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Description of Statistical Computation

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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 04 Dec 2014 08:16:20 +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/t141768126027173sx5lktroyh.htm/, Retrieved Thu, 16 May 2024 17:05:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263048, Retrieved Thu, 16 May 2024 17:05:08 +0000
QR Codes:

Original text written by user:
IsPrivate?This computation is/was private until 2014-12-19
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-04 08:16:20] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263048&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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 Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.7071 + 0.905053programma[t] -0.51212gender[t] -0.165483age[t] -0.0309203Calculation[t] -0.262306Algebraic_Reasoning[t] + 0.141024Graphical_Interpretation[t] + 0.648558Proportionality_and_Ratio[t] + 0.210871Probability_and_Sampling[t] -0.0478632Estimation[t] + 0.045835LFM[t] + 0.0066444Blogs[t] -0.0485502PRH[t] -0.0240689CH[t] + 0.0430473Hours[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.7071 +  0.905053programma[t] -0.51212gender[t] -0.165483age[t] -0.0309203Calculation[t] -0.262306Algebraic_Reasoning[t] +  0.141024Graphical_Interpretation[t] +  0.648558Proportionality_and_Ratio[t] +  0.210871Probability_and_Sampling[t] -0.0478632Estimation[t] +  0.045835LFM[t] +  0.0066444Blogs[t] -0.0485502PRH[t] -0.0240689CH[t] +  0.0430473Hours[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263048&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.7071 +  0.905053programma[t] -0.51212gender[t] -0.165483age[t] -0.0309203Calculation[t] -0.262306Algebraic_Reasoning[t] +  0.141024Graphical_Interpretation[t] +  0.648558Proportionality_and_Ratio[t] +  0.210871Probability_and_Sampling[t] -0.0478632Estimation[t] +  0.045835LFM[t] +  0.0066444Blogs[t] -0.0485502PRH[t] -0.0240689CH[t] +  0.0430473Hours[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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] = + 10.7071 + 0.905053programma[t] -0.51212gender[t] -0.165483age[t] -0.0309203Calculation[t] -0.262306Algebraic_Reasoning[t] + 0.141024Graphical_Interpretation[t] + 0.648558Proportionality_and_Ratio[t] + 0.210871Probability_and_Sampling[t] -0.0478632Estimation[t] + 0.045835LFM[t] + 0.0066444Blogs[t] -0.0485502PRH[t] -0.0240689CH[t] + 0.0430473Hours[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.70713.885722.7550.006558430.00327921
programma0.9050530.5695581.5890.1140750.0570375
gender-0.512120.381129-1.3440.1810.0904998
age-0.1654830.168087-0.98450.326390.163195
Calculation-0.03092030.123322-0.25070.8023550.401177
Algebraic_Reasoning-0.2623060.111788-2.3460.02021020.0101051
Graphical_Interpretation0.1410240.1474670.95630.3403960.170198
Proportionality_and_Ratio0.6485580.1895993.4210.0007974730.000398737
Probability_and_Sampling0.2108710.2373250.88850.3756230.187811
Estimation-0.04786320.224472-0.21320.8314290.415715
LFM0.0458350.005779217.9313.92017e-131.96009e-13
Blogs0.00664440.003692261.80.07386480.0369324
PRH-0.04855020.360014-0.13490.8928990.44645
CH-0.02406890.357815-0.067270.9464560.473228
Hours0.04304730.358560.12010.9045930.452297

\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) & 10.7071 & 3.88572 & 2.755 & 0.00655843 & 0.00327921 \tabularnewline
programma & 0.905053 & 0.569558 & 1.589 & 0.114075 & 0.0570375 \tabularnewline
gender & -0.51212 & 0.381129 & -1.344 & 0.181 & 0.0904998 \tabularnewline
age & -0.165483 & 0.168087 & -0.9845 & 0.32639 & 0.163195 \tabularnewline
Calculation & -0.0309203 & 0.123322 & -0.2507 & 0.802355 & 0.401177 \tabularnewline
Algebraic_Reasoning & -0.262306 & 0.111788 & -2.346 & 0.0202102 & 0.0101051 \tabularnewline
Graphical_Interpretation & 0.141024 & 0.147467 & 0.9563 & 0.340396 & 0.170198 \tabularnewline
Proportionality_and_Ratio & 0.648558 & 0.189599 & 3.421 & 0.000797473 & 0.000398737 \tabularnewline
Probability_and_Sampling & 0.210871 & 0.237325 & 0.8885 & 0.375623 & 0.187811 \tabularnewline
Estimation & -0.0478632 & 0.224472 & -0.2132 & 0.831429 & 0.415715 \tabularnewline
LFM & 0.045835 & 0.00577921 & 7.931 & 3.92017e-13 & 1.96009e-13 \tabularnewline
Blogs & 0.0066444 & 0.00369226 & 1.8 & 0.0738648 & 0.0369324 \tabularnewline
PRH & -0.0485502 & 0.360014 & -0.1349 & 0.892899 & 0.44645 \tabularnewline
CH & -0.0240689 & 0.357815 & -0.06727 & 0.946456 & 0.473228 \tabularnewline
Hours & 0.0430473 & 0.35856 & 0.1201 & 0.904593 & 0.452297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263048&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]10.7071[/C][C]3.88572[/C][C]2.755[/C][C]0.00655843[/C][C]0.00327921[/C][/ROW]
[ROW][C]programma[/C][C]0.905053[/C][C]0.569558[/C][C]1.589[/C][C]0.114075[/C][C]0.0570375[/C][/ROW]
[ROW][C]gender[/C][C]-0.51212[/C][C]0.381129[/C][C]-1.344[/C][C]0.181[/C][C]0.0904998[/C][/ROW]
[ROW][C]age[/C][C]-0.165483[/C][C]0.168087[/C][C]-0.9845[/C][C]0.32639[/C][C]0.163195[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.0309203[/C][C]0.123322[/C][C]-0.2507[/C][C]0.802355[/C][C]0.401177[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.262306[/C][C]0.111788[/C][C]-2.346[/C][C]0.0202102[/C][C]0.0101051[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]0.141024[/C][C]0.147467[/C][C]0.9563[/C][C]0.340396[/C][C]0.170198[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.648558[/C][C]0.189599[/C][C]3.421[/C][C]0.000797473[/C][C]0.000398737[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.210871[/C][C]0.237325[/C][C]0.8885[/C][C]0.375623[/C][C]0.187811[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.0478632[/C][C]0.224472[/C][C]-0.2132[/C][C]0.831429[/C][C]0.415715[/C][/ROW]
[ROW][C]LFM[/C][C]0.045835[/C][C]0.00577921[/C][C]7.931[/C][C]3.92017e-13[/C][C]1.96009e-13[/C][/ROW]
[ROW][C]Blogs[/C][C]0.0066444[/C][C]0.00369226[/C][C]1.8[/C][C]0.0738648[/C][C]0.0369324[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0485502[/C][C]0.360014[/C][C]-0.1349[/C][C]0.892899[/C][C]0.44645[/C][/ROW]
[ROW][C]CH[/C][C]-0.0240689[/C][C]0.357815[/C][C]-0.06727[/C][C]0.946456[/C][C]0.473228[/C][/ROW]
[ROW][C]Hours[/C][C]0.0430473[/C][C]0.35856[/C][C]0.1201[/C][C]0.904593[/C][C]0.452297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263048&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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)10.70713.885722.7550.006558430.00327921
programma0.9050530.5695581.5890.1140750.0570375
gender-0.512120.381129-1.3440.1810.0904998
age-0.1654830.168087-0.98450.326390.163195
Calculation-0.03092030.123322-0.25070.8023550.401177
Algebraic_Reasoning-0.2623060.111788-2.3460.02021020.0101051
Graphical_Interpretation0.1410240.1474670.95630.3403960.170198
Proportionality_and_Ratio0.6485580.1895993.4210.0007974730.000398737
Probability_and_Sampling0.2108710.2373250.88850.3756230.187811
Estimation-0.04786320.224472-0.21320.8314290.415715
LFM0.0458350.005779217.9313.92017e-131.96009e-13
Blogs0.00664440.003692261.80.07386480.0369324
PRH-0.04855020.360014-0.13490.8928990.44645
CH-0.02406890.357815-0.067270.9464560.473228
Hours0.04304730.358560.12010.9045930.452297






Multiple Linear Regression - Regression Statistics
Multiple R0.749334
R-squared0.561502
Adjusted R-squared0.522149
F-TEST (value)14.2686
F-TEST (DF numerator)14
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09356
Sum Squared Residuals683.748

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.749334 \tabularnewline
R-squared & 0.561502 \tabularnewline
Adjusted R-squared & 0.522149 \tabularnewline
F-TEST (value) & 14.2686 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.09356 \tabularnewline
Sum Squared Residuals & 683.748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263048&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.749334[/C][/ROW]
[ROW][C]R-squared[/C][C]0.561502[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.522149[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.2686[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/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]2.09356[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]683.748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263048&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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 R0.749334
R-squared0.561502
Adjusted R-squared0.522149
F-TEST (value)14.2686
F-TEST (DF numerator)14
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09356
Sum Squared Residuals683.748






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.358.46683-4.11683
212.712.04810.651947
318.115.76242.33763
417.8517.79830.051656
516.617.3358-0.735832
612.610.77291.82709
717.119.7877-2.68772
819.118.28450.815517
916.118.9077-2.80771
1013.3510.30913.04088
1118.417.72810.671868
1214.710.31944.38058
1310.614.1894-3.58937
1412.612.7611-0.161096
1516.214.05382.14619
1613.613.04380.556223
1718.915.7273.17303
1814.112.60881.49125
1914.514.7372-0.237167
2016.1517.3325-1.18252
2114.7513.68811.06185
2214.814.05190.748149
2312.4512.34710.10289
2412.6512.9175-0.267516
2517.3514.98842.36158
268.610.1397-1.53973
2718.417.47740.922617
2816.115.84650.253549
2911.613.0493-1.44933
3017.7515.41462.33536
3115.2514.83780.412198
3217.6515.61542.03462
3315.614.19031.40971
3416.3515.47230.877727
3517.6516.83660.813434
3613.613.15230.447672
3711.714.1188-2.41885
3814.3513.42480.925243
3914.7515.6744-0.924401
4018.2518.23470.0152725
419.916.2491-6.34912
421614.54471.45528
4318.2516.54911.7009
4416.8516.9107-0.0607025
4514.612.92471.67535
4613.8513.79990.0500515
4718.9518.53620.413796
4815.614.58941.01063
4914.8516.6327-1.7827
5011.7514.4316-2.68158
5118.4516.57681.87322
5215.915.47210.427858
5317.118.4543-1.35434
5416.18.962927.13708
5519.919.23250.667453
5610.9510.94780.00215363
5718.4516.59931.85066
5815.114.12920.970798
591516.1468-1.14678
6011.3515.0619-3.71193
6115.9514.49411.4559
6218.115.45052.64949
6314.615.7544-1.15436
6415.417.0598-1.65979
6515.417.0598-1.65979
6617.615.32512.27493
6713.3513.846-0.496014
6819.116.09443.00563
6915.3516.1748-0.824773
707.69.73193-2.13193
7113.415.4892-2.08917
7213.915.005-1.10505
7319.117.37461.72541
7415.2514.79280.457242
7512.915.9218-3.02184
7616.115.79340.306607
7717.3514.64792.70209
7813.1515.3318-2.18181
7912.1513.7961-1.64609
8012.610.47462.1254
8110.3511.8979-1.54789
8215.415.7694-0.369393
839.613.0987-3.49868
8418.214.95543.24458
8513.612.87810.721913
8614.8513.55521.29484
8714.7516.1623-1.41229
8814.114.4798-0.379836
8914.912.91461.98541
9016.2514.69231.55774
9119.2519.13970.1103
9213.611.51572.08426
9313.615.1472-1.54719
9415.6516.9518-1.30177
9512.7513.7523-1.00231
9614.613.30241.29762
979.859.9376-0.0875981
9812.6511.75780.892188
9911.912.0511-0.15108
10019.217.6541.54598
10116.615.20761.39245
10211.211.2078-0.0077948
10315.2515.3133-0.0633259
10411.914.4728-2.57282
10513.213.6963-0.496303
10616.3517.1062-0.756229
10712.411.66070.739337
10815.8513.98171.86828
10914.3515.5674-1.21743
11018.1517.57080.579162
11111.1511.8084-0.658426
11215.6516.0945-0.444458
11317.7515.28582.46424
1147.6512.1137-4.46367
11512.3512.31780.0322262
11615.612.95172.64827
11719.319.29260.00742259
11815.212.19563.0044
11917.115.46711.63291
12015.614.45981.14017
12118.415.75552.64454
12219.0516.75932.29068
12318.5515.51863.03141
12419.117.8361.26403
12513.113.1918-0.0917872
12612.8516.3264-3.47638
1279.511.6653-2.16526
1284.510.6846-6.18462
12911.8510.37991.47007
13013.615.095-1.495
13111.711.18030.519726
13212.412.5125-0.112544
13313.3515.0282-1.67822
13411.412.1344-0.734422
13514.913.94960.950418
13619.919.23250.667453
13717.7515.10972.64029
13811.212.4065-1.20652
13914.615.4694-0.869371
14017.617.7911-0.191083
14114.0513.07540.974561
14216.115.19010.909898
14313.3514.443-1.09299
14411.8512.9447-1.09472
14511.9514.0147-2.06474
14614.7515.5066-0.75664
14715.1514.15050.999473
14813.216.2043-3.00428
14916.8516.56670.283324
1507.8512.3541-4.50408
1517.712.8151-5.11511
15212.614.3072-1.70718
1537.8513.0835-5.2335
15410.9510.94780.00215363
15512.3514.3594-2.00942
1569.9512.9423-2.9923
15714.913.94960.950418
15816.6515.99220.657753
15913.414.2225-0.822537
16013.9513.70980.240153
16115.713.25342.44657
16216.8515.17881.67118
16310.9512.0514-1.10136
16415.3513.7161.634
16512.212.9343-0.734277
16615.114.9010.199007
16717.7516.32231.42768
16815.214.60720.592825
16914.614.53720.0628169
17016.6516.10160.548394
1718.19.63356-1.53356

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 8.46683 & -4.11683 \tabularnewline
2 & 12.7 & 12.0481 & 0.651947 \tabularnewline
3 & 18.1 & 15.7624 & 2.33763 \tabularnewline
4 & 17.85 & 17.7983 & 0.051656 \tabularnewline
5 & 16.6 & 17.3358 & -0.735832 \tabularnewline
6 & 12.6 & 10.7729 & 1.82709 \tabularnewline
7 & 17.1 & 19.7877 & -2.68772 \tabularnewline
8 & 19.1 & 18.2845 & 0.815517 \tabularnewline
9 & 16.1 & 18.9077 & -2.80771 \tabularnewline
10 & 13.35 & 10.3091 & 3.04088 \tabularnewline
11 & 18.4 & 17.7281 & 0.671868 \tabularnewline
12 & 14.7 & 10.3194 & 4.38058 \tabularnewline
13 & 10.6 & 14.1894 & -3.58937 \tabularnewline
14 & 12.6 & 12.7611 & -0.161096 \tabularnewline
15 & 16.2 & 14.0538 & 2.14619 \tabularnewline
16 & 13.6 & 13.0438 & 0.556223 \tabularnewline
17 & 18.9 & 15.727 & 3.17303 \tabularnewline
18 & 14.1 & 12.6088 & 1.49125 \tabularnewline
19 & 14.5 & 14.7372 & -0.237167 \tabularnewline
20 & 16.15 & 17.3325 & -1.18252 \tabularnewline
21 & 14.75 & 13.6881 & 1.06185 \tabularnewline
22 & 14.8 & 14.0519 & 0.748149 \tabularnewline
23 & 12.45 & 12.3471 & 0.10289 \tabularnewline
24 & 12.65 & 12.9175 & -0.267516 \tabularnewline
25 & 17.35 & 14.9884 & 2.36158 \tabularnewline
26 & 8.6 & 10.1397 & -1.53973 \tabularnewline
27 & 18.4 & 17.4774 & 0.922617 \tabularnewline
28 & 16.1 & 15.8465 & 0.253549 \tabularnewline
29 & 11.6 & 13.0493 & -1.44933 \tabularnewline
30 & 17.75 & 15.4146 & 2.33536 \tabularnewline
31 & 15.25 & 14.8378 & 0.412198 \tabularnewline
32 & 17.65 & 15.6154 & 2.03462 \tabularnewline
33 & 15.6 & 14.1903 & 1.40971 \tabularnewline
34 & 16.35 & 15.4723 & 0.877727 \tabularnewline
35 & 17.65 & 16.8366 & 0.813434 \tabularnewline
36 & 13.6 & 13.1523 & 0.447672 \tabularnewline
37 & 11.7 & 14.1188 & -2.41885 \tabularnewline
38 & 14.35 & 13.4248 & 0.925243 \tabularnewline
39 & 14.75 & 15.6744 & -0.924401 \tabularnewline
40 & 18.25 & 18.2347 & 0.0152725 \tabularnewline
41 & 9.9 & 16.2491 & -6.34912 \tabularnewline
42 & 16 & 14.5447 & 1.45528 \tabularnewline
43 & 18.25 & 16.5491 & 1.7009 \tabularnewline
44 & 16.85 & 16.9107 & -0.0607025 \tabularnewline
45 & 14.6 & 12.9247 & 1.67535 \tabularnewline
46 & 13.85 & 13.7999 & 0.0500515 \tabularnewline
47 & 18.95 & 18.5362 & 0.413796 \tabularnewline
48 & 15.6 & 14.5894 & 1.01063 \tabularnewline
49 & 14.85 & 16.6327 & -1.7827 \tabularnewline
50 & 11.75 & 14.4316 & -2.68158 \tabularnewline
51 & 18.45 & 16.5768 & 1.87322 \tabularnewline
52 & 15.9 & 15.4721 & 0.427858 \tabularnewline
53 & 17.1 & 18.4543 & -1.35434 \tabularnewline
54 & 16.1 & 8.96292 & 7.13708 \tabularnewline
55 & 19.9 & 19.2325 & 0.667453 \tabularnewline
56 & 10.95 & 10.9478 & 0.00215363 \tabularnewline
57 & 18.45 & 16.5993 & 1.85066 \tabularnewline
58 & 15.1 & 14.1292 & 0.970798 \tabularnewline
59 & 15 & 16.1468 & -1.14678 \tabularnewline
60 & 11.35 & 15.0619 & -3.71193 \tabularnewline
61 & 15.95 & 14.4941 & 1.4559 \tabularnewline
62 & 18.1 & 15.4505 & 2.64949 \tabularnewline
63 & 14.6 & 15.7544 & -1.15436 \tabularnewline
64 & 15.4 & 17.0598 & -1.65979 \tabularnewline
65 & 15.4 & 17.0598 & -1.65979 \tabularnewline
66 & 17.6 & 15.3251 & 2.27493 \tabularnewline
67 & 13.35 & 13.846 & -0.496014 \tabularnewline
68 & 19.1 & 16.0944 & 3.00563 \tabularnewline
69 & 15.35 & 16.1748 & -0.824773 \tabularnewline
70 & 7.6 & 9.73193 & -2.13193 \tabularnewline
71 & 13.4 & 15.4892 & -2.08917 \tabularnewline
72 & 13.9 & 15.005 & -1.10505 \tabularnewline
73 & 19.1 & 17.3746 & 1.72541 \tabularnewline
74 & 15.25 & 14.7928 & 0.457242 \tabularnewline
75 & 12.9 & 15.9218 & -3.02184 \tabularnewline
76 & 16.1 & 15.7934 & 0.306607 \tabularnewline
77 & 17.35 & 14.6479 & 2.70209 \tabularnewline
78 & 13.15 & 15.3318 & -2.18181 \tabularnewline
79 & 12.15 & 13.7961 & -1.64609 \tabularnewline
80 & 12.6 & 10.4746 & 2.1254 \tabularnewline
81 & 10.35 & 11.8979 & -1.54789 \tabularnewline
82 & 15.4 & 15.7694 & -0.369393 \tabularnewline
83 & 9.6 & 13.0987 & -3.49868 \tabularnewline
84 & 18.2 & 14.9554 & 3.24458 \tabularnewline
85 & 13.6 & 12.8781 & 0.721913 \tabularnewline
86 & 14.85 & 13.5552 & 1.29484 \tabularnewline
87 & 14.75 & 16.1623 & -1.41229 \tabularnewline
88 & 14.1 & 14.4798 & -0.379836 \tabularnewline
89 & 14.9 & 12.9146 & 1.98541 \tabularnewline
90 & 16.25 & 14.6923 & 1.55774 \tabularnewline
91 & 19.25 & 19.1397 & 0.1103 \tabularnewline
92 & 13.6 & 11.5157 & 2.08426 \tabularnewline
93 & 13.6 & 15.1472 & -1.54719 \tabularnewline
94 & 15.65 & 16.9518 & -1.30177 \tabularnewline
95 & 12.75 & 13.7523 & -1.00231 \tabularnewline
96 & 14.6 & 13.3024 & 1.29762 \tabularnewline
97 & 9.85 & 9.9376 & -0.0875981 \tabularnewline
98 & 12.65 & 11.7578 & 0.892188 \tabularnewline
99 & 11.9 & 12.0511 & -0.15108 \tabularnewline
100 & 19.2 & 17.654 & 1.54598 \tabularnewline
101 & 16.6 & 15.2076 & 1.39245 \tabularnewline
102 & 11.2 & 11.2078 & -0.0077948 \tabularnewline
103 & 15.25 & 15.3133 & -0.0633259 \tabularnewline
104 & 11.9 & 14.4728 & -2.57282 \tabularnewline
105 & 13.2 & 13.6963 & -0.496303 \tabularnewline
106 & 16.35 & 17.1062 & -0.756229 \tabularnewline
107 & 12.4 & 11.6607 & 0.739337 \tabularnewline
108 & 15.85 & 13.9817 & 1.86828 \tabularnewline
109 & 14.35 & 15.5674 & -1.21743 \tabularnewline
110 & 18.15 & 17.5708 & 0.579162 \tabularnewline
111 & 11.15 & 11.8084 & -0.658426 \tabularnewline
112 & 15.65 & 16.0945 & -0.444458 \tabularnewline
113 & 17.75 & 15.2858 & 2.46424 \tabularnewline
114 & 7.65 & 12.1137 & -4.46367 \tabularnewline
115 & 12.35 & 12.3178 & 0.0322262 \tabularnewline
116 & 15.6 & 12.9517 & 2.64827 \tabularnewline
117 & 19.3 & 19.2926 & 0.00742259 \tabularnewline
118 & 15.2 & 12.1956 & 3.0044 \tabularnewline
119 & 17.1 & 15.4671 & 1.63291 \tabularnewline
120 & 15.6 & 14.4598 & 1.14017 \tabularnewline
121 & 18.4 & 15.7555 & 2.64454 \tabularnewline
122 & 19.05 & 16.7593 & 2.29068 \tabularnewline
123 & 18.55 & 15.5186 & 3.03141 \tabularnewline
124 & 19.1 & 17.836 & 1.26403 \tabularnewline
125 & 13.1 & 13.1918 & -0.0917872 \tabularnewline
126 & 12.85 & 16.3264 & -3.47638 \tabularnewline
127 & 9.5 & 11.6653 & -2.16526 \tabularnewline
128 & 4.5 & 10.6846 & -6.18462 \tabularnewline
129 & 11.85 & 10.3799 & 1.47007 \tabularnewline
130 & 13.6 & 15.095 & -1.495 \tabularnewline
131 & 11.7 & 11.1803 & 0.519726 \tabularnewline
132 & 12.4 & 12.5125 & -0.112544 \tabularnewline
133 & 13.35 & 15.0282 & -1.67822 \tabularnewline
134 & 11.4 & 12.1344 & -0.734422 \tabularnewline
135 & 14.9 & 13.9496 & 0.950418 \tabularnewline
136 & 19.9 & 19.2325 & 0.667453 \tabularnewline
137 & 17.75 & 15.1097 & 2.64029 \tabularnewline
138 & 11.2 & 12.4065 & -1.20652 \tabularnewline
139 & 14.6 & 15.4694 & -0.869371 \tabularnewline
140 & 17.6 & 17.7911 & -0.191083 \tabularnewline
141 & 14.05 & 13.0754 & 0.974561 \tabularnewline
142 & 16.1 & 15.1901 & 0.909898 \tabularnewline
143 & 13.35 & 14.443 & -1.09299 \tabularnewline
144 & 11.85 & 12.9447 & -1.09472 \tabularnewline
145 & 11.95 & 14.0147 & -2.06474 \tabularnewline
146 & 14.75 & 15.5066 & -0.75664 \tabularnewline
147 & 15.15 & 14.1505 & 0.999473 \tabularnewline
148 & 13.2 & 16.2043 & -3.00428 \tabularnewline
149 & 16.85 & 16.5667 & 0.283324 \tabularnewline
150 & 7.85 & 12.3541 & -4.50408 \tabularnewline
151 & 7.7 & 12.8151 & -5.11511 \tabularnewline
152 & 12.6 & 14.3072 & -1.70718 \tabularnewline
153 & 7.85 & 13.0835 & -5.2335 \tabularnewline
154 & 10.95 & 10.9478 & 0.00215363 \tabularnewline
155 & 12.35 & 14.3594 & -2.00942 \tabularnewline
156 & 9.95 & 12.9423 & -2.9923 \tabularnewline
157 & 14.9 & 13.9496 & 0.950418 \tabularnewline
158 & 16.65 & 15.9922 & 0.657753 \tabularnewline
159 & 13.4 & 14.2225 & -0.822537 \tabularnewline
160 & 13.95 & 13.7098 & 0.240153 \tabularnewline
161 & 15.7 & 13.2534 & 2.44657 \tabularnewline
162 & 16.85 & 15.1788 & 1.67118 \tabularnewline
163 & 10.95 & 12.0514 & -1.10136 \tabularnewline
164 & 15.35 & 13.716 & 1.634 \tabularnewline
165 & 12.2 & 12.9343 & -0.734277 \tabularnewline
166 & 15.1 & 14.901 & 0.199007 \tabularnewline
167 & 17.75 & 16.3223 & 1.42768 \tabularnewline
168 & 15.2 & 14.6072 & 0.592825 \tabularnewline
169 & 14.6 & 14.5372 & 0.0628169 \tabularnewline
170 & 16.65 & 16.1016 & 0.548394 \tabularnewline
171 & 8.1 & 9.63356 & -1.53356 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263048&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]8.46683[/C][C]-4.11683[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]12.0481[/C][C]0.651947[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.7624[/C][C]2.33763[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.7983[/C][C]0.051656[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.3358[/C][C]-0.735832[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.7729[/C][C]1.82709[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.7877[/C][C]-2.68772[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]18.2845[/C][C]0.815517[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]18.9077[/C][C]-2.80771[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.3091[/C][C]3.04088[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.7281[/C][C]0.671868[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.3194[/C][C]4.38058[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]14.1894[/C][C]-3.58937[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]12.7611[/C][C]-0.161096[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]14.0538[/C][C]2.14619[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.0438[/C][C]0.556223[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]15.727[/C][C]3.17303[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.6088[/C][C]1.49125[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.7372[/C][C]-0.237167[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.3325[/C][C]-1.18252[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.6881[/C][C]1.06185[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]14.0519[/C][C]0.748149[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.3471[/C][C]0.10289[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9175[/C][C]-0.267516[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.9884[/C][C]2.36158[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]10.1397[/C][C]-1.53973[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.4774[/C][C]0.922617[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.8465[/C][C]0.253549[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]13.0493[/C][C]-1.44933[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.4146[/C][C]2.33536[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.8378[/C][C]0.412198[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.6154[/C][C]2.03462[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.1903[/C][C]1.40971[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]15.4723[/C][C]0.877727[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]16.8366[/C][C]0.813434[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]13.1523[/C][C]0.447672[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.1188[/C][C]-2.41885[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]13.4248[/C][C]0.925243[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.6744[/C][C]-0.924401[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]18.2347[/C][C]0.0152725[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.2491[/C][C]-6.34912[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.5447[/C][C]1.45528[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.5491[/C][C]1.7009[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]16.9107[/C][C]-0.0607025[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.9247[/C][C]1.67535[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]13.7999[/C][C]0.0500515[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.5362[/C][C]0.413796[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.5894[/C][C]1.01063[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]16.6327[/C][C]-1.7827[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]14.4316[/C][C]-2.68158[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]16.5768[/C][C]1.87322[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]15.4721[/C][C]0.427858[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]18.4543[/C][C]-1.35434[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]8.96292[/C][C]7.13708[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]19.2325[/C][C]0.667453[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.9478[/C][C]0.00215363[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]16.5993[/C][C]1.85066[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]14.1292[/C][C]0.970798[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]16.1468[/C][C]-1.14678[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]15.0619[/C][C]-3.71193[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]14.4941[/C][C]1.4559[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.4505[/C][C]2.64949[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]15.7544[/C][C]-1.15436[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]17.0598[/C][C]-1.65979[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]17.0598[/C][C]-1.65979[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.3251[/C][C]2.27493[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]13.846[/C][C]-0.496014[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.0944[/C][C]3.00563[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.1748[/C][C]-0.824773[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]9.73193[/C][C]-2.13193[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]15.4892[/C][C]-2.08917[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.005[/C][C]-1.10505[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.3746[/C][C]1.72541[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]14.7928[/C][C]0.457242[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]15.9218[/C][C]-3.02184[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.7934[/C][C]0.306607[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.6479[/C][C]2.70209[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]15.3318[/C][C]-2.18181[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]13.7961[/C][C]-1.64609[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]10.4746[/C][C]2.1254[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]11.8979[/C][C]-1.54789[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.7694[/C][C]-0.369393[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]13.0987[/C][C]-3.49868[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.9554[/C][C]3.24458[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]12.8781[/C][C]0.721913[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.5552[/C][C]1.29484[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]16.1623[/C][C]-1.41229[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]14.4798[/C][C]-0.379836[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.9146[/C][C]1.98541[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]14.6923[/C][C]1.55774[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.1397[/C][C]0.1103[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]11.5157[/C][C]2.08426[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.1472[/C][C]-1.54719[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]16.9518[/C][C]-1.30177[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]13.7523[/C][C]-1.00231[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]13.3024[/C][C]1.29762[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]9.9376[/C][C]-0.0875981[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.7578[/C][C]0.892188[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]12.0511[/C][C]-0.15108[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.654[/C][C]1.54598[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]15.2076[/C][C]1.39245[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.2078[/C][C]-0.0077948[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.3133[/C][C]-0.0633259[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]14.4728[/C][C]-2.57282[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]13.6963[/C][C]-0.496303[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.1062[/C][C]-0.756229[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]11.6607[/C][C]0.739337[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]13.9817[/C][C]1.86828[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.5674[/C][C]-1.21743[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]17.5708[/C][C]0.579162[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]11.8084[/C][C]-0.658426[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]16.0945[/C][C]-0.444458[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.2858[/C][C]2.46424[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]12.1137[/C][C]-4.46367[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]12.3178[/C][C]0.0322262[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]12.9517[/C][C]2.64827[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]19.2926[/C][C]0.00742259[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]12.1956[/C][C]3.0044[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]15.4671[/C][C]1.63291[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]14.4598[/C][C]1.14017[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.7555[/C][C]2.64454[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.7593[/C][C]2.29068[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.5186[/C][C]3.03141[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.836[/C][C]1.26403[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]13.1918[/C][C]-0.0917872[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]16.3264[/C][C]-3.47638[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]11.6653[/C][C]-2.16526[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]10.6846[/C][C]-6.18462[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]10.3799[/C][C]1.47007[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.095[/C][C]-1.495[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.1803[/C][C]0.519726[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.5125[/C][C]-0.112544[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]15.0282[/C][C]-1.67822[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.1344[/C][C]-0.734422[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.9496[/C][C]0.950418[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]19.2325[/C][C]0.667453[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]15.1097[/C][C]2.64029[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]12.4065[/C][C]-1.20652[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]15.4694[/C][C]-0.869371[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]17.7911[/C][C]-0.191083[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.0754[/C][C]0.974561[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]15.1901[/C][C]0.909898[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.443[/C][C]-1.09299[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]12.9447[/C][C]-1.09472[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]14.0147[/C][C]-2.06474[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]15.5066[/C][C]-0.75664[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.1505[/C][C]0.999473[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.2043[/C][C]-3.00428[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.5667[/C][C]0.283324[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]12.3541[/C][C]-4.50408[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]12.8151[/C][C]-5.11511[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]14.3072[/C][C]-1.70718[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]13.0835[/C][C]-5.2335[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.9478[/C][C]0.00215363[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]14.3594[/C][C]-2.00942[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]12.9423[/C][C]-2.9923[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.9496[/C][C]0.950418[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]15.9922[/C][C]0.657753[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]14.2225[/C][C]-0.822537[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.7098[/C][C]0.240153[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]13.2534[/C][C]2.44657[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.1788[/C][C]1.67118[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]12.0514[/C][C]-1.10136[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]13.716[/C][C]1.634[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.9343[/C][C]-0.734277[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.901[/C][C]0.199007[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]16.3223[/C][C]1.42768[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.6072[/C][C]0.592825[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.5372[/C][C]0.0628169[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]16.1016[/C][C]0.548394[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]9.63356[/C][C]-1.53356[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263048&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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
14.358.46683-4.11683
212.712.04810.651947
318.115.76242.33763
417.8517.79830.051656
516.617.3358-0.735832
612.610.77291.82709
717.119.7877-2.68772
819.118.28450.815517
916.118.9077-2.80771
1013.3510.30913.04088
1118.417.72810.671868
1214.710.31944.38058
1310.614.1894-3.58937
1412.612.7611-0.161096
1516.214.05382.14619
1613.613.04380.556223
1718.915.7273.17303
1814.112.60881.49125
1914.514.7372-0.237167
2016.1517.3325-1.18252
2114.7513.68811.06185
2214.814.05190.748149
2312.4512.34710.10289
2412.6512.9175-0.267516
2517.3514.98842.36158
268.610.1397-1.53973
2718.417.47740.922617
2816.115.84650.253549
2911.613.0493-1.44933
3017.7515.41462.33536
3115.2514.83780.412198
3217.6515.61542.03462
3315.614.19031.40971
3416.3515.47230.877727
3517.6516.83660.813434
3613.613.15230.447672
3711.714.1188-2.41885
3814.3513.42480.925243
3914.7515.6744-0.924401
4018.2518.23470.0152725
419.916.2491-6.34912
421614.54471.45528
4318.2516.54911.7009
4416.8516.9107-0.0607025
4514.612.92471.67535
4613.8513.79990.0500515
4718.9518.53620.413796
4815.614.58941.01063
4914.8516.6327-1.7827
5011.7514.4316-2.68158
5118.4516.57681.87322
5215.915.47210.427858
5317.118.4543-1.35434
5416.18.962927.13708
5519.919.23250.667453
5610.9510.94780.00215363
5718.4516.59931.85066
5815.114.12920.970798
591516.1468-1.14678
6011.3515.0619-3.71193
6115.9514.49411.4559
6218.115.45052.64949
6314.615.7544-1.15436
6415.417.0598-1.65979
6515.417.0598-1.65979
6617.615.32512.27493
6713.3513.846-0.496014
6819.116.09443.00563
6915.3516.1748-0.824773
707.69.73193-2.13193
7113.415.4892-2.08917
7213.915.005-1.10505
7319.117.37461.72541
7415.2514.79280.457242
7512.915.9218-3.02184
7616.115.79340.306607
7717.3514.64792.70209
7813.1515.3318-2.18181
7912.1513.7961-1.64609
8012.610.47462.1254
8110.3511.8979-1.54789
8215.415.7694-0.369393
839.613.0987-3.49868
8418.214.95543.24458
8513.612.87810.721913
8614.8513.55521.29484
8714.7516.1623-1.41229
8814.114.4798-0.379836
8914.912.91461.98541
9016.2514.69231.55774
9119.2519.13970.1103
9213.611.51572.08426
9313.615.1472-1.54719
9415.6516.9518-1.30177
9512.7513.7523-1.00231
9614.613.30241.29762
979.859.9376-0.0875981
9812.6511.75780.892188
9911.912.0511-0.15108
10019.217.6541.54598
10116.615.20761.39245
10211.211.2078-0.0077948
10315.2515.3133-0.0633259
10411.914.4728-2.57282
10513.213.6963-0.496303
10616.3517.1062-0.756229
10712.411.66070.739337
10815.8513.98171.86828
10914.3515.5674-1.21743
11018.1517.57080.579162
11111.1511.8084-0.658426
11215.6516.0945-0.444458
11317.7515.28582.46424
1147.6512.1137-4.46367
11512.3512.31780.0322262
11615.612.95172.64827
11719.319.29260.00742259
11815.212.19563.0044
11917.115.46711.63291
12015.614.45981.14017
12118.415.75552.64454
12219.0516.75932.29068
12318.5515.51863.03141
12419.117.8361.26403
12513.113.1918-0.0917872
12612.8516.3264-3.47638
1279.511.6653-2.16526
1284.510.6846-6.18462
12911.8510.37991.47007
13013.615.095-1.495
13111.711.18030.519726
13212.412.5125-0.112544
13313.3515.0282-1.67822
13411.412.1344-0.734422
13514.913.94960.950418
13619.919.23250.667453
13717.7515.10972.64029
13811.212.4065-1.20652
13914.615.4694-0.869371
14017.617.7911-0.191083
14114.0513.07540.974561
14216.115.19010.909898
14313.3514.443-1.09299
14411.8512.9447-1.09472
14511.9514.0147-2.06474
14614.7515.5066-0.75664
14715.1514.15050.999473
14813.216.2043-3.00428
14916.8516.56670.283324
1507.8512.3541-4.50408
1517.712.8151-5.11511
15212.614.3072-1.70718
1537.8513.0835-5.2335
15410.9510.94780.00215363
15512.3514.3594-2.00942
1569.9512.9423-2.9923
15714.913.94960.950418
15816.6515.99220.657753
15913.414.2225-0.822537
16013.9513.70980.240153
16115.713.25342.44657
16216.8515.17881.67118
16310.9512.0514-1.10136
16415.3513.7161.634
16512.212.9343-0.734277
16615.114.9010.199007
16717.7516.32231.42768
16815.214.60720.592825
16914.614.53720.0628169
17016.6516.10160.548394
1718.19.63356-1.53356






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.457390.9147810.54261
190.2958830.5917670.704117
200.4200030.8400070.579997
210.5512640.8974720.448736
220.6182260.7635480.381774
230.6424930.7150140.357507
240.603090.7938210.39691
250.6831320.6337360.316868
260.7322940.5354120.267706
270.673620.6527590.32638
280.595460.809080.40454
290.5552330.8895350.444767
300.4947510.9895010.505249
310.4231890.8463770.576811
320.3897310.7794620.610269
330.3264480.6528950.673552
340.3273150.6546290.672685
350.2671470.5342950.732853
360.2147390.4294770.785261
370.3853590.7707170.614641
380.3403650.6807310.659635
390.284780.569560.71522
400.2328660.4657320.767134
410.8119170.3761650.188083
420.7921840.4156320.207816
430.7631680.4736640.236832
440.7176190.5647610.282381
450.6771310.6457380.322869
460.6255310.7489380.374469
470.5720330.8559340.427967
480.5212490.9575020.478751
490.4977410.9954820.502259
500.5504730.8990550.449527
510.5160480.9679030.483952
520.4636950.9273910.536305
530.4538580.9077150.546142
540.8552830.2894350.144717
550.8306630.3386750.169337
560.8339040.3321920.166096
570.8186620.3626750.181338
580.8054840.3890330.194516
590.7752360.4495280.224764
600.8404970.3190050.159503
610.8165930.3668150.183407
620.8347330.3305340.165267
630.8258280.3483430.174172
640.8042470.3915050.195753
650.7797440.4405110.220256
660.7827830.4344330.217217
670.7783190.4433620.221681
680.7931330.4137330.206867
690.7700310.4599370.229969
700.7916950.416610.208305
710.8093130.3813750.190687
720.7877330.4245350.212267
730.7810820.4378350.218918
740.7442230.5115550.255777
750.7923330.4153340.207667
760.7565850.486830.243415
770.7549770.4900470.245023
780.7826690.4346610.217331
790.7814950.4370090.218505
800.771280.4574390.22872
810.760880.4782410.23912
820.727220.5455610.27278
830.7848660.4302680.215134
840.8318790.3362430.168121
850.8087340.3825320.191266
860.7970620.4058760.202938
870.7748030.4503940.225197
880.7448490.5103020.255151
890.7278330.5443340.272167
900.7073180.5853640.292682
910.6655160.6689670.334484
920.6722840.6554320.327716
930.6550120.6899760.344988
940.632280.735440.36772
950.5990910.8018180.400909
960.577710.844580.42229
970.5841780.8316450.415822
980.5685810.8628380.431419
990.5260360.9479290.473964
1000.5092720.9814570.490728
1010.4840740.9681480.515926
1020.4480620.8961230.551938
1030.4035690.8071380.596431
1040.4338930.8677860.566107
1050.3934820.7869630.606518
1060.3554840.7109690.644516
1070.3289760.6579530.671024
1080.3324270.6648540.667573
1090.3199920.6399840.680008
1100.2791250.5582490.720875
1110.2488790.4977590.751121
1120.2222240.4444490.777776
1130.2319130.4638260.768087
1140.3224760.6449510.677524
1150.2843320.5686650.715668
1160.3589480.7178950.641052
1170.3147160.6294320.685284
1180.3910950.7821890.608905
1190.3741560.7483120.625844
1200.3408270.6816540.659173
1210.4602860.9205730.539714
1220.4686340.9372690.531366
1230.4488010.8976030.551199
1240.4154890.8309790.584511
1250.3737110.7474210.626289
1260.4338710.8677420.566129
1270.4254040.8508080.574596
1280.6116840.7766310.388316
1290.6209490.7581020.379051
1300.5705490.8589020.429451
1310.6516560.6966870.348344
1320.5938380.8123250.406162
1330.5355610.9288790.464439
1340.4775120.9550240.522488
1350.4419340.8838690.558066
1360.3992670.7985340.600733
1370.4441310.8882610.555869
1380.3836520.7673030.616348
1390.3234380.6468770.676562
1400.2611750.5223490.738825
1410.369840.7396810.63016
1420.3954990.7909980.604501
1430.3509440.7018870.649056
1440.3720220.7440440.627978
1450.3432360.6864720.656764
1460.2702250.540450.729775
1470.2184520.4369030.781548
1480.1846590.3693180.815341
1490.124220.2484390.87578
1500.682460.6350810.31754
1510.7248560.5502870.275144
1520.5903370.8193250.409663
1530.8186830.3626340.181317

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.45739 & 0.914781 & 0.54261 \tabularnewline
19 & 0.295883 & 0.591767 & 0.704117 \tabularnewline
20 & 0.420003 & 0.840007 & 0.579997 \tabularnewline
21 & 0.551264 & 0.897472 & 0.448736 \tabularnewline
22 & 0.618226 & 0.763548 & 0.381774 \tabularnewline
23 & 0.642493 & 0.715014 & 0.357507 \tabularnewline
24 & 0.60309 & 0.793821 & 0.39691 \tabularnewline
25 & 0.683132 & 0.633736 & 0.316868 \tabularnewline
26 & 0.732294 & 0.535412 & 0.267706 \tabularnewline
27 & 0.67362 & 0.652759 & 0.32638 \tabularnewline
28 & 0.59546 & 0.80908 & 0.40454 \tabularnewline
29 & 0.555233 & 0.889535 & 0.444767 \tabularnewline
30 & 0.494751 & 0.989501 & 0.505249 \tabularnewline
31 & 0.423189 & 0.846377 & 0.576811 \tabularnewline
32 & 0.389731 & 0.779462 & 0.610269 \tabularnewline
33 & 0.326448 & 0.652895 & 0.673552 \tabularnewline
34 & 0.327315 & 0.654629 & 0.672685 \tabularnewline
35 & 0.267147 & 0.534295 & 0.732853 \tabularnewline
36 & 0.214739 & 0.429477 & 0.785261 \tabularnewline
37 & 0.385359 & 0.770717 & 0.614641 \tabularnewline
38 & 0.340365 & 0.680731 & 0.659635 \tabularnewline
39 & 0.28478 & 0.56956 & 0.71522 \tabularnewline
40 & 0.232866 & 0.465732 & 0.767134 \tabularnewline
41 & 0.811917 & 0.376165 & 0.188083 \tabularnewline
42 & 0.792184 & 0.415632 & 0.207816 \tabularnewline
43 & 0.763168 & 0.473664 & 0.236832 \tabularnewline
44 & 0.717619 & 0.564761 & 0.282381 \tabularnewline
45 & 0.677131 & 0.645738 & 0.322869 \tabularnewline
46 & 0.625531 & 0.748938 & 0.374469 \tabularnewline
47 & 0.572033 & 0.855934 & 0.427967 \tabularnewline
48 & 0.521249 & 0.957502 & 0.478751 \tabularnewline
49 & 0.497741 & 0.995482 & 0.502259 \tabularnewline
50 & 0.550473 & 0.899055 & 0.449527 \tabularnewline
51 & 0.516048 & 0.967903 & 0.483952 \tabularnewline
52 & 0.463695 & 0.927391 & 0.536305 \tabularnewline
53 & 0.453858 & 0.907715 & 0.546142 \tabularnewline
54 & 0.855283 & 0.289435 & 0.144717 \tabularnewline
55 & 0.830663 & 0.338675 & 0.169337 \tabularnewline
56 & 0.833904 & 0.332192 & 0.166096 \tabularnewline
57 & 0.818662 & 0.362675 & 0.181338 \tabularnewline
58 & 0.805484 & 0.389033 & 0.194516 \tabularnewline
59 & 0.775236 & 0.449528 & 0.224764 \tabularnewline
60 & 0.840497 & 0.319005 & 0.159503 \tabularnewline
61 & 0.816593 & 0.366815 & 0.183407 \tabularnewline
62 & 0.834733 & 0.330534 & 0.165267 \tabularnewline
63 & 0.825828 & 0.348343 & 0.174172 \tabularnewline
64 & 0.804247 & 0.391505 & 0.195753 \tabularnewline
65 & 0.779744 & 0.440511 & 0.220256 \tabularnewline
66 & 0.782783 & 0.434433 & 0.217217 \tabularnewline
67 & 0.778319 & 0.443362 & 0.221681 \tabularnewline
68 & 0.793133 & 0.413733 & 0.206867 \tabularnewline
69 & 0.770031 & 0.459937 & 0.229969 \tabularnewline
70 & 0.791695 & 0.41661 & 0.208305 \tabularnewline
71 & 0.809313 & 0.381375 & 0.190687 \tabularnewline
72 & 0.787733 & 0.424535 & 0.212267 \tabularnewline
73 & 0.781082 & 0.437835 & 0.218918 \tabularnewline
74 & 0.744223 & 0.511555 & 0.255777 \tabularnewline
75 & 0.792333 & 0.415334 & 0.207667 \tabularnewline
76 & 0.756585 & 0.48683 & 0.243415 \tabularnewline
77 & 0.754977 & 0.490047 & 0.245023 \tabularnewline
78 & 0.782669 & 0.434661 & 0.217331 \tabularnewline
79 & 0.781495 & 0.437009 & 0.218505 \tabularnewline
80 & 0.77128 & 0.457439 & 0.22872 \tabularnewline
81 & 0.76088 & 0.478241 & 0.23912 \tabularnewline
82 & 0.72722 & 0.545561 & 0.27278 \tabularnewline
83 & 0.784866 & 0.430268 & 0.215134 \tabularnewline
84 & 0.831879 & 0.336243 & 0.168121 \tabularnewline
85 & 0.808734 & 0.382532 & 0.191266 \tabularnewline
86 & 0.797062 & 0.405876 & 0.202938 \tabularnewline
87 & 0.774803 & 0.450394 & 0.225197 \tabularnewline
88 & 0.744849 & 0.510302 & 0.255151 \tabularnewline
89 & 0.727833 & 0.544334 & 0.272167 \tabularnewline
90 & 0.707318 & 0.585364 & 0.292682 \tabularnewline
91 & 0.665516 & 0.668967 & 0.334484 \tabularnewline
92 & 0.672284 & 0.655432 & 0.327716 \tabularnewline
93 & 0.655012 & 0.689976 & 0.344988 \tabularnewline
94 & 0.63228 & 0.73544 & 0.36772 \tabularnewline
95 & 0.599091 & 0.801818 & 0.400909 \tabularnewline
96 & 0.57771 & 0.84458 & 0.42229 \tabularnewline
97 & 0.584178 & 0.831645 & 0.415822 \tabularnewline
98 & 0.568581 & 0.862838 & 0.431419 \tabularnewline
99 & 0.526036 & 0.947929 & 0.473964 \tabularnewline
100 & 0.509272 & 0.981457 & 0.490728 \tabularnewline
101 & 0.484074 & 0.968148 & 0.515926 \tabularnewline
102 & 0.448062 & 0.896123 & 0.551938 \tabularnewline
103 & 0.403569 & 0.807138 & 0.596431 \tabularnewline
104 & 0.433893 & 0.867786 & 0.566107 \tabularnewline
105 & 0.393482 & 0.786963 & 0.606518 \tabularnewline
106 & 0.355484 & 0.710969 & 0.644516 \tabularnewline
107 & 0.328976 & 0.657953 & 0.671024 \tabularnewline
108 & 0.332427 & 0.664854 & 0.667573 \tabularnewline
109 & 0.319992 & 0.639984 & 0.680008 \tabularnewline
110 & 0.279125 & 0.558249 & 0.720875 \tabularnewline
111 & 0.248879 & 0.497759 & 0.751121 \tabularnewline
112 & 0.222224 & 0.444449 & 0.777776 \tabularnewline
113 & 0.231913 & 0.463826 & 0.768087 \tabularnewline
114 & 0.322476 & 0.644951 & 0.677524 \tabularnewline
115 & 0.284332 & 0.568665 & 0.715668 \tabularnewline
116 & 0.358948 & 0.717895 & 0.641052 \tabularnewline
117 & 0.314716 & 0.629432 & 0.685284 \tabularnewline
118 & 0.391095 & 0.782189 & 0.608905 \tabularnewline
119 & 0.374156 & 0.748312 & 0.625844 \tabularnewline
120 & 0.340827 & 0.681654 & 0.659173 \tabularnewline
121 & 0.460286 & 0.920573 & 0.539714 \tabularnewline
122 & 0.468634 & 0.937269 & 0.531366 \tabularnewline
123 & 0.448801 & 0.897603 & 0.551199 \tabularnewline
124 & 0.415489 & 0.830979 & 0.584511 \tabularnewline
125 & 0.373711 & 0.747421 & 0.626289 \tabularnewline
126 & 0.433871 & 0.867742 & 0.566129 \tabularnewline
127 & 0.425404 & 0.850808 & 0.574596 \tabularnewline
128 & 0.611684 & 0.776631 & 0.388316 \tabularnewline
129 & 0.620949 & 0.758102 & 0.379051 \tabularnewline
130 & 0.570549 & 0.858902 & 0.429451 \tabularnewline
131 & 0.651656 & 0.696687 & 0.348344 \tabularnewline
132 & 0.593838 & 0.812325 & 0.406162 \tabularnewline
133 & 0.535561 & 0.928879 & 0.464439 \tabularnewline
134 & 0.477512 & 0.955024 & 0.522488 \tabularnewline
135 & 0.441934 & 0.883869 & 0.558066 \tabularnewline
136 & 0.399267 & 0.798534 & 0.600733 \tabularnewline
137 & 0.444131 & 0.888261 & 0.555869 \tabularnewline
138 & 0.383652 & 0.767303 & 0.616348 \tabularnewline
139 & 0.323438 & 0.646877 & 0.676562 \tabularnewline
140 & 0.261175 & 0.522349 & 0.738825 \tabularnewline
141 & 0.36984 & 0.739681 & 0.63016 \tabularnewline
142 & 0.395499 & 0.790998 & 0.604501 \tabularnewline
143 & 0.350944 & 0.701887 & 0.649056 \tabularnewline
144 & 0.372022 & 0.744044 & 0.627978 \tabularnewline
145 & 0.343236 & 0.686472 & 0.656764 \tabularnewline
146 & 0.270225 & 0.54045 & 0.729775 \tabularnewline
147 & 0.218452 & 0.436903 & 0.781548 \tabularnewline
148 & 0.184659 & 0.369318 & 0.815341 \tabularnewline
149 & 0.12422 & 0.248439 & 0.87578 \tabularnewline
150 & 0.68246 & 0.635081 & 0.31754 \tabularnewline
151 & 0.724856 & 0.550287 & 0.275144 \tabularnewline
152 & 0.590337 & 0.819325 & 0.409663 \tabularnewline
153 & 0.818683 & 0.362634 & 0.181317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263048&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]18[/C][C]0.45739[/C][C]0.914781[/C][C]0.54261[/C][/ROW]
[ROW][C]19[/C][C]0.295883[/C][C]0.591767[/C][C]0.704117[/C][/ROW]
[ROW][C]20[/C][C]0.420003[/C][C]0.840007[/C][C]0.579997[/C][/ROW]
[ROW][C]21[/C][C]0.551264[/C][C]0.897472[/C][C]0.448736[/C][/ROW]
[ROW][C]22[/C][C]0.618226[/C][C]0.763548[/C][C]0.381774[/C][/ROW]
[ROW][C]23[/C][C]0.642493[/C][C]0.715014[/C][C]0.357507[/C][/ROW]
[ROW][C]24[/C][C]0.60309[/C][C]0.793821[/C][C]0.39691[/C][/ROW]
[ROW][C]25[/C][C]0.683132[/C][C]0.633736[/C][C]0.316868[/C][/ROW]
[ROW][C]26[/C][C]0.732294[/C][C]0.535412[/C][C]0.267706[/C][/ROW]
[ROW][C]27[/C][C]0.67362[/C][C]0.652759[/C][C]0.32638[/C][/ROW]
[ROW][C]28[/C][C]0.59546[/C][C]0.80908[/C][C]0.40454[/C][/ROW]
[ROW][C]29[/C][C]0.555233[/C][C]0.889535[/C][C]0.444767[/C][/ROW]
[ROW][C]30[/C][C]0.494751[/C][C]0.989501[/C][C]0.505249[/C][/ROW]
[ROW][C]31[/C][C]0.423189[/C][C]0.846377[/C][C]0.576811[/C][/ROW]
[ROW][C]32[/C][C]0.389731[/C][C]0.779462[/C][C]0.610269[/C][/ROW]
[ROW][C]33[/C][C]0.326448[/C][C]0.652895[/C][C]0.673552[/C][/ROW]
[ROW][C]34[/C][C]0.327315[/C][C]0.654629[/C][C]0.672685[/C][/ROW]
[ROW][C]35[/C][C]0.267147[/C][C]0.534295[/C][C]0.732853[/C][/ROW]
[ROW][C]36[/C][C]0.214739[/C][C]0.429477[/C][C]0.785261[/C][/ROW]
[ROW][C]37[/C][C]0.385359[/C][C]0.770717[/C][C]0.614641[/C][/ROW]
[ROW][C]38[/C][C]0.340365[/C][C]0.680731[/C][C]0.659635[/C][/ROW]
[ROW][C]39[/C][C]0.28478[/C][C]0.56956[/C][C]0.71522[/C][/ROW]
[ROW][C]40[/C][C]0.232866[/C][C]0.465732[/C][C]0.767134[/C][/ROW]
[ROW][C]41[/C][C]0.811917[/C][C]0.376165[/C][C]0.188083[/C][/ROW]
[ROW][C]42[/C][C]0.792184[/C][C]0.415632[/C][C]0.207816[/C][/ROW]
[ROW][C]43[/C][C]0.763168[/C][C]0.473664[/C][C]0.236832[/C][/ROW]
[ROW][C]44[/C][C]0.717619[/C][C]0.564761[/C][C]0.282381[/C][/ROW]
[ROW][C]45[/C][C]0.677131[/C][C]0.645738[/C][C]0.322869[/C][/ROW]
[ROW][C]46[/C][C]0.625531[/C][C]0.748938[/C][C]0.374469[/C][/ROW]
[ROW][C]47[/C][C]0.572033[/C][C]0.855934[/C][C]0.427967[/C][/ROW]
[ROW][C]48[/C][C]0.521249[/C][C]0.957502[/C][C]0.478751[/C][/ROW]
[ROW][C]49[/C][C]0.497741[/C][C]0.995482[/C][C]0.502259[/C][/ROW]
[ROW][C]50[/C][C]0.550473[/C][C]0.899055[/C][C]0.449527[/C][/ROW]
[ROW][C]51[/C][C]0.516048[/C][C]0.967903[/C][C]0.483952[/C][/ROW]
[ROW][C]52[/C][C]0.463695[/C][C]0.927391[/C][C]0.536305[/C][/ROW]
[ROW][C]53[/C][C]0.453858[/C][C]0.907715[/C][C]0.546142[/C][/ROW]
[ROW][C]54[/C][C]0.855283[/C][C]0.289435[/C][C]0.144717[/C][/ROW]
[ROW][C]55[/C][C]0.830663[/C][C]0.338675[/C][C]0.169337[/C][/ROW]
[ROW][C]56[/C][C]0.833904[/C][C]0.332192[/C][C]0.166096[/C][/ROW]
[ROW][C]57[/C][C]0.818662[/C][C]0.362675[/C][C]0.181338[/C][/ROW]
[ROW][C]58[/C][C]0.805484[/C][C]0.389033[/C][C]0.194516[/C][/ROW]
[ROW][C]59[/C][C]0.775236[/C][C]0.449528[/C][C]0.224764[/C][/ROW]
[ROW][C]60[/C][C]0.840497[/C][C]0.319005[/C][C]0.159503[/C][/ROW]
[ROW][C]61[/C][C]0.816593[/C][C]0.366815[/C][C]0.183407[/C][/ROW]
[ROW][C]62[/C][C]0.834733[/C][C]0.330534[/C][C]0.165267[/C][/ROW]
[ROW][C]63[/C][C]0.825828[/C][C]0.348343[/C][C]0.174172[/C][/ROW]
[ROW][C]64[/C][C]0.804247[/C][C]0.391505[/C][C]0.195753[/C][/ROW]
[ROW][C]65[/C][C]0.779744[/C][C]0.440511[/C][C]0.220256[/C][/ROW]
[ROW][C]66[/C][C]0.782783[/C][C]0.434433[/C][C]0.217217[/C][/ROW]
[ROW][C]67[/C][C]0.778319[/C][C]0.443362[/C][C]0.221681[/C][/ROW]
[ROW][C]68[/C][C]0.793133[/C][C]0.413733[/C][C]0.206867[/C][/ROW]
[ROW][C]69[/C][C]0.770031[/C][C]0.459937[/C][C]0.229969[/C][/ROW]
[ROW][C]70[/C][C]0.791695[/C][C]0.41661[/C][C]0.208305[/C][/ROW]
[ROW][C]71[/C][C]0.809313[/C][C]0.381375[/C][C]0.190687[/C][/ROW]
[ROW][C]72[/C][C]0.787733[/C][C]0.424535[/C][C]0.212267[/C][/ROW]
[ROW][C]73[/C][C]0.781082[/C][C]0.437835[/C][C]0.218918[/C][/ROW]
[ROW][C]74[/C][C]0.744223[/C][C]0.511555[/C][C]0.255777[/C][/ROW]
[ROW][C]75[/C][C]0.792333[/C][C]0.415334[/C][C]0.207667[/C][/ROW]
[ROW][C]76[/C][C]0.756585[/C][C]0.48683[/C][C]0.243415[/C][/ROW]
[ROW][C]77[/C][C]0.754977[/C][C]0.490047[/C][C]0.245023[/C][/ROW]
[ROW][C]78[/C][C]0.782669[/C][C]0.434661[/C][C]0.217331[/C][/ROW]
[ROW][C]79[/C][C]0.781495[/C][C]0.437009[/C][C]0.218505[/C][/ROW]
[ROW][C]80[/C][C]0.77128[/C][C]0.457439[/C][C]0.22872[/C][/ROW]
[ROW][C]81[/C][C]0.76088[/C][C]0.478241[/C][C]0.23912[/C][/ROW]
[ROW][C]82[/C][C]0.72722[/C][C]0.545561[/C][C]0.27278[/C][/ROW]
[ROW][C]83[/C][C]0.784866[/C][C]0.430268[/C][C]0.215134[/C][/ROW]
[ROW][C]84[/C][C]0.831879[/C][C]0.336243[/C][C]0.168121[/C][/ROW]
[ROW][C]85[/C][C]0.808734[/C][C]0.382532[/C][C]0.191266[/C][/ROW]
[ROW][C]86[/C][C]0.797062[/C][C]0.405876[/C][C]0.202938[/C][/ROW]
[ROW][C]87[/C][C]0.774803[/C][C]0.450394[/C][C]0.225197[/C][/ROW]
[ROW][C]88[/C][C]0.744849[/C][C]0.510302[/C][C]0.255151[/C][/ROW]
[ROW][C]89[/C][C]0.727833[/C][C]0.544334[/C][C]0.272167[/C][/ROW]
[ROW][C]90[/C][C]0.707318[/C][C]0.585364[/C][C]0.292682[/C][/ROW]
[ROW][C]91[/C][C]0.665516[/C][C]0.668967[/C][C]0.334484[/C][/ROW]
[ROW][C]92[/C][C]0.672284[/C][C]0.655432[/C][C]0.327716[/C][/ROW]
[ROW][C]93[/C][C]0.655012[/C][C]0.689976[/C][C]0.344988[/C][/ROW]
[ROW][C]94[/C][C]0.63228[/C][C]0.73544[/C][C]0.36772[/C][/ROW]
[ROW][C]95[/C][C]0.599091[/C][C]0.801818[/C][C]0.400909[/C][/ROW]
[ROW][C]96[/C][C]0.57771[/C][C]0.84458[/C][C]0.42229[/C][/ROW]
[ROW][C]97[/C][C]0.584178[/C][C]0.831645[/C][C]0.415822[/C][/ROW]
[ROW][C]98[/C][C]0.568581[/C][C]0.862838[/C][C]0.431419[/C][/ROW]
[ROW][C]99[/C][C]0.526036[/C][C]0.947929[/C][C]0.473964[/C][/ROW]
[ROW][C]100[/C][C]0.509272[/C][C]0.981457[/C][C]0.490728[/C][/ROW]
[ROW][C]101[/C][C]0.484074[/C][C]0.968148[/C][C]0.515926[/C][/ROW]
[ROW][C]102[/C][C]0.448062[/C][C]0.896123[/C][C]0.551938[/C][/ROW]
[ROW][C]103[/C][C]0.403569[/C][C]0.807138[/C][C]0.596431[/C][/ROW]
[ROW][C]104[/C][C]0.433893[/C][C]0.867786[/C][C]0.566107[/C][/ROW]
[ROW][C]105[/C][C]0.393482[/C][C]0.786963[/C][C]0.606518[/C][/ROW]
[ROW][C]106[/C][C]0.355484[/C][C]0.710969[/C][C]0.644516[/C][/ROW]
[ROW][C]107[/C][C]0.328976[/C][C]0.657953[/C][C]0.671024[/C][/ROW]
[ROW][C]108[/C][C]0.332427[/C][C]0.664854[/C][C]0.667573[/C][/ROW]
[ROW][C]109[/C][C]0.319992[/C][C]0.639984[/C][C]0.680008[/C][/ROW]
[ROW][C]110[/C][C]0.279125[/C][C]0.558249[/C][C]0.720875[/C][/ROW]
[ROW][C]111[/C][C]0.248879[/C][C]0.497759[/C][C]0.751121[/C][/ROW]
[ROW][C]112[/C][C]0.222224[/C][C]0.444449[/C][C]0.777776[/C][/ROW]
[ROW][C]113[/C][C]0.231913[/C][C]0.463826[/C][C]0.768087[/C][/ROW]
[ROW][C]114[/C][C]0.322476[/C][C]0.644951[/C][C]0.677524[/C][/ROW]
[ROW][C]115[/C][C]0.284332[/C][C]0.568665[/C][C]0.715668[/C][/ROW]
[ROW][C]116[/C][C]0.358948[/C][C]0.717895[/C][C]0.641052[/C][/ROW]
[ROW][C]117[/C][C]0.314716[/C][C]0.629432[/C][C]0.685284[/C][/ROW]
[ROW][C]118[/C][C]0.391095[/C][C]0.782189[/C][C]0.608905[/C][/ROW]
[ROW][C]119[/C][C]0.374156[/C][C]0.748312[/C][C]0.625844[/C][/ROW]
[ROW][C]120[/C][C]0.340827[/C][C]0.681654[/C][C]0.659173[/C][/ROW]
[ROW][C]121[/C][C]0.460286[/C][C]0.920573[/C][C]0.539714[/C][/ROW]
[ROW][C]122[/C][C]0.468634[/C][C]0.937269[/C][C]0.531366[/C][/ROW]
[ROW][C]123[/C][C]0.448801[/C][C]0.897603[/C][C]0.551199[/C][/ROW]
[ROW][C]124[/C][C]0.415489[/C][C]0.830979[/C][C]0.584511[/C][/ROW]
[ROW][C]125[/C][C]0.373711[/C][C]0.747421[/C][C]0.626289[/C][/ROW]
[ROW][C]126[/C][C]0.433871[/C][C]0.867742[/C][C]0.566129[/C][/ROW]
[ROW][C]127[/C][C]0.425404[/C][C]0.850808[/C][C]0.574596[/C][/ROW]
[ROW][C]128[/C][C]0.611684[/C][C]0.776631[/C][C]0.388316[/C][/ROW]
[ROW][C]129[/C][C]0.620949[/C][C]0.758102[/C][C]0.379051[/C][/ROW]
[ROW][C]130[/C][C]0.570549[/C][C]0.858902[/C][C]0.429451[/C][/ROW]
[ROW][C]131[/C][C]0.651656[/C][C]0.696687[/C][C]0.348344[/C][/ROW]
[ROW][C]132[/C][C]0.593838[/C][C]0.812325[/C][C]0.406162[/C][/ROW]
[ROW][C]133[/C][C]0.535561[/C][C]0.928879[/C][C]0.464439[/C][/ROW]
[ROW][C]134[/C][C]0.477512[/C][C]0.955024[/C][C]0.522488[/C][/ROW]
[ROW][C]135[/C][C]0.441934[/C][C]0.883869[/C][C]0.558066[/C][/ROW]
[ROW][C]136[/C][C]0.399267[/C][C]0.798534[/C][C]0.600733[/C][/ROW]
[ROW][C]137[/C][C]0.444131[/C][C]0.888261[/C][C]0.555869[/C][/ROW]
[ROW][C]138[/C][C]0.383652[/C][C]0.767303[/C][C]0.616348[/C][/ROW]
[ROW][C]139[/C][C]0.323438[/C][C]0.646877[/C][C]0.676562[/C][/ROW]
[ROW][C]140[/C][C]0.261175[/C][C]0.522349[/C][C]0.738825[/C][/ROW]
[ROW][C]141[/C][C]0.36984[/C][C]0.739681[/C][C]0.63016[/C][/ROW]
[ROW][C]142[/C][C]0.395499[/C][C]0.790998[/C][C]0.604501[/C][/ROW]
[ROW][C]143[/C][C]0.350944[/C][C]0.701887[/C][C]0.649056[/C][/ROW]
[ROW][C]144[/C][C]0.372022[/C][C]0.744044[/C][C]0.627978[/C][/ROW]
[ROW][C]145[/C][C]0.343236[/C][C]0.686472[/C][C]0.656764[/C][/ROW]
[ROW][C]146[/C][C]0.270225[/C][C]0.54045[/C][C]0.729775[/C][/ROW]
[ROW][C]147[/C][C]0.218452[/C][C]0.436903[/C][C]0.781548[/C][/ROW]
[ROW][C]148[/C][C]0.184659[/C][C]0.369318[/C][C]0.815341[/C][/ROW]
[ROW][C]149[/C][C]0.12422[/C][C]0.248439[/C][C]0.87578[/C][/ROW]
[ROW][C]150[/C][C]0.68246[/C][C]0.635081[/C][C]0.31754[/C][/ROW]
[ROW][C]151[/C][C]0.724856[/C][C]0.550287[/C][C]0.275144[/C][/ROW]
[ROW][C]152[/C][C]0.590337[/C][C]0.819325[/C][C]0.409663[/C][/ROW]
[ROW][C]153[/C][C]0.818683[/C][C]0.362634[/C][C]0.181317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263048&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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
180.457390.9147810.54261
190.2958830.5917670.704117
200.4200030.8400070.579997
210.5512640.8974720.448736
220.6182260.7635480.381774
230.6424930.7150140.357507
240.603090.7938210.39691
250.6831320.6337360.316868
260.7322940.5354120.267706
270.673620.6527590.32638
280.595460.809080.40454
290.5552330.8895350.444767
300.4947510.9895010.505249
310.4231890.8463770.576811
320.3897310.7794620.610269
330.3264480.6528950.673552
340.3273150.6546290.672685
350.2671470.5342950.732853
360.2147390.4294770.785261
370.3853590.7707170.614641
380.3403650.6807310.659635
390.284780.569560.71522
400.2328660.4657320.767134
410.8119170.3761650.188083
420.7921840.4156320.207816
430.7631680.4736640.236832
440.7176190.5647610.282381
450.6771310.6457380.322869
460.6255310.7489380.374469
470.5720330.8559340.427967
480.5212490.9575020.478751
490.4977410.9954820.502259
500.5504730.8990550.449527
510.5160480.9679030.483952
520.4636950.9273910.536305
530.4538580.9077150.546142
540.8552830.2894350.144717
550.8306630.3386750.169337
560.8339040.3321920.166096
570.8186620.3626750.181338
580.8054840.3890330.194516
590.7752360.4495280.224764
600.8404970.3190050.159503
610.8165930.3668150.183407
620.8347330.3305340.165267
630.8258280.3483430.174172
640.8042470.3915050.195753
650.7797440.4405110.220256
660.7827830.4344330.217217
670.7783190.4433620.221681
680.7931330.4137330.206867
690.7700310.4599370.229969
700.7916950.416610.208305
710.8093130.3813750.190687
720.7877330.4245350.212267
730.7810820.4378350.218918
740.7442230.5115550.255777
750.7923330.4153340.207667
760.7565850.486830.243415
770.7549770.4900470.245023
780.7826690.4346610.217331
790.7814950.4370090.218505
800.771280.4574390.22872
810.760880.4782410.23912
820.727220.5455610.27278
830.7848660.4302680.215134
840.8318790.3362430.168121
850.8087340.3825320.191266
860.7970620.4058760.202938
870.7748030.4503940.225197
880.7448490.5103020.255151
890.7278330.5443340.272167
900.7073180.5853640.292682
910.6655160.6689670.334484
920.6722840.6554320.327716
930.6550120.6899760.344988
940.632280.735440.36772
950.5990910.8018180.400909
960.577710.844580.42229
970.5841780.8316450.415822
980.5685810.8628380.431419
990.5260360.9479290.473964
1000.5092720.9814570.490728
1010.4840740.9681480.515926
1020.4480620.8961230.551938
1030.4035690.8071380.596431
1040.4338930.8677860.566107
1050.3934820.7869630.606518
1060.3554840.7109690.644516
1070.3289760.6579530.671024
1080.3324270.6648540.667573
1090.3199920.6399840.680008
1100.2791250.5582490.720875
1110.2488790.4977590.751121
1120.2222240.4444490.777776
1130.2319130.4638260.768087
1140.3224760.6449510.677524
1150.2843320.5686650.715668
1160.3589480.7178950.641052
1170.3147160.6294320.685284
1180.3910950.7821890.608905
1190.3741560.7483120.625844
1200.3408270.6816540.659173
1210.4602860.9205730.539714
1220.4686340.9372690.531366
1230.4488010.8976030.551199
1240.4154890.8309790.584511
1250.3737110.7474210.626289
1260.4338710.8677420.566129
1270.4254040.8508080.574596
1280.6116840.7766310.388316
1290.6209490.7581020.379051
1300.5705490.8589020.429451
1310.6516560.6966870.348344
1320.5938380.8123250.406162
1330.5355610.9288790.464439
1340.4775120.9550240.522488
1350.4419340.8838690.558066
1360.3992670.7985340.600733
1370.4441310.8882610.555869
1380.3836520.7673030.616348
1390.3234380.6468770.676562
1400.2611750.5223490.738825
1410.369840.7396810.63016
1420.3954990.7909980.604501
1430.3509440.7018870.649056
1440.3720220.7440440.627978
1450.3432360.6864720.656764
1460.2702250.540450.729775
1470.2184520.4369030.781548
1480.1846590.3693180.815341
1490.124220.2484390.87578
1500.682460.6350810.31754
1510.7248560.5502870.275144
1520.5903370.8193250.409663
1530.8186830.3626340.181317






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=263048&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=263048&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263048&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')
}