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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 computationWed, 01 Dec 2010 21:03:34 +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/01/t1291237457o7xu03xd1i8lsyv.htm/, Retrieved Sun, 05 May 2024 01:10:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104183, Retrieved Sun, 05 May 2024 01:10:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190

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] [c474a97a96075919a678ad3d2290b00b] [Current]
-    D      [Multiple Regression] [] [2010-12-02 12:01:33] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D        [Multiple Regression] [] [2010-12-02 12:56:44] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D        [Multiple Regression] [] [2010-12-06 16:31:24] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD          [Multiple Regression] [] [2010-12-06 16:36:51] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD          [Multiple Regression] [] [2010-12-06 16:38:41] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD          [Multiple Regression] [] [2010-12-06 16:40:41] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D          [Multiple Regression] [] [2010-12-06 16:44:05] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D        [Multiple Regression] [] [2010-12-06 18:03:06] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   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 
2502,66	10169,02	10433,44	24977	-7,9	-15	2,85	0,3	3,36
2466,92	9633,83	10238,83	24320	-8,8	-10	2,98	-0,1	3,37
2513,17	10066,24	9857,34	22680	-14,2	-12	3,06	-1	3,55
2443,27	10302,87	9634,97	22052	-17,8	-11	3,08	-1,2	3,53
2293,41	10430,35	9374,63	21467	-18,2	-11	3,3	-0,8	3,52
2070,83	9691,12	8679,75	21383	-22,8	-17	3,47	-1,7	3,54
2029,6	9810,31	8593	21777	-23,6	-18	3,72	-1,1	3,5
2052,02	9304,43	8398,37	21928	-27,6	-19	3,67	-0,4	3,44
1864,44	8767,96	7992,12	21814	-29,4	-22	3,82	0,6	3,38
1670,07	7764,58	7235,47	22937	-31,8	-24	3,85	0,6	3,35
1810,99	7694,78	7690,5	23595	-31,4	-24	3,9	1,9	3,68
1905,41	8331,49	8396,2	20830	-27,6	-20	3,99	2,3	3,92
1862,83	8460,94	8595,56	19650	-28,8	-25	4,35	2,6	4,05
2014,45	8531,45	8614,55	19195	-21,9	-22	4,98	3,1	4,14
2197,82	9117,03	9181,73	19644	-13,9	-17	5,46	4,7	4,53
2962,34	12123,53	11114,08	18483	-8	-9	5,19	5,5	4,54
3047,03	12989,35	11530,75	18079	-2,8	-11	5,03	5,4	4,9
3032,6	13168,91	11322,38	19178	-3,3	-13	5,38	5,9	4,92
3504,37	14084,6	12056,67	18391	-1,3	-11	5,37	5,8	4,45
3801,06	13995,33	12812,48	18441	0,5	-9	4,87	5,2	3,92
3857,62	13357,7	12656,63	18584	-1,9	-7	4,7	4,2	3,66
3674,4	12602,93	12193,88	20108	2	-3	4,4	4,4	3,74
3720,98	13547,84	12419,57	20148	1,7	-3	4,37	3,6	4,07
3844,49	13731,31	12538,12	19394	1,9	-6	4,54	3,5	4,23
4116,68	15532,18	13406,97	17745	0,1	-4	4,8	3,1	4,14
4105,18	15543,76	13200,58	17696	2,4	-8	4,56	2,9	4,18
4435,23	16903,36	13901,28	17032	2,3	-1	4,61	2,2	4,29
4296,49	16235,39	13557,69	16438	4,7	-2	4,58	1,5	4,27
4202,52	16460,95	13239,71	15683	5	-2	4,61	1,1	4,33
4562,84	17974,77	13673,28	15594	7,2	-1	4,77	1,4	4,39
4621,4	18001,37	13480,21	15713	8,5	1	4,76	1,3	4,21
4696,96	17611,14	13407,75	15937	6,8	2	4,5	1,3	3,88
4591,27	17460,53	12754,8	16171	5,8	2	4,37	1,8	3,91
4356,98	17128,37	12268,53	15928	3,7	-1	4,15	1,8	3,94
4502,64	17741,23	12631,48	16348	4,8	1	4,24	1,8	3,94
4443,91	17286,32	12512,89	15579	6,1	-1	4,22	1,7	3,64
4290,89	16775,08	12377,62	15305	6,9	-8	4,01	1,6	3,5
4199,75	16101,07	12185,15	15648	5,7	1	3,93	1,5	3,49
4138,52	16519,44	11963,12	14954	6,9	2	3,97	1,2	3,52
3970,1	15934,09	11533,59	15137	5,5	-2	3,92	1,2	3,51
3862,27	15786,78	11257,35	15839	6,5	-2	3,99	1,6	3,6
3701,61	15147,55	11036,89	16050	7,7	-2	4,1	1,6	3,57
3570,12	14990,31	10997,97	15168	6,3	-2	4,04	1,9	3,46
3801,06	16397,83	11333,88	17064	5,5	-6	3,97	2,2	3,48
3895,51	17232,97	11234,68	16005	5,3	-4	3,9	2	3,3
3917,96	16311,54	11145,65	14886	3,3	-5	3,66	1,7	3,04
3813,06	16187,64	10971,19	14931	2,2	-2	3,44	2,4	2,96
3667,03	16102,64	10872,48	14544	0,6	-1	3,27	2,6	3,07
3494,17	15650,83	10827,81	13812	0,2	-5	3,24	2,9	2,99
3363,99	14368,05	10695,25	13031	-0,7	-9	3,27	2,6	2,86
3295,32	13392,79	10324,31	12574	-1,7	-8	2,99	2,5	2,72
3277,01	12986,62	10532,54	11964	-3,7	-14	2,77	3,2	2,72
3257,16	12204,98	10554,27	11451	-7,6	-10	2,9	3,1	2,75
3161,69	11716,87	10545,38	11346	-8,2	-11	2,87	3,1	2,67
3097,31	11402,75	10486,64	11353	-7,5	-11	2,84	2,9	2,76
3061,26	11082,38	10377,18	10702	-8	-11	3,02	2,5	2,87
3119,31	11395,64	10283,19	10646	-6,9	-5	3,19	2,8	2,9
3106,22	11809,38	10682,06	10556	-4,2	-2	3,39	3,1	2,92
3080,58	11545,71	10723,78	10463	-3,6	-3	3,28	2,6	2,93
2981,85	11394,84	10539,51	10407	-1,8	-6	3,28	2,3	3,1
2921,44	11068,05	10673,38	10625	-3,2	-6	3,33	2,3	3,2
2849,27	10973	10411,75	10872	-1,3	-7	3,51	2,6	3,25
2756,76	11028,93	10001,6	10805	0,6	-6	3,65	2,9	3,31
2645,64	11079,42	10204,59	10653	1,2	-2	3,76	2	3,23
2497,84	10989,34	10032,8	10574	0,4	-2	3,67	2,2	3,24
2448,05	11383,89	10152,09	10431	3	-4	3,87	2,4	3,35
2454,62	11527,72	10364,91	10383	-0,4	0	3,99	2,3	3,19
2407,6	11037,54	10092,96	10296	0	-6	3,9	2,6	3,17
2472,81	11950,95	10418,4	10872	-1,3	-4	3,74	1,9	3,06
2408,64	11441,08	10323,73	10635	-3,1	-3	3,55	1,1	3,22
2440,25	10631,92	10601,61	10297	-4	-1	3,67	1,3	3,35
2350,44	10892,76	10540,05	10570	-4,9	-3	3,6	1,6	3,38

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'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&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'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1880.76580017901 + 0.192813678210668Nikkei[t] + 0.286352347829325DJ_Indust[t] + 0.0145348419797004Goudprijs[t] -10.0672478500057Conjunct_Seizoenzuiver[t] -2.30528030345926Cons_vertrouw[t] -18.3563447065875Rend_oblig_EUR[t] + 35.9956460531887Alg_consumptie_index_BE[t] -234.559373355687Gem_rente_kasbon_5j[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -1880.76580017901 +  0.192813678210668Nikkei[t] +  0.286352347829325DJ_Indust[t] +  0.0145348419797004Goudprijs[t] -10.0672478500057Conjunct_Seizoenzuiver[t] -2.30528030345926Cons_vertrouw[t] -18.3563447065875Rend_oblig_EUR[t] +  35.9956460531887Alg_consumptie_index_BE[t] -234.559373355687Gem_rente_kasbon_5j[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -1880.76580017901 +  0.192813678210668Nikkei[t] +  0.286352347829325DJ_Indust[t] +  0.0145348419797004Goudprijs[t] -10.0672478500057Conjunct_Seizoenzuiver[t] -2.30528030345926Cons_vertrouw[t] -18.3563447065875Rend_oblig_EUR[t] +  35.9956460531887Alg_consumptie_index_BE[t] -234.559373355687Gem_rente_kasbon_5j[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&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] = -1880.76580017901 + 0.192813678210668Nikkei[t] + 0.286352347829325DJ_Indust[t] + 0.0145348419797004Goudprijs[t] -10.0672478500057Conjunct_Seizoenzuiver[t] -2.30528030345926Cons_vertrouw[t] -18.3563447065875Rend_oblig_EUR[t] + 35.9956460531887Alg_consumptie_index_BE[t] -234.559373355687Gem_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)-1880.76580017901273.254826-6.882800
Nikkei0.1928136782106680.01573112.256800
DJ_Indust0.2863523478293250.0345448.289500
Goudprijs0.01453484197970040.0083271.74540.0857890.042894
Conjunct_Seizoenzuiver-10.06724785000576.08984-1.65310.1032810.051641
Cons_vertrouw-2.305280303459267.758898-0.29710.7673570.383678
Rend_oblig_EUR-18.356344706587581.447108-0.22540.8224150.411208
Alg_consumptie_index_BE35.995646053188719.5850681.83790.0707910.035396
Gem_rente_kasbon_5j-234.559373355687109.529101-2.14150.0361060.018053

\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) & -1880.76580017901 & 273.254826 & -6.8828 & 0 & 0 \tabularnewline
Nikkei & 0.192813678210668 & 0.015731 & 12.2568 & 0 & 0 \tabularnewline
DJ_Indust & 0.286352347829325 & 0.034544 & 8.2895 & 0 & 0 \tabularnewline
Goudprijs & 0.0145348419797004 & 0.008327 & 1.7454 & 0.085789 & 0.042894 \tabularnewline
Conjunct_Seizoenzuiver & -10.0672478500057 & 6.08984 & -1.6531 & 0.103281 & 0.051641 \tabularnewline
Cons_vertrouw & -2.30528030345926 & 7.758898 & -0.2971 & 0.767357 & 0.383678 \tabularnewline
Rend_oblig_EUR & -18.3563447065875 & 81.447108 & -0.2254 & 0.822415 & 0.411208 \tabularnewline
Alg_consumptie_index_BE & 35.9956460531887 & 19.585068 & 1.8379 & 0.070791 & 0.035396 \tabularnewline
Gem_rente_kasbon_5j & -234.559373355687 & 109.529101 & -2.1415 & 0.036106 & 0.018053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&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]-1880.76580017901[/C][C]273.254826[/C][C]-6.8828[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.192813678210668[/C][C]0.015731[/C][C]12.2568[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.286352347829325[/C][C]0.034544[/C][C]8.2895[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.0145348419797004[/C][C]0.008327[/C][C]1.7454[/C][C]0.085789[/C][C]0.042894[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-10.0672478500057[/C][C]6.08984[/C][C]-1.6531[/C][C]0.103281[/C][C]0.051641[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]-2.30528030345926[/C][C]7.758898[/C][C]-0.2971[/C][C]0.767357[/C][C]0.383678[/C][/ROW]
[ROW][C]Rend_oblig_EUR[/C][C]-18.3563447065875[/C][C]81.447108[/C][C]-0.2254[/C][C]0.822415[/C][C]0.411208[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]35.9956460531887[/C][C]19.585068[/C][C]1.8379[/C][C]0.070791[/C][C]0.035396[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-234.559373355687[/C][C]109.529101[/C][C]-2.1415[/C][C]0.036106[/C][C]0.018053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&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)-1880.76580017901273.254826-6.882800
Nikkei0.1928136782106680.01573112.256800
DJ_Indust0.2863523478293250.0345448.289500
Goudprijs0.01453484197970040.0083271.74540.0857890.042894
Conjunct_Seizoenzuiver-10.06724785000576.08984-1.65310.1032810.051641
Cons_vertrouw-2.305280303459267.758898-0.29710.7673570.383678
Rend_oblig_EUR-18.356344706587581.447108-0.22540.8224150.411208
Alg_consumptie_index_BE35.995646053188719.5850681.83790.0707910.035396
Gem_rente_kasbon_5j-234.559373355687109.529101-2.14150.0361060.018053






Multiple Linear Regression - Regression Statistics
Multiple R0.983718731063487
R-squared0.967702541845157
Adjusted R-squared0.963601277317558
F-TEST (value)235.952237494821
F-TEST (DF numerator)8
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation161.334860280945
Sum Squared Residuals1639823.03993793

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983718731063487 \tabularnewline
R-squared & 0.967702541845157 \tabularnewline
Adjusted R-squared & 0.963601277317558 \tabularnewline
F-TEST (value) & 235.952237494821 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 161.334860280945 \tabularnewline
Sum Squared Residuals & 1639823.03993793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983718731063487[/C][/ROW]
[ROW][C]R-squared[/C][C]0.967702541845157[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.963601277317558[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]235.952237494821[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]161.334860280945[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1639823.03993793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&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 R0.983718731063487
R-squared0.967702541845157
Adjusted R-squared0.963601277317558
F-TEST (value)235.952237494821
F-TEST (DF numerator)8
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation161.334860280945
Sum Squared Residuals1639823.03993793






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12502.662715.11121737621-212.451217376205
22466.922525.04678792393-58.1267879239323
32513.172458.2320752674254.937924732581
42443.272462.11526691126-18.8452669112567
52293.412420.37565727693-126.965657276927
62070.832097.57373016300-26.7437301629974
72029.62138.19060900769-108.590609007692
82052.022069.87463273278-17.8546327327776
91864.441920.80090928068-56.3609092806808
101670.071562.24769070221107.822309297789
111810.991653.09716110496157.892838895036
121905.411846.7288466962258.6811533037796
131862.831908.92945799045-46.0994579904478
142014.451830.29235960961184.157640390387
152197.821977.37911028682220.440889713182
162962.343047.09957305382-84.7595730538206
173047.033194.64081645900-147.610816459004
183032.63202.0950925811-169.495092581102
193504.373659.56124244339-155.19124244339
203801.063948.66912136436-147.609121364356
213857.623830.8370085732926.7829914267107
223674.43520.40647381457153.993526185434
233720.983665.1760561996755.803943800331
243844.493684.19213012148160.297869878519
254116.684271.70370286783-155.023702867825
264105.184188.01448667954-82.8344866795445
274435.234574.11338074336-138.883380743356
284296.494296.486939816880.00306018311966483
294202.524205.90718229782-3.38718229781947
304562.844589.98746184695-27.1474618469506
314621.44562.6666073213858.7333926786242
324696.964566.91792302561130.042076974394
334591.274377.72025694618213.549743053825
344356.984195.95741884186161.022581158144
354502.644406.8248236806695.8151763193443
364443.914332.6346476118111.275352388198
374290.894232.5198978671458.3701021328598
384199.754043.98047642233155.769523577667
394138.524018.02623632452120.493763675484
403970.13811.40438107579158.695618924211
413862.273696.03820748375166.231792516251
423701.613505.66041887935195.949581120654
433570.123503.1735846713766.9464153286269
443801.063922.97674055146-121.916740551459
453895.514073.91399629707-178.403996297075
463917.963931.52329307046-13.5632930704627
473813.063910.48894529271-97.4289452927083
483667.033858.52945154261-191.499451542606
493494.173791.34559426302-297.175594263016
503363.993532.12246397146-168.132463971461
513295.323273.3575252000621.96247479994
523277.013309.00481489556-31.9948148955643
533257.163174.0784688250683.0815311749384
543161.693103.5534227967058.1365772033041
553097.312991.46234121307105.847658786932
563061.262850.41400538995210.845994610050
573119.312858.82228946229260.487710537713
583106.223019.8450731878186.3749268121896
593080.582957.54148305236123.038516947640
602981.852812.99259262929168.857407370712
612921.442781.20598750142140.234012498581
622849.272670.49488108333178.775118916669
632756.762535.57989216804221.180107831961
642645.642570.3204224648875.319577535124
652497.842517.17044876234-19.3304487623408
662448.052581.48762006692-133.437620066922
672454.622726.19854089696-271.578540896956
682407.62579.49381485465-171.893814854649
692472.812869.19278930997-396.382789309974
702408.642693.30659962385-284.666599623851
712440.252590.90190935628-150.651909356276
722350.442646.2535309255-295.813530925502

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2502.66 & 2715.11121737621 & -212.451217376205 \tabularnewline
2 & 2466.92 & 2525.04678792393 & -58.1267879239323 \tabularnewline
3 & 2513.17 & 2458.23207526742 & 54.937924732581 \tabularnewline
4 & 2443.27 & 2462.11526691126 & -18.8452669112567 \tabularnewline
5 & 2293.41 & 2420.37565727693 & -126.965657276927 \tabularnewline
6 & 2070.83 & 2097.57373016300 & -26.7437301629974 \tabularnewline
7 & 2029.6 & 2138.19060900769 & -108.590609007692 \tabularnewline
8 & 2052.02 & 2069.87463273278 & -17.8546327327776 \tabularnewline
9 & 1864.44 & 1920.80090928068 & -56.3609092806808 \tabularnewline
10 & 1670.07 & 1562.24769070221 & 107.822309297789 \tabularnewline
11 & 1810.99 & 1653.09716110496 & 157.892838895036 \tabularnewline
12 & 1905.41 & 1846.72884669622 & 58.6811533037796 \tabularnewline
13 & 1862.83 & 1908.92945799045 & -46.0994579904478 \tabularnewline
14 & 2014.45 & 1830.29235960961 & 184.157640390387 \tabularnewline
15 & 2197.82 & 1977.37911028682 & 220.440889713182 \tabularnewline
16 & 2962.34 & 3047.09957305382 & -84.7595730538206 \tabularnewline
17 & 3047.03 & 3194.64081645900 & -147.610816459004 \tabularnewline
18 & 3032.6 & 3202.0950925811 & -169.495092581102 \tabularnewline
19 & 3504.37 & 3659.56124244339 & -155.19124244339 \tabularnewline
20 & 3801.06 & 3948.66912136436 & -147.609121364356 \tabularnewline
21 & 3857.62 & 3830.83700857329 & 26.7829914267107 \tabularnewline
22 & 3674.4 & 3520.40647381457 & 153.993526185434 \tabularnewline
23 & 3720.98 & 3665.17605619967 & 55.803943800331 \tabularnewline
24 & 3844.49 & 3684.19213012148 & 160.297869878519 \tabularnewline
25 & 4116.68 & 4271.70370286783 & -155.023702867825 \tabularnewline
26 & 4105.18 & 4188.01448667954 & -82.8344866795445 \tabularnewline
27 & 4435.23 & 4574.11338074336 & -138.883380743356 \tabularnewline
28 & 4296.49 & 4296.48693981688 & 0.00306018311966483 \tabularnewline
29 & 4202.52 & 4205.90718229782 & -3.38718229781947 \tabularnewline
30 & 4562.84 & 4589.98746184695 & -27.1474618469506 \tabularnewline
31 & 4621.4 & 4562.66660732138 & 58.7333926786242 \tabularnewline
32 & 4696.96 & 4566.91792302561 & 130.042076974394 \tabularnewline
33 & 4591.27 & 4377.72025694618 & 213.549743053825 \tabularnewline
34 & 4356.98 & 4195.95741884186 & 161.022581158144 \tabularnewline
35 & 4502.64 & 4406.82482368066 & 95.8151763193443 \tabularnewline
36 & 4443.91 & 4332.6346476118 & 111.275352388198 \tabularnewline
37 & 4290.89 & 4232.51989786714 & 58.3701021328598 \tabularnewline
38 & 4199.75 & 4043.98047642233 & 155.769523577667 \tabularnewline
39 & 4138.52 & 4018.02623632452 & 120.493763675484 \tabularnewline
40 & 3970.1 & 3811.40438107579 & 158.695618924211 \tabularnewline
41 & 3862.27 & 3696.03820748375 & 166.231792516251 \tabularnewline
42 & 3701.61 & 3505.66041887935 & 195.949581120654 \tabularnewline
43 & 3570.12 & 3503.17358467137 & 66.9464153286269 \tabularnewline
44 & 3801.06 & 3922.97674055146 & -121.916740551459 \tabularnewline
45 & 3895.51 & 4073.91399629707 & -178.403996297075 \tabularnewline
46 & 3917.96 & 3931.52329307046 & -13.5632930704627 \tabularnewline
47 & 3813.06 & 3910.48894529271 & -97.4289452927083 \tabularnewline
48 & 3667.03 & 3858.52945154261 & -191.499451542606 \tabularnewline
49 & 3494.17 & 3791.34559426302 & -297.175594263016 \tabularnewline
50 & 3363.99 & 3532.12246397146 & -168.132463971461 \tabularnewline
51 & 3295.32 & 3273.35752520006 & 21.96247479994 \tabularnewline
52 & 3277.01 & 3309.00481489556 & -31.9948148955643 \tabularnewline
53 & 3257.16 & 3174.07846882506 & 83.0815311749384 \tabularnewline
54 & 3161.69 & 3103.55342279670 & 58.1365772033041 \tabularnewline
55 & 3097.31 & 2991.46234121307 & 105.847658786932 \tabularnewline
56 & 3061.26 & 2850.41400538995 & 210.845994610050 \tabularnewline
57 & 3119.31 & 2858.82228946229 & 260.487710537713 \tabularnewline
58 & 3106.22 & 3019.84507318781 & 86.3749268121896 \tabularnewline
59 & 3080.58 & 2957.54148305236 & 123.038516947640 \tabularnewline
60 & 2981.85 & 2812.99259262929 & 168.857407370712 \tabularnewline
61 & 2921.44 & 2781.20598750142 & 140.234012498581 \tabularnewline
62 & 2849.27 & 2670.49488108333 & 178.775118916669 \tabularnewline
63 & 2756.76 & 2535.57989216804 & 221.180107831961 \tabularnewline
64 & 2645.64 & 2570.32042246488 & 75.319577535124 \tabularnewline
65 & 2497.84 & 2517.17044876234 & -19.3304487623408 \tabularnewline
66 & 2448.05 & 2581.48762006692 & -133.437620066922 \tabularnewline
67 & 2454.62 & 2726.19854089696 & -271.578540896956 \tabularnewline
68 & 2407.6 & 2579.49381485465 & -171.893814854649 \tabularnewline
69 & 2472.81 & 2869.19278930997 & -396.382789309974 \tabularnewline
70 & 2408.64 & 2693.30659962385 & -284.666599623851 \tabularnewline
71 & 2440.25 & 2590.90190935628 & -150.651909356276 \tabularnewline
72 & 2350.44 & 2646.2535309255 & -295.813530925502 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2502.66[/C][C]2715.11121737621[/C][C]-212.451217376205[/C][/ROW]
[ROW][C]2[/C][C]2466.92[/C][C]2525.04678792393[/C][C]-58.1267879239323[/C][/ROW]
[ROW][C]3[/C][C]2513.17[/C][C]2458.23207526742[/C][C]54.937924732581[/C][/ROW]
[ROW][C]4[/C][C]2443.27[/C][C]2462.11526691126[/C][C]-18.8452669112567[/C][/ROW]
[ROW][C]5[/C][C]2293.41[/C][C]2420.37565727693[/C][C]-126.965657276927[/C][/ROW]
[ROW][C]6[/C][C]2070.83[/C][C]2097.57373016300[/C][C]-26.7437301629974[/C][/ROW]
[ROW][C]7[/C][C]2029.6[/C][C]2138.19060900769[/C][C]-108.590609007692[/C][/ROW]
[ROW][C]8[/C][C]2052.02[/C][C]2069.87463273278[/C][C]-17.8546327327776[/C][/ROW]
[ROW][C]9[/C][C]1864.44[/C][C]1920.80090928068[/C][C]-56.3609092806808[/C][/ROW]
[ROW][C]10[/C][C]1670.07[/C][C]1562.24769070221[/C][C]107.822309297789[/C][/ROW]
[ROW][C]11[/C][C]1810.99[/C][C]1653.09716110496[/C][C]157.892838895036[/C][/ROW]
[ROW][C]12[/C][C]1905.41[/C][C]1846.72884669622[/C][C]58.6811533037796[/C][/ROW]
[ROW][C]13[/C][C]1862.83[/C][C]1908.92945799045[/C][C]-46.0994579904478[/C][/ROW]
[ROW][C]14[/C][C]2014.45[/C][C]1830.29235960961[/C][C]184.157640390387[/C][/ROW]
[ROW][C]15[/C][C]2197.82[/C][C]1977.37911028682[/C][C]220.440889713182[/C][/ROW]
[ROW][C]16[/C][C]2962.34[/C][C]3047.09957305382[/C][C]-84.7595730538206[/C][/ROW]
[ROW][C]17[/C][C]3047.03[/C][C]3194.64081645900[/C][C]-147.610816459004[/C][/ROW]
[ROW][C]18[/C][C]3032.6[/C][C]3202.0950925811[/C][C]-169.495092581102[/C][/ROW]
[ROW][C]19[/C][C]3504.37[/C][C]3659.56124244339[/C][C]-155.19124244339[/C][/ROW]
[ROW][C]20[/C][C]3801.06[/C][C]3948.66912136436[/C][C]-147.609121364356[/C][/ROW]
[ROW][C]21[/C][C]3857.62[/C][C]3830.83700857329[/C][C]26.7829914267107[/C][/ROW]
[ROW][C]22[/C][C]3674.4[/C][C]3520.40647381457[/C][C]153.993526185434[/C][/ROW]
[ROW][C]23[/C][C]3720.98[/C][C]3665.17605619967[/C][C]55.803943800331[/C][/ROW]
[ROW][C]24[/C][C]3844.49[/C][C]3684.19213012148[/C][C]160.297869878519[/C][/ROW]
[ROW][C]25[/C][C]4116.68[/C][C]4271.70370286783[/C][C]-155.023702867825[/C][/ROW]
[ROW][C]26[/C][C]4105.18[/C][C]4188.01448667954[/C][C]-82.8344866795445[/C][/ROW]
[ROW][C]27[/C][C]4435.23[/C][C]4574.11338074336[/C][C]-138.883380743356[/C][/ROW]
[ROW][C]28[/C][C]4296.49[/C][C]4296.48693981688[/C][C]0.00306018311966483[/C][/ROW]
[ROW][C]29[/C][C]4202.52[/C][C]4205.90718229782[/C][C]-3.38718229781947[/C][/ROW]
[ROW][C]30[/C][C]4562.84[/C][C]4589.98746184695[/C][C]-27.1474618469506[/C][/ROW]
[ROW][C]31[/C][C]4621.4[/C][C]4562.66660732138[/C][C]58.7333926786242[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]4566.91792302561[/C][C]130.042076974394[/C][/ROW]
[ROW][C]33[/C][C]4591.27[/C][C]4377.72025694618[/C][C]213.549743053825[/C][/ROW]
[ROW][C]34[/C][C]4356.98[/C][C]4195.95741884186[/C][C]161.022581158144[/C][/ROW]
[ROW][C]35[/C][C]4502.64[/C][C]4406.82482368066[/C][C]95.8151763193443[/C][/ROW]
[ROW][C]36[/C][C]4443.91[/C][C]4332.6346476118[/C][C]111.275352388198[/C][/ROW]
[ROW][C]37[/C][C]4290.89[/C][C]4232.51989786714[/C][C]58.3701021328598[/C][/ROW]
[ROW][C]38[/C][C]4199.75[/C][C]4043.98047642233[/C][C]155.769523577667[/C][/ROW]
[ROW][C]39[/C][C]4138.52[/C][C]4018.02623632452[/C][C]120.493763675484[/C][/ROW]
[ROW][C]40[/C][C]3970.1[/C][C]3811.40438107579[/C][C]158.695618924211[/C][/ROW]
[ROW][C]41[/C][C]3862.27[/C][C]3696.03820748375[/C][C]166.231792516251[/C][/ROW]
[ROW][C]42[/C][C]3701.61[/C][C]3505.66041887935[/C][C]195.949581120654[/C][/ROW]
[ROW][C]43[/C][C]3570.12[/C][C]3503.17358467137[/C][C]66.9464153286269[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]3922.97674055146[/C][C]-121.916740551459[/C][/ROW]
[ROW][C]45[/C][C]3895.51[/C][C]4073.91399629707[/C][C]-178.403996297075[/C][/ROW]
[ROW][C]46[/C][C]3917.96[/C][C]3931.52329307046[/C][C]-13.5632930704627[/C][/ROW]
[ROW][C]47[/C][C]3813.06[/C][C]3910.48894529271[/C][C]-97.4289452927083[/C][/ROW]
[ROW][C]48[/C][C]3667.03[/C][C]3858.52945154261[/C][C]-191.499451542606[/C][/ROW]
[ROW][C]49[/C][C]3494.17[/C][C]3791.34559426302[/C][C]-297.175594263016[/C][/ROW]
[ROW][C]50[/C][C]3363.99[/C][C]3532.12246397146[/C][C]-168.132463971461[/C][/ROW]
[ROW][C]51[/C][C]3295.32[/C][C]3273.35752520006[/C][C]21.96247479994[/C][/ROW]
[ROW][C]52[/C][C]3277.01[/C][C]3309.00481489556[/C][C]-31.9948148955643[/C][/ROW]
[ROW][C]53[/C][C]3257.16[/C][C]3174.07846882506[/C][C]83.0815311749384[/C][/ROW]
[ROW][C]54[/C][C]3161.69[/C][C]3103.55342279670[/C][C]58.1365772033041[/C][/ROW]
[ROW][C]55[/C][C]3097.31[/C][C]2991.46234121307[/C][C]105.847658786932[/C][/ROW]
[ROW][C]56[/C][C]3061.26[/C][C]2850.41400538995[/C][C]210.845994610050[/C][/ROW]
[ROW][C]57[/C][C]3119.31[/C][C]2858.82228946229[/C][C]260.487710537713[/C][/ROW]
[ROW][C]58[/C][C]3106.22[/C][C]3019.84507318781[/C][C]86.3749268121896[/C][/ROW]
[ROW][C]59[/C][C]3080.58[/C][C]2957.54148305236[/C][C]123.038516947640[/C][/ROW]
[ROW][C]60[/C][C]2981.85[/C][C]2812.99259262929[/C][C]168.857407370712[/C][/ROW]
[ROW][C]61[/C][C]2921.44[/C][C]2781.20598750142[/C][C]140.234012498581[/C][/ROW]
[ROW][C]62[/C][C]2849.27[/C][C]2670.49488108333[/C][C]178.775118916669[/C][/ROW]
[ROW][C]63[/C][C]2756.76[/C][C]2535.57989216804[/C][C]221.180107831961[/C][/ROW]
[ROW][C]64[/C][C]2645.64[/C][C]2570.32042246488[/C][C]75.319577535124[/C][/ROW]
[ROW][C]65[/C][C]2497.84[/C][C]2517.17044876234[/C][C]-19.3304487623408[/C][/ROW]
[ROW][C]66[/C][C]2448.05[/C][C]2581.48762006692[/C][C]-133.437620066922[/C][/ROW]
[ROW][C]67[/C][C]2454.62[/C][C]2726.19854089696[/C][C]-271.578540896956[/C][/ROW]
[ROW][C]68[/C][C]2407.6[/C][C]2579.49381485465[/C][C]-171.893814854649[/C][/ROW]
[ROW][C]69[/C][C]2472.81[/C][C]2869.19278930997[/C][C]-396.382789309974[/C][/ROW]
[ROW][C]70[/C][C]2408.64[/C][C]2693.30659962385[/C][C]-284.666599623851[/C][/ROW]
[ROW][C]71[/C][C]2440.25[/C][C]2590.90190935628[/C][C]-150.651909356276[/C][/ROW]
[ROW][C]72[/C][C]2350.44[/C][C]2646.2535309255[/C][C]-295.813530925502[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&T=4

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QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12502.662715.11121737621-212.451217376205
22466.922525.04678792393-58.1267879239323
32513.172458.2320752674254.937924732581
42443.272462.11526691126-18.8452669112567
52293.412420.37565727693-126.965657276927
62070.832097.57373016300-26.7437301629974
72029.62138.19060900769-108.590609007692
82052.022069.87463273278-17.8546327327776
91864.441920.80090928068-56.3609092806808
101670.071562.24769070221107.822309297789
111810.991653.09716110496157.892838895036
121905.411846.7288466962258.6811533037796
131862.831908.92945799045-46.0994579904478
142014.451830.29235960961184.157640390387
152197.821977.37911028682220.440889713182
162962.343047.09957305382-84.7595730538206
173047.033194.64081645900-147.610816459004
183032.63202.0950925811-169.495092581102
193504.373659.56124244339-155.19124244339
203801.063948.66912136436-147.609121364356
213857.623830.8370085732926.7829914267107
223674.43520.40647381457153.993526185434
233720.983665.1760561996755.803943800331
243844.493684.19213012148160.297869878519
254116.684271.70370286783-155.023702867825
264105.184188.01448667954-82.8344866795445
274435.234574.11338074336-138.883380743356
284296.494296.486939816880.00306018311966483
294202.524205.90718229782-3.38718229781947
304562.844589.98746184695-27.1474618469506
314621.44562.6666073213858.7333926786242
324696.964566.91792302561130.042076974394
334591.274377.72025694618213.549743053825
344356.984195.95741884186161.022581158144
354502.644406.8248236806695.8151763193443
364443.914332.6346476118111.275352388198
374290.894232.5198978671458.3701021328598
384199.754043.98047642233155.769523577667
394138.524018.02623632452120.493763675484
403970.13811.40438107579158.695618924211
413862.273696.03820748375166.231792516251
423701.613505.66041887935195.949581120654
433570.123503.1735846713766.9464153286269
443801.063922.97674055146-121.916740551459
453895.514073.91399629707-178.403996297075
463917.963931.52329307046-13.5632930704627
473813.063910.48894529271-97.4289452927083
483667.033858.52945154261-191.499451542606
493494.173791.34559426302-297.175594263016
503363.993532.12246397146-168.132463971461
513295.323273.3575252000621.96247479994
523277.013309.00481489556-31.9948148955643
533257.163174.0784688250683.0815311749384
543161.693103.5534227967058.1365772033041
553097.312991.46234121307105.847658786932
563061.262850.41400538995210.845994610050
573119.312858.82228946229260.487710537713
583106.223019.8450731878186.3749268121896
593080.582957.54148305236123.038516947640
602981.852812.99259262929168.857407370712
612921.442781.20598750142140.234012498581
622849.272670.49488108333178.775118916669
632756.762535.57989216804221.180107831961
642645.642570.3204224648875.319577535124
652497.842517.17044876234-19.3304487623408
662448.052581.48762006692-133.437620066922
672454.622726.19854089696-271.578540896956
682407.62579.49381485465-171.893814854649
692472.812869.19278930997-396.382789309974
702408.642693.30659962385-284.666599623851
712440.252590.90190935628-150.651909356276
722350.442646.2535309255-295.813530925502






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.02957058763028050.05914117526056090.97042941236972
130.007178449745215120.01435689949043020.992821550254785
140.04353591615313210.08707183230626420.956464083846868
150.02680576580087190.05361153160174380.973194234199128
160.01889682970212260.03779365940424520.981103170297877
170.008807854480455810.01761570896091160.991192145519544
180.003635526764026880.007271053528053770.996364473235973
190.01268343640792230.02536687281584460.987316563592078
200.00983129052361350.0196625810472270.990168709476386
210.00854232742104270.01708465484208540.991457672578957
220.009641418812425960.01928283762485190.990358581187574
230.01017254789748640.02034509579497270.989827452102514
240.04861824092020050.09723648184040110.9513817590798
250.03580835900164060.07161671800328110.96419164099836
260.05716900724943850.1143380144988770.942830992750561
270.0661242791144960.1322485582289920.933875720885504
280.1151466150301040.2302932300602080.884853384969896
290.116356164635030.232712329270060.88364383536497
300.1169262489683410.2338524979366810.88307375103166
310.1010269859073480.2020539718146970.898973014092652
320.09069357256846020.1813871451369200.90930642743154
330.08116864401757220.1623372880351440.918831355982428
340.0665098192092350.133019638418470.933490180790765
350.046084175673950.09216835134790.95391582432605
360.03938033648571960.0787606729714390.96061966351428
370.02579895221926750.0515979044385350.974201047780733
380.02535748311256750.0507149662251350.974642516887432
390.02550226711009530.05100453422019060.974497732889905
400.03062572442135810.06125144884271630.969374275578642
410.02968455358151200.05936910716302390.970315446418488
420.02170749823477630.04341499646955270.978292501765224
430.02623629030435840.05247258060871690.973763709695642
440.05090254466562830.1018050893312570.949097455334372
450.06599677766061050.1319935553212210.93400322233939
460.3237639539983650.6475279079967310.676236046001635
470.447568341059660.895136682119320.55243165894034
480.4181573374235330.8363146748470660.581842662576467
490.3834160010527390.7668320021054770.616583998947261
500.5826365069250180.8347269861499650.417363493074982
510.7208321518559750.5583356962880510.279167848144025
520.6845923711637640.6308152576724720.315407628836236
530.6253804182853280.7492391634293430.374619581714672
540.5589577494363680.8820845011272650.441042250563632
550.8641475987449660.2717048025100680.135852401255034
560.790143908779890.4197121824402210.209856091220110
570.8018654677463790.3962690645072420.198134532253621
580.7359396619795970.5281206760408050.264060338020403
590.6231040862391450.7537918275217090.376895913760855
600.4730032617529360.9460065235058720.526996738247064

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0295705876302805 & 0.0591411752605609 & 0.97042941236972 \tabularnewline
13 & 0.00717844974521512 & 0.0143568994904302 & 0.992821550254785 \tabularnewline
14 & 0.0435359161531321 & 0.0870718323062642 & 0.956464083846868 \tabularnewline
15 & 0.0268057658008719 & 0.0536115316017438 & 0.973194234199128 \tabularnewline
16 & 0.0188968297021226 & 0.0377936594042452 & 0.981103170297877 \tabularnewline
17 & 0.00880785448045581 & 0.0176157089609116 & 0.991192145519544 \tabularnewline
18 & 0.00363552676402688 & 0.00727105352805377 & 0.996364473235973 \tabularnewline
19 & 0.0126834364079223 & 0.0253668728158446 & 0.987316563592078 \tabularnewline
20 & 0.0098312905236135 & 0.019662581047227 & 0.990168709476386 \tabularnewline
21 & 0.0085423274210427 & 0.0170846548420854 & 0.991457672578957 \tabularnewline
22 & 0.00964141881242596 & 0.0192828376248519 & 0.990358581187574 \tabularnewline
23 & 0.0101725478974864 & 0.0203450957949727 & 0.989827452102514 \tabularnewline
24 & 0.0486182409202005 & 0.0972364818404011 & 0.9513817590798 \tabularnewline
25 & 0.0358083590016406 & 0.0716167180032811 & 0.96419164099836 \tabularnewline
26 & 0.0571690072494385 & 0.114338014498877 & 0.942830992750561 \tabularnewline
27 & 0.066124279114496 & 0.132248558228992 & 0.933875720885504 \tabularnewline
28 & 0.115146615030104 & 0.230293230060208 & 0.884853384969896 \tabularnewline
29 & 0.11635616463503 & 0.23271232927006 & 0.88364383536497 \tabularnewline
30 & 0.116926248968341 & 0.233852497936681 & 0.88307375103166 \tabularnewline
31 & 0.101026985907348 & 0.202053971814697 & 0.898973014092652 \tabularnewline
32 & 0.0906935725684602 & 0.181387145136920 & 0.90930642743154 \tabularnewline
33 & 0.0811686440175722 & 0.162337288035144 & 0.918831355982428 \tabularnewline
34 & 0.066509819209235 & 0.13301963841847 & 0.933490180790765 \tabularnewline
35 & 0.04608417567395 & 0.0921683513479 & 0.95391582432605 \tabularnewline
36 & 0.0393803364857196 & 0.078760672971439 & 0.96061966351428 \tabularnewline
37 & 0.0257989522192675 & 0.051597904438535 & 0.974201047780733 \tabularnewline
38 & 0.0253574831125675 & 0.050714966225135 & 0.974642516887432 \tabularnewline
39 & 0.0255022671100953 & 0.0510045342201906 & 0.974497732889905 \tabularnewline
40 & 0.0306257244213581 & 0.0612514488427163 & 0.969374275578642 \tabularnewline
41 & 0.0296845535815120 & 0.0593691071630239 & 0.970315446418488 \tabularnewline
42 & 0.0217074982347763 & 0.0434149964695527 & 0.978292501765224 \tabularnewline
43 & 0.0262362903043584 & 0.0524725806087169 & 0.973763709695642 \tabularnewline
44 & 0.0509025446656283 & 0.101805089331257 & 0.949097455334372 \tabularnewline
45 & 0.0659967776606105 & 0.131993555321221 & 0.93400322233939 \tabularnewline
46 & 0.323763953998365 & 0.647527907996731 & 0.676236046001635 \tabularnewline
47 & 0.44756834105966 & 0.89513668211932 & 0.55243165894034 \tabularnewline
48 & 0.418157337423533 & 0.836314674847066 & 0.581842662576467 \tabularnewline
49 & 0.383416001052739 & 0.766832002105477 & 0.616583998947261 \tabularnewline
50 & 0.582636506925018 & 0.834726986149965 & 0.417363493074982 \tabularnewline
51 & 0.720832151855975 & 0.558335696288051 & 0.279167848144025 \tabularnewline
52 & 0.684592371163764 & 0.630815257672472 & 0.315407628836236 \tabularnewline
53 & 0.625380418285328 & 0.749239163429343 & 0.374619581714672 \tabularnewline
54 & 0.558957749436368 & 0.882084501127265 & 0.441042250563632 \tabularnewline
55 & 0.864147598744966 & 0.271704802510068 & 0.135852401255034 \tabularnewline
56 & 0.79014390877989 & 0.419712182440221 & 0.209856091220110 \tabularnewline
57 & 0.801865467746379 & 0.396269064507242 & 0.198134532253621 \tabularnewline
58 & 0.735939661979597 & 0.528120676040805 & 0.264060338020403 \tabularnewline
59 & 0.623104086239145 & 0.753791827521709 & 0.376895913760855 \tabularnewline
60 & 0.473003261752936 & 0.946006523505872 & 0.526996738247064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.0295705876302805[/C][C]0.0591411752605609[/C][C]0.97042941236972[/C][/ROW]
[ROW][C]13[/C][C]0.00717844974521512[/C][C]0.0143568994904302[/C][C]0.992821550254785[/C][/ROW]
[ROW][C]14[/C][C]0.0435359161531321[/C][C]0.0870718323062642[/C][C]0.956464083846868[/C][/ROW]
[ROW][C]15[/C][C]0.0268057658008719[/C][C]0.0536115316017438[/C][C]0.973194234199128[/C][/ROW]
[ROW][C]16[/C][C]0.0188968297021226[/C][C]0.0377936594042452[/C][C]0.981103170297877[/C][/ROW]
[ROW][C]17[/C][C]0.00880785448045581[/C][C]0.0176157089609116[/C][C]0.991192145519544[/C][/ROW]
[ROW][C]18[/C][C]0.00363552676402688[/C][C]0.00727105352805377[/C][C]0.996364473235973[/C][/ROW]
[ROW][C]19[/C][C]0.0126834364079223[/C][C]0.0253668728158446[/C][C]0.987316563592078[/C][/ROW]
[ROW][C]20[/C][C]0.0098312905236135[/C][C]0.019662581047227[/C][C]0.990168709476386[/C][/ROW]
[ROW][C]21[/C][C]0.0085423274210427[/C][C]0.0170846548420854[/C][C]0.991457672578957[/C][/ROW]
[ROW][C]22[/C][C]0.00964141881242596[/C][C]0.0192828376248519[/C][C]0.990358581187574[/C][/ROW]
[ROW][C]23[/C][C]0.0101725478974864[/C][C]0.0203450957949727[/C][C]0.989827452102514[/C][/ROW]
[ROW][C]24[/C][C]0.0486182409202005[/C][C]0.0972364818404011[/C][C]0.9513817590798[/C][/ROW]
[ROW][C]25[/C][C]0.0358083590016406[/C][C]0.0716167180032811[/C][C]0.96419164099836[/C][/ROW]
[ROW][C]26[/C][C]0.0571690072494385[/C][C]0.114338014498877[/C][C]0.942830992750561[/C][/ROW]
[ROW][C]27[/C][C]0.066124279114496[/C][C]0.132248558228992[/C][C]0.933875720885504[/C][/ROW]
[ROW][C]28[/C][C]0.115146615030104[/C][C]0.230293230060208[/C][C]0.884853384969896[/C][/ROW]
[ROW][C]29[/C][C]0.11635616463503[/C][C]0.23271232927006[/C][C]0.88364383536497[/C][/ROW]
[ROW][C]30[/C][C]0.116926248968341[/C][C]0.233852497936681[/C][C]0.88307375103166[/C][/ROW]
[ROW][C]31[/C][C]0.101026985907348[/C][C]0.202053971814697[/C][C]0.898973014092652[/C][/ROW]
[ROW][C]32[/C][C]0.0906935725684602[/C][C]0.181387145136920[/C][C]0.90930642743154[/C][/ROW]
[ROW][C]33[/C][C]0.0811686440175722[/C][C]0.162337288035144[/C][C]0.918831355982428[/C][/ROW]
[ROW][C]34[/C][C]0.066509819209235[/C][C]0.13301963841847[/C][C]0.933490180790765[/C][/ROW]
[ROW][C]35[/C][C]0.04608417567395[/C][C]0.0921683513479[/C][C]0.95391582432605[/C][/ROW]
[ROW][C]36[/C][C]0.0393803364857196[/C][C]0.078760672971439[/C][C]0.96061966351428[/C][/ROW]
[ROW][C]37[/C][C]0.0257989522192675[/C][C]0.051597904438535[/C][C]0.974201047780733[/C][/ROW]
[ROW][C]38[/C][C]0.0253574831125675[/C][C]0.050714966225135[/C][C]0.974642516887432[/C][/ROW]
[ROW][C]39[/C][C]0.0255022671100953[/C][C]0.0510045342201906[/C][C]0.974497732889905[/C][/ROW]
[ROW][C]40[/C][C]0.0306257244213581[/C][C]0.0612514488427163[/C][C]0.969374275578642[/C][/ROW]
[ROW][C]41[/C][C]0.0296845535815120[/C][C]0.0593691071630239[/C][C]0.970315446418488[/C][/ROW]
[ROW][C]42[/C][C]0.0217074982347763[/C][C]0.0434149964695527[/C][C]0.978292501765224[/C][/ROW]
[ROW][C]43[/C][C]0.0262362903043584[/C][C]0.0524725806087169[/C][C]0.973763709695642[/C][/ROW]
[ROW][C]44[/C][C]0.0509025446656283[/C][C]0.101805089331257[/C][C]0.949097455334372[/C][/ROW]
[ROW][C]45[/C][C]0.0659967776606105[/C][C]0.131993555321221[/C][C]0.93400322233939[/C][/ROW]
[ROW][C]46[/C][C]0.323763953998365[/C][C]0.647527907996731[/C][C]0.676236046001635[/C][/ROW]
[ROW][C]47[/C][C]0.44756834105966[/C][C]0.89513668211932[/C][C]0.55243165894034[/C][/ROW]
[ROW][C]48[/C][C]0.418157337423533[/C][C]0.836314674847066[/C][C]0.581842662576467[/C][/ROW]
[ROW][C]49[/C][C]0.383416001052739[/C][C]0.766832002105477[/C][C]0.616583998947261[/C][/ROW]
[ROW][C]50[/C][C]0.582636506925018[/C][C]0.834726986149965[/C][C]0.417363493074982[/C][/ROW]
[ROW][C]51[/C][C]0.720832151855975[/C][C]0.558335696288051[/C][C]0.279167848144025[/C][/ROW]
[ROW][C]52[/C][C]0.684592371163764[/C][C]0.630815257672472[/C][C]0.315407628836236[/C][/ROW]
[ROW][C]53[/C][C]0.625380418285328[/C][C]0.749239163429343[/C][C]0.374619581714672[/C][/ROW]
[ROW][C]54[/C][C]0.558957749436368[/C][C]0.882084501127265[/C][C]0.441042250563632[/C][/ROW]
[ROW][C]55[/C][C]0.864147598744966[/C][C]0.271704802510068[/C][C]0.135852401255034[/C][/ROW]
[ROW][C]56[/C][C]0.79014390877989[/C][C]0.419712182440221[/C][C]0.209856091220110[/C][/ROW]
[ROW][C]57[/C][C]0.801865467746379[/C][C]0.396269064507242[/C][C]0.198134532253621[/C][/ROW]
[ROW][C]58[/C][C]0.735939661979597[/C][C]0.528120676040805[/C][C]0.264060338020403[/C][/ROW]
[ROW][C]59[/C][C]0.623104086239145[/C][C]0.753791827521709[/C][C]0.376895913760855[/C][/ROW]
[ROW][C]60[/C][C]0.473003261752936[/C][C]0.946006523505872[/C][C]0.526996738247064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.02957058763028050.05914117526056090.97042941236972
130.007178449745215120.01435689949043020.992821550254785
140.04353591615313210.08707183230626420.956464083846868
150.02680576580087190.05361153160174380.973194234199128
160.01889682970212260.03779365940424520.981103170297877
170.008807854480455810.01761570896091160.991192145519544
180.003635526764026880.007271053528053770.996364473235973
190.01268343640792230.02536687281584460.987316563592078
200.00983129052361350.0196625810472270.990168709476386
210.00854232742104270.01708465484208540.991457672578957
220.009641418812425960.01928283762485190.990358581187574
230.01017254789748640.02034509579497270.989827452102514
240.04861824092020050.09723648184040110.9513817590798
250.03580835900164060.07161671800328110.96419164099836
260.05716900724943850.1143380144988770.942830992750561
270.0661242791144960.1322485582289920.933875720885504
280.1151466150301040.2302932300602080.884853384969896
290.116356164635030.232712329270060.88364383536497
300.1169262489683410.2338524979366810.88307375103166
310.1010269859073480.2020539718146970.898973014092652
320.09069357256846020.1813871451369200.90930642743154
330.08116864401757220.1623372880351440.918831355982428
340.0665098192092350.133019638418470.933490180790765
350.046084175673950.09216835134790.95391582432605
360.03938033648571960.0787606729714390.96061966351428
370.02579895221926750.0515979044385350.974201047780733
380.02535748311256750.0507149662251350.974642516887432
390.02550226711009530.05100453422019060.974497732889905
400.03062572442135810.06125144884271630.969374275578642
410.02968455358151200.05936910716302390.970315446418488
420.02170749823477630.04341499646955270.978292501765224
430.02623629030435840.05247258060871690.973763709695642
440.05090254466562830.1018050893312570.949097455334372
450.06599677766061050.1319935553212210.93400322233939
460.3237639539983650.6475279079967310.676236046001635
470.447568341059660.895136682119320.55243165894034
480.4181573374235330.8363146748470660.581842662576467
490.3834160010527390.7668320021054770.616583998947261
500.5826365069250180.8347269861499650.417363493074982
510.7208321518559750.5583356962880510.279167848144025
520.6845923711637640.6308152576724720.315407628836236
530.6253804182853280.7492391634293430.374619581714672
540.5589577494363680.8820845011272650.441042250563632
550.8641475987449660.2717048025100680.135852401255034
560.790143908779890.4197121824402210.209856091220110
570.8018654677463790.3962690645072420.198134532253621
580.7359396619795970.5281206760408050.264060338020403
590.6231040862391450.7537918275217090.376895913760855
600.4730032617529360.9460065235058720.526996738247064






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0204081632653061NOK
5% type I error level100.204081632653061NOK
10% type I error level230.469387755102041NOK

\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 & 1 & 0.0204081632653061 & NOK \tabularnewline
5% type I error level & 10 & 0.204081632653061 & NOK \tabularnewline
10% type I error level & 23 & 0.469387755102041 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104183&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]1[/C][C]0.0204081632653061[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.204081632653061[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.469387755102041[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104183&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104183&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 level10.0204081632653061NOK
5% type I error level100.204081632653061NOK
10% type I error level230.469387755102041NOK


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')
}