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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 07 Dec 2014 19:57:25 +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/07/t14179829470bbs2ykgkgihd1s.htm/, Retrieved Thu, 16 May 2024 16:16:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263898, Retrieved Thu, 16 May 2024 16:16:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact64

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-07 19:57:25] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.5 0 0 21 21 149 18 68
1.8 0 0 23 26 152 7 55
2.1 0 1 22 22 139 31 39
2.1 0 0 21 22 148 39 32
1.9 0 1 21 18 158 46 62
1.6 0 1 21 23 128 31 33
2.1 0 1 21 12 224 67 52
2.1 0 0 21 20 159 35 62
2.2 0 1 23 22 105 52 77
1.5 0 1 22 21 159 77 76
1.9 0 1 25 19 167 37 41
2.2 0 1 21 22 165 32 48
1.6 0 1 23 15 159 36 63
1.5 0 1 22 20 119 38 30
1.9 0 0 21 19 176 69 78
0.1 0 0 21 18 54 21 19
2.2 1 0 25 15 91 26 31
1.8 0 1 21 20 163 54 66
1.6 0 0 21 21 124 36 35
2.2 1 1 20 21 137 42 42
2.1 0 0 24 15 121 23 45
1.9 0 1 23 16 153 34 21
1.6 0 1 21 23 148 112 25
1.9 0 0 24 21 221 35 44
2.2 0 1 23 18 188 47 69
1.8 0 1 21 25 149 47 54
2.4 0 1 22 9 244 37 74
2.4 1 1 20 30 148 109 80
2.5 1 0 18 20 92 24 42
1.9 0 1 21 23 150 20 61
2.1 0 0 22 16 153 22 41
1.9 0 0 22 16 94 23 46
2.1 0 0 21 19 156 32 39
1.9 0 1 23 25 146 7 63
1.5 0 1 21 25 132 30 34
1.9 0 1 25 18 161 92 51
2.1 0 1 22 23 105 43 42
1.5 0 1 22 21 97 55 31
2.1 0 0 20 10 151 16 39
2.1 1 1 21 14 131 49 20
1.8 0 1 21 22 166 71 49
2.4 0 0 21 26 157 43 53
2.1 0 1 22 23 111 29 31
1.9 0 1 21 23 145 56 39
2.1 0 1 24 24 162 46 54
1.9 0 1 22 24 163 19 49
2.4 1 1 22 18 59 23 34
2.1 0 0 21 23 187 59 46
2.2 0 1 22 15 109 30 55
2.2 1 1 19 19 90 61 42
1.8 0 0 22 16 105 7 50
2.1 1 1 23 25 83 38 13
2.4 1 1 20 23 116 32 37
2.2 1 1 20 17 42 16 25
2.1 0 1 23 19 148 19 30
1.5 1 1 20 21 155 22 28
1.9 0 1 23 18 125 48 45
1.8 0 1 21 27 116 23 35
1.8 1 0 22 21 128 26 28
1.6 0 1 21 13 138 33 41
1.2 1 0 21 8 49 9 6
1.8 1 1 19 29 96 24 45
1.5 0 1 22 28 164 34 73
2.1 0 0 21 23 162 48 17
2.4 0 0 21 21 99 18 40
2.4 0 1 21 19 202 43 64
1.5 0 0 21 19 186 33 37
1.8 1 1 21 20 66 28 25
2.1 0 0 21 18 183 71 65
2.2 0 1 22 19 214 26 100
2.1 0 1 22 17 188 67 28
1.9 1 0 18 19 104 34 35
2.1 0 0 21 25 177 80 56
1.9 0 0 23 19 126 29 29
1.6 1 0 19 22 76 16 43
2.4 1 1 19 23 99 59 59
1.9 0 1 23 26 157 58 52
1.9 0 0 21 14 139 32 50
2.1 0 0 21 16 162 43 59
1.8 1 1 21 24 108 38 27
2.1 0 0 20 20 159 29 61
2.4 1 0 19 12 74 36 28
2.1 0 1 21 24 110 32 51
2.2 1 0 19 22 96 35 35
2.1 1 0 19 12 116 21 29
2.2 1 0 19 22 87 29 48
1.6 1 1 20 20 97 12 25
2.4 1 0 19 10 127 37 44
2.1 1 1 19 23 106 37 64
1.9 1 1 19 17 80 47 32
2.4 1 0 20 22 74 51 20
2.1 1 0 19 24 91 32 28
1.8 1 0 18 18 133 21 34
2.1 1 1 19 21 74 13 31
1.8 1 1 21 20 114 14 26
1.9 1 1 18 20 140 -2 58
1.9 1 0 18 22 95 20 23
2.4 1 1 19 19 98 24 21
1.8 1 0 21 20 121 11 21
1.8 1 1 20 26 126 23 33
2.1 1 1 24 23 98 24 16
2.1 1 1 22 24 95 14 20
2.4 1 1 21 21 110 52 37
1.9 1 1 21 21 70 15 35
1.8 1 0 19 19 102 23 33
1.8 1 1 19 8 86 19 27
2.2 1 1 20 17 130 35 41
2.4 1 1 18 20 96 24 40
1.8 1 0 19 11 102 39 35
2.4 1 0 19 8 100 29 28
1.8 1 0 20 15 94 13 32
1.9 1 0 21 18 52 8 22
2.4 1 0 18 18 98 18 44
2.1 1 0 19 19 118 24 27
1.9 1 1 19 19 99 19 17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.22266 + 0.329679programma[t] -0.0221322gender[t] + 0.0102527age[t] -0.00374368NUMERACYTOT[t] + 0.00162003LFM[t] + 0.00178287PRH[t] + 0.00491664CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  1.22266 +  0.329679programma[t] -0.0221322gender[t] +  0.0102527age[t] -0.00374368NUMERACYTOT[t] +  0.00162003LFM[t] +  0.00178287PRH[t] +  0.00491664CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  1.22266 +  0.329679programma[t] -0.0221322gender[t] +  0.0102527age[t] -0.00374368NUMERACYTOT[t] +  0.00162003LFM[t] +  0.00178287PRH[t] +  0.00491664CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.22266 + 0.329679programma[t] -0.0221322gender[t] + 0.0102527age[t] -0.00374368NUMERACYTOT[t] + 0.00162003LFM[t] + 0.00178287PRH[t] + 0.00491664CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.222660.5399612.2640.02556830.0127841
programma0.3296790.08962853.6780.0003692950.000184648
gender-0.02213220.0620161-0.35690.7218850.360943
age0.01025270.02273850.45090.6529780.326489
NUMERACYTOT-0.003743680.00671513-0.55750.5783510.289175
LFM0.001620030.001071621.5120.1335420.0667709
PRH0.001782870.001596131.1170.2664960.133248
CH0.004916640.002150072.2870.02417990.01209

\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) & 1.22266 & 0.539961 & 2.264 & 0.0255683 & 0.0127841 \tabularnewline
programma & 0.329679 & 0.0896285 & 3.678 & 0.000369295 & 0.000184648 \tabularnewline
gender & -0.0221322 & 0.0620161 & -0.3569 & 0.721885 & 0.360943 \tabularnewline
age & 0.0102527 & 0.0227385 & 0.4509 & 0.652978 & 0.326489 \tabularnewline
NUMERACYTOT & -0.00374368 & 0.00671513 & -0.5575 & 0.578351 & 0.289175 \tabularnewline
LFM & 0.00162003 & 0.00107162 & 1.512 & 0.133542 & 0.0667709 \tabularnewline
PRH & 0.00178287 & 0.00159613 & 1.117 & 0.266496 & 0.133248 \tabularnewline
CH & 0.00491664 & 0.00215007 & 2.287 & 0.0241799 & 0.01209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&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]1.22266[/C][C]0.539961[/C][C]2.264[/C][C]0.0255683[/C][C]0.0127841[/C][/ROW]
[ROW][C]programma[/C][C]0.329679[/C][C]0.0896285[/C][C]3.678[/C][C]0.000369295[/C][C]0.000184648[/C][/ROW]
[ROW][C]gender[/C][C]-0.0221322[/C][C]0.0620161[/C][C]-0.3569[/C][C]0.721885[/C][C]0.360943[/C][/ROW]
[ROW][C]age[/C][C]0.0102527[/C][C]0.0227385[/C][C]0.4509[/C][C]0.652978[/C][C]0.326489[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00374368[/C][C]0.00671513[/C][C]-0.5575[/C][C]0.578351[/C][C]0.289175[/C][/ROW]
[ROW][C]LFM[/C][C]0.00162003[/C][C]0.00107162[/C][C]1.512[/C][C]0.133542[/C][C]0.0667709[/C][/ROW]
[ROW][C]PRH[/C][C]0.00178287[/C][C]0.00159613[/C][C]1.117[/C][C]0.266496[/C][C]0.133248[/C][/ROW]
[ROW][C]CH[/C][C]0.00491664[/C][C]0.00215007[/C][C]2.287[/C][C]0.0241799[/C][C]0.01209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.222660.5399612.2640.02556830.0127841
programma0.3296790.08962853.6780.0003692950.000184648
gender-0.02213220.0620161-0.35690.7218850.360943
age0.01025270.02273850.45090.6529780.326489
NUMERACYTOT-0.003743680.00671513-0.55750.5783510.289175
LFM0.001620030.001071621.5120.1335420.0667709
PRH0.001782870.001596131.1170.2664960.133248
CH0.004916640.002150072.2870.02417990.01209






Multiple Linear Regression - Regression Statistics
Multiple R0.413239
R-squared0.170767
Adjusted R-squared0.116518
F-TEST (value)3.14784
F-TEST (DF numerator)7
F-TEST (DF denominator)107
p-value0.00459628
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.302486
Sum Squared Residuals9.79029

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.413239 \tabularnewline
R-squared & 0.170767 \tabularnewline
Adjusted R-squared & 0.116518 \tabularnewline
F-TEST (value) & 3.14784 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0.00459628 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.302486 \tabularnewline
Sum Squared Residuals & 9.79029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.413239[/C][/ROW]
[ROW][C]R-squared[/C][C]0.170767[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.116518[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.14784[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]0.00459628[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.302486[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.79029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&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 R0.413239
R-squared0.170767
Adjusted R-squared0.116518
F-TEST (value)3.14784
F-TEST (DF numerator)7
F-TEST (DF denominator)107
p-value0.00459628
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.302486
Sum Squared Residuals9.79029






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.51.96716-0.46716
21.81.89028-0.0902786
32.11.815930.284069
42.11.822240.277763
51.91.99126-0.0912593
61.61.75461-0.154614
72.12.10892-0.00891727
82.11.987910.112087
92.21.995380.204625
101.52.116-0.616003
111.91.92381-0.0238111
122.21.893830.306169
131.62.0117-0.411704
141.51.75925-0.259248
151.92.15848-0.258481
160.11.58892-1.48892
172.22.09870.101303
181.82.0258-0.225802
191.61.7965-0.196501
202.22.159970.0400303
212.11.870850.22915
221.91.788170.111825
231.61.89209-0.292094
241.92.02687-0.126869
252.22.096560.103435
261.81.91292-0.112923
272.42.217480.182518
282.42.45038-0.0503819
292.52.060350.439653
301.91.90831-0.00830924
312.11.876990.223007
321.91.807780.0922229
332.11.868360.231635
341.91.9015-0.001503
351.51.75674-0.256741
361.92.06506-0.165059
372.11.793250.30675
381.51.75509-0.255089
392.11.855180.244821
402.12.091020.00897813
411.81.9699-0.1699
422.41.932220.467776
432.11.723930.376073
441.91.856230.0437737
452.11.96670.133298
461.91.87510.024904
472.41.992140.407864
482.11.986160.113835
492.21.870420.329581
502.22.114940.0850625
511.81.81674-0.0167381
522.11.938560.161443
532.42.076050.32395
542.21.89110.308896
552.11.786350.31365
561.52.08464-0.58464
571.91.878290.0217137
581.81.715770.0842307
591.82.09067-0.290668
601.61.85115-0.25115
611.21.86263-0.662626
621.82.036-0.236005
631.52.00648-0.506484
642.11.783470.31653
652.41.748490.651508
662.42.063280.336718
671.51.90892-0.408915
681.81.9504-0.150401
692.12.11321-0.013214
702.22.23966-0.039665
712.11.924130.175869
721.92.06694-0.166943
732.12.049080.0509159
741.91.785750.114246
751.62.02785-0.427846
762.42.194560.20544
771.91.95242-0.0524231
781.91.91363-0.0136257
792.12.007260.0927396
801.82.03113-0.231129
812.11.962050.137954
822.42.023950.37605
832.11.811990.288008
842.22.054790.145212
852.12.070170.029835
862.22.093430.106573
871.61.96184-0.361843
882.42.197750.202252
892.12.19126-0.0912605
901.92.0321-0.132098
912.41.984180.415824
922.11.999440.100565
931.82.08957-0.289574
942.11.941870.158131
951.82.00812-0.208119
961.92.14829-0.248288
971.91.95717-0.0571722
982.41.958680.441318
991.82.01166-0.211659
1001.82.04531-0.245307
1012.11.970390.129613
1022.11.943120.156884
1032.42.119730.280273
1041.91.97913-0.0791262
1051.82.04451-0.244511
1061.82.00101-0.201008
1072.22.146210.0537926
1082.42.034860.365138
1091.82.11282-0.31282
1102.42.068570.331434
1111.82.03403-0.234033
1121.91.90693-0.00693243
1132.42.076690.323309
1142.12.042710.0572853
1151.91.93172-0.031721

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.5 & 1.96716 & -0.46716 \tabularnewline
2 & 1.8 & 1.89028 & -0.0902786 \tabularnewline
3 & 2.1 & 1.81593 & 0.284069 \tabularnewline
4 & 2.1 & 1.82224 & 0.277763 \tabularnewline
5 & 1.9 & 1.99126 & -0.0912593 \tabularnewline
6 & 1.6 & 1.75461 & -0.154614 \tabularnewline
7 & 2.1 & 2.10892 & -0.00891727 \tabularnewline
8 & 2.1 & 1.98791 & 0.112087 \tabularnewline
9 & 2.2 & 1.99538 & 0.204625 \tabularnewline
10 & 1.5 & 2.116 & -0.616003 \tabularnewline
11 & 1.9 & 1.92381 & -0.0238111 \tabularnewline
12 & 2.2 & 1.89383 & 0.306169 \tabularnewline
13 & 1.6 & 2.0117 & -0.411704 \tabularnewline
14 & 1.5 & 1.75925 & -0.259248 \tabularnewline
15 & 1.9 & 2.15848 & -0.258481 \tabularnewline
16 & 0.1 & 1.58892 & -1.48892 \tabularnewline
17 & 2.2 & 2.0987 & 0.101303 \tabularnewline
18 & 1.8 & 2.0258 & -0.225802 \tabularnewline
19 & 1.6 & 1.7965 & -0.196501 \tabularnewline
20 & 2.2 & 2.15997 & 0.0400303 \tabularnewline
21 & 2.1 & 1.87085 & 0.22915 \tabularnewline
22 & 1.9 & 1.78817 & 0.111825 \tabularnewline
23 & 1.6 & 1.89209 & -0.292094 \tabularnewline
24 & 1.9 & 2.02687 & -0.126869 \tabularnewline
25 & 2.2 & 2.09656 & 0.103435 \tabularnewline
26 & 1.8 & 1.91292 & -0.112923 \tabularnewline
27 & 2.4 & 2.21748 & 0.182518 \tabularnewline
28 & 2.4 & 2.45038 & -0.0503819 \tabularnewline
29 & 2.5 & 2.06035 & 0.439653 \tabularnewline
30 & 1.9 & 1.90831 & -0.00830924 \tabularnewline
31 & 2.1 & 1.87699 & 0.223007 \tabularnewline
32 & 1.9 & 1.80778 & 0.0922229 \tabularnewline
33 & 2.1 & 1.86836 & 0.231635 \tabularnewline
34 & 1.9 & 1.9015 & -0.001503 \tabularnewline
35 & 1.5 & 1.75674 & -0.256741 \tabularnewline
36 & 1.9 & 2.06506 & -0.165059 \tabularnewline
37 & 2.1 & 1.79325 & 0.30675 \tabularnewline
38 & 1.5 & 1.75509 & -0.255089 \tabularnewline
39 & 2.1 & 1.85518 & 0.244821 \tabularnewline
40 & 2.1 & 2.09102 & 0.00897813 \tabularnewline
41 & 1.8 & 1.9699 & -0.1699 \tabularnewline
42 & 2.4 & 1.93222 & 0.467776 \tabularnewline
43 & 2.1 & 1.72393 & 0.376073 \tabularnewline
44 & 1.9 & 1.85623 & 0.0437737 \tabularnewline
45 & 2.1 & 1.9667 & 0.133298 \tabularnewline
46 & 1.9 & 1.8751 & 0.024904 \tabularnewline
47 & 2.4 & 1.99214 & 0.407864 \tabularnewline
48 & 2.1 & 1.98616 & 0.113835 \tabularnewline
49 & 2.2 & 1.87042 & 0.329581 \tabularnewline
50 & 2.2 & 2.11494 & 0.0850625 \tabularnewline
51 & 1.8 & 1.81674 & -0.0167381 \tabularnewline
52 & 2.1 & 1.93856 & 0.161443 \tabularnewline
53 & 2.4 & 2.07605 & 0.32395 \tabularnewline
54 & 2.2 & 1.8911 & 0.308896 \tabularnewline
55 & 2.1 & 1.78635 & 0.31365 \tabularnewline
56 & 1.5 & 2.08464 & -0.58464 \tabularnewline
57 & 1.9 & 1.87829 & 0.0217137 \tabularnewline
58 & 1.8 & 1.71577 & 0.0842307 \tabularnewline
59 & 1.8 & 2.09067 & -0.290668 \tabularnewline
60 & 1.6 & 1.85115 & -0.25115 \tabularnewline
61 & 1.2 & 1.86263 & -0.662626 \tabularnewline
62 & 1.8 & 2.036 & -0.236005 \tabularnewline
63 & 1.5 & 2.00648 & -0.506484 \tabularnewline
64 & 2.1 & 1.78347 & 0.31653 \tabularnewline
65 & 2.4 & 1.74849 & 0.651508 \tabularnewline
66 & 2.4 & 2.06328 & 0.336718 \tabularnewline
67 & 1.5 & 1.90892 & -0.408915 \tabularnewline
68 & 1.8 & 1.9504 & -0.150401 \tabularnewline
69 & 2.1 & 2.11321 & -0.013214 \tabularnewline
70 & 2.2 & 2.23966 & -0.039665 \tabularnewline
71 & 2.1 & 1.92413 & 0.175869 \tabularnewline
72 & 1.9 & 2.06694 & -0.166943 \tabularnewline
73 & 2.1 & 2.04908 & 0.0509159 \tabularnewline
74 & 1.9 & 1.78575 & 0.114246 \tabularnewline
75 & 1.6 & 2.02785 & -0.427846 \tabularnewline
76 & 2.4 & 2.19456 & 0.20544 \tabularnewline
77 & 1.9 & 1.95242 & -0.0524231 \tabularnewline
78 & 1.9 & 1.91363 & -0.0136257 \tabularnewline
79 & 2.1 & 2.00726 & 0.0927396 \tabularnewline
80 & 1.8 & 2.03113 & -0.231129 \tabularnewline
81 & 2.1 & 1.96205 & 0.137954 \tabularnewline
82 & 2.4 & 2.02395 & 0.37605 \tabularnewline
83 & 2.1 & 1.81199 & 0.288008 \tabularnewline
84 & 2.2 & 2.05479 & 0.145212 \tabularnewline
85 & 2.1 & 2.07017 & 0.029835 \tabularnewline
86 & 2.2 & 2.09343 & 0.106573 \tabularnewline
87 & 1.6 & 1.96184 & -0.361843 \tabularnewline
88 & 2.4 & 2.19775 & 0.202252 \tabularnewline
89 & 2.1 & 2.19126 & -0.0912605 \tabularnewline
90 & 1.9 & 2.0321 & -0.132098 \tabularnewline
91 & 2.4 & 1.98418 & 0.415824 \tabularnewline
92 & 2.1 & 1.99944 & 0.100565 \tabularnewline
93 & 1.8 & 2.08957 & -0.289574 \tabularnewline
94 & 2.1 & 1.94187 & 0.158131 \tabularnewline
95 & 1.8 & 2.00812 & -0.208119 \tabularnewline
96 & 1.9 & 2.14829 & -0.248288 \tabularnewline
97 & 1.9 & 1.95717 & -0.0571722 \tabularnewline
98 & 2.4 & 1.95868 & 0.441318 \tabularnewline
99 & 1.8 & 2.01166 & -0.211659 \tabularnewline
100 & 1.8 & 2.04531 & -0.245307 \tabularnewline
101 & 2.1 & 1.97039 & 0.129613 \tabularnewline
102 & 2.1 & 1.94312 & 0.156884 \tabularnewline
103 & 2.4 & 2.11973 & 0.280273 \tabularnewline
104 & 1.9 & 1.97913 & -0.0791262 \tabularnewline
105 & 1.8 & 2.04451 & -0.244511 \tabularnewline
106 & 1.8 & 2.00101 & -0.201008 \tabularnewline
107 & 2.2 & 2.14621 & 0.0537926 \tabularnewline
108 & 2.4 & 2.03486 & 0.365138 \tabularnewline
109 & 1.8 & 2.11282 & -0.31282 \tabularnewline
110 & 2.4 & 2.06857 & 0.331434 \tabularnewline
111 & 1.8 & 2.03403 & -0.234033 \tabularnewline
112 & 1.9 & 1.90693 & -0.00693243 \tabularnewline
113 & 2.4 & 2.07669 & 0.323309 \tabularnewline
114 & 2.1 & 2.04271 & 0.0572853 \tabularnewline
115 & 1.9 & 1.93172 & -0.031721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&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]1.96716[/C][C]-0.46716[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.89028[/C][C]-0.0902786[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]1.81593[/C][C]0.284069[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]1.82224[/C][C]0.277763[/C][/ROW]
[ROW][C]5[/C][C]1.9[/C][C]1.99126[/C][C]-0.0912593[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.75461[/C][C]-0.154614[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.10892[/C][C]-0.00891727[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]1.98791[/C][C]0.112087[/C][/ROW]
[ROW][C]9[/C][C]2.2[/C][C]1.99538[/C][C]0.204625[/C][/ROW]
[ROW][C]10[/C][C]1.5[/C][C]2.116[/C][C]-0.616003[/C][/ROW]
[ROW][C]11[/C][C]1.9[/C][C]1.92381[/C][C]-0.0238111[/C][/ROW]
[ROW][C]12[/C][C]2.2[/C][C]1.89383[/C][C]0.306169[/C][/ROW]
[ROW][C]13[/C][C]1.6[/C][C]2.0117[/C][C]-0.411704[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]1.75925[/C][C]-0.259248[/C][/ROW]
[ROW][C]15[/C][C]1.9[/C][C]2.15848[/C][C]-0.258481[/C][/ROW]
[ROW][C]16[/C][C]0.1[/C][C]1.58892[/C][C]-1.48892[/C][/ROW]
[ROW][C]17[/C][C]2.2[/C][C]2.0987[/C][C]0.101303[/C][/ROW]
[ROW][C]18[/C][C]1.8[/C][C]2.0258[/C][C]-0.225802[/C][/ROW]
[ROW][C]19[/C][C]1.6[/C][C]1.7965[/C][C]-0.196501[/C][/ROW]
[ROW][C]20[/C][C]2.2[/C][C]2.15997[/C][C]0.0400303[/C][/ROW]
[ROW][C]21[/C][C]2.1[/C][C]1.87085[/C][C]0.22915[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.78817[/C][C]0.111825[/C][/ROW]
[ROW][C]23[/C][C]1.6[/C][C]1.89209[/C][C]-0.292094[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]2.02687[/C][C]-0.126869[/C][/ROW]
[ROW][C]25[/C][C]2.2[/C][C]2.09656[/C][C]0.103435[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]1.91292[/C][C]-0.112923[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.21748[/C][C]0.182518[/C][/ROW]
[ROW][C]28[/C][C]2.4[/C][C]2.45038[/C][C]-0.0503819[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]2.06035[/C][C]0.439653[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.90831[/C][C]-0.00830924[/C][/ROW]
[ROW][C]31[/C][C]2.1[/C][C]1.87699[/C][C]0.223007[/C][/ROW]
[ROW][C]32[/C][C]1.9[/C][C]1.80778[/C][C]0.0922229[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]1.86836[/C][C]0.231635[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]1.9015[/C][C]-0.001503[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]1.75674[/C][C]-0.256741[/C][/ROW]
[ROW][C]36[/C][C]1.9[/C][C]2.06506[/C][C]-0.165059[/C][/ROW]
[ROW][C]37[/C][C]2.1[/C][C]1.79325[/C][C]0.30675[/C][/ROW]
[ROW][C]38[/C][C]1.5[/C][C]1.75509[/C][C]-0.255089[/C][/ROW]
[ROW][C]39[/C][C]2.1[/C][C]1.85518[/C][C]0.244821[/C][/ROW]
[ROW][C]40[/C][C]2.1[/C][C]2.09102[/C][C]0.00897813[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.9699[/C][C]-0.1699[/C][/ROW]
[ROW][C]42[/C][C]2.4[/C][C]1.93222[/C][C]0.467776[/C][/ROW]
[ROW][C]43[/C][C]2.1[/C][C]1.72393[/C][C]0.376073[/C][/ROW]
[ROW][C]44[/C][C]1.9[/C][C]1.85623[/C][C]0.0437737[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]1.9667[/C][C]0.133298[/C][/ROW]
[ROW][C]46[/C][C]1.9[/C][C]1.8751[/C][C]0.024904[/C][/ROW]
[ROW][C]47[/C][C]2.4[/C][C]1.99214[/C][C]0.407864[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]1.98616[/C][C]0.113835[/C][/ROW]
[ROW][C]49[/C][C]2.2[/C][C]1.87042[/C][C]0.329581[/C][/ROW]
[ROW][C]50[/C][C]2.2[/C][C]2.11494[/C][C]0.0850625[/C][/ROW]
[ROW][C]51[/C][C]1.8[/C][C]1.81674[/C][C]-0.0167381[/C][/ROW]
[ROW][C]52[/C][C]2.1[/C][C]1.93856[/C][C]0.161443[/C][/ROW]
[ROW][C]53[/C][C]2.4[/C][C]2.07605[/C][C]0.32395[/C][/ROW]
[ROW][C]54[/C][C]2.2[/C][C]1.8911[/C][C]0.308896[/C][/ROW]
[ROW][C]55[/C][C]2.1[/C][C]1.78635[/C][C]0.31365[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]2.08464[/C][C]-0.58464[/C][/ROW]
[ROW][C]57[/C][C]1.9[/C][C]1.87829[/C][C]0.0217137[/C][/ROW]
[ROW][C]58[/C][C]1.8[/C][C]1.71577[/C][C]0.0842307[/C][/ROW]
[ROW][C]59[/C][C]1.8[/C][C]2.09067[/C][C]-0.290668[/C][/ROW]
[ROW][C]60[/C][C]1.6[/C][C]1.85115[/C][C]-0.25115[/C][/ROW]
[ROW][C]61[/C][C]1.2[/C][C]1.86263[/C][C]-0.662626[/C][/ROW]
[ROW][C]62[/C][C]1.8[/C][C]2.036[/C][C]-0.236005[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]2.00648[/C][C]-0.506484[/C][/ROW]
[ROW][C]64[/C][C]2.1[/C][C]1.78347[/C][C]0.31653[/C][/ROW]
[ROW][C]65[/C][C]2.4[/C][C]1.74849[/C][C]0.651508[/C][/ROW]
[ROW][C]66[/C][C]2.4[/C][C]2.06328[/C][C]0.336718[/C][/ROW]
[ROW][C]67[/C][C]1.5[/C][C]1.90892[/C][C]-0.408915[/C][/ROW]
[ROW][C]68[/C][C]1.8[/C][C]1.9504[/C][C]-0.150401[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.11321[/C][C]-0.013214[/C][/ROW]
[ROW][C]70[/C][C]2.2[/C][C]2.23966[/C][C]-0.039665[/C][/ROW]
[ROW][C]71[/C][C]2.1[/C][C]1.92413[/C][C]0.175869[/C][/ROW]
[ROW][C]72[/C][C]1.9[/C][C]2.06694[/C][C]-0.166943[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.04908[/C][C]0.0509159[/C][/ROW]
[ROW][C]74[/C][C]1.9[/C][C]1.78575[/C][C]0.114246[/C][/ROW]
[ROW][C]75[/C][C]1.6[/C][C]2.02785[/C][C]-0.427846[/C][/ROW]
[ROW][C]76[/C][C]2.4[/C][C]2.19456[/C][C]0.20544[/C][/ROW]
[ROW][C]77[/C][C]1.9[/C][C]1.95242[/C][C]-0.0524231[/C][/ROW]
[ROW][C]78[/C][C]1.9[/C][C]1.91363[/C][C]-0.0136257[/C][/ROW]
[ROW][C]79[/C][C]2.1[/C][C]2.00726[/C][C]0.0927396[/C][/ROW]
[ROW][C]80[/C][C]1.8[/C][C]2.03113[/C][C]-0.231129[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]1.96205[/C][C]0.137954[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.02395[/C][C]0.37605[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]1.81199[/C][C]0.288008[/C][/ROW]
[ROW][C]84[/C][C]2.2[/C][C]2.05479[/C][C]0.145212[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.07017[/C][C]0.029835[/C][/ROW]
[ROW][C]86[/C][C]2.2[/C][C]2.09343[/C][C]0.106573[/C][/ROW]
[ROW][C]87[/C][C]1.6[/C][C]1.96184[/C][C]-0.361843[/C][/ROW]
[ROW][C]88[/C][C]2.4[/C][C]2.19775[/C][C]0.202252[/C][/ROW]
[ROW][C]89[/C][C]2.1[/C][C]2.19126[/C][C]-0.0912605[/C][/ROW]
[ROW][C]90[/C][C]1.9[/C][C]2.0321[/C][C]-0.132098[/C][/ROW]
[ROW][C]91[/C][C]2.4[/C][C]1.98418[/C][C]0.415824[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]1.99944[/C][C]0.100565[/C][/ROW]
[ROW][C]93[/C][C]1.8[/C][C]2.08957[/C][C]-0.289574[/C][/ROW]
[ROW][C]94[/C][C]2.1[/C][C]1.94187[/C][C]0.158131[/C][/ROW]
[ROW][C]95[/C][C]1.8[/C][C]2.00812[/C][C]-0.208119[/C][/ROW]
[ROW][C]96[/C][C]1.9[/C][C]2.14829[/C][C]-0.248288[/C][/ROW]
[ROW][C]97[/C][C]1.9[/C][C]1.95717[/C][C]-0.0571722[/C][/ROW]
[ROW][C]98[/C][C]2.4[/C][C]1.95868[/C][C]0.441318[/C][/ROW]
[ROW][C]99[/C][C]1.8[/C][C]2.01166[/C][C]-0.211659[/C][/ROW]
[ROW][C]100[/C][C]1.8[/C][C]2.04531[/C][C]-0.245307[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]1.97039[/C][C]0.129613[/C][/ROW]
[ROW][C]102[/C][C]2.1[/C][C]1.94312[/C][C]0.156884[/C][/ROW]
[ROW][C]103[/C][C]2.4[/C][C]2.11973[/C][C]0.280273[/C][/ROW]
[ROW][C]104[/C][C]1.9[/C][C]1.97913[/C][C]-0.0791262[/C][/ROW]
[ROW][C]105[/C][C]1.8[/C][C]2.04451[/C][C]-0.244511[/C][/ROW]
[ROW][C]106[/C][C]1.8[/C][C]2.00101[/C][C]-0.201008[/C][/ROW]
[ROW][C]107[/C][C]2.2[/C][C]2.14621[/C][C]0.0537926[/C][/ROW]
[ROW][C]108[/C][C]2.4[/C][C]2.03486[/C][C]0.365138[/C][/ROW]
[ROW][C]109[/C][C]1.8[/C][C]2.11282[/C][C]-0.31282[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.06857[/C][C]0.331434[/C][/ROW]
[ROW][C]111[/C][C]1.8[/C][C]2.03403[/C][C]-0.234033[/C][/ROW]
[ROW][C]112[/C][C]1.9[/C][C]1.90693[/C][C]-0.00693243[/C][/ROW]
[ROW][C]113[/C][C]2.4[/C][C]2.07669[/C][C]0.323309[/C][/ROW]
[ROW][C]114[/C][C]2.1[/C][C]2.04271[/C][C]0.0572853[/C][/ROW]
[ROW][C]115[/C][C]1.9[/C][C]1.93172[/C][C]-0.031721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&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
11.51.96716-0.46716
21.81.89028-0.0902786
32.11.815930.284069
42.11.822240.277763
51.91.99126-0.0912593
61.61.75461-0.154614
72.12.10892-0.00891727
82.11.987910.112087
92.21.995380.204625
101.52.116-0.616003
111.91.92381-0.0238111
122.21.893830.306169
131.62.0117-0.411704
141.51.75925-0.259248
151.92.15848-0.258481
160.11.58892-1.48892
172.22.09870.101303
181.82.0258-0.225802
191.61.7965-0.196501
202.22.159970.0400303
212.11.870850.22915
221.91.788170.111825
231.61.89209-0.292094
241.92.02687-0.126869
252.22.096560.103435
261.81.91292-0.112923
272.42.217480.182518
282.42.45038-0.0503819
292.52.060350.439653
301.91.90831-0.00830924
312.11.876990.223007
321.91.807780.0922229
332.11.868360.231635
341.91.9015-0.001503
351.51.75674-0.256741
361.92.06506-0.165059
372.11.793250.30675
381.51.75509-0.255089
392.11.855180.244821
402.12.091020.00897813
411.81.9699-0.1699
422.41.932220.467776
432.11.723930.376073
441.91.856230.0437737
452.11.96670.133298
461.91.87510.024904
472.41.992140.407864
482.11.986160.113835
492.21.870420.329581
502.22.114940.0850625
511.81.81674-0.0167381
522.11.938560.161443
532.42.076050.32395
542.21.89110.308896
552.11.786350.31365
561.52.08464-0.58464
571.91.878290.0217137
581.81.715770.0842307
591.82.09067-0.290668
601.61.85115-0.25115
611.21.86263-0.662626
621.82.036-0.236005
631.52.00648-0.506484
642.11.783470.31653
652.41.748490.651508
662.42.063280.336718
671.51.90892-0.408915
681.81.9504-0.150401
692.12.11321-0.013214
702.22.23966-0.039665
712.11.924130.175869
721.92.06694-0.166943
732.12.049080.0509159
741.91.785750.114246
751.62.02785-0.427846
762.42.194560.20544
771.91.95242-0.0524231
781.91.91363-0.0136257
792.12.007260.0927396
801.82.03113-0.231129
812.11.962050.137954
822.42.023950.37605
832.11.811990.288008
842.22.054790.145212
852.12.070170.029835
862.22.093430.106573
871.61.96184-0.361843
882.42.197750.202252
892.12.19126-0.0912605
901.92.0321-0.132098
912.41.984180.415824
922.11.999440.100565
931.82.08957-0.289574
942.11.941870.158131
951.82.00812-0.208119
961.92.14829-0.248288
971.91.95717-0.0571722
982.41.958680.441318
991.82.01166-0.211659
1001.82.04531-0.245307
1012.11.970390.129613
1022.11.943120.156884
1032.42.119730.280273
1041.91.97913-0.0791262
1051.82.04451-0.244511
1061.82.00101-0.201008
1072.22.146210.0537926
1082.42.034860.365138
1091.82.11282-0.31282
1102.42.068570.331434
1111.82.03403-0.234033
1121.91.90693-0.00693243
1132.42.076690.323309
1142.12.042710.0572853
1151.91.93172-0.031721






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7865710.4268580.213429
120.8628750.274250.137125
130.8534890.2930220.146511
140.8638290.2723410.136171
150.8012780.3974430.198722
160.9851860.02962720.0148136
170.9741280.05174420.0258721
180.9688290.06234170.0311708
190.9608570.07828550.0391428
200.9755790.04884230.0244212
210.9911210.01775720.00887858
220.9861540.0276920.013846
230.983250.03349970.0167498
240.991550.01690030.00845015
250.9866820.02663540.0133177
260.9808150.03836980.0191849
270.9753430.0493140.024657
280.9653780.06924430.0346221
290.9830930.03381410.016907
300.9750190.04996160.0249808
310.9746340.05073120.0253656
320.9771990.04560290.0228014
330.9748330.05033390.025167
340.9644810.07103830.0355191
350.9618660.0762680.038134
360.9534340.09313280.0465664
370.9665910.06681730.0334087
380.9719530.05609310.0280466
390.9692420.06151610.0307581
400.9606690.07866180.0393309
410.9574050.08518940.0425947
420.9728020.05439650.0271982
430.9776520.04469630.0223482
440.9714410.05711840.0285592
450.9619070.07618580.0380929
460.9496940.1006110.0503057
470.9559520.08809660.0440483
480.9422940.1154130.0577063
490.9478710.1042570.0521287
500.9324150.1351690.0675847
510.9115830.1768350.0884173
520.8899850.2200310.110015
530.8878510.2242980.112149
540.8721430.2557150.127857
550.878840.242320.12116
560.9608520.07829520.0391476
570.9481090.1037810.0518907
580.9317350.136530.0682651
590.9285660.1428670.0714336
600.9326680.1346640.0673321
610.9850940.02981120.0149056
620.983530.03293920.0164696
630.9935360.01292810.00646404
640.993120.01376080.0068804
650.9981570.003686030.00184302
660.9986260.002748170.00137408
670.9990370.001926910.000963455
680.9988720.00225690.00112845
690.998340.003320550.00166028
700.9977970.004405320.00220266
710.9966210.006757050.00337853
720.9958580.008284760.00414238
730.9944630.01107450.00553724
740.9915220.01695530.00847765
750.9958660.008267490.00413374
760.9939340.01213220.0060661
770.992190.01562060.00781028
780.9892470.02150530.0107527
790.9842060.03158770.0157938
800.9851740.0296520.014826
810.977790.04441990.02221
820.9767480.04650310.0232516
830.9673680.06526340.0326317
840.9530630.09387430.0469372
850.9350230.1299540.0649769
860.9096020.1807950.0903977
870.9207270.1585450.0792727
880.9227840.1544320.077216
890.8978240.2043520.102176
900.9375170.1249650.0624826
910.9175710.1648580.0824292
920.8819040.2361930.118096
930.8537980.2924040.146202
940.7987810.4024380.201219
950.7457270.5085460.254273
960.6692980.6614050.330702
970.6077050.7845890.392295
980.6195880.7608230.380412
990.5198590.9602810.480141
1000.5497150.9005690.450285
1010.4529890.9059780.547011
1020.3878970.7757940.612103
1030.3264280.6528560.673572
1040.1989490.3978980.801051

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.786571 & 0.426858 & 0.213429 \tabularnewline
12 & 0.862875 & 0.27425 & 0.137125 \tabularnewline
13 & 0.853489 & 0.293022 & 0.146511 \tabularnewline
14 & 0.863829 & 0.272341 & 0.136171 \tabularnewline
15 & 0.801278 & 0.397443 & 0.198722 \tabularnewline
16 & 0.985186 & 0.0296272 & 0.0148136 \tabularnewline
17 & 0.974128 & 0.0517442 & 0.0258721 \tabularnewline
18 & 0.968829 & 0.0623417 & 0.0311708 \tabularnewline
19 & 0.960857 & 0.0782855 & 0.0391428 \tabularnewline
20 & 0.975579 & 0.0488423 & 0.0244212 \tabularnewline
21 & 0.991121 & 0.0177572 & 0.00887858 \tabularnewline
22 & 0.986154 & 0.027692 & 0.013846 \tabularnewline
23 & 0.98325 & 0.0334997 & 0.0167498 \tabularnewline
24 & 0.99155 & 0.0169003 & 0.00845015 \tabularnewline
25 & 0.986682 & 0.0266354 & 0.0133177 \tabularnewline
26 & 0.980815 & 0.0383698 & 0.0191849 \tabularnewline
27 & 0.975343 & 0.049314 & 0.024657 \tabularnewline
28 & 0.965378 & 0.0692443 & 0.0346221 \tabularnewline
29 & 0.983093 & 0.0338141 & 0.016907 \tabularnewline
30 & 0.975019 & 0.0499616 & 0.0249808 \tabularnewline
31 & 0.974634 & 0.0507312 & 0.0253656 \tabularnewline
32 & 0.977199 & 0.0456029 & 0.0228014 \tabularnewline
33 & 0.974833 & 0.0503339 & 0.025167 \tabularnewline
34 & 0.964481 & 0.0710383 & 0.0355191 \tabularnewline
35 & 0.961866 & 0.076268 & 0.038134 \tabularnewline
36 & 0.953434 & 0.0931328 & 0.0465664 \tabularnewline
37 & 0.966591 & 0.0668173 & 0.0334087 \tabularnewline
38 & 0.971953 & 0.0560931 & 0.0280466 \tabularnewline
39 & 0.969242 & 0.0615161 & 0.0307581 \tabularnewline
40 & 0.960669 & 0.0786618 & 0.0393309 \tabularnewline
41 & 0.957405 & 0.0851894 & 0.0425947 \tabularnewline
42 & 0.972802 & 0.0543965 & 0.0271982 \tabularnewline
43 & 0.977652 & 0.0446963 & 0.0223482 \tabularnewline
44 & 0.971441 & 0.0571184 & 0.0285592 \tabularnewline
45 & 0.961907 & 0.0761858 & 0.0380929 \tabularnewline
46 & 0.949694 & 0.100611 & 0.0503057 \tabularnewline
47 & 0.955952 & 0.0880966 & 0.0440483 \tabularnewline
48 & 0.942294 & 0.115413 & 0.0577063 \tabularnewline
49 & 0.947871 & 0.104257 & 0.0521287 \tabularnewline
50 & 0.932415 & 0.135169 & 0.0675847 \tabularnewline
51 & 0.911583 & 0.176835 & 0.0884173 \tabularnewline
52 & 0.889985 & 0.220031 & 0.110015 \tabularnewline
53 & 0.887851 & 0.224298 & 0.112149 \tabularnewline
54 & 0.872143 & 0.255715 & 0.127857 \tabularnewline
55 & 0.87884 & 0.24232 & 0.12116 \tabularnewline
56 & 0.960852 & 0.0782952 & 0.0391476 \tabularnewline
57 & 0.948109 & 0.103781 & 0.0518907 \tabularnewline
58 & 0.931735 & 0.13653 & 0.0682651 \tabularnewline
59 & 0.928566 & 0.142867 & 0.0714336 \tabularnewline
60 & 0.932668 & 0.134664 & 0.0673321 \tabularnewline
61 & 0.985094 & 0.0298112 & 0.0149056 \tabularnewline
62 & 0.98353 & 0.0329392 & 0.0164696 \tabularnewline
63 & 0.993536 & 0.0129281 & 0.00646404 \tabularnewline
64 & 0.99312 & 0.0137608 & 0.0068804 \tabularnewline
65 & 0.998157 & 0.00368603 & 0.00184302 \tabularnewline
66 & 0.998626 & 0.00274817 & 0.00137408 \tabularnewline
67 & 0.999037 & 0.00192691 & 0.000963455 \tabularnewline
68 & 0.998872 & 0.0022569 & 0.00112845 \tabularnewline
69 & 0.99834 & 0.00332055 & 0.00166028 \tabularnewline
70 & 0.997797 & 0.00440532 & 0.00220266 \tabularnewline
71 & 0.996621 & 0.00675705 & 0.00337853 \tabularnewline
72 & 0.995858 & 0.00828476 & 0.00414238 \tabularnewline
73 & 0.994463 & 0.0110745 & 0.00553724 \tabularnewline
74 & 0.991522 & 0.0169553 & 0.00847765 \tabularnewline
75 & 0.995866 & 0.00826749 & 0.00413374 \tabularnewline
76 & 0.993934 & 0.0121322 & 0.0060661 \tabularnewline
77 & 0.99219 & 0.0156206 & 0.00781028 \tabularnewline
78 & 0.989247 & 0.0215053 & 0.0107527 \tabularnewline
79 & 0.984206 & 0.0315877 & 0.0157938 \tabularnewline
80 & 0.985174 & 0.029652 & 0.014826 \tabularnewline
81 & 0.97779 & 0.0444199 & 0.02221 \tabularnewline
82 & 0.976748 & 0.0465031 & 0.0232516 \tabularnewline
83 & 0.967368 & 0.0652634 & 0.0326317 \tabularnewline
84 & 0.953063 & 0.0938743 & 0.0469372 \tabularnewline
85 & 0.935023 & 0.129954 & 0.0649769 \tabularnewline
86 & 0.909602 & 0.180795 & 0.0903977 \tabularnewline
87 & 0.920727 & 0.158545 & 0.0792727 \tabularnewline
88 & 0.922784 & 0.154432 & 0.077216 \tabularnewline
89 & 0.897824 & 0.204352 & 0.102176 \tabularnewline
90 & 0.937517 & 0.124965 & 0.0624826 \tabularnewline
91 & 0.917571 & 0.164858 & 0.0824292 \tabularnewline
92 & 0.881904 & 0.236193 & 0.118096 \tabularnewline
93 & 0.853798 & 0.292404 & 0.146202 \tabularnewline
94 & 0.798781 & 0.402438 & 0.201219 \tabularnewline
95 & 0.745727 & 0.508546 & 0.254273 \tabularnewline
96 & 0.669298 & 0.661405 & 0.330702 \tabularnewline
97 & 0.607705 & 0.784589 & 0.392295 \tabularnewline
98 & 0.619588 & 0.760823 & 0.380412 \tabularnewline
99 & 0.519859 & 0.960281 & 0.480141 \tabularnewline
100 & 0.549715 & 0.900569 & 0.450285 \tabularnewline
101 & 0.452989 & 0.905978 & 0.547011 \tabularnewline
102 & 0.387897 & 0.775794 & 0.612103 \tabularnewline
103 & 0.326428 & 0.652856 & 0.673572 \tabularnewline
104 & 0.198949 & 0.397898 & 0.801051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&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]11[/C][C]0.786571[/C][C]0.426858[/C][C]0.213429[/C][/ROW]
[ROW][C]12[/C][C]0.862875[/C][C]0.27425[/C][C]0.137125[/C][/ROW]
[ROW][C]13[/C][C]0.853489[/C][C]0.293022[/C][C]0.146511[/C][/ROW]
[ROW][C]14[/C][C]0.863829[/C][C]0.272341[/C][C]0.136171[/C][/ROW]
[ROW][C]15[/C][C]0.801278[/C][C]0.397443[/C][C]0.198722[/C][/ROW]
[ROW][C]16[/C][C]0.985186[/C][C]0.0296272[/C][C]0.0148136[/C][/ROW]
[ROW][C]17[/C][C]0.974128[/C][C]0.0517442[/C][C]0.0258721[/C][/ROW]
[ROW][C]18[/C][C]0.968829[/C][C]0.0623417[/C][C]0.0311708[/C][/ROW]
[ROW][C]19[/C][C]0.960857[/C][C]0.0782855[/C][C]0.0391428[/C][/ROW]
[ROW][C]20[/C][C]0.975579[/C][C]0.0488423[/C][C]0.0244212[/C][/ROW]
[ROW][C]21[/C][C]0.991121[/C][C]0.0177572[/C][C]0.00887858[/C][/ROW]
[ROW][C]22[/C][C]0.986154[/C][C]0.027692[/C][C]0.013846[/C][/ROW]
[ROW][C]23[/C][C]0.98325[/C][C]0.0334997[/C][C]0.0167498[/C][/ROW]
[ROW][C]24[/C][C]0.99155[/C][C]0.0169003[/C][C]0.00845015[/C][/ROW]
[ROW][C]25[/C][C]0.986682[/C][C]0.0266354[/C][C]0.0133177[/C][/ROW]
[ROW][C]26[/C][C]0.980815[/C][C]0.0383698[/C][C]0.0191849[/C][/ROW]
[ROW][C]27[/C][C]0.975343[/C][C]0.049314[/C][C]0.024657[/C][/ROW]
[ROW][C]28[/C][C]0.965378[/C][C]0.0692443[/C][C]0.0346221[/C][/ROW]
[ROW][C]29[/C][C]0.983093[/C][C]0.0338141[/C][C]0.016907[/C][/ROW]
[ROW][C]30[/C][C]0.975019[/C][C]0.0499616[/C][C]0.0249808[/C][/ROW]
[ROW][C]31[/C][C]0.974634[/C][C]0.0507312[/C][C]0.0253656[/C][/ROW]
[ROW][C]32[/C][C]0.977199[/C][C]0.0456029[/C][C]0.0228014[/C][/ROW]
[ROW][C]33[/C][C]0.974833[/C][C]0.0503339[/C][C]0.025167[/C][/ROW]
[ROW][C]34[/C][C]0.964481[/C][C]0.0710383[/C][C]0.0355191[/C][/ROW]
[ROW][C]35[/C][C]0.961866[/C][C]0.076268[/C][C]0.038134[/C][/ROW]
[ROW][C]36[/C][C]0.953434[/C][C]0.0931328[/C][C]0.0465664[/C][/ROW]
[ROW][C]37[/C][C]0.966591[/C][C]0.0668173[/C][C]0.0334087[/C][/ROW]
[ROW][C]38[/C][C]0.971953[/C][C]0.0560931[/C][C]0.0280466[/C][/ROW]
[ROW][C]39[/C][C]0.969242[/C][C]0.0615161[/C][C]0.0307581[/C][/ROW]
[ROW][C]40[/C][C]0.960669[/C][C]0.0786618[/C][C]0.0393309[/C][/ROW]
[ROW][C]41[/C][C]0.957405[/C][C]0.0851894[/C][C]0.0425947[/C][/ROW]
[ROW][C]42[/C][C]0.972802[/C][C]0.0543965[/C][C]0.0271982[/C][/ROW]
[ROW][C]43[/C][C]0.977652[/C][C]0.0446963[/C][C]0.0223482[/C][/ROW]
[ROW][C]44[/C][C]0.971441[/C][C]0.0571184[/C][C]0.0285592[/C][/ROW]
[ROW][C]45[/C][C]0.961907[/C][C]0.0761858[/C][C]0.0380929[/C][/ROW]
[ROW][C]46[/C][C]0.949694[/C][C]0.100611[/C][C]0.0503057[/C][/ROW]
[ROW][C]47[/C][C]0.955952[/C][C]0.0880966[/C][C]0.0440483[/C][/ROW]
[ROW][C]48[/C][C]0.942294[/C][C]0.115413[/C][C]0.0577063[/C][/ROW]
[ROW][C]49[/C][C]0.947871[/C][C]0.104257[/C][C]0.0521287[/C][/ROW]
[ROW][C]50[/C][C]0.932415[/C][C]0.135169[/C][C]0.0675847[/C][/ROW]
[ROW][C]51[/C][C]0.911583[/C][C]0.176835[/C][C]0.0884173[/C][/ROW]
[ROW][C]52[/C][C]0.889985[/C][C]0.220031[/C][C]0.110015[/C][/ROW]
[ROW][C]53[/C][C]0.887851[/C][C]0.224298[/C][C]0.112149[/C][/ROW]
[ROW][C]54[/C][C]0.872143[/C][C]0.255715[/C][C]0.127857[/C][/ROW]
[ROW][C]55[/C][C]0.87884[/C][C]0.24232[/C][C]0.12116[/C][/ROW]
[ROW][C]56[/C][C]0.960852[/C][C]0.0782952[/C][C]0.0391476[/C][/ROW]
[ROW][C]57[/C][C]0.948109[/C][C]0.103781[/C][C]0.0518907[/C][/ROW]
[ROW][C]58[/C][C]0.931735[/C][C]0.13653[/C][C]0.0682651[/C][/ROW]
[ROW][C]59[/C][C]0.928566[/C][C]0.142867[/C][C]0.0714336[/C][/ROW]
[ROW][C]60[/C][C]0.932668[/C][C]0.134664[/C][C]0.0673321[/C][/ROW]
[ROW][C]61[/C][C]0.985094[/C][C]0.0298112[/C][C]0.0149056[/C][/ROW]
[ROW][C]62[/C][C]0.98353[/C][C]0.0329392[/C][C]0.0164696[/C][/ROW]
[ROW][C]63[/C][C]0.993536[/C][C]0.0129281[/C][C]0.00646404[/C][/ROW]
[ROW][C]64[/C][C]0.99312[/C][C]0.0137608[/C][C]0.0068804[/C][/ROW]
[ROW][C]65[/C][C]0.998157[/C][C]0.00368603[/C][C]0.00184302[/C][/ROW]
[ROW][C]66[/C][C]0.998626[/C][C]0.00274817[/C][C]0.00137408[/C][/ROW]
[ROW][C]67[/C][C]0.999037[/C][C]0.00192691[/C][C]0.000963455[/C][/ROW]
[ROW][C]68[/C][C]0.998872[/C][C]0.0022569[/C][C]0.00112845[/C][/ROW]
[ROW][C]69[/C][C]0.99834[/C][C]0.00332055[/C][C]0.00166028[/C][/ROW]
[ROW][C]70[/C][C]0.997797[/C][C]0.00440532[/C][C]0.00220266[/C][/ROW]
[ROW][C]71[/C][C]0.996621[/C][C]0.00675705[/C][C]0.00337853[/C][/ROW]
[ROW][C]72[/C][C]0.995858[/C][C]0.00828476[/C][C]0.00414238[/C][/ROW]
[ROW][C]73[/C][C]0.994463[/C][C]0.0110745[/C][C]0.00553724[/C][/ROW]
[ROW][C]74[/C][C]0.991522[/C][C]0.0169553[/C][C]0.00847765[/C][/ROW]
[ROW][C]75[/C][C]0.995866[/C][C]0.00826749[/C][C]0.00413374[/C][/ROW]
[ROW][C]76[/C][C]0.993934[/C][C]0.0121322[/C][C]0.0060661[/C][/ROW]
[ROW][C]77[/C][C]0.99219[/C][C]0.0156206[/C][C]0.00781028[/C][/ROW]
[ROW][C]78[/C][C]0.989247[/C][C]0.0215053[/C][C]0.0107527[/C][/ROW]
[ROW][C]79[/C][C]0.984206[/C][C]0.0315877[/C][C]0.0157938[/C][/ROW]
[ROW][C]80[/C][C]0.985174[/C][C]0.029652[/C][C]0.014826[/C][/ROW]
[ROW][C]81[/C][C]0.97779[/C][C]0.0444199[/C][C]0.02221[/C][/ROW]
[ROW][C]82[/C][C]0.976748[/C][C]0.0465031[/C][C]0.0232516[/C][/ROW]
[ROW][C]83[/C][C]0.967368[/C][C]0.0652634[/C][C]0.0326317[/C][/ROW]
[ROW][C]84[/C][C]0.953063[/C][C]0.0938743[/C][C]0.0469372[/C][/ROW]
[ROW][C]85[/C][C]0.935023[/C][C]0.129954[/C][C]0.0649769[/C][/ROW]
[ROW][C]86[/C][C]0.909602[/C][C]0.180795[/C][C]0.0903977[/C][/ROW]
[ROW][C]87[/C][C]0.920727[/C][C]0.158545[/C][C]0.0792727[/C][/ROW]
[ROW][C]88[/C][C]0.922784[/C][C]0.154432[/C][C]0.077216[/C][/ROW]
[ROW][C]89[/C][C]0.897824[/C][C]0.204352[/C][C]0.102176[/C][/ROW]
[ROW][C]90[/C][C]0.937517[/C][C]0.124965[/C][C]0.0624826[/C][/ROW]
[ROW][C]91[/C][C]0.917571[/C][C]0.164858[/C][C]0.0824292[/C][/ROW]
[ROW][C]92[/C][C]0.881904[/C][C]0.236193[/C][C]0.118096[/C][/ROW]
[ROW][C]93[/C][C]0.853798[/C][C]0.292404[/C][C]0.146202[/C][/ROW]
[ROW][C]94[/C][C]0.798781[/C][C]0.402438[/C][C]0.201219[/C][/ROW]
[ROW][C]95[/C][C]0.745727[/C][C]0.508546[/C][C]0.254273[/C][/ROW]
[ROW][C]96[/C][C]0.669298[/C][C]0.661405[/C][C]0.330702[/C][/ROW]
[ROW][C]97[/C][C]0.607705[/C][C]0.784589[/C][C]0.392295[/C][/ROW]
[ROW][C]98[/C][C]0.619588[/C][C]0.760823[/C][C]0.380412[/C][/ROW]
[ROW][C]99[/C][C]0.519859[/C][C]0.960281[/C][C]0.480141[/C][/ROW]
[ROW][C]100[/C][C]0.549715[/C][C]0.900569[/C][C]0.450285[/C][/ROW]
[ROW][C]101[/C][C]0.452989[/C][C]0.905978[/C][C]0.547011[/C][/ROW]
[ROW][C]102[/C][C]0.387897[/C][C]0.775794[/C][C]0.612103[/C][/ROW]
[ROW][C]103[/C][C]0.326428[/C][C]0.652856[/C][C]0.673572[/C][/ROW]
[ROW][C]104[/C][C]0.198949[/C][C]0.397898[/C][C]0.801051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7865710.4268580.213429
120.8628750.274250.137125
130.8534890.2930220.146511
140.8638290.2723410.136171
150.8012780.3974430.198722
160.9851860.02962720.0148136
170.9741280.05174420.0258721
180.9688290.06234170.0311708
190.9608570.07828550.0391428
200.9755790.04884230.0244212
210.9911210.01775720.00887858
220.9861540.0276920.013846
230.983250.03349970.0167498
240.991550.01690030.00845015
250.9866820.02663540.0133177
260.9808150.03836980.0191849
270.9753430.0493140.024657
280.9653780.06924430.0346221
290.9830930.03381410.016907
300.9750190.04996160.0249808
310.9746340.05073120.0253656
320.9771990.04560290.0228014
330.9748330.05033390.025167
340.9644810.07103830.0355191
350.9618660.0762680.038134
360.9534340.09313280.0465664
370.9665910.06681730.0334087
380.9719530.05609310.0280466
390.9692420.06151610.0307581
400.9606690.07866180.0393309
410.9574050.08518940.0425947
420.9728020.05439650.0271982
430.9776520.04469630.0223482
440.9714410.05711840.0285592
450.9619070.07618580.0380929
460.9496940.1006110.0503057
470.9559520.08809660.0440483
480.9422940.1154130.0577063
490.9478710.1042570.0521287
500.9324150.1351690.0675847
510.9115830.1768350.0884173
520.8899850.2200310.110015
530.8878510.2242980.112149
540.8721430.2557150.127857
550.878840.242320.12116
560.9608520.07829520.0391476
570.9481090.1037810.0518907
580.9317350.136530.0682651
590.9285660.1428670.0714336
600.9326680.1346640.0673321
610.9850940.02981120.0149056
620.983530.03293920.0164696
630.9935360.01292810.00646404
640.993120.01376080.0068804
650.9981570.003686030.00184302
660.9986260.002748170.00137408
670.9990370.001926910.000963455
680.9988720.00225690.00112845
690.998340.003320550.00166028
700.9977970.004405320.00220266
710.9966210.006757050.00337853
720.9958580.008284760.00414238
730.9944630.01107450.00553724
740.9915220.01695530.00847765
750.9958660.008267490.00413374
760.9939340.01213220.0060661
770.992190.01562060.00781028
780.9892470.02150530.0107527
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800.9851740.0296520.014826
810.977790.04441990.02221
820.9767480.04650310.0232516
830.9673680.06526340.0326317
840.9530630.09387430.0469372
850.9350230.1299540.0649769
860.9096020.1807950.0903977
870.9207270.1585450.0792727
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900.9375170.1249650.0624826
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920.8819040.2361930.118096
930.8537980.2924040.146202
940.7987810.4024380.201219
950.7457270.5085460.254273
960.6692980.6614050.330702
970.6077050.7845890.392295
980.6195880.7608230.380412
990.5198590.9602810.480141
1000.5497150.9005690.450285
1010.4529890.9059780.547011
1020.3878970.7757940.612103
1030.3264280.6528560.673572
1040.1989490.3978980.801051






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.0957447NOK
5% type I error level350.37234NOK
10% type I error level560.595745NOK

\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 & 9 & 0.0957447 & NOK \tabularnewline
5% type I error level & 35 & 0.37234 & NOK \tabularnewline
10% type I error level & 56 & 0.595745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263898&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]9[/C][C]0.0957447[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.37234[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]56[/C][C]0.595745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263898&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263898&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 level90.0957447NOK
5% type I error level350.37234NOK
10% type I error level560.595745NOK


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')
}