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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 10:54:20 +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/t1417691754c87igo6urqxir9x.htm/, Retrieved Thu, 16 May 2024 11:23:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263087, Retrieved Thu, 16 May 2024 11:23:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

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 10:54:20] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

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

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263087&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263087&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.9931 + 1.17595programma[t] -0.624483gender[t] -0.119664age[t] + 0.0471701LFM[t] + 0.00111338PRH[t] + 0.0245521CH[t] + 0.00609069Blogs[t] -0.00665346Hours[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] + 0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.00678706CONFSTATTOT[t] + 0.0288303CONFSOFTTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.9931 +  1.17595programma[t] -0.624483gender[t] -0.119664age[t] +  0.0471701LFM[t] +  0.00111338PRH[t] +  0.0245521CH[t] +  0.00609069Blogs[t] -0.00665346Hours[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] +  0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.00678706CONFSTATTOT[t] +  0.0288303CONFSOFTTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263087&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.9931 +  1.17595programma[t] -0.624483gender[t] -0.119664age[t] +  0.0471701LFM[t] +  0.00111338PRH[t] +  0.0245521CH[t] +  0.00609069Blogs[t] -0.00665346Hours[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] +  0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.00678706CONFSTATTOT[t] +  0.0288303CONFSOFTTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263087&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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] -0.624483gender[t] -0.119664age[t] + 0.0471701LFM[t] + 0.00111338PRH[t] + 0.0245521CH[t] + 0.00609069Blogs[t] -0.00665346Hours[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] + 0.0067238AMS.I[t] -0.0233551AMS.E[t] -0.0612843AMS.A[t] -0.00678706CONFSTATTOT[t] + 0.0288303CONFSOFTTOT[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
gender-0.6244830.413433-1.510.1330820.0665409
age-0.1196640.17377-0.68860.4921470.246074
LFM0.04717010.005975857.8936.41093e-133.20546e-13
PRH0.001113380.3739150.0029780.9976280.498814
CH0.02455210.372040.065990.9474730.473737
Blogs0.006090690.003797371.6040.1108920.0554459
Hours-0.006653460.372437-0.017860.9857710.492886
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
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
CONFSTATTOT-0.006787060.0933838-0.072680.9421610.47108
CONFSOFTTOT0.02883030.1048360.2750.7837030.391851

\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
gender & -0.624483 & 0.413433 & -1.51 & 0.133082 & 0.0665409 \tabularnewline
age & -0.119664 & 0.17377 & -0.6886 & 0.492147 & 0.246074 \tabularnewline
LFM & 0.0471701 & 0.00597585 & 7.893 & 6.41093e-13 & 3.20546e-13 \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
Blogs & 0.00609069 & 0.00379737 & 1.604 & 0.110892 & 0.0554459 \tabularnewline
Hours & -0.00665346 & 0.372437 & -0.01786 & 0.985771 & 0.492886 \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
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
CONFSTATTOT & -0.00678706 & 0.0933838 & -0.07268 & 0.942161 & 0.47108 \tabularnewline
CONFSOFTTOT & 0.0288303 & 0.104836 & 0.275 & 0.783703 & 0.391851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263087&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]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]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]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]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]Hours[/C][C]-0.00665346[/C][C]0.372437[/C][C]-0.01786[/C][C]0.985771[/C][C]0.492886[/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]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]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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263087&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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
gender-0.6244830.413433-1.510.1330820.0665409
age-0.1196640.17377-0.68860.4921470.246074
LFM0.04717010.005975857.8936.41093e-133.20546e-13
PRH0.001113380.3739150.0029780.9976280.498814
CH0.02455210.372040.065990.9474730.473737
Blogs0.006090690.003797371.6040.1108920.0554459
Hours-0.006653460.372437-0.017860.9857710.492886
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
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
CONFSTATTOT-0.006787060.0933838-0.072680.9421610.47108
CONFSOFTTOT0.02883030.1048360.2750.7837030.391851






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=263087&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=263087&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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=263087&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=263087&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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=263087&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=263087&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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=263087&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=263087&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263087&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')
}