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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 computationMon, 06 Dec 2010 18:03:06 +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/06/t12916584990tq0nwbkxfi5j2a.htm/, Retrieved Mon, 29 Apr 2024 02:37:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105750, Retrieved Mon, 29 Apr 2024 02:37:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

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] [] [2010-12-06 18:03:06] [c474a97a96075919a678ad3d2290b00b] [Current]
-   PD          [Multiple Regression] [] [2010-12-06 18:17:17] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD          [Multiple Regression] [] [2010-12-06 18:18:43] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD            [Multiple Regression] [] [2010-12-06 18:34:18] [acfa3f91ce5598ec4ba98aad4cfba2f0]
- RMPD              [] [AeNmUqHQRBiIKGZI] [-0001-11-30 00:00:00] [c87f495781bf16e372b980587f0f9312]
-   P             [Multiple Regression] [] [2010-12-06 18:38:46] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   P               [Multiple Regression] [] [2010-12-06 18:40:08] [acfa3f91ce5598ec4ba98aad4cfba2f0]
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Dataset

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

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1886.04000678096 + 0.191792840371528Nikkei[t] + 0.288303751656477DJ_Indust[t] + 0.0147718309612608Goudprijs[t] -9.98349305638133Conjunct_Seizoenzuiver[t] -2.50766896022597Cons_vertrouw[t] + 33.9568643858045Alg_consumptie_index_BE[t] -255.69184028108Gem_rente_kasbon_5j[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -1886.04000678096 +  0.191792840371528Nikkei[t] +  0.288303751656477DJ_Indust[t] +  0.0147718309612608Goudprijs[t] -9.98349305638133Conjunct_Seizoenzuiver[t] -2.50766896022597Cons_vertrouw[t] +  33.9568643858045Alg_consumptie_index_BE[t] -255.69184028108Gem_rente_kasbon_5j[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -1886.04000678096 +  0.191792840371528Nikkei[t] +  0.288303751656477DJ_Indust[t] +  0.0147718309612608Goudprijs[t] -9.98349305638133Conjunct_Seizoenzuiver[t] -2.50766896022597Cons_vertrouw[t] +  33.9568643858045Alg_consumptie_index_BE[t] -255.69184028108Gem_rente_kasbon_5j[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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] = -1886.04000678096 + 0.191792840371528Nikkei[t] + 0.288303751656477DJ_Indust[t] + 0.0147718309612608Goudprijs[t] -9.98349305638133Conjunct_Seizoenzuiver[t] -2.50766896022597Cons_vertrouw[t] + 33.9568643858045Alg_consumptie_index_BE[t] -255.69184028108Gem_rente_kasbon_5j[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1886.04000678096270.224455-6.979500
Nikkei0.1917928403715280.01495312.826500
DJ_Indust0.2883037516564770.0331938.685800
Goudprijs0.01477183096126080.0081991.80160.076320.03816
Conjunct_Seizoenzuiver-9.983493056381336.033247-1.65470.1028720.051436
Cons_vertrouw-2.507668960225977.649392-0.32780.7441130.372057
Alg_consumptie_index_BE33.956864385804517.2414661.96950.0532290.026614
Gem_rente_kasbon_5j-255.6918402810856.189516-4.55052.4e-051.2e-05

\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) & -1886.04000678096 & 270.224455 & -6.9795 & 0 & 0 \tabularnewline
Nikkei & 0.191792840371528 & 0.014953 & 12.8265 & 0 & 0 \tabularnewline
DJ_Indust & 0.288303751656477 & 0.033193 & 8.6858 & 0 & 0 \tabularnewline
Goudprijs & 0.0147718309612608 & 0.008199 & 1.8016 & 0.07632 & 0.03816 \tabularnewline
Conjunct_Seizoenzuiver & -9.98349305638133 & 6.033247 & -1.6547 & 0.102872 & 0.051436 \tabularnewline
Cons_vertrouw & -2.50766896022597 & 7.649392 & -0.3278 & 0.744113 & 0.372057 \tabularnewline
Alg_consumptie_index_BE & 33.9568643858045 & 17.241466 & 1.9695 & 0.053229 & 0.026614 \tabularnewline
Gem_rente_kasbon_5j & -255.69184028108 & 56.189516 & -4.5505 & 2.4e-05 & 1.2e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&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]-1886.04000678096[/C][C]270.224455[/C][C]-6.9795[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.191792840371528[/C][C]0.014953[/C][C]12.8265[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.288303751656477[/C][C]0.033193[/C][C]8.6858[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.0147718309612608[/C][C]0.008199[/C][C]1.8016[/C][C]0.07632[/C][C]0.03816[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-9.98349305638133[/C][C]6.033247[/C][C]-1.6547[/C][C]0.102872[/C][C]0.051436[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]-2.50766896022597[/C][C]7.649392[/C][C]-0.3278[/C][C]0.744113[/C][C]0.372057[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]33.9568643858045[/C][C]17.241466[/C][C]1.9695[/C][C]0.053229[/C][C]0.026614[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-255.69184028108[/C][C]56.189516[/C][C]-4.5505[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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)-1886.04000678096270.224455-6.979500
Nikkei0.1917928403715280.01495312.826500
DJ_Indust0.2883037516564770.0331938.685800
Goudprijs0.01477183096126080.0081991.80160.076320.03816
Conjunct_Seizoenzuiver-9.983493056381336.033247-1.65470.1028720.051436
Cons_vertrouw-2.507668960225977.649392-0.32780.7441130.372057
Alg_consumptie_index_BE33.956864385804517.2414661.96950.0532290.026614
Gem_rente_kasbon_5j-255.6918402810856.189516-4.55052.4e-051.2e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.9837054952436
R-squared0.967676501372457
Adjusted R-squared0.96414111871007
F-TEST (value)273.711955332998
F-TEST (DF numerator)7
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation160.133985897204
Sum Squared Residuals1641145.18011685

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.9837054952436 \tabularnewline
R-squared & 0.967676501372457 \tabularnewline
Adjusted R-squared & 0.96414111871007 \tabularnewline
F-TEST (value) & 273.711955332998 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 160.133985897204 \tabularnewline
Sum Squared Residuals & 1641145.18011685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.9837054952436[/C][/ROW]
[ROW][C]R-squared[/C][C]0.967676501372457[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.96414111871007[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]273.711955332998[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/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]160.133985897204[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1641145.18011685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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 R0.9837054952436
R-squared0.967676501372457
Adjusted R-squared0.96414111871007
F-TEST (value)273.711955332998
F-TEST (DF numerator)7
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation160.133985897204
Sum Squared Residuals1641145.18011685






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12350.442644.52226973597-294.08226973597
22440.252591.6935085745-151.443508574501
32408.642694.24239617322-285.602396173219
42472.812875.44001921294-402.630019212943
52407.62585.60087290272-178.000872902716
62454.622732.94972929247-278.329729292474
72448.052583.28819996326-135.238199963261
82497.842517.61342566693-19.7734256669314
92645.642573.3635519511572.2764480488502
102756.762533.52907359763223.230926402375
112849.272668.67035332043180.599646679572
122921.442777.7390316414143.700968358595
132981.852810.19182531178171.658174688216
143080.582957.18251877454123.397481225461
153106.223020.116062224986.1039377750954
163119.312856.50265543567262.807344564329
173061.262847.85807442898213.401925571025
183097.312987.19403896795110.11596103205
193161.693101.0636491829560.6263508170478
203257.163169.8406030833287.3193969166838
213277.013303.22816215898-26.2181621589782
223295.323271.3231818674423.9968181325595
233363.993532.18819246-168.198192460001
243494.173785.90185801967-291.73185801967
253667.033851.58081047638-184.550810476383
263813.063909.92717343976-96.8671734397561
273917.963935.63905870863-17.6790587086296
283895.514075.79262323747-180.282623237466
293801.063923.64733261763-122.587332617628
303570.123505.7528764202964.3671235797117
313701.613507.86986753702193.740132462985
323862.273695.22163010429167.048369895706
333970.13812.15884936956157.94115063044
344138.524018.99216934148119.527830658519
354199.754045.36120653471154.388793465295
364290.894236.4821809452854.4078190547256
374443.914335.61262334952108.297376650476
384502.644403.0619216881999.5780783118134
394356.984190.67292615312166.307073846883
404591.274377.34426916186213.925730838143
414696.964571.73034500852125.229654991476
424621.44565.3126882880356.087311711967
434562.844589.48099026381-26.6409902638104
444202.524205.08179278783-2.56179278782505
454296.494296.5678631292-0.0778631292036487
464435.234572.62116297434-137.391162974342
474105.184188.10491509472-82.924915094721
484116.684276.06118970188-159.381189701878
493844.493682.14878243838162.341217561616
503720.983662.7001734154958.2798265845135
513674.43524.36380455471150.036195445289
523857.623842.6538504821214.9661495178809
533801.063956.29842765428-155.238427654278
543504.373662.62139313115-158.251393131149
553032.63195.12832183371-162.528321833709
563047.033182.65793008691-135.627930086909
572962.343044.78372345458-82.4437234545783
582197.821982.56027771883215.259722281166
592014.451837.89267623246176.557323767537
601862.831908.05860030339-45.2286003033879
611905.411841.7198851733663.6901148266424
621810.991652.7428662126158.247133787398
631670.071569.45106639554100.618933604459
641864.441926.8019576247-62.3619576247028
652052.022073.70878135401-21.6887813540108
662029.62143.06199947823-113.46199947823
672070.832098.29620424375-27.466204243753
682293.412416.35750331553-122.947503315532
692443.272455.47321046387-12.2032104638691
702513.172451.7207157750761.4492842249306
712466.922520.65788343882-53.7378834388213
722502.662708.80824493619-206.148244936194

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2350.44 & 2644.52226973597 & -294.08226973597 \tabularnewline
2 & 2440.25 & 2591.6935085745 & -151.443508574501 \tabularnewline
3 & 2408.64 & 2694.24239617322 & -285.602396173219 \tabularnewline
4 & 2472.81 & 2875.44001921294 & -402.630019212943 \tabularnewline
5 & 2407.6 & 2585.60087290272 & -178.000872902716 \tabularnewline
6 & 2454.62 & 2732.94972929247 & -278.329729292474 \tabularnewline
7 & 2448.05 & 2583.28819996326 & -135.238199963261 \tabularnewline
8 & 2497.84 & 2517.61342566693 & -19.7734256669314 \tabularnewline
9 & 2645.64 & 2573.36355195115 & 72.2764480488502 \tabularnewline
10 & 2756.76 & 2533.52907359763 & 223.230926402375 \tabularnewline
11 & 2849.27 & 2668.67035332043 & 180.599646679572 \tabularnewline
12 & 2921.44 & 2777.7390316414 & 143.700968358595 \tabularnewline
13 & 2981.85 & 2810.19182531178 & 171.658174688216 \tabularnewline
14 & 3080.58 & 2957.18251877454 & 123.397481225461 \tabularnewline
15 & 3106.22 & 3020.1160622249 & 86.1039377750954 \tabularnewline
16 & 3119.31 & 2856.50265543567 & 262.807344564329 \tabularnewline
17 & 3061.26 & 2847.85807442898 & 213.401925571025 \tabularnewline
18 & 3097.31 & 2987.19403896795 & 110.11596103205 \tabularnewline
19 & 3161.69 & 3101.06364918295 & 60.6263508170478 \tabularnewline
20 & 3257.16 & 3169.84060308332 & 87.3193969166838 \tabularnewline
21 & 3277.01 & 3303.22816215898 & -26.2181621589782 \tabularnewline
22 & 3295.32 & 3271.32318186744 & 23.9968181325595 \tabularnewline
23 & 3363.99 & 3532.18819246 & -168.198192460001 \tabularnewline
24 & 3494.17 & 3785.90185801967 & -291.73185801967 \tabularnewline
25 & 3667.03 & 3851.58081047638 & -184.550810476383 \tabularnewline
26 & 3813.06 & 3909.92717343976 & -96.8671734397561 \tabularnewline
27 & 3917.96 & 3935.63905870863 & -17.6790587086296 \tabularnewline
28 & 3895.51 & 4075.79262323747 & -180.282623237466 \tabularnewline
29 & 3801.06 & 3923.64733261763 & -122.587332617628 \tabularnewline
30 & 3570.12 & 3505.75287642029 & 64.3671235797117 \tabularnewline
31 & 3701.61 & 3507.86986753702 & 193.740132462985 \tabularnewline
32 & 3862.27 & 3695.22163010429 & 167.048369895706 \tabularnewline
33 & 3970.1 & 3812.15884936956 & 157.94115063044 \tabularnewline
34 & 4138.52 & 4018.99216934148 & 119.527830658519 \tabularnewline
35 & 4199.75 & 4045.36120653471 & 154.388793465295 \tabularnewline
36 & 4290.89 & 4236.48218094528 & 54.4078190547256 \tabularnewline
37 & 4443.91 & 4335.61262334952 & 108.297376650476 \tabularnewline
38 & 4502.64 & 4403.06192168819 & 99.5780783118134 \tabularnewline
39 & 4356.98 & 4190.67292615312 & 166.307073846883 \tabularnewline
40 & 4591.27 & 4377.34426916186 & 213.925730838143 \tabularnewline
41 & 4696.96 & 4571.73034500852 & 125.229654991476 \tabularnewline
42 & 4621.4 & 4565.31268828803 & 56.087311711967 \tabularnewline
43 & 4562.84 & 4589.48099026381 & -26.6409902638104 \tabularnewline
44 & 4202.52 & 4205.08179278783 & -2.56179278782505 \tabularnewline
45 & 4296.49 & 4296.5678631292 & -0.0778631292036487 \tabularnewline
46 & 4435.23 & 4572.62116297434 & -137.391162974342 \tabularnewline
47 & 4105.18 & 4188.10491509472 & -82.924915094721 \tabularnewline
48 & 4116.68 & 4276.06118970188 & -159.381189701878 \tabularnewline
49 & 3844.49 & 3682.14878243838 & 162.341217561616 \tabularnewline
50 & 3720.98 & 3662.70017341549 & 58.2798265845135 \tabularnewline
51 & 3674.4 & 3524.36380455471 & 150.036195445289 \tabularnewline
52 & 3857.62 & 3842.65385048212 & 14.9661495178809 \tabularnewline
53 & 3801.06 & 3956.29842765428 & -155.238427654278 \tabularnewline
54 & 3504.37 & 3662.62139313115 & -158.251393131149 \tabularnewline
55 & 3032.6 & 3195.12832183371 & -162.528321833709 \tabularnewline
56 & 3047.03 & 3182.65793008691 & -135.627930086909 \tabularnewline
57 & 2962.34 & 3044.78372345458 & -82.4437234545783 \tabularnewline
58 & 2197.82 & 1982.56027771883 & 215.259722281166 \tabularnewline
59 & 2014.45 & 1837.89267623246 & 176.557323767537 \tabularnewline
60 & 1862.83 & 1908.05860030339 & -45.2286003033879 \tabularnewline
61 & 1905.41 & 1841.71988517336 & 63.6901148266424 \tabularnewline
62 & 1810.99 & 1652.7428662126 & 158.247133787398 \tabularnewline
63 & 1670.07 & 1569.45106639554 & 100.618933604459 \tabularnewline
64 & 1864.44 & 1926.8019576247 & -62.3619576247028 \tabularnewline
65 & 2052.02 & 2073.70878135401 & -21.6887813540108 \tabularnewline
66 & 2029.6 & 2143.06199947823 & -113.46199947823 \tabularnewline
67 & 2070.83 & 2098.29620424375 & -27.466204243753 \tabularnewline
68 & 2293.41 & 2416.35750331553 & -122.947503315532 \tabularnewline
69 & 2443.27 & 2455.47321046387 & -12.2032104638691 \tabularnewline
70 & 2513.17 & 2451.72071577507 & 61.4492842249306 \tabularnewline
71 & 2466.92 & 2520.65788343882 & -53.7378834388213 \tabularnewline
72 & 2502.66 & 2708.80824493619 & -206.148244936194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&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]2350.44[/C][C]2644.52226973597[/C][C]-294.08226973597[/C][/ROW]
[ROW][C]2[/C][C]2440.25[/C][C]2591.6935085745[/C][C]-151.443508574501[/C][/ROW]
[ROW][C]3[/C][C]2408.64[/C][C]2694.24239617322[/C][C]-285.602396173219[/C][/ROW]
[ROW][C]4[/C][C]2472.81[/C][C]2875.44001921294[/C][C]-402.630019212943[/C][/ROW]
[ROW][C]5[/C][C]2407.6[/C][C]2585.60087290272[/C][C]-178.000872902716[/C][/ROW]
[ROW][C]6[/C][C]2454.62[/C][C]2732.94972929247[/C][C]-278.329729292474[/C][/ROW]
[ROW][C]7[/C][C]2448.05[/C][C]2583.28819996326[/C][C]-135.238199963261[/C][/ROW]
[ROW][C]8[/C][C]2497.84[/C][C]2517.61342566693[/C][C]-19.7734256669314[/C][/ROW]
[ROW][C]9[/C][C]2645.64[/C][C]2573.36355195115[/C][C]72.2764480488502[/C][/ROW]
[ROW][C]10[/C][C]2756.76[/C][C]2533.52907359763[/C][C]223.230926402375[/C][/ROW]
[ROW][C]11[/C][C]2849.27[/C][C]2668.67035332043[/C][C]180.599646679572[/C][/ROW]
[ROW][C]12[/C][C]2921.44[/C][C]2777.7390316414[/C][C]143.700968358595[/C][/ROW]
[ROW][C]13[/C][C]2981.85[/C][C]2810.19182531178[/C][C]171.658174688216[/C][/ROW]
[ROW][C]14[/C][C]3080.58[/C][C]2957.18251877454[/C][C]123.397481225461[/C][/ROW]
[ROW][C]15[/C][C]3106.22[/C][C]3020.1160622249[/C][C]86.1039377750954[/C][/ROW]
[ROW][C]16[/C][C]3119.31[/C][C]2856.50265543567[/C][C]262.807344564329[/C][/ROW]
[ROW][C]17[/C][C]3061.26[/C][C]2847.85807442898[/C][C]213.401925571025[/C][/ROW]
[ROW][C]18[/C][C]3097.31[/C][C]2987.19403896795[/C][C]110.11596103205[/C][/ROW]
[ROW][C]19[/C][C]3161.69[/C][C]3101.06364918295[/C][C]60.6263508170478[/C][/ROW]
[ROW][C]20[/C][C]3257.16[/C][C]3169.84060308332[/C][C]87.3193969166838[/C][/ROW]
[ROW][C]21[/C][C]3277.01[/C][C]3303.22816215898[/C][C]-26.2181621589782[/C][/ROW]
[ROW][C]22[/C][C]3295.32[/C][C]3271.32318186744[/C][C]23.9968181325595[/C][/ROW]
[ROW][C]23[/C][C]3363.99[/C][C]3532.18819246[/C][C]-168.198192460001[/C][/ROW]
[ROW][C]24[/C][C]3494.17[/C][C]3785.90185801967[/C][C]-291.73185801967[/C][/ROW]
[ROW][C]25[/C][C]3667.03[/C][C]3851.58081047638[/C][C]-184.550810476383[/C][/ROW]
[ROW][C]26[/C][C]3813.06[/C][C]3909.92717343976[/C][C]-96.8671734397561[/C][/ROW]
[ROW][C]27[/C][C]3917.96[/C][C]3935.63905870863[/C][C]-17.6790587086296[/C][/ROW]
[ROW][C]28[/C][C]3895.51[/C][C]4075.79262323747[/C][C]-180.282623237466[/C][/ROW]
[ROW][C]29[/C][C]3801.06[/C][C]3923.64733261763[/C][C]-122.587332617628[/C][/ROW]
[ROW][C]30[/C][C]3570.12[/C][C]3505.75287642029[/C][C]64.3671235797117[/C][/ROW]
[ROW][C]31[/C][C]3701.61[/C][C]3507.86986753702[/C][C]193.740132462985[/C][/ROW]
[ROW][C]32[/C][C]3862.27[/C][C]3695.22163010429[/C][C]167.048369895706[/C][/ROW]
[ROW][C]33[/C][C]3970.1[/C][C]3812.15884936956[/C][C]157.94115063044[/C][/ROW]
[ROW][C]34[/C][C]4138.52[/C][C]4018.99216934148[/C][C]119.527830658519[/C][/ROW]
[ROW][C]35[/C][C]4199.75[/C][C]4045.36120653471[/C][C]154.388793465295[/C][/ROW]
[ROW][C]36[/C][C]4290.89[/C][C]4236.48218094528[/C][C]54.4078190547256[/C][/ROW]
[ROW][C]37[/C][C]4443.91[/C][C]4335.61262334952[/C][C]108.297376650476[/C][/ROW]
[ROW][C]38[/C][C]4502.64[/C][C]4403.06192168819[/C][C]99.5780783118134[/C][/ROW]
[ROW][C]39[/C][C]4356.98[/C][C]4190.67292615312[/C][C]166.307073846883[/C][/ROW]
[ROW][C]40[/C][C]4591.27[/C][C]4377.34426916186[/C][C]213.925730838143[/C][/ROW]
[ROW][C]41[/C][C]4696.96[/C][C]4571.73034500852[/C][C]125.229654991476[/C][/ROW]
[ROW][C]42[/C][C]4621.4[/C][C]4565.31268828803[/C][C]56.087311711967[/C][/ROW]
[ROW][C]43[/C][C]4562.84[/C][C]4589.48099026381[/C][C]-26.6409902638104[/C][/ROW]
[ROW][C]44[/C][C]4202.52[/C][C]4205.08179278783[/C][C]-2.56179278782505[/C][/ROW]
[ROW][C]45[/C][C]4296.49[/C][C]4296.5678631292[/C][C]-0.0778631292036487[/C][/ROW]
[ROW][C]46[/C][C]4435.23[/C][C]4572.62116297434[/C][C]-137.391162974342[/C][/ROW]
[ROW][C]47[/C][C]4105.18[/C][C]4188.10491509472[/C][C]-82.924915094721[/C][/ROW]
[ROW][C]48[/C][C]4116.68[/C][C]4276.06118970188[/C][C]-159.381189701878[/C][/ROW]
[ROW][C]49[/C][C]3844.49[/C][C]3682.14878243838[/C][C]162.341217561616[/C][/ROW]
[ROW][C]50[/C][C]3720.98[/C][C]3662.70017341549[/C][C]58.2798265845135[/C][/ROW]
[ROW][C]51[/C][C]3674.4[/C][C]3524.36380455471[/C][C]150.036195445289[/C][/ROW]
[ROW][C]52[/C][C]3857.62[/C][C]3842.65385048212[/C][C]14.9661495178809[/C][/ROW]
[ROW][C]53[/C][C]3801.06[/C][C]3956.29842765428[/C][C]-155.238427654278[/C][/ROW]
[ROW][C]54[/C][C]3504.37[/C][C]3662.62139313115[/C][C]-158.251393131149[/C][/ROW]
[ROW][C]55[/C][C]3032.6[/C][C]3195.12832183371[/C][C]-162.528321833709[/C][/ROW]
[ROW][C]56[/C][C]3047.03[/C][C]3182.65793008691[/C][C]-135.627930086909[/C][/ROW]
[ROW][C]57[/C][C]2962.34[/C][C]3044.78372345458[/C][C]-82.4437234545783[/C][/ROW]
[ROW][C]58[/C][C]2197.82[/C][C]1982.56027771883[/C][C]215.259722281166[/C][/ROW]
[ROW][C]59[/C][C]2014.45[/C][C]1837.89267623246[/C][C]176.557323767537[/C][/ROW]
[ROW][C]60[/C][C]1862.83[/C][C]1908.05860030339[/C][C]-45.2286003033879[/C][/ROW]
[ROW][C]61[/C][C]1905.41[/C][C]1841.71988517336[/C][C]63.6901148266424[/C][/ROW]
[ROW][C]62[/C][C]1810.99[/C][C]1652.7428662126[/C][C]158.247133787398[/C][/ROW]
[ROW][C]63[/C][C]1670.07[/C][C]1569.45106639554[/C][C]100.618933604459[/C][/ROW]
[ROW][C]64[/C][C]1864.44[/C][C]1926.8019576247[/C][C]-62.3619576247028[/C][/ROW]
[ROW][C]65[/C][C]2052.02[/C][C]2073.70878135401[/C][C]-21.6887813540108[/C][/ROW]
[ROW][C]66[/C][C]2029.6[/C][C]2143.06199947823[/C][C]-113.46199947823[/C][/ROW]
[ROW][C]67[/C][C]2070.83[/C][C]2098.29620424375[/C][C]-27.466204243753[/C][/ROW]
[ROW][C]68[/C][C]2293.41[/C][C]2416.35750331553[/C][C]-122.947503315532[/C][/ROW]
[ROW][C]69[/C][C]2443.27[/C][C]2455.47321046387[/C][C]-12.2032104638691[/C][/ROW]
[ROW][C]70[/C][C]2513.17[/C][C]2451.72071577507[/C][C]61.4492842249306[/C][/ROW]
[ROW][C]71[/C][C]2466.92[/C][C]2520.65788343882[/C][C]-53.7378834388213[/C][/ROW]
[ROW][C]72[/C][C]2502.66[/C][C]2708.80824493619[/C][C]-206.148244936194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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
12350.442644.52226973597-294.08226973597
22440.252591.6935085745-151.443508574501
32408.642694.24239617322-285.602396173219
42472.812875.44001921294-402.630019212943
52407.62585.60087290272-178.000872902716
62454.622732.94972929247-278.329729292474
72448.052583.28819996326-135.238199963261
82497.842517.61342566693-19.7734256669314
92645.642573.3635519511572.2764480488502
102756.762533.52907359763223.230926402375
112849.272668.67035332043180.599646679572
122921.442777.7390316414143.700968358595
132981.852810.19182531178171.658174688216
143080.582957.18251877454123.397481225461
153106.223020.116062224986.1039377750954
163119.312856.50265543567262.807344564329
173061.262847.85807442898213.401925571025
183097.312987.19403896795110.11596103205
193161.693101.0636491829560.6263508170478
203257.163169.8406030833287.3193969166838
213277.013303.22816215898-26.2181621589782
223295.323271.3231818674423.9968181325595
233363.993532.18819246-168.198192460001
243494.173785.90185801967-291.73185801967
253667.033851.58081047638-184.550810476383
263813.063909.92717343976-96.8671734397561
273917.963935.63905870863-17.6790587086296
283895.514075.79262323747-180.282623237466
293801.063923.64733261763-122.587332617628
303570.123505.7528764202964.3671235797117
313701.613507.86986753702193.740132462985
323862.273695.22163010429167.048369895706
333970.13812.15884936956157.94115063044
344138.524018.99216934148119.527830658519
354199.754045.36120653471154.388793465295
364290.894236.4821809452854.4078190547256
374443.914335.61262334952108.297376650476
384502.644403.0619216881999.5780783118134
394356.984190.67292615312166.307073846883
404591.274377.34426916186213.925730838143
414696.964571.73034500852125.229654991476
424621.44565.3126882880356.087311711967
434562.844589.48099026381-26.6409902638104
444202.524205.08179278783-2.56179278782505
454296.494296.5678631292-0.0778631292036487
464435.234572.62116297434-137.391162974342
474105.184188.10491509472-82.924915094721
484116.684276.06118970188-159.381189701878
493844.493682.14878243838162.341217561616
503720.983662.7001734154958.2798265845135
513674.43524.36380455471150.036195445289
523857.623842.6538504821214.9661495178809
533801.063956.29842765428-155.238427654278
543504.373662.62139313115-158.251393131149
553032.63195.12832183371-162.528321833709
563047.033182.65793008691-135.627930086909
572962.343044.78372345458-82.4437234545783
582197.821982.56027771883215.259722281166
592014.451837.89267623246176.557323767537
601862.831908.05860030339-45.2286003033879
611905.411841.7198851733663.6901148266424
621810.991652.7428662126158.247133787398
631670.071569.45106639554100.618933604459
641864.441926.8019576247-62.3619576247028
652052.022073.70878135401-21.6887813540108
662029.62143.06199947823-113.46199947823
672070.832098.29620424375-27.466204243753
682293.412416.35750331553-122.947503315532
692443.272455.47321046387-12.2032104638691
702513.172451.7207157750761.4492842249306
712466.922520.65788343882-53.7378834388213
722502.662708.80824493619-206.148244936194






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5058540906171040.9882918187657920.494145909382896
120.7009749093495960.5980501813008070.299025090650404
130.9185312865038640.1629374269922730.0814687134961363
140.8927597790214170.2144804419571670.107240220978583
150.86607823804070.2678435239185990.1339217619593
160.866480348653450.26703930269310.13351965134655
170.837541340432610.3249173191347780.162458659567389
180.9166632978059040.1666734043881910.0833367021940955
190.9050493031581320.1899013936837360.0949506968418681
200.8802317802920420.2395364394159170.119768219707958
210.844499121449820.3110017571003580.155500878550179
220.8800630996140870.2398738007718250.119936900385913
230.8537380543413090.2925238913173830.146261945658691
240.8850713489268130.2298573021463740.114928651073187
250.9204767035901520.1590465928196970.0795232964098484
260.9270635902841310.1458728194317380.0729364097158688
270.954997621983170.09000475603366140.0450023780168307
280.9652589409661540.0694821180676910.0347410590338455
290.9874513433019850.02509731339603050.0125486566980152
300.9901481042246320.0197037915507370.00985189577536851
310.986657946038760.02668410792248210.013342053961241
320.9884750765358930.02304984692821320.0115249234641066
330.9919374597770080.01612508044598390.00806254022299196
340.9931541724780570.01369165504388520.00684582752194259
350.9931002471488230.01379950570235440.00689975285117722
360.990001147994350.01999770401130060.0099988520056503
370.986579911546740.02684017690652110.0134200884532605
380.9790220414911260.0419559170177470.0209779585088735
390.9722264912029690.05554701759406220.0277735087970311
400.96140060042210.07719879915580080.0385993995779004
410.9547835010772010.09043299784559740.0452164989227987
420.942501113275520.1149977734489590.0574988867244794
430.9349966918641580.1300066162716830.0650033081358416
440.9249851404204990.1500297191590030.0750148595795013
450.9560448887810520.08791022243789570.0439551112189479
460.9710690265029520.05786194699409540.0289309734970477
470.979575907181050.04084818563790060.0204240928189503
480.973074239697020.05385152060595860.0269257603029793
490.9967080059108330.006583988178333190.00329199408916659
500.9972003080311060.005599383937788560.00279969196889428
510.9954776802799640.009044639440072310.00452231972003615
520.9960780751616070.00784384967678680.0039219248383934
530.9961582862993550.007683427401289860.00384171370064493
540.999286571073260.001426857853478730.000713428926739364
550.9979997800468840.004000439906232260.00200021995311613
560.9945378256424620.01092434871507540.00546217435753769
570.9920447255702260.01591054885954730.00795527442977364
580.9810245623556980.03795087528860370.0189754376443019
590.9960579163327780.007884167334443430.00394208366722172
600.9859927279319480.02801454413610420.0140072720680521
610.951503467090660.0969930658186810.0484965329093405

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.505854090617104 & 0.988291818765792 & 0.494145909382896 \tabularnewline
12 & 0.700974909349596 & 0.598050181300807 & 0.299025090650404 \tabularnewline
13 & 0.918531286503864 & 0.162937426992273 & 0.0814687134961363 \tabularnewline
14 & 0.892759779021417 & 0.214480441957167 & 0.107240220978583 \tabularnewline
15 & 0.8660782380407 & 0.267843523918599 & 0.1339217619593 \tabularnewline
16 & 0.86648034865345 & 0.2670393026931 & 0.13351965134655 \tabularnewline
17 & 0.83754134043261 & 0.324917319134778 & 0.162458659567389 \tabularnewline
18 & 0.916663297805904 & 0.166673404388191 & 0.0833367021940955 \tabularnewline
19 & 0.905049303158132 & 0.189901393683736 & 0.0949506968418681 \tabularnewline
20 & 0.880231780292042 & 0.239536439415917 & 0.119768219707958 \tabularnewline
21 & 0.84449912144982 & 0.311001757100358 & 0.155500878550179 \tabularnewline
22 & 0.880063099614087 & 0.239873800771825 & 0.119936900385913 \tabularnewline
23 & 0.853738054341309 & 0.292523891317383 & 0.146261945658691 \tabularnewline
24 & 0.885071348926813 & 0.229857302146374 & 0.114928651073187 \tabularnewline
25 & 0.920476703590152 & 0.159046592819697 & 0.0795232964098484 \tabularnewline
26 & 0.927063590284131 & 0.145872819431738 & 0.0729364097158688 \tabularnewline
27 & 0.95499762198317 & 0.0900047560336614 & 0.0450023780168307 \tabularnewline
28 & 0.965258940966154 & 0.069482118067691 & 0.0347410590338455 \tabularnewline
29 & 0.987451343301985 & 0.0250973133960305 & 0.0125486566980152 \tabularnewline
30 & 0.990148104224632 & 0.019703791550737 & 0.00985189577536851 \tabularnewline
31 & 0.98665794603876 & 0.0266841079224821 & 0.013342053961241 \tabularnewline
32 & 0.988475076535893 & 0.0230498469282132 & 0.0115249234641066 \tabularnewline
33 & 0.991937459777008 & 0.0161250804459839 & 0.00806254022299196 \tabularnewline
34 & 0.993154172478057 & 0.0136916550438852 & 0.00684582752194259 \tabularnewline
35 & 0.993100247148823 & 0.0137995057023544 & 0.00689975285117722 \tabularnewline
36 & 0.99000114799435 & 0.0199977040113006 & 0.0099988520056503 \tabularnewline
37 & 0.98657991154674 & 0.0268401769065211 & 0.0134200884532605 \tabularnewline
38 & 0.979022041491126 & 0.041955917017747 & 0.0209779585088735 \tabularnewline
39 & 0.972226491202969 & 0.0555470175940622 & 0.0277735087970311 \tabularnewline
40 & 0.9614006004221 & 0.0771987991558008 & 0.0385993995779004 \tabularnewline
41 & 0.954783501077201 & 0.0904329978455974 & 0.0452164989227987 \tabularnewline
42 & 0.94250111327552 & 0.114997773448959 & 0.0574988867244794 \tabularnewline
43 & 0.934996691864158 & 0.130006616271683 & 0.0650033081358416 \tabularnewline
44 & 0.924985140420499 & 0.150029719159003 & 0.0750148595795013 \tabularnewline
45 & 0.956044888781052 & 0.0879102224378957 & 0.0439551112189479 \tabularnewline
46 & 0.971069026502952 & 0.0578619469940954 & 0.0289309734970477 \tabularnewline
47 & 0.97957590718105 & 0.0408481856379006 & 0.0204240928189503 \tabularnewline
48 & 0.97307423969702 & 0.0538515206059586 & 0.0269257603029793 \tabularnewline
49 & 0.996708005910833 & 0.00658398817833319 & 0.00329199408916659 \tabularnewline
50 & 0.997200308031106 & 0.00559938393778856 & 0.00279969196889428 \tabularnewline
51 & 0.995477680279964 & 0.00904463944007231 & 0.00452231972003615 \tabularnewline
52 & 0.996078075161607 & 0.0078438496767868 & 0.0039219248383934 \tabularnewline
53 & 0.996158286299355 & 0.00768342740128986 & 0.00384171370064493 \tabularnewline
54 & 0.99928657107326 & 0.00142685785347873 & 0.000713428926739364 \tabularnewline
55 & 0.997999780046884 & 0.00400043990623226 & 0.00200021995311613 \tabularnewline
56 & 0.994537825642462 & 0.0109243487150754 & 0.00546217435753769 \tabularnewline
57 & 0.992044725570226 & 0.0159105488595473 & 0.00795527442977364 \tabularnewline
58 & 0.981024562355698 & 0.0379508752886037 & 0.0189754376443019 \tabularnewline
59 & 0.996057916332778 & 0.00788416733444343 & 0.00394208366722172 \tabularnewline
60 & 0.985992727931948 & 0.0280145441361042 & 0.0140072720680521 \tabularnewline
61 & 0.95150346709066 & 0.096993065818681 & 0.0484965329093405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.505854090617104[/C][C]0.988291818765792[/C][C]0.494145909382896[/C][/ROW]
[ROW][C]12[/C][C]0.700974909349596[/C][C]0.598050181300807[/C][C]0.299025090650404[/C][/ROW]
[ROW][C]13[/C][C]0.918531286503864[/C][C]0.162937426992273[/C][C]0.0814687134961363[/C][/ROW]
[ROW][C]14[/C][C]0.892759779021417[/C][C]0.214480441957167[/C][C]0.107240220978583[/C][/ROW]
[ROW][C]15[/C][C]0.8660782380407[/C][C]0.267843523918599[/C][C]0.1339217619593[/C][/ROW]
[ROW][C]16[/C][C]0.86648034865345[/C][C]0.2670393026931[/C][C]0.13351965134655[/C][/ROW]
[ROW][C]17[/C][C]0.83754134043261[/C][C]0.324917319134778[/C][C]0.162458659567389[/C][/ROW]
[ROW][C]18[/C][C]0.916663297805904[/C][C]0.166673404388191[/C][C]0.0833367021940955[/C][/ROW]
[ROW][C]19[/C][C]0.905049303158132[/C][C]0.189901393683736[/C][C]0.0949506968418681[/C][/ROW]
[ROW][C]20[/C][C]0.880231780292042[/C][C]0.239536439415917[/C][C]0.119768219707958[/C][/ROW]
[ROW][C]21[/C][C]0.84449912144982[/C][C]0.311001757100358[/C][C]0.155500878550179[/C][/ROW]
[ROW][C]22[/C][C]0.880063099614087[/C][C]0.239873800771825[/C][C]0.119936900385913[/C][/ROW]
[ROW][C]23[/C][C]0.853738054341309[/C][C]0.292523891317383[/C][C]0.146261945658691[/C][/ROW]
[ROW][C]24[/C][C]0.885071348926813[/C][C]0.229857302146374[/C][C]0.114928651073187[/C][/ROW]
[ROW][C]25[/C][C]0.920476703590152[/C][C]0.159046592819697[/C][C]0.0795232964098484[/C][/ROW]
[ROW][C]26[/C][C]0.927063590284131[/C][C]0.145872819431738[/C][C]0.0729364097158688[/C][/ROW]
[ROW][C]27[/C][C]0.95499762198317[/C][C]0.0900047560336614[/C][C]0.0450023780168307[/C][/ROW]
[ROW][C]28[/C][C]0.965258940966154[/C][C]0.069482118067691[/C][C]0.0347410590338455[/C][/ROW]
[ROW][C]29[/C][C]0.987451343301985[/C][C]0.0250973133960305[/C][C]0.0125486566980152[/C][/ROW]
[ROW][C]30[/C][C]0.990148104224632[/C][C]0.019703791550737[/C][C]0.00985189577536851[/C][/ROW]
[ROW][C]31[/C][C]0.98665794603876[/C][C]0.0266841079224821[/C][C]0.013342053961241[/C][/ROW]
[ROW][C]32[/C][C]0.988475076535893[/C][C]0.0230498469282132[/C][C]0.0115249234641066[/C][/ROW]
[ROW][C]33[/C][C]0.991937459777008[/C][C]0.0161250804459839[/C][C]0.00806254022299196[/C][/ROW]
[ROW][C]34[/C][C]0.993154172478057[/C][C]0.0136916550438852[/C][C]0.00684582752194259[/C][/ROW]
[ROW][C]35[/C][C]0.993100247148823[/C][C]0.0137995057023544[/C][C]0.00689975285117722[/C][/ROW]
[ROW][C]36[/C][C]0.99000114799435[/C][C]0.0199977040113006[/C][C]0.0099988520056503[/C][/ROW]
[ROW][C]37[/C][C]0.98657991154674[/C][C]0.0268401769065211[/C][C]0.0134200884532605[/C][/ROW]
[ROW][C]38[/C][C]0.979022041491126[/C][C]0.041955917017747[/C][C]0.0209779585088735[/C][/ROW]
[ROW][C]39[/C][C]0.972226491202969[/C][C]0.0555470175940622[/C][C]0.0277735087970311[/C][/ROW]
[ROW][C]40[/C][C]0.9614006004221[/C][C]0.0771987991558008[/C][C]0.0385993995779004[/C][/ROW]
[ROW][C]41[/C][C]0.954783501077201[/C][C]0.0904329978455974[/C][C]0.0452164989227987[/C][/ROW]
[ROW][C]42[/C][C]0.94250111327552[/C][C]0.114997773448959[/C][C]0.0574988867244794[/C][/ROW]
[ROW][C]43[/C][C]0.934996691864158[/C][C]0.130006616271683[/C][C]0.0650033081358416[/C][/ROW]
[ROW][C]44[/C][C]0.924985140420499[/C][C]0.150029719159003[/C][C]0.0750148595795013[/C][/ROW]
[ROW][C]45[/C][C]0.956044888781052[/C][C]0.0879102224378957[/C][C]0.0439551112189479[/C][/ROW]
[ROW][C]46[/C][C]0.971069026502952[/C][C]0.0578619469940954[/C][C]0.0289309734970477[/C][/ROW]
[ROW][C]47[/C][C]0.97957590718105[/C][C]0.0408481856379006[/C][C]0.0204240928189503[/C][/ROW]
[ROW][C]48[/C][C]0.97307423969702[/C][C]0.0538515206059586[/C][C]0.0269257603029793[/C][/ROW]
[ROW][C]49[/C][C]0.996708005910833[/C][C]0.00658398817833319[/C][C]0.00329199408916659[/C][/ROW]
[ROW][C]50[/C][C]0.997200308031106[/C][C]0.00559938393778856[/C][C]0.00279969196889428[/C][/ROW]
[ROW][C]51[/C][C]0.995477680279964[/C][C]0.00904463944007231[/C][C]0.00452231972003615[/C][/ROW]
[ROW][C]52[/C][C]0.996078075161607[/C][C]0.0078438496767868[/C][C]0.0039219248383934[/C][/ROW]
[ROW][C]53[/C][C]0.996158286299355[/C][C]0.00768342740128986[/C][C]0.00384171370064493[/C][/ROW]
[ROW][C]54[/C][C]0.99928657107326[/C][C]0.00142685785347873[/C][C]0.000713428926739364[/C][/ROW]
[ROW][C]55[/C][C]0.997999780046884[/C][C]0.00400043990623226[/C][C]0.00200021995311613[/C][/ROW]
[ROW][C]56[/C][C]0.994537825642462[/C][C]0.0109243487150754[/C][C]0.00546217435753769[/C][/ROW]
[ROW][C]57[/C][C]0.992044725570226[/C][C]0.0159105488595473[/C][C]0.00795527442977364[/C][/ROW]
[ROW][C]58[/C][C]0.981024562355698[/C][C]0.0379508752886037[/C][C]0.0189754376443019[/C][/ROW]
[ROW][C]59[/C][C]0.996057916332778[/C][C]0.00788416733444343[/C][C]0.00394208366722172[/C][/ROW]
[ROW][C]60[/C][C]0.985992727931948[/C][C]0.0280145441361042[/C][C]0.0140072720680521[/C][/ROW]
[ROW][C]61[/C][C]0.95150346709066[/C][C]0.096993065818681[/C][C]0.0484965329093405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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
110.5058540906171040.9882918187657920.494145909382896
120.7009749093495960.5980501813008070.299025090650404
130.9185312865038640.1629374269922730.0814687134961363
140.8927597790214170.2144804419571670.107240220978583
150.86607823804070.2678435239185990.1339217619593
160.866480348653450.26703930269310.13351965134655
170.837541340432610.3249173191347780.162458659567389
180.9166632978059040.1666734043881910.0833367021940955
190.9050493031581320.1899013936837360.0949506968418681
200.8802317802920420.2395364394159170.119768219707958
210.844499121449820.3110017571003580.155500878550179
220.8800630996140870.2398738007718250.119936900385913
230.8537380543413090.2925238913173830.146261945658691
240.8850713489268130.2298573021463740.114928651073187
250.9204767035901520.1590465928196970.0795232964098484
260.9270635902841310.1458728194317380.0729364097158688
270.954997621983170.09000475603366140.0450023780168307
280.9652589409661540.0694821180676910.0347410590338455
290.9874513433019850.02509731339603050.0125486566980152
300.9901481042246320.0197037915507370.00985189577536851
310.986657946038760.02668410792248210.013342053961241
320.9884750765358930.02304984692821320.0115249234641066
330.9919374597770080.01612508044598390.00806254022299196
340.9931541724780570.01369165504388520.00684582752194259
350.9931002471488230.01379950570235440.00689975285117722
360.990001147994350.01999770401130060.0099988520056503
370.986579911546740.02684017690652110.0134200884532605
380.9790220414911260.0419559170177470.0209779585088735
390.9722264912029690.05554701759406220.0277735087970311
400.96140060042210.07719879915580080.0385993995779004
410.9547835010772010.09043299784559740.0452164989227987
420.942501113275520.1149977734489590.0574988867244794
430.9349966918641580.1300066162716830.0650033081358416
440.9249851404204990.1500297191590030.0750148595795013
450.9560448887810520.08791022243789570.0439551112189479
460.9710690265029520.05786194699409540.0289309734970477
470.979575907181050.04084818563790060.0204240928189503
480.973074239697020.05385152060595860.0269257603029793
490.9967080059108330.006583988178333190.00329199408916659
500.9972003080311060.005599383937788560.00279969196889428
510.9954776802799640.009044639440072310.00452231972003615
520.9960780751616070.00784384967678680.0039219248383934
530.9961582862993550.007683427401289860.00384171370064493
540.999286571073260.001426857853478730.000713428926739364
550.9979997800468840.004000439906232260.00200021995311613
560.9945378256424620.01092434871507540.00546217435753769
570.9920447255702260.01591054885954730.00795527442977364
580.9810245623556980.03795087528860370.0189754376443019
590.9960579163327780.007884167334443430.00394208366722172
600.9859927279319480.02801454413610420.0140072720680521
610.951503467090660.0969930658186810.0484965329093405






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.156862745098039NOK
5% type I error level230.450980392156863NOK
10% type I error level320.627450980392157NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 8 & 0.156862745098039 & NOK \tabularnewline
5% type I error level & 23 & 0.450980392156863 & NOK \tabularnewline
10% type I error level & 32 & 0.627450980392157 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105750&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]8[/C][C]0.156862745098039[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.450980392156863[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.627450980392157[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105750&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105750&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 level80.156862745098039NOK
5% type I error level230.450980392156863NOK
10% type I error level320.627450980392157NOK


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')
}