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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 computationSun, 14 Dec 2014 10:32:50 +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/14/t1418553317ri7j618yujb6w5x.htm/, Retrieved Thu, 16 May 2024 03:36:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267417, Retrieved Thu, 16 May 2024 03:36:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 10:32:50] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
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Boxplots 
4.35 0 1 22 48 23 12 41 7 6 6 0 2 2 52 51 6 16 9
12.7 0 1 22 50 16 45 146 7 3 6 2 2 2 16 56 4 16 11
18.1 0 1 22 150 33 37 182 7 4 6 1 2 1 46 67 8 16 12
17.85 0 1 20 154 32 37 192 9 3 6 3 2 2 56 69 5 16 12
16.6 1 0 19 109 37 108 263 7 10 6 3 2 2 52 57 4 12 7
12.6 1 1 20 68 14 10 35 6 4 6 0 0 1 55 56 17 15 12
17.1 0 1 22 194 52 68 439 8 8 5 3 1 2 50 55 4 14 12
19.1 0 0 21 158 75 72 214 8 3 6 2 2 2 59 63 4 15 12
16.1 0 1 21 159 72 143 341 9 5 5 2 0 2 60 67 8 16 10
13.35 0 0 21 67 15 9 58 7 4 6 0 0 1 52 65 4 13 15
18.4 0 0 21 147 29 55 292 6 3 5 2 0 2 44 47 7 10 10
14.7 0 1 21 39 13 17 85 8 5 5 3 0 2 67 76 4 17 15
10.6 0 1 21 100 40 37 200 6 3 6 2 0 2 52 64 4 15 10
12.6 0 1 21 111 19 27 158 6 3 4 0 0 2 55 68 5 18 15
16.2 0 1 22 138 24 37 199 9 4 5 0 0 2 37 64 7 16 9
13.6 0 1 24 101 121 58 297 6 3 6 0 0 1 54 65 4 20 15
18.9 1 1 21 131 93 66 227 9 6 6 1 1 1 72 71 4 16 12
14.1 0 1 22 101 36 21 108 8 6 5 2 2 2 51 63 7 17 13
14.5 0 1 20 114 23 19 86 8 4 6 3 2 2 48 60 11 16 12
16.15 0 0 21 165 85 78 302 9 4 6 0 0 0 60 68 7 15 12
14.75 0 1 24 114 41 35 148 6 4 6 2 0 1 50 72 4 13 8
14.8 0 1 25 111 46 48 178 4 3 4 2 1 2 63 70 4 16 9
12.45 0 1 22 75 18 27 120 8 2 6 1 2 0 33 61 4 16 15
12.65 0 1 21 82 35 43 207 5 5 5 2 0 2 67 61 4 16 12
17.35 0 1 21 121 17 30 157 7 4 6 2 2 2 46 62 4 17 12
8.6 0 1 22 32 4 25 128 9 4 6 2 0 0 54 71 4 20 15
18.4 0 0 23 150 28 69 296 9 4 5 2 0 2 59 71 6 14 11
16.1 0 1 24 117 44 72 323 8 3 4 2 1 1 61 51 8 17 12
11.6 1 1 20 71 10 23 79 6 4 5 2 2 1 33 56 23 6 6
17.75 0 1 22 165 38 13 70 8 2 6 1 1 2 47 70 4 16 14
15.25 0 1 25 154 57 61 146 3 0 0 0 0 0 69 73 8 15 12
17.65 0 1 22 126 23 43 246 8 4 6 2 1 2 52 76 6 16 12
16.35 0 0 21 149 36 51 196 7 6 6 0 2 2 55 68 4 16 12
17.65 0 0 21 145 22 67 199 9 4 4 2 0 1 41 48 7 14 11
13.6 0 1 21 120 40 36 127 4 4 6 0 0 1 73 52 4 16 12
14.35 0 0 22 109 31 44 153 6 4 5 0 1 0 52 60 4 16 12
14.75 0 0 22 132 11 45 299 3 2 1 0 1 0 50 59 4 16 12
18.25 0 1 21 172 38 34 228 8 4 5 3 2 2 51 57 10 14 12
9.9 0 0 22 169 24 36 190 8 3 5 0 0 1 60 79 6 14 8
16 0 1 23 114 37 72 180 9 6 5 2 2 0 56 60 5 16 8
18.25 0 1 21 156 37 39 212 8 6 5 3 0 2 56 60 5 16 12
16.85 0 0 21 172 22 43 269 8 4 5 0 0 2 29 59 4 15 12
14.6 1 1 21 68 15 25 130 9 5 6 2 2 1 66 62 4 16 11
13.85 1 1 19 89 2 56 179 8 4 5 0 1 2 66 59 5 16 10
18.95 0 1 21 167 43 80 243 9 6 6 3 2 2 73 61 5 18 11
15.6 0 0 21 113 31 40 190 7 6 5 2 1 2 55 71 5 15 12
14.85 1 0 19 115 29 73 299 7 9 6 2 1 2 64 57 5 16 13
11.75 1 0 18 78 45 34 121 6 4 5 2 1 0 40 66 4 16 12
18.45 1 0 19 118 25 72 137 8 8 6 3 1 2 46 63 6 16 12
15.9 1 1 21 87 4 42 305 6 5 5 3 0 2 58 69 4 17 10
17.1 0 0 22 173 31 61 157 7 4 5 3 0 0 43 58 4 14 10
16.1 0 1 22 2 -4 23 96 8 4 6 2 2 1 61 59 4 18 11
19.9 1 0 19 162 66 74 183 8 7 6 3 2 1 51 48 9 9 8
10.95 1 1 20 49 61 16 52 7 4 6 1 2 2 50 66 18 15 12
18.45 1 0 19 122 32 66 238 9 8 6 2 1 2 52 73 6 14 9
15.1 1 1 21 96 31 9 40 9 4 6 3 2 1 54 67 5 15 12
15 1 0 19 100 39 41 226 9 3 6 2 0 1 66 61 4 13 9
11.35 1 0 20 82 19 57 190 6 5 6 2 1 2 61 68 11 16 11
15.95 1 1 21 100 31 48 214 8 8 6 2 2 2 80 75 4 20 15
18.1 1 0 19 115 36 51 145 9 4 5 1 0 1 51 62 10 14 8
14.6 1 1 21 141 42 53 119 9 10 6 3 1 0 56 69 6 12 8
15.4 0 1 21 165 21 29 222 8 5 6 2 2 2 56 58 8 15 11
15.4 0 1 21 165 21 29 222 8 5 6 2 2 2 56 60 8 15 11
17.6 1 1 19 110 25 55 159 8 3 6 1 0 2 53 74 6 15 11
13.35 0 1 25 118 32 54 165 8 3 5 1 1 2 47 55 8 16 13
19.1 0 0 21 158 26 43 249 8 3 3 0 0 2 25 62 4 11 7
15.35 1 1 20 146 28 51 125 9 4 4 1 1 1 47 63 4 16 12
7.6 0 0 25 49 32 20 122 6 5 6 1 0 2 46 69 9 7 8
13.4 1 0 19 90 41 79 186 9 5 4 2 1 2 50 58 9 11 8
13.9 1 0 20 121 29 39 148 8 4 6 0 0 0 39 58 5 9 4
19.1 0 1 22 155 33 61 274 8 7 6 3 1 0 51 68 4 15 11
15.25 1 0 19 104 17 55 172 8 5 3 1 0 1 58 72 4 16 10
12.9 1 1 20 147 13 30 84 8 4 4 1 2 0 35 62 15 14 7
16.1 1 0 19 110 32 55 168 9 7 4 3 0 2 58 62 10 15 12
17.35 1 0 19 108 30 22 102 9 7 4 3 0 2 60 65 9 13 11
13.15 1 0 18 113 34 37 106 9 7 4 3 0 2 62 69 7 13 9
12.15 1 0 19 115 59 2 2 8 7 4 3 0 2 63 66 9 12 10
12.6 1 1 21 61 13 38 139 8 7 4 0 0 0 53 72 6 16 8
10.35 1 1 19 60 23 27 95 8 7 6 2 1 2 46 62 4 14 8
15.4 1 1 20 109 10 56 130 3 1 4 1 1 0 67 75 7 16 11
9.6 1 1 20 68 5 25 72 6 2 4 2 1 2 59 58 4 14 12
18.2 1 0 19 111 31 39 141 5 3 2 1 0 2 64 66 7 15 10
13.6 1 0 19 77 19 33 113 4 6 5 1 0 1 38 55 4 10 10
14.85 1 1 22 73 32 43 206 9 8 6 3 2 2 50 47 15 16 12
14.75 0 0 21 151 30 57 268 8 8 6 1 1 1 48 72 4 14 8
14.1 1 0 19 89 25 43 175 3 0 1 0 0 0 48 62 9 16 11
14.9 1 0 19 78 48 23 77 6 3 4 1 0 2 47 64 4 12 8
16.25 1 0 19 110 35 44 125 6 6 5 1 1 2 66 64 4 16 10
19.25 0 1 23 220 67 54 255 9 5 5 2 0 2 47 19 28 16 14
13.6 1 1 19 65 15 28 111 7 7 6 1 0 1 63 50 4 15 9
13.6 0 0 20 141 22 36 132 6 3 5 0 1 2 58 68 4 14 9
15.65 1 0 19 117 18 39 211 9 3 6 2 0 0 44 70 4 16 10
12.75 0 1 22 122 33 16 92 7 4 6 2 0 1 51 79 5 11 13
14.6 1 0 19 63 46 23 76 8 4 5 3 0 2 43 69 4 15 12
9.85 0 1 25 44 24 40 171 8 1 5 0 0 2 55 71 4 18 13
12.65 1 1 19 52 14 24 83 8 5 6 2 0 2 38 48 12 13 8
19.2 1 0 19 131 12 78 266 0 0 0 0 0 0 45 73 4 7 3
16.6 1 1 19 101 38 57 186 6 4 6 1 1 0 50 74 6 7 8
11.2 1 1 20 42 12 37 50 9 6 5 2 2 1 54 66 6 17 12
15.25 0 1 20 152 28 27 117 9 4 6 1 1 2 57 71 5 18 11
11.9 0 0 21 107 41 61 219 6 1 2 0 1 2 60 74 4 15 9
13.2 1 0 19 77 12 27 246 8 3 5 0 0 2 55 78 4 8 12
16.35 0 0 21 154 31 69 279 8 7 5 2 0 2 56 75 4 13 12
12.4 0 1 23 103 33 34 148 5 3 1 0 0 2 49 53 10 13 12
15.85 1 1 19 96 34 44 137 6 5 5 1 1 0 37 60 7 15 10
18.15 0 1 22 175 21 34 181 9 3 5 2 2 2 59 70 4 18 13
11.15 1 1 20 57 20 39 98 9 6 4 2 1 2 46 69 7 16 9
15.65 1 0 18 112 44 51 226 9 9 6 3 0 2 51 65 4 14 12
17.75 0 0 21 143 52 34 234 6 4 5 0 1 2 58 78 4 15 11
7.65 1 0 20 49 7 31 138 4 3 6 0 1 1 64 78 12 19 14
12.35 0 1 21 110 29 13 85 8 9 6 2 2 2 53 59 5 16 11
15.6 0 1 21 131 11 12 66 4 5 6 0 1 0 48 72 8 12 9
19.3 0 0 21 167 26 51 236 5 3 6 3 1 1 51 70 6 16 12
15.2 1 0 19 56 24 24 106 8 6 5 2 0 1 47 63 17 11 8
17.1 0 0 21 137 7 19 135 6 2 6 1 0 1 59 63 4 16 15
15.6 1 1 19 86 60 30 122 8 4 5 3 1 2 62 71 5 15 12
18.4 0 1 21 121 13 81 218 9 5 5 2 1 1 62 74 4 19 14
19.05 0 0 21 149 20 42 199 7 4 5 2 0 1 51 67 5 15 12
18.55 0 0 22 168 52 22 112 4 0 0 0 0 0 64 66 5 14 9
19.1 0 0 21 140 28 85 278 8 2 6 1 1 2 52 62 6 14 9
13.1 1 1 22 88 25 27 94 8 5 6 2 1 2 67 80 4 17 13
12.85 0 1 22 168 39 25 113 8 3 6 2 0 1 50 73 4 16 13
9.5 0 1 22 94 9 22 84 4 0 0 0 0 0 54 67 4 20 15
4.5 0 1 22 51 19 19 86 9 5 5 3 0 2 58 61 6 16 11
11.85 1 0 21 48 13 14 62 8 6 5 1 0 2 56 73 8 9 7
13.6 0 1 22 145 60 45 222 6 3 5 0 1 1 63 74 10 13 10
11.7 0 1 23 66 19 45 167 3 0 0 0 0 0 31 32 4 15 11
12.4 1 1 19 85 34 28 82 7 3 4 0 1 0 65 69 5 19 14
13.35 0 0 22 109 14 51 207 8 5 6 2 1 2 71 69 4 16 14
11.4 1 0 21 63 17 41 184 7 4 4 0 0 2 50 84 4 17 13
14.9 1 1 19 102 45 31 83 7 5 5 2 0 1 57 64 4 16 12
19.9 1 0 19 162 66 74 183 8 7 6 3 2 1 47 58 16 9 8
11.2 1 1 20 86 48 19 89 7 8 6 2 1 2 47 59 7 11 13
14.6 1 1 18 114 29 51 225 7 6 6 1 1 2 57 78 4 14 9
17.6 0 0 21 164 -2 73 237 6 4 5 1 0 1 43 57 4 19 12
14.05 0 1 21 119 51 24 102 8 5 5 1 1 0 41 60 14 13 13
16.1 0 0 20 126 2 61 221 8 5 6 0 1 2 63 68 5 14 11
13.35 0 1 20 132 24 23 128 7 3 6 1 0 2 63 68 5 15 11
11.85 0 1 21 142 40 14 91 9 6 6 0 1 2 56 73 5 15 13
11.95 0 0 21 83 20 54 198 9 3 4 2 0 1 51 69 5 14 12
14.75 1 1 19 94 19 51 204 7 6 5 3 1 1 50 67 7 16 12
15.15 1 0 19 81 16 62 158 7 3 2 1 0 2 22 60 19 17 10
13.2 0 1 21 166 20 36 138 8 7 6 2 2 2 41 65 16 12 9
16.85 1 0 19 110 40 59 226 8 7 6 3 0 2 59 66 4 15 10
7.85 1 1 19 64 27 24 44 6 6 4 3 1 2 56 74 4 17 13
7.7 0 0 24 93 25 26 196 9 5 6 1 1 0 66 81 7 15 13
12.6 1 0 19 104 49 54 83 6 5 5 1 0 1 53 72 9 10 9
7.85 1 1 19 105 39 39 79 5 4 4 0 0 2 42 55 5 16 11
10.95 1 1 20 49 61 16 52 7 4 6 1 2 2 52 49 14 15 12
12.35 1 0 19 88 19 36 105 9 7 6 3 0 2 54 74 4 11 8
9.95 1 1 19 95 67 31 116 6 2 1 0 1 0 44 53 16 16 12
14.9 1 1 19 102 45 31 83 7 5 5 2 0 1 62 64 10 16 12
16.65 1 0 19 99 30 42 196 5 4 5 2 1 0 53 65 5 16 12
13.4 1 1 19 63 8 39 153 9 2 6 2 2 2 50 57 6 14 9
13.95 1 0 19 76 19 25 157 8 5 4 2 0 0 36 51 4 14 12
15.7 1 0 20 109 52 31 75 4 4 3 0 0 2 76 80 4 16 12
16.85 1 1 20 117 22 38 106 9 7 4 3 2 2 66 67 4 16 11
10.95 1 1 19 57 17 31 58 8 6 5 2 2 0 62 70 5 18 12
15.35 1 0 21 120 33 17 75 7 4 5 0 0 0 59 74 4 14 6
12.2 1 1 19 73 34 22 74 8 5 6 2 2 2 47 75 4 20 7
15.1 1 0 19 91 22 55 185 1 0 1 0 0 0 55 70 5 15 10
17.75 1 0 19 108 30 62 265 8 7 6 2 1 2 58 69 4 16 12
15.2 1 1 21 105 25 51 131 8 4 4 2 0 2 60 65 4 16 10
14.6 0 0 22 117 38 30 139 9 5 4 3 0 2 44 55 5 16 12
16.65 1 0 19 119 26 49 196 8 6 5 2 0 1 57 71 8 12 9
8.1 1 1 19 31 13 16 78 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 time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 8 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267417&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267417&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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 time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.9857 + 1.17536programma[t] -0.624011gender[t] -0.119453age[t] + 0.0471813LFM[t] -0.00556388PRH[t] + 0.0179099CH[t] + 0.00608458Blogs[t] -0.00743914Calculation[t] -0.263141Algebraic_Reasoning[t] + 0.140732Graphical_Interpretation[t] + 0.587657Proportionality_and_Ratio[t] + 0.239682Probability_and_Sampling[t] -0.0939824Estimation[t] + 0.00674058AMS.I[t] -0.0233561AMS.E[t] -0.0611857AMS.A[t] -0.00660012CONFSTATTOT[t] + 0.0285919CONFSOFTTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.9857 +  1.17536programma[t] -0.624011gender[t] -0.119453age[t] +  0.0471813LFM[t] -0.00556388PRH[t] +  0.0179099CH[t] +  0.00608458Blogs[t] -0.00743914Calculation[t] -0.263141Algebraic_Reasoning[t] +  0.140732Graphical_Interpretation[t] +  0.587657Proportionality_and_Ratio[t] +  0.239682Probability_and_Sampling[t] -0.0939824Estimation[t] +  0.00674058AMS.I[t] -0.0233561AMS.E[t] -0.0611857AMS.A[t] -0.00660012CONFSTATTOT[t] +  0.0285919CONFSOFTTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267417&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.9857 +  1.17536programma[t] -0.624011gender[t] -0.119453age[t] +  0.0471813LFM[t] -0.00556388PRH[t] +  0.0179099CH[t] +  0.00608458Blogs[t] -0.00743914Calculation[t] -0.263141Algebraic_Reasoning[t] +  0.140732Graphical_Interpretation[t] +  0.587657Proportionality_and_Ratio[t] +  0.239682Probability_and_Sampling[t] -0.0939824Estimation[t] +  0.00674058AMS.I[t] -0.0233561AMS.E[t] -0.0611857AMS.A[t] -0.00660012CONFSTATTOT[t] +  0.0285919CONFSOFTTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267417&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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] = + 10.9857 + 1.17536programma[t] -0.624011gender[t] -0.119453age[t] + 0.0471813LFM[t] -0.00556388PRH[t] + 0.0179099CH[t] + 0.00608458Blogs[t] -0.00743914Calculation[t] -0.263141Algebraic_Reasoning[t] + 0.140732Graphical_Interpretation[t] + 0.587657Proportionality_and_Ratio[t] + 0.239682Probability_and_Sampling[t] -0.0939824Estimation[t] + 0.00674058AMS.I[t] -0.0233561AMS.E[t] -0.0611857AMS.A[t] -0.00660012CONFSTATTOT[t] + 0.0285919CONFSOFTTOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.98574.253682.5830.01078130.00539065
programma1.175360.6104281.9250.05610120.0280506
gender-0.6240110.411183-1.5180.1312630.0656316
age-0.1194530.17278-0.69140.4904290.245214
LFM0.04718130.005922637.9664.13058e-132.06529e-13
PRH-0.005563880.010399-0.5350.5934310.296715
CH0.01790990.01313331.3640.1747450.0873727
Blogs0.006084580.003769051.6140.1085950.0542974
Calculation-0.007439140.12794-0.058150.9537120.476856
Algebraic_Reasoning-0.2631410.114994-2.2880.02354520.0117726
Graphical_Interpretation0.1407320.1525420.92260.3577410.17887
Proportionality_and_Ratio0.5876570.1954683.0060.003109770.00155488
Probability_and_Sampling0.2396820.2448440.97890.329230.164615
Estimation-0.09398240.228438-0.41140.6813690.340684
AMS.I0.006740580.01980320.34040.7340580.367029
AMS.E-0.02335610.0222122-1.0510.2947560.147378
AMS.A-0.06118570.0499292-1.2250.2223660.111183
CONFSTATTOT-0.006600120.0924796-0.071370.9432020.471601
CONFSOFTTOT0.02859190.103630.27590.7830080.391504

\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) & 10.9857 & 4.25368 & 2.583 & 0.0107813 & 0.00539065 \tabularnewline
programma & 1.17536 & 0.610428 & 1.925 & 0.0561012 & 0.0280506 \tabularnewline
gender & -0.624011 & 0.411183 & -1.518 & 0.131263 & 0.0656316 \tabularnewline
age & -0.119453 & 0.17278 & -0.6914 & 0.490429 & 0.245214 \tabularnewline
LFM & 0.0471813 & 0.00592263 & 7.966 & 4.13058e-13 & 2.06529e-13 \tabularnewline
PRH & -0.00556388 & 0.010399 & -0.535 & 0.593431 & 0.296715 \tabularnewline
CH & 0.0179099 & 0.0131333 & 1.364 & 0.174745 & 0.0873727 \tabularnewline
Blogs & 0.00608458 & 0.00376905 & 1.614 & 0.108595 & 0.0542974 \tabularnewline
Calculation & -0.00743914 & 0.12794 & -0.05815 & 0.953712 & 0.476856 \tabularnewline
Algebraic_Reasoning & -0.263141 & 0.114994 & -2.288 & 0.0235452 & 0.0117726 \tabularnewline
Graphical_Interpretation & 0.140732 & 0.152542 & 0.9226 & 0.357741 & 0.17887 \tabularnewline
Proportionality_and_Ratio & 0.587657 & 0.195468 & 3.006 & 0.00310977 & 0.00155488 \tabularnewline
Probability_and_Sampling & 0.239682 & 0.244844 & 0.9789 & 0.32923 & 0.164615 \tabularnewline
Estimation & -0.0939824 & 0.228438 & -0.4114 & 0.681369 & 0.340684 \tabularnewline
AMS.I & 0.00674058 & 0.0198032 & 0.3404 & 0.734058 & 0.367029 \tabularnewline
AMS.E & -0.0233561 & 0.0222122 & -1.051 & 0.294756 & 0.147378 \tabularnewline
AMS.A & -0.0611857 & 0.0499292 & -1.225 & 0.222366 & 0.111183 \tabularnewline
CONFSTATTOT & -0.00660012 & 0.0924796 & -0.07137 & 0.943202 & 0.471601 \tabularnewline
CONFSOFTTOT & 0.0285919 & 0.10363 & 0.2759 & 0.783008 & 0.391504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267417&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]10.9857[/C][C]4.25368[/C][C]2.583[/C][C]0.0107813[/C][C]0.00539065[/C][/ROW]
[ROW][C]programma[/C][C]1.17536[/C][C]0.610428[/C][C]1.925[/C][C]0.0561012[/C][C]0.0280506[/C][/ROW]
[ROW][C]gender[/C][C]-0.624011[/C][C]0.411183[/C][C]-1.518[/C][C]0.131263[/C][C]0.0656316[/C][/ROW]
[ROW][C]age[/C][C]-0.119453[/C][C]0.17278[/C][C]-0.6914[/C][C]0.490429[/C][C]0.245214[/C][/ROW]
[ROW][C]LFM[/C][C]0.0471813[/C][C]0.00592263[/C][C]7.966[/C][C]4.13058e-13[/C][C]2.06529e-13[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00556388[/C][C]0.010399[/C][C]-0.535[/C][C]0.593431[/C][C]0.296715[/C][/ROW]
[ROW][C]CH[/C][C]0.0179099[/C][C]0.0131333[/C][C]1.364[/C][C]0.174745[/C][C]0.0873727[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00608458[/C][C]0.00376905[/C][C]1.614[/C][C]0.108595[/C][C]0.0542974[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.00743914[/C][C]0.12794[/C][C]-0.05815[/C][C]0.953712[/C][C]0.476856[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.263141[/C][C]0.114994[/C][C]-2.288[/C][C]0.0235452[/C][C]0.0117726[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]0.140732[/C][C]0.152542[/C][C]0.9226[/C][C]0.357741[/C][C]0.17887[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.587657[/C][C]0.195468[/C][C]3.006[/C][C]0.00310977[/C][C]0.00155488[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.239682[/C][C]0.244844[/C][C]0.9789[/C][C]0.32923[/C][C]0.164615[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.0939824[/C][C]0.228438[/C][C]-0.4114[/C][C]0.681369[/C][C]0.340684[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00674058[/C][C]0.0198032[/C][C]0.3404[/C][C]0.734058[/C][C]0.367029[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0233561[/C][C]0.0222122[/C][C]-1.051[/C][C]0.294756[/C][C]0.147378[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0611857[/C][C]0.0499292[/C][C]-1.225[/C][C]0.222366[/C][C]0.111183[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.00660012[/C][C]0.0924796[/C][C]-0.07137[/C][C]0.943202[/C][C]0.471601[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.0285919[/C][C]0.10363[/C][C]0.2759[/C][C]0.783008[/C][C]0.391504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267417&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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)10.98574.253682.5830.01078130.00539065
programma1.175360.6104281.9250.05610120.0280506
gender-0.6240110.411183-1.5180.1312630.0656316
age-0.1194530.17278-0.69140.4904290.245214
LFM0.04718130.005922637.9664.13058e-132.06529e-13
PRH-0.005563880.010399-0.5350.5934310.296715
CH0.01790990.01313331.3640.1747450.0873727
Blogs0.006084580.003769051.6140.1085950.0542974
Calculation-0.007439140.12794-0.058150.9537120.476856
Algebraic_Reasoning-0.2631410.114994-2.2880.02354520.0117726
Graphical_Interpretation0.1407320.1525420.92260.3577410.17887
Proportionality_and_Ratio0.5876570.1954683.0060.003109770.00155488
Probability_and_Sampling0.2396820.2448440.97890.329230.164615
Estimation-0.09398240.228438-0.41140.6813690.340684
AMS.I0.006740580.01980320.34040.7340580.367029
AMS.E-0.02335610.0222122-1.0510.2947560.147378
AMS.A-0.06118570.0499292-1.2250.2223660.111183
CONFSTATTOT-0.006600120.0924796-0.071370.9432020.471601
CONFSOFTTOT0.02859190.103630.27590.7830080.391504






Multiple Linear Regression - Regression Statistics
Multiple R0.756186
R-squared0.571818
Adjusted R-squared0.519387
F-TEST (value)10.9062
F-TEST (DF numerator)18
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11304
Sum Squared Residuals656.345

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.756186 \tabularnewline
R-squared & 0.571818 \tabularnewline
Adjusted R-squared & 0.519387 \tabularnewline
F-TEST (value) & 10.9062 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.11304 \tabularnewline
Sum Squared Residuals & 656.345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267417&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.756186[/C][/ROW]
[ROW][C]R-squared[/C][C]0.571818[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.519387[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.9062[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/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]2.11304[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]656.345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267417&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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 R0.756186
R-squared0.571818
Adjusted R-squared0.519387
F-TEST (value)10.9062
F-TEST (DF numerator)18
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11304
Sum Squared Residuals656.345






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.358.78364-4.43364
212.711.93170.768294
318.115.60332.49665
417.8517.63120.218765
516.617.4735-0.873521
612.610.59042.00961
717.119.8986-2.79865
819.118.49390.606095
916.118.4291-2.32909
1013.3510.64542.7046
1118.417.86060.539391
1214.710.09454.60552
1310.614.0239-3.42387
1412.612.7571-0.157053
1516.213.85892.34111
1613.613.24710.352914
1718.916.00682.89319
1814.112.56751.53252
1914.514.36020.139822
2016.1517.3805-1.23055
2114.7513.55481.1952
2214.813.96670.833265
2312.4512.5877-0.13772
2412.6512.9147-0.264669
2517.3515.01472.33532
268.610.1058-1.50584
2718.417.36241.03763
2816.116.02140.0785511
2911.612.1411-0.541132
3017.7515.59572.15429
3115.2514.79740.452637
3217.6515.22272.42733
3316.3515.6930.657007
3417.6517.08070.569347
3513.613.6482-0.0482301
3614.3513.8350.514999
3714.7515.9333-1.18326
3818.2518.05040.199615
399.916.0879-6.18789
401614.67761.32237
4118.2516.5441.70604
4216.8517.0972-0.24725
4314.613.37551.22449
4413.8514.1321-0.282068
4518.9518.61460.335402
4615.614.4591.14098
4714.8516.994-2.14397
4811.7514.5281-2.77815
4918.4516.64631.80373
5015.915.34570.554284
5117.118.6413-1.54132
5216.19.123496.97651
5319.919.5390.361032
5410.9510.28050.669506
5518.4516.44232.00765
5615.114.40050.699469
571516.5183-1.51827
5811.3514.782-3.43196
5915.9514.72211.22794
6018.115.41062.68937
6114.615.8177-1.21767
6215.417.0761-1.6761
6315.417.0294-1.62939
6417.615.14322.45681
6513.3514.0387-0.688693
6619.116.06093.03915
6715.3516.5695-1.21951
687.69.4478-1.8478
6913.415.4514-2.05137
7013.915.3081-1.40814
7119.117.29471.80528
7215.2514.92080.329176
7312.915.5553-2.65535
7416.115.71470.385312
7517.3514.62812.72195
7613.1515.2394-2.08939
7712.1513.8126-1.66262
7812.610.45362.14638
7910.3511.9554-1.60542
8015.415.6144-0.214358
819.613.4823-3.88234
8218.215.01553.18447
8313.613.23270.367286
8414.8513.48451.36552
8514.7516.0327-1.28275
8614.114.4385-0.338511
8714.913.04031.85969
8816.2515.04051.20955
8919.2518.90290.347083
9013.612.0421.55796
9113.615.2189-1.61892
9215.6517.0544-1.40438
9312.7513.4654-0.715425
9414.613.32651.27352
959.859.98827-0.138271
9612.6511.57811.07192
9719.217.4141.78596
9816.614.97251.62748
9911.211.3345-0.134532
10015.2515.16380.0862157
10111.914.3375-2.43753
10213.213.6993-0.499263
10316.3516.9318-0.581805
10412.411.66380.736237
10515.8514.00661.84342
10618.1517.66870.481268
10711.1511.5869-0.43687
10815.6516.1546-0.504646
10917.7515.16562.58439
1107.6511.761-4.11097
11112.3512.3973-0.0472911
11215.612.65832.94165
11319.319.06680.233194
11415.211.58283.61717
11517.115.83571.26431
11615.614.37491.22514
11718.415.62862.77139
11819.0516.75642.29363
11918.5515.78952.76054
12019.117.73881.36119
12113.113.2715-0.17151
12212.8516.3086-3.45863
1239.511.8453-2.34529
1244.510.6018-6.10182
12511.8510.26161.5884
12613.614.6977-1.09773
12711.711.8546-0.15457
12812.412.8457-0.445697
12913.3515.2086-1.85864
13011.412.0444-0.644412
13114.914.18910.710853
13219.918.85011.04986
13311.212.5441-1.34411
13414.615.2221-0.622079
13517.618.0071-0.407121
13614.0512.69451.35551
13716.115.2410.85897
13813.3514.4064-1.05635
13911.8513.0243-1.17432
14011.9513.9187-1.96866
14114.7515.3142-0.564179
14215.1513.29361.85636
14313.215.4254-2.22536
14416.8516.60690.243129
1457.8512.2524-4.40235
1467.712.7123-5.01229
14712.614.1511-1.55106
1487.8513.3703-5.52032
14910.9510.93580.0142292
15012.3514.2788-1.92879
1519.9512.7646-2.81461
15214.913.85571.04426
15316.6516.13980.510181
15413.414.3462-0.94616
15513.9514.195-0.244994
15615.713.42542.27458
15716.8515.42381.42615
15810.9512.1916-1.24161
15915.3513.99421.35576
16012.212.6911-0.491133
16115.114.92330.176706
16217.7516.4851.26502
16315.214.8140.38601
16414.614.7484-0.148386
16516.6515.99270.657301
1668.18.89035-0.790354

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 8.78364 & -4.43364 \tabularnewline
2 & 12.7 & 11.9317 & 0.768294 \tabularnewline
3 & 18.1 & 15.6033 & 2.49665 \tabularnewline
4 & 17.85 & 17.6312 & 0.218765 \tabularnewline
5 & 16.6 & 17.4735 & -0.873521 \tabularnewline
6 & 12.6 & 10.5904 & 2.00961 \tabularnewline
7 & 17.1 & 19.8986 & -2.79865 \tabularnewline
8 & 19.1 & 18.4939 & 0.606095 \tabularnewline
9 & 16.1 & 18.4291 & -2.32909 \tabularnewline
10 & 13.35 & 10.6454 & 2.7046 \tabularnewline
11 & 18.4 & 17.8606 & 0.539391 \tabularnewline
12 & 14.7 & 10.0945 & 4.60552 \tabularnewline
13 & 10.6 & 14.0239 & -3.42387 \tabularnewline
14 & 12.6 & 12.7571 & -0.157053 \tabularnewline
15 & 16.2 & 13.8589 & 2.34111 \tabularnewline
16 & 13.6 & 13.2471 & 0.352914 \tabularnewline
17 & 18.9 & 16.0068 & 2.89319 \tabularnewline
18 & 14.1 & 12.5675 & 1.53252 \tabularnewline
19 & 14.5 & 14.3602 & 0.139822 \tabularnewline
20 & 16.15 & 17.3805 & -1.23055 \tabularnewline
21 & 14.75 & 13.5548 & 1.1952 \tabularnewline
22 & 14.8 & 13.9667 & 0.833265 \tabularnewline
23 & 12.45 & 12.5877 & -0.13772 \tabularnewline
24 & 12.65 & 12.9147 & -0.264669 \tabularnewline
25 & 17.35 & 15.0147 & 2.33532 \tabularnewline
26 & 8.6 & 10.1058 & -1.50584 \tabularnewline
27 & 18.4 & 17.3624 & 1.03763 \tabularnewline
28 & 16.1 & 16.0214 & 0.0785511 \tabularnewline
29 & 11.6 & 12.1411 & -0.541132 \tabularnewline
30 & 17.75 & 15.5957 & 2.15429 \tabularnewline
31 & 15.25 & 14.7974 & 0.452637 \tabularnewline
32 & 17.65 & 15.2227 & 2.42733 \tabularnewline
33 & 16.35 & 15.693 & 0.657007 \tabularnewline
34 & 17.65 & 17.0807 & 0.569347 \tabularnewline
35 & 13.6 & 13.6482 & -0.0482301 \tabularnewline
36 & 14.35 & 13.835 & 0.514999 \tabularnewline
37 & 14.75 & 15.9333 & -1.18326 \tabularnewline
38 & 18.25 & 18.0504 & 0.199615 \tabularnewline
39 & 9.9 & 16.0879 & -6.18789 \tabularnewline
40 & 16 & 14.6776 & 1.32237 \tabularnewline
41 & 18.25 & 16.544 & 1.70604 \tabularnewline
42 & 16.85 & 17.0972 & -0.24725 \tabularnewline
43 & 14.6 & 13.3755 & 1.22449 \tabularnewline
44 & 13.85 & 14.1321 & -0.282068 \tabularnewline
45 & 18.95 & 18.6146 & 0.335402 \tabularnewline
46 & 15.6 & 14.459 & 1.14098 \tabularnewline
47 & 14.85 & 16.994 & -2.14397 \tabularnewline
48 & 11.75 & 14.5281 & -2.77815 \tabularnewline
49 & 18.45 & 16.6463 & 1.80373 \tabularnewline
50 & 15.9 & 15.3457 & 0.554284 \tabularnewline
51 & 17.1 & 18.6413 & -1.54132 \tabularnewline
52 & 16.1 & 9.12349 & 6.97651 \tabularnewline
53 & 19.9 & 19.539 & 0.361032 \tabularnewline
54 & 10.95 & 10.2805 & 0.669506 \tabularnewline
55 & 18.45 & 16.4423 & 2.00765 \tabularnewline
56 & 15.1 & 14.4005 & 0.699469 \tabularnewline
57 & 15 & 16.5183 & -1.51827 \tabularnewline
58 & 11.35 & 14.782 & -3.43196 \tabularnewline
59 & 15.95 & 14.7221 & 1.22794 \tabularnewline
60 & 18.1 & 15.4106 & 2.68937 \tabularnewline
61 & 14.6 & 15.8177 & -1.21767 \tabularnewline
62 & 15.4 & 17.0761 & -1.6761 \tabularnewline
63 & 15.4 & 17.0294 & -1.62939 \tabularnewline
64 & 17.6 & 15.1432 & 2.45681 \tabularnewline
65 & 13.35 & 14.0387 & -0.688693 \tabularnewline
66 & 19.1 & 16.0609 & 3.03915 \tabularnewline
67 & 15.35 & 16.5695 & -1.21951 \tabularnewline
68 & 7.6 & 9.4478 & -1.8478 \tabularnewline
69 & 13.4 & 15.4514 & -2.05137 \tabularnewline
70 & 13.9 & 15.3081 & -1.40814 \tabularnewline
71 & 19.1 & 17.2947 & 1.80528 \tabularnewline
72 & 15.25 & 14.9208 & 0.329176 \tabularnewline
73 & 12.9 & 15.5553 & -2.65535 \tabularnewline
74 & 16.1 & 15.7147 & 0.385312 \tabularnewline
75 & 17.35 & 14.6281 & 2.72195 \tabularnewline
76 & 13.15 & 15.2394 & -2.08939 \tabularnewline
77 & 12.15 & 13.8126 & -1.66262 \tabularnewline
78 & 12.6 & 10.4536 & 2.14638 \tabularnewline
79 & 10.35 & 11.9554 & -1.60542 \tabularnewline
80 & 15.4 & 15.6144 & -0.214358 \tabularnewline
81 & 9.6 & 13.4823 & -3.88234 \tabularnewline
82 & 18.2 & 15.0155 & 3.18447 \tabularnewline
83 & 13.6 & 13.2327 & 0.367286 \tabularnewline
84 & 14.85 & 13.4845 & 1.36552 \tabularnewline
85 & 14.75 & 16.0327 & -1.28275 \tabularnewline
86 & 14.1 & 14.4385 & -0.338511 \tabularnewline
87 & 14.9 & 13.0403 & 1.85969 \tabularnewline
88 & 16.25 & 15.0405 & 1.20955 \tabularnewline
89 & 19.25 & 18.9029 & 0.347083 \tabularnewline
90 & 13.6 & 12.042 & 1.55796 \tabularnewline
91 & 13.6 & 15.2189 & -1.61892 \tabularnewline
92 & 15.65 & 17.0544 & -1.40438 \tabularnewline
93 & 12.75 & 13.4654 & -0.715425 \tabularnewline
94 & 14.6 & 13.3265 & 1.27352 \tabularnewline
95 & 9.85 & 9.98827 & -0.138271 \tabularnewline
96 & 12.65 & 11.5781 & 1.07192 \tabularnewline
97 & 19.2 & 17.414 & 1.78596 \tabularnewline
98 & 16.6 & 14.9725 & 1.62748 \tabularnewline
99 & 11.2 & 11.3345 & -0.134532 \tabularnewline
100 & 15.25 & 15.1638 & 0.0862157 \tabularnewline
101 & 11.9 & 14.3375 & -2.43753 \tabularnewline
102 & 13.2 & 13.6993 & -0.499263 \tabularnewline
103 & 16.35 & 16.9318 & -0.581805 \tabularnewline
104 & 12.4 & 11.6638 & 0.736237 \tabularnewline
105 & 15.85 & 14.0066 & 1.84342 \tabularnewline
106 & 18.15 & 17.6687 & 0.481268 \tabularnewline
107 & 11.15 & 11.5869 & -0.43687 \tabularnewline
108 & 15.65 & 16.1546 & -0.504646 \tabularnewline
109 & 17.75 & 15.1656 & 2.58439 \tabularnewline
110 & 7.65 & 11.761 & -4.11097 \tabularnewline
111 & 12.35 & 12.3973 & -0.0472911 \tabularnewline
112 & 15.6 & 12.6583 & 2.94165 \tabularnewline
113 & 19.3 & 19.0668 & 0.233194 \tabularnewline
114 & 15.2 & 11.5828 & 3.61717 \tabularnewline
115 & 17.1 & 15.8357 & 1.26431 \tabularnewline
116 & 15.6 & 14.3749 & 1.22514 \tabularnewline
117 & 18.4 & 15.6286 & 2.77139 \tabularnewline
118 & 19.05 & 16.7564 & 2.29363 \tabularnewline
119 & 18.55 & 15.7895 & 2.76054 \tabularnewline
120 & 19.1 & 17.7388 & 1.36119 \tabularnewline
121 & 13.1 & 13.2715 & -0.17151 \tabularnewline
122 & 12.85 & 16.3086 & -3.45863 \tabularnewline
123 & 9.5 & 11.8453 & -2.34529 \tabularnewline
124 & 4.5 & 10.6018 & -6.10182 \tabularnewline
125 & 11.85 & 10.2616 & 1.5884 \tabularnewline
126 & 13.6 & 14.6977 & -1.09773 \tabularnewline
127 & 11.7 & 11.8546 & -0.15457 \tabularnewline
128 & 12.4 & 12.8457 & -0.445697 \tabularnewline
129 & 13.35 & 15.2086 & -1.85864 \tabularnewline
130 & 11.4 & 12.0444 & -0.644412 \tabularnewline
131 & 14.9 & 14.1891 & 0.710853 \tabularnewline
132 & 19.9 & 18.8501 & 1.04986 \tabularnewline
133 & 11.2 & 12.5441 & -1.34411 \tabularnewline
134 & 14.6 & 15.2221 & -0.622079 \tabularnewline
135 & 17.6 & 18.0071 & -0.407121 \tabularnewline
136 & 14.05 & 12.6945 & 1.35551 \tabularnewline
137 & 16.1 & 15.241 & 0.85897 \tabularnewline
138 & 13.35 & 14.4064 & -1.05635 \tabularnewline
139 & 11.85 & 13.0243 & -1.17432 \tabularnewline
140 & 11.95 & 13.9187 & -1.96866 \tabularnewline
141 & 14.75 & 15.3142 & -0.564179 \tabularnewline
142 & 15.15 & 13.2936 & 1.85636 \tabularnewline
143 & 13.2 & 15.4254 & -2.22536 \tabularnewline
144 & 16.85 & 16.6069 & 0.243129 \tabularnewline
145 & 7.85 & 12.2524 & -4.40235 \tabularnewline
146 & 7.7 & 12.7123 & -5.01229 \tabularnewline
147 & 12.6 & 14.1511 & -1.55106 \tabularnewline
148 & 7.85 & 13.3703 & -5.52032 \tabularnewline
149 & 10.95 & 10.9358 & 0.0142292 \tabularnewline
150 & 12.35 & 14.2788 & -1.92879 \tabularnewline
151 & 9.95 & 12.7646 & -2.81461 \tabularnewline
152 & 14.9 & 13.8557 & 1.04426 \tabularnewline
153 & 16.65 & 16.1398 & 0.510181 \tabularnewline
154 & 13.4 & 14.3462 & -0.94616 \tabularnewline
155 & 13.95 & 14.195 & -0.244994 \tabularnewline
156 & 15.7 & 13.4254 & 2.27458 \tabularnewline
157 & 16.85 & 15.4238 & 1.42615 \tabularnewline
158 & 10.95 & 12.1916 & -1.24161 \tabularnewline
159 & 15.35 & 13.9942 & 1.35576 \tabularnewline
160 & 12.2 & 12.6911 & -0.491133 \tabularnewline
161 & 15.1 & 14.9233 & 0.176706 \tabularnewline
162 & 17.75 & 16.485 & 1.26502 \tabularnewline
163 & 15.2 & 14.814 & 0.38601 \tabularnewline
164 & 14.6 & 14.7484 & -0.148386 \tabularnewline
165 & 16.65 & 15.9927 & 0.657301 \tabularnewline
166 & 8.1 & 8.89035 & -0.790354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267417&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]8.78364[/C][C]-4.43364[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]11.9317[/C][C]0.768294[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6033[/C][C]2.49665[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.6312[/C][C]0.218765[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.4735[/C][C]-0.873521[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.5904[/C][C]2.00961[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.8986[/C][C]-2.79865[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]18.4939[/C][C]0.606095[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]18.4291[/C][C]-2.32909[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.6454[/C][C]2.7046[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.8606[/C][C]0.539391[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.0945[/C][C]4.60552[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]14.0239[/C][C]-3.42387[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]12.7571[/C][C]-0.157053[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]13.8589[/C][C]2.34111[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.2471[/C][C]0.352914[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.0068[/C][C]2.89319[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.5675[/C][C]1.53252[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.3602[/C][C]0.139822[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.3805[/C][C]-1.23055[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.5548[/C][C]1.1952[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.9667[/C][C]0.833265[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.5877[/C][C]-0.13772[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9147[/C][C]-0.264669[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]15.0147[/C][C]2.33532[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]10.1058[/C][C]-1.50584[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.3624[/C][C]1.03763[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]16.0214[/C][C]0.0785511[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.1411[/C][C]-0.541132[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.5957[/C][C]2.15429[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.7974[/C][C]0.452637[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.2227[/C][C]2.42733[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]15.693[/C][C]0.657007[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]17.0807[/C][C]0.569347[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]13.6482[/C][C]-0.0482301[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]13.835[/C][C]0.514999[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.9333[/C][C]-1.18326[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]18.0504[/C][C]0.199615[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]16.0879[/C][C]-6.18789[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6776[/C][C]1.32237[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.544[/C][C]1.70604[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]17.0972[/C][C]-0.24725[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]13.3755[/C][C]1.22449[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.1321[/C][C]-0.282068[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]18.6146[/C][C]0.335402[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.459[/C][C]1.14098[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.994[/C][C]-2.14397[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]14.5281[/C][C]-2.77815[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.6463[/C][C]1.80373[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]15.3457[/C][C]0.554284[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.6413[/C][C]-1.54132[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]9.12349[/C][C]6.97651[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]19.539[/C][C]0.361032[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.2805[/C][C]0.669506[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.4423[/C][C]2.00765[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.4005[/C][C]0.699469[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]16.5183[/C][C]-1.51827[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]14.782[/C][C]-3.43196[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.7221[/C][C]1.22794[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.4106[/C][C]2.68937[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]15.8177[/C][C]-1.21767[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]17.0761[/C][C]-1.6761[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]17.0294[/C][C]-1.62939[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]15.1432[/C][C]2.45681[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.0387[/C][C]-0.688693[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.0609[/C][C]3.03915[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]16.5695[/C][C]-1.21951[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]9.4478[/C][C]-1.8478[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]15.4514[/C][C]-2.05137[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]15.3081[/C][C]-1.40814[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]17.2947[/C][C]1.80528[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]14.9208[/C][C]0.329176[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]15.5553[/C][C]-2.65535[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.7147[/C][C]0.385312[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]14.6281[/C][C]2.72195[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.2394[/C][C]-2.08939[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]13.8126[/C][C]-1.66262[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]10.4536[/C][C]2.14638[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]11.9554[/C][C]-1.60542[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]15.6144[/C][C]-0.214358[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]13.4823[/C][C]-3.88234[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.0155[/C][C]3.18447[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.2327[/C][C]0.367286[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]13.4845[/C][C]1.36552[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.0327[/C][C]-1.28275[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]14.4385[/C][C]-0.338511[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.0403[/C][C]1.85969[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]15.0405[/C][C]1.20955[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]18.9029[/C][C]0.347083[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]12.042[/C][C]1.55796[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]15.2189[/C][C]-1.61892[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]17.0544[/C][C]-1.40438[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.4654[/C][C]-0.715425[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.3265[/C][C]1.27352[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.98827[/C][C]-0.138271[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.5781[/C][C]1.07192[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]17.414[/C][C]1.78596[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]14.9725[/C][C]1.62748[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.3345[/C][C]-0.134532[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]15.1638[/C][C]0.0862157[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]14.3375[/C][C]-2.43753[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.6993[/C][C]-0.499263[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.9318[/C][C]-0.581805[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]11.6638[/C][C]0.736237[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0066[/C][C]1.84342[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.6687[/C][C]0.481268[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.5869[/C][C]-0.43687[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.1546[/C][C]-0.504646[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.1656[/C][C]2.58439[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.761[/C][C]-4.11097[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]12.3973[/C][C]-0.0472911[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]12.6583[/C][C]2.94165[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]19.0668[/C][C]0.233194[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.5828[/C][C]3.61717[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]15.8357[/C][C]1.26431[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]14.3749[/C][C]1.22514[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]15.6286[/C][C]2.77139[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.7564[/C][C]2.29363[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]15.7895[/C][C]2.76054[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]17.7388[/C][C]1.36119[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.2715[/C][C]-0.17151[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.3086[/C][C]-3.45863[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]11.8453[/C][C]-2.34529[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.6018[/C][C]-6.10182[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]10.2616[/C][C]1.5884[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.6977[/C][C]-1.09773[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.8546[/C][C]-0.15457[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]12.8457[/C][C]-0.445697[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]15.2086[/C][C]-1.85864[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.0444[/C][C]-0.644412[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.1891[/C][C]0.710853[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]18.8501[/C][C]1.04986[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]12.5441[/C][C]-1.34411[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]15.2221[/C][C]-0.622079[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]18.0071[/C][C]-0.407121[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]12.6945[/C][C]1.35551[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.241[/C][C]0.85897[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.4064[/C][C]-1.05635[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]13.0243[/C][C]-1.17432[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]13.9187[/C][C]-1.96866[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]15.3142[/C][C]-0.564179[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]13.2936[/C][C]1.85636[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]15.4254[/C][C]-2.22536[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.6069[/C][C]0.243129[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.2524[/C][C]-4.40235[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.7123[/C][C]-5.01229[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.1511[/C][C]-1.55106[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]13.3703[/C][C]-5.52032[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]10.9358[/C][C]0.0142292[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.2788[/C][C]-1.92879[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]12.7646[/C][C]-2.81461[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]13.8557[/C][C]1.04426[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]16.1398[/C][C]0.510181[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]14.3462[/C][C]-0.94616[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.195[/C][C]-0.244994[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]13.4254[/C][C]2.27458[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.4238[/C][C]1.42615[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]12.1916[/C][C]-1.24161[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]13.9942[/C][C]1.35576[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]12.6911[/C][C]-0.491133[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]14.9233[/C][C]0.176706[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]16.485[/C][C]1.26502[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.814[/C][C]0.38601[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]14.7484[/C][C]-0.148386[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.9927[/C][C]0.657301[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]8.89035[/C][C]-0.790354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267417&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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
14.358.78364-4.43364
212.711.93170.768294
318.115.60332.49665
417.8517.63120.218765
516.617.4735-0.873521
612.610.59042.00961
717.119.8986-2.79865
819.118.49390.606095
916.118.4291-2.32909
1013.3510.64542.7046
1118.417.86060.539391
1214.710.09454.60552
1310.614.0239-3.42387
1412.612.7571-0.157053
1516.213.85892.34111
1613.613.24710.352914
1718.916.00682.89319
1814.112.56751.53252
1914.514.36020.139822
2016.1517.3805-1.23055
2114.7513.55481.1952
2214.813.96670.833265
2312.4512.5877-0.13772
2412.6512.9147-0.264669
2517.3515.01472.33532
268.610.1058-1.50584
2718.417.36241.03763
2816.116.02140.0785511
2911.612.1411-0.541132
3017.7515.59572.15429
3115.2514.79740.452637
3217.6515.22272.42733
3316.3515.6930.657007
3417.6517.08070.569347
3513.613.6482-0.0482301
3614.3513.8350.514999
3714.7515.9333-1.18326
3818.2518.05040.199615
399.916.0879-6.18789
401614.67761.32237
4118.2516.5441.70604
4216.8517.0972-0.24725
4314.613.37551.22449
4413.8514.1321-0.282068
4518.9518.61460.335402
4615.614.4591.14098
4714.8516.994-2.14397
4811.7514.5281-2.77815
4918.4516.64631.80373
5015.915.34570.554284
5117.118.6413-1.54132
5216.19.123496.97651
5319.919.5390.361032
5410.9510.28050.669506
5518.4516.44232.00765
5615.114.40050.699469
571516.5183-1.51827
5811.3514.782-3.43196
5915.9514.72211.22794
6018.115.41062.68937
6114.615.8177-1.21767
6215.417.0761-1.6761
6315.417.0294-1.62939
6417.615.14322.45681
6513.3514.0387-0.688693
6619.116.06093.03915
6715.3516.5695-1.21951
687.69.4478-1.8478
6913.415.4514-2.05137
7013.915.3081-1.40814
7119.117.29471.80528
7215.2514.92080.329176
7312.915.5553-2.65535
7416.115.71470.385312
7517.3514.62812.72195
7613.1515.2394-2.08939
7712.1513.8126-1.66262
7812.610.45362.14638
7910.3511.9554-1.60542
8015.415.6144-0.214358
819.613.4823-3.88234
8218.215.01553.18447
8313.613.23270.367286
8414.8513.48451.36552
8514.7516.0327-1.28275
8614.114.4385-0.338511
8714.913.04031.85969
8816.2515.04051.20955
8919.2518.90290.347083
9013.612.0421.55796
9113.615.2189-1.61892
9215.6517.0544-1.40438
9312.7513.4654-0.715425
9414.613.32651.27352
959.859.98827-0.138271
9612.6511.57811.07192
9719.217.4141.78596
9816.614.97251.62748
9911.211.3345-0.134532
10015.2515.16380.0862157
10111.914.3375-2.43753
10213.213.6993-0.499263
10316.3516.9318-0.581805
10412.411.66380.736237
10515.8514.00661.84342
10618.1517.66870.481268
10711.1511.5869-0.43687
10815.6516.1546-0.504646
10917.7515.16562.58439
1107.6511.761-4.11097
11112.3512.3973-0.0472911
11215.612.65832.94165
11319.319.06680.233194
11415.211.58283.61717
11517.115.83571.26431
11615.614.37491.22514
11718.415.62862.77139
11819.0516.75642.29363
11918.5515.78952.76054
12019.117.73881.36119
12113.113.2715-0.17151
12212.8516.3086-3.45863
1239.511.8453-2.34529
1244.510.6018-6.10182
12511.8510.26161.5884
12613.614.6977-1.09773
12711.711.8546-0.15457
12812.412.8457-0.445697
12913.3515.2086-1.85864
13011.412.0444-0.644412
13114.914.18910.710853
13219.918.85011.04986
13311.212.5441-1.34411
13414.615.2221-0.622079
13517.618.0071-0.407121
13614.0512.69451.35551
13716.115.2410.85897
13813.3514.4064-1.05635
13911.8513.0243-1.17432
14011.9513.9187-1.96866
14114.7515.3142-0.564179
14215.1513.29361.85636
14313.215.4254-2.22536
14416.8516.60690.243129
1457.8512.2524-4.40235
1467.712.7123-5.01229
14712.614.1511-1.55106
1487.8513.3703-5.52032
14910.9510.93580.0142292
15012.3514.2788-1.92879
1519.9512.7646-2.81461
15214.913.85571.04426
15316.6516.13980.510181
15413.414.3462-0.94616
15513.9514.195-0.244994
15615.713.42542.27458
15716.8515.42381.42615
15810.9512.1916-1.24161
15915.3513.99421.35576
16012.212.6911-0.491133
16115.114.92330.176706
16217.7516.4851.26502
16315.214.8140.38601
16414.614.7484-0.148386
16516.6515.99270.657301
1668.18.89035-0.790354






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.3824560.7649110.617544
230.2802060.5604120.719794
240.6452830.7094340.354717
250.647870.7042590.35213
260.6468130.7063740.353187
270.6273880.7452230.372612
280.6579080.6841840.342092
290.5966620.8066770.403338
300.6364020.7271960.363598
310.5684860.8630290.431514
320.5013080.9973840.498692
330.4207670.8415330.579233
340.3564290.7128580.643571
350.3569240.7138470.643076
360.297430.594860.70257
370.2380780.4761560.761922
380.1843650.368730.815635
390.5725570.8548860.427443
400.6543080.6913840.345692
410.6034150.7931710.396585
420.5400080.9199840.459992
430.4812970.9625930.518703
440.4218330.8436650.578167
450.3607280.7214550.639272
460.3118680.6237360.688132
470.3914250.782850.608575
480.385670.771340.61433
490.3418340.6836670.658166
500.3011760.6023530.698824
510.2796680.5593360.720332
520.7858820.4282370.214118
530.7502650.499470.249735
540.7176210.5647590.282379
550.724780.550440.27522
560.7297320.5405350.270268
570.6990080.6019840.300992
580.7751050.449790.224895
590.7471090.5057830.252891
600.8053090.3893810.194691
610.7919590.4160820.208041
620.7700620.4598770.229938
630.7450480.5099030.254952
640.7468960.5062090.253104
650.7608280.4783440.239172
660.7839710.4320580.216029
670.7831460.4337080.216854
680.7957360.4085270.204264
690.815720.3685590.18428
700.8063170.3873670.193683
710.7995490.4009020.200451
720.7622490.4755030.237751
730.7809310.4381380.219069
740.7419190.5161610.258081
750.7467390.5065230.253261
760.76870.46260.2313
770.7645340.4709330.235466
780.7543690.4912620.245631
790.7419150.5161710.258085
800.6999210.6001590.300079
810.8038880.3922230.196112
820.8457120.3085750.154288
830.8192170.3615660.180783
840.8007110.3985790.199289
850.7810170.4379650.218983
860.7441340.5117320.255866
870.7304940.5390110.269506
880.6979390.6041210.302061
890.6671010.6657980.332899
900.6409680.7180640.359032
910.6253450.7493090.374655
920.6108650.7782710.389135
930.5703970.8592050.429603
940.5646020.8707960.435398
950.5754470.8491060.424553
960.5440550.9118890.455945
970.550990.8980210.44901
980.5290880.9418250.470912
990.4991240.9982470.500876
1000.4476990.8953980.552301
1010.4697870.9395740.530213
1020.4226120.8452240.577388
1030.3789430.7578860.621057
1040.3459820.6919650.654018
1050.3397380.6794750.660262
1060.2927910.5855810.707209
1070.2607920.5215840.739208
1080.226570.453140.77343
1090.2423640.4847280.757636
1100.3304940.6609880.669506
1110.294340.5886790.70566
1120.4229550.8459110.577045
1130.3734240.7468480.626576
1140.4666230.9332450.533377
1150.4501870.9003740.549813
1160.441780.883560.55822
1170.5972370.8055260.402763
1180.6404910.7190170.359509
1190.6129250.774150.387075
1200.5904530.8190950.409547
1210.553330.8933390.44667
1220.5426860.9146290.457314
1230.5140130.9719740.485987
1240.6999660.6000680.300034
1250.6951710.6096580.304829
1260.6367160.7265670.363284
1270.6633150.6733710.336685
1280.6004140.7991710.399586
1290.5564850.8870310.443515
1300.5073780.9852450.492622
1310.4631380.9262760.536862
1320.397230.794460.60277
1330.3878670.7757330.612133
1340.3248550.649710.675145
1350.2611830.5223660.738817
1360.6097320.7805360.390268
1370.5274380.9451250.472562
1380.45670.91340.5433
1390.6596340.6807320.340366
1400.6671720.6656550.332828
1410.687730.6245410.31227
1420.6834840.6330310.316516
1430.5449380.9101240.455062
1440.68790.62420.3121

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.382456 & 0.764911 & 0.617544 \tabularnewline
23 & 0.280206 & 0.560412 & 0.719794 \tabularnewline
24 & 0.645283 & 0.709434 & 0.354717 \tabularnewline
25 & 0.64787 & 0.704259 & 0.35213 \tabularnewline
26 & 0.646813 & 0.706374 & 0.353187 \tabularnewline
27 & 0.627388 & 0.745223 & 0.372612 \tabularnewline
28 & 0.657908 & 0.684184 & 0.342092 \tabularnewline
29 & 0.596662 & 0.806677 & 0.403338 \tabularnewline
30 & 0.636402 & 0.727196 & 0.363598 \tabularnewline
31 & 0.568486 & 0.863029 & 0.431514 \tabularnewline
32 & 0.501308 & 0.997384 & 0.498692 \tabularnewline
33 & 0.420767 & 0.841533 & 0.579233 \tabularnewline
34 & 0.356429 & 0.712858 & 0.643571 \tabularnewline
35 & 0.356924 & 0.713847 & 0.643076 \tabularnewline
36 & 0.29743 & 0.59486 & 0.70257 \tabularnewline
37 & 0.238078 & 0.476156 & 0.761922 \tabularnewline
38 & 0.184365 & 0.36873 & 0.815635 \tabularnewline
39 & 0.572557 & 0.854886 & 0.427443 \tabularnewline
40 & 0.654308 & 0.691384 & 0.345692 \tabularnewline
41 & 0.603415 & 0.793171 & 0.396585 \tabularnewline
42 & 0.540008 & 0.919984 & 0.459992 \tabularnewline
43 & 0.481297 & 0.962593 & 0.518703 \tabularnewline
44 & 0.421833 & 0.843665 & 0.578167 \tabularnewline
45 & 0.360728 & 0.721455 & 0.639272 \tabularnewline
46 & 0.311868 & 0.623736 & 0.688132 \tabularnewline
47 & 0.391425 & 0.78285 & 0.608575 \tabularnewline
48 & 0.38567 & 0.77134 & 0.61433 \tabularnewline
49 & 0.341834 & 0.683667 & 0.658166 \tabularnewline
50 & 0.301176 & 0.602353 & 0.698824 \tabularnewline
51 & 0.279668 & 0.559336 & 0.720332 \tabularnewline
52 & 0.785882 & 0.428237 & 0.214118 \tabularnewline
53 & 0.750265 & 0.49947 & 0.249735 \tabularnewline
54 & 0.717621 & 0.564759 & 0.282379 \tabularnewline
55 & 0.72478 & 0.55044 & 0.27522 \tabularnewline
56 & 0.729732 & 0.540535 & 0.270268 \tabularnewline
57 & 0.699008 & 0.601984 & 0.300992 \tabularnewline
58 & 0.775105 & 0.44979 & 0.224895 \tabularnewline
59 & 0.747109 & 0.505783 & 0.252891 \tabularnewline
60 & 0.805309 & 0.389381 & 0.194691 \tabularnewline
61 & 0.791959 & 0.416082 & 0.208041 \tabularnewline
62 & 0.770062 & 0.459877 & 0.229938 \tabularnewline
63 & 0.745048 & 0.509903 & 0.254952 \tabularnewline
64 & 0.746896 & 0.506209 & 0.253104 \tabularnewline
65 & 0.760828 & 0.478344 & 0.239172 \tabularnewline
66 & 0.783971 & 0.432058 & 0.216029 \tabularnewline
67 & 0.783146 & 0.433708 & 0.216854 \tabularnewline
68 & 0.795736 & 0.408527 & 0.204264 \tabularnewline
69 & 0.81572 & 0.368559 & 0.18428 \tabularnewline
70 & 0.806317 & 0.387367 & 0.193683 \tabularnewline
71 & 0.799549 & 0.400902 & 0.200451 \tabularnewline
72 & 0.762249 & 0.475503 & 0.237751 \tabularnewline
73 & 0.780931 & 0.438138 & 0.219069 \tabularnewline
74 & 0.741919 & 0.516161 & 0.258081 \tabularnewline
75 & 0.746739 & 0.506523 & 0.253261 \tabularnewline
76 & 0.7687 & 0.4626 & 0.2313 \tabularnewline
77 & 0.764534 & 0.470933 & 0.235466 \tabularnewline
78 & 0.754369 & 0.491262 & 0.245631 \tabularnewline
79 & 0.741915 & 0.516171 & 0.258085 \tabularnewline
80 & 0.699921 & 0.600159 & 0.300079 \tabularnewline
81 & 0.803888 & 0.392223 & 0.196112 \tabularnewline
82 & 0.845712 & 0.308575 & 0.154288 \tabularnewline
83 & 0.819217 & 0.361566 & 0.180783 \tabularnewline
84 & 0.800711 & 0.398579 & 0.199289 \tabularnewline
85 & 0.781017 & 0.437965 & 0.218983 \tabularnewline
86 & 0.744134 & 0.511732 & 0.255866 \tabularnewline
87 & 0.730494 & 0.539011 & 0.269506 \tabularnewline
88 & 0.697939 & 0.604121 & 0.302061 \tabularnewline
89 & 0.667101 & 0.665798 & 0.332899 \tabularnewline
90 & 0.640968 & 0.718064 & 0.359032 \tabularnewline
91 & 0.625345 & 0.749309 & 0.374655 \tabularnewline
92 & 0.610865 & 0.778271 & 0.389135 \tabularnewline
93 & 0.570397 & 0.859205 & 0.429603 \tabularnewline
94 & 0.564602 & 0.870796 & 0.435398 \tabularnewline
95 & 0.575447 & 0.849106 & 0.424553 \tabularnewline
96 & 0.544055 & 0.911889 & 0.455945 \tabularnewline
97 & 0.55099 & 0.898021 & 0.44901 \tabularnewline
98 & 0.529088 & 0.941825 & 0.470912 \tabularnewline
99 & 0.499124 & 0.998247 & 0.500876 \tabularnewline
100 & 0.447699 & 0.895398 & 0.552301 \tabularnewline
101 & 0.469787 & 0.939574 & 0.530213 \tabularnewline
102 & 0.422612 & 0.845224 & 0.577388 \tabularnewline
103 & 0.378943 & 0.757886 & 0.621057 \tabularnewline
104 & 0.345982 & 0.691965 & 0.654018 \tabularnewline
105 & 0.339738 & 0.679475 & 0.660262 \tabularnewline
106 & 0.292791 & 0.585581 & 0.707209 \tabularnewline
107 & 0.260792 & 0.521584 & 0.739208 \tabularnewline
108 & 0.22657 & 0.45314 & 0.77343 \tabularnewline
109 & 0.242364 & 0.484728 & 0.757636 \tabularnewline
110 & 0.330494 & 0.660988 & 0.669506 \tabularnewline
111 & 0.29434 & 0.588679 & 0.70566 \tabularnewline
112 & 0.422955 & 0.845911 & 0.577045 \tabularnewline
113 & 0.373424 & 0.746848 & 0.626576 \tabularnewline
114 & 0.466623 & 0.933245 & 0.533377 \tabularnewline
115 & 0.450187 & 0.900374 & 0.549813 \tabularnewline
116 & 0.44178 & 0.88356 & 0.55822 \tabularnewline
117 & 0.597237 & 0.805526 & 0.402763 \tabularnewline
118 & 0.640491 & 0.719017 & 0.359509 \tabularnewline
119 & 0.612925 & 0.77415 & 0.387075 \tabularnewline
120 & 0.590453 & 0.819095 & 0.409547 \tabularnewline
121 & 0.55333 & 0.893339 & 0.44667 \tabularnewline
122 & 0.542686 & 0.914629 & 0.457314 \tabularnewline
123 & 0.514013 & 0.971974 & 0.485987 \tabularnewline
124 & 0.699966 & 0.600068 & 0.300034 \tabularnewline
125 & 0.695171 & 0.609658 & 0.304829 \tabularnewline
126 & 0.636716 & 0.726567 & 0.363284 \tabularnewline
127 & 0.663315 & 0.673371 & 0.336685 \tabularnewline
128 & 0.600414 & 0.799171 & 0.399586 \tabularnewline
129 & 0.556485 & 0.887031 & 0.443515 \tabularnewline
130 & 0.507378 & 0.985245 & 0.492622 \tabularnewline
131 & 0.463138 & 0.926276 & 0.536862 \tabularnewline
132 & 0.39723 & 0.79446 & 0.60277 \tabularnewline
133 & 0.387867 & 0.775733 & 0.612133 \tabularnewline
134 & 0.324855 & 0.64971 & 0.675145 \tabularnewline
135 & 0.261183 & 0.522366 & 0.738817 \tabularnewline
136 & 0.609732 & 0.780536 & 0.390268 \tabularnewline
137 & 0.527438 & 0.945125 & 0.472562 \tabularnewline
138 & 0.4567 & 0.9134 & 0.5433 \tabularnewline
139 & 0.659634 & 0.680732 & 0.340366 \tabularnewline
140 & 0.667172 & 0.665655 & 0.332828 \tabularnewline
141 & 0.68773 & 0.624541 & 0.31227 \tabularnewline
142 & 0.683484 & 0.633031 & 0.316516 \tabularnewline
143 & 0.544938 & 0.910124 & 0.455062 \tabularnewline
144 & 0.6879 & 0.6242 & 0.3121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267417&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]22[/C][C]0.382456[/C][C]0.764911[/C][C]0.617544[/C][/ROW]
[ROW][C]23[/C][C]0.280206[/C][C]0.560412[/C][C]0.719794[/C][/ROW]
[ROW][C]24[/C][C]0.645283[/C][C]0.709434[/C][C]0.354717[/C][/ROW]
[ROW][C]25[/C][C]0.64787[/C][C]0.704259[/C][C]0.35213[/C][/ROW]
[ROW][C]26[/C][C]0.646813[/C][C]0.706374[/C][C]0.353187[/C][/ROW]
[ROW][C]27[/C][C]0.627388[/C][C]0.745223[/C][C]0.372612[/C][/ROW]
[ROW][C]28[/C][C]0.657908[/C][C]0.684184[/C][C]0.342092[/C][/ROW]
[ROW][C]29[/C][C]0.596662[/C][C]0.806677[/C][C]0.403338[/C][/ROW]
[ROW][C]30[/C][C]0.636402[/C][C]0.727196[/C][C]0.363598[/C][/ROW]
[ROW][C]31[/C][C]0.568486[/C][C]0.863029[/C][C]0.431514[/C][/ROW]
[ROW][C]32[/C][C]0.501308[/C][C]0.997384[/C][C]0.498692[/C][/ROW]
[ROW][C]33[/C][C]0.420767[/C][C]0.841533[/C][C]0.579233[/C][/ROW]
[ROW][C]34[/C][C]0.356429[/C][C]0.712858[/C][C]0.643571[/C][/ROW]
[ROW][C]35[/C][C]0.356924[/C][C]0.713847[/C][C]0.643076[/C][/ROW]
[ROW][C]36[/C][C]0.29743[/C][C]0.59486[/C][C]0.70257[/C][/ROW]
[ROW][C]37[/C][C]0.238078[/C][C]0.476156[/C][C]0.761922[/C][/ROW]
[ROW][C]38[/C][C]0.184365[/C][C]0.36873[/C][C]0.815635[/C][/ROW]
[ROW][C]39[/C][C]0.572557[/C][C]0.854886[/C][C]0.427443[/C][/ROW]
[ROW][C]40[/C][C]0.654308[/C][C]0.691384[/C][C]0.345692[/C][/ROW]
[ROW][C]41[/C][C]0.603415[/C][C]0.793171[/C][C]0.396585[/C][/ROW]
[ROW][C]42[/C][C]0.540008[/C][C]0.919984[/C][C]0.459992[/C][/ROW]
[ROW][C]43[/C][C]0.481297[/C][C]0.962593[/C][C]0.518703[/C][/ROW]
[ROW][C]44[/C][C]0.421833[/C][C]0.843665[/C][C]0.578167[/C][/ROW]
[ROW][C]45[/C][C]0.360728[/C][C]0.721455[/C][C]0.639272[/C][/ROW]
[ROW][C]46[/C][C]0.311868[/C][C]0.623736[/C][C]0.688132[/C][/ROW]
[ROW][C]47[/C][C]0.391425[/C][C]0.78285[/C][C]0.608575[/C][/ROW]
[ROW][C]48[/C][C]0.38567[/C][C]0.77134[/C][C]0.61433[/C][/ROW]
[ROW][C]49[/C][C]0.341834[/C][C]0.683667[/C][C]0.658166[/C][/ROW]
[ROW][C]50[/C][C]0.301176[/C][C]0.602353[/C][C]0.698824[/C][/ROW]
[ROW][C]51[/C][C]0.279668[/C][C]0.559336[/C][C]0.720332[/C][/ROW]
[ROW][C]52[/C][C]0.785882[/C][C]0.428237[/C][C]0.214118[/C][/ROW]
[ROW][C]53[/C][C]0.750265[/C][C]0.49947[/C][C]0.249735[/C][/ROW]
[ROW][C]54[/C][C]0.717621[/C][C]0.564759[/C][C]0.282379[/C][/ROW]
[ROW][C]55[/C][C]0.72478[/C][C]0.55044[/C][C]0.27522[/C][/ROW]
[ROW][C]56[/C][C]0.729732[/C][C]0.540535[/C][C]0.270268[/C][/ROW]
[ROW][C]57[/C][C]0.699008[/C][C]0.601984[/C][C]0.300992[/C][/ROW]
[ROW][C]58[/C][C]0.775105[/C][C]0.44979[/C][C]0.224895[/C][/ROW]
[ROW][C]59[/C][C]0.747109[/C][C]0.505783[/C][C]0.252891[/C][/ROW]
[ROW][C]60[/C][C]0.805309[/C][C]0.389381[/C][C]0.194691[/C][/ROW]
[ROW][C]61[/C][C]0.791959[/C][C]0.416082[/C][C]0.208041[/C][/ROW]
[ROW][C]62[/C][C]0.770062[/C][C]0.459877[/C][C]0.229938[/C][/ROW]
[ROW][C]63[/C][C]0.745048[/C][C]0.509903[/C][C]0.254952[/C][/ROW]
[ROW][C]64[/C][C]0.746896[/C][C]0.506209[/C][C]0.253104[/C][/ROW]
[ROW][C]65[/C][C]0.760828[/C][C]0.478344[/C][C]0.239172[/C][/ROW]
[ROW][C]66[/C][C]0.783971[/C][C]0.432058[/C][C]0.216029[/C][/ROW]
[ROW][C]67[/C][C]0.783146[/C][C]0.433708[/C][C]0.216854[/C][/ROW]
[ROW][C]68[/C][C]0.795736[/C][C]0.408527[/C][C]0.204264[/C][/ROW]
[ROW][C]69[/C][C]0.81572[/C][C]0.368559[/C][C]0.18428[/C][/ROW]
[ROW][C]70[/C][C]0.806317[/C][C]0.387367[/C][C]0.193683[/C][/ROW]
[ROW][C]71[/C][C]0.799549[/C][C]0.400902[/C][C]0.200451[/C][/ROW]
[ROW][C]72[/C][C]0.762249[/C][C]0.475503[/C][C]0.237751[/C][/ROW]
[ROW][C]73[/C][C]0.780931[/C][C]0.438138[/C][C]0.219069[/C][/ROW]
[ROW][C]74[/C][C]0.741919[/C][C]0.516161[/C][C]0.258081[/C][/ROW]
[ROW][C]75[/C][C]0.746739[/C][C]0.506523[/C][C]0.253261[/C][/ROW]
[ROW][C]76[/C][C]0.7687[/C][C]0.4626[/C][C]0.2313[/C][/ROW]
[ROW][C]77[/C][C]0.764534[/C][C]0.470933[/C][C]0.235466[/C][/ROW]
[ROW][C]78[/C][C]0.754369[/C][C]0.491262[/C][C]0.245631[/C][/ROW]
[ROW][C]79[/C][C]0.741915[/C][C]0.516171[/C][C]0.258085[/C][/ROW]
[ROW][C]80[/C][C]0.699921[/C][C]0.600159[/C][C]0.300079[/C][/ROW]
[ROW][C]81[/C][C]0.803888[/C][C]0.392223[/C][C]0.196112[/C][/ROW]
[ROW][C]82[/C][C]0.845712[/C][C]0.308575[/C][C]0.154288[/C][/ROW]
[ROW][C]83[/C][C]0.819217[/C][C]0.361566[/C][C]0.180783[/C][/ROW]
[ROW][C]84[/C][C]0.800711[/C][C]0.398579[/C][C]0.199289[/C][/ROW]
[ROW][C]85[/C][C]0.781017[/C][C]0.437965[/C][C]0.218983[/C][/ROW]
[ROW][C]86[/C][C]0.744134[/C][C]0.511732[/C][C]0.255866[/C][/ROW]
[ROW][C]87[/C][C]0.730494[/C][C]0.539011[/C][C]0.269506[/C][/ROW]
[ROW][C]88[/C][C]0.697939[/C][C]0.604121[/C][C]0.302061[/C][/ROW]
[ROW][C]89[/C][C]0.667101[/C][C]0.665798[/C][C]0.332899[/C][/ROW]
[ROW][C]90[/C][C]0.640968[/C][C]0.718064[/C][C]0.359032[/C][/ROW]
[ROW][C]91[/C][C]0.625345[/C][C]0.749309[/C][C]0.374655[/C][/ROW]
[ROW][C]92[/C][C]0.610865[/C][C]0.778271[/C][C]0.389135[/C][/ROW]
[ROW][C]93[/C][C]0.570397[/C][C]0.859205[/C][C]0.429603[/C][/ROW]
[ROW][C]94[/C][C]0.564602[/C][C]0.870796[/C][C]0.435398[/C][/ROW]
[ROW][C]95[/C][C]0.575447[/C][C]0.849106[/C][C]0.424553[/C][/ROW]
[ROW][C]96[/C][C]0.544055[/C][C]0.911889[/C][C]0.455945[/C][/ROW]
[ROW][C]97[/C][C]0.55099[/C][C]0.898021[/C][C]0.44901[/C][/ROW]
[ROW][C]98[/C][C]0.529088[/C][C]0.941825[/C][C]0.470912[/C][/ROW]
[ROW][C]99[/C][C]0.499124[/C][C]0.998247[/C][C]0.500876[/C][/ROW]
[ROW][C]100[/C][C]0.447699[/C][C]0.895398[/C][C]0.552301[/C][/ROW]
[ROW][C]101[/C][C]0.469787[/C][C]0.939574[/C][C]0.530213[/C][/ROW]
[ROW][C]102[/C][C]0.422612[/C][C]0.845224[/C][C]0.577388[/C][/ROW]
[ROW][C]103[/C][C]0.378943[/C][C]0.757886[/C][C]0.621057[/C][/ROW]
[ROW][C]104[/C][C]0.345982[/C][C]0.691965[/C][C]0.654018[/C][/ROW]
[ROW][C]105[/C][C]0.339738[/C][C]0.679475[/C][C]0.660262[/C][/ROW]
[ROW][C]106[/C][C]0.292791[/C][C]0.585581[/C][C]0.707209[/C][/ROW]
[ROW][C]107[/C][C]0.260792[/C][C]0.521584[/C][C]0.739208[/C][/ROW]
[ROW][C]108[/C][C]0.22657[/C][C]0.45314[/C][C]0.77343[/C][/ROW]
[ROW][C]109[/C][C]0.242364[/C][C]0.484728[/C][C]0.757636[/C][/ROW]
[ROW][C]110[/C][C]0.330494[/C][C]0.660988[/C][C]0.669506[/C][/ROW]
[ROW][C]111[/C][C]0.29434[/C][C]0.588679[/C][C]0.70566[/C][/ROW]
[ROW][C]112[/C][C]0.422955[/C][C]0.845911[/C][C]0.577045[/C][/ROW]
[ROW][C]113[/C][C]0.373424[/C][C]0.746848[/C][C]0.626576[/C][/ROW]
[ROW][C]114[/C][C]0.466623[/C][C]0.933245[/C][C]0.533377[/C][/ROW]
[ROW][C]115[/C][C]0.450187[/C][C]0.900374[/C][C]0.549813[/C][/ROW]
[ROW][C]116[/C][C]0.44178[/C][C]0.88356[/C][C]0.55822[/C][/ROW]
[ROW][C]117[/C][C]0.597237[/C][C]0.805526[/C][C]0.402763[/C][/ROW]
[ROW][C]118[/C][C]0.640491[/C][C]0.719017[/C][C]0.359509[/C][/ROW]
[ROW][C]119[/C][C]0.612925[/C][C]0.77415[/C][C]0.387075[/C][/ROW]
[ROW][C]120[/C][C]0.590453[/C][C]0.819095[/C][C]0.409547[/C][/ROW]
[ROW][C]121[/C][C]0.55333[/C][C]0.893339[/C][C]0.44667[/C][/ROW]
[ROW][C]122[/C][C]0.542686[/C][C]0.914629[/C][C]0.457314[/C][/ROW]
[ROW][C]123[/C][C]0.514013[/C][C]0.971974[/C][C]0.485987[/C][/ROW]
[ROW][C]124[/C][C]0.699966[/C][C]0.600068[/C][C]0.300034[/C][/ROW]
[ROW][C]125[/C][C]0.695171[/C][C]0.609658[/C][C]0.304829[/C][/ROW]
[ROW][C]126[/C][C]0.636716[/C][C]0.726567[/C][C]0.363284[/C][/ROW]
[ROW][C]127[/C][C]0.663315[/C][C]0.673371[/C][C]0.336685[/C][/ROW]
[ROW][C]128[/C][C]0.600414[/C][C]0.799171[/C][C]0.399586[/C][/ROW]
[ROW][C]129[/C][C]0.556485[/C][C]0.887031[/C][C]0.443515[/C][/ROW]
[ROW][C]130[/C][C]0.507378[/C][C]0.985245[/C][C]0.492622[/C][/ROW]
[ROW][C]131[/C][C]0.463138[/C][C]0.926276[/C][C]0.536862[/C][/ROW]
[ROW][C]132[/C][C]0.39723[/C][C]0.79446[/C][C]0.60277[/C][/ROW]
[ROW][C]133[/C][C]0.387867[/C][C]0.775733[/C][C]0.612133[/C][/ROW]
[ROW][C]134[/C][C]0.324855[/C][C]0.64971[/C][C]0.675145[/C][/ROW]
[ROW][C]135[/C][C]0.261183[/C][C]0.522366[/C][C]0.738817[/C][/ROW]
[ROW][C]136[/C][C]0.609732[/C][C]0.780536[/C][C]0.390268[/C][/ROW]
[ROW][C]137[/C][C]0.527438[/C][C]0.945125[/C][C]0.472562[/C][/ROW]
[ROW][C]138[/C][C]0.4567[/C][C]0.9134[/C][C]0.5433[/C][/ROW]
[ROW][C]139[/C][C]0.659634[/C][C]0.680732[/C][C]0.340366[/C][/ROW]
[ROW][C]140[/C][C]0.667172[/C][C]0.665655[/C][C]0.332828[/C][/ROW]
[ROW][C]141[/C][C]0.68773[/C][C]0.624541[/C][C]0.31227[/C][/ROW]
[ROW][C]142[/C][C]0.683484[/C][C]0.633031[/C][C]0.316516[/C][/ROW]
[ROW][C]143[/C][C]0.544938[/C][C]0.910124[/C][C]0.455062[/C][/ROW]
[ROW][C]144[/C][C]0.6879[/C][C]0.6242[/C][C]0.3121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267417&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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
220.3824560.7649110.617544
230.2802060.5604120.719794
240.6452830.7094340.354717
250.647870.7042590.35213
260.6468130.7063740.353187
270.6273880.7452230.372612
280.6579080.6841840.342092
290.5966620.8066770.403338
300.6364020.7271960.363598
310.5684860.8630290.431514
320.5013080.9973840.498692
330.4207670.8415330.579233
340.3564290.7128580.643571
350.3569240.7138470.643076
360.297430.594860.70257
370.2380780.4761560.761922
380.1843650.368730.815635
390.5725570.8548860.427443
400.6543080.6913840.345692
410.6034150.7931710.396585
420.5400080.9199840.459992
430.4812970.9625930.518703
440.4218330.8436650.578167
450.3607280.7214550.639272
460.3118680.6237360.688132
470.3914250.782850.608575
480.385670.771340.61433
490.3418340.6836670.658166
500.3011760.6023530.698824
510.2796680.5593360.720332
520.7858820.4282370.214118
530.7502650.499470.249735
540.7176210.5647590.282379
550.724780.550440.27522
560.7297320.5405350.270268
570.6990080.6019840.300992
580.7751050.449790.224895
590.7471090.5057830.252891
600.8053090.3893810.194691
610.7919590.4160820.208041
620.7700620.4598770.229938
630.7450480.5099030.254952
640.7468960.5062090.253104
650.7608280.4783440.239172
660.7839710.4320580.216029
670.7831460.4337080.216854
680.7957360.4085270.204264
690.815720.3685590.18428
700.8063170.3873670.193683
710.7995490.4009020.200451
720.7622490.4755030.237751
730.7809310.4381380.219069
740.7419190.5161610.258081
750.7467390.5065230.253261
760.76870.46260.2313
770.7645340.4709330.235466
780.7543690.4912620.245631
790.7419150.5161710.258085
800.6999210.6001590.300079
810.8038880.3922230.196112
820.8457120.3085750.154288
830.8192170.3615660.180783
840.8007110.3985790.199289
850.7810170.4379650.218983
860.7441340.5117320.255866
870.7304940.5390110.269506
880.6979390.6041210.302061
890.6671010.6657980.332899
900.6409680.7180640.359032
910.6253450.7493090.374655
920.6108650.7782710.389135
930.5703970.8592050.429603
940.5646020.8707960.435398
950.5754470.8491060.424553
960.5440550.9118890.455945
970.550990.8980210.44901
980.5290880.9418250.470912
990.4991240.9982470.500876
1000.4476990.8953980.552301
1010.4697870.9395740.530213
1020.4226120.8452240.577388
1030.3789430.7578860.621057
1040.3459820.6919650.654018
1050.3397380.6794750.660262
1060.2927910.5855810.707209
1070.2607920.5215840.739208
1080.226570.453140.77343
1090.2423640.4847280.757636
1100.3304940.6609880.669506
1110.294340.5886790.70566
1120.4229550.8459110.577045
1130.3734240.7468480.626576
1140.4666230.9332450.533377
1150.4501870.9003740.549813
1160.441780.883560.55822
1170.5972370.8055260.402763
1180.6404910.7190170.359509
1190.6129250.774150.387075
1200.5904530.8190950.409547
1210.553330.8933390.44667
1220.5426860.9146290.457314
1230.5140130.9719740.485987
1240.6999660.6000680.300034
1250.6951710.6096580.304829
1260.6367160.7265670.363284
1270.6633150.6733710.336685
1280.6004140.7991710.399586
1290.5564850.8870310.443515
1300.5073780.9852450.492622
1310.4631380.9262760.536862
1320.397230.794460.60277
1330.3878670.7757330.612133
1340.3248550.649710.675145
1350.2611830.5223660.738817
1360.6097320.7805360.390268
1370.5274380.9451250.472562
1380.45670.91340.5433
1390.6596340.6807320.340366
1400.6671720.6656550.332828
1410.687730.6245410.31227
1420.6834840.6330310.316516
1430.5449380.9101240.455062
1440.68790.62420.3121






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=267417&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=267417&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267417&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 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')
}