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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sun, 28 Aug 2016 18:22:13 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Aug/28/t1472404952x65v6hzcc58ynqi.htm/, Retrieved Sat, 04 May 2024 16:50:36 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 16:50:36 +0200

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1 0.5 0.67 0.67 0 0.5 2011 1 0 7.5 149
0.89 0.5 0.83 0.33 0.5 1 2011 1 1 6 139
0.89 0.4 1 0.67 0 1 2011 1 0 6.5 148
0.89 0.5 0.83 0 0 0 2011 1 1 1 158
0.89 0.7 0.67 0 1 1 2011 1 1 1 128
0.78 0.3 0 0 0.5 0.5 2011 1 1 5.5 224
0.89 0.4 0.83 0.67 0.5 0 2011 1 0 8.5 159
1 0.4 0.5 0.67 1 1 2011 1 1 6.5 105
0.89 0.7 0.83 0 0.5 0 2011 1 1 4.5 159
0.78 0.6 0.33 0.67 0.5 0.5 2011 1 1 2 167
1 0.6 0.5 1 0 0.5 2011 1 1 5 165
0.78 0.2 0.67 0 0.5 0.5 2011 1 1 0.5 159
0.89 0.4 1 0 0.5 0.5 2011 1 1 5 119
0.89 0.4 0.5 0.67 0 1 2011 1 0 5 176
0.89 0.5 0.67 0.33 0 0 2011 1 0 2.5 54
0.89 0.3 0.17 0.67 0 0.5 2011 0 0 5 91
0.89 0.4 0.83 0.33 0.5 0.5 2011 1 1 5.5 163
0.67 0.7 0.67 0.33 0.5 1 2011 1 0 3.5 124
1 0.5 0.67 0.33 0 1 2011 0 1 3 137
0.78 0.2 0.67 0 0 1 2011 1 0 4 121
0.78 0.3 0.5 0.67 0 0.5 2011 1 1 0.5 153
0.89 0.6 1 0.33 0 1 2011 1 1 6.5 148
0.78 0.6 0.83 0.33 0 1 2011 1 0 4.5 221
0.89 0.2 0.83 0.33 0 1 2011 1 1 7.5 188
0.89 0.7 1 0.67 1 0 2011 1 1 5.5 149
0.33 0.2 0.67 0 0 0 2011 1 1 4 244
1 1 1 0.33 1 1 2011 0 1 7.5 148
0.89 0.4 0.83 0.67 0 0.5 2011 0 0 7 92
0.89 0.4 1 1 0 1 2011 1 1 4 150
0.67 0.2 0.83 0.67 0 0.5 2011 1 0 5.5 153
0.56 0.4 0.67 0.33 0 1 2011 1 0 2.5 94
0.89 0.4 0.67 0 0.5 1 2011 1 0 5.5 156
0.89 0.7 1 0.67 0.5 0.5 2011 1 1 3.5 132
1 0.2 0.67 0.67 0 0.5 2011 1 1 2.5 161
0.78 0.6 1 1 0 0.5 2011 1 1 4.5 105
0.78 0.3 1 1 0.5 0.5 2011 1 1 4.5 97
0.33 0.3 0.5 0.33 0 0 2011 1 0 4.5 151
0.78 0.2 0.67 0 0.5 0 2011 0 1 6 131
0.89 0.5 0.83 0.67 0.5 0.5 2011 1 1 2.5 166
0.89 0.7 1 0.67 0.5 1 2011 1 0 5 157
0.78 0.6 1 0.67 0.5 0.5 2011 1 1 0 111
0.89 0.4 1 0.67 0.5 1 2011 1 1 5 145
0.89 0.6 1 0.33 0.5 1 2011 1 1 6.5 162
1 0.4 1 1 0 1 2011 1 1 5 163
0.67 0.3 0.83 0.67 0 1 2011 0 1 6 59
1 0.5 0.83 0.67 0.5 0.5 2011 1 0 4.5 187
0.89 0.2 0.5 0 0 1 2011 1 1 5.5 109
0.89 0.3 0.83 0 0.5 1 2011 0 1 1 90
0.89 0.5 0.17 0 0 1 2011 1 0 7.5 105
0.78 0.7 0.83 1 0.5 1 2011 0 1 6 83
0.89 0.4 1 0.67 1 0.5 2011 0 1 5 116
0.78 0.3 1 0 0 0.5 2011 0 1 1 42
0.78 0.2 0.67 0.67 1 1 2011 1 1 5 148
1 0.5 1 0 0 0.5 2011 0 1 6.5 155
0.78 0.4 1 0 0.5 0 2011 1 1 7 125
1 0.6 1 0.67 1 1 2011 1 1 4.5 116
0.78 0.4 0.83 1 0 1 2011 0 0 0 128
0.67 0.4 0.33 0 0 0.5 2011 1 1 8.5 138
0.33 0.2 0.33 0.33 0 0 2011 0 0 3.5 49
1 0.9 1 0.67 0.5 1 2011 0 1 7.5 96
1 0.8 1 0.67 1 0.5 2011 1 1 3.5 164
0.78 0.8 0.83 0 0.5 1 2011 1 0 6 162
0.67 0.3 1 1 0.5 1 2011 1 0 1.5 99
1 0.2 0.83 0.67 0 0.5 2011 1 1 9 202
0.89 0.4 0.67 0 0.5 1 2011 1 0 3.5 186
0.89 0.2 0.83 1 0 1 2011 0 1 3.5 66
0.78 0.2 0.67 0.67 0.5 1 2011 1 0 4 183
1 0.1 0.83 0.67 0 1 2011 1 1 6.5 214
0.56 0.4 0.67 1 0.5 0 2011 1 1 7.5 188
0.67 0.5 1 0 0.5 0.5 2011 0 0 6 104
0.89 0.8 0.83 0.33 0.5 1 2011 1 0 5 177
0.89 0.4 0.67 0.67 0 0.5 2011 1 0 5.5 126
0.89 0.6 0.83 0.33 0.5 0.5 2011 0 0 3.5 76
0.89 0.5 0.83 0.67 0.5 1 2011 0 1 7.5 99
0.78 0.3 0.67 0 0 0 2011 1 0 6.5 139
1 0.4 0.33 0 0.5 0 2011 1 0 6.5 162
1 0.6 0.83 0.67 0.5 0.5 2011 0 1 6.5 108
0.89 0.4 1 0.33 0 0.5 2011 1 0 7 159
0.44 0.3 0.83 0 0 0 2011 0 0 3.5 74
0.78 0.8 0.83 0 1 1 2011 1 1 1.5 110
0.89 0.6 0.5 0.33 1 1 2011 0 0 4 96
0.67 0.3 0.5 0 0 0 2011 0 0 7.5 116
0.78 0.5 0.83 0.67 0.5 1 2011 0 0 4.5 87
0.78 0.4 1 0.33 0 1 2011 0 1 0 97
0.33 0.3 0.33 0.67 0 0 2011 0 0 3.5 127
0.89 0.7 1 0.33 0 0.5 2011 0 1 5.5 106
0.89 0.2 0.67 0.33 0.5 0.5 2011 0 1 5 80
0.89 0.4 0.83 1 0 1 2011 0 0 4.5 74
0.89 0.6 1 0.67 0.5 0.5 2011 0 0 2.5 91
0.56 0.6 0.83 0 0 1 2011 0 0 7.5 133
0.67 0.6 0.83 0.67 0.5 0.5 2011 0 1 7 74
0.67 0.4 1 0.33 0.5 1 2011 0 1 0 114
0.78 0.6 0.83 0 0 1 2011 0 1 4.5 140
0.78 0.5 1 0.33 0.5 1 2011 0 0 3 95
0.78 0.5 0.83 0 0 1 2011 0 1 1.5 98
0.89 0.6 0.67 0 0 1 2011 0 0 3.5 121
1 0.8 0.83 0.33 0.5 1 2011 0 1 2.5 126
0.89 0.5 0.83 0.67 1 0.5 2011 0 1 5.5 98
0.89 0.6 0.83 0.67 0.5 1 2011 0 1 8 95
0.78 0.4 0.83 0.67 0.5 1 2011 0 1 1 110
1 0.3 0.67 0.67 0.5 1 2011 0 1 5 70
0.78 0.3 0.83 1 0 0.5 2011 0 0 4.5 102
0.67 0.2 0 0 0 0 2011 0 1 3 86
0.78 0.4 0.83 0 0 0.5 2011 0 1 3 130
0.89 0.5 1 0 0 0.5 2011 0 1 8 96
0.67 0.3 0.17 0 0.5 0 2011 0 0 2.5 102
0.22 0.4 0.17 0 0.5 0 2011 0 0 7 100
0.44 0.5 0.5 1 0 0 2011 0 0 0 94
0.89 0.3 0.5 0.67 0 1 2011 0 0 1 52
0.67 0.5 1 0 0 0.5 2011 0 0 3.5 98
0.89 0.4 0.67 0.67 0 0.5 2011 0 0 5.5 118
0.67 0.4 0.83 0.67 0 1 2011 0 1 5.5 99
0.78 0.6 1 0 1 1 2012 1 1 0.5 48
0.78 0.3 1 0.67 1 1 2012 1 1 7.5 50
0.78 0.4 1 0.33 1 0.5 2012 1 1 9 150
1 0.3 1 1 1 1 2012 1 1 9.5 154
0.78 1 1 1 1 1 2012 0 0 8.5 109
0.67 0.4 1 0 0 0.5 2012 0 1 7 68
0.89 0.8 0.83 1 0.5 1 2012 1 1 8 194
0.89 0.3 1 0.67 1 1 2012 1 0 10 158
1 0.5 0.83 0.67 0 1 2012 1 1 7 159
0.78 0.4 1 0 0 0.5 2012 1 0 8.5 67
0.67 0.3 0.83 0.67 0 1 2012 1 0 9 147
0.89 0.5 0.83 1 0 1 2012 1 1 9.5 39
0.67 0.3 1 0.67 0 1 2012 1 1 4 100
0.67 0.3 0.67 0 0 1 2012 1 1 6 111
1 0.4 0.83 0 0 1 2012 1 1 8 138
0.67 0.3 1 0 0 0.5 2012 1 1 5.5 101
1 0.6 1 0.33 0.5 0.5 2012 0 1 9.5 131
0.89 0.6 0.83 0.67 1 1 2012 1 1 7.5 101
0.89 0.4 1 1 1 1 2012 1 1 7 114
1 0.4 1 0 0 0 2012 1 0 7.5 165
0.67 0.4 1 0.67 0 0.5 2012 1 1 8 114
0.44 0.3 0.67 0.67 0.5 1 2012 1 1 7 111
0.89 0.2 1 0.33 1 0 2012 1 1 7 75
0.56 0.5 0.83 0.67 0 1 2012 1 1 6 82
0.78 0.4 1 0.67 1 1 2012 1 1 10 121
1 0.4 1 0.67 0 0 2012 1 1 2.5 32
1 0.4 0.83 0.67 0 1 2012 1 0 9 150
0.89 0.3 0.67 0.67 0.5 0.5 2012 1 1 8 117
0.67 0.4 0.83 0.67 1 0.5 2012 0 1 6 71
0.89 0.2 1 0.33 0.5 1 2012 1 1 8.5 165
0.33 0 0 0 0 0 2012 1 1 6 154
0.89 0.4 1 0.67 0.5 1 2012 1 1 9 126
0.78 0.6 1 0 1 1 2012 1 0 8 149
1 0.4 0.67 0.67 0 0.5 2012 1 0 9 145
0.44 0.4 1 0 0 0.5 2012 1 1 5.5 120
0.67 0.4 0.83 0 0.5 0 2012 1 0 7 109
0.33 0.2 0.17 0 0.5 0 2012 1 0 5.5 132
0.89 0.4 0.83 1 1 1 2012 1 1 9 172
0.89 0.3 0.83 0 0 0.5 2012 1 0 2 169
1 0.6 0.83 0.67 1 0 2012 1 1 8.5 114
0.89 0.6 0.83 1 0 1 2012 1 1 9 156
0.89 0.4 0.83 0 0 1 2012 1 0 8.5 172
1 0.5 1 0.67 1 0.5 2012 0 1 9 68
0.89 0.4 0.83 0 0.5 1 2012 0 1 7.5 89
1 0.6 1 1 1 1 2012 1 1 10 167
0.78 0.6 0.83 0.67 0.5 1 2012 1 0 9 113
0.78 0.9 1 0.67 0.5 1 2012 0 0 7.5 115
0.67 0.4 0.83 0.67 0.5 0 2012 0 0 6 78
0.89 0.8 1 1 0.5 1 2012 0 0 10.5 118
0.67 0.5 0.83 1 0 1 2012 0 1 8.5 87
0.78 0.4 0.83 1 0 0 2012 1 0 8 173
0.89 0.4 1 0.67 1 0.5 2012 1 1 10 2
0.89 0.7 1 1 1 0.5 2012 0 0 10.5 162
0.78 0.4 1 0.33 1 1 2012 0 1 6.5 49
1 0.8 1 0.67 0.5 1 2012 0 0 9.5 122
1 0.4 1 1 1 0.5 2012 0 1 8.5 96
1 0.3 1 0.67 0 0.5 2012 0 0 7.5 100
0.67 0.5 1 0.67 0.5 1 2012 0 0 5 82
0.89 0.8 1 0.67 1 1 2012 0 1 8 100
1 0.4 0.83 0.33 0 0.5 2012 0 0 10 115
1 1 1 1 0.5 0 2012 0 1 7 141
0.89 0.5 1 0.67 1 1 2012 1 1 7.5 165
0.89 0.5 1 0.67 1 1 2012 1 1 7.5 165
0.89 0.3 1 0.33 0 1 2012 0 1 9.5 110
0.89 0.3 0.83 0.33 0.5 1 2012 1 1 6 118
0.89 0.3 0.5 0 0 1 2012 1 0 10 158
1 0.4 0.67 0.33 0.5 0.5 2012 0 1 7 146
0.67 0.5 1 0.33 0 1 2012 1 0 3 49
1 0.5 0.67 0.67 0.5 1 2012 0 0 6 90
0.89 0.4 1 0 0 0 2012 0 0 7 121
0.89 0.7 1 1 0.5 0 2012 1 1 10 155
0.89 0.5 0.5 0.33 0 0.5 2012 0 0 7 104
0.89 0.4 0.67 0.33 1 0 2012 0 1 3.5 147
1 0.7 0.67 1 0 1 2012 0 0 8 110
1 0.7 0.67 1 0 1 2012 0 0 10 108
1 0.7 0.67 1 0 1 2012 0 0 5.5 113
0.89 0.7 0.67 1 0 1 2012 0 0 6 115
0.89 0.7 0.67 0 0 0 2012 0 1 6.5 61
0.89 0.7 1 0.67 0.5 1 2012 0 1 6.5 60
0.33 0.1 0.67 0.33 0.5 0 2012 0 1 8.5 109
0.67 0.2 0.67 0.67 0.5 1 2012 0 1 4 68
0.56 0.3 0.33 0.33 0 1 2012 0 0 9.5 111
0.44 0.6 0.83 0.33 0 0.5 2012 0 0 8 77
1 0.8 1 1 1 1 2012 0 1 8.5 73
0.89 0.8 1 0.33 0.5 0.5 2012 1 0 5.5 151
0.33 0 0.17 0 0 0 2012 0 0 7 89
0.67 0.3 0.67 0.33 0 1 2012 0 0 9 78
0.67 0.6 0.83 0.33 0.5 1 2012 0 0 8 110
1 0.5 0.83 0.67 0 1 2012 1 1 10 220
0.78 0.7 1 0.33 0 0.5 2012 0 1 8 65
0.67 0.3 0.83 0 0.5 1 2012 1 0 6 141
1 0.3 1 0.67 0 0 2012 0 0 8 117
0.78 0.4 1 0.67 0 0.5 2012 1 1 5 122
0.89 0.4 0.83 1 0 1 2012 0 0 9 63
0.89 0.1 0.83 0 0 1 2012 1 1 4.5 44
0.89 0.5 1 0.67 0 1 2012 0 1 8.5 52
0 0 0 0 0 0 2012 0 0 9.5 131
0.67 0.4 1 0.33 0.5 0 2012 0 1 8.5 101
1 0.6 0.83 0.67 1 0.5 2012 0 1 7.5 42
1 0.4 1 0.33 0.5 1 2012 1 1 7.5 152
0.67 0.1 0.33 0 0.5 1 2012 1 0 5 107
0.89 0.3 0.83 0 0 1 2012 0 0 7 77
0.89 0.7 0.83 0.67 0 1 2012 1 0 8 154
0.56 0.3 0.17 0 0 1 2012 1 1 5.5 103
0.67 0.5 0.83 0.33 0.5 0 2012 0 1 8.5 96
1 0.3 0.83 0.67 1 1 2012 1 1 9.5 175
1 0.6 0.67 0.67 0.5 1 2012 0 1 7 57
1 0.9 1 1 0 1 2012 0 0 8 112
0.67 0.4 0.83 0 0.5 1 2012 1 0 8.5 143
0.44 0.3 1 0 0.5 0.5 2012 0 0 3.5 49
0.89 0.9 1 0.67 1 1 2012 1 1 6.5 110
0.44 0.5 1 0 0.5 0 2012 1 1 6.5 131
0.56 0.3 1 1 0.5 0.5 2012 1 0 10.5 167
0.89 0.6 0.83 0.67 0 0.5 2012 0 0 8.5 56
0.67 0.2 1 0.33 0 0.5 2012 1 0 8 137
0.89 0.4 0.83 1 0.5 1 2012 0 1 10 86
1 0.5 0.83 0.67 0.5 0.5 2012 1 1 10 121
0.78 0.4 0.83 0.67 0 0.5 2012 1 0 9.5 149
0.44 0 0 0 0 0 2012 1 0 9 168
0.89 0.2 1 0.33 0.5 1 2012 1 0 10 140
0.89 0.5 1 0.67 0.5 1 2012 0 1 7.5 88
0.89 0.3 1 0.67 0 0.5 2012 1 1 4.5 168
0.44 0 0 0 0 0 2012 1 1 4.5 94
1 0.5 0.83 1 0 1 2012 1 1 0.5 51
0.89 0.6 0.83 0.33 0 1 2012 0 0 6.5 48
0.67 0.3 0.83 0 0.5 0.5 2012 1 1 4.5 145
0.33 0 0 0 0 0 2012 1 1 5.5 66
0.78 0.3 0.67 0 0.5 0 2012 0 1 5 85
0.89 0.5 1 0.67 0.5 1 2012 1 0 6 109
0.78 0.4 0.67 0 0 1 2012 0 0 4 63
0.78 0.5 0.83 0.67 0 0.5 2012 0 1 8 102
0.89 0.7 1 1 1 0.5 2012 0 0 10.5 162
0.78 0.8 1 0.67 0.5 1 2012 0 1 6.5 86
0.78 0.6 1 0.33 0.5 1 2012 0 1 8 114
0.67 0.4 0.83 0.33 0 0.5 2012 1 0 8.5 164
0.89 0.5 0.83 0.33 0.5 0 2012 1 1 5.5 119
0.89 0.5 1 0 0.5 1 2012 1 0 7 126
0.78 0.3 1 0.33 0 1 2012 1 1 5 132
1 0.6 1 0 0.5 1 2012 1 1 3.5 142
1 0.3 0.67 0.67 0 0.5 2012 1 0 5 83
0.78 0.6 0.83 1 0.5 0.5 2012 0 1 9 94
0.78 0.3 0.33 0.33 0 1 2012 0 0 8.5 81
0.89 0.7 1 0.67 1 1 2012 1 1 5 166
0.89 0.7 1 1 0 1 2012 0 0 9.5 110
0.67 0.6 0.67 1 0.5 1 2012 0 1 3 64
1 0.5 1 0.33 0.5 0 2012 1 0 1.5 93
0.67 0.5 0.83 0.33 0 0.5 2012 0 0 6 104
0.56 0.4 0.67 0 0 1 2012 0 1 0.5 105
0.78 0.4 1 0.33 1 1 2012 0 1 6.5 49
1 0.7 1 1 0 1 2012 0 0 7.5 88
0.67 0.2 0.17 0 0.5 0 2012 0 1 4.5 95
0.78 0.5 0.83 0.67 0 0.5 2012 0 1 8 102
0.56 0.4 0.83 0.67 0.5 0 2012 0 0 9 99
1 0.2 1 0.67 1 1 2012 0 1 7.5 63
0.89 0.5 0.67 0.67 0 0 2012 0 0 8.5 76
0.44 0.4 0.5 0 0 1 2012 0 0 7 109
1 0.7 0.67 1 1 1 2012 0 1 9.5 117
0.89 0.6 0.83 0.67 1 0 2012 0 1 6.5 57
0.78 0.4 0.83 0 0 0 2012 0 0 9.5 120
0.89 0.5 1 0.67 1 1 2012 0 1 6 73
0.11 0 0.17 0 0 0 2012 0 0 8 91
0.89 0.7 1 0.67 0.5 1 2012 0 0 9.5 108
0.89 0.4 0.67 0.67 0 1 2012 0 1 8 105
1 0.5 0.67 1 0 1 2012 1 0 8 117
0.89 0.6 0.83 0.67 0 0.5 2012 0 0 9 119
1 0.8 0.5 0.67 0.5 0.5 2012 0 1 5 31

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
LFM[t] = + 56023 + 9.20341Calculation[t] + 18.7982Algebraic_Reasoning[t] -8.53026Graphical_Interpretation[t] -4.76999Proportionality_and_Ratio[t] -2.34705Probability_and_Sampling[t] -3.19614Estimation[t] -27.8189year[t] + 42.4313group[t] -4.10303gender[t] + 4.91456Ex[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  56023 +  9.20341Calculation[t] +  18.7982Algebraic_Reasoning[t] -8.53026Graphical_Interpretation[t] -4.76999Proportionality_and_Ratio[t] -2.34705Probability_and_Sampling[t] -3.19614Estimation[t] -27.8189year[t] +  42.4313group[t] -4.10303gender[t] +  4.91456Ex[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  56023 +  9.20341Calculation[t] +  18.7982Algebraic_Reasoning[t] -8.53026Graphical_Interpretation[t] -4.76999Proportionality_and_Ratio[t] -2.34705Probability_and_Sampling[t] -3.19614Estimation[t] -27.8189year[t] +  42.4313group[t] -4.10303gender[t] +  4.91456Ex[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
LFM[t] = + 56023 + 9.20341Calculation[t] + 18.7982Algebraic_Reasoning[t] -8.53026Graphical_Interpretation[t] -4.76999Proportionality_and_Ratio[t] -2.34705Probability_and_Sampling[t] -3.19614Estimation[t] -27.8189year[t] + 42.4313group[t] -4.10303gender[t] + 4.91456Ex[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+5.602e+04	9345	+5.9950e+00	6.588e-09	3.294e-09
Calculation	+9.203	13.15	+7.0010e-01	0.4845	0.2422
Algebraic_Reasoning	+18.8	12.25	+1.5350e+00	0.126	0.06298
Graphical_Interpretation	-8.53	9.596	-8.8900e-01	0.3748	0.1874
Proportionality_and_Ratio	-4.77	6.09	-7.8320e-01	0.4342	0.2171
Probability_and_Sampling	-2.347	5.753	-4.0800e-01	0.6836	0.3418
Estimation	-3.196	5.324	-6.0030e-01	0.5488	0.2744
year	-27.82	4.647	-5.9860e+00	6.889e-09	3.444e-09
group	+42.43	3.957	+1.0720e+01	1.528e-22	7.638e-23
gender	-4.103	4.16	-9.8620e-01	0.3249	0.1625
Ex	+4.915	0.9186	+5.3500e+00	1.893e-07	9.465e-08

\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) & +5.602e+04 &  9345 & +5.9950e+00 &  6.588e-09 &  3.294e-09 \tabularnewline
Calculation & +9.203 &  13.15 & +7.0010e-01 &  0.4845 &  0.2422 \tabularnewline
Algebraic_Reasoning & +18.8 &  12.25 & +1.5350e+00 &  0.126 &  0.06298 \tabularnewline
Graphical_Interpretation & -8.53 &  9.596 & -8.8900e-01 &  0.3748 &  0.1874 \tabularnewline
Proportionality_and_Ratio & -4.77 &  6.09 & -7.8320e-01 &  0.4342 &  0.2171 \tabularnewline
Probability_and_Sampling & -2.347 &  5.753 & -4.0800e-01 &  0.6836 &  0.3418 \tabularnewline
Estimation & -3.196 &  5.324 & -6.0030e-01 &  0.5488 &  0.2744 \tabularnewline
year & -27.82 &  4.647 & -5.9860e+00 &  6.889e-09 &  3.444e-09 \tabularnewline
group & +42.43 &  3.957 & +1.0720e+01 &  1.528e-22 &  7.638e-23 \tabularnewline
gender & -4.103 &  4.16 & -9.8620e-01 &  0.3249 &  0.1625 \tabularnewline
Ex & +4.915 &  0.9186 & +5.3500e+00 &  1.893e-07 &  9.465e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+5.602e+04[/C][C] 9345[/C][C]+5.9950e+00[/C][C] 6.588e-09[/C][C] 3.294e-09[/C][/ROW]
[ROW][C]Calculation[/C][C]+9.203[/C][C] 13.15[/C][C]+7.0010e-01[/C][C] 0.4845[/C][C] 0.2422[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]+18.8[/C][C] 12.25[/C][C]+1.5350e+00[/C][C] 0.126[/C][C] 0.06298[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-8.53[/C][C] 9.596[/C][C]-8.8900e-01[/C][C] 0.3748[/C][C] 0.1874[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]-4.77[/C][C] 6.09[/C][C]-7.8320e-01[/C][C] 0.4342[/C][C] 0.2171[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]-2.347[/C][C] 5.753[/C][C]-4.0800e-01[/C][C] 0.6836[/C][C] 0.3418[/C][/ROW]
[ROW][C]Estimation[/C][C]-3.196[/C][C] 5.324[/C][C]-6.0030e-01[/C][C] 0.5488[/C][C] 0.2744[/C][/ROW]
[ROW][C]year[/C][C]-27.82[/C][C] 4.647[/C][C]-5.9860e+00[/C][C] 6.889e-09[/C][C] 3.444e-09[/C][/ROW]
[ROW][C]group[/C][C]+42.43[/C][C] 3.957[/C][C]+1.0720e+01[/C][C] 1.528e-22[/C][C] 7.638e-23[/C][/ROW]
[ROW][C]gender[/C][C]-4.103[/C][C] 4.16[/C][C]-9.8620e-01[/C][C] 0.3249[/C][C] 0.1625[/C][/ROW]
[ROW][C]Ex[/C][C]+4.915[/C][C] 0.9186[/C][C]+5.3500e+00[/C][C] 1.893e-07[/C][C] 9.465e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+5.602e+04	9345	+5.9950e+00	6.588e-09	3.294e-09
Calculation	+9.203	13.15	+7.0010e-01	0.4845	0.2422
Algebraic_Reasoning	+18.8	12.25	+1.5350e+00	0.126	0.06298
Graphical_Interpretation	-8.53	9.596	-8.8900e-01	0.3748	0.1874
Proportionality_and_Ratio	-4.77	6.09	-7.8320e-01	0.4342	0.2171
Probability_and_Sampling	-2.347	5.753	-4.0800e-01	0.6836	0.3418
Estimation	-3.196	5.324	-6.0030e-01	0.5488	0.2744
year	-27.82	4.647	-5.9860e+00	6.889e-09	3.444e-09
group	+42.43	3.957	+1.0720e+01	1.528e-22	7.638e-23
gender	-4.103	4.16	-9.8620e-01	0.3249	0.1625
Ex	+4.915	0.9186	+5.3500e+00	1.893e-07	9.465e-08

Multiple Linear Regression - Regression Statistics
Multiple R	0.6238
R-squared	0.3892
Adjusted R-squared	0.3663
F-TEST (value)	17.01
F-TEST (DF numerator)	10
F-TEST (DF denominator)	267
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	31.71
Sum Squared Residuals	2.685e+05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6238 \tabularnewline
R-squared &  0.3892 \tabularnewline
Adjusted R-squared &  0.3663 \tabularnewline
F-TEST (value) &  17.01 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 267 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  31.71 \tabularnewline
Sum Squared Residuals &  2.685e+05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6238[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3892[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3663[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 17.01[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]267[/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] 31.71[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.685e+05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 R	0.6238
R-squared	0.3892
Adjusted R-squared	0.3663
F-TEST (value)	17.01
F-TEST (DF numerator)	10
F-TEST (DF denominator)	267
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	31.71
Sum Squared Residuals	2.685e+05

Figure 1

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Figure 3

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Figure 4

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;

Parameters (R input):

par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

R code (references can be found in the software module):

par5 <- ''
par4 <- ''
par3 <- 'First Differences'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '11'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code