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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Simple Regression Y ~ X.wasp
Title produced by softwareSimple Linear Regression
Date of computationSat, 19 Jan 2019 15:29:20 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jan/19/t15479081905vsap4klaz59fz8.htm/, Retrieved Tue, 07 May 2024 22:52:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316342, Retrieved Tue, 07 May 2024 22:52:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Simple Linear Regression] [] [2019-01-19 14:29:20] [98fe8ff2db3eaf59b568f46d4a014cb6] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316342&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]7 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316342&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316342&T=0

As an alternative you can also use a QR Code:  
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 Outputview raw output of R engine
Computing time7 seconds
R ServerBig Analytics Cloud Computing Center






Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)0.6270.02524.9930
X0.3980.0517.8130
- - -
Residual Std. Err. 0.163 on 276 df
Multiple R-sq. 0.181
95% CI Multiple R-sq. [0.101, 0.284]
Adjusted R-sq. 0.178

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & 0.627 & 0.025 & 24.993 & 0 \tabularnewline
X & 0.398 & 0.051 & 7.813 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 0.163  on  276 df \tabularnewline
Multiple R-sq.  & 0.181 \tabularnewline
95% CI Multiple R-sq.  & [0.101, 0.284] \tabularnewline
Adjusted R-sq.  & 0.178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316342&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X[/C][/ROW]
[ROW][C]coefficients:[/C][C] [/C][/ROW]
[ROW][C] [/C][C]Estimate[/C][C]Std. Error[/C][C]t value[/C][C]Pr(>|t|)[/C][/ROW]
[C](Intercept)[/C][C]0.627[/C][C]0.025[/C][C]24.993[/C][C]0[/C][/ROW]
[C]X[/C][C]0.398[/C][C]0.051[/C][C]7.813[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]0.163  on  276 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.181[/C][/ROW]
[ROW][C]95% CI Multiple R-sq. [/C][C][0.101, 0.284][/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316342&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316342&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)0.6270.02524.9930
X0.3980.0517.8130
- - -
Residual Std. Err. 0.163 on 276 df
Multiple R-sq. 0.181
95% CI Multiple R-sq. [0.101, 0.284]
Adjusted R-sq. 0.178






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Algebraic_Reasoning11.631.6361.0390
Residuals2767.370.027 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Algebraic_Reasoning & 1 & 1.63 & 1.63 & 61.039 & 0 \tabularnewline
Residuals & 276 & 7.37 & 0.027 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316342&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]1[/C][C]1.63[/C][C]1.63[/C][C]61.039[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]276[/C][C]7.37[/C][C]0.027[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316342&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316342&T=2

As an alternative you can also use a QR Code:  
QR Code


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

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Algebraic_Reasoning11.631.6361.0390
Residuals2767.370.027


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
library(boot)
cat1 <- as.numeric(par1)
cat2<- as.numeric(par2)
intercept<-as.logical(par3)
x <- na.omit(t(x))
rsq <- function(formula, data, indices) {
d <- data[indices,] # allows boot to select sample
fit <- lm(formula, data=d)
return(summary(fit)$r.square)
}
xdf<-data.frame(na.omit(t(y)))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
names(xdf)<-c('Y', 'X')
if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )
(results <- boot(data=xdf, statistic=rsq, R=1000, formula=Y~X))
sumlmxdf<-summary(lmxdf)
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
nc <- ncol(sumlmxdf$'coefficients')
nr <- nrow(sumlmxdf$'coefficients')
a<-table.row.start(a)
a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
}#end header
a<-table.row.end(a)
for(i in 1: nr){
a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
for(j in 1 : nc){
a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
}
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, '- - - ',1,TRUE)
a<-table.element(a, ' ',nc,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, '95% CI Multiple R-sq. ',1,TRUE)
a<-table.element(a, paste('[',round(boot.ci(results,type='bca')$bca[1,4], digits=3),', ', round(boot.ci(results,type='bca')$bca[1,5], digits=3), ']',sep='') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
a<-table.element(a, 'Df',1,TRUE)
a<-table.element(a, 'Sum Sq',1,TRUE)
a<-table.element(a, 'Mean Sq',1,TRUE)
a<-table.element(a, 'F value',1,TRUE)
a<-table.element(a, 'Pr(>F)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,1,TRUE)
a<-table.element(a, anova.xdf$Df[1])
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',1,TRUE)
a<-table.element(a, anova.xdf$Df[2])
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
a<-table.element(a, ' ')
a<-table.element(a, ' ')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='regressionplot.png')
plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
if(intercept == TRUE) abline(coef(lmxdf), col='red')
if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
dev.off()
library(car)
bitmap(file='residualsQQplot.png')
qqPlot(resid(lmxdf), main='QQplot of Residuals of Fit')
dev.off()
bitmap(file='residualsplot.png')
plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
dev.off()
bitmap(file='cooksDistanceLmplot.png')
plot(lmxdf, which=4)
dev.off()