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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 02 Dec 2010 12:56:44 +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/Dec/02/t1291295168eqr6dp00b8bepjj.htm/, Retrieved Sun, 05 May 2024 17:40:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104256, Retrieved Sun, 05 May 2024 17:40:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-01 21:03:34] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D    [Multiple Regression] [] [2010-12-02 12:01:33] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D        [Multiple Regression] [] [2010-12-02 12:56:44] [c474a97a96075919a678ad3d2290b00b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2502,66	10169,02	10433,44	24977	-7,9	-15	0,3	3,36	12
2466,92	9633,83	10238,83	24320	-8,8	-10	-0,1	3,37	11
2513,17	10066,24	9857,34	22680	-14,2	-12	-1	3,55	10
2443,27	10302,87	9634,97	22052	-17,8	-11	-1,2	3,53	09
2293,41	10430,35	9374,63	21467	-18,2	-11	-0,8	3,52	08
2070,83	9691,12	8679,75	21383	-22,8	-17	-1,7	3,54	07
2029,6	9810,31	8593	21777	-23,6	-18	-1,1	3,5	06
2052,02	9304,43	8398,37	21928	-27,6	-19	-0,4	3,44	05
1864,44	8767,96	7992,12	21814	-29,4	-22	0,6	3,38	04
1670,07	7764,58	7235,47	22937	-31,8	-24	0,6	3,35	03
1810,99	7694,78	7690,5	23595	-31,4	-24	1,9	3,68	02
1905,41	8331,49	8396,2	20830	-27,6	-20	2,3	3,92	01
1862,83	8460,94	8595,56	19650	-28,8	-25	2,6	4,05	12
2014,45	8531,45	8614,55	19195	-21,9	-22	3,1	4,14	11
2197,82	9117,03	9181,73	19644	-13,9	-17	4,7	4,53	10
2962,34	12123,53	11114,08	18483	-8	-9	5,5	4,54	09
3047,03	12989,35	11530,75	18079	-2,8	-11	5,4	4,9	08
3032,6	13168,91	11322,38	19178	-3,3	-13	5,9	4,92	07
3504,37	14084,6	12056,67	18391	-1,3	-11	5,8	4,45	06
3801,06	13995,33	12812,48	18441	0,5	-9	5,2	3,92	05
3857,62	13357,7	12656,63	18584	-1,9	-7	4,2	3,66	04
3674,4	12602,93	12193,88	20108	2	-3	4,4	3,74	03
3720,98	13547,84	12419,57	20148	1,7	-3	3,6	4,07	02
3844,49	13731,31	12538,12	19394	1,9	-6	3,5	4,23	01
4116,68	15532,18	13406,97	17745	0,1	-4	3,1	4,14	12
4105,18	15543,76	13200,58	17696	2,4	-8	2,9	4,18	11
4435,23	16903,36	13901,28	17032	2,3	-1	2,2	4,29	10
4296,49	16235,39	13557,69	16438	4,7	-2	1,5	4,27	09
4202,52	16460,95	13239,71	15683	5	-2	1,1	4,33	08
4562,84	17974,77	13673,28	15594	7,2	-1	1,4	4,39	07
4621,4	18001,37	13480,21	15713	8,5	1	1,3	4,21	06
4696,96	17611,14	13407,75	15937	6,8	2	1,3	3,88	05
4591,27	17460,53	12754,8	16171	5,8	2	1,8	3,91	04
4356,98	17128,37	12268,53	15928	3,7	-1	1,8	3,94	03
4502,64	17741,23	12631,48	16348	4,8	1	1,8	3,94	02
4443,91	17286,32	12512,89	15579	6,1	-1	1,7	3,64	01
4290,89	16775,08	12377,62	15305	6,9	-8	1,6	3,5	12
4199,75	16101,07	12185,15	15648	5,7	1	1,5	3,49	11
4138,52	16519,44	11963,12	14954	6,9	2	1,2	3,52	10
3970,1	15934,09	11533,59	15137	5,5	-2	1,2	3,51	09
3862,27	15786,78	11257,35	15839	6,5	-2	1,6	3,6	08
3701,61	15147,55	11036,89	16050	7,7	-2	1,6	3,57	07
3570,12	14990,31	10997,97	15168	6,3	-2	1,9	3,46	06
3801,06	16397,83	11333,88	17064	5,5	-6	2,2	3,48	05
3895,51	17232,97	11234,68	16005	5,3	-4	2	3,3	04
3917,96	16311,54	11145,65	14886	3,3	-5	1,7	3,04	03
3813,06	16187,64	10971,19	14931	2,2	-2	2,4	2,96	02
3667,03	16102,64	10872,48	14544	0,6	-1	2,6	3,07	01
3494,17	15650,83	10827,81	13812	0,2	-5	2,9	2,99	12
3363,99	14368,05	10695,25	13031	-0,7	-9	2,6	2,86	11
3295,32	13392,79	10324,31	12574	-1,7	-8	2,5	2,72	10
3277,01	12986,62	10532,54	11964	-3,7	-14	3,2	2,72	09
3257,16	12204,98	10554,27	11451	-7,6	-10	3,1	2,75	08
3161,69	11716,87	10545,38	11346	-8,2	-11	3,1	2,67	07
3097,31	11402,75	10486,64	11353	-7,5	-11	2,9	2,76	06
3061,26	11082,38	10377,18	10702	-8	-11	2,5	2,87	05
3119,31	11395,64	10283,19	10646	-6,9	-5	2,8	2,9	04
3106,22	11809,38	10682,06	10556	-4,2	-2	3,1	2,92	03
3080,58	11545,71	10723,78	10463	-3,6	-3	2,6	2,93	02
2981,85	11394,84	10539,51	10407	-1,8	-6	2,3	3,1	01
2921,44	11068,05	10673,38	10625	-3,2	-6	2,3	3,2	12
2849,27	10973	10411,75	10872	-1,3	-7	2,6	3,25	11
2756,76	11028,93	10001,6	10805	0,6	-6	2,9	3,31	10
2645,64	11079,42	10204,59	10653	1,2	-2	2	3,23	09
2497,84	10989,34	10032,8	10574	0,4	-2	2,2	3,24	08
2448,05	11383,89	10152,09	10431	3	-4	2,4	3,35	07
2454,62	11527,72	10364,91	10383	-0,4	0	2,3	3,19	06
2407,6	11037,54	10092,96	10296	0	-6	2,6	3,17	05
2472,81	11950,95	10418,4	10872	-1,3	-4	1,9	3,06	04
2408,64	11441,08	10323,73	10635	-3,1	-3	1,1	3,22	03
2440,25	10631,92	10601,61	10297	-4	-1	1,3	3,35	02
2350,44	10892,76	10540,05	10570	-4,9	-3	1,6	3,38	01

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1881.92093448974 + 0.191100321953382Nikkei[t] + 0.289259310027567DJ_Indust[t] + 0.0145639845361721Goudprijs[t] -9.42821394015824Conjunct_Seizoenzuiver[t] -3.35520018942041Cons_vertrouw[t] + 32.6392820980445Alg_consumptie_index_BE[t] -254.219100476035Gem_rente_kasbon_5j[t] -1.32190944304161Maand[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -1881.92093448974 +  0.191100321953382Nikkei[t] +  0.289259310027567DJ_Indust[t] +  0.0145639845361721Goudprijs[t] -9.42821394015824Conjunct_Seizoenzuiver[t] -3.35520018942041Cons_vertrouw[t] +  32.6392820980445Alg_consumptie_index_BE[t] -254.219100476035Gem_rente_kasbon_5j[t] -1.32190944304161Maand[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -1881.92093448974 +  0.191100321953382Nikkei[t] +  0.289259310027567DJ_Indust[t] +  0.0145639845361721Goudprijs[t] -9.42821394015824Conjunct_Seizoenzuiver[t] -3.35520018942041Cons_vertrouw[t] +  32.6392820980445Alg_consumptie_index_BE[t] -254.219100476035Gem_rente_kasbon_5j[t] -1.32190944304161Maand[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1881.92093448974 + 0.191100321953382Nikkei[t] + 0.289259310027567DJ_Indust[t] + 0.0145639845361721Goudprijs[t] -9.42821394015824Conjunct_Seizoenzuiver[t] -3.35520018942041Cons_vertrouw[t] + 32.6392820980445Alg_consumptie_index_BE[t] -254.219100476035Gem_rente_kasbon_5j[t] -1.32190944304161Maand[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1881.92093448974272.991974-6.893700
Nikkei0.1911003219533820.01543212.383500
DJ_Indust0.2892593100275670.033768.568200
Goudprijs0.01456398453617210.0083221.75010.0849750.042487
Conjunct_Seizoenzuiver-9.428213940158246.642913-1.41930.1607440.080372
Cons_vertrouw-3.355200189420418.724572-0.38460.7018520.350926
Alg_consumptie_index_BE32.639282098044518.4981631.76450.0825020.041251
Gem_rente_kasbon_5j-254.21910047603557.058457-4.45543.5e-051.8e-05
Maand-1.321909443041616.377068-0.20730.8364510.418226

\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) & -1881.92093448974 & 272.991974 & -6.8937 & 0 & 0 \tabularnewline
Nikkei & 0.191100321953382 & 0.015432 & 12.3835 & 0 & 0 \tabularnewline
DJ_Indust & 0.289259310027567 & 0.03376 & 8.5682 & 0 & 0 \tabularnewline
Goudprijs & 0.0145639845361721 & 0.008322 & 1.7501 & 0.084975 & 0.042487 \tabularnewline
Conjunct_Seizoenzuiver & -9.42821394015824 & 6.642913 & -1.4193 & 0.160744 & 0.080372 \tabularnewline
Cons_vertrouw & -3.35520018942041 & 8.724572 & -0.3846 & 0.701852 & 0.350926 \tabularnewline
Alg_consumptie_index_BE & 32.6392820980445 & 18.498163 & 1.7645 & 0.082502 & 0.041251 \tabularnewline
Gem_rente_kasbon_5j & -254.219100476035 & 57.058457 & -4.4554 & 3.5e-05 & 1.8e-05 \tabularnewline
Maand & -1.32190944304161 & 6.377068 & -0.2073 & 0.836451 & 0.418226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&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]-1881.92093448974[/C][C]272.991974[/C][C]-6.8937[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.191100321953382[/C][C]0.015432[/C][C]12.3835[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.289259310027567[/C][C]0.03376[/C][C]8.5682[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.0145639845361721[/C][C]0.008322[/C][C]1.7501[/C][C]0.084975[/C][C]0.042487[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-9.42821394015824[/C][C]6.642913[/C][C]-1.4193[/C][C]0.160744[/C][C]0.080372[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]-3.35520018942041[/C][C]8.724572[/C][C]-0.3846[/C][C]0.701852[/C][C]0.350926[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]32.6392820980445[/C][C]18.498163[/C][C]1.7645[/C][C]0.082502[/C][C]0.041251[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-254.219100476035[/C][C]57.058457[/C][C]-4.4554[/C][C]3.5e-05[/C][C]1.8e-05[/C][/ROW]
[ROW][C]Maand[/C][C]-1.32190944304161[/C][C]6.377068[/C][C]-0.2073[/C][C]0.836451[/C][C]0.418226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1881.92093448974272.991974-6.893700
Nikkei0.1911003219533820.01543212.383500
DJ_Indust0.2892593100275670.033768.568200
Goudprijs0.01456398453617210.0083221.75010.0849750.042487
Conjunct_Seizoenzuiver-9.428213940158246.642913-1.41930.1607440.080372
Cons_vertrouw-3.355200189420418.724572-0.38460.7018520.350926
Alg_consumptie_index_BE32.639282098044518.4981631.76450.0825020.041251
Gem_rente_kasbon_5j-254.21910047603557.058457-4.45543.5e-051.8e-05
Maand-1.321909443041616.377068-0.20730.8364510.418226






Multiple Linear Regression - Regression Statistics
Multiple R0.983716693372244
R-squared0.96769853281922
Adjusted R-squared0.963596759208963
F-TEST (value)235.921975410639
F-TEST (DF numerator)8
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation161.344873074614
Sum Squared Residuals1640026.58825019

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983716693372244 \tabularnewline
R-squared & 0.96769853281922 \tabularnewline
Adjusted R-squared & 0.963596759208963 \tabularnewline
F-TEST (value) & 235.921975410639 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 161.344873074614 \tabularnewline
Sum Squared Residuals & 1640026.58825019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983716693372244[/C][/ROW]
[ROW][C]R-squared[/C][C]0.96769853281922[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.963596759208963[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]235.921975410639[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]161.344873074614[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1640026.58825019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.983716693372244
R-squared0.96769853281922
Adjusted R-squared0.963596759208963
F-TEST (value)235.921975410639
F-TEST (DF numerator)8
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation161.344873074614
Sum Squared Residuals1640026.58825019






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12502.662707.67994551663-205.019945516626
22466.922516.97706924377-50.0570692437716
32513.172449.1861637627163.9838362372944
42443.272451.40226291509-8.13226291509133
52293.412412.62913109455-119.219131094554
62070.832099.50131485049-28.6713148504925
72029.62144.89554104893-115.295541048933
82052.022074.61294122374-22.5929412237412
91864.441929.17218579978-64.7321857997813
101670.071573.1998387525096.8701612474971
111810.991657.15518938415153.834810615853
121905.411846.8085776567558.6014223432489
131862.831902.31990399739-39.4899039973921
142014.451834.30236372566180.147636274342
152197.821979.00581391919218.814186080813
162962.343038.01345391172-75.6734539117243
173047.033182.33655457386-135.306554573857
183032.63196.36506079008-163.765060790078
193504.373664.26620772756-159.896207727562
203801.063959.35323829092-158.293238290915
213857.623845.2004328709612.4195671290381
223674.43526.62581228835147.774187711654
233720.983667.2105553564553.7694446435532
243844.493691.14506122603153.344938773970
254116.684268.14222608313-151.462226083132
264105.184186.3025012722-81.1225012721991
274435.234567.10274279444-131.872742794436
284296.494295.702128309350.787871690651293
294202.524206.01681959499-3.49681959499111
304562.844591.18955058552-28.3495505855178
314621.44567.0089791401454.3910208598633
324696.964572.62547954475124.334520455245
334591.274377.82315737226213.446842627739
344356.984193.71028777419163.269712225814
354502.644406.1720448962696.4679551037408
364443.914342.51306538464101.396934615356
374290.894235.4258703160655.4641296839441
384199.754037.66127687256162.088723127439
394138.524012.51476357641126.00523642359
403970.13809.55824857668160.541751423318
413862.273693.79587479137168.474125208635
423701.613508.57633496669193.033665033308
433570.123506.7016082766163.4183917233942
443801.063927.45022691712-126.390226917115
453895.514078.65670013303-183.146700133032
463917.963940.35999441493-22.3999944149300
473813.063911.68623431729-98.6262343172928
483667.033852.86926536805-185.839265368054
493494.173775.72657397467-281.556573974671
503363.993527.35301809864-163.363018098644
513295.323266.7485977663528.5714022336481
523277.013303.63485148703-26.6248514870341
533257.163166.8581184031390.301881596873
543161.693100.1509726466661.5390273533421
553097.312988.54797975787108.762020242126
563061.262851.19789412654210.062105873457
573119.312856.04379988562263.26620011438
583106.223019.6832833293086.5367166707018
593080.582960.16765850692120.412341493079
602981.852818.62664994969163.223350050313
612921.442771.31165379598150.128346204023
622849.272664.91029184400184.359708155996
632756.762530.5747812473226.185218752702
642645.642569.9328126668375.70718733317
652497.842514.72623002729-16.8862300272921
662448.052584.63066430323-136.580664303231
672454.622730.34588266038-275.725882660376
682407.62589.29918247106-181.699182471058
692472.812878.36232326484-405.552323264843
702408.642698.24117148639-289.601171486394
712440.252595.6434602399-155.393460239898
722350.442650.34214685445-299.902146854452

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2502.66 & 2707.67994551663 & -205.019945516626 \tabularnewline
2 & 2466.92 & 2516.97706924377 & -50.0570692437716 \tabularnewline
3 & 2513.17 & 2449.18616376271 & 63.9838362372944 \tabularnewline
4 & 2443.27 & 2451.40226291509 & -8.13226291509133 \tabularnewline
5 & 2293.41 & 2412.62913109455 & -119.219131094554 \tabularnewline
6 & 2070.83 & 2099.50131485049 & -28.6713148504925 \tabularnewline
7 & 2029.6 & 2144.89554104893 & -115.295541048933 \tabularnewline
8 & 2052.02 & 2074.61294122374 & -22.5929412237412 \tabularnewline
9 & 1864.44 & 1929.17218579978 & -64.7321857997813 \tabularnewline
10 & 1670.07 & 1573.19983875250 & 96.8701612474971 \tabularnewline
11 & 1810.99 & 1657.15518938415 & 153.834810615853 \tabularnewline
12 & 1905.41 & 1846.80857765675 & 58.6014223432489 \tabularnewline
13 & 1862.83 & 1902.31990399739 & -39.4899039973921 \tabularnewline
14 & 2014.45 & 1834.30236372566 & 180.147636274342 \tabularnewline
15 & 2197.82 & 1979.00581391919 & 218.814186080813 \tabularnewline
16 & 2962.34 & 3038.01345391172 & -75.6734539117243 \tabularnewline
17 & 3047.03 & 3182.33655457386 & -135.306554573857 \tabularnewline
18 & 3032.6 & 3196.36506079008 & -163.765060790078 \tabularnewline
19 & 3504.37 & 3664.26620772756 & -159.896207727562 \tabularnewline
20 & 3801.06 & 3959.35323829092 & -158.293238290915 \tabularnewline
21 & 3857.62 & 3845.20043287096 & 12.4195671290381 \tabularnewline
22 & 3674.4 & 3526.62581228835 & 147.774187711654 \tabularnewline
23 & 3720.98 & 3667.21055535645 & 53.7694446435532 \tabularnewline
24 & 3844.49 & 3691.14506122603 & 153.344938773970 \tabularnewline
25 & 4116.68 & 4268.14222608313 & -151.462226083132 \tabularnewline
26 & 4105.18 & 4186.3025012722 & -81.1225012721991 \tabularnewline
27 & 4435.23 & 4567.10274279444 & -131.872742794436 \tabularnewline
28 & 4296.49 & 4295.70212830935 & 0.787871690651293 \tabularnewline
29 & 4202.52 & 4206.01681959499 & -3.49681959499111 \tabularnewline
30 & 4562.84 & 4591.18955058552 & -28.3495505855178 \tabularnewline
31 & 4621.4 & 4567.00897914014 & 54.3910208598633 \tabularnewline
32 & 4696.96 & 4572.62547954475 & 124.334520455245 \tabularnewline
33 & 4591.27 & 4377.82315737226 & 213.446842627739 \tabularnewline
34 & 4356.98 & 4193.71028777419 & 163.269712225814 \tabularnewline
35 & 4502.64 & 4406.17204489626 & 96.4679551037408 \tabularnewline
36 & 4443.91 & 4342.51306538464 & 101.396934615356 \tabularnewline
37 & 4290.89 & 4235.42587031606 & 55.4641296839441 \tabularnewline
38 & 4199.75 & 4037.66127687256 & 162.088723127439 \tabularnewline
39 & 4138.52 & 4012.51476357641 & 126.00523642359 \tabularnewline
40 & 3970.1 & 3809.55824857668 & 160.541751423318 \tabularnewline
41 & 3862.27 & 3693.79587479137 & 168.474125208635 \tabularnewline
42 & 3701.61 & 3508.57633496669 & 193.033665033308 \tabularnewline
43 & 3570.12 & 3506.70160827661 & 63.4183917233942 \tabularnewline
44 & 3801.06 & 3927.45022691712 & -126.390226917115 \tabularnewline
45 & 3895.51 & 4078.65670013303 & -183.146700133032 \tabularnewline
46 & 3917.96 & 3940.35999441493 & -22.3999944149300 \tabularnewline
47 & 3813.06 & 3911.68623431729 & -98.6262343172928 \tabularnewline
48 & 3667.03 & 3852.86926536805 & -185.839265368054 \tabularnewline
49 & 3494.17 & 3775.72657397467 & -281.556573974671 \tabularnewline
50 & 3363.99 & 3527.35301809864 & -163.363018098644 \tabularnewline
51 & 3295.32 & 3266.74859776635 & 28.5714022336481 \tabularnewline
52 & 3277.01 & 3303.63485148703 & -26.6248514870341 \tabularnewline
53 & 3257.16 & 3166.85811840313 & 90.301881596873 \tabularnewline
54 & 3161.69 & 3100.15097264666 & 61.5390273533421 \tabularnewline
55 & 3097.31 & 2988.54797975787 & 108.762020242126 \tabularnewline
56 & 3061.26 & 2851.19789412654 & 210.062105873457 \tabularnewline
57 & 3119.31 & 2856.04379988562 & 263.26620011438 \tabularnewline
58 & 3106.22 & 3019.68328332930 & 86.5367166707018 \tabularnewline
59 & 3080.58 & 2960.16765850692 & 120.412341493079 \tabularnewline
60 & 2981.85 & 2818.62664994969 & 163.223350050313 \tabularnewline
61 & 2921.44 & 2771.31165379598 & 150.128346204023 \tabularnewline
62 & 2849.27 & 2664.91029184400 & 184.359708155996 \tabularnewline
63 & 2756.76 & 2530.5747812473 & 226.185218752702 \tabularnewline
64 & 2645.64 & 2569.93281266683 & 75.70718733317 \tabularnewline
65 & 2497.84 & 2514.72623002729 & -16.8862300272921 \tabularnewline
66 & 2448.05 & 2584.63066430323 & -136.580664303231 \tabularnewline
67 & 2454.62 & 2730.34588266038 & -275.725882660376 \tabularnewline
68 & 2407.6 & 2589.29918247106 & -181.699182471058 \tabularnewline
69 & 2472.81 & 2878.36232326484 & -405.552323264843 \tabularnewline
70 & 2408.64 & 2698.24117148639 & -289.601171486394 \tabularnewline
71 & 2440.25 & 2595.6434602399 & -155.393460239898 \tabularnewline
72 & 2350.44 & 2650.34214685445 & -299.902146854452 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&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]2502.66[/C][C]2707.67994551663[/C][C]-205.019945516626[/C][/ROW]
[ROW][C]2[/C][C]2466.92[/C][C]2516.97706924377[/C][C]-50.0570692437716[/C][/ROW]
[ROW][C]3[/C][C]2513.17[/C][C]2449.18616376271[/C][C]63.9838362372944[/C][/ROW]
[ROW][C]4[/C][C]2443.27[/C][C]2451.40226291509[/C][C]-8.13226291509133[/C][/ROW]
[ROW][C]5[/C][C]2293.41[/C][C]2412.62913109455[/C][C]-119.219131094554[/C][/ROW]
[ROW][C]6[/C][C]2070.83[/C][C]2099.50131485049[/C][C]-28.6713148504925[/C][/ROW]
[ROW][C]7[/C][C]2029.6[/C][C]2144.89554104893[/C][C]-115.295541048933[/C][/ROW]
[ROW][C]8[/C][C]2052.02[/C][C]2074.61294122374[/C][C]-22.5929412237412[/C][/ROW]
[ROW][C]9[/C][C]1864.44[/C][C]1929.17218579978[/C][C]-64.7321857997813[/C][/ROW]
[ROW][C]10[/C][C]1670.07[/C][C]1573.19983875250[/C][C]96.8701612474971[/C][/ROW]
[ROW][C]11[/C][C]1810.99[/C][C]1657.15518938415[/C][C]153.834810615853[/C][/ROW]
[ROW][C]12[/C][C]1905.41[/C][C]1846.80857765675[/C][C]58.6014223432489[/C][/ROW]
[ROW][C]13[/C][C]1862.83[/C][C]1902.31990399739[/C][C]-39.4899039973921[/C][/ROW]
[ROW][C]14[/C][C]2014.45[/C][C]1834.30236372566[/C][C]180.147636274342[/C][/ROW]
[ROW][C]15[/C][C]2197.82[/C][C]1979.00581391919[/C][C]218.814186080813[/C][/ROW]
[ROW][C]16[/C][C]2962.34[/C][C]3038.01345391172[/C][C]-75.6734539117243[/C][/ROW]
[ROW][C]17[/C][C]3047.03[/C][C]3182.33655457386[/C][C]-135.306554573857[/C][/ROW]
[ROW][C]18[/C][C]3032.6[/C][C]3196.36506079008[/C][C]-163.765060790078[/C][/ROW]
[ROW][C]19[/C][C]3504.37[/C][C]3664.26620772756[/C][C]-159.896207727562[/C][/ROW]
[ROW][C]20[/C][C]3801.06[/C][C]3959.35323829092[/C][C]-158.293238290915[/C][/ROW]
[ROW][C]21[/C][C]3857.62[/C][C]3845.20043287096[/C][C]12.4195671290381[/C][/ROW]
[ROW][C]22[/C][C]3674.4[/C][C]3526.62581228835[/C][C]147.774187711654[/C][/ROW]
[ROW][C]23[/C][C]3720.98[/C][C]3667.21055535645[/C][C]53.7694446435532[/C][/ROW]
[ROW][C]24[/C][C]3844.49[/C][C]3691.14506122603[/C][C]153.344938773970[/C][/ROW]
[ROW][C]25[/C][C]4116.68[/C][C]4268.14222608313[/C][C]-151.462226083132[/C][/ROW]
[ROW][C]26[/C][C]4105.18[/C][C]4186.3025012722[/C][C]-81.1225012721991[/C][/ROW]
[ROW][C]27[/C][C]4435.23[/C][C]4567.10274279444[/C][C]-131.872742794436[/C][/ROW]
[ROW][C]28[/C][C]4296.49[/C][C]4295.70212830935[/C][C]0.787871690651293[/C][/ROW]
[ROW][C]29[/C][C]4202.52[/C][C]4206.01681959499[/C][C]-3.49681959499111[/C][/ROW]
[ROW][C]30[/C][C]4562.84[/C][C]4591.18955058552[/C][C]-28.3495505855178[/C][/ROW]
[ROW][C]31[/C][C]4621.4[/C][C]4567.00897914014[/C][C]54.3910208598633[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]4572.62547954475[/C][C]124.334520455245[/C][/ROW]
[ROW][C]33[/C][C]4591.27[/C][C]4377.82315737226[/C][C]213.446842627739[/C][/ROW]
[ROW][C]34[/C][C]4356.98[/C][C]4193.71028777419[/C][C]163.269712225814[/C][/ROW]
[ROW][C]35[/C][C]4502.64[/C][C]4406.17204489626[/C][C]96.4679551037408[/C][/ROW]
[ROW][C]36[/C][C]4443.91[/C][C]4342.51306538464[/C][C]101.396934615356[/C][/ROW]
[ROW][C]37[/C][C]4290.89[/C][C]4235.42587031606[/C][C]55.4641296839441[/C][/ROW]
[ROW][C]38[/C][C]4199.75[/C][C]4037.66127687256[/C][C]162.088723127439[/C][/ROW]
[ROW][C]39[/C][C]4138.52[/C][C]4012.51476357641[/C][C]126.00523642359[/C][/ROW]
[ROW][C]40[/C][C]3970.1[/C][C]3809.55824857668[/C][C]160.541751423318[/C][/ROW]
[ROW][C]41[/C][C]3862.27[/C][C]3693.79587479137[/C][C]168.474125208635[/C][/ROW]
[ROW][C]42[/C][C]3701.61[/C][C]3508.57633496669[/C][C]193.033665033308[/C][/ROW]
[ROW][C]43[/C][C]3570.12[/C][C]3506.70160827661[/C][C]63.4183917233942[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]3927.45022691712[/C][C]-126.390226917115[/C][/ROW]
[ROW][C]45[/C][C]3895.51[/C][C]4078.65670013303[/C][C]-183.146700133032[/C][/ROW]
[ROW][C]46[/C][C]3917.96[/C][C]3940.35999441493[/C][C]-22.3999944149300[/C][/ROW]
[ROW][C]47[/C][C]3813.06[/C][C]3911.68623431729[/C][C]-98.6262343172928[/C][/ROW]
[ROW][C]48[/C][C]3667.03[/C][C]3852.86926536805[/C][C]-185.839265368054[/C][/ROW]
[ROW][C]49[/C][C]3494.17[/C][C]3775.72657397467[/C][C]-281.556573974671[/C][/ROW]
[ROW][C]50[/C][C]3363.99[/C][C]3527.35301809864[/C][C]-163.363018098644[/C][/ROW]
[ROW][C]51[/C][C]3295.32[/C][C]3266.74859776635[/C][C]28.5714022336481[/C][/ROW]
[ROW][C]52[/C][C]3277.01[/C][C]3303.63485148703[/C][C]-26.6248514870341[/C][/ROW]
[ROW][C]53[/C][C]3257.16[/C][C]3166.85811840313[/C][C]90.301881596873[/C][/ROW]
[ROW][C]54[/C][C]3161.69[/C][C]3100.15097264666[/C][C]61.5390273533421[/C][/ROW]
[ROW][C]55[/C][C]3097.31[/C][C]2988.54797975787[/C][C]108.762020242126[/C][/ROW]
[ROW][C]56[/C][C]3061.26[/C][C]2851.19789412654[/C][C]210.062105873457[/C][/ROW]
[ROW][C]57[/C][C]3119.31[/C][C]2856.04379988562[/C][C]263.26620011438[/C][/ROW]
[ROW][C]58[/C][C]3106.22[/C][C]3019.68328332930[/C][C]86.5367166707018[/C][/ROW]
[ROW][C]59[/C][C]3080.58[/C][C]2960.16765850692[/C][C]120.412341493079[/C][/ROW]
[ROW][C]60[/C][C]2981.85[/C][C]2818.62664994969[/C][C]163.223350050313[/C][/ROW]
[ROW][C]61[/C][C]2921.44[/C][C]2771.31165379598[/C][C]150.128346204023[/C][/ROW]
[ROW][C]62[/C][C]2849.27[/C][C]2664.91029184400[/C][C]184.359708155996[/C][/ROW]
[ROW][C]63[/C][C]2756.76[/C][C]2530.5747812473[/C][C]226.185218752702[/C][/ROW]
[ROW][C]64[/C][C]2645.64[/C][C]2569.93281266683[/C][C]75.70718733317[/C][/ROW]
[ROW][C]65[/C][C]2497.84[/C][C]2514.72623002729[/C][C]-16.8862300272921[/C][/ROW]
[ROW][C]66[/C][C]2448.05[/C][C]2584.63066430323[/C][C]-136.580664303231[/C][/ROW]
[ROW][C]67[/C][C]2454.62[/C][C]2730.34588266038[/C][C]-275.725882660376[/C][/ROW]
[ROW][C]68[/C][C]2407.6[/C][C]2589.29918247106[/C][C]-181.699182471058[/C][/ROW]
[ROW][C]69[/C][C]2472.81[/C][C]2878.36232326484[/C][C]-405.552323264843[/C][/ROW]
[ROW][C]70[/C][C]2408.64[/C][C]2698.24117148639[/C][C]-289.601171486394[/C][/ROW]
[ROW][C]71[/C][C]2440.25[/C][C]2595.6434602399[/C][C]-155.393460239898[/C][/ROW]
[ROW][C]72[/C][C]2350.44[/C][C]2650.34214685445[/C][C]-299.902146854452[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12502.662707.67994551663-205.019945516626
22466.922516.97706924377-50.0570692437716
32513.172449.1861637627163.9838362372944
42443.272451.40226291509-8.13226291509133
52293.412412.62913109455-119.219131094554
62070.832099.50131485049-28.6713148504925
72029.62144.89554104893-115.295541048933
82052.022074.61294122374-22.5929412237412
91864.441929.17218579978-64.7321857997813
101670.071573.1998387525096.8701612474971
111810.991657.15518938415153.834810615853
121905.411846.8085776567558.6014223432489
131862.831902.31990399739-39.4899039973921
142014.451834.30236372566180.147636274342
152197.821979.00581391919218.814186080813
162962.343038.01345391172-75.6734539117243
173047.033182.33655457386-135.306554573857
183032.63196.36506079008-163.765060790078
193504.373664.26620772756-159.896207727562
203801.063959.35323829092-158.293238290915
213857.623845.2004328709612.4195671290381
223674.43526.62581228835147.774187711654
233720.983667.2105553564553.7694446435532
243844.493691.14506122603153.344938773970
254116.684268.14222608313-151.462226083132
264105.184186.3025012722-81.1225012721991
274435.234567.10274279444-131.872742794436
284296.494295.702128309350.787871690651293
294202.524206.01681959499-3.49681959499111
304562.844591.18955058552-28.3495505855178
314621.44567.0089791401454.3910208598633
324696.964572.62547954475124.334520455245
334591.274377.82315737226213.446842627739
344356.984193.71028777419163.269712225814
354502.644406.1720448962696.4679551037408
364443.914342.51306538464101.396934615356
374290.894235.4258703160655.4641296839441
384199.754037.66127687256162.088723127439
394138.524012.51476357641126.00523642359
403970.13809.55824857668160.541751423318
413862.273693.79587479137168.474125208635
423701.613508.57633496669193.033665033308
433570.123506.7016082766163.4183917233942
443801.063927.45022691712-126.390226917115
453895.514078.65670013303-183.146700133032
463917.963940.35999441493-22.3999944149300
473813.063911.68623431729-98.6262343172928
483667.033852.86926536805-185.839265368054
493494.173775.72657397467-281.556573974671
503363.993527.35301809864-163.363018098644
513295.323266.7485977663528.5714022336481
523277.013303.63485148703-26.6248514870341
533257.163166.8581184031390.301881596873
543161.693100.1509726466661.5390273533421
553097.312988.54797975787108.762020242126
563061.262851.19789412654210.062105873457
573119.312856.04379988562263.26620011438
583106.223019.6832833293086.5367166707018
593080.582960.16765850692120.412341493079
602981.852818.62664994969163.223350050313
612921.442771.31165379598150.128346204023
622849.272664.91029184400184.359708155996
632756.762530.5747812473226.185218752702
642645.642569.9328126668375.70718733317
652497.842514.72623002729-16.8862300272921
662448.052584.63066430323-136.580664303231
672454.622730.34588266038-275.725882660376
682407.62589.29918247106-181.699182471058
692472.812878.36232326484-405.552323264843
702408.642698.24117148639-289.601171486394
712440.252595.6434602399-155.393460239898
722350.442650.34214685445-299.902146854452






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.01812489204535800.03624978409071590.981875107954642
130.01452538594662470.02905077189324940.985474614053375
140.04021364610463040.08042729220926080.95978635389537
150.01720332804039290.03440665608078570.982796671959607
160.01204533505206150.02409067010412300.987954664947938
170.004542803003155570.009085606006311140.995457196996844
180.001670053233633540.003340106467267090.998329946766366
190.006519306253979170.01303861250795830.99348069374602
200.004362713035127120.008725426070254240.995637286964873
210.003395454513145110.006790909026290220.996604545486855
220.001646651631438710.003293303262877430.99835334836856
230.0009667236765385980.001933447353077200.999033276323461
240.0009790197051147440.001958039410229490.999020980294885
250.003161340175931270.006322680351862540.996838659824069
260.006590041437903650.01318008287580730.993409958562096
270.01190684360901700.02381368721803400.988093156390983
280.01805660119116390.03611320238232770.981943398808836
290.01367724482064610.02735448964129220.986322755179354
300.01211863420617260.02423726841234520.987881365793827
310.01112022887164390.02224045774328770.988879771128356
320.01125647635529080.02251295271058170.98874352364471
330.01045602148189180.02091204296378360.989543978518108
340.007381190193121860.01476238038624370.992618809806878
350.004338839377295190.008677678754590380.995661160622705
360.003738708560106680.007477417120213370.996261291439893
370.002162920261599690.004325840523199370.9978370797384
380.001638059729795370.003276119459590730.998361940270205
390.001241086005094340.002482172010188680.998758913994906
400.001541181152756320.003082362305512630.998458818847244
410.001743830557690520.003487661115381030.99825616944231
420.001327442907181930.002654885814363860.998672557092818
430.003184426013341180.006368852026682360.99681557398666
440.01124123260410630.02248246520821260.988758767395894
450.01491283521440640.02982567042881280.985087164785594
460.02839097353685290.05678194707370580.971609026463147
470.03238754880947350.0647750976189470.967612451190527
480.06016791969559940.1203358393911990.9398320803044
490.05211907846772750.1042381569354550.947880921532273
500.04196675489517980.08393350979035960.95803324510482
510.06594382296107410.1318876459221480.934056177038926
520.0484730248414170.0969460496828340.951526975158583
530.03280864038561690.06561728077123370.967191359614383
540.02954801172782580.05909602345565160.970451988272174
550.05528478841665020.1105695768333000.94471521158335
560.04242977025545320.08485954051090640.957570229744547
570.04206621668933340.08413243337866680.957933783310667
580.03486746058255410.06973492116510820.965132539417446
590.02701674063230690.05403348126461370.972983259367693
600.8322769106637950.3354461786724100.167723089336205

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0181248920453580 & 0.0362497840907159 & 0.981875107954642 \tabularnewline
13 & 0.0145253859466247 & 0.0290507718932494 & 0.985474614053375 \tabularnewline
14 & 0.0402136461046304 & 0.0804272922092608 & 0.95978635389537 \tabularnewline
15 & 0.0172033280403929 & 0.0344066560807857 & 0.982796671959607 \tabularnewline
16 & 0.0120453350520615 & 0.0240906701041230 & 0.987954664947938 \tabularnewline
17 & 0.00454280300315557 & 0.00908560600631114 & 0.995457196996844 \tabularnewline
18 & 0.00167005323363354 & 0.00334010646726709 & 0.998329946766366 \tabularnewline
19 & 0.00651930625397917 & 0.0130386125079583 & 0.99348069374602 \tabularnewline
20 & 0.00436271303512712 & 0.00872542607025424 & 0.995637286964873 \tabularnewline
21 & 0.00339545451314511 & 0.00679090902629022 & 0.996604545486855 \tabularnewline
22 & 0.00164665163143871 & 0.00329330326287743 & 0.99835334836856 \tabularnewline
23 & 0.000966723676538598 & 0.00193344735307720 & 0.999033276323461 \tabularnewline
24 & 0.000979019705114744 & 0.00195803941022949 & 0.999020980294885 \tabularnewline
25 & 0.00316134017593127 & 0.00632268035186254 & 0.996838659824069 \tabularnewline
26 & 0.00659004143790365 & 0.0131800828758073 & 0.993409958562096 \tabularnewline
27 & 0.0119068436090170 & 0.0238136872180340 & 0.988093156390983 \tabularnewline
28 & 0.0180566011911639 & 0.0361132023823277 & 0.981943398808836 \tabularnewline
29 & 0.0136772448206461 & 0.0273544896412922 & 0.986322755179354 \tabularnewline
30 & 0.0121186342061726 & 0.0242372684123452 & 0.987881365793827 \tabularnewline
31 & 0.0111202288716439 & 0.0222404577432877 & 0.988879771128356 \tabularnewline
32 & 0.0112564763552908 & 0.0225129527105817 & 0.98874352364471 \tabularnewline
33 & 0.0104560214818918 & 0.0209120429637836 & 0.989543978518108 \tabularnewline
34 & 0.00738119019312186 & 0.0147623803862437 & 0.992618809806878 \tabularnewline
35 & 0.00433883937729519 & 0.00867767875459038 & 0.995661160622705 \tabularnewline
36 & 0.00373870856010668 & 0.00747741712021337 & 0.996261291439893 \tabularnewline
37 & 0.00216292026159969 & 0.00432584052319937 & 0.9978370797384 \tabularnewline
38 & 0.00163805972979537 & 0.00327611945959073 & 0.998361940270205 \tabularnewline
39 & 0.00124108600509434 & 0.00248217201018868 & 0.998758913994906 \tabularnewline
40 & 0.00154118115275632 & 0.00308236230551263 & 0.998458818847244 \tabularnewline
41 & 0.00174383055769052 & 0.00348766111538103 & 0.99825616944231 \tabularnewline
42 & 0.00132744290718193 & 0.00265488581436386 & 0.998672557092818 \tabularnewline
43 & 0.00318442601334118 & 0.00636885202668236 & 0.99681557398666 \tabularnewline
44 & 0.0112412326041063 & 0.0224824652082126 & 0.988758767395894 \tabularnewline
45 & 0.0149128352144064 & 0.0298256704288128 & 0.985087164785594 \tabularnewline
46 & 0.0283909735368529 & 0.0567819470737058 & 0.971609026463147 \tabularnewline
47 & 0.0323875488094735 & 0.064775097618947 & 0.967612451190527 \tabularnewline
48 & 0.0601679196955994 & 0.120335839391199 & 0.9398320803044 \tabularnewline
49 & 0.0521190784677275 & 0.104238156935455 & 0.947880921532273 \tabularnewline
50 & 0.0419667548951798 & 0.0839335097903596 & 0.95803324510482 \tabularnewline
51 & 0.0659438229610741 & 0.131887645922148 & 0.934056177038926 \tabularnewline
52 & 0.048473024841417 & 0.096946049682834 & 0.951526975158583 \tabularnewline
53 & 0.0328086403856169 & 0.0656172807712337 & 0.967191359614383 \tabularnewline
54 & 0.0295480117278258 & 0.0590960234556516 & 0.970451988272174 \tabularnewline
55 & 0.0552847884166502 & 0.110569576833300 & 0.94471521158335 \tabularnewline
56 & 0.0424297702554532 & 0.0848595405109064 & 0.957570229744547 \tabularnewline
57 & 0.0420662166893334 & 0.0841324333786668 & 0.957933783310667 \tabularnewline
58 & 0.0348674605825541 & 0.0697349211651082 & 0.965132539417446 \tabularnewline
59 & 0.0270167406323069 & 0.0540334812646137 & 0.972983259367693 \tabularnewline
60 & 0.832276910663795 & 0.335446178672410 & 0.167723089336205 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.0181248920453580[/C][C]0.0362497840907159[/C][C]0.981875107954642[/C][/ROW]
[ROW][C]13[/C][C]0.0145253859466247[/C][C]0.0290507718932494[/C][C]0.985474614053375[/C][/ROW]
[ROW][C]14[/C][C]0.0402136461046304[/C][C]0.0804272922092608[/C][C]0.95978635389537[/C][/ROW]
[ROW][C]15[/C][C]0.0172033280403929[/C][C]0.0344066560807857[/C][C]0.982796671959607[/C][/ROW]
[ROW][C]16[/C][C]0.0120453350520615[/C][C]0.0240906701041230[/C][C]0.987954664947938[/C][/ROW]
[ROW][C]17[/C][C]0.00454280300315557[/C][C]0.00908560600631114[/C][C]0.995457196996844[/C][/ROW]
[ROW][C]18[/C][C]0.00167005323363354[/C][C]0.00334010646726709[/C][C]0.998329946766366[/C][/ROW]
[ROW][C]19[/C][C]0.00651930625397917[/C][C]0.0130386125079583[/C][C]0.99348069374602[/C][/ROW]
[ROW][C]20[/C][C]0.00436271303512712[/C][C]0.00872542607025424[/C][C]0.995637286964873[/C][/ROW]
[ROW][C]21[/C][C]0.00339545451314511[/C][C]0.00679090902629022[/C][C]0.996604545486855[/C][/ROW]
[ROW][C]22[/C][C]0.00164665163143871[/C][C]0.00329330326287743[/C][C]0.99835334836856[/C][/ROW]
[ROW][C]23[/C][C]0.000966723676538598[/C][C]0.00193344735307720[/C][C]0.999033276323461[/C][/ROW]
[ROW][C]24[/C][C]0.000979019705114744[/C][C]0.00195803941022949[/C][C]0.999020980294885[/C][/ROW]
[ROW][C]25[/C][C]0.00316134017593127[/C][C]0.00632268035186254[/C][C]0.996838659824069[/C][/ROW]
[ROW][C]26[/C][C]0.00659004143790365[/C][C]0.0131800828758073[/C][C]0.993409958562096[/C][/ROW]
[ROW][C]27[/C][C]0.0119068436090170[/C][C]0.0238136872180340[/C][C]0.988093156390983[/C][/ROW]
[ROW][C]28[/C][C]0.0180566011911639[/C][C]0.0361132023823277[/C][C]0.981943398808836[/C][/ROW]
[ROW][C]29[/C][C]0.0136772448206461[/C][C]0.0273544896412922[/C][C]0.986322755179354[/C][/ROW]
[ROW][C]30[/C][C]0.0121186342061726[/C][C]0.0242372684123452[/C][C]0.987881365793827[/C][/ROW]
[ROW][C]31[/C][C]0.0111202288716439[/C][C]0.0222404577432877[/C][C]0.988879771128356[/C][/ROW]
[ROW][C]32[/C][C]0.0112564763552908[/C][C]0.0225129527105817[/C][C]0.98874352364471[/C][/ROW]
[ROW][C]33[/C][C]0.0104560214818918[/C][C]0.0209120429637836[/C][C]0.989543978518108[/C][/ROW]
[ROW][C]34[/C][C]0.00738119019312186[/C][C]0.0147623803862437[/C][C]0.992618809806878[/C][/ROW]
[ROW][C]35[/C][C]0.00433883937729519[/C][C]0.00867767875459038[/C][C]0.995661160622705[/C][/ROW]
[ROW][C]36[/C][C]0.00373870856010668[/C][C]0.00747741712021337[/C][C]0.996261291439893[/C][/ROW]
[ROW][C]37[/C][C]0.00216292026159969[/C][C]0.00432584052319937[/C][C]0.9978370797384[/C][/ROW]
[ROW][C]38[/C][C]0.00163805972979537[/C][C]0.00327611945959073[/C][C]0.998361940270205[/C][/ROW]
[ROW][C]39[/C][C]0.00124108600509434[/C][C]0.00248217201018868[/C][C]0.998758913994906[/C][/ROW]
[ROW][C]40[/C][C]0.00154118115275632[/C][C]0.00308236230551263[/C][C]0.998458818847244[/C][/ROW]
[ROW][C]41[/C][C]0.00174383055769052[/C][C]0.00348766111538103[/C][C]0.99825616944231[/C][/ROW]
[ROW][C]42[/C][C]0.00132744290718193[/C][C]0.00265488581436386[/C][C]0.998672557092818[/C][/ROW]
[ROW][C]43[/C][C]0.00318442601334118[/C][C]0.00636885202668236[/C][C]0.99681557398666[/C][/ROW]
[ROW][C]44[/C][C]0.0112412326041063[/C][C]0.0224824652082126[/C][C]0.988758767395894[/C][/ROW]
[ROW][C]45[/C][C]0.0149128352144064[/C][C]0.0298256704288128[/C][C]0.985087164785594[/C][/ROW]
[ROW][C]46[/C][C]0.0283909735368529[/C][C]0.0567819470737058[/C][C]0.971609026463147[/C][/ROW]
[ROW][C]47[/C][C]0.0323875488094735[/C][C]0.064775097618947[/C][C]0.967612451190527[/C][/ROW]
[ROW][C]48[/C][C]0.0601679196955994[/C][C]0.120335839391199[/C][C]0.9398320803044[/C][/ROW]
[ROW][C]49[/C][C]0.0521190784677275[/C][C]0.104238156935455[/C][C]0.947880921532273[/C][/ROW]
[ROW][C]50[/C][C]0.0419667548951798[/C][C]0.0839335097903596[/C][C]0.95803324510482[/C][/ROW]
[ROW][C]51[/C][C]0.0659438229610741[/C][C]0.131887645922148[/C][C]0.934056177038926[/C][/ROW]
[ROW][C]52[/C][C]0.048473024841417[/C][C]0.096946049682834[/C][C]0.951526975158583[/C][/ROW]
[ROW][C]53[/C][C]0.0328086403856169[/C][C]0.0656172807712337[/C][C]0.967191359614383[/C][/ROW]
[ROW][C]54[/C][C]0.0295480117278258[/C][C]0.0590960234556516[/C][C]0.970451988272174[/C][/ROW]
[ROW][C]55[/C][C]0.0552847884166502[/C][C]0.110569576833300[/C][C]0.94471521158335[/C][/ROW]
[ROW][C]56[/C][C]0.0424297702554532[/C][C]0.0848595405109064[/C][C]0.957570229744547[/C][/ROW]
[ROW][C]57[/C][C]0.0420662166893334[/C][C]0.0841324333786668[/C][C]0.957933783310667[/C][/ROW]
[ROW][C]58[/C][C]0.0348674605825541[/C][C]0.0697349211651082[/C][C]0.965132539417446[/C][/ROW]
[ROW][C]59[/C][C]0.0270167406323069[/C][C]0.0540334812646137[/C][C]0.972983259367693[/C][/ROW]
[ROW][C]60[/C][C]0.832276910663795[/C][C]0.335446178672410[/C][C]0.167723089336205[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.01812489204535800.03624978409071590.981875107954642
130.01452538594662470.02905077189324940.985474614053375
140.04021364610463040.08042729220926080.95978635389537
150.01720332804039290.03440665608078570.982796671959607
160.01204533505206150.02409067010412300.987954664947938
170.004542803003155570.009085606006311140.995457196996844
180.001670053233633540.003340106467267090.998329946766366
190.006519306253979170.01303861250795830.99348069374602
200.004362713035127120.008725426070254240.995637286964873
210.003395454513145110.006790909026290220.996604545486855
220.001646651631438710.003293303262877430.99835334836856
230.0009667236765385980.001933447353077200.999033276323461
240.0009790197051147440.001958039410229490.999020980294885
250.003161340175931270.006322680351862540.996838659824069
260.006590041437903650.01318008287580730.993409958562096
270.01190684360901700.02381368721803400.988093156390983
280.01805660119116390.03611320238232770.981943398808836
290.01367724482064610.02735448964129220.986322755179354
300.01211863420617260.02423726841234520.987881365793827
310.01112022887164390.02224045774328770.988879771128356
320.01125647635529080.02251295271058170.98874352364471
330.01045602148189180.02091204296378360.989543978518108
340.007381190193121860.01476238038624370.992618809806878
350.004338839377295190.008677678754590380.995661160622705
360.003738708560106680.007477417120213370.996261291439893
370.002162920261599690.004325840523199370.9978370797384
380.001638059729795370.003276119459590730.998361940270205
390.001241086005094340.002482172010188680.998758913994906
400.001541181152756320.003082362305512630.998458818847244
410.001743830557690520.003487661115381030.99825616944231
420.001327442907181930.002654885814363860.998672557092818
430.003184426013341180.006368852026682360.99681557398666
440.01124123260410630.02248246520821260.988758767395894
450.01491283521440640.02982567042881280.985087164785594
460.02839097353685290.05678194707370580.971609026463147
470.03238754880947350.0647750976189470.967612451190527
480.06016791969559940.1203358393911990.9398320803044
490.05211907846772750.1042381569354550.947880921532273
500.04196675489517980.08393350979035960.95803324510482
510.06594382296107410.1318876459221480.934056177038926
520.0484730248414170.0969460496828340.951526975158583
530.03280864038561690.06561728077123370.967191359614383
540.02954801172782580.05909602345565160.970451988272174
550.05528478841665020.1105695768333000.94471521158335
560.04242977025545320.08485954051090640.957570229744547
570.04206621668933340.08413243337866680.957933783310667
580.03486746058255410.06973492116510820.965132539417446
590.02701674063230690.05403348126461370.972983259367693
600.8322769106637950.3354461786724100.167723089336205






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.346938775510204NOK
5% type I error level330.673469387755102NOK
10% type I error level440.897959183673469NOK

\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 & 17 & 0.346938775510204 & NOK \tabularnewline
5% type I error level & 33 & 0.673469387755102 & NOK \tabularnewline
10% type I error level & 44 & 0.897959183673469 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104256&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]17[/C][C]0.346938775510204[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.673469387755102[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.897959183673469[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104256&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104256&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.346938775510204NOK
5% type I error level330.673469387755102NOK
10% type I error level440.897959183673469NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}