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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 04 Dec 2014 08:41:45 +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/04/t1417682865vmv1qmo9xutppsx.htm/, Retrieved Thu, 16 May 2024 12:15:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263050, Retrieved Thu, 16 May 2024 12:15:07 +0000
QR Codes:

Original text written by user:
IsPrivate?This computation is/was private until 2014-12-19
User-defined keywords
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-04 08:41:45] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
'4.35' 0 52 51 6 1 22 16 9 7 6 6 0 2 2 48 41 23 12 34
'12.7' 0 16 56 4 1 22 16 11 7 3 6 2 2 2 50 146 16 45 61
'18.1' 0 46 67 8 1 22 16 12 7 4 6 1 2 1 150 182 33 37 70
'17.85' 0 56 69 5 1 20 16 12 9 3 6 3 2 2 154 192 32 37 69
'16.6' 1 52 57 4 0 19 12 7 7 10 6 3 2 2 109 263 37 108 145
'12.6' 1 55 56 17 1 20 15 12 6 4 6 0 0 1 68 35 14 10 23
'17.1' 0 50 55 4 1 22 14 12 8 8 5 3 1 2 194 439 52 68 120
'19.1' 0 59 63 4 0 21 15 12 8 3 6 2 2 2 158 214 75 72 147
'16.1' 0 60 67 8 1 21 16 10 9 5 5 2 0 2 159 341 72 143 215
'13.35' 0 52 65 4 0 21 13 15 7 4 6 0 0 1 67 58 15 9 24
'18.4' 0 44 47 7 0 21 10 10 6 3 5 2 0 2 147 292 29 55 84
'14.7' 0 67 76 4 1 21 17 15 8 5 5 3 0 2 39 85 13 17 30
'10.6' 0 52 64 4 1 21 15 10 6 3 6 2 0 2 100 200 40 37 77
'12.6' 0 55 68 5 1 21 18 15 6 3 4 0 0 2 111 158 19 27 46
'16.2' 0 37 64 7 1 22 16 9 9 4 5 0 0 2 138 199 24 37 61
'13.6' 0 54 65 4 1 24 20 15 6 3 6 0 0 1 101 297 121 58 178
'18.9' 1 72 71 4 1 21 16 12 9 6 6 1 1 1 131 227 93 66 160
'14.1' 0 51 63 7 1 22 17 13 8 6 5 2 2 2 101 108 36 21 57
'14.5' 0 48 60 11 1 20 16 12 8 4 6 3 2 2 114 86 23 19 42
'16.15' 0 60 68 7 0 21 15 12 9 4 6 0 0 0 165 302 85 78 163
'14.75' 0 50 72 4 1 24 13 8 6 4 6 2 0 1 114 148 41 35 75
'14.8' 0 63 70 4 1 25 16 9 4 3 4 2 1 2 111 178 46 48 94
'12.45' 0 33 61 4 1 22 16 15 8 2 6 1 2 0 75 120 18 27 45
'12.65' 0 67 61 4 1 21 16 12 5 5 5 2 0 2 82 207 35 43 78
'17.35' 0 46 62 4 1 21 17 12 7 4 6 2 2 2 121 157 17 30 47
'8.6' 0 54 71 4 1 22 20 15 9 4 6 2 0 0 32 128 4 25 29
'18.4' 0 59 71 6 0 23 14 11 9 4 5 2 0 2 150 296 28 69 97
'16.1' 0 61 51 8 1 24 17 12 8 3 4 2 1 1 117 323 44 72 116
'11.6' 1 33 56 23 1 20 6 6 6 4 5 2 2 1 71 79 10 23 32
'17.75' 0 47 70 4 1 22 16 14 8 2 6 1 1 2 165 70 38 13 50
'15.25' 0 69 73 8 1 25 15 12 3 0 0 0 0 0 154 146 57 61 118
'17.65' 0 52 76 6 1 22 16 12 8 4 6 2 1 2 126 246 23 43 66
'16.35' 0 55 68 4 0 21 16 12 7 6 6 0 2 2 149 196 36 51 86
'17.65' 0 41 48 7 0 21 14 11 9 4 4 2 0 1 145 199 22 67 89
'13.6' 0 73 52 4 1 21 16 12 4 4 6 0 0 1 120 127 40 36 76
'14.35' 0 52 60 4 0 22 16 12 6 4 5 0 1 0 109 153 31 44 75
'14.75' 0 50 59 4 0 22 16 12 3 2 1 0 1 0 132 299 11 45 57
'18.25' 0 51 57 10 1 21 14 12 8 4 5 3 2 2 172 228 38 34 72
'9.9' 0 60 79 6 0 22 14 8 8 3 5 0 0 1 169 190 24 36 60
16 0 56 60 5 1 23 16 8 9 6 5 2 2 0 114 180 37 72 109
'18.25' 0 56 60 5 1 21 16 12 8 6 5 3 0 2 156 212 37 39 76
'16.85' 0 29 59 4 0 21 15 12 8 4 5 0 0 2 172 269 22 43 65
'14.6' 1 66 62 4 1 21 16 11 9 5 6 2 2 1 68 130 15 25 40
'13.85' 1 66 59 5 1 19 16 10 8 4 5 0 1 2 89 179 2 56 58
'18.95' 0 73 61 5 1 21 18 11 9 6 6 3 2 2 167 243 43 80 123
'15.6' 0 55 71 5 0 21 15 12 7 6 5 2 1 2 113 190 31 40 71
'14.85' 1 64 57 5 0 19 16 13 7 9 6 2 1 2 115 299 29 73 102
'11.75' 1 40 66 4 0 18 16 12 6 4 5 2 1 0 78 121 45 34 80
'18.45' 1 46 63 6 0 19 16 12 8 8 6 3 1 2 118 137 25 72 97
'15.9' 1 58 69 4 1 21 17 10 6 5 5 3 0 2 87 305 4 42 46
'17.1' 0 43 58 4 0 22 14 10 7 4 5 3 0 0 173 157 31 61 93
'16.1' 0 61 59 4 1 22 18 11 8 4 6 2 2 1 2 96 -4 23 19
'19.9' 1 51 48 9 0 19 9 8 8 7 6 3 2 1 162 183 66 74 140
'10.95' 1 50 66 18 1 20 15 12 7 4 6 1 2 2 49 52 61 16 78
'18.45' 1 52 73 6 0 19 14 9 9 8 6 2 1 2 122 238 32 66 98
'15.1' 1 54 67 5 1 21 15 12 9 4 6 3 2 1 96 40 31 9 40
15 1 66 61 4 0 19 13 9 9 3 6 2 0 1 100 226 39 41 80
'11.35' 1 61 68 11 0 20 16 11 6 5 6 2 1 2 82 190 19 57 76
'15.95' 1 80 75 4 1 21 20 15 8 8 6 2 2 2 100 214 31 48 79
'18.1' 1 51 62 10 0 19 14 8 9 4 5 1 0 1 115 145 36 51 87
'14.6' 1 56 69 6 1 21 12 8 9 10 6 3 1 0 141 119 42 53 95
'15.4' 0 56 58 8 1 21 15 11 8 5 6 2 2 2 165 222 21 29 49
'15.4' 0 56 60 8 1 21 15 11 8 5 6 2 2 2 165 222 21 29 49
'17.6' 1 53 74 6 1 19 15 11 8 3 6 1 0 2 110 159 25 55 80
'13.35' 0 47 55 8 1 25 16 13 8 3 5 1 1 2 118 165 32 54 86
'19.1' 0 25 62 4 0 21 11 7 8 3 3 0 0 2 158 249 26 43 69
'15.35' 1 47 63 4 1 20 16 12 9 4 4 1 1 1 146 125 28 51 79
'7.6' 0 46 69 9 0 25 7 8 6 5 6 1 0 2 49 122 32 20 52
'13.4' 1 50 58 9 0 19 11 8 9 5 4 2 1 2 90 186 41 79 120
'13.9' 1 39 58 5 0 20 9 4 8 4 6 0 0 0 121 148 29 39 69
'19.1' 0 51 68 4 1 22 15 11 8 7 6 3 1 0 155 274 33 61 94
'15.25' 1 58 72 4 0 19 16 10 8 5 3 1 0 1 104 172 17 55 72
'12.9' 1 35 62 15 1 20 14 7 8 4 4 1 2 0 147 84 13 30 43
'16.1' 1 58 62 10 0 19 15 12 9 7 4 3 0 2 110 168 32 55 87
'17.35' 1 60 65 9 0 19 13 11 9 7 4 3 0 2 108 102 30 22 52
'13.15' 1 62 69 7 0 18 13 9 9 7 4 3 0 2 113 106 34 37 71
'12.15' 1 63 66 9 0 19 12 10 8 7 4 3 0 2 115 2 59 2 61
'12.6' 1 53 72 6 1 21 16 8 8 7 4 0 0 0 61 139 13 38 51
'10.35' 1 46 62 4 1 19 14 8 8 7 6 2 1 2 60 95 23 27 50
'15.4' 1 67 75 7 1 20 16 11 3 1 4 1 1 0 109 130 10 56 67
'9.6' 1 59 58 4 1 20 14 12 6 2 4 2 1 2 68 72 5 25 30
'18.2' 1 64 66 7 0 19 15 10 5 3 2 1 0 2 111 141 31 39 70
'13.6' 1 38 55 4 0 19 10 10 4 6 5 1 0 1 77 113 19 33 52
'14.85' 1 50 47 15 1 22 16 12 9 8 6 3 2 2 73 206 32 43 75
'14.75' 0 48 72 4 0 21 14 8 8 8 6 1 1 1 151 268 30 57 87
'14.1' 1 48 62 9 0 19 16 11 3 0 1 0 0 0 89 175 25 43 69
'14.9' 1 47 64 4 0 19 12 8 6 3 4 1 0 2 78 77 48 23 72
'16.25' 1 66 64 4 0 19 16 10 6 6 5 1 1 2 110 125 35 44 79
'19.25' 0 47 19 28 1 23 16 14 9 5 5 2 0 2 220 255 67 54 121
'13.6' 1 63 50 4 1 19 15 9 7 7 6 1 0 1 65 111 15 28 43
'13.6' 0 58 68 4 0 20 14 9 6 3 5 0 1 2 141 132 22 36 58
'15.65' 1 44 70 4 0 19 16 10 9 3 6 2 0 0 117 211 18 39 57
'12.75' 0 51 79 5 1 22 11 13 7 4 6 2 0 1 122 92 33 16 50
'14.6' 1 43 69 4 0 19 15 12 8 4 5 3 0 2 63 76 46 23 69
'9.85' 0 55 71 4 1 25 18 13 8 1 5 0 0 2 44 171 24 40 64
'12.65' 1 38 48 12 1 19 13 8 8 5 6 2 0 2 52 83 14 24 38
'19.2' 1 45 73 4 0 19 7 3 0 0 0 0 0 0 131 266 12 78 90
'16.6' 1 50 74 6 1 19 7 8 6 4 6 1 1 0 101 186 38 57 96
'11.2' 1 54 66 6 1 20 17 12 9 6 5 2 2 1 42 50 12 37 49
'15.25' 0 57 71 5 1 20 18 11 9 4 6 1 1 2 152 117 28 27 56
'11.9' 0 60 74 4 0 21 15 9 6 1 2 0 1 2 107 219 41 61 102
'13.2' 1 55 78 4 0 19 8 12 8 3 5 0 0 2 77 246 12 27 40
'16.35' 0 56 75 4 0 21 13 12 8 7 5 2 0 2 154 279 31 69 100
'12.4' 0 49 53 10 1 23 13 12 5 3 1 0 0 2 103 148 33 34 67
'15.85' 1 37 60 7 1 19 15 10 6 5 5 1 1 0 96 137 34 44 78
'18.15' 0 59 70 4 1 22 18 13 9 3 5 2 2 2 175 181 21 34 55
'11.15' 1 46 69 7 1 20 16 9 9 6 4 2 1 2 57 98 20 39 59
'15.65' 1 51 65 4 0 18 14 12 9 9 6 3 0 2 112 226 44 51 96
'17.75' 0 58 78 4 0 21 15 11 6 4 5 0 1 2 143 234 52 34 86
'7.65' 1 64 78 12 0 20 19 14 4 3 6 0 1 1 49 138 7 31 38
'12.35' 0 53 59 5 1 21 16 11 8 9 6 2 2 2 110 85 29 13 43
'15.6' 0 48 72 8 1 21 12 9 4 5 6 0 1 0 131 66 11 12 23
'19.3' 0 51 70 6 0 21 16 12 5 3 6 3 1 1 167 236 26 51 77
'15.2' 1 47 63 17 0 19 11 8 8 6 5 2 0 1 56 106 24 24 48
'17.1' 0 59 63 4 0 21 16 15 6 2 6 1 0 1 137 135 7 19 26
'15.6' 1 62 71 5 1 19 15 12 8 4 5 3 1 2 86 122 60 30 91
'18.4' 0 62 74 4 1 21 19 14 9 5 5 2 1 1 121 218 13 81 94
'19.05' 0 51 67 5 0 21 15 12 7 4 5 2 0 1 149 199 20 42 62
'18.55' 0 64 66 5 0 22 14 9 4 0 0 0 0 0 168 112 52 22 74
'19.1' 0 52 62 6 0 21 14 9 8 2 6 1 1 2 140 278 28 85 114
'13.1' 1 67 80 4 1 22 17 13 8 5 6 2 1 2 88 94 25 27 52
'12.85' 0 50 73 4 1 22 16 13 8 3 6 2 0 1 168 113 39 25 64
'9.5' 0 54 67 4 1 22 20 15 4 0 0 0 0 0 94 84 9 22 31
'4.5' 0 58 61 6 1 22 16 11 9 5 5 3 0 2 51 86 19 19 38
'11.85' 1 56 73 8 0 21 9 7 8 6 5 1 0 2 48 62 13 14 27
'13.6' 0 63 74 10 1 22 13 10 6 3 5 0 1 1 145 222 60 45 105
'11.7' 0 31 32 4 1 23 15 11 3 0 0 0 0 0 66 167 19 45 64
'12.4' 1 65 69 5 1 19 19 14 7 3 4 0 1 0 85 82 34 28 62
'13.35' 0 71 69 4 0 22 16 14 8 5 6 2 1 2 109 207 14 51 65
'11.4' 1 50 84 4 0 21 17 13 7 4 4 0 0 2 63 184 17 41 58
'14.9' 1 57 64 4 1 19 16 12 7 5 5 2 0 1 102 83 45 31 76
'19.9' 1 47 58 16 0 19 9 8 8 7 6 3 2 1 162 183 66 74 140
'11.2' 1 47 59 7 1 20 11 13 7 8 6 2 1 2 86 89 48 19 68
'14.6' 1 57 78 4 1 18 14 9 7 6 6 1 1 2 114 225 29 51 80
'17.6' 0 43 57 4 0 21 19 12 6 4 5 1 0 1 164 237 -2 73 71
'14.05' 0 41 60 14 1 21 13 13 8 5 5 1 1 0 119 102 51 24 76
'16.1' 0 63 68 5 0 20 14 11 8 5 6 0 1 2 126 221 2 61 63
'13.35' 0 63 68 5 1 20 15 11 7 3 6 1 0 2 132 128 24 23 46
'11.85' 0 56 73 5 1 21 15 13 9 6 6 0 1 2 142 91 40 14 53
'11.95' 0 51 69 5 0 21 14 12 9 3 4 2 0 1 83 198 20 54 74
'14.75' 1 50 67 7 1 19 16 12 7 6 5 3 1 1 94 204 19 51 70
'15.15' 1 22 60 19 0 19 17 10 7 3 2 1 0 2 81 158 16 62 78
'13.2' 0 41 65 16 1 21 12 9 8 7 6 2 2 2 166 138 20 36 56
'16.85' 1 59 66 4 0 19 15 10 8 7 6 3 0 2 110 226 40 59 100
'7.85' 1 56 74 4 1 19 17 13 6 6 4 3 1 2 64 44 27 24 51
'7.7' 0 66 81 7 0 24 15 13 9 5 6 1 1 0 93 196 25 26 52
'12.6' 1 53 72 9 0 19 10 9 6 5 5 1 0 1 104 83 49 54 102
'7.85' 1 42 55 5 1 19 16 11 5 4 4 0 0 2 105 79 39 39 78
'10.95' 1 52 49 14 1 20 15 12 7 4 6 1 2 2 49 52 61 16 78
'12.35' 1 54 74 4 0 19 11 8 9 7 6 3 0 2 88 105 19 36 55
'9.95' 1 44 53 16 1 19 16 12 6 2 1 0 1 0 95 116 67 31 98
'14.9' 1 62 64 10 1 19 16 12 7 5 5 2 0 1 102 83 45 31 76
'16.65' 1 53 65 5 0 19 16 12 5 4 5 2 1 0 99 196 30 42 73
'13.4' 1 50 57 6 1 19 14 9 9 2 6 2 2 2 63 153 8 39 47
'13.95' 1 36 51 4 0 19 14 12 8 5 4 2 0 0 76 157 19 25 45
'15.7' 1 76 80 4 0 20 16 12 4 4 3 0 0 2 109 75 52 31 83
'16.85' 1 66 67 4 1 20 16 11 9 7 4 3 2 2 117 106 22 38 60
'10.95' 1 62 70 5 1 19 18 12 8 6 5 2 2 0 57 58 17 31 48
'15.35' 1 59 74 4 0 21 14 6 7 4 5 0 0 0 120 75 33 17 50
'12.2' 1 47 75 4 1 19 20 7 8 5 6 2 2 2 73 74 34 22 56
'15.1' 1 55 70 5 0 19 15 10 1 0 1 0 0 0 91 185 22 55 77
'17.75' 1 58 69 4 0 19 16 12 8 7 6 2 1 2 108 265 30 62 91
'15.2' 1 60 65 4 1 21 16 10 8 4 4 2 0 2 105 131 25 51 76
'14.6' 0 44 55 5 0 22 16 12 9 5 4 3 0 2 117 139 38 30 68
'16.65' 1 57 71 8 0 19 12 9 8 6 5 2 0 1 119 196 26 49 74
'8.1' 1 45 65 15 1 19 8 3 9 8 3 2 1 1 31 78 13 16 29

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&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]9 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=263050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.9931 + 1.17595programma[t] + 0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.624483gender[t] -0.119664age[t] -0.00678706CONFSTATTOT[t] + 0.0288303CONFSOFTTOT[t] -0.00732944Calculation[t] -0.263301Algebraic_Reasoning[t] + 0.14057Graphical_Interpretation[t] + 0.588002Proportionality_and_Ratio[t] + 0.239928Probability_and_Sampling[t] -0.0947946Estimation[t] + 0.0471701LFM[t] + 0.00609069Blogs[t] + 0.00111338PRH[t] + 0.0245521CH[t] -0.00665346Hours[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.9931 +  1.17595programma[t] +  0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.624483gender[t] -0.119664age[t] -0.00678706CONFSTATTOT[t] +  0.0288303CONFSOFTTOT[t] -0.00732944Calculation[t] -0.263301Algebraic_Reasoning[t] +  0.14057Graphical_Interpretation[t] +  0.588002Proportionality_and_Ratio[t] +  0.239928Probability_and_Sampling[t] -0.0947946Estimation[t] +  0.0471701LFM[t] +  0.00609069Blogs[t] +  0.00111338PRH[t] +  0.0245521CH[t] -0.00665346Hours[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.9931 +  1.17595programma[t] +  0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.624483gender[t] -0.119664age[t] -0.00678706CONFSTATTOT[t] +  0.0288303CONFSOFTTOT[t] -0.00732944Calculation[t] -0.263301Algebraic_Reasoning[t] +  0.14057Graphical_Interpretation[t] +  0.588002Proportionality_and_Ratio[t] +  0.239928Probability_and_Sampling[t] -0.0947946Estimation[t] +  0.0471701LFM[t] +  0.00609069Blogs[t] +  0.00111338PRH[t] +  0.0245521CH[t] -0.00665346Hours[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&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:

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.9931 + 1.17595programma[t] + 0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.624483gender[t] -0.119664age[t] -0.00678706CONFSTATTOT[t] + 0.0288303CONFSOFTTOT[t] -0.00732944Calculation[t] -0.263301Algebraic_Reasoning[t] + 0.14057Graphical_Interpretation[t] + 0.588002Proportionality_and_Ratio[t] + 0.239928Probability_and_Sampling[t] -0.0947946Estimation[t] + 0.0471701LFM[t] + 0.00609069Blogs[t] + 0.00111338PRH[t] + 0.0245521CH[t] -0.00665346Hours[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.99314.288592.5630.01137820.00568912
programma1.175950.6134051.9170.05717990.02859
AMS.I0.00672380.01989310.3380.7358510.367926
AMS.E-0.02335510.0222882-1.0480.2964310.148215
AMS.A-0.06128430.0504029-1.2160.225990.112995
gender-0.6244830.413433-1.510.1330820.0665409
age-0.1196640.17377-0.68860.4921470.246074
CONFSTATTOT-0.006787060.0933838-0.072680.9421610.47108
CONFSOFTTOT0.02883030.1048360.2750.7837030.391851
Calculation-0.007329440.128524-0.057030.9546010.477301
Algebraic_Reasoning-0.2633010.115734-2.2750.02435920.0121796
Graphical_Interpretation0.140570.153330.91680.3607680.180384
Proportionality_and_Ratio0.5880020.1970812.9840.003340770.00167039
Probability_and_Sampling0.2399280.2460680.9750.3311490.165575
Estimation-0.09479460.233684-0.40570.6855910.342796
LFM0.04717010.005975857.8936.41093e-133.20546e-13
Blogs0.006090690.003797371.6040.1108920.0554459
PRH0.001113380.3739150.0029780.9976280.498814
CH0.02455210.372040.065990.9474730.473737
Hours-0.006653460.372437-0.017860.9857710.492886

\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) & 10.9931 & 4.28859 & 2.563 & 0.0113782 & 0.00568912 \tabularnewline
programma & 1.17595 & 0.613405 & 1.917 & 0.0571799 & 0.02859 \tabularnewline
AMS.I & 0.0067238 & 0.0198931 & 0.338 & 0.735851 & 0.367926 \tabularnewline
AMS.E & -0.0233551 & 0.0222882 & -1.048 & 0.296431 & 0.148215 \tabularnewline
AMS.A & -0.0612843 & 0.0504029 & -1.216 & 0.22599 & 0.112995 \tabularnewline
gender & -0.624483 & 0.413433 & -1.51 & 0.133082 & 0.0665409 \tabularnewline
age & -0.119664 & 0.17377 & -0.6886 & 0.492147 & 0.246074 \tabularnewline
CONFSTATTOT & -0.00678706 & 0.0933838 & -0.07268 & 0.942161 & 0.47108 \tabularnewline
CONFSOFTTOT & 0.0288303 & 0.104836 & 0.275 & 0.783703 & 0.391851 \tabularnewline
Calculation & -0.00732944 & 0.128524 & -0.05703 & 0.954601 & 0.477301 \tabularnewline
Algebraic_Reasoning & -0.263301 & 0.115734 & -2.275 & 0.0243592 & 0.0121796 \tabularnewline
Graphical_Interpretation & 0.14057 & 0.15333 & 0.9168 & 0.360768 & 0.180384 \tabularnewline
Proportionality_and_Ratio & 0.588002 & 0.197081 & 2.984 & 0.00334077 & 0.00167039 \tabularnewline
Probability_and_Sampling & 0.239928 & 0.246068 & 0.975 & 0.331149 & 0.165575 \tabularnewline
Estimation & -0.0947946 & 0.233684 & -0.4057 & 0.685591 & 0.342796 \tabularnewline
LFM & 0.0471701 & 0.00597585 & 7.893 & 6.41093e-13 & 3.20546e-13 \tabularnewline
Blogs & 0.00609069 & 0.00379737 & 1.604 & 0.110892 & 0.0554459 \tabularnewline
PRH & 0.00111338 & 0.373915 & 0.002978 & 0.997628 & 0.498814 \tabularnewline
CH & 0.0245521 & 0.37204 & 0.06599 & 0.947473 & 0.473737 \tabularnewline
Hours & -0.00665346 & 0.372437 & -0.01786 & 0.985771 & 0.492886 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&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]10.9931[/C][C]4.28859[/C][C]2.563[/C][C]0.0113782[/C][C]0.00568912[/C][/ROW]
[ROW][C]programma[/C][C]1.17595[/C][C]0.613405[/C][C]1.917[/C][C]0.0571799[/C][C]0.02859[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0067238[/C][C]0.0198931[/C][C]0.338[/C][C]0.735851[/C][C]0.367926[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0233551[/C][C]0.0222882[/C][C]-1.048[/C][C]0.296431[/C][C]0.148215[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0612843[/C][C]0.0504029[/C][C]-1.216[/C][C]0.22599[/C][C]0.112995[/C][/ROW]
[ROW][C]gender[/C][C]-0.624483[/C][C]0.413433[/C][C]-1.51[/C][C]0.133082[/C][C]0.0665409[/C][/ROW]
[ROW][C]age[/C][C]-0.119664[/C][C]0.17377[/C][C]-0.6886[/C][C]0.492147[/C][C]0.246074[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.00678706[/C][C]0.0933838[/C][C]-0.07268[/C][C]0.942161[/C][C]0.47108[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.0288303[/C][C]0.104836[/C][C]0.275[/C][C]0.783703[/C][C]0.391851[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.00732944[/C][C]0.128524[/C][C]-0.05703[/C][C]0.954601[/C][C]0.477301[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.263301[/C][C]0.115734[/C][C]-2.275[/C][C]0.0243592[/C][C]0.0121796[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]0.14057[/C][C]0.15333[/C][C]0.9168[/C][C]0.360768[/C][C]0.180384[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.588002[/C][C]0.197081[/C][C]2.984[/C][C]0.00334077[/C][C]0.00167039[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.239928[/C][C]0.246068[/C][C]0.975[/C][C]0.331149[/C][C]0.165575[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.0947946[/C][C]0.233684[/C][C]-0.4057[/C][C]0.685591[/C][C]0.342796[/C][/ROW]
[ROW][C]LFM[/C][C]0.0471701[/C][C]0.00597585[/C][C]7.893[/C][C]6.41093e-13[/C][C]3.20546e-13[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00609069[/C][C]0.00379737[/C][C]1.604[/C][C]0.110892[/C][C]0.0554459[/C][/ROW]
[ROW][C]PRH[/C][C]0.00111338[/C][C]0.373915[/C][C]0.002978[/C][C]0.997628[/C][C]0.498814[/C][/ROW]
[ROW][C]CH[/C][C]0.0245521[/C][C]0.37204[/C][C]0.06599[/C][C]0.947473[/C][C]0.473737[/C][/ROW]
[ROW][C]Hours[/C][C]-0.00665346[/C][C]0.372437[/C][C]-0.01786[/C][C]0.985771[/C][C]0.492886[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263050&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&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:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.99314.288592.5630.01137820.00568912
programma1.175950.6134051.9170.05717990.02859
AMS.I0.00672380.01989310.3380.7358510.367926
AMS.E-0.02335510.0222882-1.0480.2964310.148215
AMS.A-0.06128430.0504029-1.2160.225990.112995
gender-0.6244830.413433-1.510.1330820.0665409
age-0.1196640.17377-0.68860.4921470.246074
CONFSTATTOT-0.006787060.0933838-0.072680.9421610.47108
CONFSOFTTOT0.02883030.1048360.2750.7837030.391851
Calculation-0.007329440.128524-0.057030.9546010.477301
Algebraic_Reasoning-0.2633010.115734-2.2750.02435920.0121796
Graphical_Interpretation0.140570.153330.91680.3607680.180384
Proportionality_and_Ratio0.5880020.1970812.9840.003340770.00167039
Probability_and_Sampling0.2399280.2460680.9750.3311490.165575
Estimation-0.09479460.233684-0.40570.6855910.342796
LFM0.04717010.005975857.8936.41093e-133.20546e-13
Blogs0.006090690.003797371.6040.1108920.0554459
PRH0.001113380.3739150.0029780.9976280.498814
CH0.02455210.372040.065990.9474730.473737
Hours-0.006653460.372437-0.017860.9857710.492886






Multiple Linear Regression - Regression Statistics
Multiple R0.756187
R-squared0.571819
Adjusted R-squared0.516097
F-TEST (value)10.262
F-TEST (DF numerator)19
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.12026
Sum Squared Residuals656.344

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.756187 \tabularnewline
R-squared & 0.571819 \tabularnewline
Adjusted R-squared & 0.516097 \tabularnewline
F-TEST (value) & 10.262 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.12026 \tabularnewline
Sum Squared Residuals & 656.344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.756187[/C][/ROW]
[ROW][C]R-squared[/C][C]0.571819[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.516097[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.262[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/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]2.12026[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]656.344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263050&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.756187
R-squared0.571819
Adjusted R-squared0.516097
F-TEST (value)10.262
F-TEST (DF numerator)19
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.12026
Sum Squared Residuals656.344






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.358.78822-4.43822
212.711.93220.767831
318.115.60312.49694
417.8517.63180.218249
516.617.4739-0.873937
612.610.59582.00416
717.119.8995-2.7995
819.118.49520.604823
916.118.4282-2.32817
1013.3510.64662.70341
1118.417.86140.538575
1214.710.09494.60512
1310.614.0237-3.42373
1412.612.7563-0.15626
1516.213.85692.34311
1613.613.25560.344416
1718.916.00192.89812
1814.112.56711.53291
1914.514.35970.140252
2016.1517.3825-1.23253
2114.7513.56081.18916
2214.813.96530.834745
2312.4512.5902-0.140217
2412.6512.9143-0.264348
2517.3515.01422.33579
268.610.1071-1.50711
2718.417.36211.0379
2816.116.02210.0779201
2911.612.1479-0.547943
3017.7515.60192.14815
3115.2514.79740.452564
3217.6515.22222.42776
3316.3515.69880.651177
3417.6517.08130.568695
3513.613.6471-0.0470955
3614.3513.83590.514095
3714.7515.9283-1.17832
3818.2518.05070.199269
399.916.0867-6.1867
401614.67761.32239
4118.2516.54351.70647
4216.8517.0971-0.247085
4314.613.37661.22344
4413.8514.1311-0.281063
4518.9518.61350.336538
4615.614.45931.14074
4714.8516.9947-2.1447
4811.7514.5253-2.77532
4918.4516.64651.80352
5015.915.34580.554249
5117.118.6356-1.53563
5216.19.12366.9764
5319.919.54110.358909
5410.9510.27410.675871
5518.4516.44272.00731
5615.114.40230.697689
571516.5207-1.52073
5811.3514.7816-3.43165
5915.9514.7221.22803
6018.115.41112.68886
6114.615.8183-1.21828
6215.417.0818-1.68182
6315.417.0351-1.63511
6417.615.1432.45696
6513.3514.0374-0.6874
6619.116.06093.03907
6715.3516.5703-1.22033
687.69.44734-1.84734
6913.415.4527-2.05267
7013.915.3026-1.40257
7119.117.29581.80418
7215.2514.92180.328233
7312.915.555-2.65503
7416.115.71550.384537
7517.3514.6292.72103
7613.1515.2401-2.0901
7712.1513.8136-1.66356
7812.610.45342.14655
7910.3511.9554-1.60544
8015.415.6083-0.208258
819.613.4831-3.88315
8218.215.01583.18416
8313.613.23440.365586
8414.8513.48441.36562
8514.7516.0324-1.28237
8614.114.4343-0.334258
8714.913.0351.86502
8816.2515.04021.20982
8919.2518.90040.349594
9013.612.04181.55824
9113.615.2177-1.61774
9215.6517.057-1.40695
9312.7513.4597-0.709737
9414.613.3291.27103
959.859.98744-0.137442
9612.6511.57751.07248
9719.217.4161.78402
9816.614.96861.63141
9911.211.3354-0.135443
10015.2515.15550.0944936
10111.914.3378-2.43781
10213.213.6954-0.495372
10316.3516.932-0.581969
10412.411.6630.736981
10515.8514.00821.8418
10618.1517.6680.481972
10711.1511.5867-0.436688
10815.6516.1499-0.499898
10917.7515.16572.58431
1107.6511.761-4.11095
11112.3512.3894-0.0393693
11215.612.65732.94273
11319.319.06750.23248
11415.211.58363.61637
11517.115.83611.26386
11615.614.37031.22966
11718.415.62852.77152
11819.0516.75692.29307
11918.5515.79052.75945
12019.117.73171.36828
12113.113.2712-0.171237
12212.8516.3087-3.45869
1239.511.8461-2.34611
1244.510.6013-6.10128
12511.8510.26141.58862
12613.614.6975-1.09753
12711.711.8562-0.156207
12812.412.8475-0.447476
12913.3515.2085-1.85852
13011.412.045-0.64503
13114.914.19040.709569
13219.918.85171.04835
13311.212.5388-1.33878
13414.615.2221-0.622141
13517.618.0059-0.405893
13614.0512.68921.36084
13716.115.23980.860168
13813.3514.4118-1.06178
13911.8513.0299-1.17991
14011.9513.9205-1.97055
14114.7515.3157-0.565703
14215.1513.29311.85686
14313.215.423-2.22303
14416.8516.60120.248838
1457.8512.2532-4.40322
1467.712.7069-5.00689
14712.614.1588-1.55882
1487.8513.3698-5.51983
14910.9510.92980.0202493
15012.3514.2795-1.92947
1519.9512.7671-2.81711
15214.913.85631.04366
15316.6516.13610.513866
15413.414.3471-0.947129
15513.9514.1921-0.242086
15615.713.42542.27464
15716.8515.4241.42603
15810.9512.1933-1.24328
15915.3513.99441.3556
16012.212.6904-0.490395
16115.114.92550.174459
16217.7516.49281.25723
16315.214.81350.386496
16414.614.749-0.148963
16516.6516.00050.649451
1668.18.89059-0.790586

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 8.78822 & -4.43822 \tabularnewline
2 & 12.7 & 11.9322 & 0.767831 \tabularnewline
3 & 18.1 & 15.6031 & 2.49694 \tabularnewline
4 & 17.85 & 17.6318 & 0.218249 \tabularnewline
5 & 16.6 & 17.4739 & -0.873937 \tabularnewline
6 & 12.6 & 10.5958 & 2.00416 \tabularnewline
7 & 17.1 & 19.8995 & -2.7995 \tabularnewline
8 & 19.1 & 18.4952 & 0.604823 \tabularnewline
9 & 16.1 & 18.4282 & -2.32817 \tabularnewline
10 & 13.35 & 10.6466 & 2.70341 \tabularnewline
11 & 18.4 & 17.8614 & 0.538575 \tabularnewline
12 & 14.7 & 10.0949 & 4.60512 \tabularnewline
13 & 10.6 & 14.0237 & -3.42373 \tabularnewline
14 & 12.6 & 12.7563 & -0.15626 \tabularnewline
15 & 16.2 & 13.8569 & 2.34311 \tabularnewline
16 & 13.6 & 13.2556 & 0.344416 \tabularnewline
17 & 18.9 & 16.0019 & 2.89812 \tabularnewline
18 & 14.1 & 12.5671 & 1.53291 \tabularnewline
19 & 14.5 & 14.3597 & 0.140252 \tabularnewline
20 & 16.15 & 17.3825 & -1.23253 \tabularnewline
21 & 14.75 & 13.5608 & 1.18916 \tabularnewline
22 & 14.8 & 13.9653 & 0.834745 \tabularnewline
23 & 12.45 & 12.5902 & -0.140217 \tabularnewline
24 & 12.65 & 12.9143 & -0.264348 \tabularnewline
25 & 17.35 & 15.0142 & 2.33579 \tabularnewline
26 & 8.6 & 10.1071 & -1.50711 \tabularnewline
27 & 18.4 & 17.3621 & 1.0379 \tabularnewline
28 & 16.1 & 16.0221 & 0.0779201 \tabularnewline
29 & 11.6 & 12.1479 & -0.547943 \tabularnewline
30 & 17.75 & 15.6019 & 2.14815 \tabularnewline
31 & 15.25 & 14.7974 & 0.452564 \tabularnewline
32 & 17.65 & 15.2222 & 2.42776 \tabularnewline
33 & 16.35 & 15.6988 & 0.651177 \tabularnewline
34 & 17.65 & 17.0813 & 0.568695 \tabularnewline
35 & 13.6 & 13.6471 & -0.0470955 \tabularnewline
36 & 14.35 & 13.8359 & 0.514095 \tabularnewline
37 & 14.75 & 15.9283 & -1.17832 \tabularnewline
38 & 18.25 & 18.0507 & 0.199269 \tabularnewline
39 & 9.9 & 16.0867 & -6.1867 \tabularnewline
40 & 16 & 14.6776 & 1.32239 \tabularnewline
41 & 18.25 & 16.5435 & 1.70647 \tabularnewline
42 & 16.85 & 17.0971 & -0.247085 \tabularnewline
43 & 14.6 & 13.3766 & 1.22344 \tabularnewline
44 & 13.85 & 14.1311 & -0.281063 \tabularnewline
45 & 18.95 & 18.6135 & 0.336538 \tabularnewline
46 & 15.6 & 14.4593 & 1.14074 \tabularnewline
47 & 14.85 & 16.9947 & -2.1447 \tabularnewline
48 & 11.75 & 14.5253 & -2.77532 \tabularnewline
49 & 18.45 & 16.6465 & 1.80352 \tabularnewline
50 & 15.9 & 15.3458 & 0.554249 \tabularnewline
51 & 17.1 & 18.6356 & -1.53563 \tabularnewline
52 & 16.1 & 9.1236 & 6.9764 \tabularnewline
53 & 19.9 & 19.5411 & 0.358909 \tabularnewline
54 & 10.95 & 10.2741 & 0.675871 \tabularnewline
55 & 18.45 & 16.4427 & 2.00731 \tabularnewline
56 & 15.1 & 14.4023 & 0.697689 \tabularnewline
57 & 15 & 16.5207 & -1.52073 \tabularnewline
58 & 11.35 & 14.7816 & -3.43165 \tabularnewline
59 & 15.95 & 14.722 & 1.22803 \tabularnewline
60 & 18.1 & 15.4111 & 2.68886 \tabularnewline
61 & 14.6 & 15.8183 & -1.21828 \tabularnewline
62 & 15.4 & 17.0818 & -1.68182 \tabularnewline
63 & 15.4 & 17.0351 & -1.63511 \tabularnewline
64 & 17.6 & 15.143 & 2.45696 \tabularnewline
65 & 13.35 & 14.0374 & -0.6874 \tabularnewline
66 & 19.1 & 16.0609 & 3.03907 \tabularnewline
67 & 15.35 & 16.5703 & -1.22033 \tabularnewline
68 & 7.6 & 9.44734 & -1.84734 \tabularnewline
69 & 13.4 & 15.4527 & -2.05267 \tabularnewline
70 & 13.9 & 15.3026 & -1.40257 \tabularnewline
71 & 19.1 & 17.2958 & 1.80418 \tabularnewline
72 & 15.25 & 14.9218 & 0.328233 \tabularnewline
73 & 12.9 & 15.555 & -2.65503 \tabularnewline
74 & 16.1 & 15.7155 & 0.384537 \tabularnewline
75 & 17.35 & 14.629 & 2.72103 \tabularnewline
76 & 13.15 & 15.2401 & -2.0901 \tabularnewline
77 & 12.15 & 13.8136 & -1.66356 \tabularnewline
78 & 12.6 & 10.4534 & 2.14655 \tabularnewline
79 & 10.35 & 11.9554 & -1.60544 \tabularnewline
80 & 15.4 & 15.6083 & -0.208258 \tabularnewline
81 & 9.6 & 13.4831 & -3.88315 \tabularnewline
82 & 18.2 & 15.0158 & 3.18416 \tabularnewline
83 & 13.6 & 13.2344 & 0.365586 \tabularnewline
84 & 14.85 & 13.4844 & 1.36562 \tabularnewline
85 & 14.75 & 16.0324 & -1.28237 \tabularnewline
86 & 14.1 & 14.4343 & -0.334258 \tabularnewline
87 & 14.9 & 13.035 & 1.86502 \tabularnewline
88 & 16.25 & 15.0402 & 1.20982 \tabularnewline
89 & 19.25 & 18.9004 & 0.349594 \tabularnewline
90 & 13.6 & 12.0418 & 1.55824 \tabularnewline
91 & 13.6 & 15.2177 & -1.61774 \tabularnewline
92 & 15.65 & 17.057 & -1.40695 \tabularnewline
93 & 12.75 & 13.4597 & -0.709737 \tabularnewline
94 & 14.6 & 13.329 & 1.27103 \tabularnewline
95 & 9.85 & 9.98744 & -0.137442 \tabularnewline
96 & 12.65 & 11.5775 & 1.07248 \tabularnewline
97 & 19.2 & 17.416 & 1.78402 \tabularnewline
98 & 16.6 & 14.9686 & 1.63141 \tabularnewline
99 & 11.2 & 11.3354 & -0.135443 \tabularnewline
100 & 15.25 & 15.1555 & 0.0944936 \tabularnewline
101 & 11.9 & 14.3378 & -2.43781 \tabularnewline
102 & 13.2 & 13.6954 & -0.495372 \tabularnewline
103 & 16.35 & 16.932 & -0.581969 \tabularnewline
104 & 12.4 & 11.663 & 0.736981 \tabularnewline
105 & 15.85 & 14.0082 & 1.8418 \tabularnewline
106 & 18.15 & 17.668 & 0.481972 \tabularnewline
107 & 11.15 & 11.5867 & -0.436688 \tabularnewline
108 & 15.65 & 16.1499 & -0.499898 \tabularnewline
109 & 17.75 & 15.1657 & 2.58431 \tabularnewline
110 & 7.65 & 11.761 & -4.11095 \tabularnewline
111 & 12.35 & 12.3894 & -0.0393693 \tabularnewline
112 & 15.6 & 12.6573 & 2.94273 \tabularnewline
113 & 19.3 & 19.0675 & 0.23248 \tabularnewline
114 & 15.2 & 11.5836 & 3.61637 \tabularnewline
115 & 17.1 & 15.8361 & 1.26386 \tabularnewline
116 & 15.6 & 14.3703 & 1.22966 \tabularnewline
117 & 18.4 & 15.6285 & 2.77152 \tabularnewline
118 & 19.05 & 16.7569 & 2.29307 \tabularnewline
119 & 18.55 & 15.7905 & 2.75945 \tabularnewline
120 & 19.1 & 17.7317 & 1.36828 \tabularnewline
121 & 13.1 & 13.2712 & -0.171237 \tabularnewline
122 & 12.85 & 16.3087 & -3.45869 \tabularnewline
123 & 9.5 & 11.8461 & -2.34611 \tabularnewline
124 & 4.5 & 10.6013 & -6.10128 \tabularnewline
125 & 11.85 & 10.2614 & 1.58862 \tabularnewline
126 & 13.6 & 14.6975 & -1.09753 \tabularnewline
127 & 11.7 & 11.8562 & -0.156207 \tabularnewline
128 & 12.4 & 12.8475 & -0.447476 \tabularnewline
129 & 13.35 & 15.2085 & -1.85852 \tabularnewline
130 & 11.4 & 12.045 & -0.64503 \tabularnewline
131 & 14.9 & 14.1904 & 0.709569 \tabularnewline
132 & 19.9 & 18.8517 & 1.04835 \tabularnewline
133 & 11.2 & 12.5388 & -1.33878 \tabularnewline
134 & 14.6 & 15.2221 & -0.622141 \tabularnewline
135 & 17.6 & 18.0059 & -0.405893 \tabularnewline
136 & 14.05 & 12.6892 & 1.36084 \tabularnewline
137 & 16.1 & 15.2398 & 0.860168 \tabularnewline
138 & 13.35 & 14.4118 & -1.06178 \tabularnewline
139 & 11.85 & 13.0299 & -1.17991 \tabularnewline
140 & 11.95 & 13.9205 & -1.97055 \tabularnewline
141 & 14.75 & 15.3157 & -0.565703 \tabularnewline
142 & 15.15 & 13.2931 & 1.85686 \tabularnewline
143 & 13.2 & 15.423 & -2.22303 \tabularnewline
144 & 16.85 & 16.6012 & 0.248838 \tabularnewline
145 & 7.85 & 12.2532 & -4.40322 \tabularnewline
146 & 7.7 & 12.7069 & -5.00689 \tabularnewline
147 & 12.6 & 14.1588 & -1.55882 \tabularnewline
148 & 7.85 & 13.3698 & -5.51983 \tabularnewline
149 & 10.95 & 10.9298 & 0.0202493 \tabularnewline
150 & 12.35 & 14.2795 & -1.92947 \tabularnewline
151 & 9.95 & 12.7671 & -2.81711 \tabularnewline
152 & 14.9 & 13.8563 & 1.04366 \tabularnewline
153 & 16.65 & 16.1361 & 0.513866 \tabularnewline
154 & 13.4 & 14.3471 & -0.947129 \tabularnewline
155 & 13.95 & 14.1921 & -0.242086 \tabularnewline
156 & 15.7 & 13.4254 & 2.27464 \tabularnewline
157 & 16.85 & 15.424 & 1.42603 \tabularnewline
158 & 10.95 & 12.1933 & -1.24328 \tabularnewline
159 & 15.35 & 13.9944 & 1.3556 \tabularnewline
160 & 12.2 & 12.6904 & -0.490395 \tabularnewline
161 & 15.1 & 14.9255 & 0.174459 \tabularnewline
162 & 17.75 & 16.4928 & 1.25723 \tabularnewline
163 & 15.2 & 14.8135 & 0.386496 \tabularnewline
164 & 14.6 & 14.749 & -0.148963 \tabularnewline
165 & 16.65 & 16.0005 & 0.649451 \tabularnewline
166 & 8.1 & 8.89059 & -0.790586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&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]4.35[/C][C]8.78822[/C][C]-4.43822[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]11.9322[/C][C]0.767831[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6031[/C][C]2.49694[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.6318[/C][C]0.218249[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.4739[/C][C]-0.873937[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.5958[/C][C]2.00416[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.8995[/C][C]-2.7995[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]18.4952[/C][C]0.604823[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]18.4282[/C][C]-2.32817[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.6466[/C][C]2.70341[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.8614[/C][C]0.538575[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.0949[/C][C]4.60512[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]14.0237[/C][C]-3.42373[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]12.7563[/C][C]-0.15626[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]13.8569[/C][C]2.34311[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.2556[/C][C]0.344416[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.0019[/C][C]2.89812[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.5671[/C][C]1.53291[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.3597[/C][C]0.140252[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.3825[/C][C]-1.23253[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.5608[/C][C]1.18916[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.9653[/C][C]0.834745[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.5902[/C][C]-0.140217[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9143[/C][C]-0.264348[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]15.0142[/C][C]2.33579[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]10.1071[/C][C]-1.50711[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.3621[/C][C]1.0379[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]16.0221[/C][C]0.0779201[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.1479[/C][C]-0.547943[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.6019[/C][C]2.14815[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.7974[/C][C]0.452564[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.2222[/C][C]2.42776[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]15.6988[/C][C]0.651177[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]17.0813[/C][C]0.568695[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]13.6471[/C][C]-0.0470955[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]13.8359[/C][C]0.514095[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.9283[/C][C]-1.17832[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]18.0507[/C][C]0.199269[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]16.0867[/C][C]-6.1867[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6776[/C][C]1.32239[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.5435[/C][C]1.70647[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]17.0971[/C][C]-0.247085[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]13.3766[/C][C]1.22344[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.1311[/C][C]-0.281063[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]18.6135[/C][C]0.336538[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.4593[/C][C]1.14074[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.9947[/C][C]-2.1447[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]14.5253[/C][C]-2.77532[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.6465[/C][C]1.80352[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]15.3458[/C][C]0.554249[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.6356[/C][C]-1.53563[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]9.1236[/C][C]6.9764[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]19.5411[/C][C]0.358909[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.2741[/C][C]0.675871[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.4427[/C][C]2.00731[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.4023[/C][C]0.697689[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]16.5207[/C][C]-1.52073[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]14.7816[/C][C]-3.43165[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.722[/C][C]1.22803[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.4111[/C][C]2.68886[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]15.8183[/C][C]-1.21828[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]17.0818[/C][C]-1.68182[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]17.0351[/C][C]-1.63511[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]15.143[/C][C]2.45696[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.0374[/C][C]-0.6874[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.0609[/C][C]3.03907[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]16.5703[/C][C]-1.22033[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]9.44734[/C][C]-1.84734[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]15.4527[/C][C]-2.05267[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]15.3026[/C][C]-1.40257[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]17.2958[/C][C]1.80418[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]14.9218[/C][C]0.328233[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]15.555[/C][C]-2.65503[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.7155[/C][C]0.384537[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]14.629[/C][C]2.72103[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.2401[/C][C]-2.0901[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]13.8136[/C][C]-1.66356[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]10.4534[/C][C]2.14655[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]11.9554[/C][C]-1.60544[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]15.6083[/C][C]-0.208258[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]13.4831[/C][C]-3.88315[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.0158[/C][C]3.18416[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.2344[/C][C]0.365586[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]13.4844[/C][C]1.36562[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.0324[/C][C]-1.28237[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]14.4343[/C][C]-0.334258[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.035[/C][C]1.86502[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]15.0402[/C][C]1.20982[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]18.9004[/C][C]0.349594[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]12.0418[/C][C]1.55824[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]15.2177[/C][C]-1.61774[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]17.057[/C][C]-1.40695[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.4597[/C][C]-0.709737[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.329[/C][C]1.27103[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.98744[/C][C]-0.137442[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.5775[/C][C]1.07248[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]17.416[/C][C]1.78402[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]14.9686[/C][C]1.63141[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.3354[/C][C]-0.135443[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]15.1555[/C][C]0.0944936[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]14.3378[/C][C]-2.43781[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.6954[/C][C]-0.495372[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.932[/C][C]-0.581969[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]11.663[/C][C]0.736981[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0082[/C][C]1.8418[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.668[/C][C]0.481972[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.5867[/C][C]-0.436688[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.1499[/C][C]-0.499898[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.1657[/C][C]2.58431[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.761[/C][C]-4.11095[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]12.3894[/C][C]-0.0393693[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]12.6573[/C][C]2.94273[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]19.0675[/C][C]0.23248[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.5836[/C][C]3.61637[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]15.8361[/C][C]1.26386[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]14.3703[/C][C]1.22966[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]15.6285[/C][C]2.77152[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.7569[/C][C]2.29307[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]15.7905[/C][C]2.75945[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]17.7317[/C][C]1.36828[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.2712[/C][C]-0.171237[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.3087[/C][C]-3.45869[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]11.8461[/C][C]-2.34611[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.6013[/C][C]-6.10128[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]10.2614[/C][C]1.58862[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.6975[/C][C]-1.09753[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.8562[/C][C]-0.156207[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]12.8475[/C][C]-0.447476[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]15.2085[/C][C]-1.85852[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.045[/C][C]-0.64503[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.1904[/C][C]0.709569[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]18.8517[/C][C]1.04835[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]12.5388[/C][C]-1.33878[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]15.2221[/C][C]-0.622141[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]18.0059[/C][C]-0.405893[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]12.6892[/C][C]1.36084[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.2398[/C][C]0.860168[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.4118[/C][C]-1.06178[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]13.0299[/C][C]-1.17991[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]13.9205[/C][C]-1.97055[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]15.3157[/C][C]-0.565703[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]13.2931[/C][C]1.85686[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]15.423[/C][C]-2.22303[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.6012[/C][C]0.248838[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.2532[/C][C]-4.40322[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.7069[/C][C]-5.00689[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.1588[/C][C]-1.55882[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]13.3698[/C][C]-5.51983[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]10.9298[/C][C]0.0202493[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.2795[/C][C]-1.92947[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]12.7671[/C][C]-2.81711[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]13.8563[/C][C]1.04366[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]16.1361[/C][C]0.513866[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]14.3471[/C][C]-0.947129[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.1921[/C][C]-0.242086[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]13.4254[/C][C]2.27464[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.424[/C][C]1.42603[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]12.1933[/C][C]-1.24328[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]13.9944[/C][C]1.3556[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]12.6904[/C][C]-0.490395[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]14.9255[/C][C]0.174459[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]16.4928[/C][C]1.25723[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.8135[/C][C]0.386496[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]14.749[/C][C]-0.148963[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]16.0005[/C][C]0.649451[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]8.89059[/C][C]-0.790586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263050&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.358.78822-4.43822
212.711.93220.767831
318.115.60312.49694
417.8517.63180.218249
516.617.4739-0.873937
612.610.59582.00416
717.119.8995-2.7995
819.118.49520.604823
916.118.4282-2.32817
1013.3510.64662.70341
1118.417.86140.538575
1214.710.09494.60512
1310.614.0237-3.42373
1412.612.7563-0.15626
1516.213.85692.34311
1613.613.25560.344416
1718.916.00192.89812
1814.112.56711.53291
1914.514.35970.140252
2016.1517.3825-1.23253
2114.7513.56081.18916
2214.813.96530.834745
2312.4512.5902-0.140217
2412.6512.9143-0.264348
2517.3515.01422.33579
268.610.1071-1.50711
2718.417.36211.0379
2816.116.02210.0779201
2911.612.1479-0.547943
3017.7515.60192.14815
3115.2514.79740.452564
3217.6515.22222.42776
3316.3515.69880.651177
3417.6517.08130.568695
3513.613.6471-0.0470955
3614.3513.83590.514095
3714.7515.9283-1.17832
3818.2518.05070.199269
399.916.0867-6.1867
401614.67761.32239
4118.2516.54351.70647
4216.8517.0971-0.247085
4314.613.37661.22344
4413.8514.1311-0.281063
4518.9518.61350.336538
4615.614.45931.14074
4714.8516.9947-2.1447
4811.7514.5253-2.77532
4918.4516.64651.80352
5015.915.34580.554249
5117.118.6356-1.53563
5216.19.12366.9764
5319.919.54110.358909
5410.9510.27410.675871
5518.4516.44272.00731
5615.114.40230.697689
571516.5207-1.52073
5811.3514.7816-3.43165
5915.9514.7221.22803
6018.115.41112.68886
6114.615.8183-1.21828
6215.417.0818-1.68182
6315.417.0351-1.63511
6417.615.1432.45696
6513.3514.0374-0.6874
6619.116.06093.03907
6715.3516.5703-1.22033
687.69.44734-1.84734
6913.415.4527-2.05267
7013.915.3026-1.40257
7119.117.29581.80418
7215.2514.92180.328233
7312.915.555-2.65503
7416.115.71550.384537
7517.3514.6292.72103
7613.1515.2401-2.0901
7712.1513.8136-1.66356
7812.610.45342.14655
7910.3511.9554-1.60544
8015.415.6083-0.208258
819.613.4831-3.88315
8218.215.01583.18416
8313.613.23440.365586
8414.8513.48441.36562
8514.7516.0324-1.28237
8614.114.4343-0.334258
8714.913.0351.86502
8816.2515.04021.20982
8919.2518.90040.349594
9013.612.04181.55824
9113.615.2177-1.61774
9215.6517.057-1.40695
9312.7513.4597-0.709737
9414.613.3291.27103
959.859.98744-0.137442
9612.6511.57751.07248
9719.217.4161.78402
9816.614.96861.63141
9911.211.3354-0.135443
10015.2515.15550.0944936
10111.914.3378-2.43781
10213.213.6954-0.495372
10316.3516.932-0.581969
10412.411.6630.736981
10515.8514.00821.8418
10618.1517.6680.481972
10711.1511.5867-0.436688
10815.6516.1499-0.499898
10917.7515.16572.58431
1107.6511.761-4.11095
11112.3512.3894-0.0393693
11215.612.65732.94273
11319.319.06750.23248
11415.211.58363.61637
11517.115.83611.26386
11615.614.37031.22966
11718.415.62852.77152
11819.0516.75692.29307
11918.5515.79052.75945
12019.117.73171.36828
12113.113.2712-0.171237
12212.8516.3087-3.45869
1239.511.8461-2.34611
1244.510.6013-6.10128
12511.8510.26141.58862
12613.614.6975-1.09753
12711.711.8562-0.156207
12812.412.8475-0.447476
12913.3515.2085-1.85852
13011.412.045-0.64503
13114.914.19040.709569
13219.918.85171.04835
13311.212.5388-1.33878
13414.615.2221-0.622141
13517.618.0059-0.405893
13614.0512.68921.36084
13716.115.23980.860168
13813.3514.4118-1.06178
13911.8513.0299-1.17991
14011.9513.9205-1.97055
14114.7515.3157-0.565703
14215.1513.29311.85686
14313.215.423-2.22303
14416.8516.60120.248838
1457.8512.2532-4.40322
1467.712.7069-5.00689
14712.614.1588-1.55882
1487.8513.3698-5.51983
14910.9510.92980.0202493
15012.3514.2795-1.92947
1519.9512.7671-2.81711
15214.913.85631.04366
15316.6516.13610.513866
15413.414.3471-0.947129
15513.9514.1921-0.242086
15615.713.42542.27464
15716.8515.4241.42603
15810.9512.1933-1.24328
15915.3513.99441.3556
16012.212.6904-0.490395
16115.114.92550.174459
16217.7516.49281.25723
16315.214.81350.386496
16414.614.749-0.148963
16516.6516.00050.649451
1668.18.89059-0.790586






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.4321560.8643130.567844
240.6964650.6070690.303535
250.5956370.8087260.404363
260.6929180.6141640.307082
270.6700430.6599140.329957
280.7033110.5933790.296689
290.626550.74690.37345
300.5767650.846470.423235
310.4887560.9775120.511244
320.4179960.8359920.582004
330.3515330.7030670.648467
340.2965050.593010.703495
350.255670.511340.74433
360.1985090.3970190.801491
370.1500350.3000690.849965
380.1095390.2190790.890461
390.4919090.9838180.508091
400.6025530.7948950.397447
410.5473670.9052660.452633
420.4831210.9662430.516879
430.4257930.8515850.574207
440.3803070.7606140.619693
450.320270.640540.67973
460.2727360.5454720.727264
470.3435560.6871130.656444
480.3454580.6909150.654542
490.3032030.6064060.696797
500.2755250.5510490.724475
510.2817240.5634480.718276
520.7853930.4292150.214607
530.7517080.4965840.248292
540.7353830.5292330.264617
550.7445970.5108060.255403
560.7485710.5028570.251429
570.7157570.5684860.284243
580.7898220.4203560.210178
590.7622790.4754430.237721
600.8214920.3570170.178508
610.8085130.3829730.191487
620.7822290.4355420.217771
630.7522160.4955680.247784
640.7532940.4934120.246706
650.7720990.4558010.227901
660.7948290.4103430.205171
670.7917150.416570.208285
680.8048440.3903130.195156
690.8227160.3545680.177284
700.8162020.3675950.183798
710.8103790.3792410.189621
720.773950.4520990.22605
730.7923060.4153890.207694
740.7539970.4920070.246003
750.7582880.4834240.241712
760.7796490.4407020.220351
770.7748740.4502510.225126
780.7633510.4732990.236649
790.750250.4995010.24975
800.7095430.5809130.290457
810.8078050.384390.192195
820.8488450.302310.151155
830.8223170.3553660.177683
840.8055040.3889910.194496
850.7849020.4301970.215098
860.750390.499220.24961
870.7312520.5374960.268748
880.6983770.6032470.301623
890.6657410.6685180.334259
900.6387070.7225860.361293
910.6241670.7516650.375833
920.6052820.7894360.394718
930.5664210.8671570.433579
940.5647870.8704250.435213
950.5789880.8420250.421012
960.5480280.9039440.451972
970.5567690.8864610.443231
980.5285640.9428710.471436
990.4992670.9985350.500733
1000.4478720.8957440.552128
1010.4698930.9397860.530107
1020.4253010.8506030.574699
1030.3811190.7622390.618881
1040.3478390.6956790.652161
1050.3473780.6947570.652622
1060.2994210.5988420.700579
1070.2666970.5333930.733303
1080.237240.474480.76276
1090.258150.51630.74185
1100.340090.680180.65991
1110.296990.5939790.70301
1120.4182430.8364860.581757
1130.3701670.7403340.629833
1140.4675710.9351410.532429
1150.4527320.9054650.547268
1160.4278290.8556580.572171
1170.5720180.8559650.427982
1180.6163940.7672110.383606
1190.5843150.8313710.415685
1200.5541530.8916940.445847
1210.5166360.9667280.483364
1220.5047070.9905850.495293
1230.4746530.9493060.525347
1240.6592690.6814620.340731
1250.6532740.6934520.346726
1260.5909920.8180150.409008
1270.6384610.7230770.361539
1280.5745080.8509840.425492
1290.5231650.9536690.476835
1300.4738450.9476910.526155
1310.4265850.853170.573415
1320.3603110.7206220.639689
1330.3444760.6889520.655524
1340.2760420.5520840.723958
1350.2132160.4264320.786784
1360.5773630.8452730.422637
1370.4806940.9613870.519306
1380.4226960.8453920.577304
1390.6186670.7626670.381333
1400.6895290.6209420.310471
1410.6681460.6637080.331854
1420.583080.833840.41692
1430.483750.96750.51625

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 & 0.432156 & 0.864313 & 0.567844 \tabularnewline
24 & 0.696465 & 0.607069 & 0.303535 \tabularnewline
25 & 0.595637 & 0.808726 & 0.404363 \tabularnewline
26 & 0.692918 & 0.614164 & 0.307082 \tabularnewline
27 & 0.670043 & 0.659914 & 0.329957 \tabularnewline
28 & 0.703311 & 0.593379 & 0.296689 \tabularnewline
29 & 0.62655 & 0.7469 & 0.37345 \tabularnewline
30 & 0.576765 & 0.84647 & 0.423235 \tabularnewline
31 & 0.488756 & 0.977512 & 0.511244 \tabularnewline
32 & 0.417996 & 0.835992 & 0.582004 \tabularnewline
33 & 0.351533 & 0.703067 & 0.648467 \tabularnewline
34 & 0.296505 & 0.59301 & 0.703495 \tabularnewline
35 & 0.25567 & 0.51134 & 0.74433 \tabularnewline
36 & 0.198509 & 0.397019 & 0.801491 \tabularnewline
37 & 0.150035 & 0.300069 & 0.849965 \tabularnewline
38 & 0.109539 & 0.219079 & 0.890461 \tabularnewline
39 & 0.491909 & 0.983818 & 0.508091 \tabularnewline
40 & 0.602553 & 0.794895 & 0.397447 \tabularnewline
41 & 0.547367 & 0.905266 & 0.452633 \tabularnewline
42 & 0.483121 & 0.966243 & 0.516879 \tabularnewline
43 & 0.425793 & 0.851585 & 0.574207 \tabularnewline
44 & 0.380307 & 0.760614 & 0.619693 \tabularnewline
45 & 0.32027 & 0.64054 & 0.67973 \tabularnewline
46 & 0.272736 & 0.545472 & 0.727264 \tabularnewline
47 & 0.343556 & 0.687113 & 0.656444 \tabularnewline
48 & 0.345458 & 0.690915 & 0.654542 \tabularnewline
49 & 0.303203 & 0.606406 & 0.696797 \tabularnewline
50 & 0.275525 & 0.551049 & 0.724475 \tabularnewline
51 & 0.281724 & 0.563448 & 0.718276 \tabularnewline
52 & 0.785393 & 0.429215 & 0.214607 \tabularnewline
53 & 0.751708 & 0.496584 & 0.248292 \tabularnewline
54 & 0.735383 & 0.529233 & 0.264617 \tabularnewline
55 & 0.744597 & 0.510806 & 0.255403 \tabularnewline
56 & 0.748571 & 0.502857 & 0.251429 \tabularnewline
57 & 0.715757 & 0.568486 & 0.284243 \tabularnewline
58 & 0.789822 & 0.420356 & 0.210178 \tabularnewline
59 & 0.762279 & 0.475443 & 0.237721 \tabularnewline
60 & 0.821492 & 0.357017 & 0.178508 \tabularnewline
61 & 0.808513 & 0.382973 & 0.191487 \tabularnewline
62 & 0.782229 & 0.435542 & 0.217771 \tabularnewline
63 & 0.752216 & 0.495568 & 0.247784 \tabularnewline
64 & 0.753294 & 0.493412 & 0.246706 \tabularnewline
65 & 0.772099 & 0.455801 & 0.227901 \tabularnewline
66 & 0.794829 & 0.410343 & 0.205171 \tabularnewline
67 & 0.791715 & 0.41657 & 0.208285 \tabularnewline
68 & 0.804844 & 0.390313 & 0.195156 \tabularnewline
69 & 0.822716 & 0.354568 & 0.177284 \tabularnewline
70 & 0.816202 & 0.367595 & 0.183798 \tabularnewline
71 & 0.810379 & 0.379241 & 0.189621 \tabularnewline
72 & 0.77395 & 0.452099 & 0.22605 \tabularnewline
73 & 0.792306 & 0.415389 & 0.207694 \tabularnewline
74 & 0.753997 & 0.492007 & 0.246003 \tabularnewline
75 & 0.758288 & 0.483424 & 0.241712 \tabularnewline
76 & 0.779649 & 0.440702 & 0.220351 \tabularnewline
77 & 0.774874 & 0.450251 & 0.225126 \tabularnewline
78 & 0.763351 & 0.473299 & 0.236649 \tabularnewline
79 & 0.75025 & 0.499501 & 0.24975 \tabularnewline
80 & 0.709543 & 0.580913 & 0.290457 \tabularnewline
81 & 0.807805 & 0.38439 & 0.192195 \tabularnewline
82 & 0.848845 & 0.30231 & 0.151155 \tabularnewline
83 & 0.822317 & 0.355366 & 0.177683 \tabularnewline
84 & 0.805504 & 0.388991 & 0.194496 \tabularnewline
85 & 0.784902 & 0.430197 & 0.215098 \tabularnewline
86 & 0.75039 & 0.49922 & 0.24961 \tabularnewline
87 & 0.731252 & 0.537496 & 0.268748 \tabularnewline
88 & 0.698377 & 0.603247 & 0.301623 \tabularnewline
89 & 0.665741 & 0.668518 & 0.334259 \tabularnewline
90 & 0.638707 & 0.722586 & 0.361293 \tabularnewline
91 & 0.624167 & 0.751665 & 0.375833 \tabularnewline
92 & 0.605282 & 0.789436 & 0.394718 \tabularnewline
93 & 0.566421 & 0.867157 & 0.433579 \tabularnewline
94 & 0.564787 & 0.870425 & 0.435213 \tabularnewline
95 & 0.578988 & 0.842025 & 0.421012 \tabularnewline
96 & 0.548028 & 0.903944 & 0.451972 \tabularnewline
97 & 0.556769 & 0.886461 & 0.443231 \tabularnewline
98 & 0.528564 & 0.942871 & 0.471436 \tabularnewline
99 & 0.499267 & 0.998535 & 0.500733 \tabularnewline
100 & 0.447872 & 0.895744 & 0.552128 \tabularnewline
101 & 0.469893 & 0.939786 & 0.530107 \tabularnewline
102 & 0.425301 & 0.850603 & 0.574699 \tabularnewline
103 & 0.381119 & 0.762239 & 0.618881 \tabularnewline
104 & 0.347839 & 0.695679 & 0.652161 \tabularnewline
105 & 0.347378 & 0.694757 & 0.652622 \tabularnewline
106 & 0.299421 & 0.598842 & 0.700579 \tabularnewline
107 & 0.266697 & 0.533393 & 0.733303 \tabularnewline
108 & 0.23724 & 0.47448 & 0.76276 \tabularnewline
109 & 0.25815 & 0.5163 & 0.74185 \tabularnewline
110 & 0.34009 & 0.68018 & 0.65991 \tabularnewline
111 & 0.29699 & 0.593979 & 0.70301 \tabularnewline
112 & 0.418243 & 0.836486 & 0.581757 \tabularnewline
113 & 0.370167 & 0.740334 & 0.629833 \tabularnewline
114 & 0.467571 & 0.935141 & 0.532429 \tabularnewline
115 & 0.452732 & 0.905465 & 0.547268 \tabularnewline
116 & 0.427829 & 0.855658 & 0.572171 \tabularnewline
117 & 0.572018 & 0.855965 & 0.427982 \tabularnewline
118 & 0.616394 & 0.767211 & 0.383606 \tabularnewline
119 & 0.584315 & 0.831371 & 0.415685 \tabularnewline
120 & 0.554153 & 0.891694 & 0.445847 \tabularnewline
121 & 0.516636 & 0.966728 & 0.483364 \tabularnewline
122 & 0.504707 & 0.990585 & 0.495293 \tabularnewline
123 & 0.474653 & 0.949306 & 0.525347 \tabularnewline
124 & 0.659269 & 0.681462 & 0.340731 \tabularnewline
125 & 0.653274 & 0.693452 & 0.346726 \tabularnewline
126 & 0.590992 & 0.818015 & 0.409008 \tabularnewline
127 & 0.638461 & 0.723077 & 0.361539 \tabularnewline
128 & 0.574508 & 0.850984 & 0.425492 \tabularnewline
129 & 0.523165 & 0.953669 & 0.476835 \tabularnewline
130 & 0.473845 & 0.947691 & 0.526155 \tabularnewline
131 & 0.426585 & 0.85317 & 0.573415 \tabularnewline
132 & 0.360311 & 0.720622 & 0.639689 \tabularnewline
133 & 0.344476 & 0.688952 & 0.655524 \tabularnewline
134 & 0.276042 & 0.552084 & 0.723958 \tabularnewline
135 & 0.213216 & 0.426432 & 0.786784 \tabularnewline
136 & 0.577363 & 0.845273 & 0.422637 \tabularnewline
137 & 0.480694 & 0.961387 & 0.519306 \tabularnewline
138 & 0.422696 & 0.845392 & 0.577304 \tabularnewline
139 & 0.618667 & 0.762667 & 0.381333 \tabularnewline
140 & 0.689529 & 0.620942 & 0.310471 \tabularnewline
141 & 0.668146 & 0.663708 & 0.331854 \tabularnewline
142 & 0.58308 & 0.83384 & 0.41692 \tabularnewline
143 & 0.48375 & 0.9675 & 0.51625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&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]23[/C][C]0.432156[/C][C]0.864313[/C][C]0.567844[/C][/ROW]
[ROW][C]24[/C][C]0.696465[/C][C]0.607069[/C][C]0.303535[/C][/ROW]
[ROW][C]25[/C][C]0.595637[/C][C]0.808726[/C][C]0.404363[/C][/ROW]
[ROW][C]26[/C][C]0.692918[/C][C]0.614164[/C][C]0.307082[/C][/ROW]
[ROW][C]27[/C][C]0.670043[/C][C]0.659914[/C][C]0.329957[/C][/ROW]
[ROW][C]28[/C][C]0.703311[/C][C]0.593379[/C][C]0.296689[/C][/ROW]
[ROW][C]29[/C][C]0.62655[/C][C]0.7469[/C][C]0.37345[/C][/ROW]
[ROW][C]30[/C][C]0.576765[/C][C]0.84647[/C][C]0.423235[/C][/ROW]
[ROW][C]31[/C][C]0.488756[/C][C]0.977512[/C][C]0.511244[/C][/ROW]
[ROW][C]32[/C][C]0.417996[/C][C]0.835992[/C][C]0.582004[/C][/ROW]
[ROW][C]33[/C][C]0.351533[/C][C]0.703067[/C][C]0.648467[/C][/ROW]
[ROW][C]34[/C][C]0.296505[/C][C]0.59301[/C][C]0.703495[/C][/ROW]
[ROW][C]35[/C][C]0.25567[/C][C]0.51134[/C][C]0.74433[/C][/ROW]
[ROW][C]36[/C][C]0.198509[/C][C]0.397019[/C][C]0.801491[/C][/ROW]
[ROW][C]37[/C][C]0.150035[/C][C]0.300069[/C][C]0.849965[/C][/ROW]
[ROW][C]38[/C][C]0.109539[/C][C]0.219079[/C][C]0.890461[/C][/ROW]
[ROW][C]39[/C][C]0.491909[/C][C]0.983818[/C][C]0.508091[/C][/ROW]
[ROW][C]40[/C][C]0.602553[/C][C]0.794895[/C][C]0.397447[/C][/ROW]
[ROW][C]41[/C][C]0.547367[/C][C]0.905266[/C][C]0.452633[/C][/ROW]
[ROW][C]42[/C][C]0.483121[/C][C]0.966243[/C][C]0.516879[/C][/ROW]
[ROW][C]43[/C][C]0.425793[/C][C]0.851585[/C][C]0.574207[/C][/ROW]
[ROW][C]44[/C][C]0.380307[/C][C]0.760614[/C][C]0.619693[/C][/ROW]
[ROW][C]45[/C][C]0.32027[/C][C]0.64054[/C][C]0.67973[/C][/ROW]
[ROW][C]46[/C][C]0.272736[/C][C]0.545472[/C][C]0.727264[/C][/ROW]
[ROW][C]47[/C][C]0.343556[/C][C]0.687113[/C][C]0.656444[/C][/ROW]
[ROW][C]48[/C][C]0.345458[/C][C]0.690915[/C][C]0.654542[/C][/ROW]
[ROW][C]49[/C][C]0.303203[/C][C]0.606406[/C][C]0.696797[/C][/ROW]
[ROW][C]50[/C][C]0.275525[/C][C]0.551049[/C][C]0.724475[/C][/ROW]
[ROW][C]51[/C][C]0.281724[/C][C]0.563448[/C][C]0.718276[/C][/ROW]
[ROW][C]52[/C][C]0.785393[/C][C]0.429215[/C][C]0.214607[/C][/ROW]
[ROW][C]53[/C][C]0.751708[/C][C]0.496584[/C][C]0.248292[/C][/ROW]
[ROW][C]54[/C][C]0.735383[/C][C]0.529233[/C][C]0.264617[/C][/ROW]
[ROW][C]55[/C][C]0.744597[/C][C]0.510806[/C][C]0.255403[/C][/ROW]
[ROW][C]56[/C][C]0.748571[/C][C]0.502857[/C][C]0.251429[/C][/ROW]
[ROW][C]57[/C][C]0.715757[/C][C]0.568486[/C][C]0.284243[/C][/ROW]
[ROW][C]58[/C][C]0.789822[/C][C]0.420356[/C][C]0.210178[/C][/ROW]
[ROW][C]59[/C][C]0.762279[/C][C]0.475443[/C][C]0.237721[/C][/ROW]
[ROW][C]60[/C][C]0.821492[/C][C]0.357017[/C][C]0.178508[/C][/ROW]
[ROW][C]61[/C][C]0.808513[/C][C]0.382973[/C][C]0.191487[/C][/ROW]
[ROW][C]62[/C][C]0.782229[/C][C]0.435542[/C][C]0.217771[/C][/ROW]
[ROW][C]63[/C][C]0.752216[/C][C]0.495568[/C][C]0.247784[/C][/ROW]
[ROW][C]64[/C][C]0.753294[/C][C]0.493412[/C][C]0.246706[/C][/ROW]
[ROW][C]65[/C][C]0.772099[/C][C]0.455801[/C][C]0.227901[/C][/ROW]
[ROW][C]66[/C][C]0.794829[/C][C]0.410343[/C][C]0.205171[/C][/ROW]
[ROW][C]67[/C][C]0.791715[/C][C]0.41657[/C][C]0.208285[/C][/ROW]
[ROW][C]68[/C][C]0.804844[/C][C]0.390313[/C][C]0.195156[/C][/ROW]
[ROW][C]69[/C][C]0.822716[/C][C]0.354568[/C][C]0.177284[/C][/ROW]
[ROW][C]70[/C][C]0.816202[/C][C]0.367595[/C][C]0.183798[/C][/ROW]
[ROW][C]71[/C][C]0.810379[/C][C]0.379241[/C][C]0.189621[/C][/ROW]
[ROW][C]72[/C][C]0.77395[/C][C]0.452099[/C][C]0.22605[/C][/ROW]
[ROW][C]73[/C][C]0.792306[/C][C]0.415389[/C][C]0.207694[/C][/ROW]
[ROW][C]74[/C][C]0.753997[/C][C]0.492007[/C][C]0.246003[/C][/ROW]
[ROW][C]75[/C][C]0.758288[/C][C]0.483424[/C][C]0.241712[/C][/ROW]
[ROW][C]76[/C][C]0.779649[/C][C]0.440702[/C][C]0.220351[/C][/ROW]
[ROW][C]77[/C][C]0.774874[/C][C]0.450251[/C][C]0.225126[/C][/ROW]
[ROW][C]78[/C][C]0.763351[/C][C]0.473299[/C][C]0.236649[/C][/ROW]
[ROW][C]79[/C][C]0.75025[/C][C]0.499501[/C][C]0.24975[/C][/ROW]
[ROW][C]80[/C][C]0.709543[/C][C]0.580913[/C][C]0.290457[/C][/ROW]
[ROW][C]81[/C][C]0.807805[/C][C]0.38439[/C][C]0.192195[/C][/ROW]
[ROW][C]82[/C][C]0.848845[/C][C]0.30231[/C][C]0.151155[/C][/ROW]
[ROW][C]83[/C][C]0.822317[/C][C]0.355366[/C][C]0.177683[/C][/ROW]
[ROW][C]84[/C][C]0.805504[/C][C]0.388991[/C][C]0.194496[/C][/ROW]
[ROW][C]85[/C][C]0.784902[/C][C]0.430197[/C][C]0.215098[/C][/ROW]
[ROW][C]86[/C][C]0.75039[/C][C]0.49922[/C][C]0.24961[/C][/ROW]
[ROW][C]87[/C][C]0.731252[/C][C]0.537496[/C][C]0.268748[/C][/ROW]
[ROW][C]88[/C][C]0.698377[/C][C]0.603247[/C][C]0.301623[/C][/ROW]
[ROW][C]89[/C][C]0.665741[/C][C]0.668518[/C][C]0.334259[/C][/ROW]
[ROW][C]90[/C][C]0.638707[/C][C]0.722586[/C][C]0.361293[/C][/ROW]
[ROW][C]91[/C][C]0.624167[/C][C]0.751665[/C][C]0.375833[/C][/ROW]
[ROW][C]92[/C][C]0.605282[/C][C]0.789436[/C][C]0.394718[/C][/ROW]
[ROW][C]93[/C][C]0.566421[/C][C]0.867157[/C][C]0.433579[/C][/ROW]
[ROW][C]94[/C][C]0.564787[/C][C]0.870425[/C][C]0.435213[/C][/ROW]
[ROW][C]95[/C][C]0.578988[/C][C]0.842025[/C][C]0.421012[/C][/ROW]
[ROW][C]96[/C][C]0.548028[/C][C]0.903944[/C][C]0.451972[/C][/ROW]
[ROW][C]97[/C][C]0.556769[/C][C]0.886461[/C][C]0.443231[/C][/ROW]
[ROW][C]98[/C][C]0.528564[/C][C]0.942871[/C][C]0.471436[/C][/ROW]
[ROW][C]99[/C][C]0.499267[/C][C]0.998535[/C][C]0.500733[/C][/ROW]
[ROW][C]100[/C][C]0.447872[/C][C]0.895744[/C][C]0.552128[/C][/ROW]
[ROW][C]101[/C][C]0.469893[/C][C]0.939786[/C][C]0.530107[/C][/ROW]
[ROW][C]102[/C][C]0.425301[/C][C]0.850603[/C][C]0.574699[/C][/ROW]
[ROW][C]103[/C][C]0.381119[/C][C]0.762239[/C][C]0.618881[/C][/ROW]
[ROW][C]104[/C][C]0.347839[/C][C]0.695679[/C][C]0.652161[/C][/ROW]
[ROW][C]105[/C][C]0.347378[/C][C]0.694757[/C][C]0.652622[/C][/ROW]
[ROW][C]106[/C][C]0.299421[/C][C]0.598842[/C][C]0.700579[/C][/ROW]
[ROW][C]107[/C][C]0.266697[/C][C]0.533393[/C][C]0.733303[/C][/ROW]
[ROW][C]108[/C][C]0.23724[/C][C]0.47448[/C][C]0.76276[/C][/ROW]
[ROW][C]109[/C][C]0.25815[/C][C]0.5163[/C][C]0.74185[/C][/ROW]
[ROW][C]110[/C][C]0.34009[/C][C]0.68018[/C][C]0.65991[/C][/ROW]
[ROW][C]111[/C][C]0.29699[/C][C]0.593979[/C][C]0.70301[/C][/ROW]
[ROW][C]112[/C][C]0.418243[/C][C]0.836486[/C][C]0.581757[/C][/ROW]
[ROW][C]113[/C][C]0.370167[/C][C]0.740334[/C][C]0.629833[/C][/ROW]
[ROW][C]114[/C][C]0.467571[/C][C]0.935141[/C][C]0.532429[/C][/ROW]
[ROW][C]115[/C][C]0.452732[/C][C]0.905465[/C][C]0.547268[/C][/ROW]
[ROW][C]116[/C][C]0.427829[/C][C]0.855658[/C][C]0.572171[/C][/ROW]
[ROW][C]117[/C][C]0.572018[/C][C]0.855965[/C][C]0.427982[/C][/ROW]
[ROW][C]118[/C][C]0.616394[/C][C]0.767211[/C][C]0.383606[/C][/ROW]
[ROW][C]119[/C][C]0.584315[/C][C]0.831371[/C][C]0.415685[/C][/ROW]
[ROW][C]120[/C][C]0.554153[/C][C]0.891694[/C][C]0.445847[/C][/ROW]
[ROW][C]121[/C][C]0.516636[/C][C]0.966728[/C][C]0.483364[/C][/ROW]
[ROW][C]122[/C][C]0.504707[/C][C]0.990585[/C][C]0.495293[/C][/ROW]
[ROW][C]123[/C][C]0.474653[/C][C]0.949306[/C][C]0.525347[/C][/ROW]
[ROW][C]124[/C][C]0.659269[/C][C]0.681462[/C][C]0.340731[/C][/ROW]
[ROW][C]125[/C][C]0.653274[/C][C]0.693452[/C][C]0.346726[/C][/ROW]
[ROW][C]126[/C][C]0.590992[/C][C]0.818015[/C][C]0.409008[/C][/ROW]
[ROW][C]127[/C][C]0.638461[/C][C]0.723077[/C][C]0.361539[/C][/ROW]
[ROW][C]128[/C][C]0.574508[/C][C]0.850984[/C][C]0.425492[/C][/ROW]
[ROW][C]129[/C][C]0.523165[/C][C]0.953669[/C][C]0.476835[/C][/ROW]
[ROW][C]130[/C][C]0.473845[/C][C]0.947691[/C][C]0.526155[/C][/ROW]
[ROW][C]131[/C][C]0.426585[/C][C]0.85317[/C][C]0.573415[/C][/ROW]
[ROW][C]132[/C][C]0.360311[/C][C]0.720622[/C][C]0.639689[/C][/ROW]
[ROW][C]133[/C][C]0.344476[/C][C]0.688952[/C][C]0.655524[/C][/ROW]
[ROW][C]134[/C][C]0.276042[/C][C]0.552084[/C][C]0.723958[/C][/ROW]
[ROW][C]135[/C][C]0.213216[/C][C]0.426432[/C][C]0.786784[/C][/ROW]
[ROW][C]136[/C][C]0.577363[/C][C]0.845273[/C][C]0.422637[/C][/ROW]
[ROW][C]137[/C][C]0.480694[/C][C]0.961387[/C][C]0.519306[/C][/ROW]
[ROW][C]138[/C][C]0.422696[/C][C]0.845392[/C][C]0.577304[/C][/ROW]
[ROW][C]139[/C][C]0.618667[/C][C]0.762667[/C][C]0.381333[/C][/ROW]
[ROW][C]140[/C][C]0.689529[/C][C]0.620942[/C][C]0.310471[/C][/ROW]
[ROW][C]141[/C][C]0.668146[/C][C]0.663708[/C][C]0.331854[/C][/ROW]
[ROW][C]142[/C][C]0.58308[/C][C]0.83384[/C][C]0.41692[/C][/ROW]
[ROW][C]143[/C][C]0.48375[/C][C]0.9675[/C][C]0.51625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263050&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.4321560.8643130.567844
240.6964650.6070690.303535
250.5956370.8087260.404363
260.6929180.6141640.307082
270.6700430.6599140.329957
280.7033110.5933790.296689
290.626550.74690.37345
300.5767650.846470.423235
310.4887560.9775120.511244
320.4179960.8359920.582004
330.3515330.7030670.648467
340.2965050.593010.703495
350.255670.511340.74433
360.1985090.3970190.801491
370.1500350.3000690.849965
380.1095390.2190790.890461
390.4919090.9838180.508091
400.6025530.7948950.397447
410.5473670.9052660.452633
420.4831210.9662430.516879
430.4257930.8515850.574207
440.3803070.7606140.619693
450.320270.640540.67973
460.2727360.5454720.727264
470.3435560.6871130.656444
480.3454580.6909150.654542
490.3032030.6064060.696797
500.2755250.5510490.724475
510.2817240.5634480.718276
520.7853930.4292150.214607
530.7517080.4965840.248292
540.7353830.5292330.264617
550.7445970.5108060.255403
560.7485710.5028570.251429
570.7157570.5684860.284243
580.7898220.4203560.210178
590.7622790.4754430.237721
600.8214920.3570170.178508
610.8085130.3829730.191487
620.7822290.4355420.217771
630.7522160.4955680.247784
640.7532940.4934120.246706
650.7720990.4558010.227901
660.7948290.4103430.205171
670.7917150.416570.208285
680.8048440.3903130.195156
690.8227160.3545680.177284
700.8162020.3675950.183798
710.8103790.3792410.189621
720.773950.4520990.22605
730.7923060.4153890.207694
740.7539970.4920070.246003
750.7582880.4834240.241712
760.7796490.4407020.220351
770.7748740.4502510.225126
780.7633510.4732990.236649
790.750250.4995010.24975
800.7095430.5809130.290457
810.8078050.384390.192195
820.8488450.302310.151155
830.8223170.3553660.177683
840.8055040.3889910.194496
850.7849020.4301970.215098
860.750390.499220.24961
870.7312520.5374960.268748
880.6983770.6032470.301623
890.6657410.6685180.334259
900.6387070.7225860.361293
910.6241670.7516650.375833
920.6052820.7894360.394718
930.5664210.8671570.433579
940.5647870.8704250.435213
950.5789880.8420250.421012
960.5480280.9039440.451972
970.5567690.8864610.443231
980.5285640.9428710.471436
990.4992670.9985350.500733
1000.4478720.8957440.552128
1010.4698930.9397860.530107
1020.4253010.8506030.574699
1030.3811190.7622390.618881
1040.3478390.6956790.652161
1050.3473780.6947570.652622
1060.2994210.5988420.700579
1070.2666970.5333930.733303
1080.237240.474480.76276
1090.258150.51630.74185
1100.340090.680180.65991
1110.296990.5939790.70301
1120.4182430.8364860.581757
1130.3701670.7403340.629833
1140.4675710.9351410.532429
1150.4527320.9054650.547268
1160.4278290.8556580.572171
1170.5720180.8559650.427982
1180.6163940.7672110.383606
1190.5843150.8313710.415685
1200.5541530.8916940.445847
1210.5166360.9667280.483364
1220.5047070.9905850.495293
1230.4746530.9493060.525347
1240.6592690.6814620.340731
1250.6532740.6934520.346726
1260.5909920.8180150.409008
1270.6384610.7230770.361539
1280.5745080.8509840.425492
1290.5231650.9536690.476835
1300.4738450.9476910.526155
1310.4265850.853170.573415
1320.3603110.7206220.639689
1330.3444760.6889520.655524
1340.2760420.5520840.723958
1350.2132160.4264320.786784
1360.5773630.8452730.422637
1370.4806940.9613870.519306
1380.4226960.8453920.577304
1390.6186670.7626670.381333
1400.6895290.6209420.310471
1410.6681460.6637080.331854
1420.583080.833840.41692
1430.483750.96750.51625






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263050&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263050&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263050&T=6

As an alternative you can also use a QR Code:  
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 level00OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}