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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 computationFri, 12 Dec 2014 14:33:58 +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/12/t1418395362hsonrkif2jljij2.htm/, Retrieved Thu, 31 Oct 2024 22:46:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266757, Retrieved Thu, 31 Oct 2024 22:46:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-12 14:33:58] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.1 0 1 22 23 48 23 12 41 34
2.7 0 1 22 22 50 16 45 146 61
2.1 0 1 22 21 150 33 37 182 70
2.1 0 1 20 25 154 32 37 192 69
2.1 1 0 19 30 109 37 108 263 145
2.1 1 1 20 17 68 14 10 35 23
2.1 0 1 22 27 194 52 68 439 120
2.1 0 0 21 23 158 75 72 214 147
2.1 0 1 21 23 159 72 143 341 215
2.1 0 0 21 18 67 15 9 58 24
2.4 0 0 21 18 147 29 55 292 84
1.95 0 1 21 23 39 13 17 85 30
2.1 0 1 21 19 100 40 37 200 77
2.1 0 1 21 15 111 19 27 158 46
1.95 0 1 22 20 138 24 37 199 61
2.1 0 1 24 16 101 121 58 297 178
2.4 1 1 21 24 131 93 66 227 160
2.1 0 1 22 25 101 36 21 108 57
2.25 0 1 20 25 114 23 19 86 42
2.4 0 0 21 19 165 85 78 302 163
2.25 0 1 24 19 114 41 35 148 75
2.55 0 1 25 16 111 46 48 178 94
1.95 0 1 22 19 75 18 27 120 45
2.4 0 1 21 19 82 35 43 207 78
2.1 0 1 21 23 121 17 30 157 47
2.1 0 1 22 21 32 4 25 128 29
2.4 0 0 23 22 150 28 69 296 97
2.1 0 1 24 19 117 44 72 323 116
2.1 1 1 20 20 71 10 23 79 32
2.25 0 1 22 20 165 38 13 70 50
2.25 0 1 25 3 154 57 61 146 118
2.4 0 1 22 23 126 23 43 246 66
2.1 0 0 22 14 138 26 22 145 48
2.1 0 0 21 23 149 36 51 196 86
2.4 0 0 21 20 145 22 67 199 89
2.1 0 1 21 15 120 40 36 127 76
1.95 0 0 22 13 138 18 21 91 39
2.1 0 0 22 16 109 31 44 153 75
2.25 0 0 22 7 132 11 45 299 57
2.25 0 1 21 24 172 38 34 228 72
2.4 0 0 22 17 169 24 36 190 60
2.25 0 1 23 24 114 37 72 180 109
2.25 0 1 21 24 156 37 39 212 76
2.1 0 0 21 19 172 22 43 269 65
2.1 1 1 21 25 68 15 25 130 40
2.1 1 1 19 20 89 2 56 179 58
2.7 0 1 21 28 167 43 80 243 123
2.1 0 0 21 23 113 31 40 190 71
2.1 1 0 19 27 115 29 73 299 102
2.25 1 0 18 18 78 45 34 121 80
2.7 1 0 19 28 118 25 72 137 97
2.4 1 1 21 21 87 4 42 305 46
2.1 0 0 22 19 173 31 61 157 93
2.1 0 1 22 23 2 -4 23 96 19
2.4 1 0 19 27 162 66 74 183 140
1.95 1 1 20 22 49 61 16 52 78
2.7 1 0 19 28 122 32 66 238 98
2.1 1 1 21 25 96 31 9 40 40
2.25 1 0 19 21 100 39 41 226 80
2.1 1 0 20 22 82 19 57 190 76
2.7 1 1 21 28 100 31 48 214 79
2.1 1 0 19 20 115 36 51 145 87
2.1 1 1 21 29 141 42 53 119 95
1.65 0 1 21 25 165 21 29 222 49
1.65 0 1 21 25 165 21 29 222 49
2.1 1 1 19 20 110 25 55 159 80
2.1 0 1 25 20 118 32 54 165 86
2.1 0 0 21 16 158 26 43 249 69
2.1 1 1 20 20 146 28 51 125 79
2.1 0 0 25 20 49 32 20 122 52
2.4 1 0 19 23 90 41 79 186 120
2.4 1 0 20 18 121 29 39 148 69
2.1 0 1 22 25 155 33 61 274 94
2.25 1 0 19 18 104 17 55 172 72
2.4 1 1 20 19 147 13 30 84 43
2.1 1 0 19 25 110 32 55 168 87
2.1 1 0 19 25 108 30 22 102 52
2.4 1 0 18 25 113 34 37 106 71
2.4 1 0 19 24 115 59 2 2 61
2.1 1 1 21 19 61 13 38 139 51
2.1 1 1 19 26 60 23 27 95 50
2.4 1 1 20 10 109 10 56 130 67
2.1 1 1 20 17 68 5 25 72 30
2.7 1 0 19 13 111 31 39 141 70
2.1 1 0 19 17 77 19 33 113 52
2.1 1 1 22 30 73 32 43 206 75
2.25 0 0 21 25 151 30 57 268 87
2.1 1 0 19 4 89 25 43 175 69
2.4 1 0 19 16 78 48 23 77 72
2.25 1 0 19 21 110 35 44 125 79
2.25 0 1 23 23 220 67 54 255 121
2.1 1 1 19 22 65 15 28 111 43
2.1 0 0 20 17 141 22 36 132 58
2.4 1 0 19 20 117 18 39 211 57
2.25 0 1 22 20 122 33 16 92 50
2.1 1 0 19 22 63 46 23 76 69
2.1 0 1 25 16 44 24 40 171 64
1.65 1 1 19 23 52 14 24 83 38
1.65 1 1 20 16 62 23 29 119 53
2.7 1 0 19 0 131 12 78 266 90
2.1 1 1 19 18 101 38 57 186 96
1.95 1 1 20 25 42 12 37 50 49
2.25 0 1 20 23 152 28 27 117 56
2.4 0 0 21 12 107 41 61 219 102
1.95 1 0 19 18 77 12 27 246 40
2.1 0 0 21 24 154 31 69 279 100
2.4 0 1 23 11 103 33 34 148 67
2.1 1 1 19 18 96 34 44 137 78
2.1 0 0 21 14 154 41 21 130 62
2.4 0 1 22 23 175 21 34 181 55
2.4 1 1 20 24 57 20 39 98 59
2.4 1 0 18 29 112 44 51 226 96
2.25 0 0 21 18 143 52 34 234 86
2.4 1 0 20 15 49 7 31 138 38
2.1 0 1 21 29 110 29 13 85 43
2.1 0 1 21 16 131 11 12 66 23
1.8 0 0 21 19 167 26 51 236 77
2.7 1 0 19 22 56 24 24 106 48
2.1 0 0 21 16 137 7 19 135 26
2.1 1 1 19 23 86 60 30 122 91
2.4 0 1 21 23 121 13 81 218 94
2.55 0 0 21 19 149 20 42 199 62
2.55 0 0 22 4 168 52 22 112 74
2.1 0 0 21 20 140 28 85 278 114
2.1 1 1 22 24 88 25 27 94 52
2.1 0 1 22 20 168 39 25 113 64
2.25 0 1 22 4 94 9 22 84 31
2.25 0 1 22 24 51 19 19 86 38
2.1 1 0 21 22 48 13 14 62 27
2.1 0 1 22 16 145 60 45 222 105
1.95 0 1 23 3 66 19 45 167 64
2.4 1 1 19 15 85 34 28 82 62
2.1 0 0 22 24 109 14 51 207 65
2.4 1 0 21 17 63 17 41 184 58
2.4 1 1 19 20 102 45 31 83 76
2.4 1 0 19 27 162 66 74 183 140
2.25 0 1 20 23 128 24 24 85 48
1.95 1 1 20 26 86 48 19 89 68
2.1 1 1 18 23 114 29 51 225 80
2.1 0 0 21 17 164 -2 73 237 71
2.55 0 1 21 20 119 51 24 102 76
2.1 0 0 20 22 126 2 61 221 63
2.1 0 1 20 19 132 24 23 128 46
2.1 0 1 21 24 142 40 14 91 53
1.95 0 0 21 19 83 20 54 198 74
2.25 1 1 19 23 94 19 51 204 70
2.4 1 0 19 15 81 16 62 158 78
1.95 0 1 21 27 166 20 36 138 56
2.1 1 0 19 26 110 40 59 226 100
2.1 1 1 19 22 64 27 24 44 51
1.95 0 0 24 22 93 25 26 196 52
2.1 1 0 19 18 104 49 54 83 102
2.1 1 1 19 15 105 39 39 79 78
1.95 1 1 20 22 49 61 16 52 78
2.1 1 0 19 27 88 19 36 105 55
1.95 1 1 19 10 95 67 31 116 98
2.4 1 1 19 20 102 45 31 83 76
2.4 1 0 19 17 99 30 42 196 73
2.4 1 1 19 23 63 8 39 153 47
1.95 1 0 19 19 76 19 25 157 45
2.7 1 0 20 13 109 52 31 75 83
2.1 1 1 20 27 117 22 38 106 60
1.95 1 1 19 23 57 17 31 58 48
2.1 1 0 21 16 120 33 17 75 50
1.95 1 1 19 25 73 34 22 74 56
2.1 1 0 19 2 91 22 55 185 77
2.25 1 0 19 26 108 30 62 265 91
2.7 1 1 21 20 105 25 51 131 76
2.1 0 0 22 23 117 38 30 139 68
2.4 1 0 19 22 119 26 49 196 74
1.35 1 1 19 24 31 13 16 78 29

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&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]7 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=266757&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.70232 + 0.0983425programma[t] -0.0584993gender[t] + 0.0181934age[t] -0.00365834NUMERACYTOT[t] + 0.00103558LFM[t] -0.00477222PRH[t] -0.00235139CH[t] -0.000436024Blogs[t] + 0.0051837Hours[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  1.70232 +  0.0983425programma[t] -0.0584993gender[t] +  0.0181934age[t] -0.00365834NUMERACYTOT[t] +  0.00103558LFM[t] -0.00477222PRH[t] -0.00235139CH[t] -0.000436024Blogs[t] +  0.0051837Hours[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  1.70232 +  0.0983425programma[t] -0.0584993gender[t] +  0.0181934age[t] -0.00365834NUMERACYTOT[t] +  0.00103558LFM[t] -0.00477222PRH[t] -0.00235139CH[t] -0.000436024Blogs[t] +  0.0051837Hours[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266757&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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
PA[t] = + 1.70232 + 0.0983425programma[t] -0.0584993gender[t] + 0.0181934age[t] -0.00365834NUMERACYTOT[t] + 0.00103558LFM[t] -0.00477222PRH[t] -0.00235139CH[t] -0.000436024Blogs[t] + 0.0051837Hours[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.702320.3889274.3772.15693e-051.07847e-05
programma0.09834250.05591621.7590.08052010.0402601
gender-0.05849930.0364215-1.6060.1101950.0550976
age0.01819340.01689091.0770.2830430.141521
NUMERACYTOT-0.003658340.00309055-1.1840.238270.119135
LFM0.001035580.0005780371.7920.07508480.0375424
PRH-0.004772220.0355733-0.13420.893450.446725
CH-0.002351390.0354026-0.066420.9471270.473564
Blogs-0.0004360240.0003681-1.1850.237950.118975
Hours0.00518370.03544670.14620.8839150.441958

\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) & 1.70232 & 0.388927 & 4.377 & 2.15693e-05 & 1.07847e-05 \tabularnewline
programma & 0.0983425 & 0.0559162 & 1.759 & 0.0805201 & 0.0402601 \tabularnewline
gender & -0.0584993 & 0.0364215 & -1.606 & 0.110195 & 0.0550976 \tabularnewline
age & 0.0181934 & 0.0168909 & 1.077 & 0.283043 & 0.141521 \tabularnewline
NUMERACYTOT & -0.00365834 & 0.00309055 & -1.184 & 0.23827 & 0.119135 \tabularnewline
LFM & 0.00103558 & 0.000578037 & 1.792 & 0.0750848 & 0.0375424 \tabularnewline
PRH & -0.00477222 & 0.0355733 & -0.1342 & 0.89345 & 0.446725 \tabularnewline
CH & -0.00235139 & 0.0354026 & -0.06642 & 0.947127 & 0.473564 \tabularnewline
Blogs & -0.000436024 & 0.0003681 & -1.185 & 0.23795 & 0.118975 \tabularnewline
Hours & 0.0051837 & 0.0354467 & 0.1462 & 0.883915 & 0.441958 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&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]1.70232[/C][C]0.388927[/C][C]4.377[/C][C]2.15693e-05[/C][C]1.07847e-05[/C][/ROW]
[ROW][C]programma[/C][C]0.0983425[/C][C]0.0559162[/C][C]1.759[/C][C]0.0805201[/C][C]0.0402601[/C][/ROW]
[ROW][C]gender[/C][C]-0.0584993[/C][C]0.0364215[/C][C]-1.606[/C][C]0.110195[/C][C]0.0550976[/C][/ROW]
[ROW][C]age[/C][C]0.0181934[/C][C]0.0168909[/C][C]1.077[/C][C]0.283043[/C][C]0.141521[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00365834[/C][C]0.00309055[/C][C]-1.184[/C][C]0.23827[/C][C]0.119135[/C][/ROW]
[ROW][C]LFM[/C][C]0.00103558[/C][C]0.000578037[/C][C]1.792[/C][C]0.0750848[/C][C]0.0375424[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00477222[/C][C]0.0355733[/C][C]-0.1342[/C][C]0.89345[/C][C]0.446725[/C][/ROW]
[ROW][C]CH[/C][C]-0.00235139[/C][C]0.0354026[/C][C]-0.06642[/C][C]0.947127[/C][C]0.473564[/C][/ROW]
[ROW][C]Blogs[/C][C]-0.000436024[/C][C]0.0003681[/C][C]-1.185[/C][C]0.23795[/C][C]0.118975[/C][/ROW]
[ROW][C]Hours[/C][C]0.0051837[/C][C]0.0354467[/C][C]0.1462[/C][C]0.883915[/C][C]0.441958[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266757&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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)1.702320.3889274.3772.15693e-051.07847e-05
programma0.09834250.05591621.7590.08052010.0402601
gender-0.05849930.0364215-1.6060.1101950.0550976
age0.01819340.01689091.0770.2830430.141521
NUMERACYTOT-0.003658340.00309055-1.1840.238270.119135
LFM0.001035580.0005780371.7920.07508480.0375424
PRH-0.004772220.0355733-0.13420.893450.446725
CH-0.002351390.0354026-0.066420.9471270.473564
Blogs-0.0004360240.0003681-1.1850.237950.118975
Hours0.00518370.03544670.14620.8839150.441958






Multiple Linear Regression - Regression Statistics
Multiple R0.356337
R-squared0.126976
Adjusted R-squared0.0781735
F-TEST (value)2.60183
F-TEST (DF numerator)9
F-TEST (DF denominator)161
p-value0.00789048
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.212551
Sum Squared Residuals7.27367

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.356337 \tabularnewline
R-squared & 0.126976 \tabularnewline
Adjusted R-squared & 0.0781735 \tabularnewline
F-TEST (value) & 2.60183 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 161 \tabularnewline
p-value & 0.00789048 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.212551 \tabularnewline
Sum Squared Residuals & 7.27367 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.356337[/C][/ROW]
[ROW][C]R-squared[/C][C]0.126976[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0781735[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.60183[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]161[/C][/ROW]
[ROW][C]p-value[/C][C]0.00789048[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.212551[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.27367[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266757&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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 R0.356337
R-squared0.126976
Adjusted R-squared0.0781735
F-TEST (value)2.60183
F-TEST (DF numerator)9
F-TEST (DF denominator)161
p-value0.00789048
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.212551
Sum Squared Residuals7.27367






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.12.030030.0699679
22.72.085750.614251
32.12.1616-0.0616045
42.12.10995-0.00995498
52.12.3559-0.255905
62.12.1279-0.0278976
72.12.16878-0.0687814
82.12.30534-0.205339
92.12.39236-0.29236
102.12.094290.00571183
112.42.211150.188848
121.951.99856-0.0485639
132.12.093980.00601915
142.12.10135-0.0013544
151.952.14172-0.19172
162.12.2059-0.105902
172.42.303490.0965089
182.12.084410.0155889
192.252.060070.189934
202.42.309960.0900405
212.252.17530.0747045
222.552.232340.317662
231.952.08379-0.133791
242.42.087220.312775
252.12.090550.00944653
262.12.017030.0829689
272.42.273510.126491
282.12.21331-0.113312
292.12.13602-0.0360184
302.252.158530.0914706
312.252.37972-0.129725
322.42.114410.285592
332.12.20405-0.104053
342.12.22316-0.123157
352.42.273420.126578
362.12.15832-0.0583232
371.952.22513-0.275133
382.12.22758-0.127585
392.252.220450.029545
402.252.128720.121278
412.42.244390.155611
422.252.233190.016809
432.252.132880.117121
442.12.20654-0.106543
452.12.12348-0.0234819
462.12.18822-0.0882212
472.72.234710.465286
482.12.16046-0.0604632
492.12.25497-0.154973
502.252.210310.0396918
512.72.320580.37942
522.42.125110.27489
532.12.33448-0.234476
542.11.983640.116357
552.42.372280.0277186
561.952.12922-0.179218
572.72.266570.43343
582.12.15299-0.0529869
592.252.20670.0432996
602.12.25538-0.155379
612.72.180750.519254
622.12.2883-0.188299
632.12.27966-0.179657
641.652.09409-0.444091
651.652.09409-0.444091
662.12.22532-0.12532
672.12.24185-0.141855
682.12.21339-0.113387
692.12.28552-0.185525
702.12.15135-0.0513497
712.42.314920.0850803
722.42.287030.11297
732.12.18001-0.0800103
742.252.27596-0.0259629
752.42.242440.157555
762.12.26448-0.164484
772.12.1969-0.0969014
782.42.226270.173728
792.42.20670.193302
802.12.17026-0.0702557
812.12.099370.000630553
822.42.293550.10645
832.12.15573-0.0557298
842.72.275460.424536
852.12.2159-0.115898
862.12.1534-0.0534004
872.252.206230.0437736
882.12.28483-0.184826
892.42.225080.174918
902.252.26795-0.0179456
912.252.27528-0.0252814
922.12.11174-0.0117448
932.12.20347-0.103473
942.42.22020.179802
952.252.121210.128786
962.12.18203-0.082028
972.12.1343-0.0342951
981.652.09509-0.445092
991.652.1566-0.506601
1002.72.391870.308127
1012.12.22774-0.127742
1021.952.146-0.195999
1032.252.123120.126883
1042.42.245440.15456
1051.952.13956-0.189558
1062.12.24259-0.142594
1072.42.174040.225963
1082.12.20028-0.100279
1092.12.21231-0.112309
1102.42.167180.232821
1112.42.153220.246782
1122.42.207230.192769
1132.252.182280.0677157
1142.42.190910.209091
1152.12.050580.049422
1162.12.11275-0.0127453
1171.82.24006-0.440059
1182.72.155480.544522
1192.12.16555-0.0655527
1202.12.1544-0.0544022
1212.42.206760.193242
1222.552.209590.340409
1232.552.296790.253209
1242.12.29243-0.192432
1252.12.19152-0.0915214
1262.12.18247-0.08247
1272.252.156170.0938259
1282.252.033220.216777
1292.12.16992-0.0699159
1302.12.19105-0.0910458
1311.952.1821-0.232098
1322.42.178530.221473
1332.12.1876-0.0876038
1342.42.228660.171335
1352.42.190430.209572
1362.42.372280.0277186
1372.252.096890.153111
1381.952.13992-0.189916
1392.12.16183-0.0618335
1402.12.29462-0.194622
1412.552.125620.424381
1422.12.19342-0.0934196
1432.12.08890.0111
1442.12.096380.00361683
1451.952.17567-0.225667
1462.252.164360.085643
1472.42.288640.111361
1481.952.14903-0.199034
1492.12.25534-0.155341
1502.12.13353-0.0335313
1511.952.15844-0.208436
1522.12.31992-0.21992
1532.12.23376-0.13376
1541.952.12922-0.179218
1552.12.20269-0.102691
1561.952.21443-0.264426
1572.42.190430.209572
1582.42.237690.162308
1592.42.115980.284023
1601.952.17089-0.220886
1612.72.306350.393654
1622.12.19888-0.0988801
1631.952.13223-0.182231
1642.12.27749-0.177486
1651.952.11601-0.166012
1662.12.31742-0.217423
1672.252.230280.0197201
1682.72.257410.442591
1692.12.17959-0.0795933
1702.42.247920.152076
1711.352.0488-0.698797

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.1 & 2.03003 & 0.0699679 \tabularnewline
2 & 2.7 & 2.08575 & 0.614251 \tabularnewline
3 & 2.1 & 2.1616 & -0.0616045 \tabularnewline
4 & 2.1 & 2.10995 & -0.00995498 \tabularnewline
5 & 2.1 & 2.3559 & -0.255905 \tabularnewline
6 & 2.1 & 2.1279 & -0.0278976 \tabularnewline
7 & 2.1 & 2.16878 & -0.0687814 \tabularnewline
8 & 2.1 & 2.30534 & -0.205339 \tabularnewline
9 & 2.1 & 2.39236 & -0.29236 \tabularnewline
10 & 2.1 & 2.09429 & 0.00571183 \tabularnewline
11 & 2.4 & 2.21115 & 0.188848 \tabularnewline
12 & 1.95 & 1.99856 & -0.0485639 \tabularnewline
13 & 2.1 & 2.09398 & 0.00601915 \tabularnewline
14 & 2.1 & 2.10135 & -0.0013544 \tabularnewline
15 & 1.95 & 2.14172 & -0.19172 \tabularnewline
16 & 2.1 & 2.2059 & -0.105902 \tabularnewline
17 & 2.4 & 2.30349 & 0.0965089 \tabularnewline
18 & 2.1 & 2.08441 & 0.0155889 \tabularnewline
19 & 2.25 & 2.06007 & 0.189934 \tabularnewline
20 & 2.4 & 2.30996 & 0.0900405 \tabularnewline
21 & 2.25 & 2.1753 & 0.0747045 \tabularnewline
22 & 2.55 & 2.23234 & 0.317662 \tabularnewline
23 & 1.95 & 2.08379 & -0.133791 \tabularnewline
24 & 2.4 & 2.08722 & 0.312775 \tabularnewline
25 & 2.1 & 2.09055 & 0.00944653 \tabularnewline
26 & 2.1 & 2.01703 & 0.0829689 \tabularnewline
27 & 2.4 & 2.27351 & 0.126491 \tabularnewline
28 & 2.1 & 2.21331 & -0.113312 \tabularnewline
29 & 2.1 & 2.13602 & -0.0360184 \tabularnewline
30 & 2.25 & 2.15853 & 0.0914706 \tabularnewline
31 & 2.25 & 2.37972 & -0.129725 \tabularnewline
32 & 2.4 & 2.11441 & 0.285592 \tabularnewline
33 & 2.1 & 2.20405 & -0.104053 \tabularnewline
34 & 2.1 & 2.22316 & -0.123157 \tabularnewline
35 & 2.4 & 2.27342 & 0.126578 \tabularnewline
36 & 2.1 & 2.15832 & -0.0583232 \tabularnewline
37 & 1.95 & 2.22513 & -0.275133 \tabularnewline
38 & 2.1 & 2.22758 & -0.127585 \tabularnewline
39 & 2.25 & 2.22045 & 0.029545 \tabularnewline
40 & 2.25 & 2.12872 & 0.121278 \tabularnewline
41 & 2.4 & 2.24439 & 0.155611 \tabularnewline
42 & 2.25 & 2.23319 & 0.016809 \tabularnewline
43 & 2.25 & 2.13288 & 0.117121 \tabularnewline
44 & 2.1 & 2.20654 & -0.106543 \tabularnewline
45 & 2.1 & 2.12348 & -0.0234819 \tabularnewline
46 & 2.1 & 2.18822 & -0.0882212 \tabularnewline
47 & 2.7 & 2.23471 & 0.465286 \tabularnewline
48 & 2.1 & 2.16046 & -0.0604632 \tabularnewline
49 & 2.1 & 2.25497 & -0.154973 \tabularnewline
50 & 2.25 & 2.21031 & 0.0396918 \tabularnewline
51 & 2.7 & 2.32058 & 0.37942 \tabularnewline
52 & 2.4 & 2.12511 & 0.27489 \tabularnewline
53 & 2.1 & 2.33448 & -0.234476 \tabularnewline
54 & 2.1 & 1.98364 & 0.116357 \tabularnewline
55 & 2.4 & 2.37228 & 0.0277186 \tabularnewline
56 & 1.95 & 2.12922 & -0.179218 \tabularnewline
57 & 2.7 & 2.26657 & 0.43343 \tabularnewline
58 & 2.1 & 2.15299 & -0.0529869 \tabularnewline
59 & 2.25 & 2.2067 & 0.0432996 \tabularnewline
60 & 2.1 & 2.25538 & -0.155379 \tabularnewline
61 & 2.7 & 2.18075 & 0.519254 \tabularnewline
62 & 2.1 & 2.2883 & -0.188299 \tabularnewline
63 & 2.1 & 2.27966 & -0.179657 \tabularnewline
64 & 1.65 & 2.09409 & -0.444091 \tabularnewline
65 & 1.65 & 2.09409 & -0.444091 \tabularnewline
66 & 2.1 & 2.22532 & -0.12532 \tabularnewline
67 & 2.1 & 2.24185 & -0.141855 \tabularnewline
68 & 2.1 & 2.21339 & -0.113387 \tabularnewline
69 & 2.1 & 2.28552 & -0.185525 \tabularnewline
70 & 2.1 & 2.15135 & -0.0513497 \tabularnewline
71 & 2.4 & 2.31492 & 0.0850803 \tabularnewline
72 & 2.4 & 2.28703 & 0.11297 \tabularnewline
73 & 2.1 & 2.18001 & -0.0800103 \tabularnewline
74 & 2.25 & 2.27596 & -0.0259629 \tabularnewline
75 & 2.4 & 2.24244 & 0.157555 \tabularnewline
76 & 2.1 & 2.26448 & -0.164484 \tabularnewline
77 & 2.1 & 2.1969 & -0.0969014 \tabularnewline
78 & 2.4 & 2.22627 & 0.173728 \tabularnewline
79 & 2.4 & 2.2067 & 0.193302 \tabularnewline
80 & 2.1 & 2.17026 & -0.0702557 \tabularnewline
81 & 2.1 & 2.09937 & 0.000630553 \tabularnewline
82 & 2.4 & 2.29355 & 0.10645 \tabularnewline
83 & 2.1 & 2.15573 & -0.0557298 \tabularnewline
84 & 2.7 & 2.27546 & 0.424536 \tabularnewline
85 & 2.1 & 2.2159 & -0.115898 \tabularnewline
86 & 2.1 & 2.1534 & -0.0534004 \tabularnewline
87 & 2.25 & 2.20623 & 0.0437736 \tabularnewline
88 & 2.1 & 2.28483 & -0.184826 \tabularnewline
89 & 2.4 & 2.22508 & 0.174918 \tabularnewline
90 & 2.25 & 2.26795 & -0.0179456 \tabularnewline
91 & 2.25 & 2.27528 & -0.0252814 \tabularnewline
92 & 2.1 & 2.11174 & -0.0117448 \tabularnewline
93 & 2.1 & 2.20347 & -0.103473 \tabularnewline
94 & 2.4 & 2.2202 & 0.179802 \tabularnewline
95 & 2.25 & 2.12121 & 0.128786 \tabularnewline
96 & 2.1 & 2.18203 & -0.082028 \tabularnewline
97 & 2.1 & 2.1343 & -0.0342951 \tabularnewline
98 & 1.65 & 2.09509 & -0.445092 \tabularnewline
99 & 1.65 & 2.1566 & -0.506601 \tabularnewline
100 & 2.7 & 2.39187 & 0.308127 \tabularnewline
101 & 2.1 & 2.22774 & -0.127742 \tabularnewline
102 & 1.95 & 2.146 & -0.195999 \tabularnewline
103 & 2.25 & 2.12312 & 0.126883 \tabularnewline
104 & 2.4 & 2.24544 & 0.15456 \tabularnewline
105 & 1.95 & 2.13956 & -0.189558 \tabularnewline
106 & 2.1 & 2.24259 & -0.142594 \tabularnewline
107 & 2.4 & 2.17404 & 0.225963 \tabularnewline
108 & 2.1 & 2.20028 & -0.100279 \tabularnewline
109 & 2.1 & 2.21231 & -0.112309 \tabularnewline
110 & 2.4 & 2.16718 & 0.232821 \tabularnewline
111 & 2.4 & 2.15322 & 0.246782 \tabularnewline
112 & 2.4 & 2.20723 & 0.192769 \tabularnewline
113 & 2.25 & 2.18228 & 0.0677157 \tabularnewline
114 & 2.4 & 2.19091 & 0.209091 \tabularnewline
115 & 2.1 & 2.05058 & 0.049422 \tabularnewline
116 & 2.1 & 2.11275 & -0.0127453 \tabularnewline
117 & 1.8 & 2.24006 & -0.440059 \tabularnewline
118 & 2.7 & 2.15548 & 0.544522 \tabularnewline
119 & 2.1 & 2.16555 & -0.0655527 \tabularnewline
120 & 2.1 & 2.1544 & -0.0544022 \tabularnewline
121 & 2.4 & 2.20676 & 0.193242 \tabularnewline
122 & 2.55 & 2.20959 & 0.340409 \tabularnewline
123 & 2.55 & 2.29679 & 0.253209 \tabularnewline
124 & 2.1 & 2.29243 & -0.192432 \tabularnewline
125 & 2.1 & 2.19152 & -0.0915214 \tabularnewline
126 & 2.1 & 2.18247 & -0.08247 \tabularnewline
127 & 2.25 & 2.15617 & 0.0938259 \tabularnewline
128 & 2.25 & 2.03322 & 0.216777 \tabularnewline
129 & 2.1 & 2.16992 & -0.0699159 \tabularnewline
130 & 2.1 & 2.19105 & -0.0910458 \tabularnewline
131 & 1.95 & 2.1821 & -0.232098 \tabularnewline
132 & 2.4 & 2.17853 & 0.221473 \tabularnewline
133 & 2.1 & 2.1876 & -0.0876038 \tabularnewline
134 & 2.4 & 2.22866 & 0.171335 \tabularnewline
135 & 2.4 & 2.19043 & 0.209572 \tabularnewline
136 & 2.4 & 2.37228 & 0.0277186 \tabularnewline
137 & 2.25 & 2.09689 & 0.153111 \tabularnewline
138 & 1.95 & 2.13992 & -0.189916 \tabularnewline
139 & 2.1 & 2.16183 & -0.0618335 \tabularnewline
140 & 2.1 & 2.29462 & -0.194622 \tabularnewline
141 & 2.55 & 2.12562 & 0.424381 \tabularnewline
142 & 2.1 & 2.19342 & -0.0934196 \tabularnewline
143 & 2.1 & 2.0889 & 0.0111 \tabularnewline
144 & 2.1 & 2.09638 & 0.00361683 \tabularnewline
145 & 1.95 & 2.17567 & -0.225667 \tabularnewline
146 & 2.25 & 2.16436 & 0.085643 \tabularnewline
147 & 2.4 & 2.28864 & 0.111361 \tabularnewline
148 & 1.95 & 2.14903 & -0.199034 \tabularnewline
149 & 2.1 & 2.25534 & -0.155341 \tabularnewline
150 & 2.1 & 2.13353 & -0.0335313 \tabularnewline
151 & 1.95 & 2.15844 & -0.208436 \tabularnewline
152 & 2.1 & 2.31992 & -0.21992 \tabularnewline
153 & 2.1 & 2.23376 & -0.13376 \tabularnewline
154 & 1.95 & 2.12922 & -0.179218 \tabularnewline
155 & 2.1 & 2.20269 & -0.102691 \tabularnewline
156 & 1.95 & 2.21443 & -0.264426 \tabularnewline
157 & 2.4 & 2.19043 & 0.209572 \tabularnewline
158 & 2.4 & 2.23769 & 0.162308 \tabularnewline
159 & 2.4 & 2.11598 & 0.284023 \tabularnewline
160 & 1.95 & 2.17089 & -0.220886 \tabularnewline
161 & 2.7 & 2.30635 & 0.393654 \tabularnewline
162 & 2.1 & 2.19888 & -0.0988801 \tabularnewline
163 & 1.95 & 2.13223 & -0.182231 \tabularnewline
164 & 2.1 & 2.27749 & -0.177486 \tabularnewline
165 & 1.95 & 2.11601 & -0.166012 \tabularnewline
166 & 2.1 & 2.31742 & -0.217423 \tabularnewline
167 & 2.25 & 2.23028 & 0.0197201 \tabularnewline
168 & 2.7 & 2.25741 & 0.442591 \tabularnewline
169 & 2.1 & 2.17959 & -0.0795933 \tabularnewline
170 & 2.4 & 2.24792 & 0.152076 \tabularnewline
171 & 1.35 & 2.0488 & -0.698797 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&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]2.1[/C][C]2.03003[/C][C]0.0699679[/C][/ROW]
[ROW][C]2[/C][C]2.7[/C][C]2.08575[/C][C]0.614251[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.1616[/C][C]-0.0616045[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]2.10995[/C][C]-0.00995498[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]2.3559[/C][C]-0.255905[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]2.1279[/C][C]-0.0278976[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.16878[/C][C]-0.0687814[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]2.30534[/C][C]-0.205339[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.39236[/C][C]-0.29236[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.09429[/C][C]0.00571183[/C][/ROW]
[ROW][C]11[/C][C]2.4[/C][C]2.21115[/C][C]0.188848[/C][/ROW]
[ROW][C]12[/C][C]1.95[/C][C]1.99856[/C][C]-0.0485639[/C][/ROW]
[ROW][C]13[/C][C]2.1[/C][C]2.09398[/C][C]0.00601915[/C][/ROW]
[ROW][C]14[/C][C]2.1[/C][C]2.10135[/C][C]-0.0013544[/C][/ROW]
[ROW][C]15[/C][C]1.95[/C][C]2.14172[/C][C]-0.19172[/C][/ROW]
[ROW][C]16[/C][C]2.1[/C][C]2.2059[/C][C]-0.105902[/C][/ROW]
[ROW][C]17[/C][C]2.4[/C][C]2.30349[/C][C]0.0965089[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]2.08441[/C][C]0.0155889[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]2.06007[/C][C]0.189934[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.30996[/C][C]0.0900405[/C][/ROW]
[ROW][C]21[/C][C]2.25[/C][C]2.1753[/C][C]0.0747045[/C][/ROW]
[ROW][C]22[/C][C]2.55[/C][C]2.23234[/C][C]0.317662[/C][/ROW]
[ROW][C]23[/C][C]1.95[/C][C]2.08379[/C][C]-0.133791[/C][/ROW]
[ROW][C]24[/C][C]2.4[/C][C]2.08722[/C][C]0.312775[/C][/ROW]
[ROW][C]25[/C][C]2.1[/C][C]2.09055[/C][C]0.00944653[/C][/ROW]
[ROW][C]26[/C][C]2.1[/C][C]2.01703[/C][C]0.0829689[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.27351[/C][C]0.126491[/C][/ROW]
[ROW][C]28[/C][C]2.1[/C][C]2.21331[/C][C]-0.113312[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]2.13602[/C][C]-0.0360184[/C][/ROW]
[ROW][C]30[/C][C]2.25[/C][C]2.15853[/C][C]0.0914706[/C][/ROW]
[ROW][C]31[/C][C]2.25[/C][C]2.37972[/C][C]-0.129725[/C][/ROW]
[ROW][C]32[/C][C]2.4[/C][C]2.11441[/C][C]0.285592[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]2.20405[/C][C]-0.104053[/C][/ROW]
[ROW][C]34[/C][C]2.1[/C][C]2.22316[/C][C]-0.123157[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]2.27342[/C][C]0.126578[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.15832[/C][C]-0.0583232[/C][/ROW]
[ROW][C]37[/C][C]1.95[/C][C]2.22513[/C][C]-0.275133[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.22758[/C][C]-0.127585[/C][/ROW]
[ROW][C]39[/C][C]2.25[/C][C]2.22045[/C][C]0.029545[/C][/ROW]
[ROW][C]40[/C][C]2.25[/C][C]2.12872[/C][C]0.121278[/C][/ROW]
[ROW][C]41[/C][C]2.4[/C][C]2.24439[/C][C]0.155611[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.23319[/C][C]0.016809[/C][/ROW]
[ROW][C]43[/C][C]2.25[/C][C]2.13288[/C][C]0.117121[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]2.20654[/C][C]-0.106543[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]2.12348[/C][C]-0.0234819[/C][/ROW]
[ROW][C]46[/C][C]2.1[/C][C]2.18822[/C][C]-0.0882212[/C][/ROW]
[ROW][C]47[/C][C]2.7[/C][C]2.23471[/C][C]0.465286[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]2.16046[/C][C]-0.0604632[/C][/ROW]
[ROW][C]49[/C][C]2.1[/C][C]2.25497[/C][C]-0.154973[/C][/ROW]
[ROW][C]50[/C][C]2.25[/C][C]2.21031[/C][C]0.0396918[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.32058[/C][C]0.37942[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.12511[/C][C]0.27489[/C][/ROW]
[ROW][C]53[/C][C]2.1[/C][C]2.33448[/C][C]-0.234476[/C][/ROW]
[ROW][C]54[/C][C]2.1[/C][C]1.98364[/C][C]0.116357[/C][/ROW]
[ROW][C]55[/C][C]2.4[/C][C]2.37228[/C][C]0.0277186[/C][/ROW]
[ROW][C]56[/C][C]1.95[/C][C]2.12922[/C][C]-0.179218[/C][/ROW]
[ROW][C]57[/C][C]2.7[/C][C]2.26657[/C][C]0.43343[/C][/ROW]
[ROW][C]58[/C][C]2.1[/C][C]2.15299[/C][C]-0.0529869[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.2067[/C][C]0.0432996[/C][/ROW]
[ROW][C]60[/C][C]2.1[/C][C]2.25538[/C][C]-0.155379[/C][/ROW]
[ROW][C]61[/C][C]2.7[/C][C]2.18075[/C][C]0.519254[/C][/ROW]
[ROW][C]62[/C][C]2.1[/C][C]2.2883[/C][C]-0.188299[/C][/ROW]
[ROW][C]63[/C][C]2.1[/C][C]2.27966[/C][C]-0.179657[/C][/ROW]
[ROW][C]64[/C][C]1.65[/C][C]2.09409[/C][C]-0.444091[/C][/ROW]
[ROW][C]65[/C][C]1.65[/C][C]2.09409[/C][C]-0.444091[/C][/ROW]
[ROW][C]66[/C][C]2.1[/C][C]2.22532[/C][C]-0.12532[/C][/ROW]
[ROW][C]67[/C][C]2.1[/C][C]2.24185[/C][C]-0.141855[/C][/ROW]
[ROW][C]68[/C][C]2.1[/C][C]2.21339[/C][C]-0.113387[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.28552[/C][C]-0.185525[/C][/ROW]
[ROW][C]70[/C][C]2.1[/C][C]2.15135[/C][C]-0.0513497[/C][/ROW]
[ROW][C]71[/C][C]2.4[/C][C]2.31492[/C][C]0.0850803[/C][/ROW]
[ROW][C]72[/C][C]2.4[/C][C]2.28703[/C][C]0.11297[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.18001[/C][C]-0.0800103[/C][/ROW]
[ROW][C]74[/C][C]2.25[/C][C]2.27596[/C][C]-0.0259629[/C][/ROW]
[ROW][C]75[/C][C]2.4[/C][C]2.24244[/C][C]0.157555[/C][/ROW]
[ROW][C]76[/C][C]2.1[/C][C]2.26448[/C][C]-0.164484[/C][/ROW]
[ROW][C]77[/C][C]2.1[/C][C]2.1969[/C][C]-0.0969014[/C][/ROW]
[ROW][C]78[/C][C]2.4[/C][C]2.22627[/C][C]0.173728[/C][/ROW]
[ROW][C]79[/C][C]2.4[/C][C]2.2067[/C][C]0.193302[/C][/ROW]
[ROW][C]80[/C][C]2.1[/C][C]2.17026[/C][C]-0.0702557[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.09937[/C][C]0.000630553[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.29355[/C][C]0.10645[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]2.15573[/C][C]-0.0557298[/C][/ROW]
[ROW][C]84[/C][C]2.7[/C][C]2.27546[/C][C]0.424536[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.2159[/C][C]-0.115898[/C][/ROW]
[ROW][C]86[/C][C]2.1[/C][C]2.1534[/C][C]-0.0534004[/C][/ROW]
[ROW][C]87[/C][C]2.25[/C][C]2.20623[/C][C]0.0437736[/C][/ROW]
[ROW][C]88[/C][C]2.1[/C][C]2.28483[/C][C]-0.184826[/C][/ROW]
[ROW][C]89[/C][C]2.4[/C][C]2.22508[/C][C]0.174918[/C][/ROW]
[ROW][C]90[/C][C]2.25[/C][C]2.26795[/C][C]-0.0179456[/C][/ROW]
[ROW][C]91[/C][C]2.25[/C][C]2.27528[/C][C]-0.0252814[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]2.11174[/C][C]-0.0117448[/C][/ROW]
[ROW][C]93[/C][C]2.1[/C][C]2.20347[/C][C]-0.103473[/C][/ROW]
[ROW][C]94[/C][C]2.4[/C][C]2.2202[/C][C]0.179802[/C][/ROW]
[ROW][C]95[/C][C]2.25[/C][C]2.12121[/C][C]0.128786[/C][/ROW]
[ROW][C]96[/C][C]2.1[/C][C]2.18203[/C][C]-0.082028[/C][/ROW]
[ROW][C]97[/C][C]2.1[/C][C]2.1343[/C][C]-0.0342951[/C][/ROW]
[ROW][C]98[/C][C]1.65[/C][C]2.09509[/C][C]-0.445092[/C][/ROW]
[ROW][C]99[/C][C]1.65[/C][C]2.1566[/C][C]-0.506601[/C][/ROW]
[ROW][C]100[/C][C]2.7[/C][C]2.39187[/C][C]0.308127[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]2.22774[/C][C]-0.127742[/C][/ROW]
[ROW][C]102[/C][C]1.95[/C][C]2.146[/C][C]-0.195999[/C][/ROW]
[ROW][C]103[/C][C]2.25[/C][C]2.12312[/C][C]0.126883[/C][/ROW]
[ROW][C]104[/C][C]2.4[/C][C]2.24544[/C][C]0.15456[/C][/ROW]
[ROW][C]105[/C][C]1.95[/C][C]2.13956[/C][C]-0.189558[/C][/ROW]
[ROW][C]106[/C][C]2.1[/C][C]2.24259[/C][C]-0.142594[/C][/ROW]
[ROW][C]107[/C][C]2.4[/C][C]2.17404[/C][C]0.225963[/C][/ROW]
[ROW][C]108[/C][C]2.1[/C][C]2.20028[/C][C]-0.100279[/C][/ROW]
[ROW][C]109[/C][C]2.1[/C][C]2.21231[/C][C]-0.112309[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.16718[/C][C]0.232821[/C][/ROW]
[ROW][C]111[/C][C]2.4[/C][C]2.15322[/C][C]0.246782[/C][/ROW]
[ROW][C]112[/C][C]2.4[/C][C]2.20723[/C][C]0.192769[/C][/ROW]
[ROW][C]113[/C][C]2.25[/C][C]2.18228[/C][C]0.0677157[/C][/ROW]
[ROW][C]114[/C][C]2.4[/C][C]2.19091[/C][C]0.209091[/C][/ROW]
[ROW][C]115[/C][C]2.1[/C][C]2.05058[/C][C]0.049422[/C][/ROW]
[ROW][C]116[/C][C]2.1[/C][C]2.11275[/C][C]-0.0127453[/C][/ROW]
[ROW][C]117[/C][C]1.8[/C][C]2.24006[/C][C]-0.440059[/C][/ROW]
[ROW][C]118[/C][C]2.7[/C][C]2.15548[/C][C]0.544522[/C][/ROW]
[ROW][C]119[/C][C]2.1[/C][C]2.16555[/C][C]-0.0655527[/C][/ROW]
[ROW][C]120[/C][C]2.1[/C][C]2.1544[/C][C]-0.0544022[/C][/ROW]
[ROW][C]121[/C][C]2.4[/C][C]2.20676[/C][C]0.193242[/C][/ROW]
[ROW][C]122[/C][C]2.55[/C][C]2.20959[/C][C]0.340409[/C][/ROW]
[ROW][C]123[/C][C]2.55[/C][C]2.29679[/C][C]0.253209[/C][/ROW]
[ROW][C]124[/C][C]2.1[/C][C]2.29243[/C][C]-0.192432[/C][/ROW]
[ROW][C]125[/C][C]2.1[/C][C]2.19152[/C][C]-0.0915214[/C][/ROW]
[ROW][C]126[/C][C]2.1[/C][C]2.18247[/C][C]-0.08247[/C][/ROW]
[ROW][C]127[/C][C]2.25[/C][C]2.15617[/C][C]0.0938259[/C][/ROW]
[ROW][C]128[/C][C]2.25[/C][C]2.03322[/C][C]0.216777[/C][/ROW]
[ROW][C]129[/C][C]2.1[/C][C]2.16992[/C][C]-0.0699159[/C][/ROW]
[ROW][C]130[/C][C]2.1[/C][C]2.19105[/C][C]-0.0910458[/C][/ROW]
[ROW][C]131[/C][C]1.95[/C][C]2.1821[/C][C]-0.232098[/C][/ROW]
[ROW][C]132[/C][C]2.4[/C][C]2.17853[/C][C]0.221473[/C][/ROW]
[ROW][C]133[/C][C]2.1[/C][C]2.1876[/C][C]-0.0876038[/C][/ROW]
[ROW][C]134[/C][C]2.4[/C][C]2.22866[/C][C]0.171335[/C][/ROW]
[ROW][C]135[/C][C]2.4[/C][C]2.19043[/C][C]0.209572[/C][/ROW]
[ROW][C]136[/C][C]2.4[/C][C]2.37228[/C][C]0.0277186[/C][/ROW]
[ROW][C]137[/C][C]2.25[/C][C]2.09689[/C][C]0.153111[/C][/ROW]
[ROW][C]138[/C][C]1.95[/C][C]2.13992[/C][C]-0.189916[/C][/ROW]
[ROW][C]139[/C][C]2.1[/C][C]2.16183[/C][C]-0.0618335[/C][/ROW]
[ROW][C]140[/C][C]2.1[/C][C]2.29462[/C][C]-0.194622[/C][/ROW]
[ROW][C]141[/C][C]2.55[/C][C]2.12562[/C][C]0.424381[/C][/ROW]
[ROW][C]142[/C][C]2.1[/C][C]2.19342[/C][C]-0.0934196[/C][/ROW]
[ROW][C]143[/C][C]2.1[/C][C]2.0889[/C][C]0.0111[/C][/ROW]
[ROW][C]144[/C][C]2.1[/C][C]2.09638[/C][C]0.00361683[/C][/ROW]
[ROW][C]145[/C][C]1.95[/C][C]2.17567[/C][C]-0.225667[/C][/ROW]
[ROW][C]146[/C][C]2.25[/C][C]2.16436[/C][C]0.085643[/C][/ROW]
[ROW][C]147[/C][C]2.4[/C][C]2.28864[/C][C]0.111361[/C][/ROW]
[ROW][C]148[/C][C]1.95[/C][C]2.14903[/C][C]-0.199034[/C][/ROW]
[ROW][C]149[/C][C]2.1[/C][C]2.25534[/C][C]-0.155341[/C][/ROW]
[ROW][C]150[/C][C]2.1[/C][C]2.13353[/C][C]-0.0335313[/C][/ROW]
[ROW][C]151[/C][C]1.95[/C][C]2.15844[/C][C]-0.208436[/C][/ROW]
[ROW][C]152[/C][C]2.1[/C][C]2.31992[/C][C]-0.21992[/C][/ROW]
[ROW][C]153[/C][C]2.1[/C][C]2.23376[/C][C]-0.13376[/C][/ROW]
[ROW][C]154[/C][C]1.95[/C][C]2.12922[/C][C]-0.179218[/C][/ROW]
[ROW][C]155[/C][C]2.1[/C][C]2.20269[/C][C]-0.102691[/C][/ROW]
[ROW][C]156[/C][C]1.95[/C][C]2.21443[/C][C]-0.264426[/C][/ROW]
[ROW][C]157[/C][C]2.4[/C][C]2.19043[/C][C]0.209572[/C][/ROW]
[ROW][C]158[/C][C]2.4[/C][C]2.23769[/C][C]0.162308[/C][/ROW]
[ROW][C]159[/C][C]2.4[/C][C]2.11598[/C][C]0.284023[/C][/ROW]
[ROW][C]160[/C][C]1.95[/C][C]2.17089[/C][C]-0.220886[/C][/ROW]
[ROW][C]161[/C][C]2.7[/C][C]2.30635[/C][C]0.393654[/C][/ROW]
[ROW][C]162[/C][C]2.1[/C][C]2.19888[/C][C]-0.0988801[/C][/ROW]
[ROW][C]163[/C][C]1.95[/C][C]2.13223[/C][C]-0.182231[/C][/ROW]
[ROW][C]164[/C][C]2.1[/C][C]2.27749[/C][C]-0.177486[/C][/ROW]
[ROW][C]165[/C][C]1.95[/C][C]2.11601[/C][C]-0.166012[/C][/ROW]
[ROW][C]166[/C][C]2.1[/C][C]2.31742[/C][C]-0.217423[/C][/ROW]
[ROW][C]167[/C][C]2.25[/C][C]2.23028[/C][C]0.0197201[/C][/ROW]
[ROW][C]168[/C][C]2.7[/C][C]2.25741[/C][C]0.442591[/C][/ROW]
[ROW][C]169[/C][C]2.1[/C][C]2.17959[/C][C]-0.0795933[/C][/ROW]
[ROW][C]170[/C][C]2.4[/C][C]2.24792[/C][C]0.152076[/C][/ROW]
[ROW][C]171[/C][C]1.35[/C][C]2.0488[/C][C]-0.698797[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266757&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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
12.12.030030.0699679
22.72.085750.614251
32.12.1616-0.0616045
42.12.10995-0.00995498
52.12.3559-0.255905
62.12.1279-0.0278976
72.12.16878-0.0687814
82.12.30534-0.205339
92.12.39236-0.29236
102.12.094290.00571183
112.42.211150.188848
121.951.99856-0.0485639
132.12.093980.00601915
142.12.10135-0.0013544
151.952.14172-0.19172
162.12.2059-0.105902
172.42.303490.0965089
182.12.084410.0155889
192.252.060070.189934
202.42.309960.0900405
212.252.17530.0747045
222.552.232340.317662
231.952.08379-0.133791
242.42.087220.312775
252.12.090550.00944653
262.12.017030.0829689
272.42.273510.126491
282.12.21331-0.113312
292.12.13602-0.0360184
302.252.158530.0914706
312.252.37972-0.129725
322.42.114410.285592
332.12.20405-0.104053
342.12.22316-0.123157
352.42.273420.126578
362.12.15832-0.0583232
371.952.22513-0.275133
382.12.22758-0.127585
392.252.220450.029545
402.252.128720.121278
412.42.244390.155611
422.252.233190.016809
432.252.132880.117121
442.12.20654-0.106543
452.12.12348-0.0234819
462.12.18822-0.0882212
472.72.234710.465286
482.12.16046-0.0604632
492.12.25497-0.154973
502.252.210310.0396918
512.72.320580.37942
522.42.125110.27489
532.12.33448-0.234476
542.11.983640.116357
552.42.372280.0277186
561.952.12922-0.179218
572.72.266570.43343
582.12.15299-0.0529869
592.252.20670.0432996
602.12.25538-0.155379
612.72.180750.519254
622.12.2883-0.188299
632.12.27966-0.179657
641.652.09409-0.444091
651.652.09409-0.444091
662.12.22532-0.12532
672.12.24185-0.141855
682.12.21339-0.113387
692.12.28552-0.185525
702.12.15135-0.0513497
712.42.314920.0850803
722.42.287030.11297
732.12.18001-0.0800103
742.252.27596-0.0259629
752.42.242440.157555
762.12.26448-0.164484
772.12.1969-0.0969014
782.42.226270.173728
792.42.20670.193302
802.12.17026-0.0702557
812.12.099370.000630553
822.42.293550.10645
832.12.15573-0.0557298
842.72.275460.424536
852.12.2159-0.115898
862.12.1534-0.0534004
872.252.206230.0437736
882.12.28483-0.184826
892.42.225080.174918
902.252.26795-0.0179456
912.252.27528-0.0252814
922.12.11174-0.0117448
932.12.20347-0.103473
942.42.22020.179802
952.252.121210.128786
962.12.18203-0.082028
972.12.1343-0.0342951
981.652.09509-0.445092
991.652.1566-0.506601
1002.72.391870.308127
1012.12.22774-0.127742
1021.952.146-0.195999
1032.252.123120.126883
1042.42.245440.15456
1051.952.13956-0.189558
1062.12.24259-0.142594
1072.42.174040.225963
1082.12.20028-0.100279
1092.12.21231-0.112309
1102.42.167180.232821
1112.42.153220.246782
1122.42.207230.192769
1132.252.182280.0677157
1142.42.190910.209091
1152.12.050580.049422
1162.12.11275-0.0127453
1171.82.24006-0.440059
1182.72.155480.544522
1192.12.16555-0.0655527
1202.12.1544-0.0544022
1212.42.206760.193242
1222.552.209590.340409
1232.552.296790.253209
1242.12.29243-0.192432
1252.12.19152-0.0915214
1262.12.18247-0.08247
1272.252.156170.0938259
1282.252.033220.216777
1292.12.16992-0.0699159
1302.12.19105-0.0910458
1311.952.1821-0.232098
1322.42.178530.221473
1332.12.1876-0.0876038
1342.42.228660.171335
1352.42.190430.209572
1362.42.372280.0277186
1372.252.096890.153111
1381.952.13992-0.189916
1392.12.16183-0.0618335
1402.12.29462-0.194622
1412.552.125620.424381
1422.12.19342-0.0934196
1432.12.08890.0111
1442.12.096380.00361683
1451.952.17567-0.225667
1462.252.164360.085643
1472.42.288640.111361
1481.952.14903-0.199034
1492.12.25534-0.155341
1502.12.13353-0.0335313
1511.952.15844-0.208436
1522.12.31992-0.21992
1532.12.23376-0.13376
1541.952.12922-0.179218
1552.12.20269-0.102691
1561.952.21443-0.264426
1572.42.190430.209572
1582.42.237690.162308
1592.42.115980.284023
1601.952.17089-0.220886
1612.72.306350.393654
1622.12.19888-0.0988801
1631.952.13223-0.182231
1642.12.27749-0.177486
1651.952.11601-0.166012
1662.12.31742-0.217423
1672.252.230280.0197201
1682.72.257410.442591
1692.12.17959-0.0795933
1702.42.247920.152076
1711.352.0488-0.698797






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8544080.2911850.145592
140.7754260.4491490.224574
150.7876640.4246720.212336
160.6903110.6193770.309689
170.6769960.6460080.323004
180.5759040.8481910.424096
190.6008380.7983250.399162
200.6012060.7975870.398794
210.5423590.9152830.457641
220.4859270.9718530.514073
230.545990.9080210.45401
240.5595710.8808590.440429
250.480690.9613810.51931
260.4445720.8891440.555428
270.3766440.7532890.623356
280.3866470.7732940.613353
290.3209930.6419850.679007
300.29240.5847990.7076
310.2658410.5316820.734159
320.2791750.558350.720825
330.2531290.5062570.746871
340.2063240.4126480.793676
350.1911690.3823380.808831
360.1517730.3035450.848227
370.181430.3628590.81857
380.1555920.3111840.844408
390.1219690.2439380.878031
400.1008750.201750.899125
410.09531590.1906320.904684
420.072660.145320.92734
430.05769810.1153960.942302
440.04588120.09176230.954119
450.03644840.07289680.963552
460.02657440.05314880.973426
470.08564220.1712840.914358
480.06883940.1376790.931161
490.05514220.1102840.944858
500.04624910.09249820.953751
510.100950.2018990.89905
520.1121740.2243480.887826
530.1300010.2600030.869999
540.1137230.2274460.886277
550.09428170.1885630.905718
560.09631540.1926310.903685
570.1748780.3497560.825122
580.1523610.3047220.847639
590.1261830.2523650.873817
600.1182020.2364030.881798
610.2416730.4833460.758327
620.2273060.4546120.772694
630.2396890.4793780.760311
640.4010610.8021210.598939
650.535420.9291590.46458
660.4963930.9927870.503607
670.4884070.9768130.511593
680.4492110.8984220.550789
690.4318050.8636110.568195
700.4061460.8122910.593854
710.3689140.7378290.631086
720.3348290.6696580.665171
730.3027540.6055080.697246
740.263540.527080.73646
750.2546680.5093360.745332
760.2418490.4836990.758151
770.2145090.4290190.785491
780.205520.4110410.79448
790.2003920.4007840.799608
800.1748260.3496510.825174
810.1495170.2990350.850483
820.1303190.2606390.869681
830.1088070.2176150.891193
840.1975430.3950870.802457
850.176090.3521790.82391
860.1547710.3095410.845229
870.129780.259560.87022
880.1241250.2482510.875875
890.1129790.2259580.887021
900.09253290.1850660.907467
910.07731140.1546230.922689
920.06269260.1253850.937307
930.05214860.1042970.947851
940.04873660.09747330.951263
950.04061110.08122220.959389
960.03301230.06602450.966988
970.02577710.05155410.974223
980.05806230.1161250.941938
990.1463710.2927410.853629
1000.1824450.364890.817555
1010.1620890.3241780.837911
1020.1567960.3135930.843204
1030.1386190.2772370.861381
1040.1271650.2543310.872835
1050.1197520.2395040.880248
1060.1061010.2122020.893899
1070.1078050.215610.892195
1080.09128780.1825760.908712
1090.07771450.1554290.922285
1100.07914820.1582960.920852
1110.08451920.1690380.915481
1120.08134490.162690.918655
1130.06718850.1343770.932812
1140.06714940.1342990.932851
1150.05403820.1080760.945962
1160.04192520.08385040.958075
1170.08365450.1673090.916346
1180.2663190.5326390.733681
1190.2295840.4591670.770416
1200.1940170.3880350.805983
1210.189470.378940.81053
1220.2423890.4847780.757611
1230.2430790.4861590.756921
1240.2265790.4531570.773421
1250.1991410.3982820.800859
1260.1770320.3540640.822968
1270.1509030.3018060.849097
1280.1938480.3876950.806152
1290.1627830.3255660.837217
1300.1427070.2854150.857293
1310.1449960.2899920.855004
1320.1447260.2894510.855274
1330.1162330.2324660.883767
1340.1217690.2435380.878231
1350.1141970.2283940.885803
1360.09688550.1937710.903114
1370.09354810.1870960.906452
1380.08575230.1715050.914248
1390.06894660.1378930.931053
1400.08007920.1601580.919921
1410.2028310.4056620.797169
1420.1602860.3205720.839714
1430.1443610.2887230.855639
1440.1458370.2916730.854163
1450.1157890.2315780.884211
1460.0852070.1704140.914793
1470.06398410.1279680.936016
1480.05549990.1110.9445
1490.08786020.175720.91214
1500.07811570.1562310.921884
1510.06731650.1346330.932684
1520.07546030.1509210.92454
1530.06818820.1363760.931812
1540.04766050.09532090.95234
1550.0408370.0816740.959163
1560.04057090.08114170.959429
1570.02135640.04271280.978644
1580.009254310.01850860.990746

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.854408 & 0.291185 & 0.145592 \tabularnewline
14 & 0.775426 & 0.449149 & 0.224574 \tabularnewline
15 & 0.787664 & 0.424672 & 0.212336 \tabularnewline
16 & 0.690311 & 0.619377 & 0.309689 \tabularnewline
17 & 0.676996 & 0.646008 & 0.323004 \tabularnewline
18 & 0.575904 & 0.848191 & 0.424096 \tabularnewline
19 & 0.600838 & 0.798325 & 0.399162 \tabularnewline
20 & 0.601206 & 0.797587 & 0.398794 \tabularnewline
21 & 0.542359 & 0.915283 & 0.457641 \tabularnewline
22 & 0.485927 & 0.971853 & 0.514073 \tabularnewline
23 & 0.54599 & 0.908021 & 0.45401 \tabularnewline
24 & 0.559571 & 0.880859 & 0.440429 \tabularnewline
25 & 0.48069 & 0.961381 & 0.51931 \tabularnewline
26 & 0.444572 & 0.889144 & 0.555428 \tabularnewline
27 & 0.376644 & 0.753289 & 0.623356 \tabularnewline
28 & 0.386647 & 0.773294 & 0.613353 \tabularnewline
29 & 0.320993 & 0.641985 & 0.679007 \tabularnewline
30 & 0.2924 & 0.584799 & 0.7076 \tabularnewline
31 & 0.265841 & 0.531682 & 0.734159 \tabularnewline
32 & 0.279175 & 0.55835 & 0.720825 \tabularnewline
33 & 0.253129 & 0.506257 & 0.746871 \tabularnewline
34 & 0.206324 & 0.412648 & 0.793676 \tabularnewline
35 & 0.191169 & 0.382338 & 0.808831 \tabularnewline
36 & 0.151773 & 0.303545 & 0.848227 \tabularnewline
37 & 0.18143 & 0.362859 & 0.81857 \tabularnewline
38 & 0.155592 & 0.311184 & 0.844408 \tabularnewline
39 & 0.121969 & 0.243938 & 0.878031 \tabularnewline
40 & 0.100875 & 0.20175 & 0.899125 \tabularnewline
41 & 0.0953159 & 0.190632 & 0.904684 \tabularnewline
42 & 0.07266 & 0.14532 & 0.92734 \tabularnewline
43 & 0.0576981 & 0.115396 & 0.942302 \tabularnewline
44 & 0.0458812 & 0.0917623 & 0.954119 \tabularnewline
45 & 0.0364484 & 0.0728968 & 0.963552 \tabularnewline
46 & 0.0265744 & 0.0531488 & 0.973426 \tabularnewline
47 & 0.0856422 & 0.171284 & 0.914358 \tabularnewline
48 & 0.0688394 & 0.137679 & 0.931161 \tabularnewline
49 & 0.0551422 & 0.110284 & 0.944858 \tabularnewline
50 & 0.0462491 & 0.0924982 & 0.953751 \tabularnewline
51 & 0.10095 & 0.201899 & 0.89905 \tabularnewline
52 & 0.112174 & 0.224348 & 0.887826 \tabularnewline
53 & 0.130001 & 0.260003 & 0.869999 \tabularnewline
54 & 0.113723 & 0.227446 & 0.886277 \tabularnewline
55 & 0.0942817 & 0.188563 & 0.905718 \tabularnewline
56 & 0.0963154 & 0.192631 & 0.903685 \tabularnewline
57 & 0.174878 & 0.349756 & 0.825122 \tabularnewline
58 & 0.152361 & 0.304722 & 0.847639 \tabularnewline
59 & 0.126183 & 0.252365 & 0.873817 \tabularnewline
60 & 0.118202 & 0.236403 & 0.881798 \tabularnewline
61 & 0.241673 & 0.483346 & 0.758327 \tabularnewline
62 & 0.227306 & 0.454612 & 0.772694 \tabularnewline
63 & 0.239689 & 0.479378 & 0.760311 \tabularnewline
64 & 0.401061 & 0.802121 & 0.598939 \tabularnewline
65 & 0.53542 & 0.929159 & 0.46458 \tabularnewline
66 & 0.496393 & 0.992787 & 0.503607 \tabularnewline
67 & 0.488407 & 0.976813 & 0.511593 \tabularnewline
68 & 0.449211 & 0.898422 & 0.550789 \tabularnewline
69 & 0.431805 & 0.863611 & 0.568195 \tabularnewline
70 & 0.406146 & 0.812291 & 0.593854 \tabularnewline
71 & 0.368914 & 0.737829 & 0.631086 \tabularnewline
72 & 0.334829 & 0.669658 & 0.665171 \tabularnewline
73 & 0.302754 & 0.605508 & 0.697246 \tabularnewline
74 & 0.26354 & 0.52708 & 0.73646 \tabularnewline
75 & 0.254668 & 0.509336 & 0.745332 \tabularnewline
76 & 0.241849 & 0.483699 & 0.758151 \tabularnewline
77 & 0.214509 & 0.429019 & 0.785491 \tabularnewline
78 & 0.20552 & 0.411041 & 0.79448 \tabularnewline
79 & 0.200392 & 0.400784 & 0.799608 \tabularnewline
80 & 0.174826 & 0.349651 & 0.825174 \tabularnewline
81 & 0.149517 & 0.299035 & 0.850483 \tabularnewline
82 & 0.130319 & 0.260639 & 0.869681 \tabularnewline
83 & 0.108807 & 0.217615 & 0.891193 \tabularnewline
84 & 0.197543 & 0.395087 & 0.802457 \tabularnewline
85 & 0.17609 & 0.352179 & 0.82391 \tabularnewline
86 & 0.154771 & 0.309541 & 0.845229 \tabularnewline
87 & 0.12978 & 0.25956 & 0.87022 \tabularnewline
88 & 0.124125 & 0.248251 & 0.875875 \tabularnewline
89 & 0.112979 & 0.225958 & 0.887021 \tabularnewline
90 & 0.0925329 & 0.185066 & 0.907467 \tabularnewline
91 & 0.0773114 & 0.154623 & 0.922689 \tabularnewline
92 & 0.0626926 & 0.125385 & 0.937307 \tabularnewline
93 & 0.0521486 & 0.104297 & 0.947851 \tabularnewline
94 & 0.0487366 & 0.0974733 & 0.951263 \tabularnewline
95 & 0.0406111 & 0.0812222 & 0.959389 \tabularnewline
96 & 0.0330123 & 0.0660245 & 0.966988 \tabularnewline
97 & 0.0257771 & 0.0515541 & 0.974223 \tabularnewline
98 & 0.0580623 & 0.116125 & 0.941938 \tabularnewline
99 & 0.146371 & 0.292741 & 0.853629 \tabularnewline
100 & 0.182445 & 0.36489 & 0.817555 \tabularnewline
101 & 0.162089 & 0.324178 & 0.837911 \tabularnewline
102 & 0.156796 & 0.313593 & 0.843204 \tabularnewline
103 & 0.138619 & 0.277237 & 0.861381 \tabularnewline
104 & 0.127165 & 0.254331 & 0.872835 \tabularnewline
105 & 0.119752 & 0.239504 & 0.880248 \tabularnewline
106 & 0.106101 & 0.212202 & 0.893899 \tabularnewline
107 & 0.107805 & 0.21561 & 0.892195 \tabularnewline
108 & 0.0912878 & 0.182576 & 0.908712 \tabularnewline
109 & 0.0777145 & 0.155429 & 0.922285 \tabularnewline
110 & 0.0791482 & 0.158296 & 0.920852 \tabularnewline
111 & 0.0845192 & 0.169038 & 0.915481 \tabularnewline
112 & 0.0813449 & 0.16269 & 0.918655 \tabularnewline
113 & 0.0671885 & 0.134377 & 0.932812 \tabularnewline
114 & 0.0671494 & 0.134299 & 0.932851 \tabularnewline
115 & 0.0540382 & 0.108076 & 0.945962 \tabularnewline
116 & 0.0419252 & 0.0838504 & 0.958075 \tabularnewline
117 & 0.0836545 & 0.167309 & 0.916346 \tabularnewline
118 & 0.266319 & 0.532639 & 0.733681 \tabularnewline
119 & 0.229584 & 0.459167 & 0.770416 \tabularnewline
120 & 0.194017 & 0.388035 & 0.805983 \tabularnewline
121 & 0.18947 & 0.37894 & 0.81053 \tabularnewline
122 & 0.242389 & 0.484778 & 0.757611 \tabularnewline
123 & 0.243079 & 0.486159 & 0.756921 \tabularnewline
124 & 0.226579 & 0.453157 & 0.773421 \tabularnewline
125 & 0.199141 & 0.398282 & 0.800859 \tabularnewline
126 & 0.177032 & 0.354064 & 0.822968 \tabularnewline
127 & 0.150903 & 0.301806 & 0.849097 \tabularnewline
128 & 0.193848 & 0.387695 & 0.806152 \tabularnewline
129 & 0.162783 & 0.325566 & 0.837217 \tabularnewline
130 & 0.142707 & 0.285415 & 0.857293 \tabularnewline
131 & 0.144996 & 0.289992 & 0.855004 \tabularnewline
132 & 0.144726 & 0.289451 & 0.855274 \tabularnewline
133 & 0.116233 & 0.232466 & 0.883767 \tabularnewline
134 & 0.121769 & 0.243538 & 0.878231 \tabularnewline
135 & 0.114197 & 0.228394 & 0.885803 \tabularnewline
136 & 0.0968855 & 0.193771 & 0.903114 \tabularnewline
137 & 0.0935481 & 0.187096 & 0.906452 \tabularnewline
138 & 0.0857523 & 0.171505 & 0.914248 \tabularnewline
139 & 0.0689466 & 0.137893 & 0.931053 \tabularnewline
140 & 0.0800792 & 0.160158 & 0.919921 \tabularnewline
141 & 0.202831 & 0.405662 & 0.797169 \tabularnewline
142 & 0.160286 & 0.320572 & 0.839714 \tabularnewline
143 & 0.144361 & 0.288723 & 0.855639 \tabularnewline
144 & 0.145837 & 0.291673 & 0.854163 \tabularnewline
145 & 0.115789 & 0.231578 & 0.884211 \tabularnewline
146 & 0.085207 & 0.170414 & 0.914793 \tabularnewline
147 & 0.0639841 & 0.127968 & 0.936016 \tabularnewline
148 & 0.0554999 & 0.111 & 0.9445 \tabularnewline
149 & 0.0878602 & 0.17572 & 0.91214 \tabularnewline
150 & 0.0781157 & 0.156231 & 0.921884 \tabularnewline
151 & 0.0673165 & 0.134633 & 0.932684 \tabularnewline
152 & 0.0754603 & 0.150921 & 0.92454 \tabularnewline
153 & 0.0681882 & 0.136376 & 0.931812 \tabularnewline
154 & 0.0476605 & 0.0953209 & 0.95234 \tabularnewline
155 & 0.040837 & 0.081674 & 0.959163 \tabularnewline
156 & 0.0405709 & 0.0811417 & 0.959429 \tabularnewline
157 & 0.0213564 & 0.0427128 & 0.978644 \tabularnewline
158 & 0.00925431 & 0.0185086 & 0.990746 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&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]13[/C][C]0.854408[/C][C]0.291185[/C][C]0.145592[/C][/ROW]
[ROW][C]14[/C][C]0.775426[/C][C]0.449149[/C][C]0.224574[/C][/ROW]
[ROW][C]15[/C][C]0.787664[/C][C]0.424672[/C][C]0.212336[/C][/ROW]
[ROW][C]16[/C][C]0.690311[/C][C]0.619377[/C][C]0.309689[/C][/ROW]
[ROW][C]17[/C][C]0.676996[/C][C]0.646008[/C][C]0.323004[/C][/ROW]
[ROW][C]18[/C][C]0.575904[/C][C]0.848191[/C][C]0.424096[/C][/ROW]
[ROW][C]19[/C][C]0.600838[/C][C]0.798325[/C][C]0.399162[/C][/ROW]
[ROW][C]20[/C][C]0.601206[/C][C]0.797587[/C][C]0.398794[/C][/ROW]
[ROW][C]21[/C][C]0.542359[/C][C]0.915283[/C][C]0.457641[/C][/ROW]
[ROW][C]22[/C][C]0.485927[/C][C]0.971853[/C][C]0.514073[/C][/ROW]
[ROW][C]23[/C][C]0.54599[/C][C]0.908021[/C][C]0.45401[/C][/ROW]
[ROW][C]24[/C][C]0.559571[/C][C]0.880859[/C][C]0.440429[/C][/ROW]
[ROW][C]25[/C][C]0.48069[/C][C]0.961381[/C][C]0.51931[/C][/ROW]
[ROW][C]26[/C][C]0.444572[/C][C]0.889144[/C][C]0.555428[/C][/ROW]
[ROW][C]27[/C][C]0.376644[/C][C]0.753289[/C][C]0.623356[/C][/ROW]
[ROW][C]28[/C][C]0.386647[/C][C]0.773294[/C][C]0.613353[/C][/ROW]
[ROW][C]29[/C][C]0.320993[/C][C]0.641985[/C][C]0.679007[/C][/ROW]
[ROW][C]30[/C][C]0.2924[/C][C]0.584799[/C][C]0.7076[/C][/ROW]
[ROW][C]31[/C][C]0.265841[/C][C]0.531682[/C][C]0.734159[/C][/ROW]
[ROW][C]32[/C][C]0.279175[/C][C]0.55835[/C][C]0.720825[/C][/ROW]
[ROW][C]33[/C][C]0.253129[/C][C]0.506257[/C][C]0.746871[/C][/ROW]
[ROW][C]34[/C][C]0.206324[/C][C]0.412648[/C][C]0.793676[/C][/ROW]
[ROW][C]35[/C][C]0.191169[/C][C]0.382338[/C][C]0.808831[/C][/ROW]
[ROW][C]36[/C][C]0.151773[/C][C]0.303545[/C][C]0.848227[/C][/ROW]
[ROW][C]37[/C][C]0.18143[/C][C]0.362859[/C][C]0.81857[/C][/ROW]
[ROW][C]38[/C][C]0.155592[/C][C]0.311184[/C][C]0.844408[/C][/ROW]
[ROW][C]39[/C][C]0.121969[/C][C]0.243938[/C][C]0.878031[/C][/ROW]
[ROW][C]40[/C][C]0.100875[/C][C]0.20175[/C][C]0.899125[/C][/ROW]
[ROW][C]41[/C][C]0.0953159[/C][C]0.190632[/C][C]0.904684[/C][/ROW]
[ROW][C]42[/C][C]0.07266[/C][C]0.14532[/C][C]0.92734[/C][/ROW]
[ROW][C]43[/C][C]0.0576981[/C][C]0.115396[/C][C]0.942302[/C][/ROW]
[ROW][C]44[/C][C]0.0458812[/C][C]0.0917623[/C][C]0.954119[/C][/ROW]
[ROW][C]45[/C][C]0.0364484[/C][C]0.0728968[/C][C]0.963552[/C][/ROW]
[ROW][C]46[/C][C]0.0265744[/C][C]0.0531488[/C][C]0.973426[/C][/ROW]
[ROW][C]47[/C][C]0.0856422[/C][C]0.171284[/C][C]0.914358[/C][/ROW]
[ROW][C]48[/C][C]0.0688394[/C][C]0.137679[/C][C]0.931161[/C][/ROW]
[ROW][C]49[/C][C]0.0551422[/C][C]0.110284[/C][C]0.944858[/C][/ROW]
[ROW][C]50[/C][C]0.0462491[/C][C]0.0924982[/C][C]0.953751[/C][/ROW]
[ROW][C]51[/C][C]0.10095[/C][C]0.201899[/C][C]0.89905[/C][/ROW]
[ROW][C]52[/C][C]0.112174[/C][C]0.224348[/C][C]0.887826[/C][/ROW]
[ROW][C]53[/C][C]0.130001[/C][C]0.260003[/C][C]0.869999[/C][/ROW]
[ROW][C]54[/C][C]0.113723[/C][C]0.227446[/C][C]0.886277[/C][/ROW]
[ROW][C]55[/C][C]0.0942817[/C][C]0.188563[/C][C]0.905718[/C][/ROW]
[ROW][C]56[/C][C]0.0963154[/C][C]0.192631[/C][C]0.903685[/C][/ROW]
[ROW][C]57[/C][C]0.174878[/C][C]0.349756[/C][C]0.825122[/C][/ROW]
[ROW][C]58[/C][C]0.152361[/C][C]0.304722[/C][C]0.847639[/C][/ROW]
[ROW][C]59[/C][C]0.126183[/C][C]0.252365[/C][C]0.873817[/C][/ROW]
[ROW][C]60[/C][C]0.118202[/C][C]0.236403[/C][C]0.881798[/C][/ROW]
[ROW][C]61[/C][C]0.241673[/C][C]0.483346[/C][C]0.758327[/C][/ROW]
[ROW][C]62[/C][C]0.227306[/C][C]0.454612[/C][C]0.772694[/C][/ROW]
[ROW][C]63[/C][C]0.239689[/C][C]0.479378[/C][C]0.760311[/C][/ROW]
[ROW][C]64[/C][C]0.401061[/C][C]0.802121[/C][C]0.598939[/C][/ROW]
[ROW][C]65[/C][C]0.53542[/C][C]0.929159[/C][C]0.46458[/C][/ROW]
[ROW][C]66[/C][C]0.496393[/C][C]0.992787[/C][C]0.503607[/C][/ROW]
[ROW][C]67[/C][C]0.488407[/C][C]0.976813[/C][C]0.511593[/C][/ROW]
[ROW][C]68[/C][C]0.449211[/C][C]0.898422[/C][C]0.550789[/C][/ROW]
[ROW][C]69[/C][C]0.431805[/C][C]0.863611[/C][C]0.568195[/C][/ROW]
[ROW][C]70[/C][C]0.406146[/C][C]0.812291[/C][C]0.593854[/C][/ROW]
[ROW][C]71[/C][C]0.368914[/C][C]0.737829[/C][C]0.631086[/C][/ROW]
[ROW][C]72[/C][C]0.334829[/C][C]0.669658[/C][C]0.665171[/C][/ROW]
[ROW][C]73[/C][C]0.302754[/C][C]0.605508[/C][C]0.697246[/C][/ROW]
[ROW][C]74[/C][C]0.26354[/C][C]0.52708[/C][C]0.73646[/C][/ROW]
[ROW][C]75[/C][C]0.254668[/C][C]0.509336[/C][C]0.745332[/C][/ROW]
[ROW][C]76[/C][C]0.241849[/C][C]0.483699[/C][C]0.758151[/C][/ROW]
[ROW][C]77[/C][C]0.214509[/C][C]0.429019[/C][C]0.785491[/C][/ROW]
[ROW][C]78[/C][C]0.20552[/C][C]0.411041[/C][C]0.79448[/C][/ROW]
[ROW][C]79[/C][C]0.200392[/C][C]0.400784[/C][C]0.799608[/C][/ROW]
[ROW][C]80[/C][C]0.174826[/C][C]0.349651[/C][C]0.825174[/C][/ROW]
[ROW][C]81[/C][C]0.149517[/C][C]0.299035[/C][C]0.850483[/C][/ROW]
[ROW][C]82[/C][C]0.130319[/C][C]0.260639[/C][C]0.869681[/C][/ROW]
[ROW][C]83[/C][C]0.108807[/C][C]0.217615[/C][C]0.891193[/C][/ROW]
[ROW][C]84[/C][C]0.197543[/C][C]0.395087[/C][C]0.802457[/C][/ROW]
[ROW][C]85[/C][C]0.17609[/C][C]0.352179[/C][C]0.82391[/C][/ROW]
[ROW][C]86[/C][C]0.154771[/C][C]0.309541[/C][C]0.845229[/C][/ROW]
[ROW][C]87[/C][C]0.12978[/C][C]0.25956[/C][C]0.87022[/C][/ROW]
[ROW][C]88[/C][C]0.124125[/C][C]0.248251[/C][C]0.875875[/C][/ROW]
[ROW][C]89[/C][C]0.112979[/C][C]0.225958[/C][C]0.887021[/C][/ROW]
[ROW][C]90[/C][C]0.0925329[/C][C]0.185066[/C][C]0.907467[/C][/ROW]
[ROW][C]91[/C][C]0.0773114[/C][C]0.154623[/C][C]0.922689[/C][/ROW]
[ROW][C]92[/C][C]0.0626926[/C][C]0.125385[/C][C]0.937307[/C][/ROW]
[ROW][C]93[/C][C]0.0521486[/C][C]0.104297[/C][C]0.947851[/C][/ROW]
[ROW][C]94[/C][C]0.0487366[/C][C]0.0974733[/C][C]0.951263[/C][/ROW]
[ROW][C]95[/C][C]0.0406111[/C][C]0.0812222[/C][C]0.959389[/C][/ROW]
[ROW][C]96[/C][C]0.0330123[/C][C]0.0660245[/C][C]0.966988[/C][/ROW]
[ROW][C]97[/C][C]0.0257771[/C][C]0.0515541[/C][C]0.974223[/C][/ROW]
[ROW][C]98[/C][C]0.0580623[/C][C]0.116125[/C][C]0.941938[/C][/ROW]
[ROW][C]99[/C][C]0.146371[/C][C]0.292741[/C][C]0.853629[/C][/ROW]
[ROW][C]100[/C][C]0.182445[/C][C]0.36489[/C][C]0.817555[/C][/ROW]
[ROW][C]101[/C][C]0.162089[/C][C]0.324178[/C][C]0.837911[/C][/ROW]
[ROW][C]102[/C][C]0.156796[/C][C]0.313593[/C][C]0.843204[/C][/ROW]
[ROW][C]103[/C][C]0.138619[/C][C]0.277237[/C][C]0.861381[/C][/ROW]
[ROW][C]104[/C][C]0.127165[/C][C]0.254331[/C][C]0.872835[/C][/ROW]
[ROW][C]105[/C][C]0.119752[/C][C]0.239504[/C][C]0.880248[/C][/ROW]
[ROW][C]106[/C][C]0.106101[/C][C]0.212202[/C][C]0.893899[/C][/ROW]
[ROW][C]107[/C][C]0.107805[/C][C]0.21561[/C][C]0.892195[/C][/ROW]
[ROW][C]108[/C][C]0.0912878[/C][C]0.182576[/C][C]0.908712[/C][/ROW]
[ROW][C]109[/C][C]0.0777145[/C][C]0.155429[/C][C]0.922285[/C][/ROW]
[ROW][C]110[/C][C]0.0791482[/C][C]0.158296[/C][C]0.920852[/C][/ROW]
[ROW][C]111[/C][C]0.0845192[/C][C]0.169038[/C][C]0.915481[/C][/ROW]
[ROW][C]112[/C][C]0.0813449[/C][C]0.16269[/C][C]0.918655[/C][/ROW]
[ROW][C]113[/C][C]0.0671885[/C][C]0.134377[/C][C]0.932812[/C][/ROW]
[ROW][C]114[/C][C]0.0671494[/C][C]0.134299[/C][C]0.932851[/C][/ROW]
[ROW][C]115[/C][C]0.0540382[/C][C]0.108076[/C][C]0.945962[/C][/ROW]
[ROW][C]116[/C][C]0.0419252[/C][C]0.0838504[/C][C]0.958075[/C][/ROW]
[ROW][C]117[/C][C]0.0836545[/C][C]0.167309[/C][C]0.916346[/C][/ROW]
[ROW][C]118[/C][C]0.266319[/C][C]0.532639[/C][C]0.733681[/C][/ROW]
[ROW][C]119[/C][C]0.229584[/C][C]0.459167[/C][C]0.770416[/C][/ROW]
[ROW][C]120[/C][C]0.194017[/C][C]0.388035[/C][C]0.805983[/C][/ROW]
[ROW][C]121[/C][C]0.18947[/C][C]0.37894[/C][C]0.81053[/C][/ROW]
[ROW][C]122[/C][C]0.242389[/C][C]0.484778[/C][C]0.757611[/C][/ROW]
[ROW][C]123[/C][C]0.243079[/C][C]0.486159[/C][C]0.756921[/C][/ROW]
[ROW][C]124[/C][C]0.226579[/C][C]0.453157[/C][C]0.773421[/C][/ROW]
[ROW][C]125[/C][C]0.199141[/C][C]0.398282[/C][C]0.800859[/C][/ROW]
[ROW][C]126[/C][C]0.177032[/C][C]0.354064[/C][C]0.822968[/C][/ROW]
[ROW][C]127[/C][C]0.150903[/C][C]0.301806[/C][C]0.849097[/C][/ROW]
[ROW][C]128[/C][C]0.193848[/C][C]0.387695[/C][C]0.806152[/C][/ROW]
[ROW][C]129[/C][C]0.162783[/C][C]0.325566[/C][C]0.837217[/C][/ROW]
[ROW][C]130[/C][C]0.142707[/C][C]0.285415[/C][C]0.857293[/C][/ROW]
[ROW][C]131[/C][C]0.144996[/C][C]0.289992[/C][C]0.855004[/C][/ROW]
[ROW][C]132[/C][C]0.144726[/C][C]0.289451[/C][C]0.855274[/C][/ROW]
[ROW][C]133[/C][C]0.116233[/C][C]0.232466[/C][C]0.883767[/C][/ROW]
[ROW][C]134[/C][C]0.121769[/C][C]0.243538[/C][C]0.878231[/C][/ROW]
[ROW][C]135[/C][C]0.114197[/C][C]0.228394[/C][C]0.885803[/C][/ROW]
[ROW][C]136[/C][C]0.0968855[/C][C]0.193771[/C][C]0.903114[/C][/ROW]
[ROW][C]137[/C][C]0.0935481[/C][C]0.187096[/C][C]0.906452[/C][/ROW]
[ROW][C]138[/C][C]0.0857523[/C][C]0.171505[/C][C]0.914248[/C][/ROW]
[ROW][C]139[/C][C]0.0689466[/C][C]0.137893[/C][C]0.931053[/C][/ROW]
[ROW][C]140[/C][C]0.0800792[/C][C]0.160158[/C][C]0.919921[/C][/ROW]
[ROW][C]141[/C][C]0.202831[/C][C]0.405662[/C][C]0.797169[/C][/ROW]
[ROW][C]142[/C][C]0.160286[/C][C]0.320572[/C][C]0.839714[/C][/ROW]
[ROW][C]143[/C][C]0.144361[/C][C]0.288723[/C][C]0.855639[/C][/ROW]
[ROW][C]144[/C][C]0.145837[/C][C]0.291673[/C][C]0.854163[/C][/ROW]
[ROW][C]145[/C][C]0.115789[/C][C]0.231578[/C][C]0.884211[/C][/ROW]
[ROW][C]146[/C][C]0.085207[/C][C]0.170414[/C][C]0.914793[/C][/ROW]
[ROW][C]147[/C][C]0.0639841[/C][C]0.127968[/C][C]0.936016[/C][/ROW]
[ROW][C]148[/C][C]0.0554999[/C][C]0.111[/C][C]0.9445[/C][/ROW]
[ROW][C]149[/C][C]0.0878602[/C][C]0.17572[/C][C]0.91214[/C][/ROW]
[ROW][C]150[/C][C]0.0781157[/C][C]0.156231[/C][C]0.921884[/C][/ROW]
[ROW][C]151[/C][C]0.0673165[/C][C]0.134633[/C][C]0.932684[/C][/ROW]
[ROW][C]152[/C][C]0.0754603[/C][C]0.150921[/C][C]0.92454[/C][/ROW]
[ROW][C]153[/C][C]0.0681882[/C][C]0.136376[/C][C]0.931812[/C][/ROW]
[ROW][C]154[/C][C]0.0476605[/C][C]0.0953209[/C][C]0.95234[/C][/ROW]
[ROW][C]155[/C][C]0.040837[/C][C]0.081674[/C][C]0.959163[/C][/ROW]
[ROW][C]156[/C][C]0.0405709[/C][C]0.0811417[/C][C]0.959429[/C][/ROW]
[ROW][C]157[/C][C]0.0213564[/C][C]0.0427128[/C][C]0.978644[/C][/ROW]
[ROW][C]158[/C][C]0.00925431[/C][C]0.0185086[/C][C]0.990746[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266757&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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
130.8544080.2911850.145592
140.7754260.4491490.224574
150.7876640.4246720.212336
160.6903110.6193770.309689
170.6769960.6460080.323004
180.5759040.8481910.424096
190.6008380.7983250.399162
200.6012060.7975870.398794
210.5423590.9152830.457641
220.4859270.9718530.514073
230.545990.9080210.45401
240.5595710.8808590.440429
250.480690.9613810.51931
260.4445720.8891440.555428
270.3766440.7532890.623356
280.3866470.7732940.613353
290.3209930.6419850.679007
300.29240.5847990.7076
310.2658410.5316820.734159
320.2791750.558350.720825
330.2531290.5062570.746871
340.2063240.4126480.793676
350.1911690.3823380.808831
360.1517730.3035450.848227
370.181430.3628590.81857
380.1555920.3111840.844408
390.1219690.2439380.878031
400.1008750.201750.899125
410.09531590.1906320.904684
420.072660.145320.92734
430.05769810.1153960.942302
440.04588120.09176230.954119
450.03644840.07289680.963552
460.02657440.05314880.973426
470.08564220.1712840.914358
480.06883940.1376790.931161
490.05514220.1102840.944858
500.04624910.09249820.953751
510.100950.2018990.89905
520.1121740.2243480.887826
530.1300010.2600030.869999
540.1137230.2274460.886277
550.09428170.1885630.905718
560.09631540.1926310.903685
570.1748780.3497560.825122
580.1523610.3047220.847639
590.1261830.2523650.873817
600.1182020.2364030.881798
610.2416730.4833460.758327
620.2273060.4546120.772694
630.2396890.4793780.760311
640.4010610.8021210.598939
650.535420.9291590.46458
660.4963930.9927870.503607
670.4884070.9768130.511593
680.4492110.8984220.550789
690.4318050.8636110.568195
700.4061460.8122910.593854
710.3689140.7378290.631086
720.3348290.6696580.665171
730.3027540.6055080.697246
740.263540.527080.73646
750.2546680.5093360.745332
760.2418490.4836990.758151
770.2145090.4290190.785491
780.205520.4110410.79448
790.2003920.4007840.799608
800.1748260.3496510.825174
810.1495170.2990350.850483
820.1303190.2606390.869681
830.1088070.2176150.891193
840.1975430.3950870.802457
850.176090.3521790.82391
860.1547710.3095410.845229
870.129780.259560.87022
880.1241250.2482510.875875
890.1129790.2259580.887021
900.09253290.1850660.907467
910.07731140.1546230.922689
920.06269260.1253850.937307
930.05214860.1042970.947851
940.04873660.09747330.951263
950.04061110.08122220.959389
960.03301230.06602450.966988
970.02577710.05155410.974223
980.05806230.1161250.941938
990.1463710.2927410.853629
1000.1824450.364890.817555
1010.1620890.3241780.837911
1020.1567960.3135930.843204
1030.1386190.2772370.861381
1040.1271650.2543310.872835
1050.1197520.2395040.880248
1060.1061010.2122020.893899
1070.1078050.215610.892195
1080.09128780.1825760.908712
1090.07771450.1554290.922285
1100.07914820.1582960.920852
1110.08451920.1690380.915481
1120.08134490.162690.918655
1130.06718850.1343770.932812
1140.06714940.1342990.932851
1150.05403820.1080760.945962
1160.04192520.08385040.958075
1170.08365450.1673090.916346
1180.2663190.5326390.733681
1190.2295840.4591670.770416
1200.1940170.3880350.805983
1210.189470.378940.81053
1220.2423890.4847780.757611
1230.2430790.4861590.756921
1240.2265790.4531570.773421
1250.1991410.3982820.800859
1260.1770320.3540640.822968
1270.1509030.3018060.849097
1280.1938480.3876950.806152
1290.1627830.3255660.837217
1300.1427070.2854150.857293
1310.1449960.2899920.855004
1320.1447260.2894510.855274
1330.1162330.2324660.883767
1340.1217690.2435380.878231
1350.1141970.2283940.885803
1360.09688550.1937710.903114
1370.09354810.1870960.906452
1380.08575230.1715050.914248
1390.06894660.1378930.931053
1400.08007920.1601580.919921
1410.2028310.4056620.797169
1420.1602860.3205720.839714
1430.1443610.2887230.855639
1440.1458370.2916730.854163
1450.1157890.2315780.884211
1460.0852070.1704140.914793
1470.06398410.1279680.936016
1480.05549990.1110.9445
1490.08786020.175720.91214
1500.07811570.1562310.921884
1510.06731650.1346330.932684
1520.07546030.1509210.92454
1530.06818820.1363760.931812
1540.04766050.09532090.95234
1550.0408370.0816740.959163
1560.04057090.08114170.959429
1570.02135640.04271280.978644
1580.009254310.01850860.990746






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0136986OK
10% type I error level140.0958904OK

\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 & 2 & 0.0136986 & OK \tabularnewline
10% type I error level & 14 & 0.0958904 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266757&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]2[/C][C]0.0136986[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.0958904[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266757&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266757&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 level20.0136986OK
10% type I error level140.0958904OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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')
}