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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 14 Dec 2014 09:14:20 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418548659f7iborkshigvwha.htm/, Retrieved Thu, 31 Oct 2024 23:36:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267346, Retrieved Thu, 31 Oct 2024 23:36:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 09:14:20] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataseries X:
1.5 0 0 21 21 149 18 68 96
1.8 0 0 23 26 152 7 55 75
2.1 0 1 22 22 139 31 39 70
2.1 0 0 21 22 148 39 32 88
1.9 0 1 21 18 158 46 62 114
1.6 0 1 21 23 128 31 33 69
2.1 0 1 21 12 224 67 52 176
2.1 0 0 21 20 159 35 62 114
2.2 0 1 23 22 105 52 77 121
1.5 0 1 22 21 159 77 76 110
1.9 0 1 25 19 167 37 41 158
2.2 0 1 21 22 165 32 48 116
1.6 0 1 23 15 159 36 63 181
1.5 0 1 22 20 119 38 30 77
1.9 0 0 21 19 176 69 78 141
0.1 0 0 21 18 54 21 19 35
2.2 1 0 25 15 91 26 31 80
1.8 0 1 21 20 163 54 66 152
1.6 0 0 21 21 124 36 35 97
2.2 1 1 20 21 137 42 42 99
2.1 0 0 24 15 121 23 45 84
1.9 0 1 23 16 153 34 21 68
1.6 0 1 21 23 148 112 25 101
1.9 0 0 24 21 221 35 44 107
2.2 0 1 23 18 188 47 69 88
1.8 0 1 21 25 149 47 54 112
2.4 0 1 22 9 244 37 74 171
2.4 1 1 20 30 148 109 80 137
2.5 1 0 18 20 92 24 42 77
1.9 0 1 21 23 150 20 61 66
2.1 0 0 22 16 153 22 41 93
1.9 0 0 22 16 94 23 46 105
2.1 0 0 21 19 156 32 39 131
1.9 0 1 23 25 146 7 63 89
1.5 0 1 21 25 132 30 34 102
1.9 0 1 25 18 161 92 51 161
2.1 0 1 22 23 105 43 42 120
1.5 0 1 22 21 97 55 31 127
2.1 0 0 20 10 151 16 39 77
2.1 1 1 21 14 131 49 20 108
1.8 0 1 21 22 166 71 49 85
2.4 0 0 21 26 157 43 53 168
2.1 0 1 22 23 111 29 31 48
1.9 0 1 21 23 145 56 39 152
2.1 0 1 24 24 162 46 54 75
1.9 0 1 22 24 163 19 49 107
2.4 1 1 22 18 59 23 34 62
2.1 0 0 21 23 187 59 46 121
2.2 0 1 22 15 109 30 55 124
2.2 1 1 19 19 90 61 42 72
1.8 0 0 22 16 105 7 50 40
2.1 1 1 23 25 83 38 13 58
2.4 1 1 20 23 116 32 37 97
2.2 1 1 20 17 42 16 25 88
2.1 0 1 23 19 148 19 30 126
1.5 1 1 20 21 155 22 28 104
1.9 0 1 23 18 125 48 45 148
1.8 0 1 21 27 116 23 35 146
1.8 1 0 22 21 128 26 28 80
1.6 0 1 21 13 138 33 41 97
1.2 1 0 21 8 49 9 6 25
1.8 1 1 19 29 96 24 45 99
1.5 0 1 22 28 164 34 73 118
2.1 0 0 21 23 162 48 17 58
2.4 0 0 21 21 99 18 40 63
2.4 0 1 21 19 202 43 64 139
1.5 0 0 21 19 186 33 37 50
1.8 1 1 21 20 66 28 25 60
2.1 0 0 21 18 183 71 65 152
2.2 0 1 22 19 214 26 100 142
2.1 0 1 22 17 188 67 28 94
1.9 1 0 18 19 104 34 35 66
2.1 0 0 21 25 177 80 56 127
1.9 0 0 23 19 126 29 29 67
1.6 1 0 19 22 76 16 43 90
2.4 1 1 19 23 99 59 59 75
1.9 0 1 23 26 157 58 52 96
1.9 0 0 21 14 139 32 50 128
2.1 0 0 21 16 162 43 59 146
1.8 1 1 21 24 108 38 27 69
2.1 0 0 20 20 159 29 61 186
2.4 1 0 19 12 74 36 28 81
2.1 0 1 21 24 110 32 51 85
2.2 1 0 19 22 96 35 35 54
2.1 1 0 19 12 116 21 29 46
2.2 1 0 19 22 87 29 48 106
1.6 1 1 20 20 97 12 25 34
2.4 1 0 19 10 127 37 44 60
2.1 1 1 19 23 106 37 64 95
1.9 1 1 19 17 80 47 32 57
2.4 1 0 20 22 74 51 20 62
2.1 1 0 19 24 91 32 28 36
1.8 1 0 18 18 133 21 34 56
2.1 1 1 19 21 74 13 31 54
1.8 1 1 21 20 114 14 26 64
1.9 1 1 18 20 140 -2 58 76
1.9 1 0 18 22 95 20 23 98
2.4 1 1 19 19 98 24 21 88
1.8 1 0 21 20 121 11 21 35
1.8 1 1 20 26 126 23 33 102
2.1 1 1 24 23 98 24 16 61
2.1 1 1 22 24 95 14 20 80
2.4 1 1 21 21 110 52 37 49
1.9 1 1 21 21 70 15 35 78
1.8 1 0 19 19 102 23 33 90
1.8 1 1 19 8 86 19 27 45
2.2 1 1 20 17 130 35 41 55
2.4 1 1 18 20 96 24 40 96
1.8 1 0 19 11 102 39 35 43
2.4 1 0 19 8 100 29 28 52
1.8 1 0 20 15 94 13 32 60
1.9 1 0 21 18 52 8 22 54
2.4 1 0 18 18 98 18 44 51
2.1 1 0 19 19 118 24 27 51
1.9 1 1 19 19 99 19 17 38




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=0

As an alternative you can also use a 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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.20359 + 0.345333programma[t] -0.0297883gender[t] + 0.00938839age[t] -0.00363078NUMERACYTOT[t] + 0.00138745LFM[t] + 0.00137072PRH[t] + 0.00404821CH[t] + 0.00122842Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  1.20359 +  0.345333programma[t] -0.0297883gender[t] +  0.00938839age[t] -0.00363078NUMERACYTOT[t] +  0.00138745LFM[t] +  0.00137072PRH[t] +  0.00404821CH[t] +  0.00122842Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  1.20359 +  0.345333programma[t] -0.0297883gender[t] +  0.00938839age[t] -0.00363078NUMERACYTOT[t] +  0.00138745LFM[t] +  0.00137072PRH[t] +  0.00404821CH[t] +  0.00122842Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.20359 + 0.345333programma[t] -0.0297883gender[t] + 0.00938839age[t] -0.00363078NUMERACYTOT[t] + 0.00138745LFM[t] + 0.00137072PRH[t] + 0.00404821CH[t] + 0.00122842Blogs[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.203590.5395952.2310.0278180.013909
programma0.3453330.09060923.8110.0002323640.000116182
gender-0.02978830.0623197-0.4780.6336410.31682
age0.009388390.02272490.41310.6803440.340172
NUMERACYTOT-0.003630780.00670799-0.54130.5894630.294731
LFM0.001387450.001090351.2720.2059850.102992
PRH0.001370720.001636230.83770.4040670.202033
CH0.004048210.002283451.7730.07912660.0395633
Blogs0.001228420.001097691.1190.265630.132815

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.20359 & 0.539595 & 2.231 & 0.027818 & 0.013909 \tabularnewline
programma & 0.345333 & 0.0906092 & 3.811 & 0.000232364 & 0.000116182 \tabularnewline
gender & -0.0297883 & 0.0623197 & -0.478 & 0.633641 & 0.31682 \tabularnewline
age & 0.00938839 & 0.0227249 & 0.4131 & 0.680344 & 0.340172 \tabularnewline
NUMERACYTOT & -0.00363078 & 0.00670799 & -0.5413 & 0.589463 & 0.294731 \tabularnewline
LFM & 0.00138745 & 0.00109035 & 1.272 & 0.205985 & 0.102992 \tabularnewline
PRH & 0.00137072 & 0.00163623 & 0.8377 & 0.404067 & 0.202033 \tabularnewline
CH & 0.00404821 & 0.00228345 & 1.773 & 0.0791266 & 0.0395633 \tabularnewline
Blogs & 0.00122842 & 0.00109769 & 1.119 & 0.26563 & 0.132815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.20359[/C][C]0.539595[/C][C]2.231[/C][C]0.027818[/C][C]0.013909[/C][/ROW]
[ROW][C]programma[/C][C]0.345333[/C][C]0.0906092[/C][C]3.811[/C][C]0.000232364[/C][C]0.000116182[/C][/ROW]
[ROW][C]gender[/C][C]-0.0297883[/C][C]0.0623197[/C][C]-0.478[/C][C]0.633641[/C][C]0.31682[/C][/ROW]
[ROW][C]age[/C][C]0.00938839[/C][C]0.0227249[/C][C]0.4131[/C][C]0.680344[/C][C]0.340172[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00363078[/C][C]0.00670799[/C][C]-0.5413[/C][C]0.589463[/C][C]0.294731[/C][/ROW]
[ROW][C]LFM[/C][C]0.00138745[/C][C]0.00109035[/C][C]1.272[/C][C]0.205985[/C][C]0.102992[/C][/ROW]
[ROW][C]PRH[/C][C]0.00137072[/C][C]0.00163623[/C][C]0.8377[/C][C]0.404067[/C][C]0.202033[/C][/ROW]
[ROW][C]CH[/C][C]0.00404821[/C][C]0.00228345[/C][C]1.773[/C][C]0.0791266[/C][C]0.0395633[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00122842[/C][C]0.00109769[/C][C]1.119[/C][C]0.26563[/C][C]0.132815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.203590.5395952.2310.0278180.013909
programma0.3453330.09060923.8110.0002323640.000116182
gender-0.02978830.0623197-0.4780.6336410.31682
age0.009388390.02272490.41310.6803440.340172
NUMERACYTOT-0.003630780.00670799-0.54130.5894630.294731
LFM0.001387450.001090351.2720.2059850.102992
PRH0.001370720.001636230.83770.4040670.202033
CH0.004048210.002283451.7730.07912660.0395633
Blogs0.001228420.001097691.1190.265630.132815







Multiple Linear Regression - Regression Statistics
Multiple R0.424794
R-squared0.18045
Adjusted R-squared0.118597
F-TEST (value)2.9174
F-TEST (DF numerator)8
F-TEST (DF denominator)106
p-value0.00555181
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.30213
Sum Squared Residuals9.67597

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.424794 \tabularnewline
R-squared & 0.18045 \tabularnewline
Adjusted R-squared & 0.118597 \tabularnewline
F-TEST (value) & 2.9174 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.00555181 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.30213 \tabularnewline
Sum Squared Residuals & 9.67597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.424794[/C][/ROW]
[ROW][C]R-squared[/C][C]0.18045[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.118597[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.9174[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.00555181[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.30213[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.67597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.424794
R-squared0.18045
Adjusted R-squared0.118597
F-TEST (value)2.9174
F-TEST (DF numerator)8
F-TEST (DF denominator)106
p-value0.00555181
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.30213
Sum Squared Residuals9.67597







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.51.94911-0.44911
21.81.86039-0.0603941
32.11.779690.320312
42.11.817310.282686
51.91.9789-0.0789039
61.61.72589-0.125889
72.12.15673-0.0567253
82.11.987740.11226
92.21.987170.212831
101.52.07304-0.573041
111.91.98202-0.0820153
122.21.900680.299315
131.62.08261-0.482605
141.51.74096-0.24096
151.92.1595-0.259501
160.11.55901-1.45901
172.22.114840.0851627
181.82.05242-0.252418
191.61.80673-0.206735
202.22.169950.0300532
212.11.859220.240785
221.91.759070.140927
231.61.87159-0.27159
241.92.01683-0.11683
252.22.037070.162926
261.81.90753-0.10753
272.42.246550.153448
282.42.44488-0.044882
292.52.070460.429544
301.91.8510.0490006
312.11.87470.225302
321.91.829190.0708083
332.11.910870.189129
341.91.87550.0245046
351.51.76739-0.267392
361.92.09688-0.196878
372.11.81890.281102
381.51.79558-0.295577
392.11.838960.261044
402.12.12802-0.0280162
411.81.9215-0.121498
422.42.004050.395953
432.11.675060.424944
441.91.90999-0.00999153
452.11.910540.18946
461.91.875210.0247902
472.41.987510.412486
482.11.992420.107579
492.21.893210.306785
502.22.095490.104514
511.81.758870.0411323
522.11.935420.164579
532.42.097140.302856
542.21.934690.265309
552.11.828360.271636
561.52.11697-0.616974
571.91.92758-0.027583
581.81.786440.0135648
591.82.10408-0.304078
601.61.84559-0.245594
611.21.85236-0.652356
621.82.0621-0.262098
631.51.9933-0.493305
642.11.747870.352131
652.41.725850.67415
662.42.071020.328984
671.51.84627-0.346267
681.81.94854-0.148539
692.12.13647-0.036471
702.22.22317-0.0231718
712.11.900120.199876
721.92.06259-0.162593
732.12.047920.0520769
741.91.764810.135189
751.62.05944-0.459435
762.42.163210.236786
771.91.9211-0.0211022
781.91.94628-0.046283
792.12.044560.0554439
801.82.02515-0.225148
812.12.054530.0454748
822.42.04860.351396
832.11.791180.308823
842.22.036620.163381
852.12.047370.0526307
862.22.132410.0675878
871.61.92829-0.328291
882.42.169750.230255
892.12.18758-0.0875792
901.92.01077-0.110775
912.41.986520.41348
922.11.967860.132141
931.82.07231-0.272308
942.11.933590.166411
951.82.00491-0.204908
961.92.13517-0.235169
971.92.01075-0.110754
982.41.990510.409488
991.81.98443-0.184432
1001.82.07774-0.277739
1012.11.969520.130478
1022.11.968780.131222
1032.42.073920.326081
1041.91.99523-0.0952324
1051.82.07551-0.275514
1061.81.97841-0.178414
1072.22.107060.0929358
1082.42.061460.338539
1091.82.07685-0.276853
1102.42.053980.346019
1111.82.03372-0.233718
1121.91.91924-0.0192351
1132.42.053980.346025
1142.12.026890.0731134
1151.91.90743-0.00743165

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.5 & 1.94911 & -0.44911 \tabularnewline
2 & 1.8 & 1.86039 & -0.0603941 \tabularnewline
3 & 2.1 & 1.77969 & 0.320312 \tabularnewline
4 & 2.1 & 1.81731 & 0.282686 \tabularnewline
5 & 1.9 & 1.9789 & -0.0789039 \tabularnewline
6 & 1.6 & 1.72589 & -0.125889 \tabularnewline
7 & 2.1 & 2.15673 & -0.0567253 \tabularnewline
8 & 2.1 & 1.98774 & 0.11226 \tabularnewline
9 & 2.2 & 1.98717 & 0.212831 \tabularnewline
10 & 1.5 & 2.07304 & -0.573041 \tabularnewline
11 & 1.9 & 1.98202 & -0.0820153 \tabularnewline
12 & 2.2 & 1.90068 & 0.299315 \tabularnewline
13 & 1.6 & 2.08261 & -0.482605 \tabularnewline
14 & 1.5 & 1.74096 & -0.24096 \tabularnewline
15 & 1.9 & 2.1595 & -0.259501 \tabularnewline
16 & 0.1 & 1.55901 & -1.45901 \tabularnewline
17 & 2.2 & 2.11484 & 0.0851627 \tabularnewline
18 & 1.8 & 2.05242 & -0.252418 \tabularnewline
19 & 1.6 & 1.80673 & -0.206735 \tabularnewline
20 & 2.2 & 2.16995 & 0.0300532 \tabularnewline
21 & 2.1 & 1.85922 & 0.240785 \tabularnewline
22 & 1.9 & 1.75907 & 0.140927 \tabularnewline
23 & 1.6 & 1.87159 & -0.27159 \tabularnewline
24 & 1.9 & 2.01683 & -0.11683 \tabularnewline
25 & 2.2 & 2.03707 & 0.162926 \tabularnewline
26 & 1.8 & 1.90753 & -0.10753 \tabularnewline
27 & 2.4 & 2.24655 & 0.153448 \tabularnewline
28 & 2.4 & 2.44488 & -0.044882 \tabularnewline
29 & 2.5 & 2.07046 & 0.429544 \tabularnewline
30 & 1.9 & 1.851 & 0.0490006 \tabularnewline
31 & 2.1 & 1.8747 & 0.225302 \tabularnewline
32 & 1.9 & 1.82919 & 0.0708083 \tabularnewline
33 & 2.1 & 1.91087 & 0.189129 \tabularnewline
34 & 1.9 & 1.8755 & 0.0245046 \tabularnewline
35 & 1.5 & 1.76739 & -0.267392 \tabularnewline
36 & 1.9 & 2.09688 & -0.196878 \tabularnewline
37 & 2.1 & 1.8189 & 0.281102 \tabularnewline
38 & 1.5 & 1.79558 & -0.295577 \tabularnewline
39 & 2.1 & 1.83896 & 0.261044 \tabularnewline
40 & 2.1 & 2.12802 & -0.0280162 \tabularnewline
41 & 1.8 & 1.9215 & -0.121498 \tabularnewline
42 & 2.4 & 2.00405 & 0.395953 \tabularnewline
43 & 2.1 & 1.67506 & 0.424944 \tabularnewline
44 & 1.9 & 1.90999 & -0.00999153 \tabularnewline
45 & 2.1 & 1.91054 & 0.18946 \tabularnewline
46 & 1.9 & 1.87521 & 0.0247902 \tabularnewline
47 & 2.4 & 1.98751 & 0.412486 \tabularnewline
48 & 2.1 & 1.99242 & 0.107579 \tabularnewline
49 & 2.2 & 1.89321 & 0.306785 \tabularnewline
50 & 2.2 & 2.09549 & 0.104514 \tabularnewline
51 & 1.8 & 1.75887 & 0.0411323 \tabularnewline
52 & 2.1 & 1.93542 & 0.164579 \tabularnewline
53 & 2.4 & 2.09714 & 0.302856 \tabularnewline
54 & 2.2 & 1.93469 & 0.265309 \tabularnewline
55 & 2.1 & 1.82836 & 0.271636 \tabularnewline
56 & 1.5 & 2.11697 & -0.616974 \tabularnewline
57 & 1.9 & 1.92758 & -0.027583 \tabularnewline
58 & 1.8 & 1.78644 & 0.0135648 \tabularnewline
59 & 1.8 & 2.10408 & -0.304078 \tabularnewline
60 & 1.6 & 1.84559 & -0.245594 \tabularnewline
61 & 1.2 & 1.85236 & -0.652356 \tabularnewline
62 & 1.8 & 2.0621 & -0.262098 \tabularnewline
63 & 1.5 & 1.9933 & -0.493305 \tabularnewline
64 & 2.1 & 1.74787 & 0.352131 \tabularnewline
65 & 2.4 & 1.72585 & 0.67415 \tabularnewline
66 & 2.4 & 2.07102 & 0.328984 \tabularnewline
67 & 1.5 & 1.84627 & -0.346267 \tabularnewline
68 & 1.8 & 1.94854 & -0.148539 \tabularnewline
69 & 2.1 & 2.13647 & -0.036471 \tabularnewline
70 & 2.2 & 2.22317 & -0.0231718 \tabularnewline
71 & 2.1 & 1.90012 & 0.199876 \tabularnewline
72 & 1.9 & 2.06259 & -0.162593 \tabularnewline
73 & 2.1 & 2.04792 & 0.0520769 \tabularnewline
74 & 1.9 & 1.76481 & 0.135189 \tabularnewline
75 & 1.6 & 2.05944 & -0.459435 \tabularnewline
76 & 2.4 & 2.16321 & 0.236786 \tabularnewline
77 & 1.9 & 1.9211 & -0.0211022 \tabularnewline
78 & 1.9 & 1.94628 & -0.046283 \tabularnewline
79 & 2.1 & 2.04456 & 0.0554439 \tabularnewline
80 & 1.8 & 2.02515 & -0.225148 \tabularnewline
81 & 2.1 & 2.05453 & 0.0454748 \tabularnewline
82 & 2.4 & 2.0486 & 0.351396 \tabularnewline
83 & 2.1 & 1.79118 & 0.308823 \tabularnewline
84 & 2.2 & 2.03662 & 0.163381 \tabularnewline
85 & 2.1 & 2.04737 & 0.0526307 \tabularnewline
86 & 2.2 & 2.13241 & 0.0675878 \tabularnewline
87 & 1.6 & 1.92829 & -0.328291 \tabularnewline
88 & 2.4 & 2.16975 & 0.230255 \tabularnewline
89 & 2.1 & 2.18758 & -0.0875792 \tabularnewline
90 & 1.9 & 2.01077 & -0.110775 \tabularnewline
91 & 2.4 & 1.98652 & 0.41348 \tabularnewline
92 & 2.1 & 1.96786 & 0.132141 \tabularnewline
93 & 1.8 & 2.07231 & -0.272308 \tabularnewline
94 & 2.1 & 1.93359 & 0.166411 \tabularnewline
95 & 1.8 & 2.00491 & -0.204908 \tabularnewline
96 & 1.9 & 2.13517 & -0.235169 \tabularnewline
97 & 1.9 & 2.01075 & -0.110754 \tabularnewline
98 & 2.4 & 1.99051 & 0.409488 \tabularnewline
99 & 1.8 & 1.98443 & -0.184432 \tabularnewline
100 & 1.8 & 2.07774 & -0.277739 \tabularnewline
101 & 2.1 & 1.96952 & 0.130478 \tabularnewline
102 & 2.1 & 1.96878 & 0.131222 \tabularnewline
103 & 2.4 & 2.07392 & 0.326081 \tabularnewline
104 & 1.9 & 1.99523 & -0.0952324 \tabularnewline
105 & 1.8 & 2.07551 & -0.275514 \tabularnewline
106 & 1.8 & 1.97841 & -0.178414 \tabularnewline
107 & 2.2 & 2.10706 & 0.0929358 \tabularnewline
108 & 2.4 & 2.06146 & 0.338539 \tabularnewline
109 & 1.8 & 2.07685 & -0.276853 \tabularnewline
110 & 2.4 & 2.05398 & 0.346019 \tabularnewline
111 & 1.8 & 2.03372 & -0.233718 \tabularnewline
112 & 1.9 & 1.91924 & -0.0192351 \tabularnewline
113 & 2.4 & 2.05398 & 0.346025 \tabularnewline
114 & 2.1 & 2.02689 & 0.0731134 \tabularnewline
115 & 1.9 & 1.90743 & -0.00743165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]1.5[/C][C]1.94911[/C][C]-0.44911[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.86039[/C][C]-0.0603941[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]1.77969[/C][C]0.320312[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]1.81731[/C][C]0.282686[/C][/ROW]
[ROW][C]5[/C][C]1.9[/C][C]1.9789[/C][C]-0.0789039[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.72589[/C][C]-0.125889[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.15673[/C][C]-0.0567253[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]1.98774[/C][C]0.11226[/C][/ROW]
[ROW][C]9[/C][C]2.2[/C][C]1.98717[/C][C]0.212831[/C][/ROW]
[ROW][C]10[/C][C]1.5[/C][C]2.07304[/C][C]-0.573041[/C][/ROW]
[ROW][C]11[/C][C]1.9[/C][C]1.98202[/C][C]-0.0820153[/C][/ROW]
[ROW][C]12[/C][C]2.2[/C][C]1.90068[/C][C]0.299315[/C][/ROW]
[ROW][C]13[/C][C]1.6[/C][C]2.08261[/C][C]-0.482605[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]1.74096[/C][C]-0.24096[/C][/ROW]
[ROW][C]15[/C][C]1.9[/C][C]2.1595[/C][C]-0.259501[/C][/ROW]
[ROW][C]16[/C][C]0.1[/C][C]1.55901[/C][C]-1.45901[/C][/ROW]
[ROW][C]17[/C][C]2.2[/C][C]2.11484[/C][C]0.0851627[/C][/ROW]
[ROW][C]18[/C][C]1.8[/C][C]2.05242[/C][C]-0.252418[/C][/ROW]
[ROW][C]19[/C][C]1.6[/C][C]1.80673[/C][C]-0.206735[/C][/ROW]
[ROW][C]20[/C][C]2.2[/C][C]2.16995[/C][C]0.0300532[/C][/ROW]
[ROW][C]21[/C][C]2.1[/C][C]1.85922[/C][C]0.240785[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.75907[/C][C]0.140927[/C][/ROW]
[ROW][C]23[/C][C]1.6[/C][C]1.87159[/C][C]-0.27159[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]2.01683[/C][C]-0.11683[/C][/ROW]
[ROW][C]25[/C][C]2.2[/C][C]2.03707[/C][C]0.162926[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]1.90753[/C][C]-0.10753[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.24655[/C][C]0.153448[/C][/ROW]
[ROW][C]28[/C][C]2.4[/C][C]2.44488[/C][C]-0.044882[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]2.07046[/C][C]0.429544[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.851[/C][C]0.0490006[/C][/ROW]
[ROW][C]31[/C][C]2.1[/C][C]1.8747[/C][C]0.225302[/C][/ROW]
[ROW][C]32[/C][C]1.9[/C][C]1.82919[/C][C]0.0708083[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]1.91087[/C][C]0.189129[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]1.8755[/C][C]0.0245046[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]1.76739[/C][C]-0.267392[/C][/ROW]
[ROW][C]36[/C][C]1.9[/C][C]2.09688[/C][C]-0.196878[/C][/ROW]
[ROW][C]37[/C][C]2.1[/C][C]1.8189[/C][C]0.281102[/C][/ROW]
[ROW][C]38[/C][C]1.5[/C][C]1.79558[/C][C]-0.295577[/C][/ROW]
[ROW][C]39[/C][C]2.1[/C][C]1.83896[/C][C]0.261044[/C][/ROW]
[ROW][C]40[/C][C]2.1[/C][C]2.12802[/C][C]-0.0280162[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.9215[/C][C]-0.121498[/C][/ROW]
[ROW][C]42[/C][C]2.4[/C][C]2.00405[/C][C]0.395953[/C][/ROW]
[ROW][C]43[/C][C]2.1[/C][C]1.67506[/C][C]0.424944[/C][/ROW]
[ROW][C]44[/C][C]1.9[/C][C]1.90999[/C][C]-0.00999153[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]1.91054[/C][C]0.18946[/C][/ROW]
[ROW][C]46[/C][C]1.9[/C][C]1.87521[/C][C]0.0247902[/C][/ROW]
[ROW][C]47[/C][C]2.4[/C][C]1.98751[/C][C]0.412486[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]1.99242[/C][C]0.107579[/C][/ROW]
[ROW][C]49[/C][C]2.2[/C][C]1.89321[/C][C]0.306785[/C][/ROW]
[ROW][C]50[/C][C]2.2[/C][C]2.09549[/C][C]0.104514[/C][/ROW]
[ROW][C]51[/C][C]1.8[/C][C]1.75887[/C][C]0.0411323[/C][/ROW]
[ROW][C]52[/C][C]2.1[/C][C]1.93542[/C][C]0.164579[/C][/ROW]
[ROW][C]53[/C][C]2.4[/C][C]2.09714[/C][C]0.302856[/C][/ROW]
[ROW][C]54[/C][C]2.2[/C][C]1.93469[/C][C]0.265309[/C][/ROW]
[ROW][C]55[/C][C]2.1[/C][C]1.82836[/C][C]0.271636[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]2.11697[/C][C]-0.616974[/C][/ROW]
[ROW][C]57[/C][C]1.9[/C][C]1.92758[/C][C]-0.027583[/C][/ROW]
[ROW][C]58[/C][C]1.8[/C][C]1.78644[/C][C]0.0135648[/C][/ROW]
[ROW][C]59[/C][C]1.8[/C][C]2.10408[/C][C]-0.304078[/C][/ROW]
[ROW][C]60[/C][C]1.6[/C][C]1.84559[/C][C]-0.245594[/C][/ROW]
[ROW][C]61[/C][C]1.2[/C][C]1.85236[/C][C]-0.652356[/C][/ROW]
[ROW][C]62[/C][C]1.8[/C][C]2.0621[/C][C]-0.262098[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]1.9933[/C][C]-0.493305[/C][/ROW]
[ROW][C]64[/C][C]2.1[/C][C]1.74787[/C][C]0.352131[/C][/ROW]
[ROW][C]65[/C][C]2.4[/C][C]1.72585[/C][C]0.67415[/C][/ROW]
[ROW][C]66[/C][C]2.4[/C][C]2.07102[/C][C]0.328984[/C][/ROW]
[ROW][C]67[/C][C]1.5[/C][C]1.84627[/C][C]-0.346267[/C][/ROW]
[ROW][C]68[/C][C]1.8[/C][C]1.94854[/C][C]-0.148539[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.13647[/C][C]-0.036471[/C][/ROW]
[ROW][C]70[/C][C]2.2[/C][C]2.22317[/C][C]-0.0231718[/C][/ROW]
[ROW][C]71[/C][C]2.1[/C][C]1.90012[/C][C]0.199876[/C][/ROW]
[ROW][C]72[/C][C]1.9[/C][C]2.06259[/C][C]-0.162593[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.04792[/C][C]0.0520769[/C][/ROW]
[ROW][C]74[/C][C]1.9[/C][C]1.76481[/C][C]0.135189[/C][/ROW]
[ROW][C]75[/C][C]1.6[/C][C]2.05944[/C][C]-0.459435[/C][/ROW]
[ROW][C]76[/C][C]2.4[/C][C]2.16321[/C][C]0.236786[/C][/ROW]
[ROW][C]77[/C][C]1.9[/C][C]1.9211[/C][C]-0.0211022[/C][/ROW]
[ROW][C]78[/C][C]1.9[/C][C]1.94628[/C][C]-0.046283[/C][/ROW]
[ROW][C]79[/C][C]2.1[/C][C]2.04456[/C][C]0.0554439[/C][/ROW]
[ROW][C]80[/C][C]1.8[/C][C]2.02515[/C][C]-0.225148[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.05453[/C][C]0.0454748[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.0486[/C][C]0.351396[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]1.79118[/C][C]0.308823[/C][/ROW]
[ROW][C]84[/C][C]2.2[/C][C]2.03662[/C][C]0.163381[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.04737[/C][C]0.0526307[/C][/ROW]
[ROW][C]86[/C][C]2.2[/C][C]2.13241[/C][C]0.0675878[/C][/ROW]
[ROW][C]87[/C][C]1.6[/C][C]1.92829[/C][C]-0.328291[/C][/ROW]
[ROW][C]88[/C][C]2.4[/C][C]2.16975[/C][C]0.230255[/C][/ROW]
[ROW][C]89[/C][C]2.1[/C][C]2.18758[/C][C]-0.0875792[/C][/ROW]
[ROW][C]90[/C][C]1.9[/C][C]2.01077[/C][C]-0.110775[/C][/ROW]
[ROW][C]91[/C][C]2.4[/C][C]1.98652[/C][C]0.41348[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]1.96786[/C][C]0.132141[/C][/ROW]
[ROW][C]93[/C][C]1.8[/C][C]2.07231[/C][C]-0.272308[/C][/ROW]
[ROW][C]94[/C][C]2.1[/C][C]1.93359[/C][C]0.166411[/C][/ROW]
[ROW][C]95[/C][C]1.8[/C][C]2.00491[/C][C]-0.204908[/C][/ROW]
[ROW][C]96[/C][C]1.9[/C][C]2.13517[/C][C]-0.235169[/C][/ROW]
[ROW][C]97[/C][C]1.9[/C][C]2.01075[/C][C]-0.110754[/C][/ROW]
[ROW][C]98[/C][C]2.4[/C][C]1.99051[/C][C]0.409488[/C][/ROW]
[ROW][C]99[/C][C]1.8[/C][C]1.98443[/C][C]-0.184432[/C][/ROW]
[ROW][C]100[/C][C]1.8[/C][C]2.07774[/C][C]-0.277739[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]1.96952[/C][C]0.130478[/C][/ROW]
[ROW][C]102[/C][C]2.1[/C][C]1.96878[/C][C]0.131222[/C][/ROW]
[ROW][C]103[/C][C]2.4[/C][C]2.07392[/C][C]0.326081[/C][/ROW]
[ROW][C]104[/C][C]1.9[/C][C]1.99523[/C][C]-0.0952324[/C][/ROW]
[ROW][C]105[/C][C]1.8[/C][C]2.07551[/C][C]-0.275514[/C][/ROW]
[ROW][C]106[/C][C]1.8[/C][C]1.97841[/C][C]-0.178414[/C][/ROW]
[ROW][C]107[/C][C]2.2[/C][C]2.10706[/C][C]0.0929358[/C][/ROW]
[ROW][C]108[/C][C]2.4[/C][C]2.06146[/C][C]0.338539[/C][/ROW]
[ROW][C]109[/C][C]1.8[/C][C]2.07685[/C][C]-0.276853[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.05398[/C][C]0.346019[/C][/ROW]
[ROW][C]111[/C][C]1.8[/C][C]2.03372[/C][C]-0.233718[/C][/ROW]
[ROW][C]112[/C][C]1.9[/C][C]1.91924[/C][C]-0.0192351[/C][/ROW]
[ROW][C]113[/C][C]2.4[/C][C]2.05398[/C][C]0.346025[/C][/ROW]
[ROW][C]114[/C][C]2.1[/C][C]2.02689[/C][C]0.0731134[/C][/ROW]
[ROW][C]115[/C][C]1.9[/C][C]1.90743[/C][C]-0.00743165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.51.94911-0.44911
21.81.86039-0.0603941
32.11.779690.320312
42.11.817310.282686
51.91.9789-0.0789039
61.61.72589-0.125889
72.12.15673-0.0567253
82.11.987740.11226
92.21.987170.212831
101.52.07304-0.573041
111.91.98202-0.0820153
122.21.900680.299315
131.62.08261-0.482605
141.51.74096-0.24096
151.92.1595-0.259501
160.11.55901-1.45901
172.22.114840.0851627
181.82.05242-0.252418
191.61.80673-0.206735
202.22.169950.0300532
212.11.859220.240785
221.91.759070.140927
231.61.87159-0.27159
241.92.01683-0.11683
252.22.037070.162926
261.81.90753-0.10753
272.42.246550.153448
282.42.44488-0.044882
292.52.070460.429544
301.91.8510.0490006
312.11.87470.225302
321.91.829190.0708083
332.11.910870.189129
341.91.87550.0245046
351.51.76739-0.267392
361.92.09688-0.196878
372.11.81890.281102
381.51.79558-0.295577
392.11.838960.261044
402.12.12802-0.0280162
411.81.9215-0.121498
422.42.004050.395953
432.11.675060.424944
441.91.90999-0.00999153
452.11.910540.18946
461.91.875210.0247902
472.41.987510.412486
482.11.992420.107579
492.21.893210.306785
502.22.095490.104514
511.81.758870.0411323
522.11.935420.164579
532.42.097140.302856
542.21.934690.265309
552.11.828360.271636
561.52.11697-0.616974
571.91.92758-0.027583
581.81.786440.0135648
591.82.10408-0.304078
601.61.84559-0.245594
611.21.85236-0.652356
621.82.0621-0.262098
631.51.9933-0.493305
642.11.747870.352131
652.41.725850.67415
662.42.071020.328984
671.51.84627-0.346267
681.81.94854-0.148539
692.12.13647-0.036471
702.22.22317-0.0231718
712.11.900120.199876
721.92.06259-0.162593
732.12.047920.0520769
741.91.764810.135189
751.62.05944-0.459435
762.42.163210.236786
771.91.9211-0.0211022
781.91.94628-0.046283
792.12.044560.0554439
801.82.02515-0.225148
812.12.054530.0454748
822.42.04860.351396
832.11.791180.308823
842.22.036620.163381
852.12.047370.0526307
862.22.132410.0675878
871.61.92829-0.328291
882.42.169750.230255
892.12.18758-0.0875792
901.92.01077-0.110775
912.41.986520.41348
922.11.967860.132141
931.82.07231-0.272308
942.11.933590.166411
951.82.00491-0.204908
961.92.13517-0.235169
971.92.01075-0.110754
982.41.990510.409488
991.81.98443-0.184432
1001.82.07774-0.277739
1012.11.969520.130478
1022.11.968780.131222
1032.42.073920.326081
1041.91.99523-0.0952324
1051.82.07551-0.275514
1061.81.97841-0.178414
1072.22.107060.0929358
1082.42.061460.338539
1091.82.07685-0.276853
1102.42.053980.346019
1111.82.03372-0.233718
1121.91.91924-0.0192351
1132.42.053980.346025
1142.12.026890.0731134
1151.91.90743-0.00743165







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8594970.2810050.140503
130.8844090.2311830.115591
140.8447710.3104580.155229
150.7782270.4435460.221773
160.9593340.0813320.040666
170.9322510.1354970.0677485
180.9653840.06923160.0346158
190.9490330.1019330.0509667
200.9814540.03709210.018546
210.9937220.01255590.00627794
220.9895530.02089330.0104466
230.9867440.02651170.0132558
240.9938030.01239370.00619686
250.9900520.01989590.00994793
260.9852480.02950470.0147524
270.9802640.03947140.0197357
280.9716840.05663280.0283164
290.9862180.0275640.013782
300.9792280.04154370.0207719
310.9786620.04267530.0213376
320.9805030.03899460.0194973
330.9771410.04571840.0228592
340.967450.06510050.0325503
350.9655420.06891560.0344578
360.9577910.08441770.0422088
370.966050.06789980.0339499
380.9722720.05545530.0277276
390.9713370.05732670.0286634
400.9629770.07404550.0370228
410.9583470.08330640.0416532
420.9650010.06999750.0349987
430.9750460.04990750.0249538
440.9675950.06480920.0324046
450.9589290.08214110.0410706
460.9463670.1072660.053633
470.9534950.09301080.0465054
480.9390380.1219230.0609617
490.9418570.1162850.0581426
500.9251540.1496920.0748459
510.9035850.1928310.0964154
520.8802530.2394930.119747
530.8754360.2491290.124564
540.8547650.2904710.145235
550.8570980.2858040.142902
560.9551370.08972570.0448628
570.9409120.1181760.0590882
580.9224140.1551730.0775864
590.919460.1610790.0805397
600.923350.15330.0766499
610.9809050.03819070.0190953
620.9800140.0399720.019986
630.9914460.01710740.00855368
640.9916370.01672570.00836286
650.9983110.003378890.00168944
660.9986040.002792420.00139621
670.9986710.002658510.00132926
680.9984530.003093080.00154654
690.9978230.004353390.0021767
700.9971790.005642940.00282147
710.9958110.008378050.00418902
720.9947730.01045310.00522655
730.9930570.01388650.00694324
740.9896920.02061540.0103077
750.9953130.009373460.00468673
760.9933110.01337780.00668889
770.9905460.01890780.00945392
780.9870270.02594520.0129726
790.9811430.03771470.0188573
800.9824880.03502330.0175117
810.9749240.05015130.0250756
820.9709120.05817680.0290884
830.9597420.0805170.0402585
840.9435810.1128370.0564187
850.9225760.1548470.0774237
860.8918930.2162130.108107
870.8935260.2129480.106474
880.8950610.2098770.104939
890.8633150.2733710.136685
900.9111250.1777510.0888753
910.8844480.2311030.115552
920.8398160.3203680.160184
930.8042750.3914490.195725
940.7372650.5254690.262735
950.6734160.6531670.326584
960.5858940.8282120.414106
970.5149470.9701070.485053
980.5325550.934890.467445
990.4324770.8649540.567523
1000.4334090.8668170.566591
1010.3332580.6665150.666742
1020.276630.5532610.72337
1030.2041420.4082830.795858

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.859497 & 0.281005 & 0.140503 \tabularnewline
13 & 0.884409 & 0.231183 & 0.115591 \tabularnewline
14 & 0.844771 & 0.310458 & 0.155229 \tabularnewline
15 & 0.778227 & 0.443546 & 0.221773 \tabularnewline
16 & 0.959334 & 0.081332 & 0.040666 \tabularnewline
17 & 0.932251 & 0.135497 & 0.0677485 \tabularnewline
18 & 0.965384 & 0.0692316 & 0.0346158 \tabularnewline
19 & 0.949033 & 0.101933 & 0.0509667 \tabularnewline
20 & 0.981454 & 0.0370921 & 0.018546 \tabularnewline
21 & 0.993722 & 0.0125559 & 0.00627794 \tabularnewline
22 & 0.989553 & 0.0208933 & 0.0104466 \tabularnewline
23 & 0.986744 & 0.0265117 & 0.0132558 \tabularnewline
24 & 0.993803 & 0.0123937 & 0.00619686 \tabularnewline
25 & 0.990052 & 0.0198959 & 0.00994793 \tabularnewline
26 & 0.985248 & 0.0295047 & 0.0147524 \tabularnewline
27 & 0.980264 & 0.0394714 & 0.0197357 \tabularnewline
28 & 0.971684 & 0.0566328 & 0.0283164 \tabularnewline
29 & 0.986218 & 0.027564 & 0.013782 \tabularnewline
30 & 0.979228 & 0.0415437 & 0.0207719 \tabularnewline
31 & 0.978662 & 0.0426753 & 0.0213376 \tabularnewline
32 & 0.980503 & 0.0389946 & 0.0194973 \tabularnewline
33 & 0.977141 & 0.0457184 & 0.0228592 \tabularnewline
34 & 0.96745 & 0.0651005 & 0.0325503 \tabularnewline
35 & 0.965542 & 0.0689156 & 0.0344578 \tabularnewline
36 & 0.957791 & 0.0844177 & 0.0422088 \tabularnewline
37 & 0.96605 & 0.0678998 & 0.0339499 \tabularnewline
38 & 0.972272 & 0.0554553 & 0.0277276 \tabularnewline
39 & 0.971337 & 0.0573267 & 0.0286634 \tabularnewline
40 & 0.962977 & 0.0740455 & 0.0370228 \tabularnewline
41 & 0.958347 & 0.0833064 & 0.0416532 \tabularnewline
42 & 0.965001 & 0.0699975 & 0.0349987 \tabularnewline
43 & 0.975046 & 0.0499075 & 0.0249538 \tabularnewline
44 & 0.967595 & 0.0648092 & 0.0324046 \tabularnewline
45 & 0.958929 & 0.0821411 & 0.0410706 \tabularnewline
46 & 0.946367 & 0.107266 & 0.053633 \tabularnewline
47 & 0.953495 & 0.0930108 & 0.0465054 \tabularnewline
48 & 0.939038 & 0.121923 & 0.0609617 \tabularnewline
49 & 0.941857 & 0.116285 & 0.0581426 \tabularnewline
50 & 0.925154 & 0.149692 & 0.0748459 \tabularnewline
51 & 0.903585 & 0.192831 & 0.0964154 \tabularnewline
52 & 0.880253 & 0.239493 & 0.119747 \tabularnewline
53 & 0.875436 & 0.249129 & 0.124564 \tabularnewline
54 & 0.854765 & 0.290471 & 0.145235 \tabularnewline
55 & 0.857098 & 0.285804 & 0.142902 \tabularnewline
56 & 0.955137 & 0.0897257 & 0.0448628 \tabularnewline
57 & 0.940912 & 0.118176 & 0.0590882 \tabularnewline
58 & 0.922414 & 0.155173 & 0.0775864 \tabularnewline
59 & 0.91946 & 0.161079 & 0.0805397 \tabularnewline
60 & 0.92335 & 0.1533 & 0.0766499 \tabularnewline
61 & 0.980905 & 0.0381907 & 0.0190953 \tabularnewline
62 & 0.980014 & 0.039972 & 0.019986 \tabularnewline
63 & 0.991446 & 0.0171074 & 0.00855368 \tabularnewline
64 & 0.991637 & 0.0167257 & 0.00836286 \tabularnewline
65 & 0.998311 & 0.00337889 & 0.00168944 \tabularnewline
66 & 0.998604 & 0.00279242 & 0.00139621 \tabularnewline
67 & 0.998671 & 0.00265851 & 0.00132926 \tabularnewline
68 & 0.998453 & 0.00309308 & 0.00154654 \tabularnewline
69 & 0.997823 & 0.00435339 & 0.0021767 \tabularnewline
70 & 0.997179 & 0.00564294 & 0.00282147 \tabularnewline
71 & 0.995811 & 0.00837805 & 0.00418902 \tabularnewline
72 & 0.994773 & 0.0104531 & 0.00522655 \tabularnewline
73 & 0.993057 & 0.0138865 & 0.00694324 \tabularnewline
74 & 0.989692 & 0.0206154 & 0.0103077 \tabularnewline
75 & 0.995313 & 0.00937346 & 0.00468673 \tabularnewline
76 & 0.993311 & 0.0133778 & 0.00668889 \tabularnewline
77 & 0.990546 & 0.0189078 & 0.00945392 \tabularnewline
78 & 0.987027 & 0.0259452 & 0.0129726 \tabularnewline
79 & 0.981143 & 0.0377147 & 0.0188573 \tabularnewline
80 & 0.982488 & 0.0350233 & 0.0175117 \tabularnewline
81 & 0.974924 & 0.0501513 & 0.0250756 \tabularnewline
82 & 0.970912 & 0.0581768 & 0.0290884 \tabularnewline
83 & 0.959742 & 0.080517 & 0.0402585 \tabularnewline
84 & 0.943581 & 0.112837 & 0.0564187 \tabularnewline
85 & 0.922576 & 0.154847 & 0.0774237 \tabularnewline
86 & 0.891893 & 0.216213 & 0.108107 \tabularnewline
87 & 0.893526 & 0.212948 & 0.106474 \tabularnewline
88 & 0.895061 & 0.209877 & 0.104939 \tabularnewline
89 & 0.863315 & 0.273371 & 0.136685 \tabularnewline
90 & 0.911125 & 0.177751 & 0.0888753 \tabularnewline
91 & 0.884448 & 0.231103 & 0.115552 \tabularnewline
92 & 0.839816 & 0.320368 & 0.160184 \tabularnewline
93 & 0.804275 & 0.391449 & 0.195725 \tabularnewline
94 & 0.737265 & 0.525469 & 0.262735 \tabularnewline
95 & 0.673416 & 0.653167 & 0.326584 \tabularnewline
96 & 0.585894 & 0.828212 & 0.414106 \tabularnewline
97 & 0.514947 & 0.970107 & 0.485053 \tabularnewline
98 & 0.532555 & 0.93489 & 0.467445 \tabularnewline
99 & 0.432477 & 0.864954 & 0.567523 \tabularnewline
100 & 0.433409 & 0.866817 & 0.566591 \tabularnewline
101 & 0.333258 & 0.666515 & 0.666742 \tabularnewline
102 & 0.27663 & 0.553261 & 0.72337 \tabularnewline
103 & 0.204142 & 0.408283 & 0.795858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]12[/C][C]0.859497[/C][C]0.281005[/C][C]0.140503[/C][/ROW]
[ROW][C]13[/C][C]0.884409[/C][C]0.231183[/C][C]0.115591[/C][/ROW]
[ROW][C]14[/C][C]0.844771[/C][C]0.310458[/C][C]0.155229[/C][/ROW]
[ROW][C]15[/C][C]0.778227[/C][C]0.443546[/C][C]0.221773[/C][/ROW]
[ROW][C]16[/C][C]0.959334[/C][C]0.081332[/C][C]0.040666[/C][/ROW]
[ROW][C]17[/C][C]0.932251[/C][C]0.135497[/C][C]0.0677485[/C][/ROW]
[ROW][C]18[/C][C]0.965384[/C][C]0.0692316[/C][C]0.0346158[/C][/ROW]
[ROW][C]19[/C][C]0.949033[/C][C]0.101933[/C][C]0.0509667[/C][/ROW]
[ROW][C]20[/C][C]0.981454[/C][C]0.0370921[/C][C]0.018546[/C][/ROW]
[ROW][C]21[/C][C]0.993722[/C][C]0.0125559[/C][C]0.00627794[/C][/ROW]
[ROW][C]22[/C][C]0.989553[/C][C]0.0208933[/C][C]0.0104466[/C][/ROW]
[ROW][C]23[/C][C]0.986744[/C][C]0.0265117[/C][C]0.0132558[/C][/ROW]
[ROW][C]24[/C][C]0.993803[/C][C]0.0123937[/C][C]0.00619686[/C][/ROW]
[ROW][C]25[/C][C]0.990052[/C][C]0.0198959[/C][C]0.00994793[/C][/ROW]
[ROW][C]26[/C][C]0.985248[/C][C]0.0295047[/C][C]0.0147524[/C][/ROW]
[ROW][C]27[/C][C]0.980264[/C][C]0.0394714[/C][C]0.0197357[/C][/ROW]
[ROW][C]28[/C][C]0.971684[/C][C]0.0566328[/C][C]0.0283164[/C][/ROW]
[ROW][C]29[/C][C]0.986218[/C][C]0.027564[/C][C]0.013782[/C][/ROW]
[ROW][C]30[/C][C]0.979228[/C][C]0.0415437[/C][C]0.0207719[/C][/ROW]
[ROW][C]31[/C][C]0.978662[/C][C]0.0426753[/C][C]0.0213376[/C][/ROW]
[ROW][C]32[/C][C]0.980503[/C][C]0.0389946[/C][C]0.0194973[/C][/ROW]
[ROW][C]33[/C][C]0.977141[/C][C]0.0457184[/C][C]0.0228592[/C][/ROW]
[ROW][C]34[/C][C]0.96745[/C][C]0.0651005[/C][C]0.0325503[/C][/ROW]
[ROW][C]35[/C][C]0.965542[/C][C]0.0689156[/C][C]0.0344578[/C][/ROW]
[ROW][C]36[/C][C]0.957791[/C][C]0.0844177[/C][C]0.0422088[/C][/ROW]
[ROW][C]37[/C][C]0.96605[/C][C]0.0678998[/C][C]0.0339499[/C][/ROW]
[ROW][C]38[/C][C]0.972272[/C][C]0.0554553[/C][C]0.0277276[/C][/ROW]
[ROW][C]39[/C][C]0.971337[/C][C]0.0573267[/C][C]0.0286634[/C][/ROW]
[ROW][C]40[/C][C]0.962977[/C][C]0.0740455[/C][C]0.0370228[/C][/ROW]
[ROW][C]41[/C][C]0.958347[/C][C]0.0833064[/C][C]0.0416532[/C][/ROW]
[ROW][C]42[/C][C]0.965001[/C][C]0.0699975[/C][C]0.0349987[/C][/ROW]
[ROW][C]43[/C][C]0.975046[/C][C]0.0499075[/C][C]0.0249538[/C][/ROW]
[ROW][C]44[/C][C]0.967595[/C][C]0.0648092[/C][C]0.0324046[/C][/ROW]
[ROW][C]45[/C][C]0.958929[/C][C]0.0821411[/C][C]0.0410706[/C][/ROW]
[ROW][C]46[/C][C]0.946367[/C][C]0.107266[/C][C]0.053633[/C][/ROW]
[ROW][C]47[/C][C]0.953495[/C][C]0.0930108[/C][C]0.0465054[/C][/ROW]
[ROW][C]48[/C][C]0.939038[/C][C]0.121923[/C][C]0.0609617[/C][/ROW]
[ROW][C]49[/C][C]0.941857[/C][C]0.116285[/C][C]0.0581426[/C][/ROW]
[ROW][C]50[/C][C]0.925154[/C][C]0.149692[/C][C]0.0748459[/C][/ROW]
[ROW][C]51[/C][C]0.903585[/C][C]0.192831[/C][C]0.0964154[/C][/ROW]
[ROW][C]52[/C][C]0.880253[/C][C]0.239493[/C][C]0.119747[/C][/ROW]
[ROW][C]53[/C][C]0.875436[/C][C]0.249129[/C][C]0.124564[/C][/ROW]
[ROW][C]54[/C][C]0.854765[/C][C]0.290471[/C][C]0.145235[/C][/ROW]
[ROW][C]55[/C][C]0.857098[/C][C]0.285804[/C][C]0.142902[/C][/ROW]
[ROW][C]56[/C][C]0.955137[/C][C]0.0897257[/C][C]0.0448628[/C][/ROW]
[ROW][C]57[/C][C]0.940912[/C][C]0.118176[/C][C]0.0590882[/C][/ROW]
[ROW][C]58[/C][C]0.922414[/C][C]0.155173[/C][C]0.0775864[/C][/ROW]
[ROW][C]59[/C][C]0.91946[/C][C]0.161079[/C][C]0.0805397[/C][/ROW]
[ROW][C]60[/C][C]0.92335[/C][C]0.1533[/C][C]0.0766499[/C][/ROW]
[ROW][C]61[/C][C]0.980905[/C][C]0.0381907[/C][C]0.0190953[/C][/ROW]
[ROW][C]62[/C][C]0.980014[/C][C]0.039972[/C][C]0.019986[/C][/ROW]
[ROW][C]63[/C][C]0.991446[/C][C]0.0171074[/C][C]0.00855368[/C][/ROW]
[ROW][C]64[/C][C]0.991637[/C][C]0.0167257[/C][C]0.00836286[/C][/ROW]
[ROW][C]65[/C][C]0.998311[/C][C]0.00337889[/C][C]0.00168944[/C][/ROW]
[ROW][C]66[/C][C]0.998604[/C][C]0.00279242[/C][C]0.00139621[/C][/ROW]
[ROW][C]67[/C][C]0.998671[/C][C]0.00265851[/C][C]0.00132926[/C][/ROW]
[ROW][C]68[/C][C]0.998453[/C][C]0.00309308[/C][C]0.00154654[/C][/ROW]
[ROW][C]69[/C][C]0.997823[/C][C]0.00435339[/C][C]0.0021767[/C][/ROW]
[ROW][C]70[/C][C]0.997179[/C][C]0.00564294[/C][C]0.00282147[/C][/ROW]
[ROW][C]71[/C][C]0.995811[/C][C]0.00837805[/C][C]0.00418902[/C][/ROW]
[ROW][C]72[/C][C]0.994773[/C][C]0.0104531[/C][C]0.00522655[/C][/ROW]
[ROW][C]73[/C][C]0.993057[/C][C]0.0138865[/C][C]0.00694324[/C][/ROW]
[ROW][C]74[/C][C]0.989692[/C][C]0.0206154[/C][C]0.0103077[/C][/ROW]
[ROW][C]75[/C][C]0.995313[/C][C]0.00937346[/C][C]0.00468673[/C][/ROW]
[ROW][C]76[/C][C]0.993311[/C][C]0.0133778[/C][C]0.00668889[/C][/ROW]
[ROW][C]77[/C][C]0.990546[/C][C]0.0189078[/C][C]0.00945392[/C][/ROW]
[ROW][C]78[/C][C]0.987027[/C][C]0.0259452[/C][C]0.0129726[/C][/ROW]
[ROW][C]79[/C][C]0.981143[/C][C]0.0377147[/C][C]0.0188573[/C][/ROW]
[ROW][C]80[/C][C]0.982488[/C][C]0.0350233[/C][C]0.0175117[/C][/ROW]
[ROW][C]81[/C][C]0.974924[/C][C]0.0501513[/C][C]0.0250756[/C][/ROW]
[ROW][C]82[/C][C]0.970912[/C][C]0.0581768[/C][C]0.0290884[/C][/ROW]
[ROW][C]83[/C][C]0.959742[/C][C]0.080517[/C][C]0.0402585[/C][/ROW]
[ROW][C]84[/C][C]0.943581[/C][C]0.112837[/C][C]0.0564187[/C][/ROW]
[ROW][C]85[/C][C]0.922576[/C][C]0.154847[/C][C]0.0774237[/C][/ROW]
[ROW][C]86[/C][C]0.891893[/C][C]0.216213[/C][C]0.108107[/C][/ROW]
[ROW][C]87[/C][C]0.893526[/C][C]0.212948[/C][C]0.106474[/C][/ROW]
[ROW][C]88[/C][C]0.895061[/C][C]0.209877[/C][C]0.104939[/C][/ROW]
[ROW][C]89[/C][C]0.863315[/C][C]0.273371[/C][C]0.136685[/C][/ROW]
[ROW][C]90[/C][C]0.911125[/C][C]0.177751[/C][C]0.0888753[/C][/ROW]
[ROW][C]91[/C][C]0.884448[/C][C]0.231103[/C][C]0.115552[/C][/ROW]
[ROW][C]92[/C][C]0.839816[/C][C]0.320368[/C][C]0.160184[/C][/ROW]
[ROW][C]93[/C][C]0.804275[/C][C]0.391449[/C][C]0.195725[/C][/ROW]
[ROW][C]94[/C][C]0.737265[/C][C]0.525469[/C][C]0.262735[/C][/ROW]
[ROW][C]95[/C][C]0.673416[/C][C]0.653167[/C][C]0.326584[/C][/ROW]
[ROW][C]96[/C][C]0.585894[/C][C]0.828212[/C][C]0.414106[/C][/ROW]
[ROW][C]97[/C][C]0.514947[/C][C]0.970107[/C][C]0.485053[/C][/ROW]
[ROW][C]98[/C][C]0.532555[/C][C]0.93489[/C][C]0.467445[/C][/ROW]
[ROW][C]99[/C][C]0.432477[/C][C]0.864954[/C][C]0.567523[/C][/ROW]
[ROW][C]100[/C][C]0.433409[/C][C]0.866817[/C][C]0.566591[/C][/ROW]
[ROW][C]101[/C][C]0.333258[/C][C]0.666515[/C][C]0.666742[/C][/ROW]
[ROW][C]102[/C][C]0.27663[/C][C]0.553261[/C][C]0.72337[/C][/ROW]
[ROW][C]103[/C][C]0.204142[/C][C]0.408283[/C][C]0.795858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8594970.2810050.140503
130.8844090.2311830.115591
140.8447710.3104580.155229
150.7782270.4435460.221773
160.9593340.0813320.040666
170.9322510.1354970.0677485
180.9653840.06923160.0346158
190.9490330.1019330.0509667
200.9814540.03709210.018546
210.9937220.01255590.00627794
220.9895530.02089330.0104466
230.9867440.02651170.0132558
240.9938030.01239370.00619686
250.9900520.01989590.00994793
260.9852480.02950470.0147524
270.9802640.03947140.0197357
280.9716840.05663280.0283164
290.9862180.0275640.013782
300.9792280.04154370.0207719
310.9786620.04267530.0213376
320.9805030.03899460.0194973
330.9771410.04571840.0228592
340.967450.06510050.0325503
350.9655420.06891560.0344578
360.9577910.08441770.0422088
370.966050.06789980.0339499
380.9722720.05545530.0277276
390.9713370.05732670.0286634
400.9629770.07404550.0370228
410.9583470.08330640.0416532
420.9650010.06999750.0349987
430.9750460.04990750.0249538
440.9675950.06480920.0324046
450.9589290.08214110.0410706
460.9463670.1072660.053633
470.9534950.09301080.0465054
480.9390380.1219230.0609617
490.9418570.1162850.0581426
500.9251540.1496920.0748459
510.9035850.1928310.0964154
520.8802530.2394930.119747
530.8754360.2491290.124564
540.8547650.2904710.145235
550.8570980.2858040.142902
560.9551370.08972570.0448628
570.9409120.1181760.0590882
580.9224140.1551730.0775864
590.919460.1610790.0805397
600.923350.15330.0766499
610.9809050.03819070.0190953
620.9800140.0399720.019986
630.9914460.01710740.00855368
640.9916370.01672570.00836286
650.9983110.003378890.00168944
660.9986040.002792420.00139621
670.9986710.002658510.00132926
680.9984530.003093080.00154654
690.9978230.004353390.0021767
700.9971790.005642940.00282147
710.9958110.008378050.00418902
720.9947730.01045310.00522655
730.9930570.01388650.00694324
740.9896920.02061540.0103077
750.9953130.009373460.00468673
760.9933110.01337780.00668889
770.9905460.01890780.00945392
780.9870270.02594520.0129726
790.9811430.03771470.0188573
800.9824880.03502330.0175117
810.9749240.05015130.0250756
820.9709120.05817680.0290884
830.9597420.0805170.0402585
840.9435810.1128370.0564187
850.9225760.1548470.0774237
860.8918930.2162130.108107
870.8935260.2129480.106474
880.8950610.2098770.104939
890.8633150.2733710.136685
900.9111250.1777510.0888753
910.8844480.2311030.115552
920.8398160.3203680.160184
930.8042750.3914490.195725
940.7372650.5254690.262735
950.6734160.6531670.326584
960.5858940.8282120.414106
970.5149470.9701070.485053
980.5325550.934890.467445
990.4324770.8649540.567523
1000.4334090.8668170.566591
1010.3332580.6665150.666742
1020.276630.5532610.72337
1030.2041420.4082830.795858







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0869565NOK
5% type I error level340.369565NOK
10% type I error level530.576087NOK

\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 & 8 & 0.0869565 & NOK \tabularnewline
5% type I error level & 34 & 0.369565 & NOK \tabularnewline
10% type I error level & 53 & 0.576087 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]8[/C][C]0.0869565[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.369565[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.576087[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0869565NOK
5% type I error level340.369565NOK
10% type I error level530.576087NOK



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')
}