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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 13:45:53 +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/2010/Nov/30/t1291124856ydnzobkecmm18un.htm/, Retrieved Thu, 31 Oct 2024 22:58:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103446, Retrieved Thu, 31 Oct 2024 22:58:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact194
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS8: model 2] [2010-11-26 13:52:51] [1fd136673b2a4fecb5c545b9b4a05d64]
-    D    [Multiple Regression] [] [2010-11-30 13:45:53] [912a7c71b856221ca57f8714938acfc7] [Current]
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Dataseries X:
2	12
2	11
2	14
1	12
2	21
2	12
2	22
2	11
2	10
2	13
1	10
2	8
1	15
2	14
2	10
1	14
1	14
2	11
1	10
2	13
1	7
2	14
2	12
2	14
1	11
2	9
1	11
2	15
2	14
1	13
2	9
1	15
2	10
2	11
1	13
1	8
1	20
1	12
2	10
1	10
1	9
2	14
1	8
1	14
2	11
2	13
2	9
2	11
2	15
1	11
2	10
1	14
1	18
2	14
1	11
2	12
2	13
2	9
1	10
2	15
1	20
1	12
2	12
2	14
2	13
1	11
2	17
1	12
2	13
1	14
1	13
2	15
2	13
1	10
1	11
2	19
2	13
2	17
1	13
1	9
1	11
1	10
2	9
1	12
2	12
2	13
1	13
2	12
2	15
2	22
2	13
2	15
2	13
2	15
2	10
2	11
2	16
2	11
1	11
1	10
2	10
1	16
2	12
1	11
2	16
1	19
2	11
1	16
1	15
2	24
2	14
2	15
2	11
1	15
2	12
1	10
2	14
2	13
2	9
2	15
2	15
2	14
2	11
2	8
2	11
2	11
1	8
2	10
2	11
2	13
1	11
1	20
2	10
1	15
1	12
2	14
1	23
1	14
2	16
2	11
1	12
2	10
1	14
2	12
1	12
2	11
2	12
1	13
1	11
1	19
2	12
2	17
1	9
2	12
2	19
2	18
2	15
2	14
2	11
2	9
2	18
2	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 12.9137999388331 + 0.323663672507733x[t] + 0.934728575797638M1[t] -0.516961686524345M2[t] -1.87410454366720M3[t] -0.636699995630935M4[t] + 0.91160974204709M5[t] + 1.22044286151192M6[t] -0.898179717499406M7[t] -1.10405174269112M8[t] -1.92307692307692M9[t] -0.71720489788521M10[t] -1.87328251192189M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  12.9137999388331 +  0.323663672507733x[t] +  0.934728575797638M1[t] -0.516961686524345M2[t] -1.87410454366720M3[t] -0.636699995630935M4[t] +  0.91160974204709M5[t] +  1.22044286151192M6[t] -0.898179717499406M7[t] -1.10405174269112M8[t] -1.92307692307692M9[t] -0.71720489788521M10[t] -1.87328251192189M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  12.9137999388331 +  0.323663672507733x[t] +  0.934728575797638M1[t] -0.516961686524345M2[t] -1.87410454366720M3[t] -0.636699995630935M4[t] +  0.91160974204709M5[t] +  1.22044286151192M6[t] -0.898179717499406M7[t] -1.10405174269112M8[t] -1.92307692307692M9[t] -0.71720489788521M10[t] -1.87328251192189M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 12.9137999388331 + 0.323663672507733x[t] + 0.934728575797638M1[t] -0.516961686524345M2[t] -1.87410454366720M3[t] -0.636699995630935M4[t] + 0.91160974204709M5[t] + 1.22044286151192M6[t] -0.898179717499406M7[t] -1.10405174269112M8[t] -1.92307692307692M9[t] -0.71720489788521M10[t] -1.87328251192189M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.91379993883311.21208610.654200
x0.3236636725077330.5061730.63940.5235230.261762
M10.9347285757976381.1924740.78390.4343680.217184
M2-0.5169616865243451.191166-0.4340.6649190.332459
M3-1.874104543667201.191166-1.57330.1177620.058881
M4-0.6366999956309351.192474-0.53390.5941840.297092
M50.911609742047091.1911660.76530.4452980.222649
M61.220442861511921.1924741.02350.3077520.153876
M7-0.8981797174994061.213381-0.74020.4603260.230163
M8-1.104051742691121.215254-0.90850.3650840.182542
M9-1.923076923076921.212756-1.58570.1149260.057463
M10-0.717204897885211.213381-0.59110.5553630.277681
M11-1.873282511921891.215254-1.54150.1253230.062661

\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) & 12.9137999388331 & 1.212086 & 10.6542 & 0 & 0 \tabularnewline
x & 0.323663672507733 & 0.506173 & 0.6394 & 0.523523 & 0.261762 \tabularnewline
M1 & 0.934728575797638 & 1.192474 & 0.7839 & 0.434368 & 0.217184 \tabularnewline
M2 & -0.516961686524345 & 1.191166 & -0.434 & 0.664919 & 0.332459 \tabularnewline
M3 & -1.87410454366720 & 1.191166 & -1.5733 & 0.117762 & 0.058881 \tabularnewline
M4 & -0.636699995630935 & 1.192474 & -0.5339 & 0.594184 & 0.297092 \tabularnewline
M5 & 0.91160974204709 & 1.191166 & 0.7653 & 0.445298 & 0.222649 \tabularnewline
M6 & 1.22044286151192 & 1.192474 & 1.0235 & 0.307752 & 0.153876 \tabularnewline
M7 & -0.898179717499406 & 1.213381 & -0.7402 & 0.460326 & 0.230163 \tabularnewline
M8 & -1.10405174269112 & 1.215254 & -0.9085 & 0.365084 & 0.182542 \tabularnewline
M9 & -1.92307692307692 & 1.212756 & -1.5857 & 0.114926 & 0.057463 \tabularnewline
M10 & -0.71720489788521 & 1.213381 & -0.5911 & 0.555363 & 0.277681 \tabularnewline
M11 & -1.87328251192189 & 1.215254 & -1.5415 & 0.125323 & 0.062661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&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]12.9137999388331[/C][C]1.212086[/C][C]10.6542[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]0.323663672507733[/C][C]0.506173[/C][C]0.6394[/C][C]0.523523[/C][C]0.261762[/C][/ROW]
[ROW][C]M1[/C][C]0.934728575797638[/C][C]1.192474[/C][C]0.7839[/C][C]0.434368[/C][C]0.217184[/C][/ROW]
[ROW][C]M2[/C][C]-0.516961686524345[/C][C]1.191166[/C][C]-0.434[/C][C]0.664919[/C][C]0.332459[/C][/ROW]
[ROW][C]M3[/C][C]-1.87410454366720[/C][C]1.191166[/C][C]-1.5733[/C][C]0.117762[/C][C]0.058881[/C][/ROW]
[ROW][C]M4[/C][C]-0.636699995630935[/C][C]1.192474[/C][C]-0.5339[/C][C]0.594184[/C][C]0.297092[/C][/ROW]
[ROW][C]M5[/C][C]0.91160974204709[/C][C]1.191166[/C][C]0.7653[/C][C]0.445298[/C][C]0.222649[/C][/ROW]
[ROW][C]M6[/C][C]1.22044286151192[/C][C]1.192474[/C][C]1.0235[/C][C]0.307752[/C][C]0.153876[/C][/ROW]
[ROW][C]M7[/C][C]-0.898179717499406[/C][C]1.213381[/C][C]-0.7402[/C][C]0.460326[/C][C]0.230163[/C][/ROW]
[ROW][C]M8[/C][C]-1.10405174269112[/C][C]1.215254[/C][C]-0.9085[/C][C]0.365084[/C][C]0.182542[/C][/ROW]
[ROW][C]M9[/C][C]-1.92307692307692[/C][C]1.212756[/C][C]-1.5857[/C][C]0.114926[/C][C]0.057463[/C][/ROW]
[ROW][C]M10[/C][C]-0.71720489788521[/C][C]1.213381[/C][C]-0.5911[/C][C]0.555363[/C][C]0.277681[/C][/ROW]
[ROW][C]M11[/C][C]-1.87328251192189[/C][C]1.215254[/C][C]-1.5415[/C][C]0.125323[/C][C]0.062661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.91379993883311.21208610.654200
x0.3236636725077330.5061730.63940.5235230.261762
M10.9347285757976381.1924740.78390.4343680.217184
M2-0.5169616865243451.191166-0.4340.6649190.332459
M3-1.874104543667201.191166-1.57330.1177620.058881
M4-0.6366999956309351.192474-0.53390.5941840.297092
M50.911609742047091.1911660.76530.4452980.222649
M61.220442861511921.1924741.02350.3077520.153876
M7-0.8981797174994061.213381-0.74020.4603260.230163
M8-1.104051742691121.215254-0.90850.3650840.182542
M9-1.923076923076921.212756-1.58570.1149260.057463
M10-0.717204897885211.213381-0.59110.5553630.277681
M11-1.873282511921891.215254-1.54150.1253230.062661







Multiple Linear Regression - Regression Statistics
Multiple R0.342663940796646
R-squared0.117418576322287
Adjusted R-squared0.0463381932073034
F-TEST (value)1.65191254150029
F-TEST (DF numerator)12
F-TEST (DF denominator)149
p-value0.083263726308721
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.09193395483857
Sum Squared Residuals1424.44828158147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.342663940796646 \tabularnewline
R-squared & 0.117418576322287 \tabularnewline
Adjusted R-squared & 0.0463381932073034 \tabularnewline
F-TEST (value) & 1.65191254150029 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.083263726308721 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.09193395483857 \tabularnewline
Sum Squared Residuals & 1424.44828158147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.342663940796646[/C][/ROW]
[ROW][C]R-squared[/C][C]0.117418576322287[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0463381932073034[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.65191254150029[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.083263726308721[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.09193395483857[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1424.44828158147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.342663940796646
R-squared0.117418576322287
Adjusted R-squared0.0463381932073034
F-TEST (value)1.65191254150029
F-TEST (DF numerator)12
F-TEST (DF denominator)149
p-value0.083263726308721
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.09193395483857
Sum Squared Residuals1424.44828158147







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.4958558596462-2.49585585964622
21113.0441655973242-2.04416559732419
31411.68702274018132.31297725981866
41212.6007636157099-0.600763615709873
52114.47273702589566.52726297410438
61214.7815701453605-2.78157014536045
72212.66294756634919.33705243365087
81112.4570755411574-1.4570755411574
91011.6380503607716-1.63805036077161
101312.84392238596330.156077614036677
111011.3641810994189-1.36418109941890
12813.5611272838485-5.56112728384854
131514.17219218713840.827807812861563
141413.04416559732420.955834402675809
151011.6870227401813-1.68702274018133
161412.60076361570991.39923638429013
171414.1490733533879-0.14907335338789
181114.7815701453605-3.78157014536046
191012.3392838938414-2.33928389384139
201312.45707554115740.542924458842584
21711.3143866882639-4.31438668826388
221412.84392238596331.15607761403668
231211.68784477192660.312155228073354
241413.56112728384850.438872716151467
251114.1721921871384-3.17219218713844
26913.0441655973242-4.04416559732419
271111.3633590676736-0.3633590676736
281512.92442728821762.0755727117824
291414.4727370258956-0.472737025895624
301314.4579064728527-1.45790647285272
31912.6629475663491-3.66294756634912
321512.13341186864972.86658813135032
331011.6380503607716-1.63805036077161
341112.8439223859633-1.84392238596332
351311.36418109941891.63581890058109
36813.2374636113408-5.2374636113408
372014.17219218713845.82780781286156
381212.7205019248165-0.720501924816458
391011.6870227401813-1.68702274018133
401012.6007636157099-2.60076361570987
41914.1490733533879-5.14907335338789
421414.7815701453605-0.781570145360455
43812.3392838938414-4.33928389384139
441412.13341186864971.86658813135032
451111.6380503607716-0.63805036077161
461312.84392238596330.156077614036678
47911.6878447719266-2.68784477192664
481113.5611272838485-2.56112728384853
491514.49585585964620.504144140353831
501112.7205019248165-1.72050192481646
511011.6870227401813-1.68702274018133
521412.60076361570991.39923638429013
531814.14907335338793.85092664661211
541414.7815701453605-0.781570145360455
551112.3392838938414-1.33928389384139
561212.4570755411574-0.457075541157416
571311.63805036077161.36194963922839
58912.8439223859633-3.84392238596332
591011.3641810994189-1.36418109941891
601513.56112728384851.43887271615147
612014.17219218713845.82780781286156
621212.7205019248165-0.720501924816458
631211.68702274018130.312977259818668
641412.92442728821761.07557271178240
651314.4727370258956-1.47273702589562
661114.4579064728527-3.45790647285272
671712.66294756634914.33705243365087
681212.1334118686497-0.133411868649683
691311.63805036077161.36194963922839
701412.52025871345561.47974128654441
711311.36418109941891.63581890058109
721513.56112728384851.43887271615147
731314.4958558596462-1.49585585964617
741012.7205019248165-2.72050192481646
751111.3633590676736-0.3633590676736
761912.92442728821766.0755727117824
771314.4727370258956-1.47273702589562
781714.78157014536052.21842985463954
791312.33928389384140.660716106158607
80912.1334118686497-3.13341186864968
811111.3143866882639-0.314386688263877
821012.5202587134556-2.52025871345559
83911.6878447719266-2.68784477192664
841213.2374636113408-1.2374636113408
851214.4958558596462-2.49585585964617
861313.0441655973242-0.0441655973241913
871311.36335906767361.63664093232640
881212.9244272882176-0.924427288217599
891514.47273702589560.527262974104376
902214.78157014536057.21842985463954
911312.66294756634910.337052433650874
921512.45707554115742.54292445884258
931311.63805036077161.36194963922839
941512.84392238596332.15607761403668
951011.6878447719266-1.68784477192665
961113.5611272838485-2.56112728384853
971614.49585585964621.50414414035383
981113.0441655973242-2.04416559732419
991111.3633590676736-0.3633590676736
1001012.6007636157099-2.60076361570987
1011014.4727370258956-4.47273702589563
1021614.45790647285271.54209352714728
1031212.6629475663491-0.662947566349126
1041112.1334118686497-1.13341186864968
1051611.63805036077164.36194963922839
1061912.52025871345566.47974128654441
1071111.6878447719266-0.687844771926646
1081613.23746361134082.7625363886592
1091514.17219218713840.827807812861564
1102413.044165597324210.9558344026758
1111411.68702274018132.31297725981867
1121512.92442728821762.0755727117824
1131114.4727370258956-3.47273702589562
1141514.45790647285270.542093527147277
1151212.6629475663491-0.662947566349126
1161012.1334118686497-2.13341186864968
1171411.63805036077162.36194963922839
1181312.84392238596330.156077614036678
119911.6878447719266-2.68784477192664
1201513.56112728384851.43887271615147
1211514.49585585964620.504144140353831
1221413.04416559732420.955834402675809
1231111.6870227401813-0.687022740181332
124812.9244272882176-4.9244272882176
1251114.4727370258956-3.47273702589562
1261114.7815701453605-3.78157014536046
127812.3392838938414-4.33928389384139
1281012.4570755411574-2.45707554115742
1291111.6380503607716-0.63805036077161
1301312.84392238596330.156077614036678
1311111.3641810994189-0.364181099418912
1322013.23746361134086.7625363886592
1331014.4958558596462-4.49585585964617
1341512.72050192481652.27949807518354
1351211.36335906767360.636640932326403
1361412.92442728821761.07557271178240
1372314.14907335338798.85092664661211
1381414.4579064728527-0.457906472852723
1391612.66294756634913.33705243365087
1401112.4570755411574-1.45707554115742
1411211.31438668826390.685613311736124
1421012.8439223859633-2.84392238596332
1431411.36418109941892.63581890058109
1441213.5611272838485-1.56112728384853
1451214.1721921871384-2.17219218713844
1461113.0441655973242-2.04416559732419
1471211.68702274018130.312977259818668
1481312.60076361570990.399236384290135
1491114.1490733533879-3.14907335338789
1501914.45790647285274.54209352714728
1511212.6629475663491-0.662947566349126
1521712.45707554115744.54292445884258
153911.3143866882639-2.31438668826387
1541212.8439223859633-0.843922385963322
1551911.68784477192667.31215522807335
1561813.56112728384854.43887271615147
1571514.49585585964620.504144140353831
1581413.04416559732420.955834402675809
1591111.6870227401813-0.687022740181332
160912.9244272882176-3.9244272882176
1611814.47273702589563.52726297410438
1621614.78157014536051.21842985463954

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 14.4958558596462 & -2.49585585964622 \tabularnewline
2 & 11 & 13.0441655973242 & -2.04416559732419 \tabularnewline
3 & 14 & 11.6870227401813 & 2.31297725981866 \tabularnewline
4 & 12 & 12.6007636157099 & -0.600763615709873 \tabularnewline
5 & 21 & 14.4727370258956 & 6.52726297410438 \tabularnewline
6 & 12 & 14.7815701453605 & -2.78157014536045 \tabularnewline
7 & 22 & 12.6629475663491 & 9.33705243365087 \tabularnewline
8 & 11 & 12.4570755411574 & -1.4570755411574 \tabularnewline
9 & 10 & 11.6380503607716 & -1.63805036077161 \tabularnewline
10 & 13 & 12.8439223859633 & 0.156077614036677 \tabularnewline
11 & 10 & 11.3641810994189 & -1.36418109941890 \tabularnewline
12 & 8 & 13.5611272838485 & -5.56112728384854 \tabularnewline
13 & 15 & 14.1721921871384 & 0.827807812861563 \tabularnewline
14 & 14 & 13.0441655973242 & 0.955834402675809 \tabularnewline
15 & 10 & 11.6870227401813 & -1.68702274018133 \tabularnewline
16 & 14 & 12.6007636157099 & 1.39923638429013 \tabularnewline
17 & 14 & 14.1490733533879 & -0.14907335338789 \tabularnewline
18 & 11 & 14.7815701453605 & -3.78157014536046 \tabularnewline
19 & 10 & 12.3392838938414 & -2.33928389384139 \tabularnewline
20 & 13 & 12.4570755411574 & 0.542924458842584 \tabularnewline
21 & 7 & 11.3143866882639 & -4.31438668826388 \tabularnewline
22 & 14 & 12.8439223859633 & 1.15607761403668 \tabularnewline
23 & 12 & 11.6878447719266 & 0.312155228073354 \tabularnewline
24 & 14 & 13.5611272838485 & 0.438872716151467 \tabularnewline
25 & 11 & 14.1721921871384 & -3.17219218713844 \tabularnewline
26 & 9 & 13.0441655973242 & -4.04416559732419 \tabularnewline
27 & 11 & 11.3633590676736 & -0.3633590676736 \tabularnewline
28 & 15 & 12.9244272882176 & 2.0755727117824 \tabularnewline
29 & 14 & 14.4727370258956 & -0.472737025895624 \tabularnewline
30 & 13 & 14.4579064728527 & -1.45790647285272 \tabularnewline
31 & 9 & 12.6629475663491 & -3.66294756634912 \tabularnewline
32 & 15 & 12.1334118686497 & 2.86658813135032 \tabularnewline
33 & 10 & 11.6380503607716 & -1.63805036077161 \tabularnewline
34 & 11 & 12.8439223859633 & -1.84392238596332 \tabularnewline
35 & 13 & 11.3641810994189 & 1.63581890058109 \tabularnewline
36 & 8 & 13.2374636113408 & -5.2374636113408 \tabularnewline
37 & 20 & 14.1721921871384 & 5.82780781286156 \tabularnewline
38 & 12 & 12.7205019248165 & -0.720501924816458 \tabularnewline
39 & 10 & 11.6870227401813 & -1.68702274018133 \tabularnewline
40 & 10 & 12.6007636157099 & -2.60076361570987 \tabularnewline
41 & 9 & 14.1490733533879 & -5.14907335338789 \tabularnewline
42 & 14 & 14.7815701453605 & -0.781570145360455 \tabularnewline
43 & 8 & 12.3392838938414 & -4.33928389384139 \tabularnewline
44 & 14 & 12.1334118686497 & 1.86658813135032 \tabularnewline
45 & 11 & 11.6380503607716 & -0.63805036077161 \tabularnewline
46 & 13 & 12.8439223859633 & 0.156077614036678 \tabularnewline
47 & 9 & 11.6878447719266 & -2.68784477192664 \tabularnewline
48 & 11 & 13.5611272838485 & -2.56112728384853 \tabularnewline
49 & 15 & 14.4958558596462 & 0.504144140353831 \tabularnewline
50 & 11 & 12.7205019248165 & -1.72050192481646 \tabularnewline
51 & 10 & 11.6870227401813 & -1.68702274018133 \tabularnewline
52 & 14 & 12.6007636157099 & 1.39923638429013 \tabularnewline
53 & 18 & 14.1490733533879 & 3.85092664661211 \tabularnewline
54 & 14 & 14.7815701453605 & -0.781570145360455 \tabularnewline
55 & 11 & 12.3392838938414 & -1.33928389384139 \tabularnewline
56 & 12 & 12.4570755411574 & -0.457075541157416 \tabularnewline
57 & 13 & 11.6380503607716 & 1.36194963922839 \tabularnewline
58 & 9 & 12.8439223859633 & -3.84392238596332 \tabularnewline
59 & 10 & 11.3641810994189 & -1.36418109941891 \tabularnewline
60 & 15 & 13.5611272838485 & 1.43887271615147 \tabularnewline
61 & 20 & 14.1721921871384 & 5.82780781286156 \tabularnewline
62 & 12 & 12.7205019248165 & -0.720501924816458 \tabularnewline
63 & 12 & 11.6870227401813 & 0.312977259818668 \tabularnewline
64 & 14 & 12.9244272882176 & 1.07557271178240 \tabularnewline
65 & 13 & 14.4727370258956 & -1.47273702589562 \tabularnewline
66 & 11 & 14.4579064728527 & -3.45790647285272 \tabularnewline
67 & 17 & 12.6629475663491 & 4.33705243365087 \tabularnewline
68 & 12 & 12.1334118686497 & -0.133411868649683 \tabularnewline
69 & 13 & 11.6380503607716 & 1.36194963922839 \tabularnewline
70 & 14 & 12.5202587134556 & 1.47974128654441 \tabularnewline
71 & 13 & 11.3641810994189 & 1.63581890058109 \tabularnewline
72 & 15 & 13.5611272838485 & 1.43887271615147 \tabularnewline
73 & 13 & 14.4958558596462 & -1.49585585964617 \tabularnewline
74 & 10 & 12.7205019248165 & -2.72050192481646 \tabularnewline
75 & 11 & 11.3633590676736 & -0.3633590676736 \tabularnewline
76 & 19 & 12.9244272882176 & 6.0755727117824 \tabularnewline
77 & 13 & 14.4727370258956 & -1.47273702589562 \tabularnewline
78 & 17 & 14.7815701453605 & 2.21842985463954 \tabularnewline
79 & 13 & 12.3392838938414 & 0.660716106158607 \tabularnewline
80 & 9 & 12.1334118686497 & -3.13341186864968 \tabularnewline
81 & 11 & 11.3143866882639 & -0.314386688263877 \tabularnewline
82 & 10 & 12.5202587134556 & -2.52025871345559 \tabularnewline
83 & 9 & 11.6878447719266 & -2.68784477192664 \tabularnewline
84 & 12 & 13.2374636113408 & -1.2374636113408 \tabularnewline
85 & 12 & 14.4958558596462 & -2.49585585964617 \tabularnewline
86 & 13 & 13.0441655973242 & -0.0441655973241913 \tabularnewline
87 & 13 & 11.3633590676736 & 1.63664093232640 \tabularnewline
88 & 12 & 12.9244272882176 & -0.924427288217599 \tabularnewline
89 & 15 & 14.4727370258956 & 0.527262974104376 \tabularnewline
90 & 22 & 14.7815701453605 & 7.21842985463954 \tabularnewline
91 & 13 & 12.6629475663491 & 0.337052433650874 \tabularnewline
92 & 15 & 12.4570755411574 & 2.54292445884258 \tabularnewline
93 & 13 & 11.6380503607716 & 1.36194963922839 \tabularnewline
94 & 15 & 12.8439223859633 & 2.15607761403668 \tabularnewline
95 & 10 & 11.6878447719266 & -1.68784477192665 \tabularnewline
96 & 11 & 13.5611272838485 & -2.56112728384853 \tabularnewline
97 & 16 & 14.4958558596462 & 1.50414414035383 \tabularnewline
98 & 11 & 13.0441655973242 & -2.04416559732419 \tabularnewline
99 & 11 & 11.3633590676736 & -0.3633590676736 \tabularnewline
100 & 10 & 12.6007636157099 & -2.60076361570987 \tabularnewline
101 & 10 & 14.4727370258956 & -4.47273702589563 \tabularnewline
102 & 16 & 14.4579064728527 & 1.54209352714728 \tabularnewline
103 & 12 & 12.6629475663491 & -0.662947566349126 \tabularnewline
104 & 11 & 12.1334118686497 & -1.13341186864968 \tabularnewline
105 & 16 & 11.6380503607716 & 4.36194963922839 \tabularnewline
106 & 19 & 12.5202587134556 & 6.47974128654441 \tabularnewline
107 & 11 & 11.6878447719266 & -0.687844771926646 \tabularnewline
108 & 16 & 13.2374636113408 & 2.7625363886592 \tabularnewline
109 & 15 & 14.1721921871384 & 0.827807812861564 \tabularnewline
110 & 24 & 13.0441655973242 & 10.9558344026758 \tabularnewline
111 & 14 & 11.6870227401813 & 2.31297725981867 \tabularnewline
112 & 15 & 12.9244272882176 & 2.0755727117824 \tabularnewline
113 & 11 & 14.4727370258956 & -3.47273702589562 \tabularnewline
114 & 15 & 14.4579064728527 & 0.542093527147277 \tabularnewline
115 & 12 & 12.6629475663491 & -0.662947566349126 \tabularnewline
116 & 10 & 12.1334118686497 & -2.13341186864968 \tabularnewline
117 & 14 & 11.6380503607716 & 2.36194963922839 \tabularnewline
118 & 13 & 12.8439223859633 & 0.156077614036678 \tabularnewline
119 & 9 & 11.6878447719266 & -2.68784477192664 \tabularnewline
120 & 15 & 13.5611272838485 & 1.43887271615147 \tabularnewline
121 & 15 & 14.4958558596462 & 0.504144140353831 \tabularnewline
122 & 14 & 13.0441655973242 & 0.955834402675809 \tabularnewline
123 & 11 & 11.6870227401813 & -0.687022740181332 \tabularnewline
124 & 8 & 12.9244272882176 & -4.9244272882176 \tabularnewline
125 & 11 & 14.4727370258956 & -3.47273702589562 \tabularnewline
126 & 11 & 14.7815701453605 & -3.78157014536046 \tabularnewline
127 & 8 & 12.3392838938414 & -4.33928389384139 \tabularnewline
128 & 10 & 12.4570755411574 & -2.45707554115742 \tabularnewline
129 & 11 & 11.6380503607716 & -0.63805036077161 \tabularnewline
130 & 13 & 12.8439223859633 & 0.156077614036678 \tabularnewline
131 & 11 & 11.3641810994189 & -0.364181099418912 \tabularnewline
132 & 20 & 13.2374636113408 & 6.7625363886592 \tabularnewline
133 & 10 & 14.4958558596462 & -4.49585585964617 \tabularnewline
134 & 15 & 12.7205019248165 & 2.27949807518354 \tabularnewline
135 & 12 & 11.3633590676736 & 0.636640932326403 \tabularnewline
136 & 14 & 12.9244272882176 & 1.07557271178240 \tabularnewline
137 & 23 & 14.1490733533879 & 8.85092664661211 \tabularnewline
138 & 14 & 14.4579064728527 & -0.457906472852723 \tabularnewline
139 & 16 & 12.6629475663491 & 3.33705243365087 \tabularnewline
140 & 11 & 12.4570755411574 & -1.45707554115742 \tabularnewline
141 & 12 & 11.3143866882639 & 0.685613311736124 \tabularnewline
142 & 10 & 12.8439223859633 & -2.84392238596332 \tabularnewline
143 & 14 & 11.3641810994189 & 2.63581890058109 \tabularnewline
144 & 12 & 13.5611272838485 & -1.56112728384853 \tabularnewline
145 & 12 & 14.1721921871384 & -2.17219218713844 \tabularnewline
146 & 11 & 13.0441655973242 & -2.04416559732419 \tabularnewline
147 & 12 & 11.6870227401813 & 0.312977259818668 \tabularnewline
148 & 13 & 12.6007636157099 & 0.399236384290135 \tabularnewline
149 & 11 & 14.1490733533879 & -3.14907335338789 \tabularnewline
150 & 19 & 14.4579064728527 & 4.54209352714728 \tabularnewline
151 & 12 & 12.6629475663491 & -0.662947566349126 \tabularnewline
152 & 17 & 12.4570755411574 & 4.54292445884258 \tabularnewline
153 & 9 & 11.3143866882639 & -2.31438668826387 \tabularnewline
154 & 12 & 12.8439223859633 & -0.843922385963322 \tabularnewline
155 & 19 & 11.6878447719266 & 7.31215522807335 \tabularnewline
156 & 18 & 13.5611272838485 & 4.43887271615147 \tabularnewline
157 & 15 & 14.4958558596462 & 0.504144140353831 \tabularnewline
158 & 14 & 13.0441655973242 & 0.955834402675809 \tabularnewline
159 & 11 & 11.6870227401813 & -0.687022740181332 \tabularnewline
160 & 9 & 12.9244272882176 & -3.9244272882176 \tabularnewline
161 & 18 & 14.4727370258956 & 3.52726297410438 \tabularnewline
162 & 16 & 14.7815701453605 & 1.21842985463954 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&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[/C][C]14.4958558596462[/C][C]-2.49585585964622[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]13.0441655973242[/C][C]-2.04416559732419[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]11.6870227401813[/C][C]2.31297725981866[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.6007636157099[/C][C]-0.600763615709873[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]14.4727370258956[/C][C]6.52726297410438[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]14.7815701453605[/C][C]-2.78157014536045[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.6629475663491[/C][C]9.33705243365087[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.4570755411574[/C][C]-1.4570755411574[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]11.6380503607716[/C][C]-1.63805036077161[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.8439223859633[/C][C]0.156077614036677[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.3641810994189[/C][C]-1.36418109941890[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]13.5611272838485[/C][C]-5.56112728384854[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]14.1721921871384[/C][C]0.827807812861563[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]13.0441655973242[/C][C]0.955834402675809[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]11.6870227401813[/C][C]-1.68702274018133[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.6007636157099[/C][C]1.39923638429013[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.1490733533879[/C][C]-0.14907335338789[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]14.7815701453605[/C][C]-3.78157014536046[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.3392838938414[/C][C]-2.33928389384139[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.4570755411574[/C][C]0.542924458842584[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]11.3143866882639[/C][C]-4.31438668826388[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.8439223859633[/C][C]1.15607761403668[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]11.6878447719266[/C][C]0.312155228073354[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.5611272838485[/C][C]0.438872716151467[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]14.1721921871384[/C][C]-3.17219218713844[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]13.0441655973242[/C][C]-4.04416559732419[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.3633590676736[/C][C]-0.3633590676736[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.9244272882176[/C][C]2.0755727117824[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]14.4727370258956[/C][C]-0.472737025895624[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]14.4579064728527[/C][C]-1.45790647285272[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.6629475663491[/C][C]-3.66294756634912[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.1334118686497[/C][C]2.86658813135032[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.6380503607716[/C][C]-1.63805036077161[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.8439223859633[/C][C]-1.84392238596332[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]11.3641810994189[/C][C]1.63581890058109[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]13.2374636113408[/C][C]-5.2374636113408[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]14.1721921871384[/C][C]5.82780781286156[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.7205019248165[/C][C]-0.720501924816458[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.6870227401813[/C][C]-1.68702274018133[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.6007636157099[/C][C]-2.60076361570987[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]14.1490733533879[/C][C]-5.14907335338789[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.7815701453605[/C][C]-0.781570145360455[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.3392838938414[/C][C]-4.33928389384139[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]12.1334118686497[/C][C]1.86658813135032[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.6380503607716[/C][C]-0.63805036077161[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.8439223859633[/C][C]0.156077614036678[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]11.6878447719266[/C][C]-2.68784477192664[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]13.5611272838485[/C][C]-2.56112728384853[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]14.4958558596462[/C][C]0.504144140353831[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.7205019248165[/C][C]-1.72050192481646[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.6870227401813[/C][C]-1.68702274018133[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.6007636157099[/C][C]1.39923638429013[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.1490733533879[/C][C]3.85092664661211[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.7815701453605[/C][C]-0.781570145360455[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.3392838938414[/C][C]-1.33928389384139[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4570755411574[/C][C]-0.457075541157416[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.6380503607716[/C][C]1.36194963922839[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.8439223859633[/C][C]-3.84392238596332[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]11.3641810994189[/C][C]-1.36418109941891[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.5611272838485[/C][C]1.43887271615147[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]14.1721921871384[/C][C]5.82780781286156[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.7205019248165[/C][C]-0.720501924816458[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]11.6870227401813[/C][C]0.312977259818668[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.9244272882176[/C][C]1.07557271178240[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]14.4727370258956[/C][C]-1.47273702589562[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]14.4579064728527[/C][C]-3.45790647285272[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.6629475663491[/C][C]4.33705243365087[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.1334118686497[/C][C]-0.133411868649683[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]11.6380503607716[/C][C]1.36194963922839[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.5202587134556[/C][C]1.47974128654441[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]11.3641810994189[/C][C]1.63581890058109[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.5611272838485[/C][C]1.43887271615147[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]14.4958558596462[/C][C]-1.49585585964617[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.7205019248165[/C][C]-2.72050192481646[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]11.3633590676736[/C][C]-0.3633590676736[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.9244272882176[/C][C]6.0755727117824[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]14.4727370258956[/C][C]-1.47273702589562[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.7815701453605[/C][C]2.21842985463954[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.3392838938414[/C][C]0.660716106158607[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.1334118686497[/C][C]-3.13341186864968[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.3143866882639[/C][C]-0.314386688263877[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]12.5202587134556[/C][C]-2.52025871345559[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]11.6878447719266[/C][C]-2.68784477192664[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]13.2374636113408[/C][C]-1.2374636113408[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]14.4958558596462[/C][C]-2.49585585964617[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.0441655973242[/C][C]-0.0441655973241913[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]11.3633590676736[/C][C]1.63664093232640[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.9244272882176[/C][C]-0.924427288217599[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.4727370258956[/C][C]0.527262974104376[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]14.7815701453605[/C][C]7.21842985463954[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.6629475663491[/C][C]0.337052433650874[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.4570755411574[/C][C]2.54292445884258[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.6380503607716[/C][C]1.36194963922839[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.8439223859633[/C][C]2.15607761403668[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]11.6878447719266[/C][C]-1.68784477192665[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]13.5611272838485[/C][C]-2.56112728384853[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.4958558596462[/C][C]1.50414414035383[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]13.0441655973242[/C][C]-2.04416559732419[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]11.3633590676736[/C][C]-0.3633590676736[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.6007636157099[/C][C]-2.60076361570987[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]14.4727370258956[/C][C]-4.47273702589563[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.4579064728527[/C][C]1.54209352714728[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.6629475663491[/C][C]-0.662947566349126[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]12.1334118686497[/C][C]-1.13341186864968[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]11.6380503607716[/C][C]4.36194963922839[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.5202587134556[/C][C]6.47974128654441[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.6878447719266[/C][C]-0.687844771926646[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]13.2374636113408[/C][C]2.7625363886592[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.1721921871384[/C][C]0.827807812861564[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]13.0441655973242[/C][C]10.9558344026758[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.6870227401813[/C][C]2.31297725981867[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.9244272882176[/C][C]2.0755727117824[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]14.4727370258956[/C][C]-3.47273702589562[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.4579064728527[/C][C]0.542093527147277[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]12.6629475663491[/C][C]-0.662947566349126[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]12.1334118686497[/C][C]-2.13341186864968[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]11.6380503607716[/C][C]2.36194963922839[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]12.8439223859633[/C][C]0.156077614036678[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]11.6878447719266[/C][C]-2.68784477192664[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.5611272838485[/C][C]1.43887271615147[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.4958558596462[/C][C]0.504144140353831[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.0441655973242[/C][C]0.955834402675809[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.6870227401813[/C][C]-0.687022740181332[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.9244272882176[/C][C]-4.9244272882176[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]14.4727370258956[/C][C]-3.47273702589562[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]14.7815701453605[/C][C]-3.78157014536046[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]12.3392838938414[/C][C]-4.33928389384139[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]12.4570755411574[/C][C]-2.45707554115742[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.6380503607716[/C][C]-0.63805036077161[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.8439223859633[/C][C]0.156077614036678[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]11.3641810994189[/C][C]-0.364181099418912[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.2374636113408[/C][C]6.7625363886592[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]14.4958558596462[/C][C]-4.49585585964617[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.7205019248165[/C][C]2.27949807518354[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.3633590676736[/C][C]0.636640932326403[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.9244272882176[/C][C]1.07557271178240[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]14.1490733533879[/C][C]8.85092664661211[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]14.4579064728527[/C][C]-0.457906472852723[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]12.6629475663491[/C][C]3.33705243365087[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.4570755411574[/C][C]-1.45707554115742[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.3143866882639[/C][C]0.685613311736124[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.8439223859633[/C][C]-2.84392238596332[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.3641810994189[/C][C]2.63581890058109[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]13.5611272838485[/C][C]-1.56112728384853[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]14.1721921871384[/C][C]-2.17219218713844[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]13.0441655973242[/C][C]-2.04416559732419[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.6870227401813[/C][C]0.312977259818668[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]12.6007636157099[/C][C]0.399236384290135[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]14.1490733533879[/C][C]-3.14907335338789[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]14.4579064728527[/C][C]4.54209352714728[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.6629475663491[/C][C]-0.662947566349126[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]12.4570755411574[/C][C]4.54292445884258[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.3143866882639[/C][C]-2.31438668826387[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]12.8439223859633[/C][C]-0.843922385963322[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]11.6878447719266[/C][C]7.31215522807335[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.5611272838485[/C][C]4.43887271615147[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]14.4958558596462[/C][C]0.504144140353831[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.0441655973242[/C][C]0.955834402675809[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]11.6870227401813[/C][C]-0.687022740181332[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]12.9244272882176[/C][C]-3.9244272882176[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.4727370258956[/C][C]3.52726297410438[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.7815701453605[/C][C]1.21842985463954[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.4958558596462-2.49585585964622
21113.0441655973242-2.04416559732419
31411.68702274018132.31297725981866
41212.6007636157099-0.600763615709873
52114.47273702589566.52726297410438
61214.7815701453605-2.78157014536045
72212.66294756634919.33705243365087
81112.4570755411574-1.4570755411574
91011.6380503607716-1.63805036077161
101312.84392238596330.156077614036677
111011.3641810994189-1.36418109941890
12813.5611272838485-5.56112728384854
131514.17219218713840.827807812861563
141413.04416559732420.955834402675809
151011.6870227401813-1.68702274018133
161412.60076361570991.39923638429013
171414.1490733533879-0.14907335338789
181114.7815701453605-3.78157014536046
191012.3392838938414-2.33928389384139
201312.45707554115740.542924458842584
21711.3143866882639-4.31438668826388
221412.84392238596331.15607761403668
231211.68784477192660.312155228073354
241413.56112728384850.438872716151467
251114.1721921871384-3.17219218713844
26913.0441655973242-4.04416559732419
271111.3633590676736-0.3633590676736
281512.92442728821762.0755727117824
291414.4727370258956-0.472737025895624
301314.4579064728527-1.45790647285272
31912.6629475663491-3.66294756634912
321512.13341186864972.86658813135032
331011.6380503607716-1.63805036077161
341112.8439223859633-1.84392238596332
351311.36418109941891.63581890058109
36813.2374636113408-5.2374636113408
372014.17219218713845.82780781286156
381212.7205019248165-0.720501924816458
391011.6870227401813-1.68702274018133
401012.6007636157099-2.60076361570987
41914.1490733533879-5.14907335338789
421414.7815701453605-0.781570145360455
43812.3392838938414-4.33928389384139
441412.13341186864971.86658813135032
451111.6380503607716-0.63805036077161
461312.84392238596330.156077614036678
47911.6878447719266-2.68784477192664
481113.5611272838485-2.56112728384853
491514.49585585964620.504144140353831
501112.7205019248165-1.72050192481646
511011.6870227401813-1.68702274018133
521412.60076361570991.39923638429013
531814.14907335338793.85092664661211
541414.7815701453605-0.781570145360455
551112.3392838938414-1.33928389384139
561212.4570755411574-0.457075541157416
571311.63805036077161.36194963922839
58912.8439223859633-3.84392238596332
591011.3641810994189-1.36418109941891
601513.56112728384851.43887271615147
612014.17219218713845.82780781286156
621212.7205019248165-0.720501924816458
631211.68702274018130.312977259818668
641412.92442728821761.07557271178240
651314.4727370258956-1.47273702589562
661114.4579064728527-3.45790647285272
671712.66294756634914.33705243365087
681212.1334118686497-0.133411868649683
691311.63805036077161.36194963922839
701412.52025871345561.47974128654441
711311.36418109941891.63581890058109
721513.56112728384851.43887271615147
731314.4958558596462-1.49585585964617
741012.7205019248165-2.72050192481646
751111.3633590676736-0.3633590676736
761912.92442728821766.0755727117824
771314.4727370258956-1.47273702589562
781714.78157014536052.21842985463954
791312.33928389384140.660716106158607
80912.1334118686497-3.13341186864968
811111.3143866882639-0.314386688263877
821012.5202587134556-2.52025871345559
83911.6878447719266-2.68784477192664
841213.2374636113408-1.2374636113408
851214.4958558596462-2.49585585964617
861313.0441655973242-0.0441655973241913
871311.36335906767361.63664093232640
881212.9244272882176-0.924427288217599
891514.47273702589560.527262974104376
902214.78157014536057.21842985463954
911312.66294756634910.337052433650874
921512.45707554115742.54292445884258
931311.63805036077161.36194963922839
941512.84392238596332.15607761403668
951011.6878447719266-1.68784477192665
961113.5611272838485-2.56112728384853
971614.49585585964621.50414414035383
981113.0441655973242-2.04416559732419
991111.3633590676736-0.3633590676736
1001012.6007636157099-2.60076361570987
1011014.4727370258956-4.47273702589563
1021614.45790647285271.54209352714728
1031212.6629475663491-0.662947566349126
1041112.1334118686497-1.13341186864968
1051611.63805036077164.36194963922839
1061912.52025871345566.47974128654441
1071111.6878447719266-0.687844771926646
1081613.23746361134082.7625363886592
1091514.17219218713840.827807812861564
1102413.044165597324210.9558344026758
1111411.68702274018132.31297725981867
1121512.92442728821762.0755727117824
1131114.4727370258956-3.47273702589562
1141514.45790647285270.542093527147277
1151212.6629475663491-0.662947566349126
1161012.1334118686497-2.13341186864968
1171411.63805036077162.36194963922839
1181312.84392238596330.156077614036678
119911.6878447719266-2.68784477192664
1201513.56112728384851.43887271615147
1211514.49585585964620.504144140353831
1221413.04416559732420.955834402675809
1231111.6870227401813-0.687022740181332
124812.9244272882176-4.9244272882176
1251114.4727370258956-3.47273702589562
1261114.7815701453605-3.78157014536046
127812.3392838938414-4.33928389384139
1281012.4570755411574-2.45707554115742
1291111.6380503607716-0.63805036077161
1301312.84392238596330.156077614036678
1311111.3641810994189-0.364181099418912
1322013.23746361134086.7625363886592
1331014.4958558596462-4.49585585964617
1341512.72050192481652.27949807518354
1351211.36335906767360.636640932326403
1361412.92442728821761.07557271178240
1372314.14907335338798.85092664661211
1381414.4579064728527-0.457906472852723
1391612.66294756634913.33705243365087
1401112.4570755411574-1.45707554115742
1411211.31438668826390.685613311736124
1421012.8439223859633-2.84392238596332
1431411.36418109941892.63581890058109
1441213.5611272838485-1.56112728384853
1451214.1721921871384-2.17219218713844
1461113.0441655973242-2.04416559732419
1471211.68702274018130.312977259818668
1481312.60076361570990.399236384290135
1491114.1490733533879-3.14907335338789
1501914.45790647285274.54209352714728
1511212.6629475663491-0.662947566349126
1521712.45707554115744.54292445884258
153911.3143866882639-2.31438668826387
1541212.8439223859633-0.843922385963322
1551911.68784477192667.31215522807335
1561813.56112728384854.43887271615147
1571514.49585585964620.504144140353831
1581413.04416559732420.955834402675809
1591111.6870227401813-0.687022740181332
160912.9244272882176-3.9244272882176
1611814.47273702589563.52726297410438
1621614.78157014536051.21842985463954







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.3377808310632680.6755616621265350.662219168936732
170.628718567486920.742562865026160.37128143251308
180.5016714246458150.996657150708370.498328575354185
190.7529447535436120.4941104929127750.247055246456388
200.6725125195629350.6549749608741310.327487480437065
210.6033871538594920.7932256922810150.396612846140508
220.5073178690801620.9853642618396770.492682130919838
230.4392151749657810.8784303499315630.560784825034219
240.5154518784970940.9690962430058120.484548121502906
250.4360906677703130.8721813355406260.563909332229687
260.4359205028785430.8718410057570860.564079497121457
270.4010712755026980.8021425510053970.598928724497302
280.3393310069606930.6786620139213850.660668993039307
290.3951896229987650.7903792459975290.604810377001235
300.4072514984387980.8145029968775970.592748501561202
310.6265732651292180.7468534697415640.373426734870782
320.642923608584310.714152782831380.35707639141569
330.5844735139937870.8310529720124250.415526486006213
340.5444964242050120.9110071515899760.455503575794988
350.5060742974475690.9878514051048630.493925702552431
360.493264775391860.986529550783720.50673522460814
370.6987939661251030.6024120677497940.301206033874897
380.6526544357342380.6946911285315250.347345564265762
390.6109227948948860.7781544102102290.389077205105114
400.6008286462995870.7983427074008260.399171353700413
410.7165864351758490.5668271296483020.283413564824151
420.6744861420632320.6510277158735350.325513857936768
430.7105584241999650.5788831516000710.289441575800035
440.6770484320753570.6459031358492870.322951567924643
450.633363469074160.733273061851680.36663653092584
460.5789018617360570.8421962765278860.421098138263943
470.5761139688131670.8477720623736650.423886031186833
480.5424563277094790.9150873445810420.457543672290521
490.4886287724457940.9772575448915880.511371227554206
500.4444235729958580.8888471459917150.555576427004142
510.4041633955622370.8083267911244740.595836604437763
520.3629739547675780.7259479095351560.637026045232422
530.3906042870655070.7812085741310150.609395712934493
540.3492412912187750.698482582437550.650758708781225
550.30589629935930.61179259871860.6941037006407
560.2698594733627230.5397189467254460.730140526637277
570.2552027182822650.510405436564530.744797281717735
580.272835091283840.545670182567680.72716490871616
590.2353397981588790.4706795963177580.764660201841121
600.2438892764068920.4877785528137840.756110723593108
610.3559629702631770.7119259405263540.644037029736823
620.3173776924947380.6347553849894770.682622307505262
630.2749698060409540.5499396120819080.725030193959046
640.2366678112145530.4733356224291050.763332188785448
650.2178450206146320.4356900412292640.782154979385368
660.2145051687113060.4290103374226120.785494831288694
670.2475458356428650.4950916712857290.752454164357135
680.2099809742593450.4199619485186890.790019025740655
690.1890682722364640.3781365444729270.810931727763536
700.1757813229970550.351562645994110.824218677002945
710.1588079387923010.3176158775846020.841192061207699
720.1512807692633820.3025615385267640.848719230736618
730.1393438711743690.2786877423487380.860656128825631
740.1322527637733570.2645055275467130.867747236226643
750.1087229554036450.2174459108072890.891277044596355
760.1806193370739860.3612386741479730.819380662926014
770.1606660662468380.3213321324936760.839333933753162
780.1598692743490170.3197385486980330.840130725650983
790.1334193005211720.2668386010423440.866580699478828
800.1336336426287670.2672672852575350.866366357371233
810.1130711174592370.2261422349184740.886928882540763
820.1057011821841060.2114023643682110.894298817815894
830.1029565245180730.2059130490361450.897043475481927
840.0935387451275040.1870774902550080.906461254872496
850.09046501564839780.1809300312967960.909534984351602
860.07570911022808650.1514182204561730.924290889771914
870.06530868230892650.1306173646178530.934691317691073
880.05550254899733150.1110050979946630.944497451002668
890.04363300025306590.08726600050613170.956366999746934
900.1303034346250500.2606068692501010.86969656537495
910.1074272913971070.2148545827942130.892572708602893
920.1014805805459390.2029611610918780.898519419454061
930.08539121501848020.1707824300369600.91460878498152
940.07552704124483360.1510540824896670.924472958755166
950.06521150132613240.1304230026522650.934788498673868
960.0680896452146740.1361792904293480.931910354785326
970.05863700174942330.1172740034988470.941362998250577
980.0584573592810550.116914718562110.941542640718945
990.04642281482084870.09284562964169740.953577185179151
1000.04302377221892140.08604754443784270.956976227781079
1010.05653455400500870.1130691080100170.943465445994991
1020.04686829822082900.09373659644165810.953131701779171
1030.03645387075990230.07290774151980470.963546129240098
1040.02865160867870330.05730321735740660.971348391321297
1050.03801256227884130.07602512455768260.961987437721159
1060.081795382388860.163590764777720.91820461761114
1070.06765039151960080.1353007830392020.9323496084804
1080.06420012522483450.1284002504496690.935799874775165
1090.05176733827199020.1035346765439800.94823266172801
1100.3524293903571950.704858780714390.647570609642805
1110.3287149542613490.6574299085226990.671285045738651
1120.3280841444514770.6561682889029540.671915855548523
1130.3525768443936720.7051536887873440.647423155606328
1140.3042559743341590.6085119486683170.695744025665841
1150.2589144217909440.5178288435818890.741085578209056
1160.2421363987004410.4842727974008810.75786360129956
1170.2376498908198950.475299781639790.762350109180105
1180.1998275200342230.3996550400684460.800172479965777
1190.2253158255561310.4506316511122620.77468417444387
1200.1948347208640860.3896694417281710.805165279135914
1210.1729452972758480.3458905945516950.827054702724152
1220.1395997218923540.2791994437847080.860400278107646
1230.1098595615149410.2197191230298810.89014043848506
1240.1255228657584270.2510457315168550.874477134241573
1250.1725056565886980.3450113131773960.827494343411302
1260.2163552717335910.4327105434671830.783644728266409
1270.2749773100186620.5499546200373240.725022689981338
1280.274358510679440.548717021358880.72564148932056
1290.2234194979479990.4468389958959980.776580502052001
1300.1855769648482260.3711539296964510.814423035151774
1310.2245636871448470.4491273742896940.775436312855153
1320.2872387792915420.5744775585830830.712761220708458
1330.2901001535624080.5802003071248160.709899846437592
1340.2638148630070560.5276297260141120.736185136992944
1350.2111697202500860.4223394405001720.788830279749914
1360.1755549129681050.3511098259362090.824445087031895
1370.525507001933660.948985996132680.47449299806634
1380.4744753858022940.9489507716045880.525524614197706
1390.4598484128130280.9196968256260560.540151587186972
1400.5330887695460920.9338224609078170.466911230453909
1410.4750373168360130.9500746336720260.524962683163987
1420.3926212152112660.7852424304225330.607378784788734
1430.3601571408491690.7203142816983390.639842859150831
1440.4450097336761950.890019467352390.554990266323805
1450.3527033109000120.7054066218000250.647296689099988
1460.2666719887122010.5333439774244010.7333280112878

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.337780831063268 & 0.675561662126535 & 0.662219168936732 \tabularnewline
17 & 0.62871856748692 & 0.74256286502616 & 0.37128143251308 \tabularnewline
18 & 0.501671424645815 & 0.99665715070837 & 0.498328575354185 \tabularnewline
19 & 0.752944753543612 & 0.494110492912775 & 0.247055246456388 \tabularnewline
20 & 0.672512519562935 & 0.654974960874131 & 0.327487480437065 \tabularnewline
21 & 0.603387153859492 & 0.793225692281015 & 0.396612846140508 \tabularnewline
22 & 0.507317869080162 & 0.985364261839677 & 0.492682130919838 \tabularnewline
23 & 0.439215174965781 & 0.878430349931563 & 0.560784825034219 \tabularnewline
24 & 0.515451878497094 & 0.969096243005812 & 0.484548121502906 \tabularnewline
25 & 0.436090667770313 & 0.872181335540626 & 0.563909332229687 \tabularnewline
26 & 0.435920502878543 & 0.871841005757086 & 0.564079497121457 \tabularnewline
27 & 0.401071275502698 & 0.802142551005397 & 0.598928724497302 \tabularnewline
28 & 0.339331006960693 & 0.678662013921385 & 0.660668993039307 \tabularnewline
29 & 0.395189622998765 & 0.790379245997529 & 0.604810377001235 \tabularnewline
30 & 0.407251498438798 & 0.814502996877597 & 0.592748501561202 \tabularnewline
31 & 0.626573265129218 & 0.746853469741564 & 0.373426734870782 \tabularnewline
32 & 0.64292360858431 & 0.71415278283138 & 0.35707639141569 \tabularnewline
33 & 0.584473513993787 & 0.831052972012425 & 0.415526486006213 \tabularnewline
34 & 0.544496424205012 & 0.911007151589976 & 0.455503575794988 \tabularnewline
35 & 0.506074297447569 & 0.987851405104863 & 0.493925702552431 \tabularnewline
36 & 0.49326477539186 & 0.98652955078372 & 0.50673522460814 \tabularnewline
37 & 0.698793966125103 & 0.602412067749794 & 0.301206033874897 \tabularnewline
38 & 0.652654435734238 & 0.694691128531525 & 0.347345564265762 \tabularnewline
39 & 0.610922794894886 & 0.778154410210229 & 0.389077205105114 \tabularnewline
40 & 0.600828646299587 & 0.798342707400826 & 0.399171353700413 \tabularnewline
41 & 0.716586435175849 & 0.566827129648302 & 0.283413564824151 \tabularnewline
42 & 0.674486142063232 & 0.651027715873535 & 0.325513857936768 \tabularnewline
43 & 0.710558424199965 & 0.578883151600071 & 0.289441575800035 \tabularnewline
44 & 0.677048432075357 & 0.645903135849287 & 0.322951567924643 \tabularnewline
45 & 0.63336346907416 & 0.73327306185168 & 0.36663653092584 \tabularnewline
46 & 0.578901861736057 & 0.842196276527886 & 0.421098138263943 \tabularnewline
47 & 0.576113968813167 & 0.847772062373665 & 0.423886031186833 \tabularnewline
48 & 0.542456327709479 & 0.915087344581042 & 0.457543672290521 \tabularnewline
49 & 0.488628772445794 & 0.977257544891588 & 0.511371227554206 \tabularnewline
50 & 0.444423572995858 & 0.888847145991715 & 0.555576427004142 \tabularnewline
51 & 0.404163395562237 & 0.808326791124474 & 0.595836604437763 \tabularnewline
52 & 0.362973954767578 & 0.725947909535156 & 0.637026045232422 \tabularnewline
53 & 0.390604287065507 & 0.781208574131015 & 0.609395712934493 \tabularnewline
54 & 0.349241291218775 & 0.69848258243755 & 0.650758708781225 \tabularnewline
55 & 0.3058962993593 & 0.6117925987186 & 0.6941037006407 \tabularnewline
56 & 0.269859473362723 & 0.539718946725446 & 0.730140526637277 \tabularnewline
57 & 0.255202718282265 & 0.51040543656453 & 0.744797281717735 \tabularnewline
58 & 0.27283509128384 & 0.54567018256768 & 0.72716490871616 \tabularnewline
59 & 0.235339798158879 & 0.470679596317758 & 0.764660201841121 \tabularnewline
60 & 0.243889276406892 & 0.487778552813784 & 0.756110723593108 \tabularnewline
61 & 0.355962970263177 & 0.711925940526354 & 0.644037029736823 \tabularnewline
62 & 0.317377692494738 & 0.634755384989477 & 0.682622307505262 \tabularnewline
63 & 0.274969806040954 & 0.549939612081908 & 0.725030193959046 \tabularnewline
64 & 0.236667811214553 & 0.473335622429105 & 0.763332188785448 \tabularnewline
65 & 0.217845020614632 & 0.435690041229264 & 0.782154979385368 \tabularnewline
66 & 0.214505168711306 & 0.429010337422612 & 0.785494831288694 \tabularnewline
67 & 0.247545835642865 & 0.495091671285729 & 0.752454164357135 \tabularnewline
68 & 0.209980974259345 & 0.419961948518689 & 0.790019025740655 \tabularnewline
69 & 0.189068272236464 & 0.378136544472927 & 0.810931727763536 \tabularnewline
70 & 0.175781322997055 & 0.35156264599411 & 0.824218677002945 \tabularnewline
71 & 0.158807938792301 & 0.317615877584602 & 0.841192061207699 \tabularnewline
72 & 0.151280769263382 & 0.302561538526764 & 0.848719230736618 \tabularnewline
73 & 0.139343871174369 & 0.278687742348738 & 0.860656128825631 \tabularnewline
74 & 0.132252763773357 & 0.264505527546713 & 0.867747236226643 \tabularnewline
75 & 0.108722955403645 & 0.217445910807289 & 0.891277044596355 \tabularnewline
76 & 0.180619337073986 & 0.361238674147973 & 0.819380662926014 \tabularnewline
77 & 0.160666066246838 & 0.321332132493676 & 0.839333933753162 \tabularnewline
78 & 0.159869274349017 & 0.319738548698033 & 0.840130725650983 \tabularnewline
79 & 0.133419300521172 & 0.266838601042344 & 0.866580699478828 \tabularnewline
80 & 0.133633642628767 & 0.267267285257535 & 0.866366357371233 \tabularnewline
81 & 0.113071117459237 & 0.226142234918474 & 0.886928882540763 \tabularnewline
82 & 0.105701182184106 & 0.211402364368211 & 0.894298817815894 \tabularnewline
83 & 0.102956524518073 & 0.205913049036145 & 0.897043475481927 \tabularnewline
84 & 0.093538745127504 & 0.187077490255008 & 0.906461254872496 \tabularnewline
85 & 0.0904650156483978 & 0.180930031296796 & 0.909534984351602 \tabularnewline
86 & 0.0757091102280865 & 0.151418220456173 & 0.924290889771914 \tabularnewline
87 & 0.0653086823089265 & 0.130617364617853 & 0.934691317691073 \tabularnewline
88 & 0.0555025489973315 & 0.111005097994663 & 0.944497451002668 \tabularnewline
89 & 0.0436330002530659 & 0.0872660005061317 & 0.956366999746934 \tabularnewline
90 & 0.130303434625050 & 0.260606869250101 & 0.86969656537495 \tabularnewline
91 & 0.107427291397107 & 0.214854582794213 & 0.892572708602893 \tabularnewline
92 & 0.101480580545939 & 0.202961161091878 & 0.898519419454061 \tabularnewline
93 & 0.0853912150184802 & 0.170782430036960 & 0.91460878498152 \tabularnewline
94 & 0.0755270412448336 & 0.151054082489667 & 0.924472958755166 \tabularnewline
95 & 0.0652115013261324 & 0.130423002652265 & 0.934788498673868 \tabularnewline
96 & 0.068089645214674 & 0.136179290429348 & 0.931910354785326 \tabularnewline
97 & 0.0586370017494233 & 0.117274003498847 & 0.941362998250577 \tabularnewline
98 & 0.058457359281055 & 0.11691471856211 & 0.941542640718945 \tabularnewline
99 & 0.0464228148208487 & 0.0928456296416974 & 0.953577185179151 \tabularnewline
100 & 0.0430237722189214 & 0.0860475444378427 & 0.956976227781079 \tabularnewline
101 & 0.0565345540050087 & 0.113069108010017 & 0.943465445994991 \tabularnewline
102 & 0.0468682982208290 & 0.0937365964416581 & 0.953131701779171 \tabularnewline
103 & 0.0364538707599023 & 0.0729077415198047 & 0.963546129240098 \tabularnewline
104 & 0.0286516086787033 & 0.0573032173574066 & 0.971348391321297 \tabularnewline
105 & 0.0380125622788413 & 0.0760251245576826 & 0.961987437721159 \tabularnewline
106 & 0.08179538238886 & 0.16359076477772 & 0.91820461761114 \tabularnewline
107 & 0.0676503915196008 & 0.135300783039202 & 0.9323496084804 \tabularnewline
108 & 0.0642001252248345 & 0.128400250449669 & 0.935799874775165 \tabularnewline
109 & 0.0517673382719902 & 0.103534676543980 & 0.94823266172801 \tabularnewline
110 & 0.352429390357195 & 0.70485878071439 & 0.647570609642805 \tabularnewline
111 & 0.328714954261349 & 0.657429908522699 & 0.671285045738651 \tabularnewline
112 & 0.328084144451477 & 0.656168288902954 & 0.671915855548523 \tabularnewline
113 & 0.352576844393672 & 0.705153688787344 & 0.647423155606328 \tabularnewline
114 & 0.304255974334159 & 0.608511948668317 & 0.695744025665841 \tabularnewline
115 & 0.258914421790944 & 0.517828843581889 & 0.741085578209056 \tabularnewline
116 & 0.242136398700441 & 0.484272797400881 & 0.75786360129956 \tabularnewline
117 & 0.237649890819895 & 0.47529978163979 & 0.762350109180105 \tabularnewline
118 & 0.199827520034223 & 0.399655040068446 & 0.800172479965777 \tabularnewline
119 & 0.225315825556131 & 0.450631651112262 & 0.77468417444387 \tabularnewline
120 & 0.194834720864086 & 0.389669441728171 & 0.805165279135914 \tabularnewline
121 & 0.172945297275848 & 0.345890594551695 & 0.827054702724152 \tabularnewline
122 & 0.139599721892354 & 0.279199443784708 & 0.860400278107646 \tabularnewline
123 & 0.109859561514941 & 0.219719123029881 & 0.89014043848506 \tabularnewline
124 & 0.125522865758427 & 0.251045731516855 & 0.874477134241573 \tabularnewline
125 & 0.172505656588698 & 0.345011313177396 & 0.827494343411302 \tabularnewline
126 & 0.216355271733591 & 0.432710543467183 & 0.783644728266409 \tabularnewline
127 & 0.274977310018662 & 0.549954620037324 & 0.725022689981338 \tabularnewline
128 & 0.27435851067944 & 0.54871702135888 & 0.72564148932056 \tabularnewline
129 & 0.223419497947999 & 0.446838995895998 & 0.776580502052001 \tabularnewline
130 & 0.185576964848226 & 0.371153929696451 & 0.814423035151774 \tabularnewline
131 & 0.224563687144847 & 0.449127374289694 & 0.775436312855153 \tabularnewline
132 & 0.287238779291542 & 0.574477558583083 & 0.712761220708458 \tabularnewline
133 & 0.290100153562408 & 0.580200307124816 & 0.709899846437592 \tabularnewline
134 & 0.263814863007056 & 0.527629726014112 & 0.736185136992944 \tabularnewline
135 & 0.211169720250086 & 0.422339440500172 & 0.788830279749914 \tabularnewline
136 & 0.175554912968105 & 0.351109825936209 & 0.824445087031895 \tabularnewline
137 & 0.52550700193366 & 0.94898599613268 & 0.47449299806634 \tabularnewline
138 & 0.474475385802294 & 0.948950771604588 & 0.525524614197706 \tabularnewline
139 & 0.459848412813028 & 0.919696825626056 & 0.540151587186972 \tabularnewline
140 & 0.533088769546092 & 0.933822460907817 & 0.466911230453909 \tabularnewline
141 & 0.475037316836013 & 0.950074633672026 & 0.524962683163987 \tabularnewline
142 & 0.392621215211266 & 0.785242430422533 & 0.607378784788734 \tabularnewline
143 & 0.360157140849169 & 0.720314281698339 & 0.639842859150831 \tabularnewline
144 & 0.445009733676195 & 0.89001946735239 & 0.554990266323805 \tabularnewline
145 & 0.352703310900012 & 0.705406621800025 & 0.647296689099988 \tabularnewline
146 & 0.266671988712201 & 0.533343977424401 & 0.7333280112878 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&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]16[/C][C]0.337780831063268[/C][C]0.675561662126535[/C][C]0.662219168936732[/C][/ROW]
[ROW][C]17[/C][C]0.62871856748692[/C][C]0.74256286502616[/C][C]0.37128143251308[/C][/ROW]
[ROW][C]18[/C][C]0.501671424645815[/C][C]0.99665715070837[/C][C]0.498328575354185[/C][/ROW]
[ROW][C]19[/C][C]0.752944753543612[/C][C]0.494110492912775[/C][C]0.247055246456388[/C][/ROW]
[ROW][C]20[/C][C]0.672512519562935[/C][C]0.654974960874131[/C][C]0.327487480437065[/C][/ROW]
[ROW][C]21[/C][C]0.603387153859492[/C][C]0.793225692281015[/C][C]0.396612846140508[/C][/ROW]
[ROW][C]22[/C][C]0.507317869080162[/C][C]0.985364261839677[/C][C]0.492682130919838[/C][/ROW]
[ROW][C]23[/C][C]0.439215174965781[/C][C]0.878430349931563[/C][C]0.560784825034219[/C][/ROW]
[ROW][C]24[/C][C]0.515451878497094[/C][C]0.969096243005812[/C][C]0.484548121502906[/C][/ROW]
[ROW][C]25[/C][C]0.436090667770313[/C][C]0.872181335540626[/C][C]0.563909332229687[/C][/ROW]
[ROW][C]26[/C][C]0.435920502878543[/C][C]0.871841005757086[/C][C]0.564079497121457[/C][/ROW]
[ROW][C]27[/C][C]0.401071275502698[/C][C]0.802142551005397[/C][C]0.598928724497302[/C][/ROW]
[ROW][C]28[/C][C]0.339331006960693[/C][C]0.678662013921385[/C][C]0.660668993039307[/C][/ROW]
[ROW][C]29[/C][C]0.395189622998765[/C][C]0.790379245997529[/C][C]0.604810377001235[/C][/ROW]
[ROW][C]30[/C][C]0.407251498438798[/C][C]0.814502996877597[/C][C]0.592748501561202[/C][/ROW]
[ROW][C]31[/C][C]0.626573265129218[/C][C]0.746853469741564[/C][C]0.373426734870782[/C][/ROW]
[ROW][C]32[/C][C]0.64292360858431[/C][C]0.71415278283138[/C][C]0.35707639141569[/C][/ROW]
[ROW][C]33[/C][C]0.584473513993787[/C][C]0.831052972012425[/C][C]0.415526486006213[/C][/ROW]
[ROW][C]34[/C][C]0.544496424205012[/C][C]0.911007151589976[/C][C]0.455503575794988[/C][/ROW]
[ROW][C]35[/C][C]0.506074297447569[/C][C]0.987851405104863[/C][C]0.493925702552431[/C][/ROW]
[ROW][C]36[/C][C]0.49326477539186[/C][C]0.98652955078372[/C][C]0.50673522460814[/C][/ROW]
[ROW][C]37[/C][C]0.698793966125103[/C][C]0.602412067749794[/C][C]0.301206033874897[/C][/ROW]
[ROW][C]38[/C][C]0.652654435734238[/C][C]0.694691128531525[/C][C]0.347345564265762[/C][/ROW]
[ROW][C]39[/C][C]0.610922794894886[/C][C]0.778154410210229[/C][C]0.389077205105114[/C][/ROW]
[ROW][C]40[/C][C]0.600828646299587[/C][C]0.798342707400826[/C][C]0.399171353700413[/C][/ROW]
[ROW][C]41[/C][C]0.716586435175849[/C][C]0.566827129648302[/C][C]0.283413564824151[/C][/ROW]
[ROW][C]42[/C][C]0.674486142063232[/C][C]0.651027715873535[/C][C]0.325513857936768[/C][/ROW]
[ROW][C]43[/C][C]0.710558424199965[/C][C]0.578883151600071[/C][C]0.289441575800035[/C][/ROW]
[ROW][C]44[/C][C]0.677048432075357[/C][C]0.645903135849287[/C][C]0.322951567924643[/C][/ROW]
[ROW][C]45[/C][C]0.63336346907416[/C][C]0.73327306185168[/C][C]0.36663653092584[/C][/ROW]
[ROW][C]46[/C][C]0.578901861736057[/C][C]0.842196276527886[/C][C]0.421098138263943[/C][/ROW]
[ROW][C]47[/C][C]0.576113968813167[/C][C]0.847772062373665[/C][C]0.423886031186833[/C][/ROW]
[ROW][C]48[/C][C]0.542456327709479[/C][C]0.915087344581042[/C][C]0.457543672290521[/C][/ROW]
[ROW][C]49[/C][C]0.488628772445794[/C][C]0.977257544891588[/C][C]0.511371227554206[/C][/ROW]
[ROW][C]50[/C][C]0.444423572995858[/C][C]0.888847145991715[/C][C]0.555576427004142[/C][/ROW]
[ROW][C]51[/C][C]0.404163395562237[/C][C]0.808326791124474[/C][C]0.595836604437763[/C][/ROW]
[ROW][C]52[/C][C]0.362973954767578[/C][C]0.725947909535156[/C][C]0.637026045232422[/C][/ROW]
[ROW][C]53[/C][C]0.390604287065507[/C][C]0.781208574131015[/C][C]0.609395712934493[/C][/ROW]
[ROW][C]54[/C][C]0.349241291218775[/C][C]0.69848258243755[/C][C]0.650758708781225[/C][/ROW]
[ROW][C]55[/C][C]0.3058962993593[/C][C]0.6117925987186[/C][C]0.6941037006407[/C][/ROW]
[ROW][C]56[/C][C]0.269859473362723[/C][C]0.539718946725446[/C][C]0.730140526637277[/C][/ROW]
[ROW][C]57[/C][C]0.255202718282265[/C][C]0.51040543656453[/C][C]0.744797281717735[/C][/ROW]
[ROW][C]58[/C][C]0.27283509128384[/C][C]0.54567018256768[/C][C]0.72716490871616[/C][/ROW]
[ROW][C]59[/C][C]0.235339798158879[/C][C]0.470679596317758[/C][C]0.764660201841121[/C][/ROW]
[ROW][C]60[/C][C]0.243889276406892[/C][C]0.487778552813784[/C][C]0.756110723593108[/C][/ROW]
[ROW][C]61[/C][C]0.355962970263177[/C][C]0.711925940526354[/C][C]0.644037029736823[/C][/ROW]
[ROW][C]62[/C][C]0.317377692494738[/C][C]0.634755384989477[/C][C]0.682622307505262[/C][/ROW]
[ROW][C]63[/C][C]0.274969806040954[/C][C]0.549939612081908[/C][C]0.725030193959046[/C][/ROW]
[ROW][C]64[/C][C]0.236667811214553[/C][C]0.473335622429105[/C][C]0.763332188785448[/C][/ROW]
[ROW][C]65[/C][C]0.217845020614632[/C][C]0.435690041229264[/C][C]0.782154979385368[/C][/ROW]
[ROW][C]66[/C][C]0.214505168711306[/C][C]0.429010337422612[/C][C]0.785494831288694[/C][/ROW]
[ROW][C]67[/C][C]0.247545835642865[/C][C]0.495091671285729[/C][C]0.752454164357135[/C][/ROW]
[ROW][C]68[/C][C]0.209980974259345[/C][C]0.419961948518689[/C][C]0.790019025740655[/C][/ROW]
[ROW][C]69[/C][C]0.189068272236464[/C][C]0.378136544472927[/C][C]0.810931727763536[/C][/ROW]
[ROW][C]70[/C][C]0.175781322997055[/C][C]0.35156264599411[/C][C]0.824218677002945[/C][/ROW]
[ROW][C]71[/C][C]0.158807938792301[/C][C]0.317615877584602[/C][C]0.841192061207699[/C][/ROW]
[ROW][C]72[/C][C]0.151280769263382[/C][C]0.302561538526764[/C][C]0.848719230736618[/C][/ROW]
[ROW][C]73[/C][C]0.139343871174369[/C][C]0.278687742348738[/C][C]0.860656128825631[/C][/ROW]
[ROW][C]74[/C][C]0.132252763773357[/C][C]0.264505527546713[/C][C]0.867747236226643[/C][/ROW]
[ROW][C]75[/C][C]0.108722955403645[/C][C]0.217445910807289[/C][C]0.891277044596355[/C][/ROW]
[ROW][C]76[/C][C]0.180619337073986[/C][C]0.361238674147973[/C][C]0.819380662926014[/C][/ROW]
[ROW][C]77[/C][C]0.160666066246838[/C][C]0.321332132493676[/C][C]0.839333933753162[/C][/ROW]
[ROW][C]78[/C][C]0.159869274349017[/C][C]0.319738548698033[/C][C]0.840130725650983[/C][/ROW]
[ROW][C]79[/C][C]0.133419300521172[/C][C]0.266838601042344[/C][C]0.866580699478828[/C][/ROW]
[ROW][C]80[/C][C]0.133633642628767[/C][C]0.267267285257535[/C][C]0.866366357371233[/C][/ROW]
[ROW][C]81[/C][C]0.113071117459237[/C][C]0.226142234918474[/C][C]0.886928882540763[/C][/ROW]
[ROW][C]82[/C][C]0.105701182184106[/C][C]0.211402364368211[/C][C]0.894298817815894[/C][/ROW]
[ROW][C]83[/C][C]0.102956524518073[/C][C]0.205913049036145[/C][C]0.897043475481927[/C][/ROW]
[ROW][C]84[/C][C]0.093538745127504[/C][C]0.187077490255008[/C][C]0.906461254872496[/C][/ROW]
[ROW][C]85[/C][C]0.0904650156483978[/C][C]0.180930031296796[/C][C]0.909534984351602[/C][/ROW]
[ROW][C]86[/C][C]0.0757091102280865[/C][C]0.151418220456173[/C][C]0.924290889771914[/C][/ROW]
[ROW][C]87[/C][C]0.0653086823089265[/C][C]0.130617364617853[/C][C]0.934691317691073[/C][/ROW]
[ROW][C]88[/C][C]0.0555025489973315[/C][C]0.111005097994663[/C][C]0.944497451002668[/C][/ROW]
[ROW][C]89[/C][C]0.0436330002530659[/C][C]0.0872660005061317[/C][C]0.956366999746934[/C][/ROW]
[ROW][C]90[/C][C]0.130303434625050[/C][C]0.260606869250101[/C][C]0.86969656537495[/C][/ROW]
[ROW][C]91[/C][C]0.107427291397107[/C][C]0.214854582794213[/C][C]0.892572708602893[/C][/ROW]
[ROW][C]92[/C][C]0.101480580545939[/C][C]0.202961161091878[/C][C]0.898519419454061[/C][/ROW]
[ROW][C]93[/C][C]0.0853912150184802[/C][C]0.170782430036960[/C][C]0.91460878498152[/C][/ROW]
[ROW][C]94[/C][C]0.0755270412448336[/C][C]0.151054082489667[/C][C]0.924472958755166[/C][/ROW]
[ROW][C]95[/C][C]0.0652115013261324[/C][C]0.130423002652265[/C][C]0.934788498673868[/C][/ROW]
[ROW][C]96[/C][C]0.068089645214674[/C][C]0.136179290429348[/C][C]0.931910354785326[/C][/ROW]
[ROW][C]97[/C][C]0.0586370017494233[/C][C]0.117274003498847[/C][C]0.941362998250577[/C][/ROW]
[ROW][C]98[/C][C]0.058457359281055[/C][C]0.11691471856211[/C][C]0.941542640718945[/C][/ROW]
[ROW][C]99[/C][C]0.0464228148208487[/C][C]0.0928456296416974[/C][C]0.953577185179151[/C][/ROW]
[ROW][C]100[/C][C]0.0430237722189214[/C][C]0.0860475444378427[/C][C]0.956976227781079[/C][/ROW]
[ROW][C]101[/C][C]0.0565345540050087[/C][C]0.113069108010017[/C][C]0.943465445994991[/C][/ROW]
[ROW][C]102[/C][C]0.0468682982208290[/C][C]0.0937365964416581[/C][C]0.953131701779171[/C][/ROW]
[ROW][C]103[/C][C]0.0364538707599023[/C][C]0.0729077415198047[/C][C]0.963546129240098[/C][/ROW]
[ROW][C]104[/C][C]0.0286516086787033[/C][C]0.0573032173574066[/C][C]0.971348391321297[/C][/ROW]
[ROW][C]105[/C][C]0.0380125622788413[/C][C]0.0760251245576826[/C][C]0.961987437721159[/C][/ROW]
[ROW][C]106[/C][C]0.08179538238886[/C][C]0.16359076477772[/C][C]0.91820461761114[/C][/ROW]
[ROW][C]107[/C][C]0.0676503915196008[/C][C]0.135300783039202[/C][C]0.9323496084804[/C][/ROW]
[ROW][C]108[/C][C]0.0642001252248345[/C][C]0.128400250449669[/C][C]0.935799874775165[/C][/ROW]
[ROW][C]109[/C][C]0.0517673382719902[/C][C]0.103534676543980[/C][C]0.94823266172801[/C][/ROW]
[ROW][C]110[/C][C]0.352429390357195[/C][C]0.70485878071439[/C][C]0.647570609642805[/C][/ROW]
[ROW][C]111[/C][C]0.328714954261349[/C][C]0.657429908522699[/C][C]0.671285045738651[/C][/ROW]
[ROW][C]112[/C][C]0.328084144451477[/C][C]0.656168288902954[/C][C]0.671915855548523[/C][/ROW]
[ROW][C]113[/C][C]0.352576844393672[/C][C]0.705153688787344[/C][C]0.647423155606328[/C][/ROW]
[ROW][C]114[/C][C]0.304255974334159[/C][C]0.608511948668317[/C][C]0.695744025665841[/C][/ROW]
[ROW][C]115[/C][C]0.258914421790944[/C][C]0.517828843581889[/C][C]0.741085578209056[/C][/ROW]
[ROW][C]116[/C][C]0.242136398700441[/C][C]0.484272797400881[/C][C]0.75786360129956[/C][/ROW]
[ROW][C]117[/C][C]0.237649890819895[/C][C]0.47529978163979[/C][C]0.762350109180105[/C][/ROW]
[ROW][C]118[/C][C]0.199827520034223[/C][C]0.399655040068446[/C][C]0.800172479965777[/C][/ROW]
[ROW][C]119[/C][C]0.225315825556131[/C][C]0.450631651112262[/C][C]0.77468417444387[/C][/ROW]
[ROW][C]120[/C][C]0.194834720864086[/C][C]0.389669441728171[/C][C]0.805165279135914[/C][/ROW]
[ROW][C]121[/C][C]0.172945297275848[/C][C]0.345890594551695[/C][C]0.827054702724152[/C][/ROW]
[ROW][C]122[/C][C]0.139599721892354[/C][C]0.279199443784708[/C][C]0.860400278107646[/C][/ROW]
[ROW][C]123[/C][C]0.109859561514941[/C][C]0.219719123029881[/C][C]0.89014043848506[/C][/ROW]
[ROW][C]124[/C][C]0.125522865758427[/C][C]0.251045731516855[/C][C]0.874477134241573[/C][/ROW]
[ROW][C]125[/C][C]0.172505656588698[/C][C]0.345011313177396[/C][C]0.827494343411302[/C][/ROW]
[ROW][C]126[/C][C]0.216355271733591[/C][C]0.432710543467183[/C][C]0.783644728266409[/C][/ROW]
[ROW][C]127[/C][C]0.274977310018662[/C][C]0.549954620037324[/C][C]0.725022689981338[/C][/ROW]
[ROW][C]128[/C][C]0.27435851067944[/C][C]0.54871702135888[/C][C]0.72564148932056[/C][/ROW]
[ROW][C]129[/C][C]0.223419497947999[/C][C]0.446838995895998[/C][C]0.776580502052001[/C][/ROW]
[ROW][C]130[/C][C]0.185576964848226[/C][C]0.371153929696451[/C][C]0.814423035151774[/C][/ROW]
[ROW][C]131[/C][C]0.224563687144847[/C][C]0.449127374289694[/C][C]0.775436312855153[/C][/ROW]
[ROW][C]132[/C][C]0.287238779291542[/C][C]0.574477558583083[/C][C]0.712761220708458[/C][/ROW]
[ROW][C]133[/C][C]0.290100153562408[/C][C]0.580200307124816[/C][C]0.709899846437592[/C][/ROW]
[ROW][C]134[/C][C]0.263814863007056[/C][C]0.527629726014112[/C][C]0.736185136992944[/C][/ROW]
[ROW][C]135[/C][C]0.211169720250086[/C][C]0.422339440500172[/C][C]0.788830279749914[/C][/ROW]
[ROW][C]136[/C][C]0.175554912968105[/C][C]0.351109825936209[/C][C]0.824445087031895[/C][/ROW]
[ROW][C]137[/C][C]0.52550700193366[/C][C]0.94898599613268[/C][C]0.47449299806634[/C][/ROW]
[ROW][C]138[/C][C]0.474475385802294[/C][C]0.948950771604588[/C][C]0.525524614197706[/C][/ROW]
[ROW][C]139[/C][C]0.459848412813028[/C][C]0.919696825626056[/C][C]0.540151587186972[/C][/ROW]
[ROW][C]140[/C][C]0.533088769546092[/C][C]0.933822460907817[/C][C]0.466911230453909[/C][/ROW]
[ROW][C]141[/C][C]0.475037316836013[/C][C]0.950074633672026[/C][C]0.524962683163987[/C][/ROW]
[ROW][C]142[/C][C]0.392621215211266[/C][C]0.785242430422533[/C][C]0.607378784788734[/C][/ROW]
[ROW][C]143[/C][C]0.360157140849169[/C][C]0.720314281698339[/C][C]0.639842859150831[/C][/ROW]
[ROW][C]144[/C][C]0.445009733676195[/C][C]0.89001946735239[/C][C]0.554990266323805[/C][/ROW]
[ROW][C]145[/C][C]0.352703310900012[/C][C]0.705406621800025[/C][C]0.647296689099988[/C][/ROW]
[ROW][C]146[/C][C]0.266671988712201[/C][C]0.533343977424401[/C][C]0.7333280112878[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.3377808310632680.6755616621265350.662219168936732
170.628718567486920.742562865026160.37128143251308
180.5016714246458150.996657150708370.498328575354185
190.7529447535436120.4941104929127750.247055246456388
200.6725125195629350.6549749608741310.327487480437065
210.6033871538594920.7932256922810150.396612846140508
220.5073178690801620.9853642618396770.492682130919838
230.4392151749657810.8784303499315630.560784825034219
240.5154518784970940.9690962430058120.484548121502906
250.4360906677703130.8721813355406260.563909332229687
260.4359205028785430.8718410057570860.564079497121457
270.4010712755026980.8021425510053970.598928724497302
280.3393310069606930.6786620139213850.660668993039307
290.3951896229987650.7903792459975290.604810377001235
300.4072514984387980.8145029968775970.592748501561202
310.6265732651292180.7468534697415640.373426734870782
320.642923608584310.714152782831380.35707639141569
330.5844735139937870.8310529720124250.415526486006213
340.5444964242050120.9110071515899760.455503575794988
350.5060742974475690.9878514051048630.493925702552431
360.493264775391860.986529550783720.50673522460814
370.6987939661251030.6024120677497940.301206033874897
380.6526544357342380.6946911285315250.347345564265762
390.6109227948948860.7781544102102290.389077205105114
400.6008286462995870.7983427074008260.399171353700413
410.7165864351758490.5668271296483020.283413564824151
420.6744861420632320.6510277158735350.325513857936768
430.7105584241999650.5788831516000710.289441575800035
440.6770484320753570.6459031358492870.322951567924643
450.633363469074160.733273061851680.36663653092584
460.5789018617360570.8421962765278860.421098138263943
470.5761139688131670.8477720623736650.423886031186833
480.5424563277094790.9150873445810420.457543672290521
490.4886287724457940.9772575448915880.511371227554206
500.4444235729958580.8888471459917150.555576427004142
510.4041633955622370.8083267911244740.595836604437763
520.3629739547675780.7259479095351560.637026045232422
530.3906042870655070.7812085741310150.609395712934493
540.3492412912187750.698482582437550.650758708781225
550.30589629935930.61179259871860.6941037006407
560.2698594733627230.5397189467254460.730140526637277
570.2552027182822650.510405436564530.744797281717735
580.272835091283840.545670182567680.72716490871616
590.2353397981588790.4706795963177580.764660201841121
600.2438892764068920.4877785528137840.756110723593108
610.3559629702631770.7119259405263540.644037029736823
620.3173776924947380.6347553849894770.682622307505262
630.2749698060409540.5499396120819080.725030193959046
640.2366678112145530.4733356224291050.763332188785448
650.2178450206146320.4356900412292640.782154979385368
660.2145051687113060.4290103374226120.785494831288694
670.2475458356428650.4950916712857290.752454164357135
680.2099809742593450.4199619485186890.790019025740655
690.1890682722364640.3781365444729270.810931727763536
700.1757813229970550.351562645994110.824218677002945
710.1588079387923010.3176158775846020.841192061207699
720.1512807692633820.3025615385267640.848719230736618
730.1393438711743690.2786877423487380.860656128825631
740.1322527637733570.2645055275467130.867747236226643
750.1087229554036450.2174459108072890.891277044596355
760.1806193370739860.3612386741479730.819380662926014
770.1606660662468380.3213321324936760.839333933753162
780.1598692743490170.3197385486980330.840130725650983
790.1334193005211720.2668386010423440.866580699478828
800.1336336426287670.2672672852575350.866366357371233
810.1130711174592370.2261422349184740.886928882540763
820.1057011821841060.2114023643682110.894298817815894
830.1029565245180730.2059130490361450.897043475481927
840.0935387451275040.1870774902550080.906461254872496
850.09046501564839780.1809300312967960.909534984351602
860.07570911022808650.1514182204561730.924290889771914
870.06530868230892650.1306173646178530.934691317691073
880.05550254899733150.1110050979946630.944497451002668
890.04363300025306590.08726600050613170.956366999746934
900.1303034346250500.2606068692501010.86969656537495
910.1074272913971070.2148545827942130.892572708602893
920.1014805805459390.2029611610918780.898519419454061
930.08539121501848020.1707824300369600.91460878498152
940.07552704124483360.1510540824896670.924472958755166
950.06521150132613240.1304230026522650.934788498673868
960.0680896452146740.1361792904293480.931910354785326
970.05863700174942330.1172740034988470.941362998250577
980.0584573592810550.116914718562110.941542640718945
990.04642281482084870.09284562964169740.953577185179151
1000.04302377221892140.08604754443784270.956976227781079
1010.05653455400500870.1130691080100170.943465445994991
1020.04686829822082900.09373659644165810.953131701779171
1030.03645387075990230.07290774151980470.963546129240098
1040.02865160867870330.05730321735740660.971348391321297
1050.03801256227884130.07602512455768260.961987437721159
1060.081795382388860.163590764777720.91820461761114
1070.06765039151960080.1353007830392020.9323496084804
1080.06420012522483450.1284002504496690.935799874775165
1090.05176733827199020.1035346765439800.94823266172801
1100.3524293903571950.704858780714390.647570609642805
1110.3287149542613490.6574299085226990.671285045738651
1120.3280841444514770.6561682889029540.671915855548523
1130.3525768443936720.7051536887873440.647423155606328
1140.3042559743341590.6085119486683170.695744025665841
1150.2589144217909440.5178288435818890.741085578209056
1160.2421363987004410.4842727974008810.75786360129956
1170.2376498908198950.475299781639790.762350109180105
1180.1998275200342230.3996550400684460.800172479965777
1190.2253158255561310.4506316511122620.77468417444387
1200.1948347208640860.3896694417281710.805165279135914
1210.1729452972758480.3458905945516950.827054702724152
1220.1395997218923540.2791994437847080.860400278107646
1230.1098595615149410.2197191230298810.89014043848506
1240.1255228657584270.2510457315168550.874477134241573
1250.1725056565886980.3450113131773960.827494343411302
1260.2163552717335910.4327105434671830.783644728266409
1270.2749773100186620.5499546200373240.725022689981338
1280.274358510679440.548717021358880.72564148932056
1290.2234194979479990.4468389958959980.776580502052001
1300.1855769648482260.3711539296964510.814423035151774
1310.2245636871448470.4491273742896940.775436312855153
1320.2872387792915420.5744775585830830.712761220708458
1330.2901001535624080.5802003071248160.709899846437592
1340.2638148630070560.5276297260141120.736185136992944
1350.2111697202500860.4223394405001720.788830279749914
1360.1755549129681050.3511098259362090.824445087031895
1370.525507001933660.948985996132680.47449299806634
1380.4744753858022940.9489507716045880.525524614197706
1390.4598484128130280.9196968256260560.540151587186972
1400.5330887695460920.9338224609078170.466911230453909
1410.4750373168360130.9500746336720260.524962683163987
1420.3926212152112660.7852424304225330.607378784788734
1430.3601571408491690.7203142816983390.639842859150831
1440.4450097336761950.890019467352390.554990266323805
1450.3527033109000120.7054066218000250.647296689099988
1460.2666719887122010.5333439774244010.7333280112878







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0534351145038168OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 7 & 0.0534351145038168 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103446&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0534351145038168[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103446&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103446&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0534351145038168OK



Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Include Monthly 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}