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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 05 Dec 2010 21:30:44 +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/05/t1291584605kfxyc501sssbvd6.htm/, Retrieved Wed, 01 May 2024 22:19:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105503, Retrieved Wed, 01 May 2024 22:19:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2010-12-05 21:30:44] [c474a97a96075919a678ad3d2290b00b] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -437.150130487514 + 0.0822460593228443Nikkei[t] + 0.157632205011041DJ_Indust[t] -0.0356323334275397Goudprijs[t] -4.97051484989923Conjunct_Seizoenzuiver[t] -0.505827584503886Cons_vertrouw[t] + 55.8217095027264Alg_consumptie_index_BE[t] -32.8367316002704Gem_rente_kasbon_5j[t] + 0.273525857881991Y1[t] -0.0033510421496098Y2[t] + 0.118967771142002Y3[t] + 0.141804980701454Y4[t] -53.7213924732792M1[t] -40.1662586828167M2[t] -26.8055842768575M3[t] + 53.3258062955028M4[t] + 59.9822935133613M5[t] + 49.9516938387192M6[t] + 76.3109196672503M7[t] -6.06146549961345M8[t] -77.0522153153102M9[t] -18.0618843132753M10[t] + 17.6333117110356M11[t] -9.75190391682969t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -437.150130487514 +  0.0822460593228443Nikkei[t] +  0.157632205011041DJ_Indust[t] -0.0356323334275397Goudprijs[t] -4.97051484989923Conjunct_Seizoenzuiver[t] -0.505827584503886Cons_vertrouw[t] +  55.8217095027264Alg_consumptie_index_BE[t] -32.8367316002704Gem_rente_kasbon_5j[t] +  0.273525857881991Y1[t] -0.0033510421496098Y2[t] +  0.118967771142002Y3[t] +  0.141804980701454Y4[t] -53.7213924732792M1[t] -40.1662586828167M2[t] -26.8055842768575M3[t] +  53.3258062955028M4[t] +  59.9822935133613M5[t] +  49.9516938387192M6[t] +  76.3109196672503M7[t] -6.06146549961345M8[t] -77.0522153153102M9[t] -18.0618843132753M10[t] +  17.6333117110356M11[t] -9.75190391682969t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -437.150130487514 +  0.0822460593228443Nikkei[t] +  0.157632205011041DJ_Indust[t] -0.0356323334275397Goudprijs[t] -4.97051484989923Conjunct_Seizoenzuiver[t] -0.505827584503886Cons_vertrouw[t] +  55.8217095027264Alg_consumptie_index_BE[t] -32.8367316002704Gem_rente_kasbon_5j[t] +  0.273525857881991Y1[t] -0.0033510421496098Y2[t] +  0.118967771142002Y3[t] +  0.141804980701454Y4[t] -53.7213924732792M1[t] -40.1662586828167M2[t] -26.8055842768575M3[t] +  53.3258062955028M4[t] +  59.9822935133613M5[t] +  49.9516938387192M6[t] +  76.3109196672503M7[t] -6.06146549961345M8[t] -77.0522153153102M9[t] -18.0618843132753M10[t] +  17.6333117110356M11[t] -9.75190391682969t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&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] = -437.150130487514 + 0.0822460593228443Nikkei[t] + 0.157632205011041DJ_Indust[t] -0.0356323334275397Goudprijs[t] -4.97051484989923Conjunct_Seizoenzuiver[t] -0.505827584503886Cons_vertrouw[t] + 55.8217095027264Alg_consumptie_index_BE[t] -32.8367316002704Gem_rente_kasbon_5j[t] + 0.273525857881991Y1[t] -0.0033510421496098Y2[t] + 0.118967771142002Y3[t] + 0.141804980701454Y4[t] -53.7213924732792M1[t] -40.1662586828167M2[t] -26.8055842768575M3[t] + 53.3258062955028M4[t] + 59.9822935133613M5[t] + 49.9516938387192M6[t] + 76.3109196672503M7[t] -6.06146549961345M8[t] -77.0522153153102M9[t] -18.0618843132753M10[t] + 17.6333117110356M11[t] -9.75190391682969t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-437.150130487514557.507462-0.78410.4371740.218587
Nikkei0.08224605932284430.0124056.630300
DJ_Indust0.1576322050110410.0243636.470100
Goudprijs-0.03563233342753970.015009-2.3740.0220260.011013
Conjunct_Seizoenzuiver-4.970514849899235.318399-0.93460.3551010.177551
Cons_vertrouw-0.5058275845038865.08118-0.09950.9211540.460577
Alg_consumptie_index_BE55.821709502726411.9570794.66852.9e-051.4e-05
Gem_rente_kasbon_5j-32.836731600270444.625418-0.73580.4657390.232869
Y10.2735258578819910.1059412.58190.0132330.006617
Y2-0.00335104214960980.109178-0.03070.9756530.487826
Y30.1189677711420020.1084081.09740.2784320.139216
Y40.1418049807014540.0788591.79820.0790080.039504
M1-53.721392473279250.379829-1.06630.2920920.146046
M2-40.166258682816751.427424-0.7810.4389680.219484
M3-26.805584276857550.167432-0.53430.5958080.297904
M453.325806295502851.5796881.03390.3068540.153427
M559.982293513361356.4111561.06330.2934440.146722
M649.951693838719259.756320.83590.4077150.203857
M776.310919667250361.0560071.24990.2179610.108981
M8-6.0614654996134555.750862-0.10870.9139160.456958
M9-77.052215315310254.904333-1.40340.1675190.083759
M10-18.061884313275353.197752-0.33950.7358310.367915
M1117.633311711035652.0601990.33870.7364390.36822
t-9.751903916829693.70872-2.62950.0117380.005869

\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) & -437.150130487514 & 557.507462 & -0.7841 & 0.437174 & 0.218587 \tabularnewline
Nikkei & 0.0822460593228443 & 0.012405 & 6.6303 & 0 & 0 \tabularnewline
DJ_Indust & 0.157632205011041 & 0.024363 & 6.4701 & 0 & 0 \tabularnewline
Goudprijs & -0.0356323334275397 & 0.015009 & -2.374 & 0.022026 & 0.011013 \tabularnewline
Conjunct_Seizoenzuiver & -4.97051484989923 & 5.318399 & -0.9346 & 0.355101 & 0.177551 \tabularnewline
Cons_vertrouw & -0.505827584503886 & 5.08118 & -0.0995 & 0.921154 & 0.460577 \tabularnewline
Alg_consumptie_index_BE & 55.8217095027264 & 11.957079 & 4.6685 & 2.9e-05 & 1.4e-05 \tabularnewline
Gem_rente_kasbon_5j & -32.8367316002704 & 44.625418 & -0.7358 & 0.465739 & 0.232869 \tabularnewline
Y1 & 0.273525857881991 & 0.105941 & 2.5819 & 0.013233 & 0.006617 \tabularnewline
Y2 & -0.0033510421496098 & 0.109178 & -0.0307 & 0.975653 & 0.487826 \tabularnewline
Y3 & 0.118967771142002 & 0.108408 & 1.0974 & 0.278432 & 0.139216 \tabularnewline
Y4 & 0.141804980701454 & 0.078859 & 1.7982 & 0.079008 & 0.039504 \tabularnewline
M1 & -53.7213924732792 & 50.379829 & -1.0663 & 0.292092 & 0.146046 \tabularnewline
M2 & -40.1662586828167 & 51.427424 & -0.781 & 0.438968 & 0.219484 \tabularnewline
M3 & -26.8055842768575 & 50.167432 & -0.5343 & 0.595808 & 0.297904 \tabularnewline
M4 & 53.3258062955028 & 51.579688 & 1.0339 & 0.306854 & 0.153427 \tabularnewline
M5 & 59.9822935133613 & 56.411156 & 1.0633 & 0.293444 & 0.146722 \tabularnewline
M6 & 49.9516938387192 & 59.75632 & 0.8359 & 0.407715 & 0.203857 \tabularnewline
M7 & 76.3109196672503 & 61.056007 & 1.2499 & 0.217961 & 0.108981 \tabularnewline
M8 & -6.06146549961345 & 55.750862 & -0.1087 & 0.913916 & 0.456958 \tabularnewline
M9 & -77.0522153153102 & 54.904333 & -1.4034 & 0.167519 & 0.083759 \tabularnewline
M10 & -18.0618843132753 & 53.197752 & -0.3395 & 0.735831 & 0.367915 \tabularnewline
M11 & 17.6333117110356 & 52.060199 & 0.3387 & 0.736439 & 0.36822 \tabularnewline
t & -9.75190391682969 & 3.70872 & -2.6295 & 0.011738 & 0.005869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&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]-437.150130487514[/C][C]557.507462[/C][C]-0.7841[/C][C]0.437174[/C][C]0.218587[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.0822460593228443[/C][C]0.012405[/C][C]6.6303[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.157632205011041[/C][C]0.024363[/C][C]6.4701[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]-0.0356323334275397[/C][C]0.015009[/C][C]-2.374[/C][C]0.022026[/C][C]0.011013[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-4.97051484989923[/C][C]5.318399[/C][C]-0.9346[/C][C]0.355101[/C][C]0.177551[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]-0.505827584503886[/C][C]5.08118[/C][C]-0.0995[/C][C]0.921154[/C][C]0.460577[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]55.8217095027264[/C][C]11.957079[/C][C]4.6685[/C][C]2.9e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-32.8367316002704[/C][C]44.625418[/C][C]-0.7358[/C][C]0.465739[/C][C]0.232869[/C][/ROW]
[ROW][C]Y1[/C][C]0.273525857881991[/C][C]0.105941[/C][C]2.5819[/C][C]0.013233[/C][C]0.006617[/C][/ROW]
[ROW][C]Y2[/C][C]-0.0033510421496098[/C][C]0.109178[/C][C]-0.0307[/C][C]0.975653[/C][C]0.487826[/C][/ROW]
[ROW][C]Y3[/C][C]0.118967771142002[/C][C]0.108408[/C][C]1.0974[/C][C]0.278432[/C][C]0.139216[/C][/ROW]
[ROW][C]Y4[/C][C]0.141804980701454[/C][C]0.078859[/C][C]1.7982[/C][C]0.079008[/C][C]0.039504[/C][/ROW]
[ROW][C]M1[/C][C]-53.7213924732792[/C][C]50.379829[/C][C]-1.0663[/C][C]0.292092[/C][C]0.146046[/C][/ROW]
[ROW][C]M2[/C][C]-40.1662586828167[/C][C]51.427424[/C][C]-0.781[/C][C]0.438968[/C][C]0.219484[/C][/ROW]
[ROW][C]M3[/C][C]-26.8055842768575[/C][C]50.167432[/C][C]-0.5343[/C][C]0.595808[/C][C]0.297904[/C][/ROW]
[ROW][C]M4[/C][C]53.3258062955028[/C][C]51.579688[/C][C]1.0339[/C][C]0.306854[/C][C]0.153427[/C][/ROW]
[ROW][C]M5[/C][C]59.9822935133613[/C][C]56.411156[/C][C]1.0633[/C][C]0.293444[/C][C]0.146722[/C][/ROW]
[ROW][C]M6[/C][C]49.9516938387192[/C][C]59.75632[/C][C]0.8359[/C][C]0.407715[/C][C]0.203857[/C][/ROW]
[ROW][C]M7[/C][C]76.3109196672503[/C][C]61.056007[/C][C]1.2499[/C][C]0.217961[/C][C]0.108981[/C][/ROW]
[ROW][C]M8[/C][C]-6.06146549961345[/C][C]55.750862[/C][C]-0.1087[/C][C]0.913916[/C][C]0.456958[/C][/ROW]
[ROW][C]M9[/C][C]-77.0522153153102[/C][C]54.904333[/C][C]-1.4034[/C][C]0.167519[/C][C]0.083759[/C][/ROW]
[ROW][C]M10[/C][C]-18.0618843132753[/C][C]53.197752[/C][C]-0.3395[/C][C]0.735831[/C][C]0.367915[/C][/ROW]
[ROW][C]M11[/C][C]17.6333117110356[/C][C]52.060199[/C][C]0.3387[/C][C]0.736439[/C][C]0.36822[/C][/ROW]
[ROW][C]t[/C][C]-9.75190391682969[/C][C]3.70872[/C][C]-2.6295[/C][C]0.011738[/C][C]0.005869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&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)-437.150130487514557.507462-0.78410.4371740.218587
Nikkei0.08224605932284430.0124056.630300
DJ_Indust0.1576322050110410.0243636.470100
Goudprijs-0.03563233342753970.015009-2.3740.0220260.011013
Conjunct_Seizoenzuiver-4.970514849899235.318399-0.93460.3551010.177551
Cons_vertrouw-0.5058275845038865.08118-0.09950.9211540.460577
Alg_consumptie_index_BE55.821709502726411.9570794.66852.9e-051.4e-05
Gem_rente_kasbon_5j-32.836731600270444.625418-0.73580.4657390.232869
Y10.2735258578819910.1059412.58190.0132330.006617
Y2-0.00335104214960980.109178-0.03070.9756530.487826
Y30.1189677711420020.1084081.09740.2784320.139216
Y40.1418049807014540.0788591.79820.0790080.039504
M1-53.721392473279250.379829-1.06630.2920920.146046
M2-40.166258682816751.427424-0.7810.4389680.219484
M3-26.805584276857550.167432-0.53430.5958080.297904
M453.325806295502851.5796881.03390.3068540.153427
M559.982293513361356.4111561.06330.2934440.146722
M649.951693838719259.756320.83590.4077150.203857
M776.310919667250361.0560071.24990.2179610.108981
M8-6.0614654996134555.750862-0.10870.9139160.456958
M9-77.052215315310254.904333-1.40340.1675190.083759
M10-18.061884313275353.197752-0.33950.7358310.367915
M1117.633311711035652.0601990.33870.7364390.36822
t-9.751903916829693.70872-2.62950.0117380.005869






Multiple Linear Regression - Regression Statistics
Multiple R0.99701564320794
R-squared0.99404019280134
Adjusted R-squared0.990924839038406
F-TEST (value)319.077789696899
F-TEST (DF numerator)23
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation81.0167551044207
Sum Squared Residuals288803.442736586

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99701564320794 \tabularnewline
R-squared & 0.99404019280134 \tabularnewline
Adjusted R-squared & 0.990924839038406 \tabularnewline
F-TEST (value) & 319.077789696899 \tabularnewline
F-TEST (DF numerator) & 23 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 81.0167551044207 \tabularnewline
Sum Squared Residuals & 288803.442736586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99701564320794[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99404019280134[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.990924839038406[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]319.077789696899[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]23[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/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]81.0167551044207[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]288803.442736586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&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 R0.99701564320794
R-squared0.99404019280134
Adjusted R-squared0.990924839038406
F-TEST (value)319.077789696899
F-TEST (DF numerator)23
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation81.0167551044207
Sum Squared Residuals288803.442736586






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12293.412314.09027639276-20.6802763927601
22070.832085.23326404422-14.4032640442217
32029.62048.08337850274-18.4833785027381
42052.022063.95680329801-11.93680329801
51864.441983.55904105403-119.119041054026
61670.071648.0398562600322.0301437399714
71810.991711.2156106319399.7743893680729
81905.411894.8191595443710.5908404556259
91862.831894.79700019651-31.9670001965138
102014.451935.4214256787979.0285743212066
112197.822189.979207470767.84079252923915
122962.342824.76477178693137.575228213071
133047.033090.84076132081-43.8107613208125
143032.63132.07592685282-99.4759268528232
153504.373466.4106394771937.9593605228114
163801.063868.33603465777-67.2760346577655
173857.623836.6333060038720.9866939961319
183674.43683.21063167134-8.8106316713421
193720.983809.57240307486-88.5924030748574
203844.493829.9349568436714.5550431563330
214116.684101.4354185569815.2445814430163
224105.184152.54896983023-47.3689698302346
234435.234395.9370987921839.2929012078197
244296.494370.98878675174-74.4987867517418
254202.524275.23017188377-72.7101718837678
264562.844490.7866070937372.053392906266
274621.44583.932821380537.4671786194967
284696.964605.5507273855191.4092726144865
294591.274560.7130151158330.5569848841681
304356.984485.4887378022-128.508737802202
314502.644541.83228975478-39.1922897547817
324443.914458.58791416613-14.6779141661265
334290.894263.4048747939527.4851252060536
344199.754153.2528770442646.4971229557413
354138.524168.38511977971-29.8651197797128
363970.13984.96386458212-14.8638645821219
373862.273776.8157844394785.454215560534
383701.613631.6569183656169.9530816343925
393570.123602.64067283117-32.5206728311659
403801.063724.1253369150576.9346630849504
413895.513835.7476351209159.7623648790891
423917.963754.89279280640163.067207193605
433813.063792.5107069723620.5492930276419
443667.033721.84149336537-54.8114933653671
453494.173622.83850288249-128.668502882494
463363.993511.43696877591-147.446968775907
473295.323351.18397292840-55.8639729283959
483277.013337.38421243919-60.3742124391898
493257.163197.3454899767859.8145100232234
503161.693137.4608956349124.2291043650907
513097.313050.1637403940047.1462596059962
523061.263054.433187882696.82681211730801
533119.313047.7230614501471.5869385498575
543106.223124.00432994056-17.7843299405627
553080.583080.90717831594-0.327178315941167
562981.852914.3894210237567.4605789762528
572921.442803.53420357006117.905796429938
582849.272779.9797586708169.2902413291934
592756.762718.1646010289538.59539897105
602645.642633.4783644400212.1616355599831
612497.842505.90751598642-8.06751598641694
622448.052500.40638800870-52.3563880087043
632454.622526.1887474144-71.5687474144003
642407.62503.55790986097-95.9579098609695
652472.812536.58394125522-63.7739412552211
662408.642438.63365151947-29.9936515194696
672440.252432.461811250137.78818874986562
682350.442373.55705505672-23.1170550567181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2293.41 & 2314.09027639276 & -20.6802763927601 \tabularnewline
2 & 2070.83 & 2085.23326404422 & -14.4032640442217 \tabularnewline
3 & 2029.6 & 2048.08337850274 & -18.4833785027381 \tabularnewline
4 & 2052.02 & 2063.95680329801 & -11.93680329801 \tabularnewline
5 & 1864.44 & 1983.55904105403 & -119.119041054026 \tabularnewline
6 & 1670.07 & 1648.03985626003 & 22.0301437399714 \tabularnewline
7 & 1810.99 & 1711.21561063193 & 99.7743893680729 \tabularnewline
8 & 1905.41 & 1894.81915954437 & 10.5908404556259 \tabularnewline
9 & 1862.83 & 1894.79700019651 & -31.9670001965138 \tabularnewline
10 & 2014.45 & 1935.42142567879 & 79.0285743212066 \tabularnewline
11 & 2197.82 & 2189.97920747076 & 7.84079252923915 \tabularnewline
12 & 2962.34 & 2824.76477178693 & 137.575228213071 \tabularnewline
13 & 3047.03 & 3090.84076132081 & -43.8107613208125 \tabularnewline
14 & 3032.6 & 3132.07592685282 & -99.4759268528232 \tabularnewline
15 & 3504.37 & 3466.41063947719 & 37.9593605228114 \tabularnewline
16 & 3801.06 & 3868.33603465777 & -67.2760346577655 \tabularnewline
17 & 3857.62 & 3836.63330600387 & 20.9866939961319 \tabularnewline
18 & 3674.4 & 3683.21063167134 & -8.8106316713421 \tabularnewline
19 & 3720.98 & 3809.57240307486 & -88.5924030748574 \tabularnewline
20 & 3844.49 & 3829.93495684367 & 14.5550431563330 \tabularnewline
21 & 4116.68 & 4101.43541855698 & 15.2445814430163 \tabularnewline
22 & 4105.18 & 4152.54896983023 & -47.3689698302346 \tabularnewline
23 & 4435.23 & 4395.93709879218 & 39.2929012078197 \tabularnewline
24 & 4296.49 & 4370.98878675174 & -74.4987867517418 \tabularnewline
25 & 4202.52 & 4275.23017188377 & -72.7101718837678 \tabularnewline
26 & 4562.84 & 4490.78660709373 & 72.053392906266 \tabularnewline
27 & 4621.4 & 4583.9328213805 & 37.4671786194967 \tabularnewline
28 & 4696.96 & 4605.55072738551 & 91.4092726144865 \tabularnewline
29 & 4591.27 & 4560.71301511583 & 30.5569848841681 \tabularnewline
30 & 4356.98 & 4485.4887378022 & -128.508737802202 \tabularnewline
31 & 4502.64 & 4541.83228975478 & -39.1922897547817 \tabularnewline
32 & 4443.91 & 4458.58791416613 & -14.6779141661265 \tabularnewline
33 & 4290.89 & 4263.40487479395 & 27.4851252060536 \tabularnewline
34 & 4199.75 & 4153.25287704426 & 46.4971229557413 \tabularnewline
35 & 4138.52 & 4168.38511977971 & -29.8651197797128 \tabularnewline
36 & 3970.1 & 3984.96386458212 & -14.8638645821219 \tabularnewline
37 & 3862.27 & 3776.81578443947 & 85.454215560534 \tabularnewline
38 & 3701.61 & 3631.65691836561 & 69.9530816343925 \tabularnewline
39 & 3570.12 & 3602.64067283117 & -32.5206728311659 \tabularnewline
40 & 3801.06 & 3724.12533691505 & 76.9346630849504 \tabularnewline
41 & 3895.51 & 3835.74763512091 & 59.7623648790891 \tabularnewline
42 & 3917.96 & 3754.89279280640 & 163.067207193605 \tabularnewline
43 & 3813.06 & 3792.51070697236 & 20.5492930276419 \tabularnewline
44 & 3667.03 & 3721.84149336537 & -54.8114933653671 \tabularnewline
45 & 3494.17 & 3622.83850288249 & -128.668502882494 \tabularnewline
46 & 3363.99 & 3511.43696877591 & -147.446968775907 \tabularnewline
47 & 3295.32 & 3351.18397292840 & -55.8639729283959 \tabularnewline
48 & 3277.01 & 3337.38421243919 & -60.3742124391898 \tabularnewline
49 & 3257.16 & 3197.34548997678 & 59.8145100232234 \tabularnewline
50 & 3161.69 & 3137.46089563491 & 24.2291043650907 \tabularnewline
51 & 3097.31 & 3050.16374039400 & 47.1462596059962 \tabularnewline
52 & 3061.26 & 3054.43318788269 & 6.82681211730801 \tabularnewline
53 & 3119.31 & 3047.72306145014 & 71.5869385498575 \tabularnewline
54 & 3106.22 & 3124.00432994056 & -17.7843299405627 \tabularnewline
55 & 3080.58 & 3080.90717831594 & -0.327178315941167 \tabularnewline
56 & 2981.85 & 2914.38942102375 & 67.4605789762528 \tabularnewline
57 & 2921.44 & 2803.53420357006 & 117.905796429938 \tabularnewline
58 & 2849.27 & 2779.97975867081 & 69.2902413291934 \tabularnewline
59 & 2756.76 & 2718.16460102895 & 38.59539897105 \tabularnewline
60 & 2645.64 & 2633.47836444002 & 12.1616355599831 \tabularnewline
61 & 2497.84 & 2505.90751598642 & -8.06751598641694 \tabularnewline
62 & 2448.05 & 2500.40638800870 & -52.3563880087043 \tabularnewline
63 & 2454.62 & 2526.1887474144 & -71.5687474144003 \tabularnewline
64 & 2407.6 & 2503.55790986097 & -95.9579098609695 \tabularnewline
65 & 2472.81 & 2536.58394125522 & -63.7739412552211 \tabularnewline
66 & 2408.64 & 2438.63365151947 & -29.9936515194696 \tabularnewline
67 & 2440.25 & 2432.46181125013 & 7.78818874986562 \tabularnewline
68 & 2350.44 & 2373.55705505672 & -23.1170550567181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&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]2293.41[/C][C]2314.09027639276[/C][C]-20.6802763927601[/C][/ROW]
[ROW][C]2[/C][C]2070.83[/C][C]2085.23326404422[/C][C]-14.4032640442217[/C][/ROW]
[ROW][C]3[/C][C]2029.6[/C][C]2048.08337850274[/C][C]-18.4833785027381[/C][/ROW]
[ROW][C]4[/C][C]2052.02[/C][C]2063.95680329801[/C][C]-11.93680329801[/C][/ROW]
[ROW][C]5[/C][C]1864.44[/C][C]1983.55904105403[/C][C]-119.119041054026[/C][/ROW]
[ROW][C]6[/C][C]1670.07[/C][C]1648.03985626003[/C][C]22.0301437399714[/C][/ROW]
[ROW][C]7[/C][C]1810.99[/C][C]1711.21561063193[/C][C]99.7743893680729[/C][/ROW]
[ROW][C]8[/C][C]1905.41[/C][C]1894.81915954437[/C][C]10.5908404556259[/C][/ROW]
[ROW][C]9[/C][C]1862.83[/C][C]1894.79700019651[/C][C]-31.9670001965138[/C][/ROW]
[ROW][C]10[/C][C]2014.45[/C][C]1935.42142567879[/C][C]79.0285743212066[/C][/ROW]
[ROW][C]11[/C][C]2197.82[/C][C]2189.97920747076[/C][C]7.84079252923915[/C][/ROW]
[ROW][C]12[/C][C]2962.34[/C][C]2824.76477178693[/C][C]137.575228213071[/C][/ROW]
[ROW][C]13[/C][C]3047.03[/C][C]3090.84076132081[/C][C]-43.8107613208125[/C][/ROW]
[ROW][C]14[/C][C]3032.6[/C][C]3132.07592685282[/C][C]-99.4759268528232[/C][/ROW]
[ROW][C]15[/C][C]3504.37[/C][C]3466.41063947719[/C][C]37.9593605228114[/C][/ROW]
[ROW][C]16[/C][C]3801.06[/C][C]3868.33603465777[/C][C]-67.2760346577655[/C][/ROW]
[ROW][C]17[/C][C]3857.62[/C][C]3836.63330600387[/C][C]20.9866939961319[/C][/ROW]
[ROW][C]18[/C][C]3674.4[/C][C]3683.21063167134[/C][C]-8.8106316713421[/C][/ROW]
[ROW][C]19[/C][C]3720.98[/C][C]3809.57240307486[/C][C]-88.5924030748574[/C][/ROW]
[ROW][C]20[/C][C]3844.49[/C][C]3829.93495684367[/C][C]14.5550431563330[/C][/ROW]
[ROW][C]21[/C][C]4116.68[/C][C]4101.43541855698[/C][C]15.2445814430163[/C][/ROW]
[ROW][C]22[/C][C]4105.18[/C][C]4152.54896983023[/C][C]-47.3689698302346[/C][/ROW]
[ROW][C]23[/C][C]4435.23[/C][C]4395.93709879218[/C][C]39.2929012078197[/C][/ROW]
[ROW][C]24[/C][C]4296.49[/C][C]4370.98878675174[/C][C]-74.4987867517418[/C][/ROW]
[ROW][C]25[/C][C]4202.52[/C][C]4275.23017188377[/C][C]-72.7101718837678[/C][/ROW]
[ROW][C]26[/C][C]4562.84[/C][C]4490.78660709373[/C][C]72.053392906266[/C][/ROW]
[ROW][C]27[/C][C]4621.4[/C][C]4583.9328213805[/C][C]37.4671786194967[/C][/ROW]
[ROW][C]28[/C][C]4696.96[/C][C]4605.55072738551[/C][C]91.4092726144865[/C][/ROW]
[ROW][C]29[/C][C]4591.27[/C][C]4560.71301511583[/C][C]30.5569848841681[/C][/ROW]
[ROW][C]30[/C][C]4356.98[/C][C]4485.4887378022[/C][C]-128.508737802202[/C][/ROW]
[ROW][C]31[/C][C]4502.64[/C][C]4541.83228975478[/C][C]-39.1922897547817[/C][/ROW]
[ROW][C]32[/C][C]4443.91[/C][C]4458.58791416613[/C][C]-14.6779141661265[/C][/ROW]
[ROW][C]33[/C][C]4290.89[/C][C]4263.40487479395[/C][C]27.4851252060536[/C][/ROW]
[ROW][C]34[/C][C]4199.75[/C][C]4153.25287704426[/C][C]46.4971229557413[/C][/ROW]
[ROW][C]35[/C][C]4138.52[/C][C]4168.38511977971[/C][C]-29.8651197797128[/C][/ROW]
[ROW][C]36[/C][C]3970.1[/C][C]3984.96386458212[/C][C]-14.8638645821219[/C][/ROW]
[ROW][C]37[/C][C]3862.27[/C][C]3776.81578443947[/C][C]85.454215560534[/C][/ROW]
[ROW][C]38[/C][C]3701.61[/C][C]3631.65691836561[/C][C]69.9530816343925[/C][/ROW]
[ROW][C]39[/C][C]3570.12[/C][C]3602.64067283117[/C][C]-32.5206728311659[/C][/ROW]
[ROW][C]40[/C][C]3801.06[/C][C]3724.12533691505[/C][C]76.9346630849504[/C][/ROW]
[ROW][C]41[/C][C]3895.51[/C][C]3835.74763512091[/C][C]59.7623648790891[/C][/ROW]
[ROW][C]42[/C][C]3917.96[/C][C]3754.89279280640[/C][C]163.067207193605[/C][/ROW]
[ROW][C]43[/C][C]3813.06[/C][C]3792.51070697236[/C][C]20.5492930276419[/C][/ROW]
[ROW][C]44[/C][C]3667.03[/C][C]3721.84149336537[/C][C]-54.8114933653671[/C][/ROW]
[ROW][C]45[/C][C]3494.17[/C][C]3622.83850288249[/C][C]-128.668502882494[/C][/ROW]
[ROW][C]46[/C][C]3363.99[/C][C]3511.43696877591[/C][C]-147.446968775907[/C][/ROW]
[ROW][C]47[/C][C]3295.32[/C][C]3351.18397292840[/C][C]-55.8639729283959[/C][/ROW]
[ROW][C]48[/C][C]3277.01[/C][C]3337.38421243919[/C][C]-60.3742124391898[/C][/ROW]
[ROW][C]49[/C][C]3257.16[/C][C]3197.34548997678[/C][C]59.8145100232234[/C][/ROW]
[ROW][C]50[/C][C]3161.69[/C][C]3137.46089563491[/C][C]24.2291043650907[/C][/ROW]
[ROW][C]51[/C][C]3097.31[/C][C]3050.16374039400[/C][C]47.1462596059962[/C][/ROW]
[ROW][C]52[/C][C]3061.26[/C][C]3054.43318788269[/C][C]6.82681211730801[/C][/ROW]
[ROW][C]53[/C][C]3119.31[/C][C]3047.72306145014[/C][C]71.5869385498575[/C][/ROW]
[ROW][C]54[/C][C]3106.22[/C][C]3124.00432994056[/C][C]-17.7843299405627[/C][/ROW]
[ROW][C]55[/C][C]3080.58[/C][C]3080.90717831594[/C][C]-0.327178315941167[/C][/ROW]
[ROW][C]56[/C][C]2981.85[/C][C]2914.38942102375[/C][C]67.4605789762528[/C][/ROW]
[ROW][C]57[/C][C]2921.44[/C][C]2803.53420357006[/C][C]117.905796429938[/C][/ROW]
[ROW][C]58[/C][C]2849.27[/C][C]2779.97975867081[/C][C]69.2902413291934[/C][/ROW]
[ROW][C]59[/C][C]2756.76[/C][C]2718.16460102895[/C][C]38.59539897105[/C][/ROW]
[ROW][C]60[/C][C]2645.64[/C][C]2633.47836444002[/C][C]12.1616355599831[/C][/ROW]
[ROW][C]61[/C][C]2497.84[/C][C]2505.90751598642[/C][C]-8.06751598641694[/C][/ROW]
[ROW][C]62[/C][C]2448.05[/C][C]2500.40638800870[/C][C]-52.3563880087043[/C][/ROW]
[ROW][C]63[/C][C]2454.62[/C][C]2526.1887474144[/C][C]-71.5687474144003[/C][/ROW]
[ROW][C]64[/C][C]2407.6[/C][C]2503.55790986097[/C][C]-95.9579098609695[/C][/ROW]
[ROW][C]65[/C][C]2472.81[/C][C]2536.58394125522[/C][C]-63.7739412552211[/C][/ROW]
[ROW][C]66[/C][C]2408.64[/C][C]2438.63365151947[/C][C]-29.9936515194696[/C][/ROW]
[ROW][C]67[/C][C]2440.25[/C][C]2432.46181125013[/C][C]7.78818874986562[/C][/ROW]
[ROW][C]68[/C][C]2350.44[/C][C]2373.55705505672[/C][C]-23.1170550567181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&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
12293.412314.09027639276-20.6802763927601
22070.832085.23326404422-14.4032640442217
32029.62048.08337850274-18.4833785027381
42052.022063.95680329801-11.93680329801
51864.441983.55904105403-119.119041054026
61670.071648.0398562600322.0301437399714
71810.991711.2156106319399.7743893680729
81905.411894.8191595443710.5908404556259
91862.831894.79700019651-31.9670001965138
102014.451935.4214256787979.0285743212066
112197.822189.979207470767.84079252923915
122962.342824.76477178693137.575228213071
133047.033090.84076132081-43.8107613208125
143032.63132.07592685282-99.4759268528232
153504.373466.4106394771937.9593605228114
163801.063868.33603465777-67.2760346577655
173857.623836.6333060038720.9866939961319
183674.43683.21063167134-8.8106316713421
193720.983809.57240307486-88.5924030748574
203844.493829.9349568436714.5550431563330
214116.684101.4354185569815.2445814430163
224105.184152.54896983023-47.3689698302346
234435.234395.9370987921839.2929012078197
244296.494370.98878675174-74.4987867517418
254202.524275.23017188377-72.7101718837678
264562.844490.7866070937372.053392906266
274621.44583.932821380537.4671786194967
284696.964605.5507273855191.4092726144865
294591.274560.7130151158330.5569848841681
304356.984485.4887378022-128.508737802202
314502.644541.83228975478-39.1922897547817
324443.914458.58791416613-14.6779141661265
334290.894263.4048747939527.4851252060536
344199.754153.2528770442646.4971229557413
354138.524168.38511977971-29.8651197797128
363970.13984.96386458212-14.8638645821219
373862.273776.8157844394785.454215560534
383701.613631.6569183656169.9530816343925
393570.123602.64067283117-32.5206728311659
403801.063724.1253369150576.9346630849504
413895.513835.7476351209159.7623648790891
423917.963754.89279280640163.067207193605
433813.063792.5107069723620.5492930276419
443667.033721.84149336537-54.8114933653671
453494.173622.83850288249-128.668502882494
463363.993511.43696877591-147.446968775907
473295.323351.18397292840-55.8639729283959
483277.013337.38421243919-60.3742124391898
493257.163197.3454899767859.8145100232234
503161.693137.4608956349124.2291043650907
513097.313050.1637403940047.1462596059962
523061.263054.433187882696.82681211730801
533119.313047.7230614501471.5869385498575
543106.223124.00432994056-17.7843299405627
553080.583080.90717831594-0.327178315941167
562981.852914.3894210237567.4605789762528
572921.442803.53420357006117.905796429938
582849.272779.9797586708169.2902413291934
592756.762718.1646010289538.59539897105
602645.642633.4783644400212.1616355599831
612497.842505.90751598642-8.06751598641694
622448.052500.40638800870-52.3563880087043
632454.622526.1887474144-71.5687474144003
642407.62503.55790986097-95.9579098609695
652472.812536.58394125522-63.7739412552211
662408.642438.63365151947-29.9936515194696
672440.252432.461811250137.78818874986562
682350.442373.55705505672-23.1170550567181






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
270.5790317058104660.8419365883790670.420968294189534
280.6408511578877560.7182976842244870.359148842112243
290.5475410796111320.9049178407777370.452458920388868
300.7867524717141850.4264950565716290.213247528285815
310.7887203226387370.4225593547225250.211279677361263
320.8983987220196140.2032025559607730.101601277980386
330.8293635017489930.3412729965020130.170636498251007
340.9411475043218990.1177049913562030.0588524956781013
350.920130784465360.1597384310692810.0798692155346404
360.8630241214487240.2739517571025520.136975878551276
370.7714303705080410.4571392589839180.228569629491959
380.7271687682198350.5456624635603290.272831231780165
390.9997654635895320.0004690728209350740.000234536410467537
400.9991797733457390.001640453308522540.000820226654261268
410.9950382380065830.009923523986833060.00496176199341653

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
27 & 0.579031705810466 & 0.841936588379067 & 0.420968294189534 \tabularnewline
28 & 0.640851157887756 & 0.718297684224487 & 0.359148842112243 \tabularnewline
29 & 0.547541079611132 & 0.904917840777737 & 0.452458920388868 \tabularnewline
30 & 0.786752471714185 & 0.426495056571629 & 0.213247528285815 \tabularnewline
31 & 0.788720322638737 & 0.422559354722525 & 0.211279677361263 \tabularnewline
32 & 0.898398722019614 & 0.203202555960773 & 0.101601277980386 \tabularnewline
33 & 0.829363501748993 & 0.341272996502013 & 0.170636498251007 \tabularnewline
34 & 0.941147504321899 & 0.117704991356203 & 0.0588524956781013 \tabularnewline
35 & 0.92013078446536 & 0.159738431069281 & 0.0798692155346404 \tabularnewline
36 & 0.863024121448724 & 0.273951757102552 & 0.136975878551276 \tabularnewline
37 & 0.771430370508041 & 0.457139258983918 & 0.228569629491959 \tabularnewline
38 & 0.727168768219835 & 0.545662463560329 & 0.272831231780165 \tabularnewline
39 & 0.999765463589532 & 0.000469072820935074 & 0.000234536410467537 \tabularnewline
40 & 0.999179773345739 & 0.00164045330852254 & 0.000820226654261268 \tabularnewline
41 & 0.995038238006583 & 0.00992352398683306 & 0.00496176199341653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&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]27[/C][C]0.579031705810466[/C][C]0.841936588379067[/C][C]0.420968294189534[/C][/ROW]
[ROW][C]28[/C][C]0.640851157887756[/C][C]0.718297684224487[/C][C]0.359148842112243[/C][/ROW]
[ROW][C]29[/C][C]0.547541079611132[/C][C]0.904917840777737[/C][C]0.452458920388868[/C][/ROW]
[ROW][C]30[/C][C]0.786752471714185[/C][C]0.426495056571629[/C][C]0.213247528285815[/C][/ROW]
[ROW][C]31[/C][C]0.788720322638737[/C][C]0.422559354722525[/C][C]0.211279677361263[/C][/ROW]
[ROW][C]32[/C][C]0.898398722019614[/C][C]0.203202555960773[/C][C]0.101601277980386[/C][/ROW]
[ROW][C]33[/C][C]0.829363501748993[/C][C]0.341272996502013[/C][C]0.170636498251007[/C][/ROW]
[ROW][C]34[/C][C]0.941147504321899[/C][C]0.117704991356203[/C][C]0.0588524956781013[/C][/ROW]
[ROW][C]35[/C][C]0.92013078446536[/C][C]0.159738431069281[/C][C]0.0798692155346404[/C][/ROW]
[ROW][C]36[/C][C]0.863024121448724[/C][C]0.273951757102552[/C][C]0.136975878551276[/C][/ROW]
[ROW][C]37[/C][C]0.771430370508041[/C][C]0.457139258983918[/C][C]0.228569629491959[/C][/ROW]
[ROW][C]38[/C][C]0.727168768219835[/C][C]0.545662463560329[/C][C]0.272831231780165[/C][/ROW]
[ROW][C]39[/C][C]0.999765463589532[/C][C]0.000469072820935074[/C][C]0.000234536410467537[/C][/ROW]
[ROW][C]40[/C][C]0.999179773345739[/C][C]0.00164045330852254[/C][C]0.000820226654261268[/C][/ROW]
[ROW][C]41[/C][C]0.995038238006583[/C][C]0.00992352398683306[/C][C]0.00496176199341653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&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
270.5790317058104660.8419365883790670.420968294189534
280.6408511578877560.7182976842244870.359148842112243
290.5475410796111320.9049178407777370.452458920388868
300.7867524717141850.4264950565716290.213247528285815
310.7887203226387370.4225593547225250.211279677361263
320.8983987220196140.2032025559607730.101601277980386
330.8293635017489930.3412729965020130.170636498251007
340.9411475043218990.1177049913562030.0588524956781013
350.920130784465360.1597384310692810.0798692155346404
360.8630241214487240.2739517571025520.136975878551276
370.7714303705080410.4571392589839180.228569629491959
380.7271687682198350.5456624635603290.272831231780165
390.9997654635895320.0004690728209350740.000234536410467537
400.9991797733457390.001640453308522540.000820226654261268
410.9950382380065830.009923523986833060.00496176199341653






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.2NOK
5% type I error level30.2NOK
10% type I error level30.2NOK

\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 & 3 & 0.2 & NOK \tabularnewline
5% type I error level & 3 & 0.2 & NOK \tabularnewline
10% type I error level & 3 & 0.2 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105503&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]3[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.2[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105503&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105503&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.2NOK
5% type I error level30.2NOK
10% type I error level30.2NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}