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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 computationWed, 10 Dec 2014 09:16:32 +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/10/t1418203003oygo68d63x2fjgn.htm/, Retrieved Tue, 28 May 2024 17:36:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264867, Retrieved Tue, 28 May 2024 17:36:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-10 09:16:32] [cb9023b84cdbf59fe6647357de544f7b] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
12.9 86 68 18 96 149
12.8 71 32 39 88 148
7.4 108 62 46 114 158
6.7 64 33 31 69 128
12.6 119 52 67 176 224
14.8 97 62 35 114 159
13.3 129 77 52 121 105
11.1 153 76 77 110 159
8.2 78 41 37 158 167
11.4 80 48 32 116 165
6.4 99 63 36 181 159
12 147 78 69 141 176
6.3 40 19 21 35 54
11.3 57 31 26 80 91
11.9 120 66 54 152 163
9.3 71 35 36 97 124
10 68 45 23 84 121
13.8 137 25 112 101 148
10.8 79 44 35 107 221
11.7 101 54 47 112 149
10.9 111 74 37 171 244
16.1 189 80 109 137 148
9.9 81 61 20 66 150
11.5 63 41 22 93 153
8.3 69 46 23 105 94
11.7 71 39 32 131 156
9 64 34 30 102 132
10.8 85 42 43 120 105
10.4 55 39 16 77 151
12.7 69 20 49 108 131
11.8 96 53 43 168 157
13 100 54 46 75 162
10.8 68 49 19 107 163
12.3 57 34 23 62 59
11.3 105 46 59 121 187
11.6 69 37 32 97 116
10.9 49 30 19 126 148
12.1 50 28 22 104 155
13.3 93 45 48 148 125
10.1 58 35 23 146 116
14.3 74 41 33 97 138
9.3 107 73 34 118 164
12.5 65 17 48 58 162
7.6 58 40 18 63 99
9.2 70 37 33 50 186
14.5 95 28 67 94 188
12.3 136 56 80 127 177
12.6 82 50 32 128 139
13 102 59 43 146 162
12.6 65 27 38 69 108
13.2 90 61 29 186 159
7.7 83 51 32 85 110
10.5 70 35 35 54 96
10.9 77 48 29 106 87
4.3 37 25 12 34 97
10.3 81 44 37 60 127
11.4 71 20 51 62 74
5.6 40 26 14 64 114
8.8 43 23 20 98 95
9 32 21 11 35 121
9.6 76 41 35 55 130
6.4 30 22 8 54 52
11.6 51 27 24 51 118
4.35 34 12 23 41 48
12.7 61 45 16 146 50
18.1 70 37 33 182 150
17.85 69 37 32 192 154
16.6 145 108 37 263 109
12.6 23 10 14 35 68
17.1 120 68 52 439 194
19.1 147 72 75 214 158
16.1 215 143 72 341 159
13.35 24 9 15 58 67
18.4 84 55 29 292 147
14.7 30 17 13 85 39
10.6 77 37 40 200 100
12.6 46 27 19 158 111
16.2 61 37 24 199 138
13.6 178 58 121 297 101
18.9 160 66 93 227 131
14.1 57 21 36 108 101
14.5 42 19 23 86 114
16.15 163 78 85 302 165
14.75 75 35 41 148 114
14.8 94 48 46 178 111
12.45 45 27 18 120 75
12.65 78 43 35 207 82
17.35 47 30 17 157 121
8.6 29 25 4 128 32
18.4 97 69 28 296 150
16.1 116 72 44 323 117
11.6 32 23 10 79 71
17.75 50 13 38 70 165
15.25 118 61 57 146 154
17.65 66 43 23 246 126
15.6 48 22 26 145 138
16.35 86 51 36 196 149
17.65 89 67 22 199 145
13.6 76 36 40 127 120
11.7 39 21 18 91 138
14.35 75 44 31 153 109
14.75 57 45 11 299 132
18.25 72 34 38 228 172
9.9 60 36 24 190 169
16 109 72 37 180 114
18.25 76 39 37 212 156
16.85 65 43 22 269 172
14.6 40 25 15 130 68
13.85 58 56 2 179 89
18.95 123 80 43 243 167
15.6 71 40 31 190 113
14.85 102 73 29 299 115
11.75 80 34 45 121 78
18.45 97 72 25 137 118
15.9 46 42 4 305 87
17.1 93 61 31 157 173
16.1 19 23 -4 96 2
19.9 140 74 66 183 162
10.95 78 16 61 52 49
18.45 98 66 32 238 122
15.1 40 9 31 40 96
15 80 41 39 226 100
11.35 76 57 19 190 82
15.95 79 48 31 214 100
18.1 87 51 36 145 115
14.6 95 53 42 119 141
15.4 49 29 21 222 165
15.4 49 29 21 222 165
17.6 80 55 25 159 110
13.35 86 54 32 165 118
19.1 69 43 26 249 158
15.35 79 51 28 125 146
7.6 52 20 32 122 49
13.4 120 79 41 186 90
13.9 69 39 29 148 121
19.1 94 61 33 274 155
15.25 72 55 17 172 104
12.9 43 30 13 84 147
16.1 87 55 32 168 110
17.35 52 22 30 102 108
13.15 71 37 34 106 113
12.15 61 2 59 2 115
12.6 51 38 13 139 61
10.35 50 27 23 95 60
15.4 67 56 10 130 109
9.6 30 25 5 72 68
18.2 70 39 31 141 111
13.6 52 33 19 113 77
14.85 75 43 32 206 73
14.75 87 57 30 268 151
14.1 69 43 25 175 89
14.9 72 23 48 77 78
16.25 79 44 35 125 110
19.25 121 54 67 255 220
13.6 43 28 15 111 65
13.6 58 36 22 132 141
15.65 57 39 18 211 117
12.75 50 16 33 92 122
14.6 69 23 46 76 63
9.85 64 40 24 171 44
12.65 38 24 14 83 52
11.9 53 29 23 119 62
19.2 90 78 12 266 131
16.6 96 57 38 186 101
11.2 49 37 12 50 42
15.25 56 27 28 117 152
11.9 102 61 41 219 107
13.2 40 27 12 246 77
16.35 100 69 31 279 154
12.4 67 34 33 148 103
15.85 78 44 34 137 96
14.35 62 21 41 130 154
18.15 55 34 21 181 175
11.15 59 39 20 98 57
15.65 96 51 44 226 112
17.75 86 34 52 234 143
7.65 38 31 7 138 49
12.35 43 13 29 85 110
15.6 23 12 11 66 131
19.3 77 51 26 236 167
15.2 48 24 24 106 56
17.1 26 19 7 135 137
15.6 91 30 60 122 86
18.4 94 81 13 218 121
19.05 62 42 20 199 149
18.55 74 22 52 112 168
19.1 114 85 28 278 140
13.1 52 27 25 94 88
12.85 64 25 39 113 168
9.5 31 22 9 84 94
4.5 38 19 19 86 51
11.85 27 14 13 62 48
13.6 105 45 60 222 145
11.7 64 45 19 167 66
12.4 62 28 34 82 85
13.35 65 51 14 207 109
11.4 58 41 17 184 63
14.9 76 31 45 83 102
19.9 140 74 66 183 162
17.75 48 24 24 85 128
11.2 68 19 48 89 86
14.6 80 51 29 225 114
17.6 71 73 -2 237 164
14.05 76 24 51 102 119
16.1 63 61 2 221 126
13.35 46 23 24 128 132
11.85 53 14 40 91 142
11.95 74 54 20 198 83
14.75 70 51 19 204 94
15.15 78 62 16 158 81
13.2 56 36 20 138 166
16.85 100 59 40 226 110
7.85 51 24 27 44 64
7.7 52 26 25 196 93
12.6 102 54 49 83 104
7.85 78 39 39 79 105
10.95 78 16 61 52 49
12.35 55 36 19 105 88
9.95 98 31 67 116 95
14.9 76 31 45 83 102
16.65 73 42 30 196 99
13.4 47 39 8 153 63
13.95 45 25 19 157 76
15.7 83 31 52 75 109
16.85 60 38 22 106 117
10.95 48 31 17 58 57
15.35 50 17 33 75 120
12.2 56 22 34 74 73
15.1 77 55 22 185 91
17.75 91 62 30 265 108
15.2 76 51 25 131 105
14.6 68 30 38 139 117
16.65 74 49 26 196 119
8.1 29 16 13 78 31

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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&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'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.5244 + 0.298993Hours[t] -0.312669CH[t] -0.289814PRH[t] + 0.0262618Blogged[t] + 0.0124089LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.5244 +  0.298993Hours[t] -0.312669CH[t] -0.289814PRH[t] +  0.0262618Blogged[t] +  0.0124089LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.5244 +  0.298993Hours[t] -0.312669CH[t] -0.289814PRH[t] +  0.0262618Blogged[t] +  0.0124089LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&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] = + 8.5244 + 0.298993Hours[t] -0.312669CH[t] -0.289814PRH[t] + 0.0262618Blogged[t] + 0.0124089LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.52440.60406414.116.38957e-333.19478e-33
Hours0.2989930.4411810.67770.4986420.249321
CH-0.3126690.441298-0.70850.4793460.239673
PRH-0.2898140.442154-0.65550.5128320.256416
Blogged0.02626180.003261938.0514.52757e-142.26379e-14
LFM0.01240890.005084522.4410.01542840.00771422

\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) & 8.5244 & 0.604064 & 14.11 & 6.38957e-33 & 3.19478e-33 \tabularnewline
Hours & 0.298993 & 0.441181 & 0.6777 & 0.498642 & 0.249321 \tabularnewline
CH & -0.312669 & 0.441298 & -0.7085 & 0.479346 & 0.239673 \tabularnewline
PRH & -0.289814 & 0.442154 & -0.6555 & 0.512832 & 0.256416 \tabularnewline
Blogged & 0.0262618 & 0.00326193 & 8.051 & 4.52757e-14 & 2.26379e-14 \tabularnewline
LFM & 0.0124089 & 0.00508452 & 2.441 & 0.0154284 & 0.00771422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&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]8.5244[/C][C]0.604064[/C][C]14.11[/C][C]6.38957e-33[/C][C]3.19478e-33[/C][/ROW]
[ROW][C]Hours[/C][C]0.298993[/C][C]0.441181[/C][C]0.6777[/C][C]0.498642[/C][C]0.249321[/C][/ROW]
[ROW][C]CH[/C][C]-0.312669[/C][C]0.441298[/C][C]-0.7085[/C][C]0.479346[/C][C]0.239673[/C][/ROW]
[ROW][C]PRH[/C][C]-0.289814[/C][C]0.442154[/C][C]-0.6555[/C][C]0.512832[/C][C]0.256416[/C][/ROW]
[ROW][C]Blogged[/C][C]0.0262618[/C][C]0.00326193[/C][C]8.051[/C][C]4.52757e-14[/C][C]2.26379e-14[/C][/ROW]
[ROW][C]LFM[/C][C]0.0124089[/C][C]0.00508452[/C][C]2.441[/C][C]0.0154284[/C][C]0.00771422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&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)8.52440.60406414.116.38957e-333.19478e-33
Hours0.2989930.4411810.67770.4986420.249321
CH-0.3126690.441298-0.70850.4793460.239673
PRH-0.2898140.442154-0.65550.5128320.256416
Blogged0.02626180.003261938.0514.52757e-142.26379e-14
LFM0.01240890.005084522.4410.01542840.00771422






Multiple Linear Regression - Regression Statistics
Multiple R0.587332
R-squared0.344959
Adjusted R-squared0.330594
F-TEST (value)24.0139
F-TEST (DF numerator)5
F-TEST (DF denominator)228
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.70675
Sum Squared Residuals1670.44

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.587332 \tabularnewline
R-squared & 0.344959 \tabularnewline
Adjusted R-squared & 0.330594 \tabularnewline
F-TEST (value) & 24.0139 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 228 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.70675 \tabularnewline
Sum Squared Residuals & 1670.44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.587332[/C][/ROW]
[ROW][C]R-squared[/C][C]0.344959[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.330594[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.0139[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]228[/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]2.70675[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1670.44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&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 R0.587332
R-squared0.344959
Adjusted R-squared0.330594
F-TEST (value)24.0139
F-TEST (DF numerator)5
F-TEST (DF denominator)228
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.70675
Sum Squared Residuals1670.44






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.12970.770281
212.812.59230.2077
37.413.0532-5.65317
46.711.758-5.05804
512.615.8299-3.2299
614.812.96461.83539
713.312.42930.87074
811.113.0536-1.9536
98.214.525-6.32496
1011.413.2555-1.85552
116.414.7197-8.31966
121213.9779-1.97789
136.310.0466-3.74656
1411.311.5693-0.26925
1511.914.1319-2.23189
169.312.4623-3.16228
171011.8276-1.82757
1813.813.69950.100526
1910.813.7963-2.9963
2011.713.0076-1.30755
2110.915.3705-4.47054
2216.113.86522.23481
239.911.4684-1.56836
2411.512.5065-1.00653
258.312.0303-3.73035
2611.713.6608-1.96085
27912.6515-3.65146
2810.812.799-1.99905
2910.412.0338-1.63381
3012.713.1625-0.462482
3111.814.5544-2.75445
321312.1880.811999
3310.812.8613-2.06135
3412.310.63091.66911
3511.313.935-2.635
3611.612.2989-0.698941
3710.913.434-2.53403
3812.112.998-0.898019
3913.313.7874-0.487424
4010.113.5305-3.43051
4114.312.52641.77359
429.312.9721-3.67209
4312.512.26590.234083
447.611.0256-3.42556
459.211.9424-2.74244
4614.513.55790.942066
4712.314.0245-1.72447
4812.613.2207-0.620675
491313.9567-0.956675
5012.611.65620.943831
5113.214.8141-1.61407
527.711.7179-4.01788
5310.510.9764-0.476391
5410.911.9975-1.09747
554.310.3892-6.08922
5610.311.4139-1.11391
5711.411.26550.134514
585.611.3927-5.7927
598.812.1459-3.34593
60910.7588-1.75882
619.611.3425-1.7425
626.410.3604-3.96036
6311.611.1790.420955
644.359.94477-5.59477
6512.712.51050.189485
6618.114.96233.13772
6717.8515.26542.58465
6816.615.64640.953559
6912.69.980122.61988
7017.122.008-4.908
7119.115.80883.29123
7216.118.1579-2.0579
7313.3510.89362.45642
7418.417.5310.86903
7514.711.12743.57256
7610.614.8788-4.27879
7712.613.8563-1.2563
7816.215.17721.02279
7913.617.5959-3.99588
8018.916.36142.5386
8114.112.65721.44278
8214.512.14882.3512
8316.1518.2164-2.0664
8414.7513.42441.32556
8514.814.34220.457831
8612.4512.40250.047545
8712.6514.7113-2.06132
8817.3513.89473.45525
898.611.9778-3.37782
9018.417.47260.927398
9116.117.878-1.77801
9211.610.95840.641634
9317.7512.28225.46779
9415.2513.95861.29145
9517.6516.17141.47862
9615.613.98261.61743
9716.3514.85461.49539
9817.6514.83542.81456
9913.613.22350.376464
10011.712.5047-0.804681
10114.3513.57780.77217
10214.7517.7992-3.04921
10318.2516.53021.71976
1049.915.3392-5.43921
1051614.02111.97891
10618.2515.83392.41605
10716.8517.337-0.487032
10814.612.5782.02197
10913.8513.58220.267833
11018.9516.27892.67108
11115.614.65390.946143
11214.8517.0715-2.22155
11311.7512.917-1.16703
11418.4512.83135.61868
11515.917.0762-1.17616
11617.114.54362.55645
11716.110.71915.38091
11819.914.93434.96567
11910.9511.1381-0.188128
12018.4515.67972.77029
12115.110.92764.17241
1221515.4977-0.497719
12311.3513.9265-2.57655
12415.9515.01340.936583
12518.113.39244.70765
12614.613.05991.54011
12715.415.8992-0.499156
12815.415.8992-0.499156
12917.613.54234.0577
13013.3513.8771-0.527071
13119.116.67482.42521
13215.3513.17842.17164
1337.612.3566-4.75658
13413.413.8218-0.421828
13513.913.9444-0.0444434
13619.117.11221.98781
13715.2513.73581.51418
13812.912.26360.636448
13916.113.84292.25709
14017.3512.51784.83224
14113.1512.51640.633573
14212.1510.51811.63186
14312.612.53140.068624
14410.3511.6057-1.25567
14515.412.91592.48406
1469.610.9631-1.36305
14718.213.35594.84411
14813.612.17061.42944
14914.8514.54580.304161
15014.7516.9321-2.18215
15114.114.165-0.0650084
15214.911.93952.96052
15316.2512.89163.35838
15419.2517.82761.4224
15513.612.00081.5992
15613.613.45020.149783
15715.6515.14930.500655
15812.7512.8375-0.0874517
15914.611.40973.19027
1609.8513.2344-3.38442
16112.6511.14971.50032
16211.912.5324-0.632411
16319.216.1793.02096
16416.614.53072.06935
16511.29.96281.2372
16615.2513.66991.58006
16711.915.1456-3.24559
16813.215.9802-2.78018
16916.3517.1033-0.753322
17012.413.5272-1.12718
17115.8513.02392.82614
17214.3513.93850.411457
17318.1515.17712.97288
17411.1511.4556-0.30558
17515.6515.8548-0.204753
17617.7516.45651.29355
1777.6512.3969-4.74687
17812.3512.509-0.159025
17915.611.82013.77989
18019.316.33572.96435
18115.211.89513.30488
18217.113.57423.52582
18315.613.23492.36506
18418.414.76253.63747
18519.0515.20863.84138
18618.5513.72684.82316
18719.116.9562.14402
18813.111.94521.15479
18912.8513.5928-0.742756
1909.511.6786-2.17857
1914.511.3303-6.83032
19211.8510.67611.17388
19313.616.0891-2.48912
19411.713.2881-1.58809
19512.411.66180.738223
19613.3514.7442-1.3942
19711.413.7337-2.33366
19814.911.95892.94107
19919.914.93434.96567
20017.7512.23715.51294
20111.212.4086-1.2086
20214.615.4166-0.816637
20317.615.76681.83318
20414.0513.11870.931349
20516.115.07591.02409
20613.3513.13060.219359
20711.8512.553-0.702985
20811.9514.1993-2.24926
20914.7514.52520.224826
21015.1512.97782.17216
21113.213.8997-0.699654
21216.8515.68381.16619
2137.8510.3937-2.54369
2147.714.9986-7.29863
21512.611.40691.19308
2167.8511.7266-3.87663
21710.9511.1381-0.188128
21812.3512.05590.294061
2199.9512.9406-2.99063
22014.911.95892.94107
22116.6514.90021.74983
22213.412.86430.53571
22313.9513.72210.227925
22415.711.89993.80005
22516.8512.44224.40776
22610.9510.4870.463021
22715.3512.05353.29649
22812.211.38480.815169
22915.113.96181.1382
23017.7515.95241.79759
23115.212.79962.40037
23214.613.56511.03485
23316.6514.41792.23209
2348.110.858-2.75801

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.1297 & 0.770281 \tabularnewline
2 & 12.8 & 12.5923 & 0.2077 \tabularnewline
3 & 7.4 & 13.0532 & -5.65317 \tabularnewline
4 & 6.7 & 11.758 & -5.05804 \tabularnewline
5 & 12.6 & 15.8299 & -3.2299 \tabularnewline
6 & 14.8 & 12.9646 & 1.83539 \tabularnewline
7 & 13.3 & 12.4293 & 0.87074 \tabularnewline
8 & 11.1 & 13.0536 & -1.9536 \tabularnewline
9 & 8.2 & 14.525 & -6.32496 \tabularnewline
10 & 11.4 & 13.2555 & -1.85552 \tabularnewline
11 & 6.4 & 14.7197 & -8.31966 \tabularnewline
12 & 12 & 13.9779 & -1.97789 \tabularnewline
13 & 6.3 & 10.0466 & -3.74656 \tabularnewline
14 & 11.3 & 11.5693 & -0.26925 \tabularnewline
15 & 11.9 & 14.1319 & -2.23189 \tabularnewline
16 & 9.3 & 12.4623 & -3.16228 \tabularnewline
17 & 10 & 11.8276 & -1.82757 \tabularnewline
18 & 13.8 & 13.6995 & 0.100526 \tabularnewline
19 & 10.8 & 13.7963 & -2.9963 \tabularnewline
20 & 11.7 & 13.0076 & -1.30755 \tabularnewline
21 & 10.9 & 15.3705 & -4.47054 \tabularnewline
22 & 16.1 & 13.8652 & 2.23481 \tabularnewline
23 & 9.9 & 11.4684 & -1.56836 \tabularnewline
24 & 11.5 & 12.5065 & -1.00653 \tabularnewline
25 & 8.3 & 12.0303 & -3.73035 \tabularnewline
26 & 11.7 & 13.6608 & -1.96085 \tabularnewline
27 & 9 & 12.6515 & -3.65146 \tabularnewline
28 & 10.8 & 12.799 & -1.99905 \tabularnewline
29 & 10.4 & 12.0338 & -1.63381 \tabularnewline
30 & 12.7 & 13.1625 & -0.462482 \tabularnewline
31 & 11.8 & 14.5544 & -2.75445 \tabularnewline
32 & 13 & 12.188 & 0.811999 \tabularnewline
33 & 10.8 & 12.8613 & -2.06135 \tabularnewline
34 & 12.3 & 10.6309 & 1.66911 \tabularnewline
35 & 11.3 & 13.935 & -2.635 \tabularnewline
36 & 11.6 & 12.2989 & -0.698941 \tabularnewline
37 & 10.9 & 13.434 & -2.53403 \tabularnewline
38 & 12.1 & 12.998 & -0.898019 \tabularnewline
39 & 13.3 & 13.7874 & -0.487424 \tabularnewline
40 & 10.1 & 13.5305 & -3.43051 \tabularnewline
41 & 14.3 & 12.5264 & 1.77359 \tabularnewline
42 & 9.3 & 12.9721 & -3.67209 \tabularnewline
43 & 12.5 & 12.2659 & 0.234083 \tabularnewline
44 & 7.6 & 11.0256 & -3.42556 \tabularnewline
45 & 9.2 & 11.9424 & -2.74244 \tabularnewline
46 & 14.5 & 13.5579 & 0.942066 \tabularnewline
47 & 12.3 & 14.0245 & -1.72447 \tabularnewline
48 & 12.6 & 13.2207 & -0.620675 \tabularnewline
49 & 13 & 13.9567 & -0.956675 \tabularnewline
50 & 12.6 & 11.6562 & 0.943831 \tabularnewline
51 & 13.2 & 14.8141 & -1.61407 \tabularnewline
52 & 7.7 & 11.7179 & -4.01788 \tabularnewline
53 & 10.5 & 10.9764 & -0.476391 \tabularnewline
54 & 10.9 & 11.9975 & -1.09747 \tabularnewline
55 & 4.3 & 10.3892 & -6.08922 \tabularnewline
56 & 10.3 & 11.4139 & -1.11391 \tabularnewline
57 & 11.4 & 11.2655 & 0.134514 \tabularnewline
58 & 5.6 & 11.3927 & -5.7927 \tabularnewline
59 & 8.8 & 12.1459 & -3.34593 \tabularnewline
60 & 9 & 10.7588 & -1.75882 \tabularnewline
61 & 9.6 & 11.3425 & -1.7425 \tabularnewline
62 & 6.4 & 10.3604 & -3.96036 \tabularnewline
63 & 11.6 & 11.179 & 0.420955 \tabularnewline
64 & 4.35 & 9.94477 & -5.59477 \tabularnewline
65 & 12.7 & 12.5105 & 0.189485 \tabularnewline
66 & 18.1 & 14.9623 & 3.13772 \tabularnewline
67 & 17.85 & 15.2654 & 2.58465 \tabularnewline
68 & 16.6 & 15.6464 & 0.953559 \tabularnewline
69 & 12.6 & 9.98012 & 2.61988 \tabularnewline
70 & 17.1 & 22.008 & -4.908 \tabularnewline
71 & 19.1 & 15.8088 & 3.29123 \tabularnewline
72 & 16.1 & 18.1579 & -2.0579 \tabularnewline
73 & 13.35 & 10.8936 & 2.45642 \tabularnewline
74 & 18.4 & 17.531 & 0.86903 \tabularnewline
75 & 14.7 & 11.1274 & 3.57256 \tabularnewline
76 & 10.6 & 14.8788 & -4.27879 \tabularnewline
77 & 12.6 & 13.8563 & -1.2563 \tabularnewline
78 & 16.2 & 15.1772 & 1.02279 \tabularnewline
79 & 13.6 & 17.5959 & -3.99588 \tabularnewline
80 & 18.9 & 16.3614 & 2.5386 \tabularnewline
81 & 14.1 & 12.6572 & 1.44278 \tabularnewline
82 & 14.5 & 12.1488 & 2.3512 \tabularnewline
83 & 16.15 & 18.2164 & -2.0664 \tabularnewline
84 & 14.75 & 13.4244 & 1.32556 \tabularnewline
85 & 14.8 & 14.3422 & 0.457831 \tabularnewline
86 & 12.45 & 12.4025 & 0.047545 \tabularnewline
87 & 12.65 & 14.7113 & -2.06132 \tabularnewline
88 & 17.35 & 13.8947 & 3.45525 \tabularnewline
89 & 8.6 & 11.9778 & -3.37782 \tabularnewline
90 & 18.4 & 17.4726 & 0.927398 \tabularnewline
91 & 16.1 & 17.878 & -1.77801 \tabularnewline
92 & 11.6 & 10.9584 & 0.641634 \tabularnewline
93 & 17.75 & 12.2822 & 5.46779 \tabularnewline
94 & 15.25 & 13.9586 & 1.29145 \tabularnewline
95 & 17.65 & 16.1714 & 1.47862 \tabularnewline
96 & 15.6 & 13.9826 & 1.61743 \tabularnewline
97 & 16.35 & 14.8546 & 1.49539 \tabularnewline
98 & 17.65 & 14.8354 & 2.81456 \tabularnewline
99 & 13.6 & 13.2235 & 0.376464 \tabularnewline
100 & 11.7 & 12.5047 & -0.804681 \tabularnewline
101 & 14.35 & 13.5778 & 0.77217 \tabularnewline
102 & 14.75 & 17.7992 & -3.04921 \tabularnewline
103 & 18.25 & 16.5302 & 1.71976 \tabularnewline
104 & 9.9 & 15.3392 & -5.43921 \tabularnewline
105 & 16 & 14.0211 & 1.97891 \tabularnewline
106 & 18.25 & 15.8339 & 2.41605 \tabularnewline
107 & 16.85 & 17.337 & -0.487032 \tabularnewline
108 & 14.6 & 12.578 & 2.02197 \tabularnewline
109 & 13.85 & 13.5822 & 0.267833 \tabularnewline
110 & 18.95 & 16.2789 & 2.67108 \tabularnewline
111 & 15.6 & 14.6539 & 0.946143 \tabularnewline
112 & 14.85 & 17.0715 & -2.22155 \tabularnewline
113 & 11.75 & 12.917 & -1.16703 \tabularnewline
114 & 18.45 & 12.8313 & 5.61868 \tabularnewline
115 & 15.9 & 17.0762 & -1.17616 \tabularnewline
116 & 17.1 & 14.5436 & 2.55645 \tabularnewline
117 & 16.1 & 10.7191 & 5.38091 \tabularnewline
118 & 19.9 & 14.9343 & 4.96567 \tabularnewline
119 & 10.95 & 11.1381 & -0.188128 \tabularnewline
120 & 18.45 & 15.6797 & 2.77029 \tabularnewline
121 & 15.1 & 10.9276 & 4.17241 \tabularnewline
122 & 15 & 15.4977 & -0.497719 \tabularnewline
123 & 11.35 & 13.9265 & -2.57655 \tabularnewline
124 & 15.95 & 15.0134 & 0.936583 \tabularnewline
125 & 18.1 & 13.3924 & 4.70765 \tabularnewline
126 & 14.6 & 13.0599 & 1.54011 \tabularnewline
127 & 15.4 & 15.8992 & -0.499156 \tabularnewline
128 & 15.4 & 15.8992 & -0.499156 \tabularnewline
129 & 17.6 & 13.5423 & 4.0577 \tabularnewline
130 & 13.35 & 13.8771 & -0.527071 \tabularnewline
131 & 19.1 & 16.6748 & 2.42521 \tabularnewline
132 & 15.35 & 13.1784 & 2.17164 \tabularnewline
133 & 7.6 & 12.3566 & -4.75658 \tabularnewline
134 & 13.4 & 13.8218 & -0.421828 \tabularnewline
135 & 13.9 & 13.9444 & -0.0444434 \tabularnewline
136 & 19.1 & 17.1122 & 1.98781 \tabularnewline
137 & 15.25 & 13.7358 & 1.51418 \tabularnewline
138 & 12.9 & 12.2636 & 0.636448 \tabularnewline
139 & 16.1 & 13.8429 & 2.25709 \tabularnewline
140 & 17.35 & 12.5178 & 4.83224 \tabularnewline
141 & 13.15 & 12.5164 & 0.633573 \tabularnewline
142 & 12.15 & 10.5181 & 1.63186 \tabularnewline
143 & 12.6 & 12.5314 & 0.068624 \tabularnewline
144 & 10.35 & 11.6057 & -1.25567 \tabularnewline
145 & 15.4 & 12.9159 & 2.48406 \tabularnewline
146 & 9.6 & 10.9631 & -1.36305 \tabularnewline
147 & 18.2 & 13.3559 & 4.84411 \tabularnewline
148 & 13.6 & 12.1706 & 1.42944 \tabularnewline
149 & 14.85 & 14.5458 & 0.304161 \tabularnewline
150 & 14.75 & 16.9321 & -2.18215 \tabularnewline
151 & 14.1 & 14.165 & -0.0650084 \tabularnewline
152 & 14.9 & 11.9395 & 2.96052 \tabularnewline
153 & 16.25 & 12.8916 & 3.35838 \tabularnewline
154 & 19.25 & 17.8276 & 1.4224 \tabularnewline
155 & 13.6 & 12.0008 & 1.5992 \tabularnewline
156 & 13.6 & 13.4502 & 0.149783 \tabularnewline
157 & 15.65 & 15.1493 & 0.500655 \tabularnewline
158 & 12.75 & 12.8375 & -0.0874517 \tabularnewline
159 & 14.6 & 11.4097 & 3.19027 \tabularnewline
160 & 9.85 & 13.2344 & -3.38442 \tabularnewline
161 & 12.65 & 11.1497 & 1.50032 \tabularnewline
162 & 11.9 & 12.5324 & -0.632411 \tabularnewline
163 & 19.2 & 16.179 & 3.02096 \tabularnewline
164 & 16.6 & 14.5307 & 2.06935 \tabularnewline
165 & 11.2 & 9.9628 & 1.2372 \tabularnewline
166 & 15.25 & 13.6699 & 1.58006 \tabularnewline
167 & 11.9 & 15.1456 & -3.24559 \tabularnewline
168 & 13.2 & 15.9802 & -2.78018 \tabularnewline
169 & 16.35 & 17.1033 & -0.753322 \tabularnewline
170 & 12.4 & 13.5272 & -1.12718 \tabularnewline
171 & 15.85 & 13.0239 & 2.82614 \tabularnewline
172 & 14.35 & 13.9385 & 0.411457 \tabularnewline
173 & 18.15 & 15.1771 & 2.97288 \tabularnewline
174 & 11.15 & 11.4556 & -0.30558 \tabularnewline
175 & 15.65 & 15.8548 & -0.204753 \tabularnewline
176 & 17.75 & 16.4565 & 1.29355 \tabularnewline
177 & 7.65 & 12.3969 & -4.74687 \tabularnewline
178 & 12.35 & 12.509 & -0.159025 \tabularnewline
179 & 15.6 & 11.8201 & 3.77989 \tabularnewline
180 & 19.3 & 16.3357 & 2.96435 \tabularnewline
181 & 15.2 & 11.8951 & 3.30488 \tabularnewline
182 & 17.1 & 13.5742 & 3.52582 \tabularnewline
183 & 15.6 & 13.2349 & 2.36506 \tabularnewline
184 & 18.4 & 14.7625 & 3.63747 \tabularnewline
185 & 19.05 & 15.2086 & 3.84138 \tabularnewline
186 & 18.55 & 13.7268 & 4.82316 \tabularnewline
187 & 19.1 & 16.956 & 2.14402 \tabularnewline
188 & 13.1 & 11.9452 & 1.15479 \tabularnewline
189 & 12.85 & 13.5928 & -0.742756 \tabularnewline
190 & 9.5 & 11.6786 & -2.17857 \tabularnewline
191 & 4.5 & 11.3303 & -6.83032 \tabularnewline
192 & 11.85 & 10.6761 & 1.17388 \tabularnewline
193 & 13.6 & 16.0891 & -2.48912 \tabularnewline
194 & 11.7 & 13.2881 & -1.58809 \tabularnewline
195 & 12.4 & 11.6618 & 0.738223 \tabularnewline
196 & 13.35 & 14.7442 & -1.3942 \tabularnewline
197 & 11.4 & 13.7337 & -2.33366 \tabularnewline
198 & 14.9 & 11.9589 & 2.94107 \tabularnewline
199 & 19.9 & 14.9343 & 4.96567 \tabularnewline
200 & 17.75 & 12.2371 & 5.51294 \tabularnewline
201 & 11.2 & 12.4086 & -1.2086 \tabularnewline
202 & 14.6 & 15.4166 & -0.816637 \tabularnewline
203 & 17.6 & 15.7668 & 1.83318 \tabularnewline
204 & 14.05 & 13.1187 & 0.931349 \tabularnewline
205 & 16.1 & 15.0759 & 1.02409 \tabularnewline
206 & 13.35 & 13.1306 & 0.219359 \tabularnewline
207 & 11.85 & 12.553 & -0.702985 \tabularnewline
208 & 11.95 & 14.1993 & -2.24926 \tabularnewline
209 & 14.75 & 14.5252 & 0.224826 \tabularnewline
210 & 15.15 & 12.9778 & 2.17216 \tabularnewline
211 & 13.2 & 13.8997 & -0.699654 \tabularnewline
212 & 16.85 & 15.6838 & 1.16619 \tabularnewline
213 & 7.85 & 10.3937 & -2.54369 \tabularnewline
214 & 7.7 & 14.9986 & -7.29863 \tabularnewline
215 & 12.6 & 11.4069 & 1.19308 \tabularnewline
216 & 7.85 & 11.7266 & -3.87663 \tabularnewline
217 & 10.95 & 11.1381 & -0.188128 \tabularnewline
218 & 12.35 & 12.0559 & 0.294061 \tabularnewline
219 & 9.95 & 12.9406 & -2.99063 \tabularnewline
220 & 14.9 & 11.9589 & 2.94107 \tabularnewline
221 & 16.65 & 14.9002 & 1.74983 \tabularnewline
222 & 13.4 & 12.8643 & 0.53571 \tabularnewline
223 & 13.95 & 13.7221 & 0.227925 \tabularnewline
224 & 15.7 & 11.8999 & 3.80005 \tabularnewline
225 & 16.85 & 12.4422 & 4.40776 \tabularnewline
226 & 10.95 & 10.487 & 0.463021 \tabularnewline
227 & 15.35 & 12.0535 & 3.29649 \tabularnewline
228 & 12.2 & 11.3848 & 0.815169 \tabularnewline
229 & 15.1 & 13.9618 & 1.1382 \tabularnewline
230 & 17.75 & 15.9524 & 1.79759 \tabularnewline
231 & 15.2 & 12.7996 & 2.40037 \tabularnewline
232 & 14.6 & 13.5651 & 1.03485 \tabularnewline
233 & 16.65 & 14.4179 & 2.23209 \tabularnewline
234 & 8.1 & 10.858 & -2.75801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&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]12.9[/C][C]12.1297[/C][C]0.770281[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]12.5923[/C][C]0.2077[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]13.0532[/C][C]-5.65317[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]11.758[/C][C]-5.05804[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]15.8299[/C][C]-3.2299[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]12.9646[/C][C]1.83539[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]12.4293[/C][C]0.87074[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]13.0536[/C][C]-1.9536[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]14.525[/C][C]-6.32496[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]13.2555[/C][C]-1.85552[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]14.7197[/C][C]-8.31966[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.9779[/C][C]-1.97789[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]10.0466[/C][C]-3.74656[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]11.5693[/C][C]-0.26925[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]14.1319[/C][C]-2.23189[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]12.4623[/C][C]-3.16228[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.8276[/C][C]-1.82757[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]13.6995[/C][C]0.100526[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]13.7963[/C][C]-2.9963[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]13.0076[/C][C]-1.30755[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]15.3705[/C][C]-4.47054[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]13.8652[/C][C]2.23481[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]11.4684[/C][C]-1.56836[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]12.5065[/C][C]-1.00653[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]12.0303[/C][C]-3.73035[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]13.6608[/C][C]-1.96085[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]12.6515[/C][C]-3.65146[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]12.799[/C][C]-1.99905[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]12.0338[/C][C]-1.63381[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]13.1625[/C][C]-0.462482[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]14.5544[/C][C]-2.75445[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.188[/C][C]0.811999[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]12.8613[/C][C]-2.06135[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]10.6309[/C][C]1.66911[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]13.935[/C][C]-2.635[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]12.2989[/C][C]-0.698941[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]13.434[/C][C]-2.53403[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]12.998[/C][C]-0.898019[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]13.7874[/C][C]-0.487424[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]13.5305[/C][C]-3.43051[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]12.5264[/C][C]1.77359[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]12.9721[/C][C]-3.67209[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]12.2659[/C][C]0.234083[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]11.0256[/C][C]-3.42556[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]11.9424[/C][C]-2.74244[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]13.5579[/C][C]0.942066[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]14.0245[/C][C]-1.72447[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]13.2207[/C][C]-0.620675[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.9567[/C][C]-0.956675[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]11.6562[/C][C]0.943831[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]14.8141[/C][C]-1.61407[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]11.7179[/C][C]-4.01788[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]10.9764[/C][C]-0.476391[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]11.9975[/C][C]-1.09747[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]10.3892[/C][C]-6.08922[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]11.4139[/C][C]-1.11391[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]11.2655[/C][C]0.134514[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]11.3927[/C][C]-5.7927[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]12.1459[/C][C]-3.34593[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]10.7588[/C][C]-1.75882[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]11.3425[/C][C]-1.7425[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]10.3604[/C][C]-3.96036[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]11.179[/C][C]0.420955[/C][/ROW]
[ROW][C]64[/C][C]4.35[/C][C]9.94477[/C][C]-5.59477[/C][/ROW]
[ROW][C]65[/C][C]12.7[/C][C]12.5105[/C][C]0.189485[/C][/ROW]
[ROW][C]66[/C][C]18.1[/C][C]14.9623[/C][C]3.13772[/C][/ROW]
[ROW][C]67[/C][C]17.85[/C][C]15.2654[/C][C]2.58465[/C][/ROW]
[ROW][C]68[/C][C]16.6[/C][C]15.6464[/C][C]0.953559[/C][/ROW]
[ROW][C]69[/C][C]12.6[/C][C]9.98012[/C][C]2.61988[/C][/ROW]
[ROW][C]70[/C][C]17.1[/C][C]22.008[/C][C]-4.908[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]15.8088[/C][C]3.29123[/C][/ROW]
[ROW][C]72[/C][C]16.1[/C][C]18.1579[/C][C]-2.0579[/C][/ROW]
[ROW][C]73[/C][C]13.35[/C][C]10.8936[/C][C]2.45642[/C][/ROW]
[ROW][C]74[/C][C]18.4[/C][C]17.531[/C][C]0.86903[/C][/ROW]
[ROW][C]75[/C][C]14.7[/C][C]11.1274[/C][C]3.57256[/C][/ROW]
[ROW][C]76[/C][C]10.6[/C][C]14.8788[/C][C]-4.27879[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]13.8563[/C][C]-1.2563[/C][/ROW]
[ROW][C]78[/C][C]16.2[/C][C]15.1772[/C][C]1.02279[/C][/ROW]
[ROW][C]79[/C][C]13.6[/C][C]17.5959[/C][C]-3.99588[/C][/ROW]
[ROW][C]80[/C][C]18.9[/C][C]16.3614[/C][C]2.5386[/C][/ROW]
[ROW][C]81[/C][C]14.1[/C][C]12.6572[/C][C]1.44278[/C][/ROW]
[ROW][C]82[/C][C]14.5[/C][C]12.1488[/C][C]2.3512[/C][/ROW]
[ROW][C]83[/C][C]16.15[/C][C]18.2164[/C][C]-2.0664[/C][/ROW]
[ROW][C]84[/C][C]14.75[/C][C]13.4244[/C][C]1.32556[/C][/ROW]
[ROW][C]85[/C][C]14.8[/C][C]14.3422[/C][C]0.457831[/C][/ROW]
[ROW][C]86[/C][C]12.45[/C][C]12.4025[/C][C]0.047545[/C][/ROW]
[ROW][C]87[/C][C]12.65[/C][C]14.7113[/C][C]-2.06132[/C][/ROW]
[ROW][C]88[/C][C]17.35[/C][C]13.8947[/C][C]3.45525[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]11.9778[/C][C]-3.37782[/C][/ROW]
[ROW][C]90[/C][C]18.4[/C][C]17.4726[/C][C]0.927398[/C][/ROW]
[ROW][C]91[/C][C]16.1[/C][C]17.878[/C][C]-1.77801[/C][/ROW]
[ROW][C]92[/C][C]11.6[/C][C]10.9584[/C][C]0.641634[/C][/ROW]
[ROW][C]93[/C][C]17.75[/C][C]12.2822[/C][C]5.46779[/C][/ROW]
[ROW][C]94[/C][C]15.25[/C][C]13.9586[/C][C]1.29145[/C][/ROW]
[ROW][C]95[/C][C]17.65[/C][C]16.1714[/C][C]1.47862[/C][/ROW]
[ROW][C]96[/C][C]15.6[/C][C]13.9826[/C][C]1.61743[/C][/ROW]
[ROW][C]97[/C][C]16.35[/C][C]14.8546[/C][C]1.49539[/C][/ROW]
[ROW][C]98[/C][C]17.65[/C][C]14.8354[/C][C]2.81456[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]13.2235[/C][C]0.376464[/C][/ROW]
[ROW][C]100[/C][C]11.7[/C][C]12.5047[/C][C]-0.804681[/C][/ROW]
[ROW][C]101[/C][C]14.35[/C][C]13.5778[/C][C]0.77217[/C][/ROW]
[ROW][C]102[/C][C]14.75[/C][C]17.7992[/C][C]-3.04921[/C][/ROW]
[ROW][C]103[/C][C]18.25[/C][C]16.5302[/C][C]1.71976[/C][/ROW]
[ROW][C]104[/C][C]9.9[/C][C]15.3392[/C][C]-5.43921[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.0211[/C][C]1.97891[/C][/ROW]
[ROW][C]106[/C][C]18.25[/C][C]15.8339[/C][C]2.41605[/C][/ROW]
[ROW][C]107[/C][C]16.85[/C][C]17.337[/C][C]-0.487032[/C][/ROW]
[ROW][C]108[/C][C]14.6[/C][C]12.578[/C][C]2.02197[/C][/ROW]
[ROW][C]109[/C][C]13.85[/C][C]13.5822[/C][C]0.267833[/C][/ROW]
[ROW][C]110[/C][C]18.95[/C][C]16.2789[/C][C]2.67108[/C][/ROW]
[ROW][C]111[/C][C]15.6[/C][C]14.6539[/C][C]0.946143[/C][/ROW]
[ROW][C]112[/C][C]14.85[/C][C]17.0715[/C][C]-2.22155[/C][/ROW]
[ROW][C]113[/C][C]11.75[/C][C]12.917[/C][C]-1.16703[/C][/ROW]
[ROW][C]114[/C][C]18.45[/C][C]12.8313[/C][C]5.61868[/C][/ROW]
[ROW][C]115[/C][C]15.9[/C][C]17.0762[/C][C]-1.17616[/C][/ROW]
[ROW][C]116[/C][C]17.1[/C][C]14.5436[/C][C]2.55645[/C][/ROW]
[ROW][C]117[/C][C]16.1[/C][C]10.7191[/C][C]5.38091[/C][/ROW]
[ROW][C]118[/C][C]19.9[/C][C]14.9343[/C][C]4.96567[/C][/ROW]
[ROW][C]119[/C][C]10.95[/C][C]11.1381[/C][C]-0.188128[/C][/ROW]
[ROW][C]120[/C][C]18.45[/C][C]15.6797[/C][C]2.77029[/C][/ROW]
[ROW][C]121[/C][C]15.1[/C][C]10.9276[/C][C]4.17241[/C][/ROW]
[ROW][C]122[/C][C]15[/C][C]15.4977[/C][C]-0.497719[/C][/ROW]
[ROW][C]123[/C][C]11.35[/C][C]13.9265[/C][C]-2.57655[/C][/ROW]
[ROW][C]124[/C][C]15.95[/C][C]15.0134[/C][C]0.936583[/C][/ROW]
[ROW][C]125[/C][C]18.1[/C][C]13.3924[/C][C]4.70765[/C][/ROW]
[ROW][C]126[/C][C]14.6[/C][C]13.0599[/C][C]1.54011[/C][/ROW]
[ROW][C]127[/C][C]15.4[/C][C]15.8992[/C][C]-0.499156[/C][/ROW]
[ROW][C]128[/C][C]15.4[/C][C]15.8992[/C][C]-0.499156[/C][/ROW]
[ROW][C]129[/C][C]17.6[/C][C]13.5423[/C][C]4.0577[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.8771[/C][C]-0.527071[/C][/ROW]
[ROW][C]131[/C][C]19.1[/C][C]16.6748[/C][C]2.42521[/C][/ROW]
[ROW][C]132[/C][C]15.35[/C][C]13.1784[/C][C]2.17164[/C][/ROW]
[ROW][C]133[/C][C]7.6[/C][C]12.3566[/C][C]-4.75658[/C][/ROW]
[ROW][C]134[/C][C]13.4[/C][C]13.8218[/C][C]-0.421828[/C][/ROW]
[ROW][C]135[/C][C]13.9[/C][C]13.9444[/C][C]-0.0444434[/C][/ROW]
[ROW][C]136[/C][C]19.1[/C][C]17.1122[/C][C]1.98781[/C][/ROW]
[ROW][C]137[/C][C]15.25[/C][C]13.7358[/C][C]1.51418[/C][/ROW]
[ROW][C]138[/C][C]12.9[/C][C]12.2636[/C][C]0.636448[/C][/ROW]
[ROW][C]139[/C][C]16.1[/C][C]13.8429[/C][C]2.25709[/C][/ROW]
[ROW][C]140[/C][C]17.35[/C][C]12.5178[/C][C]4.83224[/C][/ROW]
[ROW][C]141[/C][C]13.15[/C][C]12.5164[/C][C]0.633573[/C][/ROW]
[ROW][C]142[/C][C]12.15[/C][C]10.5181[/C][C]1.63186[/C][/ROW]
[ROW][C]143[/C][C]12.6[/C][C]12.5314[/C][C]0.068624[/C][/ROW]
[ROW][C]144[/C][C]10.35[/C][C]11.6057[/C][C]-1.25567[/C][/ROW]
[ROW][C]145[/C][C]15.4[/C][C]12.9159[/C][C]2.48406[/C][/ROW]
[ROW][C]146[/C][C]9.6[/C][C]10.9631[/C][C]-1.36305[/C][/ROW]
[ROW][C]147[/C][C]18.2[/C][C]13.3559[/C][C]4.84411[/C][/ROW]
[ROW][C]148[/C][C]13.6[/C][C]12.1706[/C][C]1.42944[/C][/ROW]
[ROW][C]149[/C][C]14.85[/C][C]14.5458[/C][C]0.304161[/C][/ROW]
[ROW][C]150[/C][C]14.75[/C][C]16.9321[/C][C]-2.18215[/C][/ROW]
[ROW][C]151[/C][C]14.1[/C][C]14.165[/C][C]-0.0650084[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]11.9395[/C][C]2.96052[/C][/ROW]
[ROW][C]153[/C][C]16.25[/C][C]12.8916[/C][C]3.35838[/C][/ROW]
[ROW][C]154[/C][C]19.25[/C][C]17.8276[/C][C]1.4224[/C][/ROW]
[ROW][C]155[/C][C]13.6[/C][C]12.0008[/C][C]1.5992[/C][/ROW]
[ROW][C]156[/C][C]13.6[/C][C]13.4502[/C][C]0.149783[/C][/ROW]
[ROW][C]157[/C][C]15.65[/C][C]15.1493[/C][C]0.500655[/C][/ROW]
[ROW][C]158[/C][C]12.75[/C][C]12.8375[/C][C]-0.0874517[/C][/ROW]
[ROW][C]159[/C][C]14.6[/C][C]11.4097[/C][C]3.19027[/C][/ROW]
[ROW][C]160[/C][C]9.85[/C][C]13.2344[/C][C]-3.38442[/C][/ROW]
[ROW][C]161[/C][C]12.65[/C][C]11.1497[/C][C]1.50032[/C][/ROW]
[ROW][C]162[/C][C]11.9[/C][C]12.5324[/C][C]-0.632411[/C][/ROW]
[ROW][C]163[/C][C]19.2[/C][C]16.179[/C][C]3.02096[/C][/ROW]
[ROW][C]164[/C][C]16.6[/C][C]14.5307[/C][C]2.06935[/C][/ROW]
[ROW][C]165[/C][C]11.2[/C][C]9.9628[/C][C]1.2372[/C][/ROW]
[ROW][C]166[/C][C]15.25[/C][C]13.6699[/C][C]1.58006[/C][/ROW]
[ROW][C]167[/C][C]11.9[/C][C]15.1456[/C][C]-3.24559[/C][/ROW]
[ROW][C]168[/C][C]13.2[/C][C]15.9802[/C][C]-2.78018[/C][/ROW]
[ROW][C]169[/C][C]16.35[/C][C]17.1033[/C][C]-0.753322[/C][/ROW]
[ROW][C]170[/C][C]12.4[/C][C]13.5272[/C][C]-1.12718[/C][/ROW]
[ROW][C]171[/C][C]15.85[/C][C]13.0239[/C][C]2.82614[/C][/ROW]
[ROW][C]172[/C][C]14.35[/C][C]13.9385[/C][C]0.411457[/C][/ROW]
[ROW][C]173[/C][C]18.15[/C][C]15.1771[/C][C]2.97288[/C][/ROW]
[ROW][C]174[/C][C]11.15[/C][C]11.4556[/C][C]-0.30558[/C][/ROW]
[ROW][C]175[/C][C]15.65[/C][C]15.8548[/C][C]-0.204753[/C][/ROW]
[ROW][C]176[/C][C]17.75[/C][C]16.4565[/C][C]1.29355[/C][/ROW]
[ROW][C]177[/C][C]7.65[/C][C]12.3969[/C][C]-4.74687[/C][/ROW]
[ROW][C]178[/C][C]12.35[/C][C]12.509[/C][C]-0.159025[/C][/ROW]
[ROW][C]179[/C][C]15.6[/C][C]11.8201[/C][C]3.77989[/C][/ROW]
[ROW][C]180[/C][C]19.3[/C][C]16.3357[/C][C]2.96435[/C][/ROW]
[ROW][C]181[/C][C]15.2[/C][C]11.8951[/C][C]3.30488[/C][/ROW]
[ROW][C]182[/C][C]17.1[/C][C]13.5742[/C][C]3.52582[/C][/ROW]
[ROW][C]183[/C][C]15.6[/C][C]13.2349[/C][C]2.36506[/C][/ROW]
[ROW][C]184[/C][C]18.4[/C][C]14.7625[/C][C]3.63747[/C][/ROW]
[ROW][C]185[/C][C]19.05[/C][C]15.2086[/C][C]3.84138[/C][/ROW]
[ROW][C]186[/C][C]18.55[/C][C]13.7268[/C][C]4.82316[/C][/ROW]
[ROW][C]187[/C][C]19.1[/C][C]16.956[/C][C]2.14402[/C][/ROW]
[ROW][C]188[/C][C]13.1[/C][C]11.9452[/C][C]1.15479[/C][/ROW]
[ROW][C]189[/C][C]12.85[/C][C]13.5928[/C][C]-0.742756[/C][/ROW]
[ROW][C]190[/C][C]9.5[/C][C]11.6786[/C][C]-2.17857[/C][/ROW]
[ROW][C]191[/C][C]4.5[/C][C]11.3303[/C][C]-6.83032[/C][/ROW]
[ROW][C]192[/C][C]11.85[/C][C]10.6761[/C][C]1.17388[/C][/ROW]
[ROW][C]193[/C][C]13.6[/C][C]16.0891[/C][C]-2.48912[/C][/ROW]
[ROW][C]194[/C][C]11.7[/C][C]13.2881[/C][C]-1.58809[/C][/ROW]
[ROW][C]195[/C][C]12.4[/C][C]11.6618[/C][C]0.738223[/C][/ROW]
[ROW][C]196[/C][C]13.35[/C][C]14.7442[/C][C]-1.3942[/C][/ROW]
[ROW][C]197[/C][C]11.4[/C][C]13.7337[/C][C]-2.33366[/C][/ROW]
[ROW][C]198[/C][C]14.9[/C][C]11.9589[/C][C]2.94107[/C][/ROW]
[ROW][C]199[/C][C]19.9[/C][C]14.9343[/C][C]4.96567[/C][/ROW]
[ROW][C]200[/C][C]17.75[/C][C]12.2371[/C][C]5.51294[/C][/ROW]
[ROW][C]201[/C][C]11.2[/C][C]12.4086[/C][C]-1.2086[/C][/ROW]
[ROW][C]202[/C][C]14.6[/C][C]15.4166[/C][C]-0.816637[/C][/ROW]
[ROW][C]203[/C][C]17.6[/C][C]15.7668[/C][C]1.83318[/C][/ROW]
[ROW][C]204[/C][C]14.05[/C][C]13.1187[/C][C]0.931349[/C][/ROW]
[ROW][C]205[/C][C]16.1[/C][C]15.0759[/C][C]1.02409[/C][/ROW]
[ROW][C]206[/C][C]13.35[/C][C]13.1306[/C][C]0.219359[/C][/ROW]
[ROW][C]207[/C][C]11.85[/C][C]12.553[/C][C]-0.702985[/C][/ROW]
[ROW][C]208[/C][C]11.95[/C][C]14.1993[/C][C]-2.24926[/C][/ROW]
[ROW][C]209[/C][C]14.75[/C][C]14.5252[/C][C]0.224826[/C][/ROW]
[ROW][C]210[/C][C]15.15[/C][C]12.9778[/C][C]2.17216[/C][/ROW]
[ROW][C]211[/C][C]13.2[/C][C]13.8997[/C][C]-0.699654[/C][/ROW]
[ROW][C]212[/C][C]16.85[/C][C]15.6838[/C][C]1.16619[/C][/ROW]
[ROW][C]213[/C][C]7.85[/C][C]10.3937[/C][C]-2.54369[/C][/ROW]
[ROW][C]214[/C][C]7.7[/C][C]14.9986[/C][C]-7.29863[/C][/ROW]
[ROW][C]215[/C][C]12.6[/C][C]11.4069[/C][C]1.19308[/C][/ROW]
[ROW][C]216[/C][C]7.85[/C][C]11.7266[/C][C]-3.87663[/C][/ROW]
[ROW][C]217[/C][C]10.95[/C][C]11.1381[/C][C]-0.188128[/C][/ROW]
[ROW][C]218[/C][C]12.35[/C][C]12.0559[/C][C]0.294061[/C][/ROW]
[ROW][C]219[/C][C]9.95[/C][C]12.9406[/C][C]-2.99063[/C][/ROW]
[ROW][C]220[/C][C]14.9[/C][C]11.9589[/C][C]2.94107[/C][/ROW]
[ROW][C]221[/C][C]16.65[/C][C]14.9002[/C][C]1.74983[/C][/ROW]
[ROW][C]222[/C][C]13.4[/C][C]12.8643[/C][C]0.53571[/C][/ROW]
[ROW][C]223[/C][C]13.95[/C][C]13.7221[/C][C]0.227925[/C][/ROW]
[ROW][C]224[/C][C]15.7[/C][C]11.8999[/C][C]3.80005[/C][/ROW]
[ROW][C]225[/C][C]16.85[/C][C]12.4422[/C][C]4.40776[/C][/ROW]
[ROW][C]226[/C][C]10.95[/C][C]10.487[/C][C]0.463021[/C][/ROW]
[ROW][C]227[/C][C]15.35[/C][C]12.0535[/C][C]3.29649[/C][/ROW]
[ROW][C]228[/C][C]12.2[/C][C]11.3848[/C][C]0.815169[/C][/ROW]
[ROW][C]229[/C][C]15.1[/C][C]13.9618[/C][C]1.1382[/C][/ROW]
[ROW][C]230[/C][C]17.75[/C][C]15.9524[/C][C]1.79759[/C][/ROW]
[ROW][C]231[/C][C]15.2[/C][C]12.7996[/C][C]2.40037[/C][/ROW]
[ROW][C]232[/C][C]14.6[/C][C]13.5651[/C][C]1.03485[/C][/ROW]
[ROW][C]233[/C][C]16.65[/C][C]14.4179[/C][C]2.23209[/C][/ROW]
[ROW][C]234[/C][C]8.1[/C][C]10.858[/C][C]-2.75801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&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
112.912.12970.770281
212.812.59230.2077
37.413.0532-5.65317
46.711.758-5.05804
512.615.8299-3.2299
614.812.96461.83539
713.312.42930.87074
811.113.0536-1.9536
98.214.525-6.32496
1011.413.2555-1.85552
116.414.7197-8.31966
121213.9779-1.97789
136.310.0466-3.74656
1411.311.5693-0.26925
1511.914.1319-2.23189
169.312.4623-3.16228
171011.8276-1.82757
1813.813.69950.100526
1910.813.7963-2.9963
2011.713.0076-1.30755
2110.915.3705-4.47054
2216.113.86522.23481
239.911.4684-1.56836
2411.512.5065-1.00653
258.312.0303-3.73035
2611.713.6608-1.96085
27912.6515-3.65146
2810.812.799-1.99905
2910.412.0338-1.63381
3012.713.1625-0.462482
3111.814.5544-2.75445
321312.1880.811999
3310.812.8613-2.06135
3412.310.63091.66911
3511.313.935-2.635
3611.612.2989-0.698941
3710.913.434-2.53403
3812.112.998-0.898019
3913.313.7874-0.487424
4010.113.5305-3.43051
4114.312.52641.77359
429.312.9721-3.67209
4312.512.26590.234083
447.611.0256-3.42556
459.211.9424-2.74244
4614.513.55790.942066
4712.314.0245-1.72447
4812.613.2207-0.620675
491313.9567-0.956675
5012.611.65620.943831
5113.214.8141-1.61407
527.711.7179-4.01788
5310.510.9764-0.476391
5410.911.9975-1.09747
554.310.3892-6.08922
5610.311.4139-1.11391
5711.411.26550.134514
585.611.3927-5.7927
598.812.1459-3.34593
60910.7588-1.75882
619.611.3425-1.7425
626.410.3604-3.96036
6311.611.1790.420955
644.359.94477-5.59477
6512.712.51050.189485
6618.114.96233.13772
6717.8515.26542.58465
6816.615.64640.953559
6912.69.980122.61988
7017.122.008-4.908
7119.115.80883.29123
7216.118.1579-2.0579
7313.3510.89362.45642
7418.417.5310.86903
7514.711.12743.57256
7610.614.8788-4.27879
7712.613.8563-1.2563
7816.215.17721.02279
7913.617.5959-3.99588
8018.916.36142.5386
8114.112.65721.44278
8214.512.14882.3512
8316.1518.2164-2.0664
8414.7513.42441.32556
8514.814.34220.457831
8612.4512.40250.047545
8712.6514.7113-2.06132
8817.3513.89473.45525
898.611.9778-3.37782
9018.417.47260.927398
9116.117.878-1.77801
9211.610.95840.641634
9317.7512.28225.46779
9415.2513.95861.29145
9517.6516.17141.47862
9615.613.98261.61743
9716.3514.85461.49539
9817.6514.83542.81456
9913.613.22350.376464
10011.712.5047-0.804681
10114.3513.57780.77217
10214.7517.7992-3.04921
10318.2516.53021.71976
1049.915.3392-5.43921
1051614.02111.97891
10618.2515.83392.41605
10716.8517.337-0.487032
10814.612.5782.02197
10913.8513.58220.267833
11018.9516.27892.67108
11115.614.65390.946143
11214.8517.0715-2.22155
11311.7512.917-1.16703
11418.4512.83135.61868
11515.917.0762-1.17616
11617.114.54362.55645
11716.110.71915.38091
11819.914.93434.96567
11910.9511.1381-0.188128
12018.4515.67972.77029
12115.110.92764.17241
1221515.4977-0.497719
12311.3513.9265-2.57655
12415.9515.01340.936583
12518.113.39244.70765
12614.613.05991.54011
12715.415.8992-0.499156
12815.415.8992-0.499156
12917.613.54234.0577
13013.3513.8771-0.527071
13119.116.67482.42521
13215.3513.17842.17164
1337.612.3566-4.75658
13413.413.8218-0.421828
13513.913.9444-0.0444434
13619.117.11221.98781
13715.2513.73581.51418
13812.912.26360.636448
13916.113.84292.25709
14017.3512.51784.83224
14113.1512.51640.633573
14212.1510.51811.63186
14312.612.53140.068624
14410.3511.6057-1.25567
14515.412.91592.48406
1469.610.9631-1.36305
14718.213.35594.84411
14813.612.17061.42944
14914.8514.54580.304161
15014.7516.9321-2.18215
15114.114.165-0.0650084
15214.911.93952.96052
15316.2512.89163.35838
15419.2517.82761.4224
15513.612.00081.5992
15613.613.45020.149783
15715.6515.14930.500655
15812.7512.8375-0.0874517
15914.611.40973.19027
1609.8513.2344-3.38442
16112.6511.14971.50032
16211.912.5324-0.632411
16319.216.1793.02096
16416.614.53072.06935
16511.29.96281.2372
16615.2513.66991.58006
16711.915.1456-3.24559
16813.215.9802-2.78018
16916.3517.1033-0.753322
17012.413.5272-1.12718
17115.8513.02392.82614
17214.3513.93850.411457
17318.1515.17712.97288
17411.1511.4556-0.30558
17515.6515.8548-0.204753
17617.7516.45651.29355
1777.6512.3969-4.74687
17812.3512.509-0.159025
17915.611.82013.77989
18019.316.33572.96435
18115.211.89513.30488
18217.113.57423.52582
18315.613.23492.36506
18418.414.76253.63747
18519.0515.20863.84138
18618.5513.72684.82316
18719.116.9562.14402
18813.111.94521.15479
18912.8513.5928-0.742756
1909.511.6786-2.17857
1914.511.3303-6.83032
19211.8510.67611.17388
19313.616.0891-2.48912
19411.713.2881-1.58809
19512.411.66180.738223
19613.3514.7442-1.3942
19711.413.7337-2.33366
19814.911.95892.94107
19919.914.93434.96567
20017.7512.23715.51294
20111.212.4086-1.2086
20214.615.4166-0.816637
20317.615.76681.83318
20414.0513.11870.931349
20516.115.07591.02409
20613.3513.13060.219359
20711.8512.553-0.702985
20811.9514.1993-2.24926
20914.7514.52520.224826
21015.1512.97782.17216
21113.213.8997-0.699654
21216.8515.68381.16619
2137.8510.3937-2.54369
2147.714.9986-7.29863
21512.611.40691.19308
2167.8511.7266-3.87663
21710.9511.1381-0.188128
21812.3512.05590.294061
2199.9512.9406-2.99063
22014.911.95892.94107
22116.6514.90021.74983
22213.412.86430.53571
22313.9513.72210.227925
22415.711.89993.80005
22516.8512.44224.40776
22610.9510.4870.463021
22715.3512.05353.29649
22812.211.38480.815169
22915.113.96181.1382
23017.7515.95241.79759
23115.212.79962.40037
23214.613.56511.03485
23316.6514.41792.23209
2348.110.858-2.75801






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9429060.1141880.0570942
100.8901170.2197660.109883
110.9236720.1526560.0763278
120.8756580.2486840.124342
130.8168530.3662950.183147
140.8360120.3279770.163988
150.7840680.4318630.215932
160.7166180.5667640.283382
170.6398640.7202710.360136
180.62470.7506010.3753
190.5673020.8653960.432698
200.4946610.9893210.505339
210.4480340.8960670.551966
220.3987380.7974760.601262
230.3495480.6990970.650452
240.319660.639320.68034
250.272080.5441590.72792
260.2667460.5334930.733254
270.2267930.4535850.773207
280.1911440.3822890.808856
290.1559170.3118330.844083
300.1615140.3230290.838486
310.1445630.2891260.855437
320.1181070.2362150.881893
330.09760580.1952120.902394
340.1086240.2172480.891376
350.09158550.1831710.908414
360.07695170.1539030.923048
370.06924880.1384980.930751
380.06673520.133470.933265
390.06732970.1346590.93267
400.05554340.1110870.944457
410.07460720.1492140.925393
420.08082470.1616490.919175
430.06438090.1287620.935619
440.0739990.1479980.926001
450.08204770.1640950.917952
460.07493370.1498670.925066
470.06673590.1334720.933264
480.06363790.1272760.936362
490.05999010.119980.94001
500.05229220.1045840.947708
510.05697580.1139520.943024
520.08009410.1601880.919906
530.06532620.1306520.934674
540.05372660.1074530.946273
550.1582110.3164210.841789
560.1460040.2920080.853996
570.1214380.2428770.878562
580.2260190.4520390.773981
590.2204270.4408540.779573
600.2183890.4367790.781611
610.2272390.4544790.772761
620.2512280.5024560.748772
630.2524370.5048740.747563
640.2836360.5672720.716364
650.3014320.6028640.698568
660.5089470.9821060.491053
670.6161760.7676480.383824
680.6042360.7915270.395764
690.7667950.4664110.233205
700.7823650.4352690.217635
710.8169020.3661970.183098
720.8314290.3371430.168571
730.8603670.2792670.139633
740.8660270.2679450.133973
750.9110580.1778840.0889419
760.930930.138140.0690702
770.9188680.1622640.0811319
780.9186140.1627720.0813858
790.928230.143540.0717698
800.9189230.1621540.0810769
810.9132160.1735690.0867843
820.9202050.159590.079795
830.9151640.1696730.0848365
840.9285670.1428660.0714329
850.9168420.1663160.083158
860.9018040.1963920.0981961
870.8898220.2203570.110178
880.9236590.1526820.076341
890.9265890.1468230.0734114
900.9234360.1531280.076564
910.910460.1790810.0895404
920.9065340.1869310.0934656
930.9592790.08144270.0407213
940.9581070.08378570.0418928
950.9580190.08396230.0419811
960.9548690.09026270.0451313
970.9505930.09881430.0494072
980.9574260.08514880.0425744
990.9499270.1001450.0500726
1000.945330.109340.0546698
1010.9368870.1262270.0631133
1020.9300270.1399450.0699726
1030.9258830.1482350.0741174
1040.9711020.05779680.0288984
1050.9701870.05962540.0298127
1060.9710790.05784190.0289209
1070.9647820.07043670.0352183
1080.9658510.06829730.0341487
1090.9597090.08058190.0402909
1100.9613490.07730290.0386514
1110.9548930.09021460.0451073
1120.9510170.09796660.0489833
1130.9425020.1149960.057498
1140.9684530.0630930.0315465
1150.96310.07380030.0369001
1160.9661380.06772460.0338623
1170.9929940.01401220.00700609
1180.9951250.009749020.00487451
1190.9936050.01279090.00639543
1200.9937110.0125780.00628898
1210.9958380.008324770.00416238
1220.9945950.01081080.00540541
1230.9947730.01045410.00522707
1240.9936610.01267880.00633938
1250.9959190.008162270.00408113
1260.9954540.009091350.00454567
1270.9940740.01185260.00592628
1280.9923580.01528320.00764161
1290.9939340.01213140.00606572
1300.9930320.01393510.00696754
1310.9930570.01388540.00694272
1320.9923180.01536450.00768223
1330.9945840.01083250.00541626
1340.9939660.01206740.00603372
1350.9925450.01491080.00745539
1360.9914370.0171260.00856302
1370.9893790.02124130.0106206
1380.9887640.02247190.011236
1390.9869010.02619870.0130993
1400.9929810.01403850.00701925
1410.9912760.01744820.00872408
1420.9892070.02158550.0107928
1430.9861760.02764790.0138239
1440.9829980.03400480.0170024
1450.9803480.03930440.0196522
1460.977830.0443410.0221705
1470.986320.02735980.0136799
1480.9834820.0330360.016518
1490.9813020.03739660.0186983
1500.980890.03821910.0191095
1510.9755930.04881480.0244074
1520.9764620.04707620.0235381
1530.9759360.04812830.0240641
1540.9720090.05598260.0279913
1550.9698450.06030910.0301546
1560.9661630.06767480.0338374
1570.958690.08261970.0413098
1580.950680.098640.04932
1590.9617150.07656930.0382847
1600.9591210.08175770.0408789
1610.9567120.08657510.0432875
1620.9465130.1069740.0534868
1630.9427610.1144770.0572385
1640.9339040.1321910.0660955
1650.9209560.1580880.0790439
1660.9082170.1835670.0917834
1670.9235010.1529980.0764992
1680.9139510.1720980.0860492
1690.9081930.1836140.0918069
1700.893340.213320.10666
1710.8894350.221130.110565
1720.8721180.2557640.127882
1730.8586540.2826930.141346
1740.8319240.3361520.168076
1750.8021810.3956380.197819
1760.7839040.4321910.216096
1770.8107620.3784760.189238
1780.7809230.4381550.219077
1790.781010.4379810.21899
1800.7644220.4711550.235578
1810.8344850.331030.165515
1820.853280.293440.14672
1830.855540.288920.14446
1840.8404480.3191040.159552
1850.8578020.2843960.142198
1860.8866630.2266730.113337
1870.8634330.2731330.136567
1880.8404020.3191960.159598
1890.833150.3337010.16685
1900.8290920.3418150.170908
1910.932510.1349790.0674896
1920.927090.1458210.0729104
1930.9275120.1449760.0724881
1940.90950.1809990.0904997
1950.8852190.2295620.114781
1960.8680540.2638930.131946
1970.842160.3156810.15784
1980.8289150.342170.171085
1990.8147190.3705620.185281
2000.8899040.2201930.110096
2010.8609420.2781170.139058
2020.8291960.3416070.170804
2030.7969850.4060310.203015
2040.7538050.4923910.246195
2050.7044290.5911420.295571
2060.6460910.7078190.353909
2070.5942460.8115090.405754
2080.5925170.8149660.407483
2090.5257880.9484240.474212
2100.4614210.9228420.538579
2110.5161540.9676910.483846
2120.447530.895060.55247
2130.4478620.8957240.552138
2140.8923880.2152240.107612
2150.8503850.2992290.149615
2160.9954090.009181030.00459051
2170.9965620.006875440.00343772
2180.9969930.006013180.00300659
2190.9997990.0004029190.000201459
2200.9993450.001310720.000655361
2210.9978520.004295860.00214793
2220.9956030.008794690.00439734
2230.9941210.01175760.0058788
2240.9816770.03664670.0183233
2250.970250.05949960.0297498

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.942906 & 0.114188 & 0.0570942 \tabularnewline
10 & 0.890117 & 0.219766 & 0.109883 \tabularnewline
11 & 0.923672 & 0.152656 & 0.0763278 \tabularnewline
12 & 0.875658 & 0.248684 & 0.124342 \tabularnewline
13 & 0.816853 & 0.366295 & 0.183147 \tabularnewline
14 & 0.836012 & 0.327977 & 0.163988 \tabularnewline
15 & 0.784068 & 0.431863 & 0.215932 \tabularnewline
16 & 0.716618 & 0.566764 & 0.283382 \tabularnewline
17 & 0.639864 & 0.720271 & 0.360136 \tabularnewline
18 & 0.6247 & 0.750601 & 0.3753 \tabularnewline
19 & 0.567302 & 0.865396 & 0.432698 \tabularnewline
20 & 0.494661 & 0.989321 & 0.505339 \tabularnewline
21 & 0.448034 & 0.896067 & 0.551966 \tabularnewline
22 & 0.398738 & 0.797476 & 0.601262 \tabularnewline
23 & 0.349548 & 0.699097 & 0.650452 \tabularnewline
24 & 0.31966 & 0.63932 & 0.68034 \tabularnewline
25 & 0.27208 & 0.544159 & 0.72792 \tabularnewline
26 & 0.266746 & 0.533493 & 0.733254 \tabularnewline
27 & 0.226793 & 0.453585 & 0.773207 \tabularnewline
28 & 0.191144 & 0.382289 & 0.808856 \tabularnewline
29 & 0.155917 & 0.311833 & 0.844083 \tabularnewline
30 & 0.161514 & 0.323029 & 0.838486 \tabularnewline
31 & 0.144563 & 0.289126 & 0.855437 \tabularnewline
32 & 0.118107 & 0.236215 & 0.881893 \tabularnewline
33 & 0.0976058 & 0.195212 & 0.902394 \tabularnewline
34 & 0.108624 & 0.217248 & 0.891376 \tabularnewline
35 & 0.0915855 & 0.183171 & 0.908414 \tabularnewline
36 & 0.0769517 & 0.153903 & 0.923048 \tabularnewline
37 & 0.0692488 & 0.138498 & 0.930751 \tabularnewline
38 & 0.0667352 & 0.13347 & 0.933265 \tabularnewline
39 & 0.0673297 & 0.134659 & 0.93267 \tabularnewline
40 & 0.0555434 & 0.111087 & 0.944457 \tabularnewline
41 & 0.0746072 & 0.149214 & 0.925393 \tabularnewline
42 & 0.0808247 & 0.161649 & 0.919175 \tabularnewline
43 & 0.0643809 & 0.128762 & 0.935619 \tabularnewline
44 & 0.073999 & 0.147998 & 0.926001 \tabularnewline
45 & 0.0820477 & 0.164095 & 0.917952 \tabularnewline
46 & 0.0749337 & 0.149867 & 0.925066 \tabularnewline
47 & 0.0667359 & 0.133472 & 0.933264 \tabularnewline
48 & 0.0636379 & 0.127276 & 0.936362 \tabularnewline
49 & 0.0599901 & 0.11998 & 0.94001 \tabularnewline
50 & 0.0522922 & 0.104584 & 0.947708 \tabularnewline
51 & 0.0569758 & 0.113952 & 0.943024 \tabularnewline
52 & 0.0800941 & 0.160188 & 0.919906 \tabularnewline
53 & 0.0653262 & 0.130652 & 0.934674 \tabularnewline
54 & 0.0537266 & 0.107453 & 0.946273 \tabularnewline
55 & 0.158211 & 0.316421 & 0.841789 \tabularnewline
56 & 0.146004 & 0.292008 & 0.853996 \tabularnewline
57 & 0.121438 & 0.242877 & 0.878562 \tabularnewline
58 & 0.226019 & 0.452039 & 0.773981 \tabularnewline
59 & 0.220427 & 0.440854 & 0.779573 \tabularnewline
60 & 0.218389 & 0.436779 & 0.781611 \tabularnewline
61 & 0.227239 & 0.454479 & 0.772761 \tabularnewline
62 & 0.251228 & 0.502456 & 0.748772 \tabularnewline
63 & 0.252437 & 0.504874 & 0.747563 \tabularnewline
64 & 0.283636 & 0.567272 & 0.716364 \tabularnewline
65 & 0.301432 & 0.602864 & 0.698568 \tabularnewline
66 & 0.508947 & 0.982106 & 0.491053 \tabularnewline
67 & 0.616176 & 0.767648 & 0.383824 \tabularnewline
68 & 0.604236 & 0.791527 & 0.395764 \tabularnewline
69 & 0.766795 & 0.466411 & 0.233205 \tabularnewline
70 & 0.782365 & 0.435269 & 0.217635 \tabularnewline
71 & 0.816902 & 0.366197 & 0.183098 \tabularnewline
72 & 0.831429 & 0.337143 & 0.168571 \tabularnewline
73 & 0.860367 & 0.279267 & 0.139633 \tabularnewline
74 & 0.866027 & 0.267945 & 0.133973 \tabularnewline
75 & 0.911058 & 0.177884 & 0.0889419 \tabularnewline
76 & 0.93093 & 0.13814 & 0.0690702 \tabularnewline
77 & 0.918868 & 0.162264 & 0.0811319 \tabularnewline
78 & 0.918614 & 0.162772 & 0.0813858 \tabularnewline
79 & 0.92823 & 0.14354 & 0.0717698 \tabularnewline
80 & 0.918923 & 0.162154 & 0.0810769 \tabularnewline
81 & 0.913216 & 0.173569 & 0.0867843 \tabularnewline
82 & 0.920205 & 0.15959 & 0.079795 \tabularnewline
83 & 0.915164 & 0.169673 & 0.0848365 \tabularnewline
84 & 0.928567 & 0.142866 & 0.0714329 \tabularnewline
85 & 0.916842 & 0.166316 & 0.083158 \tabularnewline
86 & 0.901804 & 0.196392 & 0.0981961 \tabularnewline
87 & 0.889822 & 0.220357 & 0.110178 \tabularnewline
88 & 0.923659 & 0.152682 & 0.076341 \tabularnewline
89 & 0.926589 & 0.146823 & 0.0734114 \tabularnewline
90 & 0.923436 & 0.153128 & 0.076564 \tabularnewline
91 & 0.91046 & 0.179081 & 0.0895404 \tabularnewline
92 & 0.906534 & 0.186931 & 0.0934656 \tabularnewline
93 & 0.959279 & 0.0814427 & 0.0407213 \tabularnewline
94 & 0.958107 & 0.0837857 & 0.0418928 \tabularnewline
95 & 0.958019 & 0.0839623 & 0.0419811 \tabularnewline
96 & 0.954869 & 0.0902627 & 0.0451313 \tabularnewline
97 & 0.950593 & 0.0988143 & 0.0494072 \tabularnewline
98 & 0.957426 & 0.0851488 & 0.0425744 \tabularnewline
99 & 0.949927 & 0.100145 & 0.0500726 \tabularnewline
100 & 0.94533 & 0.10934 & 0.0546698 \tabularnewline
101 & 0.936887 & 0.126227 & 0.0631133 \tabularnewline
102 & 0.930027 & 0.139945 & 0.0699726 \tabularnewline
103 & 0.925883 & 0.148235 & 0.0741174 \tabularnewline
104 & 0.971102 & 0.0577968 & 0.0288984 \tabularnewline
105 & 0.970187 & 0.0596254 & 0.0298127 \tabularnewline
106 & 0.971079 & 0.0578419 & 0.0289209 \tabularnewline
107 & 0.964782 & 0.0704367 & 0.0352183 \tabularnewline
108 & 0.965851 & 0.0682973 & 0.0341487 \tabularnewline
109 & 0.959709 & 0.0805819 & 0.0402909 \tabularnewline
110 & 0.961349 & 0.0773029 & 0.0386514 \tabularnewline
111 & 0.954893 & 0.0902146 & 0.0451073 \tabularnewline
112 & 0.951017 & 0.0979666 & 0.0489833 \tabularnewline
113 & 0.942502 & 0.114996 & 0.057498 \tabularnewline
114 & 0.968453 & 0.063093 & 0.0315465 \tabularnewline
115 & 0.9631 & 0.0738003 & 0.0369001 \tabularnewline
116 & 0.966138 & 0.0677246 & 0.0338623 \tabularnewline
117 & 0.992994 & 0.0140122 & 0.00700609 \tabularnewline
118 & 0.995125 & 0.00974902 & 0.00487451 \tabularnewline
119 & 0.993605 & 0.0127909 & 0.00639543 \tabularnewline
120 & 0.993711 & 0.012578 & 0.00628898 \tabularnewline
121 & 0.995838 & 0.00832477 & 0.00416238 \tabularnewline
122 & 0.994595 & 0.0108108 & 0.00540541 \tabularnewline
123 & 0.994773 & 0.0104541 & 0.00522707 \tabularnewline
124 & 0.993661 & 0.0126788 & 0.00633938 \tabularnewline
125 & 0.995919 & 0.00816227 & 0.00408113 \tabularnewline
126 & 0.995454 & 0.00909135 & 0.00454567 \tabularnewline
127 & 0.994074 & 0.0118526 & 0.00592628 \tabularnewline
128 & 0.992358 & 0.0152832 & 0.00764161 \tabularnewline
129 & 0.993934 & 0.0121314 & 0.00606572 \tabularnewline
130 & 0.993032 & 0.0139351 & 0.00696754 \tabularnewline
131 & 0.993057 & 0.0138854 & 0.00694272 \tabularnewline
132 & 0.992318 & 0.0153645 & 0.00768223 \tabularnewline
133 & 0.994584 & 0.0108325 & 0.00541626 \tabularnewline
134 & 0.993966 & 0.0120674 & 0.00603372 \tabularnewline
135 & 0.992545 & 0.0149108 & 0.00745539 \tabularnewline
136 & 0.991437 & 0.017126 & 0.00856302 \tabularnewline
137 & 0.989379 & 0.0212413 & 0.0106206 \tabularnewline
138 & 0.988764 & 0.0224719 & 0.011236 \tabularnewline
139 & 0.986901 & 0.0261987 & 0.0130993 \tabularnewline
140 & 0.992981 & 0.0140385 & 0.00701925 \tabularnewline
141 & 0.991276 & 0.0174482 & 0.00872408 \tabularnewline
142 & 0.989207 & 0.0215855 & 0.0107928 \tabularnewline
143 & 0.986176 & 0.0276479 & 0.0138239 \tabularnewline
144 & 0.982998 & 0.0340048 & 0.0170024 \tabularnewline
145 & 0.980348 & 0.0393044 & 0.0196522 \tabularnewline
146 & 0.97783 & 0.044341 & 0.0221705 \tabularnewline
147 & 0.98632 & 0.0273598 & 0.0136799 \tabularnewline
148 & 0.983482 & 0.033036 & 0.016518 \tabularnewline
149 & 0.981302 & 0.0373966 & 0.0186983 \tabularnewline
150 & 0.98089 & 0.0382191 & 0.0191095 \tabularnewline
151 & 0.975593 & 0.0488148 & 0.0244074 \tabularnewline
152 & 0.976462 & 0.0470762 & 0.0235381 \tabularnewline
153 & 0.975936 & 0.0481283 & 0.0240641 \tabularnewline
154 & 0.972009 & 0.0559826 & 0.0279913 \tabularnewline
155 & 0.969845 & 0.0603091 & 0.0301546 \tabularnewline
156 & 0.966163 & 0.0676748 & 0.0338374 \tabularnewline
157 & 0.95869 & 0.0826197 & 0.0413098 \tabularnewline
158 & 0.95068 & 0.09864 & 0.04932 \tabularnewline
159 & 0.961715 & 0.0765693 & 0.0382847 \tabularnewline
160 & 0.959121 & 0.0817577 & 0.0408789 \tabularnewline
161 & 0.956712 & 0.0865751 & 0.0432875 \tabularnewline
162 & 0.946513 & 0.106974 & 0.0534868 \tabularnewline
163 & 0.942761 & 0.114477 & 0.0572385 \tabularnewline
164 & 0.933904 & 0.132191 & 0.0660955 \tabularnewline
165 & 0.920956 & 0.158088 & 0.0790439 \tabularnewline
166 & 0.908217 & 0.183567 & 0.0917834 \tabularnewline
167 & 0.923501 & 0.152998 & 0.0764992 \tabularnewline
168 & 0.913951 & 0.172098 & 0.0860492 \tabularnewline
169 & 0.908193 & 0.183614 & 0.0918069 \tabularnewline
170 & 0.89334 & 0.21332 & 0.10666 \tabularnewline
171 & 0.889435 & 0.22113 & 0.110565 \tabularnewline
172 & 0.872118 & 0.255764 & 0.127882 \tabularnewline
173 & 0.858654 & 0.282693 & 0.141346 \tabularnewline
174 & 0.831924 & 0.336152 & 0.168076 \tabularnewline
175 & 0.802181 & 0.395638 & 0.197819 \tabularnewline
176 & 0.783904 & 0.432191 & 0.216096 \tabularnewline
177 & 0.810762 & 0.378476 & 0.189238 \tabularnewline
178 & 0.780923 & 0.438155 & 0.219077 \tabularnewline
179 & 0.78101 & 0.437981 & 0.21899 \tabularnewline
180 & 0.764422 & 0.471155 & 0.235578 \tabularnewline
181 & 0.834485 & 0.33103 & 0.165515 \tabularnewline
182 & 0.85328 & 0.29344 & 0.14672 \tabularnewline
183 & 0.85554 & 0.28892 & 0.14446 \tabularnewline
184 & 0.840448 & 0.319104 & 0.159552 \tabularnewline
185 & 0.857802 & 0.284396 & 0.142198 \tabularnewline
186 & 0.886663 & 0.226673 & 0.113337 \tabularnewline
187 & 0.863433 & 0.273133 & 0.136567 \tabularnewline
188 & 0.840402 & 0.319196 & 0.159598 \tabularnewline
189 & 0.83315 & 0.333701 & 0.16685 \tabularnewline
190 & 0.829092 & 0.341815 & 0.170908 \tabularnewline
191 & 0.93251 & 0.134979 & 0.0674896 \tabularnewline
192 & 0.92709 & 0.145821 & 0.0729104 \tabularnewline
193 & 0.927512 & 0.144976 & 0.0724881 \tabularnewline
194 & 0.9095 & 0.180999 & 0.0904997 \tabularnewline
195 & 0.885219 & 0.229562 & 0.114781 \tabularnewline
196 & 0.868054 & 0.263893 & 0.131946 \tabularnewline
197 & 0.84216 & 0.315681 & 0.15784 \tabularnewline
198 & 0.828915 & 0.34217 & 0.171085 \tabularnewline
199 & 0.814719 & 0.370562 & 0.185281 \tabularnewline
200 & 0.889904 & 0.220193 & 0.110096 \tabularnewline
201 & 0.860942 & 0.278117 & 0.139058 \tabularnewline
202 & 0.829196 & 0.341607 & 0.170804 \tabularnewline
203 & 0.796985 & 0.406031 & 0.203015 \tabularnewline
204 & 0.753805 & 0.492391 & 0.246195 \tabularnewline
205 & 0.704429 & 0.591142 & 0.295571 \tabularnewline
206 & 0.646091 & 0.707819 & 0.353909 \tabularnewline
207 & 0.594246 & 0.811509 & 0.405754 \tabularnewline
208 & 0.592517 & 0.814966 & 0.407483 \tabularnewline
209 & 0.525788 & 0.948424 & 0.474212 \tabularnewline
210 & 0.461421 & 0.922842 & 0.538579 \tabularnewline
211 & 0.516154 & 0.967691 & 0.483846 \tabularnewline
212 & 0.44753 & 0.89506 & 0.55247 \tabularnewline
213 & 0.447862 & 0.895724 & 0.552138 \tabularnewline
214 & 0.892388 & 0.215224 & 0.107612 \tabularnewline
215 & 0.850385 & 0.299229 & 0.149615 \tabularnewline
216 & 0.995409 & 0.00918103 & 0.00459051 \tabularnewline
217 & 0.996562 & 0.00687544 & 0.00343772 \tabularnewline
218 & 0.996993 & 0.00601318 & 0.00300659 \tabularnewline
219 & 0.999799 & 0.000402919 & 0.000201459 \tabularnewline
220 & 0.999345 & 0.00131072 & 0.000655361 \tabularnewline
221 & 0.997852 & 0.00429586 & 0.00214793 \tabularnewline
222 & 0.995603 & 0.00879469 & 0.00439734 \tabularnewline
223 & 0.994121 & 0.0117576 & 0.0058788 \tabularnewline
224 & 0.981677 & 0.0366467 & 0.0183233 \tabularnewline
225 & 0.97025 & 0.0594996 & 0.0297498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&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]9[/C][C]0.942906[/C][C]0.114188[/C][C]0.0570942[/C][/ROW]
[ROW][C]10[/C][C]0.890117[/C][C]0.219766[/C][C]0.109883[/C][/ROW]
[ROW][C]11[/C][C]0.923672[/C][C]0.152656[/C][C]0.0763278[/C][/ROW]
[ROW][C]12[/C][C]0.875658[/C][C]0.248684[/C][C]0.124342[/C][/ROW]
[ROW][C]13[/C][C]0.816853[/C][C]0.366295[/C][C]0.183147[/C][/ROW]
[ROW][C]14[/C][C]0.836012[/C][C]0.327977[/C][C]0.163988[/C][/ROW]
[ROW][C]15[/C][C]0.784068[/C][C]0.431863[/C][C]0.215932[/C][/ROW]
[ROW][C]16[/C][C]0.716618[/C][C]0.566764[/C][C]0.283382[/C][/ROW]
[ROW][C]17[/C][C]0.639864[/C][C]0.720271[/C][C]0.360136[/C][/ROW]
[ROW][C]18[/C][C]0.6247[/C][C]0.750601[/C][C]0.3753[/C][/ROW]
[ROW][C]19[/C][C]0.567302[/C][C]0.865396[/C][C]0.432698[/C][/ROW]
[ROW][C]20[/C][C]0.494661[/C][C]0.989321[/C][C]0.505339[/C][/ROW]
[ROW][C]21[/C][C]0.448034[/C][C]0.896067[/C][C]0.551966[/C][/ROW]
[ROW][C]22[/C][C]0.398738[/C][C]0.797476[/C][C]0.601262[/C][/ROW]
[ROW][C]23[/C][C]0.349548[/C][C]0.699097[/C][C]0.650452[/C][/ROW]
[ROW][C]24[/C][C]0.31966[/C][C]0.63932[/C][C]0.68034[/C][/ROW]
[ROW][C]25[/C][C]0.27208[/C][C]0.544159[/C][C]0.72792[/C][/ROW]
[ROW][C]26[/C][C]0.266746[/C][C]0.533493[/C][C]0.733254[/C][/ROW]
[ROW][C]27[/C][C]0.226793[/C][C]0.453585[/C][C]0.773207[/C][/ROW]
[ROW][C]28[/C][C]0.191144[/C][C]0.382289[/C][C]0.808856[/C][/ROW]
[ROW][C]29[/C][C]0.155917[/C][C]0.311833[/C][C]0.844083[/C][/ROW]
[ROW][C]30[/C][C]0.161514[/C][C]0.323029[/C][C]0.838486[/C][/ROW]
[ROW][C]31[/C][C]0.144563[/C][C]0.289126[/C][C]0.855437[/C][/ROW]
[ROW][C]32[/C][C]0.118107[/C][C]0.236215[/C][C]0.881893[/C][/ROW]
[ROW][C]33[/C][C]0.0976058[/C][C]0.195212[/C][C]0.902394[/C][/ROW]
[ROW][C]34[/C][C]0.108624[/C][C]0.217248[/C][C]0.891376[/C][/ROW]
[ROW][C]35[/C][C]0.0915855[/C][C]0.183171[/C][C]0.908414[/C][/ROW]
[ROW][C]36[/C][C]0.0769517[/C][C]0.153903[/C][C]0.923048[/C][/ROW]
[ROW][C]37[/C][C]0.0692488[/C][C]0.138498[/C][C]0.930751[/C][/ROW]
[ROW][C]38[/C][C]0.0667352[/C][C]0.13347[/C][C]0.933265[/C][/ROW]
[ROW][C]39[/C][C]0.0673297[/C][C]0.134659[/C][C]0.93267[/C][/ROW]
[ROW][C]40[/C][C]0.0555434[/C][C]0.111087[/C][C]0.944457[/C][/ROW]
[ROW][C]41[/C][C]0.0746072[/C][C]0.149214[/C][C]0.925393[/C][/ROW]
[ROW][C]42[/C][C]0.0808247[/C][C]0.161649[/C][C]0.919175[/C][/ROW]
[ROW][C]43[/C][C]0.0643809[/C][C]0.128762[/C][C]0.935619[/C][/ROW]
[ROW][C]44[/C][C]0.073999[/C][C]0.147998[/C][C]0.926001[/C][/ROW]
[ROW][C]45[/C][C]0.0820477[/C][C]0.164095[/C][C]0.917952[/C][/ROW]
[ROW][C]46[/C][C]0.0749337[/C][C]0.149867[/C][C]0.925066[/C][/ROW]
[ROW][C]47[/C][C]0.0667359[/C][C]0.133472[/C][C]0.933264[/C][/ROW]
[ROW][C]48[/C][C]0.0636379[/C][C]0.127276[/C][C]0.936362[/C][/ROW]
[ROW][C]49[/C][C]0.0599901[/C][C]0.11998[/C][C]0.94001[/C][/ROW]
[ROW][C]50[/C][C]0.0522922[/C][C]0.104584[/C][C]0.947708[/C][/ROW]
[ROW][C]51[/C][C]0.0569758[/C][C]0.113952[/C][C]0.943024[/C][/ROW]
[ROW][C]52[/C][C]0.0800941[/C][C]0.160188[/C][C]0.919906[/C][/ROW]
[ROW][C]53[/C][C]0.0653262[/C][C]0.130652[/C][C]0.934674[/C][/ROW]
[ROW][C]54[/C][C]0.0537266[/C][C]0.107453[/C][C]0.946273[/C][/ROW]
[ROW][C]55[/C][C]0.158211[/C][C]0.316421[/C][C]0.841789[/C][/ROW]
[ROW][C]56[/C][C]0.146004[/C][C]0.292008[/C][C]0.853996[/C][/ROW]
[ROW][C]57[/C][C]0.121438[/C][C]0.242877[/C][C]0.878562[/C][/ROW]
[ROW][C]58[/C][C]0.226019[/C][C]0.452039[/C][C]0.773981[/C][/ROW]
[ROW][C]59[/C][C]0.220427[/C][C]0.440854[/C][C]0.779573[/C][/ROW]
[ROW][C]60[/C][C]0.218389[/C][C]0.436779[/C][C]0.781611[/C][/ROW]
[ROW][C]61[/C][C]0.227239[/C][C]0.454479[/C][C]0.772761[/C][/ROW]
[ROW][C]62[/C][C]0.251228[/C][C]0.502456[/C][C]0.748772[/C][/ROW]
[ROW][C]63[/C][C]0.252437[/C][C]0.504874[/C][C]0.747563[/C][/ROW]
[ROW][C]64[/C][C]0.283636[/C][C]0.567272[/C][C]0.716364[/C][/ROW]
[ROW][C]65[/C][C]0.301432[/C][C]0.602864[/C][C]0.698568[/C][/ROW]
[ROW][C]66[/C][C]0.508947[/C][C]0.982106[/C][C]0.491053[/C][/ROW]
[ROW][C]67[/C][C]0.616176[/C][C]0.767648[/C][C]0.383824[/C][/ROW]
[ROW][C]68[/C][C]0.604236[/C][C]0.791527[/C][C]0.395764[/C][/ROW]
[ROW][C]69[/C][C]0.766795[/C][C]0.466411[/C][C]0.233205[/C][/ROW]
[ROW][C]70[/C][C]0.782365[/C][C]0.435269[/C][C]0.217635[/C][/ROW]
[ROW][C]71[/C][C]0.816902[/C][C]0.366197[/C][C]0.183098[/C][/ROW]
[ROW][C]72[/C][C]0.831429[/C][C]0.337143[/C][C]0.168571[/C][/ROW]
[ROW][C]73[/C][C]0.860367[/C][C]0.279267[/C][C]0.139633[/C][/ROW]
[ROW][C]74[/C][C]0.866027[/C][C]0.267945[/C][C]0.133973[/C][/ROW]
[ROW][C]75[/C][C]0.911058[/C][C]0.177884[/C][C]0.0889419[/C][/ROW]
[ROW][C]76[/C][C]0.93093[/C][C]0.13814[/C][C]0.0690702[/C][/ROW]
[ROW][C]77[/C][C]0.918868[/C][C]0.162264[/C][C]0.0811319[/C][/ROW]
[ROW][C]78[/C][C]0.918614[/C][C]0.162772[/C][C]0.0813858[/C][/ROW]
[ROW][C]79[/C][C]0.92823[/C][C]0.14354[/C][C]0.0717698[/C][/ROW]
[ROW][C]80[/C][C]0.918923[/C][C]0.162154[/C][C]0.0810769[/C][/ROW]
[ROW][C]81[/C][C]0.913216[/C][C]0.173569[/C][C]0.0867843[/C][/ROW]
[ROW][C]82[/C][C]0.920205[/C][C]0.15959[/C][C]0.079795[/C][/ROW]
[ROW][C]83[/C][C]0.915164[/C][C]0.169673[/C][C]0.0848365[/C][/ROW]
[ROW][C]84[/C][C]0.928567[/C][C]0.142866[/C][C]0.0714329[/C][/ROW]
[ROW][C]85[/C][C]0.916842[/C][C]0.166316[/C][C]0.083158[/C][/ROW]
[ROW][C]86[/C][C]0.901804[/C][C]0.196392[/C][C]0.0981961[/C][/ROW]
[ROW][C]87[/C][C]0.889822[/C][C]0.220357[/C][C]0.110178[/C][/ROW]
[ROW][C]88[/C][C]0.923659[/C][C]0.152682[/C][C]0.076341[/C][/ROW]
[ROW][C]89[/C][C]0.926589[/C][C]0.146823[/C][C]0.0734114[/C][/ROW]
[ROW][C]90[/C][C]0.923436[/C][C]0.153128[/C][C]0.076564[/C][/ROW]
[ROW][C]91[/C][C]0.91046[/C][C]0.179081[/C][C]0.0895404[/C][/ROW]
[ROW][C]92[/C][C]0.906534[/C][C]0.186931[/C][C]0.0934656[/C][/ROW]
[ROW][C]93[/C][C]0.959279[/C][C]0.0814427[/C][C]0.0407213[/C][/ROW]
[ROW][C]94[/C][C]0.958107[/C][C]0.0837857[/C][C]0.0418928[/C][/ROW]
[ROW][C]95[/C][C]0.958019[/C][C]0.0839623[/C][C]0.0419811[/C][/ROW]
[ROW][C]96[/C][C]0.954869[/C][C]0.0902627[/C][C]0.0451313[/C][/ROW]
[ROW][C]97[/C][C]0.950593[/C][C]0.0988143[/C][C]0.0494072[/C][/ROW]
[ROW][C]98[/C][C]0.957426[/C][C]0.0851488[/C][C]0.0425744[/C][/ROW]
[ROW][C]99[/C][C]0.949927[/C][C]0.100145[/C][C]0.0500726[/C][/ROW]
[ROW][C]100[/C][C]0.94533[/C][C]0.10934[/C][C]0.0546698[/C][/ROW]
[ROW][C]101[/C][C]0.936887[/C][C]0.126227[/C][C]0.0631133[/C][/ROW]
[ROW][C]102[/C][C]0.930027[/C][C]0.139945[/C][C]0.0699726[/C][/ROW]
[ROW][C]103[/C][C]0.925883[/C][C]0.148235[/C][C]0.0741174[/C][/ROW]
[ROW][C]104[/C][C]0.971102[/C][C]0.0577968[/C][C]0.0288984[/C][/ROW]
[ROW][C]105[/C][C]0.970187[/C][C]0.0596254[/C][C]0.0298127[/C][/ROW]
[ROW][C]106[/C][C]0.971079[/C][C]0.0578419[/C][C]0.0289209[/C][/ROW]
[ROW][C]107[/C][C]0.964782[/C][C]0.0704367[/C][C]0.0352183[/C][/ROW]
[ROW][C]108[/C][C]0.965851[/C][C]0.0682973[/C][C]0.0341487[/C][/ROW]
[ROW][C]109[/C][C]0.959709[/C][C]0.0805819[/C][C]0.0402909[/C][/ROW]
[ROW][C]110[/C][C]0.961349[/C][C]0.0773029[/C][C]0.0386514[/C][/ROW]
[ROW][C]111[/C][C]0.954893[/C][C]0.0902146[/C][C]0.0451073[/C][/ROW]
[ROW][C]112[/C][C]0.951017[/C][C]0.0979666[/C][C]0.0489833[/C][/ROW]
[ROW][C]113[/C][C]0.942502[/C][C]0.114996[/C][C]0.057498[/C][/ROW]
[ROW][C]114[/C][C]0.968453[/C][C]0.063093[/C][C]0.0315465[/C][/ROW]
[ROW][C]115[/C][C]0.9631[/C][C]0.0738003[/C][C]0.0369001[/C][/ROW]
[ROW][C]116[/C][C]0.966138[/C][C]0.0677246[/C][C]0.0338623[/C][/ROW]
[ROW][C]117[/C][C]0.992994[/C][C]0.0140122[/C][C]0.00700609[/C][/ROW]
[ROW][C]118[/C][C]0.995125[/C][C]0.00974902[/C][C]0.00487451[/C][/ROW]
[ROW][C]119[/C][C]0.993605[/C][C]0.0127909[/C][C]0.00639543[/C][/ROW]
[ROW][C]120[/C][C]0.993711[/C][C]0.012578[/C][C]0.00628898[/C][/ROW]
[ROW][C]121[/C][C]0.995838[/C][C]0.00832477[/C][C]0.00416238[/C][/ROW]
[ROW][C]122[/C][C]0.994595[/C][C]0.0108108[/C][C]0.00540541[/C][/ROW]
[ROW][C]123[/C][C]0.994773[/C][C]0.0104541[/C][C]0.00522707[/C][/ROW]
[ROW][C]124[/C][C]0.993661[/C][C]0.0126788[/C][C]0.00633938[/C][/ROW]
[ROW][C]125[/C][C]0.995919[/C][C]0.00816227[/C][C]0.00408113[/C][/ROW]
[ROW][C]126[/C][C]0.995454[/C][C]0.00909135[/C][C]0.00454567[/C][/ROW]
[ROW][C]127[/C][C]0.994074[/C][C]0.0118526[/C][C]0.00592628[/C][/ROW]
[ROW][C]128[/C][C]0.992358[/C][C]0.0152832[/C][C]0.00764161[/C][/ROW]
[ROW][C]129[/C][C]0.993934[/C][C]0.0121314[/C][C]0.00606572[/C][/ROW]
[ROW][C]130[/C][C]0.993032[/C][C]0.0139351[/C][C]0.00696754[/C][/ROW]
[ROW][C]131[/C][C]0.993057[/C][C]0.0138854[/C][C]0.00694272[/C][/ROW]
[ROW][C]132[/C][C]0.992318[/C][C]0.0153645[/C][C]0.00768223[/C][/ROW]
[ROW][C]133[/C][C]0.994584[/C][C]0.0108325[/C][C]0.00541626[/C][/ROW]
[ROW][C]134[/C][C]0.993966[/C][C]0.0120674[/C][C]0.00603372[/C][/ROW]
[ROW][C]135[/C][C]0.992545[/C][C]0.0149108[/C][C]0.00745539[/C][/ROW]
[ROW][C]136[/C][C]0.991437[/C][C]0.017126[/C][C]0.00856302[/C][/ROW]
[ROW][C]137[/C][C]0.989379[/C][C]0.0212413[/C][C]0.0106206[/C][/ROW]
[ROW][C]138[/C][C]0.988764[/C][C]0.0224719[/C][C]0.011236[/C][/ROW]
[ROW][C]139[/C][C]0.986901[/C][C]0.0261987[/C][C]0.0130993[/C][/ROW]
[ROW][C]140[/C][C]0.992981[/C][C]0.0140385[/C][C]0.00701925[/C][/ROW]
[ROW][C]141[/C][C]0.991276[/C][C]0.0174482[/C][C]0.00872408[/C][/ROW]
[ROW][C]142[/C][C]0.989207[/C][C]0.0215855[/C][C]0.0107928[/C][/ROW]
[ROW][C]143[/C][C]0.986176[/C][C]0.0276479[/C][C]0.0138239[/C][/ROW]
[ROW][C]144[/C][C]0.982998[/C][C]0.0340048[/C][C]0.0170024[/C][/ROW]
[ROW][C]145[/C][C]0.980348[/C][C]0.0393044[/C][C]0.0196522[/C][/ROW]
[ROW][C]146[/C][C]0.97783[/C][C]0.044341[/C][C]0.0221705[/C][/ROW]
[ROW][C]147[/C][C]0.98632[/C][C]0.0273598[/C][C]0.0136799[/C][/ROW]
[ROW][C]148[/C][C]0.983482[/C][C]0.033036[/C][C]0.016518[/C][/ROW]
[ROW][C]149[/C][C]0.981302[/C][C]0.0373966[/C][C]0.0186983[/C][/ROW]
[ROW][C]150[/C][C]0.98089[/C][C]0.0382191[/C][C]0.0191095[/C][/ROW]
[ROW][C]151[/C][C]0.975593[/C][C]0.0488148[/C][C]0.0244074[/C][/ROW]
[ROW][C]152[/C][C]0.976462[/C][C]0.0470762[/C][C]0.0235381[/C][/ROW]
[ROW][C]153[/C][C]0.975936[/C][C]0.0481283[/C][C]0.0240641[/C][/ROW]
[ROW][C]154[/C][C]0.972009[/C][C]0.0559826[/C][C]0.0279913[/C][/ROW]
[ROW][C]155[/C][C]0.969845[/C][C]0.0603091[/C][C]0.0301546[/C][/ROW]
[ROW][C]156[/C][C]0.966163[/C][C]0.0676748[/C][C]0.0338374[/C][/ROW]
[ROW][C]157[/C][C]0.95869[/C][C]0.0826197[/C][C]0.0413098[/C][/ROW]
[ROW][C]158[/C][C]0.95068[/C][C]0.09864[/C][C]0.04932[/C][/ROW]
[ROW][C]159[/C][C]0.961715[/C][C]0.0765693[/C][C]0.0382847[/C][/ROW]
[ROW][C]160[/C][C]0.959121[/C][C]0.0817577[/C][C]0.0408789[/C][/ROW]
[ROW][C]161[/C][C]0.956712[/C][C]0.0865751[/C][C]0.0432875[/C][/ROW]
[ROW][C]162[/C][C]0.946513[/C][C]0.106974[/C][C]0.0534868[/C][/ROW]
[ROW][C]163[/C][C]0.942761[/C][C]0.114477[/C][C]0.0572385[/C][/ROW]
[ROW][C]164[/C][C]0.933904[/C][C]0.132191[/C][C]0.0660955[/C][/ROW]
[ROW][C]165[/C][C]0.920956[/C][C]0.158088[/C][C]0.0790439[/C][/ROW]
[ROW][C]166[/C][C]0.908217[/C][C]0.183567[/C][C]0.0917834[/C][/ROW]
[ROW][C]167[/C][C]0.923501[/C][C]0.152998[/C][C]0.0764992[/C][/ROW]
[ROW][C]168[/C][C]0.913951[/C][C]0.172098[/C][C]0.0860492[/C][/ROW]
[ROW][C]169[/C][C]0.908193[/C][C]0.183614[/C][C]0.0918069[/C][/ROW]
[ROW][C]170[/C][C]0.89334[/C][C]0.21332[/C][C]0.10666[/C][/ROW]
[ROW][C]171[/C][C]0.889435[/C][C]0.22113[/C][C]0.110565[/C][/ROW]
[ROW][C]172[/C][C]0.872118[/C][C]0.255764[/C][C]0.127882[/C][/ROW]
[ROW][C]173[/C][C]0.858654[/C][C]0.282693[/C][C]0.141346[/C][/ROW]
[ROW][C]174[/C][C]0.831924[/C][C]0.336152[/C][C]0.168076[/C][/ROW]
[ROW][C]175[/C][C]0.802181[/C][C]0.395638[/C][C]0.197819[/C][/ROW]
[ROW][C]176[/C][C]0.783904[/C][C]0.432191[/C][C]0.216096[/C][/ROW]
[ROW][C]177[/C][C]0.810762[/C][C]0.378476[/C][C]0.189238[/C][/ROW]
[ROW][C]178[/C][C]0.780923[/C][C]0.438155[/C][C]0.219077[/C][/ROW]
[ROW][C]179[/C][C]0.78101[/C][C]0.437981[/C][C]0.21899[/C][/ROW]
[ROW][C]180[/C][C]0.764422[/C][C]0.471155[/C][C]0.235578[/C][/ROW]
[ROW][C]181[/C][C]0.834485[/C][C]0.33103[/C][C]0.165515[/C][/ROW]
[ROW][C]182[/C][C]0.85328[/C][C]0.29344[/C][C]0.14672[/C][/ROW]
[ROW][C]183[/C][C]0.85554[/C][C]0.28892[/C][C]0.14446[/C][/ROW]
[ROW][C]184[/C][C]0.840448[/C][C]0.319104[/C][C]0.159552[/C][/ROW]
[ROW][C]185[/C][C]0.857802[/C][C]0.284396[/C][C]0.142198[/C][/ROW]
[ROW][C]186[/C][C]0.886663[/C][C]0.226673[/C][C]0.113337[/C][/ROW]
[ROW][C]187[/C][C]0.863433[/C][C]0.273133[/C][C]0.136567[/C][/ROW]
[ROW][C]188[/C][C]0.840402[/C][C]0.319196[/C][C]0.159598[/C][/ROW]
[ROW][C]189[/C][C]0.83315[/C][C]0.333701[/C][C]0.16685[/C][/ROW]
[ROW][C]190[/C][C]0.829092[/C][C]0.341815[/C][C]0.170908[/C][/ROW]
[ROW][C]191[/C][C]0.93251[/C][C]0.134979[/C][C]0.0674896[/C][/ROW]
[ROW][C]192[/C][C]0.92709[/C][C]0.145821[/C][C]0.0729104[/C][/ROW]
[ROW][C]193[/C][C]0.927512[/C][C]0.144976[/C][C]0.0724881[/C][/ROW]
[ROW][C]194[/C][C]0.9095[/C][C]0.180999[/C][C]0.0904997[/C][/ROW]
[ROW][C]195[/C][C]0.885219[/C][C]0.229562[/C][C]0.114781[/C][/ROW]
[ROW][C]196[/C][C]0.868054[/C][C]0.263893[/C][C]0.131946[/C][/ROW]
[ROW][C]197[/C][C]0.84216[/C][C]0.315681[/C][C]0.15784[/C][/ROW]
[ROW][C]198[/C][C]0.828915[/C][C]0.34217[/C][C]0.171085[/C][/ROW]
[ROW][C]199[/C][C]0.814719[/C][C]0.370562[/C][C]0.185281[/C][/ROW]
[ROW][C]200[/C][C]0.889904[/C][C]0.220193[/C][C]0.110096[/C][/ROW]
[ROW][C]201[/C][C]0.860942[/C][C]0.278117[/C][C]0.139058[/C][/ROW]
[ROW][C]202[/C][C]0.829196[/C][C]0.341607[/C][C]0.170804[/C][/ROW]
[ROW][C]203[/C][C]0.796985[/C][C]0.406031[/C][C]0.203015[/C][/ROW]
[ROW][C]204[/C][C]0.753805[/C][C]0.492391[/C][C]0.246195[/C][/ROW]
[ROW][C]205[/C][C]0.704429[/C][C]0.591142[/C][C]0.295571[/C][/ROW]
[ROW][C]206[/C][C]0.646091[/C][C]0.707819[/C][C]0.353909[/C][/ROW]
[ROW][C]207[/C][C]0.594246[/C][C]0.811509[/C][C]0.405754[/C][/ROW]
[ROW][C]208[/C][C]0.592517[/C][C]0.814966[/C][C]0.407483[/C][/ROW]
[ROW][C]209[/C][C]0.525788[/C][C]0.948424[/C][C]0.474212[/C][/ROW]
[ROW][C]210[/C][C]0.461421[/C][C]0.922842[/C][C]0.538579[/C][/ROW]
[ROW][C]211[/C][C]0.516154[/C][C]0.967691[/C][C]0.483846[/C][/ROW]
[ROW][C]212[/C][C]0.44753[/C][C]0.89506[/C][C]0.55247[/C][/ROW]
[ROW][C]213[/C][C]0.447862[/C][C]0.895724[/C][C]0.552138[/C][/ROW]
[ROW][C]214[/C][C]0.892388[/C][C]0.215224[/C][C]0.107612[/C][/ROW]
[ROW][C]215[/C][C]0.850385[/C][C]0.299229[/C][C]0.149615[/C][/ROW]
[ROW][C]216[/C][C]0.995409[/C][C]0.00918103[/C][C]0.00459051[/C][/ROW]
[ROW][C]217[/C][C]0.996562[/C][C]0.00687544[/C][C]0.00343772[/C][/ROW]
[ROW][C]218[/C][C]0.996993[/C][C]0.00601318[/C][C]0.00300659[/C][/ROW]
[ROW][C]219[/C][C]0.999799[/C][C]0.000402919[/C][C]0.000201459[/C][/ROW]
[ROW][C]220[/C][C]0.999345[/C][C]0.00131072[/C][C]0.000655361[/C][/ROW]
[ROW][C]221[/C][C]0.997852[/C][C]0.00429586[/C][C]0.00214793[/C][/ROW]
[ROW][C]222[/C][C]0.995603[/C][C]0.00879469[/C][C]0.00439734[/C][/ROW]
[ROW][C]223[/C][C]0.994121[/C][C]0.0117576[/C][C]0.0058788[/C][/ROW]
[ROW][C]224[/C][C]0.981677[/C][C]0.0366467[/C][C]0.0183233[/C][/ROW]
[ROW][C]225[/C][C]0.97025[/C][C]0.0594996[/C][C]0.0297498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&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
90.9429060.1141880.0570942
100.8901170.2197660.109883
110.9236720.1526560.0763278
120.8756580.2486840.124342
130.8168530.3662950.183147
140.8360120.3279770.163988
150.7840680.4318630.215932
160.7166180.5667640.283382
170.6398640.7202710.360136
180.62470.7506010.3753
190.5673020.8653960.432698
200.4946610.9893210.505339
210.4480340.8960670.551966
220.3987380.7974760.601262
230.3495480.6990970.650452
240.319660.639320.68034
250.272080.5441590.72792
260.2667460.5334930.733254
270.2267930.4535850.773207
280.1911440.3822890.808856
290.1559170.3118330.844083
300.1615140.3230290.838486
310.1445630.2891260.855437
320.1181070.2362150.881893
330.09760580.1952120.902394
340.1086240.2172480.891376
350.09158550.1831710.908414
360.07695170.1539030.923048
370.06924880.1384980.930751
380.06673520.133470.933265
390.06732970.1346590.93267
400.05554340.1110870.944457
410.07460720.1492140.925393
420.08082470.1616490.919175
430.06438090.1287620.935619
440.0739990.1479980.926001
450.08204770.1640950.917952
460.07493370.1498670.925066
470.06673590.1334720.933264
480.06363790.1272760.936362
490.05999010.119980.94001
500.05229220.1045840.947708
510.05697580.1139520.943024
520.08009410.1601880.919906
530.06532620.1306520.934674
540.05372660.1074530.946273
550.1582110.3164210.841789
560.1460040.2920080.853996
570.1214380.2428770.878562
580.2260190.4520390.773981
590.2204270.4408540.779573
600.2183890.4367790.781611
610.2272390.4544790.772761
620.2512280.5024560.748772
630.2524370.5048740.747563
640.2836360.5672720.716364
650.3014320.6028640.698568
660.5089470.9821060.491053
670.6161760.7676480.383824
680.6042360.7915270.395764
690.7667950.4664110.233205
700.7823650.4352690.217635
710.8169020.3661970.183098
720.8314290.3371430.168571
730.8603670.2792670.139633
740.8660270.2679450.133973
750.9110580.1778840.0889419
760.930930.138140.0690702
770.9188680.1622640.0811319
780.9186140.1627720.0813858
790.928230.143540.0717698
800.9189230.1621540.0810769
810.9132160.1735690.0867843
820.9202050.159590.079795
830.9151640.1696730.0848365
840.9285670.1428660.0714329
850.9168420.1663160.083158
860.9018040.1963920.0981961
870.8898220.2203570.110178
880.9236590.1526820.076341
890.9265890.1468230.0734114
900.9234360.1531280.076564
910.910460.1790810.0895404
920.9065340.1869310.0934656
930.9592790.08144270.0407213
940.9581070.08378570.0418928
950.9580190.08396230.0419811
960.9548690.09026270.0451313
970.9505930.09881430.0494072
980.9574260.08514880.0425744
990.9499270.1001450.0500726
1000.945330.109340.0546698
1010.9368870.1262270.0631133
1020.9300270.1399450.0699726
1030.9258830.1482350.0741174
1040.9711020.05779680.0288984
1050.9701870.05962540.0298127
1060.9710790.05784190.0289209
1070.9647820.07043670.0352183
1080.9658510.06829730.0341487
1090.9597090.08058190.0402909
1100.9613490.07730290.0386514
1110.9548930.09021460.0451073
1120.9510170.09796660.0489833
1130.9425020.1149960.057498
1140.9684530.0630930.0315465
1150.96310.07380030.0369001
1160.9661380.06772460.0338623
1170.9929940.01401220.00700609
1180.9951250.009749020.00487451
1190.9936050.01279090.00639543
1200.9937110.0125780.00628898
1210.9958380.008324770.00416238
1220.9945950.01081080.00540541
1230.9947730.01045410.00522707
1240.9936610.01267880.00633938
1250.9959190.008162270.00408113
1260.9954540.009091350.00454567
1270.9940740.01185260.00592628
1280.9923580.01528320.00764161
1290.9939340.01213140.00606572
1300.9930320.01393510.00696754
1310.9930570.01388540.00694272
1320.9923180.01536450.00768223
1330.9945840.01083250.00541626
1340.9939660.01206740.00603372
1350.9925450.01491080.00745539
1360.9914370.0171260.00856302
1370.9893790.02124130.0106206
1380.9887640.02247190.011236
1390.9869010.02619870.0130993
1400.9929810.01403850.00701925
1410.9912760.01744820.00872408
1420.9892070.02158550.0107928
1430.9861760.02764790.0138239
1440.9829980.03400480.0170024
1450.9803480.03930440.0196522
1460.977830.0443410.0221705
1470.986320.02735980.0136799
1480.9834820.0330360.016518
1490.9813020.03739660.0186983
1500.980890.03821910.0191095
1510.9755930.04881480.0244074
1520.9764620.04707620.0235381
1530.9759360.04812830.0240641
1540.9720090.05598260.0279913
1550.9698450.06030910.0301546
1560.9661630.06767480.0338374
1570.958690.08261970.0413098
1580.950680.098640.04932
1590.9617150.07656930.0382847
1600.9591210.08175770.0408789
1610.9567120.08657510.0432875
1620.9465130.1069740.0534868
1630.9427610.1144770.0572385
1640.9339040.1321910.0660955
1650.9209560.1580880.0790439
1660.9082170.1835670.0917834
1670.9235010.1529980.0764992
1680.9139510.1720980.0860492
1690.9081930.1836140.0918069
1700.893340.213320.10666
1710.8894350.221130.110565
1720.8721180.2557640.127882
1730.8586540.2826930.141346
1740.8319240.3361520.168076
1750.8021810.3956380.197819
1760.7839040.4321910.216096
1770.8107620.3784760.189238
1780.7809230.4381550.219077
1790.781010.4379810.21899
1800.7644220.4711550.235578
1810.8344850.331030.165515
1820.853280.293440.14672
1830.855540.288920.14446
1840.8404480.3191040.159552
1850.8578020.2843960.142198
1860.8866630.2266730.113337
1870.8634330.2731330.136567
1880.8404020.3191960.159598
1890.833150.3337010.16685
1900.8290920.3418150.170908
1910.932510.1349790.0674896
1920.927090.1458210.0729104
1930.9275120.1449760.0724881
1940.90950.1809990.0904997
1950.8852190.2295620.114781
1960.8680540.2638930.131946
1970.842160.3156810.15784
1980.8289150.342170.171085
1990.8147190.3705620.185281
2000.8899040.2201930.110096
2010.8609420.2781170.139058
2020.8291960.3416070.170804
2030.7969850.4060310.203015
2040.7538050.4923910.246195
2050.7044290.5911420.295571
2060.6460910.7078190.353909
2070.5942460.8115090.405754
2080.5925170.8149660.407483
2090.5257880.9484240.474212
2100.4614210.9228420.538579
2110.5161540.9676910.483846
2120.447530.895060.55247
2130.4478620.8957240.552138
2140.8923880.2152240.107612
2150.8503850.2992290.149615
2160.9954090.009181030.00459051
2170.9965620.006875440.00343772
2180.9969930.006013180.00300659
2190.9997990.0004029190.000201459
2200.9993450.001310720.000655361
2210.9978520.004295860.00214793
2220.9956030.008794690.00439734
2230.9941210.01175760.0058788
2240.9816770.03664670.0183233
2250.970250.05949960.0297498






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.0506912NOK
5% type I error level460.211982NOK
10% type I error level730.336406NOK

\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 & 11 & 0.0506912 & NOK \tabularnewline
5% type I error level & 46 & 0.211982 & NOK \tabularnewline
10% type I error level & 73 & 0.336406 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264867&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]11[/C][C]0.0506912[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.211982[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.336406[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264867&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264867&T=6

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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 level110.0506912NOK
5% type I error level460.211982NOK
10% type I error level730.336406NOK


Figures (Output of Computation)

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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')
}