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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 computationThu, 18 Dec 2014 15:16:24 +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/18/t1418915820o3d4p36hzidng0m.htm/, Retrieved Fri, 17 May 2024 01:02:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271063, Retrieved Fri, 17 May 2024 01:02:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:16:24] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 12 18 68 1.8
12.2 18 8 31 39 2.1
12.8 12 11 39 32 2.2
7.4 24 13 46 62 2.3
6.7 16 11 31 33 2.1
12.6 19 10 67 52 2.7
14.8 16 7 35 62 2.1
13.3 15 10 52 77 2.4
11.1 28 15 77 76 2.9
8.2 21 12 37 41 2.2
11.4 18 12 32 48 2.1
6.4 22 10 36 63 2.2
10.6 19 10 38 30 2.2
12 22 14 69 78 2.7
6.3 25 6 21 19 1.9
11.9 16 14 54 66 2.5
9.3 19 11 36 35 2.2
10 26 12 23 45 1.9
6.4 24 15 34 21 2.1
13.8 20 13 112 25 3.5
10.8 19 11 35 44 2.1
13.8 19 12 47 69 2.3
11.7 23 7 47 54 2.3
10.9 18 11 37 74 2.2
9.9 21 12 20 61 1.9
11.5 20 13 22 41 1.9
8.3 15 9 23 46 1.9
11.7 19 11 32 39 2.1
9 19 12 30 34 2
9.7 7 15 92 51 3.2
10.8 20 12 43 42 2.3
10.3 20 6 55 31 2.5
10.4 19 5 16 39 1.8
9.3 20 11 71 49 2.8
11.8 18 6 43 53 2.3
5.9 14 12 29 31 2
11.4 17 10 56 39 2.5
13 17 6 46 54 2.3
10.8 8 12 19 49 1.8
11.3 22 6 59 46 2.6
11.8 20 12 30 55 2
12.7 22 8 7 50 1.6
10.9 14 12 19 30 1.8
13.3 21 14 48 45 2.4
10.1 20 12 23 35 1.9
14.3 18 14 33 41 2.1
9.3 24 11 34 73 2.1
12.5 19 10 48 17 2.4
7.6 16 7 18 40 1.8
15.9 16 12 43 64 2.3
9.2 16 7 33 37 2.1
11.1 22 12 71 65 2.8
13 21 10 26 100 2
14.5 15 10 67 28 2.7
12.3 15 12 80 56 2.9
11.4 14 12 29 29 2
13 16 10 43 59 2.3
13.2 26 11 29 61 2
7.7 18 12 32 51 2.1
4.35 17 9 23 12 1
12.7 6 11 16 45 1
18.1 22 12 33 37 4
17.85 20 12 32 37 4
17.1 17 12 52 68 4
19.1 20 12 75 72 4
16.1 23 10 72 143 4
13.35 18 15 15 9 2
18.4 13 10 29 55 4
14.7 22 15 13 17 1
10.6 20 10 40 37 3
12.6 20 15 19 27 3
13.6 16 15 121 58 3
14.1 16 13 36 21 3
14.5 15 12 23 19 3
16.15 19 12 85 78 4
14.75 19 8 41 35 3
14.8 24 9 46 48 3
12.45 9 15 18 27 2
12.65 22 12 35 43 2
17.35 15 12 17 30 3
8.6 22 15 4 25 1
18.4 22 11 28 69 4
16.1 24 12 44 72 3
17.75 21 14 38 13 4
15.25 25 12 57 61 4
17.65 26 12 23 43 4
16.35 21 12 36 51 4
17.65 14 11 22 67 4
13.6 28 12 40 36 3
14.35 21 12 31 44 3
14.75 16 12 11 45 4
18.25 16 12 38 34 4
9.9 25 8 24 36 4
16 21 8 37 72 3
18.25 22 12 37 39 4
16.85 9 12 22 43 4
18.95 24 11 43 80 4
15.6 22 12 31 40 3
17.1 10 10 31 61 4
15.4 21 11 21 29 4
15.4 20 11 21 29 4
13.35 17 13 32 54 3
19.1 7 7 26 43 4
7.6 14 8 32 20 1
19.1 23 11 33 61 4
14.75 18 8 30 57 4
19.25 17 14 67 54 4
13.6 20 9 22 36 4
12.75 19 13 33 16 4
9.85 19 13 24 40 1
15.25 23 11 28 27 4
11.9 20 9 41 61 3
16.35 19 12 31 69 4
12.4 16 12 33 34 3
18.15 21 13 21 34 4
17.75 20 11 52 34 4
12.35 20 11 29 13 3
15.6 19 9 11 12 4
19.3 19 12 26 51 4
17.1 20 15 7 19 4
18.4 22 14 13 81 3
19.05 19 12 20 42 4
18.55 23 9 52 22 4
19.1 16 9 28 85 4
12.85 18 13 39 25 4
9.5 23 15 9 22 2
4.5 20 11 19 19 1
13.6 23 10 60 45 4
11.7 13 11 19 45 2
13.35 26 14 14 51 3
17.6 13 12 -2 73 4
14.05 10 13 51 24 3
16.1 21 11 2 61 4
13.35 24 11 24 23 4
11.85 21 13 40 14 4
11.95 23 12 20 54 2
13.2 16 9 20 36 4
7.7 26 13 25 26 2
14.6 16 12 38 30 3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 4.72399 -0.101868AMS.I2[t] + 0.141686CONFSOFTTOT[t] -0.0254778PRH[t] + 0.0351555CH[t] + 2.84112PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  4.72399 -0.101868AMS.I2[t] +  0.141686CONFSOFTTOT[t] -0.0254778PRH[t] +  0.0351555CH[t] +  2.84112PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  4.72399 -0.101868AMS.I2[t] +  0.141686CONFSOFTTOT[t] -0.0254778PRH[t] +  0.0351555CH[t] +  2.84112PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 4.72399 -0.101868AMS.I2[t] + 0.141686CONFSOFTTOT[t] -0.0254778PRH[t] + 0.0351555CH[t] + 2.84112PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.723991.370123.4480.0007570270.000378514
AMS.I2-0.1018680.0432887-2.3530.02007710.0100386
CONFSOFTTOT0.1416860.08447681.6770.09584920.0479246
PRH-0.02547780.00986345-2.5830.01087350.00543675
CH0.03515550.00969763.6250.0004100670.000205034
PR2.841120.20625513.772.23482e-271.11741e-27

\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) & 4.72399 & 1.37012 & 3.448 & 0.000757027 & 0.000378514 \tabularnewline
AMS.I2 & -0.101868 & 0.0432887 & -2.353 & 0.0200771 & 0.0100386 \tabularnewline
CONFSOFTTOT & 0.141686 & 0.0844768 & 1.677 & 0.0958492 & 0.0479246 \tabularnewline
PRH & -0.0254778 & 0.00986345 & -2.583 & 0.0108735 & 0.00543675 \tabularnewline
CH & 0.0351555 & 0.0096976 & 3.625 & 0.000410067 & 0.000205034 \tabularnewline
PR & 2.84112 & 0.206255 & 13.77 & 2.23482e-27 & 1.11741e-27 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&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]4.72399[/C][C]1.37012[/C][C]3.448[/C][C]0.000757027[/C][C]0.000378514[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.101868[/C][C]0.0432887[/C][C]-2.353[/C][C]0.0200771[/C][C]0.0100386[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.141686[/C][C]0.0844768[/C][C]1.677[/C][C]0.0958492[/C][C]0.0479246[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0254778[/C][C]0.00986345[/C][C]-2.583[/C][C]0.0108735[/C][C]0.00543675[/C][/ROW]
[ROW][C]CH[/C][C]0.0351555[/C][C]0.0096976[/C][C]3.625[/C][C]0.000410067[/C][C]0.000205034[/C][/ROW]
[ROW][C]PR[/C][C]2.84112[/C][C]0.206255[/C][C]13.77[/C][C]2.23482e-27[/C][C]1.11741e-27[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&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)4.723991.370123.4480.0007570270.000378514
AMS.I2-0.1018680.0432887-2.3530.02007710.0100386
CONFSOFTTOT0.1416860.08447681.6770.09584920.0479246
PRH-0.02547780.00986345-2.5830.01087350.00543675
CH0.03515550.00969763.6250.0004100670.000205034
PR2.841120.20625513.772.23482e-271.11741e-27






Multiple Linear Regression - Regression Statistics
Multiple R0.788673
R-squared0.622004
Adjusted R-squared0.607794
F-TEST (value)43.7712
F-TEST (DF numerator)5
F-TEST (DF denominator)133
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19438
Sum Squared Residuals640.437

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.788673 \tabularnewline
R-squared & 0.622004 \tabularnewline
Adjusted R-squared & 0.607794 \tabularnewline
F-TEST (value) & 43.7712 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 133 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19438 \tabularnewline
Sum Squared Residuals & 640.437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.788673[/C][/ROW]
[ROW][C]R-squared[/C][C]0.622004[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.607794[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]43.7712[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]133[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.19438[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]640.437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&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 R0.788673
R-squared0.622004
Adjusted R-squared0.607794
F-TEST (value)43.7712
F-TEST (DF numerator)5
F-TEST (DF denominator)133
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19438
Sum Squared Residuals640.437






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.65530.244727
212.210.57151.62853
312.811.44191.35807
47.411.6633-4.26332
56.710.9893-4.28933
612.611.99750.602535
714.811.34023.45982
813.312.81370.486347
911.112.9463-1.84626
108.211.0342-2.83416
1111.411.4291-0.0291325
126.411.4478-5.04782
1310.610.54230.0576596
141213.1217-1.12169
156.38.55847-2.25847
1611.913.125-1.22498
179.310.9108-1.61076
181010.1698-0.169798
196.410.2428-3.84283
2013.812.49791.30215
2110.810.9685-0.168524
2213.812.25161.54841
2311.710.60841.09165
2410.912.3582-1.45821
259.911.3181-1.41806
2611.510.80750.692452
278.310.9004-2.60044
2811.710.86920.83082
29910.6019-1.60193
309.714.6768-4.97677
3110.811.3024-0.502433
3210.310.3281-0.0280992
3310.49.574370.825626
349.312.114-2.81402
3511.811.04280.757236
365.911.0313-5.13128
3711.411.4562-0.0562116
381311.10341.89665
3910.811.9618-1.16184
4011.310.83390.466104
4111.811.23830.561672
4212.79.741612.95839
4310.910.68270.217319
4413.311.74611.55387
4510.110.4295-0.329451
4614.311.44092.85906
479.311.5042-2.20417
4812.510.39882.10123
497.610.1475-2.54755
5015.912.48333.41668
519.210.5123-1.31225
5211.112.6145-1.51445
531312.5370.463005
5414.511.56122.93879
5512.313.0659-0.765943
5611.410.9610.439028
571312.02420.975825
5813.210.72182.47815
597.711.5346-3.8346
604.356.94441-2.59441
6112.79.68683.0132
6218.116.00762.09239
6317.8516.23681.61318
6417.117.1227-0.0226883
6519.116.37172.72828
6616.118.3552-2.25521
6713.3510.63212.71786
6818.417.37581.02424
6914.77.715746.98426
7010.612.9085-2.3085
7112.613.8004-1.20041
7213.612.6990.901035
7314.113.28050.819543
7414.513.50150.99846
7516.1516.4297-0.279739
7614.7512.63122.11879
7714.812.59322.20681
7812.4512.10530.344686
7912.6510.48532.16466
8017.3514.04113.30888
818.68.226290.373715
8218.417.11831.28171
8316.113.91292.18707
8417.7515.42172.32827
8515.2515.9343-0.684268
8617.6516.06581.58415
8716.3516.5252-0.175219
8817.6518.0158-0.365786
8913.612.34181.25822
9014.3513.56540.784604
9114.7517.4606-2.71057
9218.2516.3861.86404
939.915.3294-5.42941
941613.83012.16986
9518.2515.9762.27399
9616.8517.8231-0.97308
9718.9516.91912.03091
9815.613.32292.27709
9917.117.8413-0.741339
10015.415.9923-0.592281
10115.416.0941-0.694149
10213.3514.4406-1.09063
10319.117.21651.88352
1047.67.160270.439728
10519.116.60782.49221
10614.7516.6279-1.87788
10719.2516.53172.71828
10813.616.0314-2.43139
10912.7515.7166-2.96663
1109.858.266291.58371
11115.2515.5399-0.28989
11211.913.5851-1.68507
11316.3517.4891-1.13914
11412.413.6722-1.27223
11518.1516.45141.69857
11617.7515.48012.26989
11712.3512.4867-0.136715
11815.615.56980.0302189
11919.316.98372.31627
12017.116.6660.433973
12118.415.50632.89375
12219.0516.82022.2298
12318.5514.46934.08073
12419.118.00861.09139
12512.8515.982-3.13203
1269.510.7327-1.23269
1274.57.27018-2.77018
12813.615.2157-1.61571
12911.711.7384-0.0384193
13013.3514.0186-0.668639
13117.619.0817-1.48174
13214.0513.6150.435036
13316.117.6013-1.50133
13413.3515.3993-2.04931
13511.8515.2642-3.41424
13611.9511.15230.797653
13713.216.4898-3.28982
1387.79.87669-2.17669
13914.613.40421.19579

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.6553 & 0.244727 \tabularnewline
2 & 12.2 & 10.5715 & 1.62853 \tabularnewline
3 & 12.8 & 11.4419 & 1.35807 \tabularnewline
4 & 7.4 & 11.6633 & -4.26332 \tabularnewline
5 & 6.7 & 10.9893 & -4.28933 \tabularnewline
6 & 12.6 & 11.9975 & 0.602535 \tabularnewline
7 & 14.8 & 11.3402 & 3.45982 \tabularnewline
8 & 13.3 & 12.8137 & 0.486347 \tabularnewline
9 & 11.1 & 12.9463 & -1.84626 \tabularnewline
10 & 8.2 & 11.0342 & -2.83416 \tabularnewline
11 & 11.4 & 11.4291 & -0.0291325 \tabularnewline
12 & 6.4 & 11.4478 & -5.04782 \tabularnewline
13 & 10.6 & 10.5423 & 0.0576596 \tabularnewline
14 & 12 & 13.1217 & -1.12169 \tabularnewline
15 & 6.3 & 8.55847 & -2.25847 \tabularnewline
16 & 11.9 & 13.125 & -1.22498 \tabularnewline
17 & 9.3 & 10.9108 & -1.61076 \tabularnewline
18 & 10 & 10.1698 & -0.169798 \tabularnewline
19 & 6.4 & 10.2428 & -3.84283 \tabularnewline
20 & 13.8 & 12.4979 & 1.30215 \tabularnewline
21 & 10.8 & 10.9685 & -0.168524 \tabularnewline
22 & 13.8 & 12.2516 & 1.54841 \tabularnewline
23 & 11.7 & 10.6084 & 1.09165 \tabularnewline
24 & 10.9 & 12.3582 & -1.45821 \tabularnewline
25 & 9.9 & 11.3181 & -1.41806 \tabularnewline
26 & 11.5 & 10.8075 & 0.692452 \tabularnewline
27 & 8.3 & 10.9004 & -2.60044 \tabularnewline
28 & 11.7 & 10.8692 & 0.83082 \tabularnewline
29 & 9 & 10.6019 & -1.60193 \tabularnewline
30 & 9.7 & 14.6768 & -4.97677 \tabularnewline
31 & 10.8 & 11.3024 & -0.502433 \tabularnewline
32 & 10.3 & 10.3281 & -0.0280992 \tabularnewline
33 & 10.4 & 9.57437 & 0.825626 \tabularnewline
34 & 9.3 & 12.114 & -2.81402 \tabularnewline
35 & 11.8 & 11.0428 & 0.757236 \tabularnewline
36 & 5.9 & 11.0313 & -5.13128 \tabularnewline
37 & 11.4 & 11.4562 & -0.0562116 \tabularnewline
38 & 13 & 11.1034 & 1.89665 \tabularnewline
39 & 10.8 & 11.9618 & -1.16184 \tabularnewline
40 & 11.3 & 10.8339 & 0.466104 \tabularnewline
41 & 11.8 & 11.2383 & 0.561672 \tabularnewline
42 & 12.7 & 9.74161 & 2.95839 \tabularnewline
43 & 10.9 & 10.6827 & 0.217319 \tabularnewline
44 & 13.3 & 11.7461 & 1.55387 \tabularnewline
45 & 10.1 & 10.4295 & -0.329451 \tabularnewline
46 & 14.3 & 11.4409 & 2.85906 \tabularnewline
47 & 9.3 & 11.5042 & -2.20417 \tabularnewline
48 & 12.5 & 10.3988 & 2.10123 \tabularnewline
49 & 7.6 & 10.1475 & -2.54755 \tabularnewline
50 & 15.9 & 12.4833 & 3.41668 \tabularnewline
51 & 9.2 & 10.5123 & -1.31225 \tabularnewline
52 & 11.1 & 12.6145 & -1.51445 \tabularnewline
53 & 13 & 12.537 & 0.463005 \tabularnewline
54 & 14.5 & 11.5612 & 2.93879 \tabularnewline
55 & 12.3 & 13.0659 & -0.765943 \tabularnewline
56 & 11.4 & 10.961 & 0.439028 \tabularnewline
57 & 13 & 12.0242 & 0.975825 \tabularnewline
58 & 13.2 & 10.7218 & 2.47815 \tabularnewline
59 & 7.7 & 11.5346 & -3.8346 \tabularnewline
60 & 4.35 & 6.94441 & -2.59441 \tabularnewline
61 & 12.7 & 9.6868 & 3.0132 \tabularnewline
62 & 18.1 & 16.0076 & 2.09239 \tabularnewline
63 & 17.85 & 16.2368 & 1.61318 \tabularnewline
64 & 17.1 & 17.1227 & -0.0226883 \tabularnewline
65 & 19.1 & 16.3717 & 2.72828 \tabularnewline
66 & 16.1 & 18.3552 & -2.25521 \tabularnewline
67 & 13.35 & 10.6321 & 2.71786 \tabularnewline
68 & 18.4 & 17.3758 & 1.02424 \tabularnewline
69 & 14.7 & 7.71574 & 6.98426 \tabularnewline
70 & 10.6 & 12.9085 & -2.3085 \tabularnewline
71 & 12.6 & 13.8004 & -1.20041 \tabularnewline
72 & 13.6 & 12.699 & 0.901035 \tabularnewline
73 & 14.1 & 13.2805 & 0.819543 \tabularnewline
74 & 14.5 & 13.5015 & 0.99846 \tabularnewline
75 & 16.15 & 16.4297 & -0.279739 \tabularnewline
76 & 14.75 & 12.6312 & 2.11879 \tabularnewline
77 & 14.8 & 12.5932 & 2.20681 \tabularnewline
78 & 12.45 & 12.1053 & 0.344686 \tabularnewline
79 & 12.65 & 10.4853 & 2.16466 \tabularnewline
80 & 17.35 & 14.0411 & 3.30888 \tabularnewline
81 & 8.6 & 8.22629 & 0.373715 \tabularnewline
82 & 18.4 & 17.1183 & 1.28171 \tabularnewline
83 & 16.1 & 13.9129 & 2.18707 \tabularnewline
84 & 17.75 & 15.4217 & 2.32827 \tabularnewline
85 & 15.25 & 15.9343 & -0.684268 \tabularnewline
86 & 17.65 & 16.0658 & 1.58415 \tabularnewline
87 & 16.35 & 16.5252 & -0.175219 \tabularnewline
88 & 17.65 & 18.0158 & -0.365786 \tabularnewline
89 & 13.6 & 12.3418 & 1.25822 \tabularnewline
90 & 14.35 & 13.5654 & 0.784604 \tabularnewline
91 & 14.75 & 17.4606 & -2.71057 \tabularnewline
92 & 18.25 & 16.386 & 1.86404 \tabularnewline
93 & 9.9 & 15.3294 & -5.42941 \tabularnewline
94 & 16 & 13.8301 & 2.16986 \tabularnewline
95 & 18.25 & 15.976 & 2.27399 \tabularnewline
96 & 16.85 & 17.8231 & -0.97308 \tabularnewline
97 & 18.95 & 16.9191 & 2.03091 \tabularnewline
98 & 15.6 & 13.3229 & 2.27709 \tabularnewline
99 & 17.1 & 17.8413 & -0.741339 \tabularnewline
100 & 15.4 & 15.9923 & -0.592281 \tabularnewline
101 & 15.4 & 16.0941 & -0.694149 \tabularnewline
102 & 13.35 & 14.4406 & -1.09063 \tabularnewline
103 & 19.1 & 17.2165 & 1.88352 \tabularnewline
104 & 7.6 & 7.16027 & 0.439728 \tabularnewline
105 & 19.1 & 16.6078 & 2.49221 \tabularnewline
106 & 14.75 & 16.6279 & -1.87788 \tabularnewline
107 & 19.25 & 16.5317 & 2.71828 \tabularnewline
108 & 13.6 & 16.0314 & -2.43139 \tabularnewline
109 & 12.75 & 15.7166 & -2.96663 \tabularnewline
110 & 9.85 & 8.26629 & 1.58371 \tabularnewline
111 & 15.25 & 15.5399 & -0.28989 \tabularnewline
112 & 11.9 & 13.5851 & -1.68507 \tabularnewline
113 & 16.35 & 17.4891 & -1.13914 \tabularnewline
114 & 12.4 & 13.6722 & -1.27223 \tabularnewline
115 & 18.15 & 16.4514 & 1.69857 \tabularnewline
116 & 17.75 & 15.4801 & 2.26989 \tabularnewline
117 & 12.35 & 12.4867 & -0.136715 \tabularnewline
118 & 15.6 & 15.5698 & 0.0302189 \tabularnewline
119 & 19.3 & 16.9837 & 2.31627 \tabularnewline
120 & 17.1 & 16.666 & 0.433973 \tabularnewline
121 & 18.4 & 15.5063 & 2.89375 \tabularnewline
122 & 19.05 & 16.8202 & 2.2298 \tabularnewline
123 & 18.55 & 14.4693 & 4.08073 \tabularnewline
124 & 19.1 & 18.0086 & 1.09139 \tabularnewline
125 & 12.85 & 15.982 & -3.13203 \tabularnewline
126 & 9.5 & 10.7327 & -1.23269 \tabularnewline
127 & 4.5 & 7.27018 & -2.77018 \tabularnewline
128 & 13.6 & 15.2157 & -1.61571 \tabularnewline
129 & 11.7 & 11.7384 & -0.0384193 \tabularnewline
130 & 13.35 & 14.0186 & -0.668639 \tabularnewline
131 & 17.6 & 19.0817 & -1.48174 \tabularnewline
132 & 14.05 & 13.615 & 0.435036 \tabularnewline
133 & 16.1 & 17.6013 & -1.50133 \tabularnewline
134 & 13.35 & 15.3993 & -2.04931 \tabularnewline
135 & 11.85 & 15.2642 & -3.41424 \tabularnewline
136 & 11.95 & 11.1523 & 0.797653 \tabularnewline
137 & 13.2 & 16.4898 & -3.28982 \tabularnewline
138 & 7.7 & 9.87669 & -2.17669 \tabularnewline
139 & 14.6 & 13.4042 & 1.19579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12.9[/C][C]12.6553[/C][C]0.244727[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5715[/C][C]1.62853[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.4419[/C][C]1.35807[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6633[/C][C]-4.26332[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.9893[/C][C]-4.28933[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9975[/C][C]0.602535[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.3402[/C][C]3.45982[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]12.8137[/C][C]0.486347[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.9463[/C][C]-1.84626[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.0342[/C][C]-2.83416[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.4291[/C][C]-0.0291325[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.4478[/C][C]-5.04782[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.5423[/C][C]0.0576596[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.1217[/C][C]-1.12169[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.55847[/C][C]-2.25847[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.125[/C][C]-1.22498[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9108[/C][C]-1.61076[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]10.1698[/C][C]-0.169798[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.2428[/C][C]-3.84283[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.4979[/C][C]1.30215[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.9685[/C][C]-0.168524[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.2516[/C][C]1.54841[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.6084[/C][C]1.09165[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.3582[/C][C]-1.45821[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3181[/C][C]-1.41806[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.8075[/C][C]0.692452[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]10.9004[/C][C]-2.60044[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]10.8692[/C][C]0.83082[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.6019[/C][C]-1.60193[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]14.6768[/C][C]-4.97677[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.3024[/C][C]-0.502433[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.3281[/C][C]-0.0280992[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.57437[/C][C]0.825626[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.114[/C][C]-2.81402[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.0428[/C][C]0.757236[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.0313[/C][C]-5.13128[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.4562[/C][C]-0.0562116[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.1034[/C][C]1.89665[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.9618[/C][C]-1.16184[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.8339[/C][C]0.466104[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.2383[/C][C]0.561672[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.74161[/C][C]2.95839[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.6827[/C][C]0.217319[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.7461[/C][C]1.55387[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.4295[/C][C]-0.329451[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.4409[/C][C]2.85906[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.5042[/C][C]-2.20417[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.3988[/C][C]2.10123[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.1475[/C][C]-2.54755[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4833[/C][C]3.41668[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.5123[/C][C]-1.31225[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.6145[/C][C]-1.51445[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.537[/C][C]0.463005[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.5612[/C][C]2.93879[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0659[/C][C]-0.765943[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.961[/C][C]0.439028[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.0242[/C][C]0.975825[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]10.7218[/C][C]2.47815[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.5346[/C][C]-3.8346[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]6.94441[/C][C]-2.59441[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]9.6868[/C][C]3.0132[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.0076[/C][C]2.09239[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.2368[/C][C]1.61318[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.1227[/C][C]-0.0226883[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.3717[/C][C]2.72828[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.3552[/C][C]-2.25521[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.6321[/C][C]2.71786[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.3758[/C][C]1.02424[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.71574[/C][C]6.98426[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]12.9085[/C][C]-2.3085[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.8004[/C][C]-1.20041[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.699[/C][C]0.901035[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.2805[/C][C]0.819543[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.5015[/C][C]0.99846[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.4297[/C][C]-0.279739[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.6312[/C][C]2.11879[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.5932[/C][C]2.20681[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.1053[/C][C]0.344686[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.4853[/C][C]2.16466[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]14.0411[/C][C]3.30888[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.22629[/C][C]0.373715[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]17.1183[/C][C]1.28171[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]13.9129[/C][C]2.18707[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.4217[/C][C]2.32827[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.9343[/C][C]-0.684268[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.0658[/C][C]1.58415[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.5252[/C][C]-0.175219[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.0158[/C][C]-0.365786[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.3418[/C][C]1.25822[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.5654[/C][C]0.784604[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.4606[/C][C]-2.71057[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.386[/C][C]1.86404[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.3294[/C][C]-5.42941[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.8301[/C][C]2.16986[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]15.976[/C][C]2.27399[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.8231[/C][C]-0.97308[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.9191[/C][C]2.03091[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.3229[/C][C]2.27709[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.8413[/C][C]-0.741339[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]15.9923[/C][C]-0.592281[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0941[/C][C]-0.694149[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.4406[/C][C]-1.09063[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.2165[/C][C]1.88352[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]7.16027[/C][C]0.439728[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.6078[/C][C]2.49221[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.6279[/C][C]-1.87788[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.5317[/C][C]2.71828[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0314[/C][C]-2.43139[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.7166[/C][C]-2.96663[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.26629[/C][C]1.58371[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.5399[/C][C]-0.28989[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.5851[/C][C]-1.68507[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.4891[/C][C]-1.13914[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.6722[/C][C]-1.27223[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.4514[/C][C]1.69857[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.4801[/C][C]2.26989[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.4867[/C][C]-0.136715[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.5698[/C][C]0.0302189[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.9837[/C][C]2.31627[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.666[/C][C]0.433973[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.5063[/C][C]2.89375[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8202[/C][C]2.2298[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.4693[/C][C]4.08073[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.0086[/C][C]1.09139[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.982[/C][C]-3.13203[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.7327[/C][C]-1.23269[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.27018[/C][C]-2.77018[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.2157[/C][C]-1.61571[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]11.7384[/C][C]-0.0384193[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]14.0186[/C][C]-0.668639[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]19.0817[/C][C]-1.48174[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.615[/C][C]0.435036[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.6013[/C][C]-1.50133[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.3993[/C][C]-2.04931[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.2642[/C][C]-3.41424[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.1523[/C][C]0.797653[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.4898[/C][C]-3.28982[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.87669[/C][C]-2.17669[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.4042[/C][C]1.19579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&T=4

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.65530.244727
212.210.57151.62853
312.811.44191.35807
47.411.6633-4.26332
56.710.9893-4.28933
612.611.99750.602535
714.811.34023.45982
813.312.81370.486347
911.112.9463-1.84626
108.211.0342-2.83416
1111.411.4291-0.0291325
126.411.4478-5.04782
1310.610.54230.0576596
141213.1217-1.12169
156.38.55847-2.25847
1611.913.125-1.22498
179.310.9108-1.61076
181010.1698-0.169798
196.410.2428-3.84283
2013.812.49791.30215
2110.810.9685-0.168524
2213.812.25161.54841
2311.710.60841.09165
2410.912.3582-1.45821
259.911.3181-1.41806
2611.510.80750.692452
278.310.9004-2.60044
2811.710.86920.83082
29910.6019-1.60193
309.714.6768-4.97677
3110.811.3024-0.502433
3210.310.3281-0.0280992
3310.49.574370.825626
349.312.114-2.81402
3511.811.04280.757236
365.911.0313-5.13128
3711.411.4562-0.0562116
381311.10341.89665
3910.811.9618-1.16184
4011.310.83390.466104
4111.811.23830.561672
4212.79.741612.95839
4310.910.68270.217319
4413.311.74611.55387
4510.110.4295-0.329451
4614.311.44092.85906
479.311.5042-2.20417
4812.510.39882.10123
497.610.1475-2.54755
5015.912.48333.41668
519.210.5123-1.31225
5211.112.6145-1.51445
531312.5370.463005
5414.511.56122.93879
5512.313.0659-0.765943
5611.410.9610.439028
571312.02420.975825
5813.210.72182.47815
597.711.5346-3.8346
604.356.94441-2.59441
6112.79.68683.0132
6218.116.00762.09239
6317.8516.23681.61318
6417.117.1227-0.0226883
6519.116.37172.72828
6616.118.3552-2.25521
6713.3510.63212.71786
6818.417.37581.02424
6914.77.715746.98426
7010.612.9085-2.3085
7112.613.8004-1.20041
7213.612.6990.901035
7314.113.28050.819543
7414.513.50150.99846
7516.1516.4297-0.279739
7614.7512.63122.11879
7714.812.59322.20681
7812.4512.10530.344686
7912.6510.48532.16466
8017.3514.04113.30888
818.68.226290.373715
8218.417.11831.28171
8316.113.91292.18707
8417.7515.42172.32827
8515.2515.9343-0.684268
8617.6516.06581.58415
8716.3516.5252-0.175219
8817.6518.0158-0.365786
8913.612.34181.25822
9014.3513.56540.784604
9114.7517.4606-2.71057
9218.2516.3861.86404
939.915.3294-5.42941
941613.83012.16986
9518.2515.9762.27399
9616.8517.8231-0.97308
9718.9516.91912.03091
9815.613.32292.27709
9917.117.8413-0.741339
10015.415.9923-0.592281
10115.416.0941-0.694149
10213.3514.4406-1.09063
10319.117.21651.88352
1047.67.160270.439728
10519.116.60782.49221
10614.7516.6279-1.87788
10719.2516.53172.71828
10813.616.0314-2.43139
10912.7515.7166-2.96663
1109.858.266291.58371
11115.2515.5399-0.28989
11211.913.5851-1.68507
11316.3517.4891-1.13914
11412.413.6722-1.27223
11518.1516.45141.69857
11617.7515.48012.26989
11712.3512.4867-0.136715
11815.615.56980.0302189
11919.316.98372.31627
12017.116.6660.433973
12118.415.50632.89375
12219.0516.82022.2298
12318.5514.46934.08073
12419.118.00861.09139
12512.8515.982-3.13203
1269.510.7327-1.23269
1274.57.27018-2.77018
12813.615.2157-1.61571
12911.711.7384-0.0384193
13013.3514.0186-0.668639
13117.619.0817-1.48174
13214.0513.6150.435036
13316.117.6013-1.50133
13413.3515.3993-2.04931
13511.8515.2642-3.41424
13611.9511.15230.797653
13713.216.4898-3.28982
1387.79.87669-2.17669
13914.613.40421.19579






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7183910.5632190.281609
100.576060.847880.42394
110.5528750.8942510.447125
120.701680.5966410.29832
130.6006030.7987930.399397
140.4949210.9898410.505079
150.4194860.8389710.580514
160.3276730.6553460.672327
170.2527440.5054870.747256
180.3668020.7336030.633198
190.3403980.6807970.659602
200.2671070.5342150.732893
210.2030550.4061090.796945
220.1727080.3454160.827292
230.1304240.2608490.869576
240.09616840.1923370.903832
250.09240070.1848010.907599
260.1190070.2380140.880993
270.1870050.3740090.812995
280.18930.37860.8107
290.1670810.3341620.832919
300.3446420.6892840.655358
310.3090690.6181370.690931
320.2609430.5218860.739057
330.2146810.4293620.785319
340.2173440.4346870.782656
350.1779210.3558410.822079
360.4084230.8168450.591577
370.3533220.7066450.646678
380.3118210.6236420.688179
390.275940.5518790.72406
400.2306610.4613220.769339
410.1979780.3959560.802022
420.2278120.4556240.772188
430.1925390.3850770.807461
440.2961530.5923050.703847
450.2554750.5109510.744525
460.3977070.7954140.602293
470.4055540.8111070.594446
480.4442470.8884940.555753
490.5012620.9974750.498738
500.631720.7365610.36828
510.603140.793720.39686
520.5812670.8374660.418733
530.5358340.9283330.464166
540.5762880.8474240.423712
550.5453210.9093590.454679
560.5001050.9997890.499895
570.4634790.9269580.536521
580.4792990.9585980.520701
590.5784360.8431290.421564
600.6607350.6785290.339265
610.6932790.6134420.306721
620.6948080.6103840.305192
630.6620590.6758830.337941
640.6234880.7530240.376512
650.6181460.7637070.381854
660.6873320.6253360.312668
670.7173340.5653320.282666
680.6833330.6333350.316667
690.9523950.09520960.0476048
700.9573160.0853670.0426835
710.9499650.100070.0500351
720.9497730.1004550.0502273
730.9362590.1274820.0637412
740.9235410.1529180.076459
750.9294760.1410480.0705239
760.9271850.145630.0728151
770.9225660.1548680.0774339
780.9023160.1953670.0976836
790.8958990.2082020.104101
800.9301510.1396990.0698494
810.9133740.1732520.0866258
820.8941230.2117530.105877
830.8805420.2389160.119458
840.8813360.2373280.118664
850.8803210.2393580.119679
860.8692310.2615380.130769
870.8435930.3128150.156407
880.8149340.3701320.185066
890.7840740.4318510.215926
900.7450710.5098580.254929
910.7662840.4674320.233716
920.7480920.5038170.251908
930.90590.18820.0941
940.8938710.2122580.106129
950.8928370.2143270.107163
960.8692220.2615560.130778
970.846680.306640.15332
980.851110.2977810.14889
990.826520.3469610.17348
1000.789850.4203010.21015
1010.7482270.5035460.251773
1020.7256220.5487560.274378
1030.7333290.5333420.266671
1040.7107760.5784480.289224
1050.7067970.5864070.293203
1060.6818630.6362730.318137
1070.652130.695740.34787
1080.6379640.7240710.362036
1090.6639570.6720860.336043
1100.6578530.6842930.342147
1110.5947040.8105920.405296
1120.5793440.8413120.420656
1130.5681730.8636550.431827
1140.5158380.9683240.484162
1150.4967460.9934920.503254
1160.4754340.9508670.524566
1170.4154190.8308370.584581
1180.4126680.8253360.587332
1190.4089090.8178190.591091
1200.4397980.8795970.560202
1210.4294340.8588670.570566
1220.578130.843740.42187
1230.9813820.03723520.0186176
1240.9658870.06822640.0341132
1250.9644670.0710670.0355335
1260.9320170.1359670.0679834
1270.9246430.1507140.075357
1280.9421320.1157370.0578684
1290.8862790.2274410.113721
1300.7643440.4713120.235656

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.718391 & 0.563219 & 0.281609 \tabularnewline
10 & 0.57606 & 0.84788 & 0.42394 \tabularnewline
11 & 0.552875 & 0.894251 & 0.447125 \tabularnewline
12 & 0.70168 & 0.596641 & 0.29832 \tabularnewline
13 & 0.600603 & 0.798793 & 0.399397 \tabularnewline
14 & 0.494921 & 0.989841 & 0.505079 \tabularnewline
15 & 0.419486 & 0.838971 & 0.580514 \tabularnewline
16 & 0.327673 & 0.655346 & 0.672327 \tabularnewline
17 & 0.252744 & 0.505487 & 0.747256 \tabularnewline
18 & 0.366802 & 0.733603 & 0.633198 \tabularnewline
19 & 0.340398 & 0.680797 & 0.659602 \tabularnewline
20 & 0.267107 & 0.534215 & 0.732893 \tabularnewline
21 & 0.203055 & 0.406109 & 0.796945 \tabularnewline
22 & 0.172708 & 0.345416 & 0.827292 \tabularnewline
23 & 0.130424 & 0.260849 & 0.869576 \tabularnewline
24 & 0.0961684 & 0.192337 & 0.903832 \tabularnewline
25 & 0.0924007 & 0.184801 & 0.907599 \tabularnewline
26 & 0.119007 & 0.238014 & 0.880993 \tabularnewline
27 & 0.187005 & 0.374009 & 0.812995 \tabularnewline
28 & 0.1893 & 0.3786 & 0.8107 \tabularnewline
29 & 0.167081 & 0.334162 & 0.832919 \tabularnewline
30 & 0.344642 & 0.689284 & 0.655358 \tabularnewline
31 & 0.309069 & 0.618137 & 0.690931 \tabularnewline
32 & 0.260943 & 0.521886 & 0.739057 \tabularnewline
33 & 0.214681 & 0.429362 & 0.785319 \tabularnewline
34 & 0.217344 & 0.434687 & 0.782656 \tabularnewline
35 & 0.177921 & 0.355841 & 0.822079 \tabularnewline
36 & 0.408423 & 0.816845 & 0.591577 \tabularnewline
37 & 0.353322 & 0.706645 & 0.646678 \tabularnewline
38 & 0.311821 & 0.623642 & 0.688179 \tabularnewline
39 & 0.27594 & 0.551879 & 0.72406 \tabularnewline
40 & 0.230661 & 0.461322 & 0.769339 \tabularnewline
41 & 0.197978 & 0.395956 & 0.802022 \tabularnewline
42 & 0.227812 & 0.455624 & 0.772188 \tabularnewline
43 & 0.192539 & 0.385077 & 0.807461 \tabularnewline
44 & 0.296153 & 0.592305 & 0.703847 \tabularnewline
45 & 0.255475 & 0.510951 & 0.744525 \tabularnewline
46 & 0.397707 & 0.795414 & 0.602293 \tabularnewline
47 & 0.405554 & 0.811107 & 0.594446 \tabularnewline
48 & 0.444247 & 0.888494 & 0.555753 \tabularnewline
49 & 0.501262 & 0.997475 & 0.498738 \tabularnewline
50 & 0.63172 & 0.736561 & 0.36828 \tabularnewline
51 & 0.60314 & 0.79372 & 0.39686 \tabularnewline
52 & 0.581267 & 0.837466 & 0.418733 \tabularnewline
53 & 0.535834 & 0.928333 & 0.464166 \tabularnewline
54 & 0.576288 & 0.847424 & 0.423712 \tabularnewline
55 & 0.545321 & 0.909359 & 0.454679 \tabularnewline
56 & 0.500105 & 0.999789 & 0.499895 \tabularnewline
57 & 0.463479 & 0.926958 & 0.536521 \tabularnewline
58 & 0.479299 & 0.958598 & 0.520701 \tabularnewline
59 & 0.578436 & 0.843129 & 0.421564 \tabularnewline
60 & 0.660735 & 0.678529 & 0.339265 \tabularnewline
61 & 0.693279 & 0.613442 & 0.306721 \tabularnewline
62 & 0.694808 & 0.610384 & 0.305192 \tabularnewline
63 & 0.662059 & 0.675883 & 0.337941 \tabularnewline
64 & 0.623488 & 0.753024 & 0.376512 \tabularnewline
65 & 0.618146 & 0.763707 & 0.381854 \tabularnewline
66 & 0.687332 & 0.625336 & 0.312668 \tabularnewline
67 & 0.717334 & 0.565332 & 0.282666 \tabularnewline
68 & 0.683333 & 0.633335 & 0.316667 \tabularnewline
69 & 0.952395 & 0.0952096 & 0.0476048 \tabularnewline
70 & 0.957316 & 0.085367 & 0.0426835 \tabularnewline
71 & 0.949965 & 0.10007 & 0.0500351 \tabularnewline
72 & 0.949773 & 0.100455 & 0.0502273 \tabularnewline
73 & 0.936259 & 0.127482 & 0.0637412 \tabularnewline
74 & 0.923541 & 0.152918 & 0.076459 \tabularnewline
75 & 0.929476 & 0.141048 & 0.0705239 \tabularnewline
76 & 0.927185 & 0.14563 & 0.0728151 \tabularnewline
77 & 0.922566 & 0.154868 & 0.0774339 \tabularnewline
78 & 0.902316 & 0.195367 & 0.0976836 \tabularnewline
79 & 0.895899 & 0.208202 & 0.104101 \tabularnewline
80 & 0.930151 & 0.139699 & 0.0698494 \tabularnewline
81 & 0.913374 & 0.173252 & 0.0866258 \tabularnewline
82 & 0.894123 & 0.211753 & 0.105877 \tabularnewline
83 & 0.880542 & 0.238916 & 0.119458 \tabularnewline
84 & 0.881336 & 0.237328 & 0.118664 \tabularnewline
85 & 0.880321 & 0.239358 & 0.119679 \tabularnewline
86 & 0.869231 & 0.261538 & 0.130769 \tabularnewline
87 & 0.843593 & 0.312815 & 0.156407 \tabularnewline
88 & 0.814934 & 0.370132 & 0.185066 \tabularnewline
89 & 0.784074 & 0.431851 & 0.215926 \tabularnewline
90 & 0.745071 & 0.509858 & 0.254929 \tabularnewline
91 & 0.766284 & 0.467432 & 0.233716 \tabularnewline
92 & 0.748092 & 0.503817 & 0.251908 \tabularnewline
93 & 0.9059 & 0.1882 & 0.0941 \tabularnewline
94 & 0.893871 & 0.212258 & 0.106129 \tabularnewline
95 & 0.892837 & 0.214327 & 0.107163 \tabularnewline
96 & 0.869222 & 0.261556 & 0.130778 \tabularnewline
97 & 0.84668 & 0.30664 & 0.15332 \tabularnewline
98 & 0.85111 & 0.297781 & 0.14889 \tabularnewline
99 & 0.82652 & 0.346961 & 0.17348 \tabularnewline
100 & 0.78985 & 0.420301 & 0.21015 \tabularnewline
101 & 0.748227 & 0.503546 & 0.251773 \tabularnewline
102 & 0.725622 & 0.548756 & 0.274378 \tabularnewline
103 & 0.733329 & 0.533342 & 0.266671 \tabularnewline
104 & 0.710776 & 0.578448 & 0.289224 \tabularnewline
105 & 0.706797 & 0.586407 & 0.293203 \tabularnewline
106 & 0.681863 & 0.636273 & 0.318137 \tabularnewline
107 & 0.65213 & 0.69574 & 0.34787 \tabularnewline
108 & 0.637964 & 0.724071 & 0.362036 \tabularnewline
109 & 0.663957 & 0.672086 & 0.336043 \tabularnewline
110 & 0.657853 & 0.684293 & 0.342147 \tabularnewline
111 & 0.594704 & 0.810592 & 0.405296 \tabularnewline
112 & 0.579344 & 0.841312 & 0.420656 \tabularnewline
113 & 0.568173 & 0.863655 & 0.431827 \tabularnewline
114 & 0.515838 & 0.968324 & 0.484162 \tabularnewline
115 & 0.496746 & 0.993492 & 0.503254 \tabularnewline
116 & 0.475434 & 0.950867 & 0.524566 \tabularnewline
117 & 0.415419 & 0.830837 & 0.584581 \tabularnewline
118 & 0.412668 & 0.825336 & 0.587332 \tabularnewline
119 & 0.408909 & 0.817819 & 0.591091 \tabularnewline
120 & 0.439798 & 0.879597 & 0.560202 \tabularnewline
121 & 0.429434 & 0.858867 & 0.570566 \tabularnewline
122 & 0.57813 & 0.84374 & 0.42187 \tabularnewline
123 & 0.981382 & 0.0372352 & 0.0186176 \tabularnewline
124 & 0.965887 & 0.0682264 & 0.0341132 \tabularnewline
125 & 0.964467 & 0.071067 & 0.0355335 \tabularnewline
126 & 0.932017 & 0.135967 & 0.0679834 \tabularnewline
127 & 0.924643 & 0.150714 & 0.075357 \tabularnewline
128 & 0.942132 & 0.115737 & 0.0578684 \tabularnewline
129 & 0.886279 & 0.227441 & 0.113721 \tabularnewline
130 & 0.764344 & 0.471312 & 0.235656 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.718391[/C][C]0.563219[/C][C]0.281609[/C][/ROW]
[ROW][C]10[/C][C]0.57606[/C][C]0.84788[/C][C]0.42394[/C][/ROW]
[ROW][C]11[/C][C]0.552875[/C][C]0.894251[/C][C]0.447125[/C][/ROW]
[ROW][C]12[/C][C]0.70168[/C][C]0.596641[/C][C]0.29832[/C][/ROW]
[ROW][C]13[/C][C]0.600603[/C][C]0.798793[/C][C]0.399397[/C][/ROW]
[ROW][C]14[/C][C]0.494921[/C][C]0.989841[/C][C]0.505079[/C][/ROW]
[ROW][C]15[/C][C]0.419486[/C][C]0.838971[/C][C]0.580514[/C][/ROW]
[ROW][C]16[/C][C]0.327673[/C][C]0.655346[/C][C]0.672327[/C][/ROW]
[ROW][C]17[/C][C]0.252744[/C][C]0.505487[/C][C]0.747256[/C][/ROW]
[ROW][C]18[/C][C]0.366802[/C][C]0.733603[/C][C]0.633198[/C][/ROW]
[ROW][C]19[/C][C]0.340398[/C][C]0.680797[/C][C]0.659602[/C][/ROW]
[ROW][C]20[/C][C]0.267107[/C][C]0.534215[/C][C]0.732893[/C][/ROW]
[ROW][C]21[/C][C]0.203055[/C][C]0.406109[/C][C]0.796945[/C][/ROW]
[ROW][C]22[/C][C]0.172708[/C][C]0.345416[/C][C]0.827292[/C][/ROW]
[ROW][C]23[/C][C]0.130424[/C][C]0.260849[/C][C]0.869576[/C][/ROW]
[ROW][C]24[/C][C]0.0961684[/C][C]0.192337[/C][C]0.903832[/C][/ROW]
[ROW][C]25[/C][C]0.0924007[/C][C]0.184801[/C][C]0.907599[/C][/ROW]
[ROW][C]26[/C][C]0.119007[/C][C]0.238014[/C][C]0.880993[/C][/ROW]
[ROW][C]27[/C][C]0.187005[/C][C]0.374009[/C][C]0.812995[/C][/ROW]
[ROW][C]28[/C][C]0.1893[/C][C]0.3786[/C][C]0.8107[/C][/ROW]
[ROW][C]29[/C][C]0.167081[/C][C]0.334162[/C][C]0.832919[/C][/ROW]
[ROW][C]30[/C][C]0.344642[/C][C]0.689284[/C][C]0.655358[/C][/ROW]
[ROW][C]31[/C][C]0.309069[/C][C]0.618137[/C][C]0.690931[/C][/ROW]
[ROW][C]32[/C][C]0.260943[/C][C]0.521886[/C][C]0.739057[/C][/ROW]
[ROW][C]33[/C][C]0.214681[/C][C]0.429362[/C][C]0.785319[/C][/ROW]
[ROW][C]34[/C][C]0.217344[/C][C]0.434687[/C][C]0.782656[/C][/ROW]
[ROW][C]35[/C][C]0.177921[/C][C]0.355841[/C][C]0.822079[/C][/ROW]
[ROW][C]36[/C][C]0.408423[/C][C]0.816845[/C][C]0.591577[/C][/ROW]
[ROW][C]37[/C][C]0.353322[/C][C]0.706645[/C][C]0.646678[/C][/ROW]
[ROW][C]38[/C][C]0.311821[/C][C]0.623642[/C][C]0.688179[/C][/ROW]
[ROW][C]39[/C][C]0.27594[/C][C]0.551879[/C][C]0.72406[/C][/ROW]
[ROW][C]40[/C][C]0.230661[/C][C]0.461322[/C][C]0.769339[/C][/ROW]
[ROW][C]41[/C][C]0.197978[/C][C]0.395956[/C][C]0.802022[/C][/ROW]
[ROW][C]42[/C][C]0.227812[/C][C]0.455624[/C][C]0.772188[/C][/ROW]
[ROW][C]43[/C][C]0.192539[/C][C]0.385077[/C][C]0.807461[/C][/ROW]
[ROW][C]44[/C][C]0.296153[/C][C]0.592305[/C][C]0.703847[/C][/ROW]
[ROW][C]45[/C][C]0.255475[/C][C]0.510951[/C][C]0.744525[/C][/ROW]
[ROW][C]46[/C][C]0.397707[/C][C]0.795414[/C][C]0.602293[/C][/ROW]
[ROW][C]47[/C][C]0.405554[/C][C]0.811107[/C][C]0.594446[/C][/ROW]
[ROW][C]48[/C][C]0.444247[/C][C]0.888494[/C][C]0.555753[/C][/ROW]
[ROW][C]49[/C][C]0.501262[/C][C]0.997475[/C][C]0.498738[/C][/ROW]
[ROW][C]50[/C][C]0.63172[/C][C]0.736561[/C][C]0.36828[/C][/ROW]
[ROW][C]51[/C][C]0.60314[/C][C]0.79372[/C][C]0.39686[/C][/ROW]
[ROW][C]52[/C][C]0.581267[/C][C]0.837466[/C][C]0.418733[/C][/ROW]
[ROW][C]53[/C][C]0.535834[/C][C]0.928333[/C][C]0.464166[/C][/ROW]
[ROW][C]54[/C][C]0.576288[/C][C]0.847424[/C][C]0.423712[/C][/ROW]
[ROW][C]55[/C][C]0.545321[/C][C]0.909359[/C][C]0.454679[/C][/ROW]
[ROW][C]56[/C][C]0.500105[/C][C]0.999789[/C][C]0.499895[/C][/ROW]
[ROW][C]57[/C][C]0.463479[/C][C]0.926958[/C][C]0.536521[/C][/ROW]
[ROW][C]58[/C][C]0.479299[/C][C]0.958598[/C][C]0.520701[/C][/ROW]
[ROW][C]59[/C][C]0.578436[/C][C]0.843129[/C][C]0.421564[/C][/ROW]
[ROW][C]60[/C][C]0.660735[/C][C]0.678529[/C][C]0.339265[/C][/ROW]
[ROW][C]61[/C][C]0.693279[/C][C]0.613442[/C][C]0.306721[/C][/ROW]
[ROW][C]62[/C][C]0.694808[/C][C]0.610384[/C][C]0.305192[/C][/ROW]
[ROW][C]63[/C][C]0.662059[/C][C]0.675883[/C][C]0.337941[/C][/ROW]
[ROW][C]64[/C][C]0.623488[/C][C]0.753024[/C][C]0.376512[/C][/ROW]
[ROW][C]65[/C][C]0.618146[/C][C]0.763707[/C][C]0.381854[/C][/ROW]
[ROW][C]66[/C][C]0.687332[/C][C]0.625336[/C][C]0.312668[/C][/ROW]
[ROW][C]67[/C][C]0.717334[/C][C]0.565332[/C][C]0.282666[/C][/ROW]
[ROW][C]68[/C][C]0.683333[/C][C]0.633335[/C][C]0.316667[/C][/ROW]
[ROW][C]69[/C][C]0.952395[/C][C]0.0952096[/C][C]0.0476048[/C][/ROW]
[ROW][C]70[/C][C]0.957316[/C][C]0.085367[/C][C]0.0426835[/C][/ROW]
[ROW][C]71[/C][C]0.949965[/C][C]0.10007[/C][C]0.0500351[/C][/ROW]
[ROW][C]72[/C][C]0.949773[/C][C]0.100455[/C][C]0.0502273[/C][/ROW]
[ROW][C]73[/C][C]0.936259[/C][C]0.127482[/C][C]0.0637412[/C][/ROW]
[ROW][C]74[/C][C]0.923541[/C][C]0.152918[/C][C]0.076459[/C][/ROW]
[ROW][C]75[/C][C]0.929476[/C][C]0.141048[/C][C]0.0705239[/C][/ROW]
[ROW][C]76[/C][C]0.927185[/C][C]0.14563[/C][C]0.0728151[/C][/ROW]
[ROW][C]77[/C][C]0.922566[/C][C]0.154868[/C][C]0.0774339[/C][/ROW]
[ROW][C]78[/C][C]0.902316[/C][C]0.195367[/C][C]0.0976836[/C][/ROW]
[ROW][C]79[/C][C]0.895899[/C][C]0.208202[/C][C]0.104101[/C][/ROW]
[ROW][C]80[/C][C]0.930151[/C][C]0.139699[/C][C]0.0698494[/C][/ROW]
[ROW][C]81[/C][C]0.913374[/C][C]0.173252[/C][C]0.0866258[/C][/ROW]
[ROW][C]82[/C][C]0.894123[/C][C]0.211753[/C][C]0.105877[/C][/ROW]
[ROW][C]83[/C][C]0.880542[/C][C]0.238916[/C][C]0.119458[/C][/ROW]
[ROW][C]84[/C][C]0.881336[/C][C]0.237328[/C][C]0.118664[/C][/ROW]
[ROW][C]85[/C][C]0.880321[/C][C]0.239358[/C][C]0.119679[/C][/ROW]
[ROW][C]86[/C][C]0.869231[/C][C]0.261538[/C][C]0.130769[/C][/ROW]
[ROW][C]87[/C][C]0.843593[/C][C]0.312815[/C][C]0.156407[/C][/ROW]
[ROW][C]88[/C][C]0.814934[/C][C]0.370132[/C][C]0.185066[/C][/ROW]
[ROW][C]89[/C][C]0.784074[/C][C]0.431851[/C][C]0.215926[/C][/ROW]
[ROW][C]90[/C][C]0.745071[/C][C]0.509858[/C][C]0.254929[/C][/ROW]
[ROW][C]91[/C][C]0.766284[/C][C]0.467432[/C][C]0.233716[/C][/ROW]
[ROW][C]92[/C][C]0.748092[/C][C]0.503817[/C][C]0.251908[/C][/ROW]
[ROW][C]93[/C][C]0.9059[/C][C]0.1882[/C][C]0.0941[/C][/ROW]
[ROW][C]94[/C][C]0.893871[/C][C]0.212258[/C][C]0.106129[/C][/ROW]
[ROW][C]95[/C][C]0.892837[/C][C]0.214327[/C][C]0.107163[/C][/ROW]
[ROW][C]96[/C][C]0.869222[/C][C]0.261556[/C][C]0.130778[/C][/ROW]
[ROW][C]97[/C][C]0.84668[/C][C]0.30664[/C][C]0.15332[/C][/ROW]
[ROW][C]98[/C][C]0.85111[/C][C]0.297781[/C][C]0.14889[/C][/ROW]
[ROW][C]99[/C][C]0.82652[/C][C]0.346961[/C][C]0.17348[/C][/ROW]
[ROW][C]100[/C][C]0.78985[/C][C]0.420301[/C][C]0.21015[/C][/ROW]
[ROW][C]101[/C][C]0.748227[/C][C]0.503546[/C][C]0.251773[/C][/ROW]
[ROW][C]102[/C][C]0.725622[/C][C]0.548756[/C][C]0.274378[/C][/ROW]
[ROW][C]103[/C][C]0.733329[/C][C]0.533342[/C][C]0.266671[/C][/ROW]
[ROW][C]104[/C][C]0.710776[/C][C]0.578448[/C][C]0.289224[/C][/ROW]
[ROW][C]105[/C][C]0.706797[/C][C]0.586407[/C][C]0.293203[/C][/ROW]
[ROW][C]106[/C][C]0.681863[/C][C]0.636273[/C][C]0.318137[/C][/ROW]
[ROW][C]107[/C][C]0.65213[/C][C]0.69574[/C][C]0.34787[/C][/ROW]
[ROW][C]108[/C][C]0.637964[/C][C]0.724071[/C][C]0.362036[/C][/ROW]
[ROW][C]109[/C][C]0.663957[/C][C]0.672086[/C][C]0.336043[/C][/ROW]
[ROW][C]110[/C][C]0.657853[/C][C]0.684293[/C][C]0.342147[/C][/ROW]
[ROW][C]111[/C][C]0.594704[/C][C]0.810592[/C][C]0.405296[/C][/ROW]
[ROW][C]112[/C][C]0.579344[/C][C]0.841312[/C][C]0.420656[/C][/ROW]
[ROW][C]113[/C][C]0.568173[/C][C]0.863655[/C][C]0.431827[/C][/ROW]
[ROW][C]114[/C][C]0.515838[/C][C]0.968324[/C][C]0.484162[/C][/ROW]
[ROW][C]115[/C][C]0.496746[/C][C]0.993492[/C][C]0.503254[/C][/ROW]
[ROW][C]116[/C][C]0.475434[/C][C]0.950867[/C][C]0.524566[/C][/ROW]
[ROW][C]117[/C][C]0.415419[/C][C]0.830837[/C][C]0.584581[/C][/ROW]
[ROW][C]118[/C][C]0.412668[/C][C]0.825336[/C][C]0.587332[/C][/ROW]
[ROW][C]119[/C][C]0.408909[/C][C]0.817819[/C][C]0.591091[/C][/ROW]
[ROW][C]120[/C][C]0.439798[/C][C]0.879597[/C][C]0.560202[/C][/ROW]
[ROW][C]121[/C][C]0.429434[/C][C]0.858867[/C][C]0.570566[/C][/ROW]
[ROW][C]122[/C][C]0.57813[/C][C]0.84374[/C][C]0.42187[/C][/ROW]
[ROW][C]123[/C][C]0.981382[/C][C]0.0372352[/C][C]0.0186176[/C][/ROW]
[ROW][C]124[/C][C]0.965887[/C][C]0.0682264[/C][C]0.0341132[/C][/ROW]
[ROW][C]125[/C][C]0.964467[/C][C]0.071067[/C][C]0.0355335[/C][/ROW]
[ROW][C]126[/C][C]0.932017[/C][C]0.135967[/C][C]0.0679834[/C][/ROW]
[ROW][C]127[/C][C]0.924643[/C][C]0.150714[/C][C]0.075357[/C][/ROW]
[ROW][C]128[/C][C]0.942132[/C][C]0.115737[/C][C]0.0578684[/C][/ROW]
[ROW][C]129[/C][C]0.886279[/C][C]0.227441[/C][C]0.113721[/C][/ROW]
[ROW][C]130[/C][C]0.764344[/C][C]0.471312[/C][C]0.235656[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7183910.5632190.281609
100.576060.847880.42394
110.5528750.8942510.447125
120.701680.5966410.29832
130.6006030.7987930.399397
140.4949210.9898410.505079
150.4194860.8389710.580514
160.3276730.6553460.672327
170.2527440.5054870.747256
180.3668020.7336030.633198
190.3403980.6807970.659602
200.2671070.5342150.732893
210.2030550.4061090.796945
220.1727080.3454160.827292
230.1304240.2608490.869576
240.09616840.1923370.903832
250.09240070.1848010.907599
260.1190070.2380140.880993
270.1870050.3740090.812995
280.18930.37860.8107
290.1670810.3341620.832919
300.3446420.6892840.655358
310.3090690.6181370.690931
320.2609430.5218860.739057
330.2146810.4293620.785319
340.2173440.4346870.782656
350.1779210.3558410.822079
360.4084230.8168450.591577
370.3533220.7066450.646678
380.3118210.6236420.688179
390.275940.5518790.72406
400.2306610.4613220.769339
410.1979780.3959560.802022
420.2278120.4556240.772188
430.1925390.3850770.807461
440.2961530.5923050.703847
450.2554750.5109510.744525
460.3977070.7954140.602293
470.4055540.8111070.594446
480.4442470.8884940.555753
490.5012620.9974750.498738
500.631720.7365610.36828
510.603140.793720.39686
520.5812670.8374660.418733
530.5358340.9283330.464166
540.5762880.8474240.423712
550.5453210.9093590.454679
560.5001050.9997890.499895
570.4634790.9269580.536521
580.4792990.9585980.520701
590.5784360.8431290.421564
600.6607350.6785290.339265
610.6932790.6134420.306721
620.6948080.6103840.305192
630.6620590.6758830.337941
640.6234880.7530240.376512
650.6181460.7637070.381854
660.6873320.6253360.312668
670.7173340.5653320.282666
680.6833330.6333350.316667
690.9523950.09520960.0476048
700.9573160.0853670.0426835
710.9499650.100070.0500351
720.9497730.1004550.0502273
730.9362590.1274820.0637412
740.9235410.1529180.076459
750.9294760.1410480.0705239
760.9271850.145630.0728151
770.9225660.1548680.0774339
780.9023160.1953670.0976836
790.8958990.2082020.104101
800.9301510.1396990.0698494
810.9133740.1732520.0866258
820.8941230.2117530.105877
830.8805420.2389160.119458
840.8813360.2373280.118664
850.8803210.2393580.119679
860.8692310.2615380.130769
870.8435930.3128150.156407
880.8149340.3701320.185066
890.7840740.4318510.215926
900.7450710.5098580.254929
910.7662840.4674320.233716
920.7480920.5038170.251908
930.90590.18820.0941
940.8938710.2122580.106129
950.8928370.2143270.107163
960.8692220.2615560.130778
970.846680.306640.15332
980.851110.2977810.14889
990.826520.3469610.17348
1000.789850.4203010.21015
1010.7482270.5035460.251773
1020.7256220.5487560.274378
1030.7333290.5333420.266671
1040.7107760.5784480.289224
1050.7067970.5864070.293203
1060.6818630.6362730.318137
1070.652130.695740.34787
1080.6379640.7240710.362036
1090.6639570.6720860.336043
1100.6578530.6842930.342147
1110.5947040.8105920.405296
1120.5793440.8413120.420656
1130.5681730.8636550.431827
1140.5158380.9683240.484162
1150.4967460.9934920.503254
1160.4754340.9508670.524566
1170.4154190.8308370.584581
1180.4126680.8253360.587332
1190.4089090.8178190.591091
1200.4397980.8795970.560202
1210.4294340.8588670.570566
1220.578130.843740.42187
1230.9813820.03723520.0186176
1240.9658870.06822640.0341132
1250.9644670.0710670.0355335
1260.9320170.1359670.0679834
1270.9246430.1507140.075357
1280.9421320.1157370.0578684
1290.8862790.2274410.113721
1300.7643440.4713120.235656






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00819672OK
10% type I error level50.0409836OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00819672 & OK \tabularnewline
10% type I error level & 5 & 0.0409836 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271063&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00819672[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0409836[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271063&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271063&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00819672OK
10% type I error level50.0409836OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}