742432 & 0.515135 & 0.257568 \tabularnewline
164 & 0.971599 & 0.0568019 & 0.0284009 \tabularnewline
165 & 0.965083 & 0.0698347 & 0.0349173 \tabularnewline
166 & 0.960254 & 0.0794918 & 0.0397459 \tabularnewline
167 & 0.953375 & 0.0932499 & 0.046625 \tabularnewline
168 & 0.952154 & 0.0956926 & 0.0478463 \tabularnewline
169 & 0.94302 & 0.11396 & 0.0569798 \tabularnewline
170 & 0.947353 & 0.105295 & 0.0526474 \tabularnewline
171 & 0.940206 & 0.119588 & 0.0597938 \tabularnewline
172 & 0.93754 & 0.124919 & 0.0624597 \tabularnewline
173 & 0.936153 & 0.127694 & 0.0638472 \tabularnewline
174 & 0.926584 & 0.146832 & 0.073416 \tabularnewline
175 & 0.914794 & 0.170412 & 0.0852059 \tabularnewline
176 & 0.921009 & 0.157982 & 0.0789911 \tabularnewline
177 & 0.912291 & 0.175418 & 0.087709 \tabularnewline
178 & 0.910167 & 0.179665 & 0.0898325 \tabularnewline
179 & 0.898763 & 0.202473 & 0.101237 \tabularnewline
180 & 0.895695 & 0.208609 & 0.104305 \tabularnewline
181 & 0.911043 & 0.177915 & 0.0889575 \tabularnewline
182 & 0.916799 & 0.166401 & 0.0832006 \tabularnewline
183 & 0.913658 & 0.172684 & 0.0863418 \tabularnewline
184 & 0.89808 & 0.203841 & 0.10192 \tabularnewline
185 & 0.904754 & 0.190492 & 0.0952459 \tabularnewline
186 & 0.889098 & 0.221804 & 0.110902 \tabularnewline
187 & 0.90123 & 0.197539 & 0.0987697 \tabularnewline
188 & 0.903436 & 0.193127 & 0.0965637 \tabularnewline
189 & 0.901199 & 0.197601 & 0.0988007 \tabularnewline
190 & 0.884326 & 0.231349 & 0.115674 \tabularnewline
191 & 0.88071 & 0.23858 & 0.11929 \tabularnewline
192 & 0.864098 & 0.271804 & 0.135902 \tabularnewline
193 & 0.888638 & 0.222724 & 0.111362 \tabularnewline
194 & 0.903689 & 0.192622 & 0.0963108 \tabularnewline
195 & 0.892657 & 0.214685 & 0.107343 \tabularnewline
196 & 0.883294 & 0.233412 & 0.116706 \tabularnewline
197 & 0.893294 & 0.213411 & 0.106706 \tabularnewline
198 & 0.873561 & 0.252878 & 0.126439 \tabularnewline
199 & 0.863452 & 0.273095 & 0.136548 \tabularnewline
200 & 0.840738 & 0.318523 & 0.159262 \tabularnewline
201 & 0.818522 & 0.362957 & 0.181478 \tabularnewline
202 & 0.796129 & 0.407741 & 0.203871 \tabularnewline
203 & 0.809668 & 0.380665 & 0.190332 \tabularnewline
204 & 0.778709 & 0.442582 & 0.221291 \tabularnewline
205 & 0.762714 & 0.474572 & 0.237286 \tabularnewline
206 & 0.757317 & 0.485366 & 0.242683 \tabularnewline
207 & 0.750494 & 0.499011 & 0.249506 \tabularnewline
208 & 0.718929 & 0.562141 & 0.281071 \tabularnewline
209 & 0.695959 & 0.608082 & 0.304041 \tabularnewline
210 & 0.692348 & 0.615304 & 0.307652 \tabularnewline
211 & 0.653935 & 0.69213 & 0.346065 \tabularnewline
212 & 0.612107 & 0.775786 & 0.387893 \tabularnewline
213 & 0.604397 & 0.791206 & 0.395603 \tabularnewline
214 & 0.56028 & 0.879439 & 0.43972 \tabularnewline
215 & 0.563905 & 0.872191 & 0.436095 \tabularnewline
216 & 0.52443 & 0.951139 & 0.47557 \tabularnewline
217 & 0.521451 & 0.957099 & 0.478549 \tabularnewline
218 & 0.485535 & 0.971069 & 0.514465 \tabularnewline
219 & 0.447552 & 0.895104 & 0.552448 \tabularnewline
220 & 0.412442 & 0.824884 & 0.587558 \tabularnewline
221 & 0.415987 & 0.831974 & 0.584013 \tabularnewline
222 & 0.463526 & 0.927053 & 0.536474 \tabularnewline
223 & 0.428488 & 0.856976 & 0.571512 \tabularnewline
224 & 0.577389 & 0.845223 & 0.422611 \tabularnewline
225 & 0.552219 & 0.895562 & 0.447781 \tabularnewline
226 & 0.603733 & 0.792533 & 0.396267 \tabularnewline
227 & 0.61234 & 0.775321 & 0.38766 \tabularnewline
228 & 0.66286 & 0.67428 & 0.33714 \tabularnewline
229 & 0.737935 & 0.52413 & 0.262065 \tabularnewline
230 & 0.762311 & 0.475378 & 0.237689 \tabularnewline
231 & 0.737659 & 0.524681 & 0.262341 \tabularnewline
232 & 0.702573 & 0.594854 & 0.297427 \tabularnewline
233 & 0.676316 & 0.647368 & 0.323684 \tabularnewline
234 & 0.651616 & 0.696767 & 0.348384 \tabularnewline
235 & 0.62008 & 0.75984 & 0.37992 \tabularnewline
236 & 0.69303 & 0.613939 & 0.30697 \tabularnewline
237 & 0.663921 & 0.672159 & 0.336079 \tabularnewline
238 & 0.611213 & 0.777574 & 0.388787 \tabularnewline
239 & 0.619175 & 0.76165 & 0.380825 \tabularnewline
240 & 0.564069 & 0.871862 & 0.435931 \tabularnewline
241 & 0.525804 & 0.948392 & 0.474196 \tabularnewline
242 & 0.46352 & 0.927039 & 0.53648 \tabularnewline
243 & 0.408667 & 0.817334 & 0.591333 \tabularnewline
244 & 0.351828 & 0.703657 & 0.648172 \tabularnewline
245 & 0.300856 & 0.601711 & 0.699144 \tabularnewline
246 & 0.241835 & 0.48367 & 0.758165 \tabularnewline
247 & 0.199105 & 0.398211 & 0.800895 \tabularnewline
248 & 0.420748 & 0.841496 & 0.579252 \tabularnewline
249 & 0.358483 & 0.716965 & 0.641517 \tabularnewline
250 & 0.291156 & 0.582312 & 0.708844 \tabularnewline
251 & 0.225465 & 0.45093 & 0.774535 \tabularnewline
252 & 0.185158 & 0.370316 & 0.814842 \tabularnewline
253 & 0.181512 & 0.363024 & 0.818488 \tabularnewline
254 & 0.307272 & 0.614543 & 0.692728 \tabularnewline
255 & 0.272957 & 0.545913 & 0.727043 \tabularnewline
256 & 0.197478 & 0.394957 & 0.802522 \tabularnewline
257 & 0.126681 & 0.253363 & 0.873319 \tabularnewline
258 & 0.0696005 & 0.139201 & 0.930399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]20[/C][C]0.716437[/C][C]0.567127[/C][C]0.283563[/C][/ROW]
[ROW][C]21[/C][C]0.581498[/C][C]0.837004[/C][C]0.418502[/C][/ROW]
[ROW][C]22[/C][C]0.443492[/C][C]0.886984[/C][C]0.556508[/C][/ROW]
[ROW][C]23[/C][C]0.369547[/C][C]0.739093[/C][C]0.630453[/C][/ROW]
[ROW][C]24[/C][C]0.29935[/C][C]0.5987[/C][C]0.70065[/C][/ROW]
[ROW][C]25[/C][C]0.243873[/C][C]0.487745[/C][C]0.756127[/C][/ROW]
[ROW][C]26[/C][C]0.172381[/C][C]0.344761[/C][C]0.827619[/C][/ROW]
[ROW][C]27[/C][C]0.134846[/C][C]0.269692[/C][C]0.865154[/C][/ROW]
[ROW][C]28[/C][C]0.095813[/C][C]0.191626[/C][C]0.904187[/C][/ROW]
[ROW][C]29[/C][C]0.0615741[/C][C]0.123148[/C][C]0.938426[/C][/ROW]
[ROW][C]30[/C][C]0.0565664[/C][C]0.113133[/C][C]0.943434[/C][/ROW]
[ROW][C]31[/C][C]0.0464438[/C][C]0.0928875[/C][C]0.953556[/C][/ROW]
[ROW][C]32[/C][C]0.0290795[/C][C]0.058159[/C][C]0.97092[/C][/ROW]
[ROW][C]33[/C][C]0.0933369[/C][C]0.186674[/C][C]0.906663[/C][/ROW]
[ROW][C]34[/C][C]0.274677[/C][C]0.549354[/C][C]0.725323[/C][/ROW]
[ROW][C]35[/C][C]0.222552[/C][C]0.445104[/C][C]0.777448[/C][/ROW]
[ROW][C]36[/C][C]0.253679[/C][C]0.507359[/C][C]0.746321[/C][/ROW]
[ROW][C]37[/C][C]0.272521[/C][C]0.545042[/C][C]0.727479[/C][/ROW]
[ROW][C]38[/C][C]0.29064[/C][C]0.581279[/C][C]0.70936[/C][/ROW]
[ROW][C]39[/C][C]0.270189[/C][C]0.540378[/C][C]0.729811[/C][/ROW]
[ROW][C]40[/C][C]0.231412[/C][C]0.462825[/C][C]0.768588[/C][/ROW]
[ROW][C]41[/C][C]0.272332[/C][C]0.544663[/C][C]0.727668[/C][/ROW]
[ROW][C]42[/C][C]0.267134[/C][C]0.534267[/C][C]0.732866[/C][/ROW]
[ROW][C]43[/C][C]0.241042[/C][C]0.482083[/C][C]0.758958[/C][/ROW]
[ROW][C]44[/C][C]0.227709[/C][C]0.455417[/C][C]0.772291[/C][/ROW]
[ROW][C]45[/C][C]0.195223[/C][C]0.390446[/C][C]0.804777[/C][/ROW]
[ROW][C]46[/C][C]0.159777[/C][C]0.319554[/C][C]0.840223[/C][/ROW]
[ROW][C]47[/C][C]0.17321[/C][C]0.34642[/C][C]0.82679[/C][/ROW]
[ROW][C]48[/C][C]0.171725[/C][C]0.343449[/C][C]0.828275[/C][/ROW]
[ROW][C]49[/C][C]0.234625[/C][C]0.46925[/C][C]0.765375[/C][/ROW]
[ROW][C]50[/C][C]0.261161[/C][C]0.522322[/C][C]0.738839[/C][/ROW]
[ROW][C]51[/C][C]0.220402[/C][C]0.440804[/C][C]0.779598[/C][/ROW]
[ROW][C]52[/C][C]0.248969[/C][C]0.497937[/C][C]0.751031[/C][/ROW]
[ROW][C]53[/C][C]0.232694[/C][C]0.465387[/C][C]0.767306[/C][/ROW]
[ROW][C]54[/C][C]0.197477[/C][C]0.394954[/C][C]0.802523[/C][/ROW]
[ROW][C]55[/C][C]0.3126[/C][C]0.6252[/C][C]0.6874[/C][/ROW]
[ROW][C]56[/C][C]0.270908[/C][C]0.541816[/C][C]0.729092[/C][/ROW]
[ROW][C]57[/C][C]0.62658[/C][C]0.746841[/C][C]0.37342[/C][/ROW]
[ROW][C]58[/C][C]0.65924[/C][C]0.68152[/C][C]0.34076[/C][/ROW]
[ROW][C]59[/C][C]0.624491[/C][C]0.751018[/C][C]0.375509[/C][/ROW]
[ROW][C]60[/C][C]0.619162[/C][C]0.761676[/C][C]0.380838[/C][/ROW]
[ROW][C]61[/C][C]0.627761[/C][C]0.744477[/C][C]0.372239[/C][/ROW]
[ROW][C]62[/C][C]0.620661[/C][C]0.758679[/C][C]0.379339[/C][/ROW]
[ROW][C]63[/C][C]0.725309[/C][C]0.549382[/C][C]0.274691[/C][/ROW]
[ROW][C]64[/C][C]0.78527[/C][C]0.42946[/C][C]0.21473[/C][/ROW]
[ROW][C]65[/C][C]0.79079[/C][C]0.418421[/C][C]0.20921[/C][/ROW]
[ROW][C]66[/C][C]0.768036[/C][C]0.463927[/C][C]0.231964[/C][/ROW]
[ROW][C]67[/C][C]0.739996[/C][C]0.520008[/C][C]0.260004[/C][/ROW]
[ROW][C]68[/C][C]0.706291[/C][C]0.587419[/C][C]0.293709[/C][/ROW]
[ROW][C]69[/C][C]0.737927[/C][C]0.524147[/C][C]0.262073[/C][/ROW]
[ROW][C]70[/C][C]0.713449[/C][C]0.573102[/C][C]0.286551[/C][/ROW]
[ROW][C]71[/C][C]0.677323[/C][C]0.645355[/C][C]0.322677[/C][/ROW]
[ROW][C]72[/C][C]0.644625[/C][C]0.710751[/C][C]0.355375[/C][/ROW]
[ROW][C]73[/C][C]0.616601[/C][C]0.766797[/C][C]0.383399[/C][/ROW]
[ROW][C]74[/C][C]0.661407[/C][C]0.677187[/C][C]0.338593[/C][/ROW]
[ROW][C]75[/C][C]0.633853[/C][C]0.732295[/C][C]0.366147[/C][/ROW]
[ROW][C]76[/C][C]0.620764[/C][C]0.758471[/C][C]0.379236[/C][/ROW]
[ROW][C]77[/C][C]0.597945[/C][C]0.804111[/C][C]0.402055[/C][/ROW]
[ROW][C]78[/C][C]0.58126[/C][C]0.83748[/C][C]0.41874[/C][/ROW]
[ROW][C]79[/C][C]0.541647[/C][C]0.916707[/C][C]0.458353[/C][/ROW]
[ROW][C]80[/C][C]0.538124[/C][C]0.923752[/C][C]0.461876[/C][/ROW]
[ROW][C]81[/C][C]0.498909[/C][C]0.997818[/C][C]0.501091[/C][/ROW]
[ROW][C]82[/C][C]0.5199[/C][C]0.960201[/C][C]0.4801[/C][/ROW]
[ROW][C]83[/C][C]0.482625[/C][C]0.96525[/C][C]0.517375[/C][/ROW]
[ROW][C]84[/C][C]0.677422[/C][C]0.645156[/C][C]0.322578[/C][/ROW]
[ROW][C]85[/C][C]0.670491[/C][C]0.659017[/C][C]0.329509[/C][/ROW]
[ROW][C]86[/C][C]0.635809[/C][C]0.728382[/C][C]0.364191[/C][/ROW]
[ROW][C]87[/C][C]0.610822[/C][C]0.778356[/C][C]0.389178[/C][/ROW]
[ROW][C]88[/C][C]0.629764[/C][C]0.740471[/C][C]0.370236[/C][/ROW]
[ROW][C]89[/C][C]0.612663[/C][C]0.774674[/C][C]0.387337[/C][/ROW]
[ROW][C]90[/C][C]0.607896[/C][C]0.784208[/C][C]0.392104[/C][/ROW]
[ROW][C]91[/C][C]0.616317[/C][C]0.767366[/C][C]0.383683[/C][/ROW]
[ROW][C]92[/C][C]0.763276[/C][C]0.473448[/C][C]0.236724[/C][/ROW]
[ROW][C]93[/C][C]0.756487[/C][C]0.487026[/C][C]0.243513[/C][/ROW]
[ROW][C]94[/C][C]0.735586[/C][C]0.528827[/C][C]0.264414[/C][/ROW]
[ROW][C]95[/C][C]0.719821[/C][C]0.560359[/C][C]0.280179[/C][/ROW]
[ROW][C]96[/C][C]0.693177[/C][C]0.613646[/C][C]0.306823[/C][/ROW]
[ROW][C]97[/C][C]0.732963[/C][C]0.534075[/C][C]0.267037[/C][/ROW]
[ROW][C]98[/C][C]0.718103[/C][C]0.563794[/C][C]0.281897[/C][/ROW]
[ROW][C]99[/C][C]0.772059[/C][C]0.455883[/C][C]0.227941[/C][/ROW]
[ROW][C]100[/C][C]0.773879[/C][C]0.452242[/C][C]0.226121[/C][/ROW]
[ROW][C]101[/C][C]0.757096[/C][C]0.485808[/C][C]0.242904[/C][/ROW]
[ROW][C]102[/C][C]0.72889[/C][C]0.54222[/C][C]0.27111[/C][/ROW]
[ROW][C]103[/C][C]0.714343[/C][C]0.571314[/C][C]0.285657[/C][/ROW]
[ROW][C]104[/C][C]0.700289[/C][C]0.599421[/C][C]0.299711[/C][/ROW]
[ROW][C]105[/C][C]0.747364[/C][C]0.505273[/C][C]0.252636[/C][/ROW]
[ROW][C]106[/C][C]0.727676[/C][C]0.544649[/C][C]0.272324[/C][/ROW]
[ROW][C]107[/C][C]0.785903[/C][C]0.428195[/C][C]0.214097[/C][/ROW]
[ROW][C]108[/C][C]0.864336[/C][C]0.271327[/C][C]0.135664[/C][/ROW]
[ROW][C]109[/C][C]0.845811[/C][C]0.308378[/C][C]0.154189[/C][/ROW]
[ROW][C]110[/C][C]0.831982[/C][C]0.336035[/C][C]0.168018[/C][/ROW]
[ROW][C]111[/C][C]0.813393[/C][C]0.373214[/C][C]0.186607[/C][/ROW]
[ROW][C]112[/C][C]0.793565[/C][C]0.412869[/C][C]0.206435[/C][/ROW]
[ROW][C]113[/C][C]0.845893[/C][C]0.308214[/C][C]0.154107[/C][/ROW]
[ROW][C]114[/C][C]0.827074[/C][C]0.345852[/C][C]0.172926[/C][/ROW]
[ROW][C]115[/C][C]0.901371[/C][C]0.197257[/C][C]0.0986286[/C][/ROW]
[ROW][C]116[/C][C]0.893585[/C][C]0.212831[/C][C]0.106415[/C][/ROW]
[ROW][C]117[/C][C]0.885039[/C][C]0.229922[/C][C]0.114961[/C][/ROW]
[ROW][C]118[/C][C]0.876709[/C][C]0.246581[/C][C]0.123291[/C][/ROW]
[ROW][C]119[/C][C]0.884542[/C][C]0.230917[/C][C]0.115458[/C][/ROW]
[ROW][C]120[/C][C]0.870416[/C][C]0.259167[/C][C]0.129584[/C][/ROW]
[ROW][C]121[/C][C]0.895024[/C][C]0.209951[/C][C]0.104976[/C][/ROW]
[ROW][C]122[/C][C]0.904611[/C][C]0.190778[/C][C]0.0953892[/C][/ROW]
[ROW][C]123[/C][C]0.897357[/C][C]0.205285[/C][C]0.102643[/C][/ROW]
[ROW][C]124[/C][C]0.940595[/C][C]0.11881[/C][C]0.0594049[/C][/ROW]
[ROW][C]125[/C][C]0.941426[/C][C]0.117148[/C][C]0.0585741[/C][/ROW]
[ROW][C]126[/C][C]0.932296[/C][C]0.135407[/C][C]0.0677037[/C][/ROW]
[ROW][C]127[/C][C]0.923912[/C][C]0.152177[/C][C]0.0760885[/C][/ROW]
[ROW][C]128[/C][C]0.911194[/C][C]0.177611[/C][C]0.0888056[/C][/ROW]
[ROW][C]129[/C][C]0.906984[/C][C]0.186032[/C][C]0.0930161[/C][/ROW]
[ROW][C]130[/C][C]0.892574[/C][C]0.214851[/C][C]0.107426[/C][/ROW]
[ROW][C]131[/C][C]0.879543[/C][C]0.240913[/C][C]0.120457[/C][/ROW]
[ROW][C]132[/C][C]0.877089[/C][C]0.245823[/C][C]0.122911[/C][/ROW]
[ROW][C]133[/C][C]0.862324[/C][C]0.275352[/C][C]0.137676[/C][/ROW]
[ROW][C]134[/C][C]0.844301[/C][C]0.311398[/C][C]0.155699[/C][/ROW]
[ROW][C]135[/C][C]0.821805[/C][C]0.35639[/C][C]0.178195[/C][/ROW]
[ROW][C]136[/C][C]0.79884[/C][C]0.40232[/C][C]0.20116[/C][/ROW]
[ROW][C]137[/C][C]0.818186[/C][C]0.363628[/C][C]0.181814[/C][/ROW]
[ROW][C]138[/C][C]0.808931[/C][C]0.382139[/C][C]0.191069[/C][/ROW]
[ROW][C]139[/C][C]0.787459[/C][C]0.425082[/C][C]0.212541[/C][/ROW]
[ROW][C]140[/C][C]0.761541[/C][C]0.476917[/C][C]0.238459[/C][/ROW]
[ROW][C]141[/C][C]0.734105[/C][C]0.531791[/C][C]0.265895[/C][/ROW]
[ROW][C]142[/C][C]0.734229[/C][C]0.531542[/C][C]0.265771[/C][/ROW]
[ROW][C]143[/C][C]0.706801[/C][C]0.586398[/C][C]0.293199[/C][/ROW]
[ROW][C]144[/C][C]0.744735[/C][C]0.51053[/C][C]0.255265[/C][/ROW]
[ROW][C]145[/C][C]0.722184[/C][C]0.555633[/C][C]0.277816[/C][/ROW]
[ROW][C]146[/C][C]0.691133[/C][C]0.617734[/C][C]0.308867[/C][/ROW]
[ROW][C]147[/C][C]0.661304[/C][C]0.677391[/C][C]0.338696[/C][/ROW]
[ROW][C]148[/C][C]0.628337[/C][C]0.743326[/C][C]0.371663[/C][/ROW]
[ROW][C]149[/C][C]0.60062[/C][C]0.798761[/C][C]0.39938[/C][/ROW]
[ROW][C]150[/C][C]0.596594[/C][C]0.806813[/C][C]0.403406[/C][/ROW]
[ROW][C]151[/C][C]0.855645[/C][C]0.28871[/C][C]0.144355[/C][/ROW]
[ROW][C]152[/C][C]0.837892[/C][C]0.324216[/C][C]0.162108[/C][/ROW]
[ROW][C]153[/C][C]0.841172[/C][C]0.317657[/C][C]0.158828[/C][/ROW]
[ROW][C]154[/C][C]0.823808[/C][C]0.352383[/C][C]0.176192[/C][/ROW]
[ROW][C]155[/C][C]0.834177[/C][C]0.331646[/C][C]0.165823[/C][/ROW]
[ROW][C]156[/C][C]0.816199[/C][C]0.367602[/C][C]0.183801[/C][/ROW]
[ROW][C]157[/C][C]0.795111[/C][C]0.409778[/C][C]0.204889[/C][/ROW]
[ROW][C]158[/C][C]0.770508[/C][C]0.458985[/C][C]0.229492[/C][/ROW]
[ROW][C]159[/C][C]0.772021[/C][C]0.455957[/C][C]0.227979[/C][/ROW]
[ROW][C]160[/C][C]0.769707[/C][C]0.460585[/C][C]0.230293[/C][/ROW]
[ROW][C]161[/C][C]0.752225[/C][C]0.495551[/C][C]0.247775[/C][/ROW]
[ROW][C]162[/C][C]0.749737[/C][C]0.500526[/C][C]0.250263[/C][/ROW]
[ROW][C]163[/C][C]0.742432[/C][C]0.515135[/C][C]0.257568[/C][/ROW]
[ROW][C]164[/C][C]0.971599[/C][C]0.0568019[/C][C]0.0284009[/C][/ROW]
[ROW][C]165[/C][C]0.965083[/C][C]0.0698347[/C][C]0.0349173[/C][/ROW]
[ROW][C]166[/C][C]0.960254[/C][C]0.0794918[/C][C]0.0397459[/C][/ROW]
[ROW][C]167[/C][C]0.953375[/C][C]0.0932499[/C][C]0.046625[/C][/ROW]
[ROW][C]168[/C][C]0.952154[/C][C]0.0956926[/C][C]0.0478463[/C][/ROW]
[ROW][C]169[/C][C]0.94302[/C][C]0.11396[/C][C]0.0569798[/C][/ROW]
[ROW][C]170[/C][C]0.947353[/C][C]0.105295[/C][C]0.0526474[/C][/ROW]
[ROW][C]171[/C][C]0.940206[/C][C]0.119588[/C][C]0.0597938[/C][/ROW]
[ROW][C]172[/C][C]0.93754[/C][C]0.124919[/C][C]0.0624597[/C][/ROW]
[ROW][C]173[/C][C]0.936153[/C][C]0.127694[/C][C]0.0638472[/C][/ROW]
[ROW][C]174[/C][C]0.926584[/C][C]0.146832[/C][C]0.073416[/C][/ROW]
[ROW][C]175[/C][C]0.914794[/C][C]0.170412[/C][C]0.0852059[/C][/ROW]
[ROW][C]176[/C][C]0.921009[/C][C]0.157982[/C][C]0.0789911[/C][/ROW]
[ROW][C]177[/C][C]0.912291[/C][C]0.175418[/C][C]0.087709[/C][/ROW]
[ROW][C]178[/C][C]0.910167[/C][C]0.179665[/C][C]0.0898325[/C][/ROW]
[ROW][C]179[/C][C]0.898763[/C][C]0.202473[/C][C]0.101237[/C][/ROW]
[ROW][C]180[/C][C]0.895695[/C][C]0.208609[/C][C]0.104305[/C][/ROW]
[ROW][C]181[/C][C]0.911043[/C][C]0.177915[/C][C]0.0889575[/C][/ROW]
[ROW][C]182[/C][C]0.916799[/C][C]0.166401[/C][C]0.0832006[/C][/ROW]
[ROW][C]183[/C][C]0.913658[/C][C]0.172684[/C][C]0.0863418[/C][/ROW]
[ROW][C]184[/C][C]0.89808[/C][C]0.203841[/C][C]0.10192[/C][/ROW]
[ROW][C]185[/C][C]0.904754[/C][C]0.190492[/C][C]0.0952459[/C][/ROW]
[ROW][C]186[/C][C]0.889098[/C][C]0.221804[/C][C]0.110902[/C][/ROW]
[ROW][C]187[/C][C]0.90123[/C][C]0.197539[/C][C]0.0987697[/C][/ROW]
[ROW][C]188[/C][C]0.903436[/C][C]0.193127[/C][C]0.0965637[/C][/ROW]
[ROW][C]189[/C][C]0.901199[/C][C]0.197601[/C][C]0.0988007[/C][/ROW]
[ROW][C]190[/C][C]0.884326[/C][C]0.231349[/C][C]0.115674[/C][/ROW]
[ROW][C]191[/C][C]0.88071[/C][C]0.23858[/C][C]0.11929[/C][/ROW]
[ROW][C]192[/C][C]0.864098[/C][C]0.271804[/C][C]0.135902[/C][/ROW]
[ROW][C]193[/C][C]0.888638[/C][C]0.222724[/C][C]0.111362[/C][/ROW]
[ROW][C]194[/C][C]0.903689[/C][C]0.192622[/C][C]0.0963108[/C][/ROW]
[ROW][C]195[/C][C]0.892657[/C][C]0.214685[/C][C]0.107343[/C][/ROW]
[ROW][C]196[/C][C]0.883294[/C][C]0.233412[/C][C]0.116706[/C][/ROW]
[ROW][C]197[/C][C]0.893294[/C][C]0.213411[/C][C]0.106706[/C][/ROW]
[ROW][C]198[/C][C]0.873561[/C][C]0.252878[/C][C]0.126439[/C][/ROW]
[ROW][C]199[/C][C]0.863452[/C][C]0.273095[/C][C]0.136548[/C][/ROW]
[ROW][C]200[/C][C]0.840738[/C][C]0.318523[/C][C]0.159262[/C][/ROW]
[ROW][C]201[/C][C]0.818522[/C][C]0.362957[/C][C]0.181478[/C][/ROW]
[ROW][C]202[/C][C]0.796129[/C][C]0.407741[/C][C]0.203871[/C][/ROW]
[ROW][C]203[/C][C]0.809668[/C][C]0.380665[/C][C]0.190332[/C][/ROW]
[ROW][C]204[/C][C]0.778709[/C][C]0.442582[/C][C]0.221291[/C][/ROW]
[ROW][C]205[/C][C]0.762714[/C][C]0.474572[/C][C]0.237286[/C][/ROW]
[ROW][C]206[/C][C]0.757317[/C][C]0.485366[/C][C]0.242683[/C][/ROW]
[ROW][C]207[/C][C]0.750494[/C][C]0.499011[/C][C]0.249506[/C][/ROW]
[ROW][C]208[/C][C]0.718929[/C][C]0.562141[/C][C]0.281071[/C][/ROW]
[ROW][C]209[/C][C]0.695959[/C][C]0.608082[/C][C]0.304041[/C][/ROW]
[ROW][C]210[/C][C]0.692348[/C][C]0.615304[/C][C]0.307652[/C][/ROW]
[ROW][C]211[/C][C]0.653935[/C][C]0.69213[/C][C]0.346065[/C][/ROW]
[ROW][C]212[/C][C]0.612107[/C][C]0.775786[/C][C]0.387893[/C][/ROW]
[ROW][C]213[/C][C]0.604397[/C][C]0.791206[/C][C]0.395603[/C][/ROW]
[ROW][C]214[/C][C]0.56028[/C][C]0.879439[/C][C]0.43972[/C][/ROW]
[ROW][C]215[/C][C]0.563905[/C][C]0.872191[/C][C]0.436095[/C][/ROW]
[ROW][C]216[/C][C]0.52443[/C][C]0.951139[/C][C]0.47557[/C][/ROW]
[ROW][C]217[/C][C]0.521451[/C][C]0.957099[/C][C]0.478549[/C][/ROW]
[ROW][C]218[/C][C]0.485535[/C][C]0.971069[/C][C]0.514465[/C][/ROW]
[ROW][C]219[/C][C]0.447552[/C][C]0.895104[/C][C]0.552448[/C][/ROW]
[ROW][C]220[/C][C]0.412442[/C][C]0.824884[/C][C]0.587558[/C][/ROW]
[ROW][C]221[/C][C]0.415987[/C][C]0.831974[/C][C]0.584013[/C][/ROW]
[ROW][C]222[/C][C]0.463526[/C][C]0.927053[/C][C]0.536474[/C][/ROW]
[ROW][C]223[/C][C]0.428488[/C][C]0.856976[/C][C]0.571512[/C][/ROW]
[ROW][C]224[/C][C]0.577389[/C][C]0.845223[/C][C]0.422611[/C][/ROW]
[ROW][C]225[/C][C]0.552219[/C][C]0.895562[/C][C]0.447781[/C][/ROW]
[ROW][C]226[/C][C]0.603733[/C][C]0.792533[/C][C]0.396267[/C][/ROW]
[ROW][C]227[/C][C]0.61234[/C][C]0.775321[/C][C]0.38766[/C][/ROW]
[ROW][C]228[/C][C]0.66286[/C][C]0.67428[/C][C]0.33714[/C][/ROW]
[ROW][C]229[/C][C]0.737935[/C][C]0.52413[/C][C]0.262065[/C][/ROW]
[ROW][C]230[/C][C]0.762311[/C][C]0.475378[/C][C]0.237689[/C][/ROW]
[ROW][C]231[/C][C]0.737659[/C][C]0.524681[/C][C]0.262341[/C][/ROW]
[ROW][C]232[/C][C]0.702573[/C][C]0.594854[/C][C]0.297427[/C][/ROW]
[ROW][C]233[/C][C]0.676316[/C][C]0.647368[/C][C]0.323684[/C][/ROW]
[ROW][C]234[/C][C]0.651616[/C][C]0.696767[/C][C]0.348384[/C][/ROW]
[ROW][C]235[/C][C]0.62008[/C][C]0.75984[/C][C]0.37992[/C][/ROW]
[ROW][C]236[/C][C]0.69303[/C][C]0.613939[/C][C]0.30697[/C][/ROW]
[ROW][C]237[/C][C]0.663921[/C][C]0.672159[/C][C]0.336079[/C][/ROW]
[ROW][C]238[/C][C]0.611213[/C][C]0.777574[/C][C]0.388787[/C][/ROW]
[ROW][C]239[/C][C]0.619175[/C][C]0.76165[/C][C]0.380825[/C][/ROW]
[ROW][C]240[/C][C]0.564069[/C][C]0.871862[/C][C]0.435931[/C][/ROW]
[ROW][C]241[/C][C]0.525804[/C][C]0.948392[/C][C]0.474196[/C][/ROW]
[ROW][C]242[/C][C]0.46352[/C][C]0.927039[/C][C]0.53648[/C][/ROW]
[ROW][C]243[/C][C]0.408667[/C][C]0.817334[/C][C]0.591333[/C][/ROW]
[ROW][C]244[/C][C]0.351828[/C][C]0.703657[/C][C]0.648172[/C][/ROW]
[ROW][C]245[/C][C]0.300856[/C][C]0.601711[/C][C]0.699144[/C][/ROW]
[ROW][C]246[/C][C]0.241835[/C][C]0.48367[/C][C]0.758165[/C][/ROW]
[ROW][C]247[/C][C]0.199105[/C][C]0.398211[/C][C]0.800895[/C][/ROW]
[ROW][C]248[/C][C]0.420748[/C][C]0.841496[/C][C]0.579252[/C][/ROW]
[ROW][C]249[/C][C]0.358483[/C][C]0.716965[/C][C]0.641517[/C][/ROW]
[ROW][C]250[/C][C]0.291156[/C][C]0.582312[/C][C]0.708844[/C][/ROW]
[ROW][C]251[/C][C]0.225465[/C][C]0.45093[/C][C]0.774535[/C][/ROW]
[ROW][C]252[/C][C]0.185158[/C][C]0.370316[/C][C]0.814842[/C][/ROW]
[ROW][C]253[/C][C]0.181512[/C][C]0.363024[/C][C]0.818488[/C][/ROW]
[ROW][C]254[/C][C]0.307272[/C][C]0.614543[/C][C]0.692728[/C][/ROW]
[ROW][C]255[/C][C]0.272957[/C][C]0.545913[/C][C]0.727043[/C][/ROW]
[ROW][C]256[/C][C]0.197478[/C][C]0.394957[/C][C]0.802522[/C][/ROW]
[ROW][C]257[/C][C]0.126681[/C][C]0.253363[/C][C]0.873319[/C][/ROW]
[ROW][C]258[/C][C]0.0696005[/C][C]0.139201[/C][C]0.930399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.7164370.5671270.283563
210.5814980.8370040.418502
220.4434920.8869840.556508
230.3695470.7390930.630453
240.299350.59870.70065
250.2438730.4877450.756127
260.1723810.3447610.827619
270.1348460.2696920.865154
280.0958130.1916260.904187
290.06157410.1231480.938426
300.05656640.1131330.943434
310.04644380.09288750.953556
320.02907950.0581590.97092
330.09333690.1866740.906663
340.2746770.5493540.725323
350.2225520.4451040.777448
360.2536790.5073590.746321
370.2725210.5450420.727479
380.290640.5812790.70936
390.2701890.5403780.729811
400.2314120.4628250.768588
410.2723320.5446630.727668
420.2671340.5342670.732866
430.2410420.4820830.758958
440.2277090.4554170.772291
450.1952230.3904460.804777
460.1597770.3195540.840223
470.173210.346420.82679
480.1717250.3434490.828275
490.2346250.469250.765375
500.2611610.5223220.738839
510.2204020.4408040.779598
520.2489690.4979370.751031
530.2326940.4653870.767306
540.1974770.3949540.802523
550.31260.62520.6874
560.2709080.5418160.729092
570.626580.7468410.37342
580.659240.681520.34076
590.6244910.7510180.375509
600.6191620.7616760.380838
610.6277610.7444770.372239
620.6206610.7586790.379339
630.7253090.5493820.274691
640.785270.429460.21473
650.790790.4184210.20921
660.7680360.4639270.231964
670.7399960.5200080.260004
680.7062910.5874190.293709
690.7379270.5241470.262073
700.7134490.5731020.286551
710.6773230.6453550.322677
720.6446250.7107510.355375
730.6166010.7667970.383399
740.6614070.6771870.338593
750.6338530.7322950.366147
760.6207640.7584710.379236
770.5979450.8041110.402055
780.581260.837480.41874
790.5416470.9167070.458353
800.5381240.9237520.461876
810.4989090.9978180.501091
820.51990.9602010.4801
830.4826250.965250.517375
840.6774220.6451560.322578
850.6704910.6590170.329509
860.6358090.7283820.364191
870.6108220.7783560.389178
880.6297640.7404710.370236
890.6126630.7746740.387337
900.6078960.7842080.392104
910.6163170.7673660.383683
920.7632760.4734480.236724
930.7564870.4870260.243513
940.7355860.5288270.264414
950.7198210.5603590.280179
960.6931770.6136460.306823
970.7329630.5340750.267037
980.7181030.5637940.281897
990.7720590.4558830.227941
1000.7738790.4522420.226121
1010.7570960.4858080.242904
1020.728890.542220.27111
1030.7143430.5713140.285657
1040.7002890.5994210.299711
1050.7473640.5052730.252636
1060.7276760.5446490.272324
1070.7859030.4281950.214097
1080.8643360.2713270.135664
1090.8458110.3083780.154189
1100.8319820.3360350.168018
1110.8133930.3732140.186607
1120.7935650.4128690.206435
1130.8458930.3082140.154107
1140.8270740.3458520.172926
1150.9013710.1972570.0986286
1160.8935850.2128310.106415
1170.8850390.2299220.114961
1180.8767090.2465810.123291
1190.8845420.2309170.115458
1200.8704160.2591670.129584
1210.8950240.2099510.104976
1220.9046110.1907780.0953892
1230.8973570.2052850.102643
1240.9405950.118810.0594049
1250.9414260.1171480.0585741
1260.9322960.1354070.0677037
1270.9239120.1521770.0760885
1280.9111940.1776110.0888056
1290.9069840.1860320.0930161
1300.8925740.2148510.107426
1310.8795430.2409130.120457
1320.8770890.2458230.122911
1330.8623240.2753520.137676
1340.8443010.3113980.155699
1350.8218050.356390.178195
1360.798840.402320.20116
1370.8181860.3636280.181814
1380.8089310.3821390.191069
1390.7874590.4250820.212541
1400.7615410.4769170.238459
1410.7341050.5317910.265895
1420.7342290.5315420.265771
1430.7068010.5863980.293199
1440.7447350.510530.255265
1450.7221840.5556330.277816
1460.6911330.6177340.308867
1470.6613040.6773910.338696
1480.6283370.7433260.371663
1490.600620.7987610.39938
1500.5965940.8068130.403406
1510.8556450.288710.144355
1520.8378920.3242160.162108
1530.8411720.3176570.158828
1540.8238080.3523830.176192
1550.8341770.3316460.165823
1560.8161990.3676020.183801
1570.7951110.4097780.204889
1580.7705080.4589850.229492
1590.7720210.4559570.227979
1600.7697070.4605850.230293
1610.7522250.4955510.247775
1620.7497370.5005260.250263
1630.7424320.5151350.257568
1640.9715990.05680190.0284009
1650.9650830.06983470.0349173
1660.9602540.07949180.0397459
1670.9533750.09324990.046625
1680.9521540.09569260.0478463
1690.943020.113960.0569798
1700.9473530.1052950.0526474
1710.9402060.1195880.0597938
1720.937540.1249190.0624597
1730.9361530.1276940.0638472
1740.9265840.1468320.073416
1750.9147940.1704120.0852059
1760.9210090.1579820.0789911
1770.9122910.1754180.087709
1780.9101670.1796650.0898325
1790.8987630.2024730.101237
1800.8956950.2086090.104305
1810.9110430.1779150.0889575
1820.9167990.1664010.0832006
1830.9136580.1726840.0863418
1840.898080.2038410.10192
1850.9047540.1904920.0952459
1860.8890980.2218040.110902
1870.901230.1975390.0987697
1880.9034360.1931270.0965637
1890.9011990.1976010.0988007
1900.8843260.2313490.115674
1910.880710.238580.11929
1920.8640980.2718040.135902
1930.8886380.2227240.111362
1940.9036890.1926220.0963108
1950.8926570.2146850.107343
1960.8832940.2334120.116706
1970.8932940.2134110.106706
1980.8735610.2528780.126439
1990.8634520.2730950.136548
2000.8407380.3185230.159262
2010.8185220.3629570.181478
2020.7961290.4077410.203871
2030.8096680.3806650.190332
2040.7787090.4425820.221291
2050.7627140.4745720.237286
2060.7573170.4853660.242683
2070.7504940.4990110.249506
2080.7189290.5621410.281071
2090.6959590.6080820.304041
2100.6923480.6153040.307652
2110.6539350.692130.346065
2120.6121070.7757860.387893
2130.6043970.7912060.395603
2140.560280.8794390.43972
2150.5639050.8721910.436095
2160.524430.9511390.47557
2170.5214510.9570990.478549
2180.4855350.9710690.514465
2190.4475520.8951040.552448
2200.4124420.8248840.587558
2210.4159870.8319740.584013
2220.4635260.9270530.536474
2230.4284880.8569760.571512
2240.5773890.8452230.422611
2250.5522190.8955620.447781
2260.6037330.7925330.396267
2270.612340.7753210.38766
2280.662860.674280.33714
2290.7379350.524130.262065
2300.7623110.4753780.237689
2310.7376590.5246810.262341
2320.7025730.5948540.297427
2330.6763160.6473680.323684
2340.6516160.6967670.348384
2350.620080.759840.37992
2360.693030.6139390.30697
2370.6639210.6721590.336079
2380.6112130.7775740.388787
2390.6191750.761650.380825
2400.5640690.8718620.435931
2410.5258040.9483920.474196
2420.463520.9270390.53648
2430.4086670.8173340.591333
2440.3518280.7036570.648172
2450.3008560.6017110.699144
2460.2418350.483670.758165
2470.1991050.3982110.800895
2480.4207480.8414960.579252
2490.3584830.7169650.641517
2500.2911560.5823120.708844
2510.2254650.450930.774535
2520.1851580.3703160.814842
2530.1815120.3630240.818488
2540.3072720.6145430.692728
2550.2729570.5459130.727043
2560.1974780.3949570.802522
2570.1266810.2533630.873319
2580.06960050.1392010.930399







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0292887OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 7 & 0.0292887 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269098&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0292887[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269098&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269098&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0292887OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$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')
}