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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 22 Dec 2011 07:17:38 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t1324556318k1pkwmrkn1xaqc3.htm/, Retrieved Fri, 03 May 2024 05:54:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159358, Retrieved Fri, 03 May 2024 05:54:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Multiple Regression] [Multiple Regressi...] [2010-11-29 14:00:19] [b9eaf9df71639055b3e2389f5099ca2c]
-   P   [Multiple Regression] [Minitutorial Mult...] [2010-11-30 14:04:11] [b9eaf9df71639055b3e2389f5099ca2c]
- R  D      [Multiple Regression] [Multiple Regressi...] [2011-12-22 12:17:38] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9/06/2002	169498	2,47	0,97	0,80	5,06
16/06/2002	125451	2,51	0,95	0,80	5,07
23/06/2002	140449	2,51	0,96	0,80	5,04
30/06/2002	141653	2,51	0,97	0,81	5,08
7/07/2002	136394	2,49	0,96	0,81	5,15
14/07/2002	167588	2,52	0,96	0,83	5,16
21/07/2002	191807	2,51	0,94	0,79	5,09
28/07/2002	149736	2,54	0,95	0,80	5,14
4/08/2002	196066	2,53	0,95	0,81	5,17
11/08/2002	239155	2,55	0,95	0,80	5,13
18/08/2002	178421	2,50	0,95	0,81	5,19
25/08/2002	139871	2,59	0,97	0,81	5,17
1/09/2002	118159	2,56	0,97	0,82	5,17
8/09/2002	109763	2,59	0,95	0,81	5,21
15/09/2002	97415	2,58	0,96	0,82	5,21
22/09/2002	119190	2,62	0,96	0,82	5,23
29/09/2002	97903	2,59	0,94	0,80	5,17
6/10/2002	96953	2,58	0,96	0,79	5,16
13/10/2002	87888	2,57	0,98	0,79	5,15
20/10/2002	84637	2,57	0,97	0,80	5,12
27/10/2002	90549	2,55	0,96	0,80	5,12
3/11/2002	95680	2,51	0,95	0,80	5,10
10/11/2002	99371	2,50	0,96	0,80	5,13
17/11/2002	79984	2,59	0,96	0,80	5,14
24/11/2002	86752	2,63	0,97	0,80	5,16
1/12/2002	85733	2,63	0,96	0,80	5,17
8/12/2002	84906	2,61	0,95	0,78	5,15
15/12/2002	78356	2,64	0,95	0,78	5,12
22/12/2002	108895	2,67	0,94	0,76	5,08
29/12/2002	101768	2,63	0,94	0,77	5,07
5/01/2003	73285	2,58	0,98	0,82	5,16
12/01/2003	65724	2,56	0,93	0,81	5,14
19/01/2003	67457	2,57	0,93	0,81	5,13
26/01/2003	67203	2,55	0,96	0,80	5,11
2/02/2003	69273	2,58	0,97	0,79	5,12
9/02/2003	80807	2,50	0,97	0,81	5,09
16/02/2003	75129	2,56	0,95	0,81	5,10
23/02/2003	74991	2,62	0,95	0,81	4,95
2/03/2003	68157	2,71	0,96	0,81	5,11
9/03/2003	73858	2,74	0,98	0,82	5,11
16/03/2003	71349	2,76	0,98	0,81	5,10
23/03/2003	85634	2,66	0,97	0,80	5,11
30/03/2003	91624	2,61	0,98	0,83	5,12
6/04/2003	116014	2,68	0,98	0,81	5,09
13/04/2003	120033	2,70	0,99	0,83	5,10
20/04/2003	108651	2,70	0,99	0,84	5,06
27/04/2003	105378	2,72	0,97	0,84	5,03
4/05/2003	138939	2,77	0,98	0,85	5,05
11/05/2003	132974	2,76	0,97	0,85	5,04
18/05/2003	135277	2,72	0,97	0,86	5,02
25/05/2003	152741	2,69	0,97	0,85	4,97
1/06/2003	158417	2,70	0,98	0,87	4,91
8/06/2003	157460	2,69	0,97	0,86	4,91
15/06/2003	193997	2,66	0,97	0,87	4,98
22/06/2003	154089	2,74	0,98	0,87	4,98
29/06/2003	147570	2,76	0,98	0,86	4,97
6/07/2003	162924	2,79	0,95	0,87	4,97
13/07/2003	153629	2,78	0,97	0,88	4,90
20/07/2003	155907	2,80	0,97	0,87	4,91
27/07/2003	197675	2,78	0,97	0,87	4,88
3/08/2003	250708	2,76	0,97	0,86	4,86
10/08/2003	266652	2,73	0,98	0,86	4,87
17/08/2003	209842	2,72	0,98	0,88	4,86
24/08/2003	165826	2,73	0,98	0,88	4,89
31/08/2003	137152	2,74	0,96	0,87	4,90
7/09/2003	150581	2,72	0,98	0,88	4,88
14/09/2003	145973	2,71	1,00	0,89	4,85
21/09/2003	126532	2,66	1,01	0,89	4,85
28/09/2003	115437	2,68	1,02	0,88	4,84
5/10/2003	119526	2,67	1,01	0,88	4,91
12/10/2003	110856	2,68	1,01	0,88	4,94
19/10/2003	97243	2,67	1,02	0,88	4,92
26/10/2003	103876	2,71	1,01	0,87	4,93
2/11/2003	116370	2,69	1,01	0,87	4,97
9/11/2003	109616	2,64	1,01	0,86	4,89
16/11/2003	98365	2,66	1,02	0,88	4,88
23/11/2003	90440	2,70	1,02	0,87	4,93
30/11/2003	88899	2,69	1,02	0,86	4,94
7/12/2003	92358	2,71	1,01	0,85	4,99
14/12/2003	88394	2,74	1,01	0,86	5,00
21/12/2003	98219	2,78	0,99	0,84	5,02
28/12/2003	113546	2,79	1,00	0,85	5,06
4/01/2004	107168	2,75	1,01	0,88	5,01
11/01/2004	77540	2,69	0,99	0,88	5,02
18/01/2004	74944	2,69	1,00	0,89	4,97
25/01/2004	75641	2,69	1,02	0,88	4,96
1/02/2004	75910	2,72	1,01	0,88	4,95
8/02/2004	87384	2,69	1,01	0,88	4,92
15/02/2004	84615	2,70	1,01	0,89	4,88
22/02/2004	80420	2,68	1,03	0,89	4,86
29/02/2004	80784	2,70	1,02	0,89	4,94
7/03/2004	79933	2,72	1,02	0,88	4,83
14/03/2004	82118	2,70	1,03	0,89	4,95
21/03/2004	91420	2,66	1,03	0,89	4,95
28/03/2004	112426	2,68	1,02	0,89	4,94
4/04/2004	114528	2,65	1,02	0,89	4,93
11/04/2004	131025	2,69	1,02	0,90	4,97
18/04/2004	116460	2,66	1,02	0,88	4,95
25/04/2004	111258	2,69	1,03	0,90	4,92
2/05/2004	155318	2,69	1,02	0,88	4,82
9/05/2004	155078	2,65	1,02	0,90	4,82
16/05/2004	134794	2,66	1,02	0,89	4,84
23/05/2004	139985	2,63	1,03	0,89	4,83
30/05/2004	198778	2,65	1,02	0,88	4,79
6/06/2004	172436	2,60	1,02	0,89	4,81
13/06/2004	169585	2,57	1,02	0,91	4,85
20/06/2004	203702	2,65	1,02	0,91	4,84
27/06/2004	282392	2,69	1,02	0,90	4,82
4/07/2004	220658	2,71	1,00	0,93	4,92
11/07/2004	194472	2,72	1,04	0,94	4,92
18/07/2004	269246	2,73	1,04	0,95	4,90
25/07/2004	215340	2,72	1,03	0,95	4,91
1/08/2004	218319	2,73	1,02	0,93	4,85
8/08/2004	195724	2,72	1,04	0,95	4,86
15/08/2004	174614	2,70	1,05	0,95	4,88
22/08/2004	172085	2,72	1,03	0,94	4,85
29/08/2004	152347	2,70	0,99	0,92	4,91
5/09/2004	189615	2,72	1,03	0,94	4,89
12/09/2004	173804	2,70	1,08	0,95	4,92
19/09/2004	145683	2,65	1,09	0,97	4,82
26/09/2004	133550	2,66	1,08	0,96	4,82
3/10/2004	121156	2,69	1,05	0,92	4,87
10/10/2004	112040	2,70	1,06	0,94	4,88
17/10/2004	120767	2,71	1,04	0,94	4,90
24/10/2004	127019	2,69	1,06	0,92	4,88
31/10/2004	136295	2,72	1,06	0,91	4,89
7/11/2004	113425	2,71	1,07	0,93	4,88
14/11/2004	107815	2,71	1,08	0,93	4,87
21/11/2004	100298	2,74	1,08	0,94	4,85
28/11/2004	97048	2,82	1,05	0,92	4,87
5/12/2004	98750	2,76	1,04	0,91	4,88
12/12/2004	98235	2,77	1,04	0,91	4,87
19/12/2004	101254	2,77	1,04	0,90	4,93
26/12/2004	139589	2,81	1,04	0,89	4,93
2/01/2005	134921	2,77	1,06	0,91	4,74
9/01/2005	80355	2,76	1,08	0,93	4,77
16/01/2005	80396	2,73	1,08	0,94	4,81
23/01/2005	82183	2,72	1,08	0,93	4,82
30/01/2005	79709	2,73	1,07	0,91	4,79
6/02/2005	90781	2,71	1,06	0,92	4,75

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
QBEFRU[t] = + 1212426.92642868 -5203328.63805973Tijd[t] -104930.121917316PBEFRU[t] -946228.035093211PSOCOLA[t] + 1102337.83746774PSOORA[t] -154284.674992912`PSTIM `[t] -513.642229153251t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEFRU[t] =  +  1212426.92642868 -5203328.63805973Tijd[t] -104930.121917316PBEFRU[t] -946228.035093211PSOCOLA[t] +  1102337.83746774PSOORA[t] -154284.674992912`PSTIM
`[t] -513.642229153251t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEFRU[t] =  +  1212426.92642868 -5203328.63805973Tijd[t] -104930.121917316PBEFRU[t] -946228.035093211PSOCOLA[t] +  1102337.83746774PSOORA[t] -154284.674992912`PSTIM
`[t] -513.642229153251t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&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
QBEFRU[t] = + 1212426.92642868 -5203328.63805973Tijd[t] -104930.121917316PBEFRU[t] -946228.035093211PSOCOLA[t] + 1102337.83746774PSOORA[t] -154284.674992912`PSTIM `[t] -513.642229153251t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1212426.92642868386282.3537573.13870.0020910.001045
Tijd-5203328.638059731177656.970009-4.41842e-051e-05
PBEFRU-104930.12191731652493.875255-1.99890.0476570.023829
PSOCOLA-946228.035093211200072.867688-4.72946e-063e-06
PSOORA1102337.83746774162351.4823016.789800
`PSTIM `-154284.67499291248500.393939-3.18110.0018260.000913
t-513.642229153251265.308617-1.9360.0549870.027493

\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) & 1212426.92642868 & 386282.353757 & 3.1387 & 0.002091 & 0.001045 \tabularnewline
Tijd & -5203328.63805973 & 1177656.970009 & -4.4184 & 2e-05 & 1e-05 \tabularnewline
PBEFRU & -104930.121917316 & 52493.875255 & -1.9989 & 0.047657 & 0.023829 \tabularnewline
PSOCOLA & -946228.035093211 & 200072.867688 & -4.7294 & 6e-06 & 3e-06 \tabularnewline
PSOORA & 1102337.83746774 & 162351.482301 & 6.7898 & 0 & 0 \tabularnewline
`PSTIM
` & -154284.674992912 & 48500.393939 & -3.1811 & 0.001826 & 0.000913 \tabularnewline
t & -513.642229153251 & 265.308617 & -1.936 & 0.054987 & 0.027493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&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]1212426.92642868[/C][C]386282.353757[/C][C]3.1387[/C][C]0.002091[/C][C]0.001045[/C][/ROW]
[ROW][C]Tijd[/C][C]-5203328.63805973[/C][C]1177656.970009[/C][C]-4.4184[/C][C]2e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]PBEFRU[/C][C]-104930.121917316[/C][C]52493.875255[/C][C]-1.9989[/C][C]0.047657[/C][C]0.023829[/C][/ROW]
[ROW][C]PSOCOLA[/C][C]-946228.035093211[/C][C]200072.867688[/C][C]-4.7294[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]PSOORA[/C][C]1102337.83746774[/C][C]162351.482301[/C][C]6.7898[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`PSTIM
`[/C][C]-154284.674992912[/C][C]48500.393939[/C][C]-3.1811[/C][C]0.001826[/C][C]0.000913[/C][/ROW]
[ROW][C]t[/C][C]-513.642229153251[/C][C]265.308617[/C][C]-1.936[/C][C]0.054987[/C][C]0.027493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&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)1212426.92642868386282.3537573.13870.0020910.001045
Tijd-5203328.638059731177656.970009-4.41842e-051e-05
PBEFRU-104930.12191731652493.875255-1.99890.0476570.023829
PSOCOLA-946228.035093211200072.867688-4.72946e-063e-06
PSOORA1102337.83746774162351.4823016.789800
`PSTIM `-154284.67499291248500.393939-3.18110.0018260.000913
t-513.642229153251265.308617-1.9360.0549870.027493






Multiple Linear Regression - Regression Statistics
Multiple R0.69440044104494
R-squared0.482191972523407
Adjusted R-squared0.458832211885365
F-TEST (value)20.6419911571418
F-TEST (DF numerator)6
F-TEST (DF denominator)133
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation34365.393750337
Sum Squared Residuals157070378252.889

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.69440044104494 \tabularnewline
R-squared & 0.482191972523407 \tabularnewline
Adjusted R-squared & 0.458832211885365 \tabularnewline
F-TEST (value) & 20.6419911571418 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 133 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 34365.393750337 \tabularnewline
Sum Squared Residuals & 157070378252.889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.69440044104494[/C][/ROW]
[ROW][C]R-squared[/C][C]0.482191972523407[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.458832211885365[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.6419911571418[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]133[/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]34365.393750337[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]157070378252.889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&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 R0.69440044104494
R-squared0.482191972523407
Adjusted R-squared0.458832211885365
F-TEST (value)20.6419911571418
F-TEST (DF numerator)6
F-TEST (DF denominator)133
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation34365.393750337
Sum Squared Residuals157070378252.889






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1169498132185.90565274237312.0943472582
2125451141824.529702758-16373.5297027579
3140449133444.9045763887004.09542361213
4141653125288.73057519116364.2694248086
5136394135932.304900917461.695099083075
6167588150175.60375989417412.3962401061
7191807133743.17194879658063.8280512039
8149736121329.42508244728406.5749175529
9196066137356.65058557358709.3494144266
10239155127618.232446059111536.767553941
11178421131843.2120908246577.7879091801
12139871103772.80959004836098.1904099518
13118159125263.743282945-7104.74328294466
14109763121310.497526361-11547.497526361
1597415121385.759342745-23970.7593427447
16119190111567.7235396597622.27646034092
1797903118315.374222728-20412.3742227282
189695397261.2675515928-308.267551592771
198788878595.86691203439292.13308796571
2084637101377.077980635-16740.0779806347
2190549110604.972863117-20055.9728631166
2295680133145.149568339-37465.1495683393
2399371117936.0373416-18565.0373416004
2479984104781.88677392-24797.8867739203
258675285869.1152012447882.884798755334
268573398729.005628367-12996.005628367
278490689299.0615409594-4393.06154095942
287835688749.934506038-10393.934506038
2910889577159.17782262331735.8221773771
3010176891892.84419673289875.1558032672
317328593300.0938093913-20015.0938093913
3265724116074.397225726-50350.3972257258
336745797869.9268546285-30412.9268546285
346720344945.98746350622257.012536494
356927384200.127789509-14927.1277895089
3680807109664.005476533-28857.0054765332
3775129111144.083047926-36015.0830479262
3874991118385.147916321-43394.1479163206
3968157102422.449429704-34265.4494297038
407385884798.2633249442-10940.2633249442
417134966644.02914179654704.97085820346
428563467457.996439800318176.0035601997
439162488194.41043878383429.58956121616
4411601484998.46834984431015.531650156
4512003388881.75991266331151.2400873371
46108651101016.7896397297634.2103602714
47105378117411.552505706-12033.5525057055
48138939125583.52632636913355.4736736314
49132974133487.43768271-513.437682709789
50135277147643.197470245-12366.1974702449
51152741143331.4395390399409.56046096077
52158417176165.930048432-17748.9300484316
53157460172109.762069256-14649.7620692562
54193997171936.74567734622060.254322654
55154089150535.6843984253553.31560157462
56147570135412.17916072812157.8208392724
57162924181490.071760511-18566.0717605113
58153629182326.70800532-28697.7080053196
59155907164550.470545685-8643.47054568496
60197675168166.20333713729508.7966628632
61250708170859.27679951279848.7232004881
62266652160215.364417929106436.635582071
63209842182067.58019814627774.4198018543
64165826173603.049790945-7777.04979094462
65137152176125.395210788-38973.3952107883
66150581180940.730340693-30359.7303406927
67145973176183.26128968-30210.2612896799
68126532169433.358841827-42901.358841827
69115437145857.816235014-30420.8162350136
70119526151838.888347231-32312.8883472308
71110856143828.967281847-32972.967281847
7297243136169.602053523-38926.6020535231
73103876126536.372806733-22660.3728067328
74116370128231.820557688-11861.8205576876
75109616132630.955169819-23014.9551698193
7698365142492.908771335-44127.9087713352
7790440117391.32466183-26951.3246618299
7888899103707.633647907-14808.6336479068
799235897389.513645733-5031.51364573296
8088394101693.134911084-13299.1349110836
819821989259.03378516398959.96621483608
8211354681570.436888141331975.5631118587
83107168112251.660444217-5083.66044421724
8477540117240.239848097-39700.2398480969
8574944107826.629758393-32882.6297583934
867564160732.595568686414908.4044313136
8775910131689.725501669-55779.7255016689
8887384129864.877362852-42480.877362852
8984615136409.049471949-51794.0494719488
9080420113067.492662165-32647.4926621652
918078498487.1045291943-17703.1045291943
9279933133413.197480945-53480.1974809449
9382118111986.66150342-29868.6615034201
9491420109611.790939646-18191.7909396458
95112426111946.240161694479.759838306303
96114528137760.609808967-23232.6098089667
97131025133357.929169597-2332.92916959655
98116460112487.3024399813972.69756001926
99111258121494.948293033-10236.9482930329
100155318139014.6547159716303.3452840298
101155078161109.914186076-6031.91418607628
102134794141802.838936426-7008.838936426
103139985132882.6068370017102.39316299901
104198778131245.59121868467532.4087813157
105172436156898.49684160315537.5031583973
106169585172378.91141395-2793.91141395046
107203702161984.48957568441717.5104243159
108282392146306.740989362136085.259010638
109220658190461.14791241530196.8520875854
110194472159475.9900587634996.0099412403
111269246169425.64710869299820.3528913084
112215340175284.26832343740055.7316765628
113218319179342.48211236238976.5178876384
114195724179185.577945716538.4220542999
115174614165950.651849868663.34815013975
116172085173596.217305092-1511.21730509221
117152347179454.549214842-27107.5492148416
118189615172095.358033917519.6419661
119173804130744.27687621843059.723123782
120145683161470.60690354-15787.6069035398
121133550156327.087694363-22777.0876943635
122121156135966.611508494-14810.6115084943
123112040143627.767745267-31587.7677452673
124120767156086.161535553-35319.1615355528
125127019117967.9678299919051.03217000899
12613629599922.666855317636372.3331446824
127113425120982.410293427-7557.41029342713
128107815110897.034496549-3082.03449654892
129100298119692.26051769-19394.2605176899
13097048112386.299372013-15338.2993720128
13198750120591.856477663-21841.8564776629
13298235119057.151476437-20822.1514764371
13310125496748.44207020334505.55792979667
13413958979499.608286851660089.3917131484
135134921116054.790456318866.2095437004
1368035596917.8705974303-16562.8705974303
1378039686237.8887541648-5841.88875416484
1388218356041.087972985626141.9120270144
1397970928355.973729431351353.0262705687
14090781126667.741872236-35886.741872236

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 169498 & 132185.905652742 & 37312.0943472582 \tabularnewline
2 & 125451 & 141824.529702758 & -16373.5297027579 \tabularnewline
3 & 140449 & 133444.904576388 & 7004.09542361213 \tabularnewline
4 & 141653 & 125288.730575191 & 16364.2694248086 \tabularnewline
5 & 136394 & 135932.304900917 & 461.695099083075 \tabularnewline
6 & 167588 & 150175.603759894 & 17412.3962401061 \tabularnewline
7 & 191807 & 133743.171948796 & 58063.8280512039 \tabularnewline
8 & 149736 & 121329.425082447 & 28406.5749175529 \tabularnewline
9 & 196066 & 137356.650585573 & 58709.3494144266 \tabularnewline
10 & 239155 & 127618.232446059 & 111536.767553941 \tabularnewline
11 & 178421 & 131843.21209082 & 46577.7879091801 \tabularnewline
12 & 139871 & 103772.809590048 & 36098.1904099518 \tabularnewline
13 & 118159 & 125263.743282945 & -7104.74328294466 \tabularnewline
14 & 109763 & 121310.497526361 & -11547.497526361 \tabularnewline
15 & 97415 & 121385.759342745 & -23970.7593427447 \tabularnewline
16 & 119190 & 111567.723539659 & 7622.27646034092 \tabularnewline
17 & 97903 & 118315.374222728 & -20412.3742227282 \tabularnewline
18 & 96953 & 97261.2675515928 & -308.267551592771 \tabularnewline
19 & 87888 & 78595.8669120343 & 9292.13308796571 \tabularnewline
20 & 84637 & 101377.077980635 & -16740.0779806347 \tabularnewline
21 & 90549 & 110604.972863117 & -20055.9728631166 \tabularnewline
22 & 95680 & 133145.149568339 & -37465.1495683393 \tabularnewline
23 & 99371 & 117936.0373416 & -18565.0373416004 \tabularnewline
24 & 79984 & 104781.88677392 & -24797.8867739203 \tabularnewline
25 & 86752 & 85869.1152012447 & 882.884798755334 \tabularnewline
26 & 85733 & 98729.005628367 & -12996.005628367 \tabularnewline
27 & 84906 & 89299.0615409594 & -4393.06154095942 \tabularnewline
28 & 78356 & 88749.934506038 & -10393.934506038 \tabularnewline
29 & 108895 & 77159.177822623 & 31735.8221773771 \tabularnewline
30 & 101768 & 91892.8441967328 & 9875.1558032672 \tabularnewline
31 & 73285 & 93300.0938093913 & -20015.0938093913 \tabularnewline
32 & 65724 & 116074.397225726 & -50350.3972257258 \tabularnewline
33 & 67457 & 97869.9268546285 & -30412.9268546285 \tabularnewline
34 & 67203 & 44945.987463506 & 22257.012536494 \tabularnewline
35 & 69273 & 84200.127789509 & -14927.1277895089 \tabularnewline
36 & 80807 & 109664.005476533 & -28857.0054765332 \tabularnewline
37 & 75129 & 111144.083047926 & -36015.0830479262 \tabularnewline
38 & 74991 & 118385.147916321 & -43394.1479163206 \tabularnewline
39 & 68157 & 102422.449429704 & -34265.4494297038 \tabularnewline
40 & 73858 & 84798.2633249442 & -10940.2633249442 \tabularnewline
41 & 71349 & 66644.0291417965 & 4704.97085820346 \tabularnewline
42 & 85634 & 67457.9964398003 & 18176.0035601997 \tabularnewline
43 & 91624 & 88194.4104387838 & 3429.58956121616 \tabularnewline
44 & 116014 & 84998.468349844 & 31015.531650156 \tabularnewline
45 & 120033 & 88881.759912663 & 31151.2400873371 \tabularnewline
46 & 108651 & 101016.789639729 & 7634.2103602714 \tabularnewline
47 & 105378 & 117411.552505706 & -12033.5525057055 \tabularnewline
48 & 138939 & 125583.526326369 & 13355.4736736314 \tabularnewline
49 & 132974 & 133487.43768271 & -513.437682709789 \tabularnewline
50 & 135277 & 147643.197470245 & -12366.1974702449 \tabularnewline
51 & 152741 & 143331.439539039 & 9409.56046096077 \tabularnewline
52 & 158417 & 176165.930048432 & -17748.9300484316 \tabularnewline
53 & 157460 & 172109.762069256 & -14649.7620692562 \tabularnewline
54 & 193997 & 171936.745677346 & 22060.254322654 \tabularnewline
55 & 154089 & 150535.684398425 & 3553.31560157462 \tabularnewline
56 & 147570 & 135412.179160728 & 12157.8208392724 \tabularnewline
57 & 162924 & 181490.071760511 & -18566.0717605113 \tabularnewline
58 & 153629 & 182326.70800532 & -28697.7080053196 \tabularnewline
59 & 155907 & 164550.470545685 & -8643.47054568496 \tabularnewline
60 & 197675 & 168166.203337137 & 29508.7966628632 \tabularnewline
61 & 250708 & 170859.276799512 & 79848.7232004881 \tabularnewline
62 & 266652 & 160215.364417929 & 106436.635582071 \tabularnewline
63 & 209842 & 182067.580198146 & 27774.4198018543 \tabularnewline
64 & 165826 & 173603.049790945 & -7777.04979094462 \tabularnewline
65 & 137152 & 176125.395210788 & -38973.3952107883 \tabularnewline
66 & 150581 & 180940.730340693 & -30359.7303406927 \tabularnewline
67 & 145973 & 176183.26128968 & -30210.2612896799 \tabularnewline
68 & 126532 & 169433.358841827 & -42901.358841827 \tabularnewline
69 & 115437 & 145857.816235014 & -30420.8162350136 \tabularnewline
70 & 119526 & 151838.888347231 & -32312.8883472308 \tabularnewline
71 & 110856 & 143828.967281847 & -32972.967281847 \tabularnewline
72 & 97243 & 136169.602053523 & -38926.6020535231 \tabularnewline
73 & 103876 & 126536.372806733 & -22660.3728067328 \tabularnewline
74 & 116370 & 128231.820557688 & -11861.8205576876 \tabularnewline
75 & 109616 & 132630.955169819 & -23014.9551698193 \tabularnewline
76 & 98365 & 142492.908771335 & -44127.9087713352 \tabularnewline
77 & 90440 & 117391.32466183 & -26951.3246618299 \tabularnewline
78 & 88899 & 103707.633647907 & -14808.6336479068 \tabularnewline
79 & 92358 & 97389.513645733 & -5031.51364573296 \tabularnewline
80 & 88394 & 101693.134911084 & -13299.1349110836 \tabularnewline
81 & 98219 & 89259.0337851639 & 8959.96621483608 \tabularnewline
82 & 113546 & 81570.4368881413 & 31975.5631118587 \tabularnewline
83 & 107168 & 112251.660444217 & -5083.66044421724 \tabularnewline
84 & 77540 & 117240.239848097 & -39700.2398480969 \tabularnewline
85 & 74944 & 107826.629758393 & -32882.6297583934 \tabularnewline
86 & 75641 & 60732.5955686864 & 14908.4044313136 \tabularnewline
87 & 75910 & 131689.725501669 & -55779.7255016689 \tabularnewline
88 & 87384 & 129864.877362852 & -42480.877362852 \tabularnewline
89 & 84615 & 136409.049471949 & -51794.0494719488 \tabularnewline
90 & 80420 & 113067.492662165 & -32647.4926621652 \tabularnewline
91 & 80784 & 98487.1045291943 & -17703.1045291943 \tabularnewline
92 & 79933 & 133413.197480945 & -53480.1974809449 \tabularnewline
93 & 82118 & 111986.66150342 & -29868.6615034201 \tabularnewline
94 & 91420 & 109611.790939646 & -18191.7909396458 \tabularnewline
95 & 112426 & 111946.240161694 & 479.759838306303 \tabularnewline
96 & 114528 & 137760.609808967 & -23232.6098089667 \tabularnewline
97 & 131025 & 133357.929169597 & -2332.92916959655 \tabularnewline
98 & 116460 & 112487.302439981 & 3972.69756001926 \tabularnewline
99 & 111258 & 121494.948293033 & -10236.9482930329 \tabularnewline
100 & 155318 & 139014.65471597 & 16303.3452840298 \tabularnewline
101 & 155078 & 161109.914186076 & -6031.91418607628 \tabularnewline
102 & 134794 & 141802.838936426 & -7008.838936426 \tabularnewline
103 & 139985 & 132882.606837001 & 7102.39316299901 \tabularnewline
104 & 198778 & 131245.591218684 & 67532.4087813157 \tabularnewline
105 & 172436 & 156898.496841603 & 15537.5031583973 \tabularnewline
106 & 169585 & 172378.91141395 & -2793.91141395046 \tabularnewline
107 & 203702 & 161984.489575684 & 41717.5104243159 \tabularnewline
108 & 282392 & 146306.740989362 & 136085.259010638 \tabularnewline
109 & 220658 & 190461.147912415 & 30196.8520875854 \tabularnewline
110 & 194472 & 159475.99005876 & 34996.0099412403 \tabularnewline
111 & 269246 & 169425.647108692 & 99820.3528913084 \tabularnewline
112 & 215340 & 175284.268323437 & 40055.7316765628 \tabularnewline
113 & 218319 & 179342.482112362 & 38976.5178876384 \tabularnewline
114 & 195724 & 179185.5779457 & 16538.4220542999 \tabularnewline
115 & 174614 & 165950.65184986 & 8663.34815013975 \tabularnewline
116 & 172085 & 173596.217305092 & -1511.21730509221 \tabularnewline
117 & 152347 & 179454.549214842 & -27107.5492148416 \tabularnewline
118 & 189615 & 172095.3580339 & 17519.6419661 \tabularnewline
119 & 173804 & 130744.276876218 & 43059.723123782 \tabularnewline
120 & 145683 & 161470.60690354 & -15787.6069035398 \tabularnewline
121 & 133550 & 156327.087694363 & -22777.0876943635 \tabularnewline
122 & 121156 & 135966.611508494 & -14810.6115084943 \tabularnewline
123 & 112040 & 143627.767745267 & -31587.7677452673 \tabularnewline
124 & 120767 & 156086.161535553 & -35319.1615355528 \tabularnewline
125 & 127019 & 117967.967829991 & 9051.03217000899 \tabularnewline
126 & 136295 & 99922.6668553176 & 36372.3331446824 \tabularnewline
127 & 113425 & 120982.410293427 & -7557.41029342713 \tabularnewline
128 & 107815 & 110897.034496549 & -3082.03449654892 \tabularnewline
129 & 100298 & 119692.26051769 & -19394.2605176899 \tabularnewline
130 & 97048 & 112386.299372013 & -15338.2993720128 \tabularnewline
131 & 98750 & 120591.856477663 & -21841.8564776629 \tabularnewline
132 & 98235 & 119057.151476437 & -20822.1514764371 \tabularnewline
133 & 101254 & 96748.4420702033 & 4505.55792979667 \tabularnewline
134 & 139589 & 79499.6082868516 & 60089.3917131484 \tabularnewline
135 & 134921 & 116054.7904563 & 18866.2095437004 \tabularnewline
136 & 80355 & 96917.8705974303 & -16562.8705974303 \tabularnewline
137 & 80396 & 86237.8887541648 & -5841.88875416484 \tabularnewline
138 & 82183 & 56041.0879729856 & 26141.9120270144 \tabularnewline
139 & 79709 & 28355.9737294313 & 51353.0262705687 \tabularnewline
140 & 90781 & 126667.741872236 & -35886.741872236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&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]169498[/C][C]132185.905652742[/C][C]37312.0943472582[/C][/ROW]
[ROW][C]2[/C][C]125451[/C][C]141824.529702758[/C][C]-16373.5297027579[/C][/ROW]
[ROW][C]3[/C][C]140449[/C][C]133444.904576388[/C][C]7004.09542361213[/C][/ROW]
[ROW][C]4[/C][C]141653[/C][C]125288.730575191[/C][C]16364.2694248086[/C][/ROW]
[ROW][C]5[/C][C]136394[/C][C]135932.304900917[/C][C]461.695099083075[/C][/ROW]
[ROW][C]6[/C][C]167588[/C][C]150175.603759894[/C][C]17412.3962401061[/C][/ROW]
[ROW][C]7[/C][C]191807[/C][C]133743.171948796[/C][C]58063.8280512039[/C][/ROW]
[ROW][C]8[/C][C]149736[/C][C]121329.425082447[/C][C]28406.5749175529[/C][/ROW]
[ROW][C]9[/C][C]196066[/C][C]137356.650585573[/C][C]58709.3494144266[/C][/ROW]
[ROW][C]10[/C][C]239155[/C][C]127618.232446059[/C][C]111536.767553941[/C][/ROW]
[ROW][C]11[/C][C]178421[/C][C]131843.21209082[/C][C]46577.7879091801[/C][/ROW]
[ROW][C]12[/C][C]139871[/C][C]103772.809590048[/C][C]36098.1904099518[/C][/ROW]
[ROW][C]13[/C][C]118159[/C][C]125263.743282945[/C][C]-7104.74328294466[/C][/ROW]
[ROW][C]14[/C][C]109763[/C][C]121310.497526361[/C][C]-11547.497526361[/C][/ROW]
[ROW][C]15[/C][C]97415[/C][C]121385.759342745[/C][C]-23970.7593427447[/C][/ROW]
[ROW][C]16[/C][C]119190[/C][C]111567.723539659[/C][C]7622.27646034092[/C][/ROW]
[ROW][C]17[/C][C]97903[/C][C]118315.374222728[/C][C]-20412.3742227282[/C][/ROW]
[ROW][C]18[/C][C]96953[/C][C]97261.2675515928[/C][C]-308.267551592771[/C][/ROW]
[ROW][C]19[/C][C]87888[/C][C]78595.8669120343[/C][C]9292.13308796571[/C][/ROW]
[ROW][C]20[/C][C]84637[/C][C]101377.077980635[/C][C]-16740.0779806347[/C][/ROW]
[ROW][C]21[/C][C]90549[/C][C]110604.972863117[/C][C]-20055.9728631166[/C][/ROW]
[ROW][C]22[/C][C]95680[/C][C]133145.149568339[/C][C]-37465.1495683393[/C][/ROW]
[ROW][C]23[/C][C]99371[/C][C]117936.0373416[/C][C]-18565.0373416004[/C][/ROW]
[ROW][C]24[/C][C]79984[/C][C]104781.88677392[/C][C]-24797.8867739203[/C][/ROW]
[ROW][C]25[/C][C]86752[/C][C]85869.1152012447[/C][C]882.884798755334[/C][/ROW]
[ROW][C]26[/C][C]85733[/C][C]98729.005628367[/C][C]-12996.005628367[/C][/ROW]
[ROW][C]27[/C][C]84906[/C][C]89299.0615409594[/C][C]-4393.06154095942[/C][/ROW]
[ROW][C]28[/C][C]78356[/C][C]88749.934506038[/C][C]-10393.934506038[/C][/ROW]
[ROW][C]29[/C][C]108895[/C][C]77159.177822623[/C][C]31735.8221773771[/C][/ROW]
[ROW][C]30[/C][C]101768[/C][C]91892.8441967328[/C][C]9875.1558032672[/C][/ROW]
[ROW][C]31[/C][C]73285[/C][C]93300.0938093913[/C][C]-20015.0938093913[/C][/ROW]
[ROW][C]32[/C][C]65724[/C][C]116074.397225726[/C][C]-50350.3972257258[/C][/ROW]
[ROW][C]33[/C][C]67457[/C][C]97869.9268546285[/C][C]-30412.9268546285[/C][/ROW]
[ROW][C]34[/C][C]67203[/C][C]44945.987463506[/C][C]22257.012536494[/C][/ROW]
[ROW][C]35[/C][C]69273[/C][C]84200.127789509[/C][C]-14927.1277895089[/C][/ROW]
[ROW][C]36[/C][C]80807[/C][C]109664.005476533[/C][C]-28857.0054765332[/C][/ROW]
[ROW][C]37[/C][C]75129[/C][C]111144.083047926[/C][C]-36015.0830479262[/C][/ROW]
[ROW][C]38[/C][C]74991[/C][C]118385.147916321[/C][C]-43394.1479163206[/C][/ROW]
[ROW][C]39[/C][C]68157[/C][C]102422.449429704[/C][C]-34265.4494297038[/C][/ROW]
[ROW][C]40[/C][C]73858[/C][C]84798.2633249442[/C][C]-10940.2633249442[/C][/ROW]
[ROW][C]41[/C][C]71349[/C][C]66644.0291417965[/C][C]4704.97085820346[/C][/ROW]
[ROW][C]42[/C][C]85634[/C][C]67457.9964398003[/C][C]18176.0035601997[/C][/ROW]
[ROW][C]43[/C][C]91624[/C][C]88194.4104387838[/C][C]3429.58956121616[/C][/ROW]
[ROW][C]44[/C][C]116014[/C][C]84998.468349844[/C][C]31015.531650156[/C][/ROW]
[ROW][C]45[/C][C]120033[/C][C]88881.759912663[/C][C]31151.2400873371[/C][/ROW]
[ROW][C]46[/C][C]108651[/C][C]101016.789639729[/C][C]7634.2103602714[/C][/ROW]
[ROW][C]47[/C][C]105378[/C][C]117411.552505706[/C][C]-12033.5525057055[/C][/ROW]
[ROW][C]48[/C][C]138939[/C][C]125583.526326369[/C][C]13355.4736736314[/C][/ROW]
[ROW][C]49[/C][C]132974[/C][C]133487.43768271[/C][C]-513.437682709789[/C][/ROW]
[ROW][C]50[/C][C]135277[/C][C]147643.197470245[/C][C]-12366.1974702449[/C][/ROW]
[ROW][C]51[/C][C]152741[/C][C]143331.439539039[/C][C]9409.56046096077[/C][/ROW]
[ROW][C]52[/C][C]158417[/C][C]176165.930048432[/C][C]-17748.9300484316[/C][/ROW]
[ROW][C]53[/C][C]157460[/C][C]172109.762069256[/C][C]-14649.7620692562[/C][/ROW]
[ROW][C]54[/C][C]193997[/C][C]171936.745677346[/C][C]22060.254322654[/C][/ROW]
[ROW][C]55[/C][C]154089[/C][C]150535.684398425[/C][C]3553.31560157462[/C][/ROW]
[ROW][C]56[/C][C]147570[/C][C]135412.179160728[/C][C]12157.8208392724[/C][/ROW]
[ROW][C]57[/C][C]162924[/C][C]181490.071760511[/C][C]-18566.0717605113[/C][/ROW]
[ROW][C]58[/C][C]153629[/C][C]182326.70800532[/C][C]-28697.7080053196[/C][/ROW]
[ROW][C]59[/C][C]155907[/C][C]164550.470545685[/C][C]-8643.47054568496[/C][/ROW]
[ROW][C]60[/C][C]197675[/C][C]168166.203337137[/C][C]29508.7966628632[/C][/ROW]
[ROW][C]61[/C][C]250708[/C][C]170859.276799512[/C][C]79848.7232004881[/C][/ROW]
[ROW][C]62[/C][C]266652[/C][C]160215.364417929[/C][C]106436.635582071[/C][/ROW]
[ROW][C]63[/C][C]209842[/C][C]182067.580198146[/C][C]27774.4198018543[/C][/ROW]
[ROW][C]64[/C][C]165826[/C][C]173603.049790945[/C][C]-7777.04979094462[/C][/ROW]
[ROW][C]65[/C][C]137152[/C][C]176125.395210788[/C][C]-38973.3952107883[/C][/ROW]
[ROW][C]66[/C][C]150581[/C][C]180940.730340693[/C][C]-30359.7303406927[/C][/ROW]
[ROW][C]67[/C][C]145973[/C][C]176183.26128968[/C][C]-30210.2612896799[/C][/ROW]
[ROW][C]68[/C][C]126532[/C][C]169433.358841827[/C][C]-42901.358841827[/C][/ROW]
[ROW][C]69[/C][C]115437[/C][C]145857.816235014[/C][C]-30420.8162350136[/C][/ROW]
[ROW][C]70[/C][C]119526[/C][C]151838.888347231[/C][C]-32312.8883472308[/C][/ROW]
[ROW][C]71[/C][C]110856[/C][C]143828.967281847[/C][C]-32972.967281847[/C][/ROW]
[ROW][C]72[/C][C]97243[/C][C]136169.602053523[/C][C]-38926.6020535231[/C][/ROW]
[ROW][C]73[/C][C]103876[/C][C]126536.372806733[/C][C]-22660.3728067328[/C][/ROW]
[ROW][C]74[/C][C]116370[/C][C]128231.820557688[/C][C]-11861.8205576876[/C][/ROW]
[ROW][C]75[/C][C]109616[/C][C]132630.955169819[/C][C]-23014.9551698193[/C][/ROW]
[ROW][C]76[/C][C]98365[/C][C]142492.908771335[/C][C]-44127.9087713352[/C][/ROW]
[ROW][C]77[/C][C]90440[/C][C]117391.32466183[/C][C]-26951.3246618299[/C][/ROW]
[ROW][C]78[/C][C]88899[/C][C]103707.633647907[/C][C]-14808.6336479068[/C][/ROW]
[ROW][C]79[/C][C]92358[/C][C]97389.513645733[/C][C]-5031.51364573296[/C][/ROW]
[ROW][C]80[/C][C]88394[/C][C]101693.134911084[/C][C]-13299.1349110836[/C][/ROW]
[ROW][C]81[/C][C]98219[/C][C]89259.0337851639[/C][C]8959.96621483608[/C][/ROW]
[ROW][C]82[/C][C]113546[/C][C]81570.4368881413[/C][C]31975.5631118587[/C][/ROW]
[ROW][C]83[/C][C]107168[/C][C]112251.660444217[/C][C]-5083.66044421724[/C][/ROW]
[ROW][C]84[/C][C]77540[/C][C]117240.239848097[/C][C]-39700.2398480969[/C][/ROW]
[ROW][C]85[/C][C]74944[/C][C]107826.629758393[/C][C]-32882.6297583934[/C][/ROW]
[ROW][C]86[/C][C]75641[/C][C]60732.5955686864[/C][C]14908.4044313136[/C][/ROW]
[ROW][C]87[/C][C]75910[/C][C]131689.725501669[/C][C]-55779.7255016689[/C][/ROW]
[ROW][C]88[/C][C]87384[/C][C]129864.877362852[/C][C]-42480.877362852[/C][/ROW]
[ROW][C]89[/C][C]84615[/C][C]136409.049471949[/C][C]-51794.0494719488[/C][/ROW]
[ROW][C]90[/C][C]80420[/C][C]113067.492662165[/C][C]-32647.4926621652[/C][/ROW]
[ROW][C]91[/C][C]80784[/C][C]98487.1045291943[/C][C]-17703.1045291943[/C][/ROW]
[ROW][C]92[/C][C]79933[/C][C]133413.197480945[/C][C]-53480.1974809449[/C][/ROW]
[ROW][C]93[/C][C]82118[/C][C]111986.66150342[/C][C]-29868.6615034201[/C][/ROW]
[ROW][C]94[/C][C]91420[/C][C]109611.790939646[/C][C]-18191.7909396458[/C][/ROW]
[ROW][C]95[/C][C]112426[/C][C]111946.240161694[/C][C]479.759838306303[/C][/ROW]
[ROW][C]96[/C][C]114528[/C][C]137760.609808967[/C][C]-23232.6098089667[/C][/ROW]
[ROW][C]97[/C][C]131025[/C][C]133357.929169597[/C][C]-2332.92916959655[/C][/ROW]
[ROW][C]98[/C][C]116460[/C][C]112487.302439981[/C][C]3972.69756001926[/C][/ROW]
[ROW][C]99[/C][C]111258[/C][C]121494.948293033[/C][C]-10236.9482930329[/C][/ROW]
[ROW][C]100[/C][C]155318[/C][C]139014.65471597[/C][C]16303.3452840298[/C][/ROW]
[ROW][C]101[/C][C]155078[/C][C]161109.914186076[/C][C]-6031.91418607628[/C][/ROW]
[ROW][C]102[/C][C]134794[/C][C]141802.838936426[/C][C]-7008.838936426[/C][/ROW]
[ROW][C]103[/C][C]139985[/C][C]132882.606837001[/C][C]7102.39316299901[/C][/ROW]
[ROW][C]104[/C][C]198778[/C][C]131245.591218684[/C][C]67532.4087813157[/C][/ROW]
[ROW][C]105[/C][C]172436[/C][C]156898.496841603[/C][C]15537.5031583973[/C][/ROW]
[ROW][C]106[/C][C]169585[/C][C]172378.91141395[/C][C]-2793.91141395046[/C][/ROW]
[ROW][C]107[/C][C]203702[/C][C]161984.489575684[/C][C]41717.5104243159[/C][/ROW]
[ROW][C]108[/C][C]282392[/C][C]146306.740989362[/C][C]136085.259010638[/C][/ROW]
[ROW][C]109[/C][C]220658[/C][C]190461.147912415[/C][C]30196.8520875854[/C][/ROW]
[ROW][C]110[/C][C]194472[/C][C]159475.99005876[/C][C]34996.0099412403[/C][/ROW]
[ROW][C]111[/C][C]269246[/C][C]169425.647108692[/C][C]99820.3528913084[/C][/ROW]
[ROW][C]112[/C][C]215340[/C][C]175284.268323437[/C][C]40055.7316765628[/C][/ROW]
[ROW][C]113[/C][C]218319[/C][C]179342.482112362[/C][C]38976.5178876384[/C][/ROW]
[ROW][C]114[/C][C]195724[/C][C]179185.5779457[/C][C]16538.4220542999[/C][/ROW]
[ROW][C]115[/C][C]174614[/C][C]165950.65184986[/C][C]8663.34815013975[/C][/ROW]
[ROW][C]116[/C][C]172085[/C][C]173596.217305092[/C][C]-1511.21730509221[/C][/ROW]
[ROW][C]117[/C][C]152347[/C][C]179454.549214842[/C][C]-27107.5492148416[/C][/ROW]
[ROW][C]118[/C][C]189615[/C][C]172095.3580339[/C][C]17519.6419661[/C][/ROW]
[ROW][C]119[/C][C]173804[/C][C]130744.276876218[/C][C]43059.723123782[/C][/ROW]
[ROW][C]120[/C][C]145683[/C][C]161470.60690354[/C][C]-15787.6069035398[/C][/ROW]
[ROW][C]121[/C][C]133550[/C][C]156327.087694363[/C][C]-22777.0876943635[/C][/ROW]
[ROW][C]122[/C][C]121156[/C][C]135966.611508494[/C][C]-14810.6115084943[/C][/ROW]
[ROW][C]123[/C][C]112040[/C][C]143627.767745267[/C][C]-31587.7677452673[/C][/ROW]
[ROW][C]124[/C][C]120767[/C][C]156086.161535553[/C][C]-35319.1615355528[/C][/ROW]
[ROW][C]125[/C][C]127019[/C][C]117967.967829991[/C][C]9051.03217000899[/C][/ROW]
[ROW][C]126[/C][C]136295[/C][C]99922.6668553176[/C][C]36372.3331446824[/C][/ROW]
[ROW][C]127[/C][C]113425[/C][C]120982.410293427[/C][C]-7557.41029342713[/C][/ROW]
[ROW][C]128[/C][C]107815[/C][C]110897.034496549[/C][C]-3082.03449654892[/C][/ROW]
[ROW][C]129[/C][C]100298[/C][C]119692.26051769[/C][C]-19394.2605176899[/C][/ROW]
[ROW][C]130[/C][C]97048[/C][C]112386.299372013[/C][C]-15338.2993720128[/C][/ROW]
[ROW][C]131[/C][C]98750[/C][C]120591.856477663[/C][C]-21841.8564776629[/C][/ROW]
[ROW][C]132[/C][C]98235[/C][C]119057.151476437[/C][C]-20822.1514764371[/C][/ROW]
[ROW][C]133[/C][C]101254[/C][C]96748.4420702033[/C][C]4505.55792979667[/C][/ROW]
[ROW][C]134[/C][C]139589[/C][C]79499.6082868516[/C][C]60089.3917131484[/C][/ROW]
[ROW][C]135[/C][C]134921[/C][C]116054.7904563[/C][C]18866.2095437004[/C][/ROW]
[ROW][C]136[/C][C]80355[/C][C]96917.8705974303[/C][C]-16562.8705974303[/C][/ROW]
[ROW][C]137[/C][C]80396[/C][C]86237.8887541648[/C][C]-5841.88875416484[/C][/ROW]
[ROW][C]138[/C][C]82183[/C][C]56041.0879729856[/C][C]26141.9120270144[/C][/ROW]
[ROW][C]139[/C][C]79709[/C][C]28355.9737294313[/C][C]51353.0262705687[/C][/ROW]
[ROW][C]140[/C][C]90781[/C][C]126667.741872236[/C][C]-35886.741872236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&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
1169498132185.90565274237312.0943472582
2125451141824.529702758-16373.5297027579
3140449133444.9045763887004.09542361213
4141653125288.73057519116364.2694248086
5136394135932.304900917461.695099083075
6167588150175.60375989417412.3962401061
7191807133743.17194879658063.8280512039
8149736121329.42508244728406.5749175529
9196066137356.65058557358709.3494144266
10239155127618.232446059111536.767553941
11178421131843.2120908246577.7879091801
12139871103772.80959004836098.1904099518
13118159125263.743282945-7104.74328294466
14109763121310.497526361-11547.497526361
1597415121385.759342745-23970.7593427447
16119190111567.7235396597622.27646034092
1797903118315.374222728-20412.3742227282
189695397261.2675515928-308.267551592771
198788878595.86691203439292.13308796571
2084637101377.077980635-16740.0779806347
2190549110604.972863117-20055.9728631166
2295680133145.149568339-37465.1495683393
2399371117936.0373416-18565.0373416004
2479984104781.88677392-24797.8867739203
258675285869.1152012447882.884798755334
268573398729.005628367-12996.005628367
278490689299.0615409594-4393.06154095942
287835688749.934506038-10393.934506038
2910889577159.17782262331735.8221773771
3010176891892.84419673289875.1558032672
317328593300.0938093913-20015.0938093913
3265724116074.397225726-50350.3972257258
336745797869.9268546285-30412.9268546285
346720344945.98746350622257.012536494
356927384200.127789509-14927.1277895089
3680807109664.005476533-28857.0054765332
3775129111144.083047926-36015.0830479262
3874991118385.147916321-43394.1479163206
3968157102422.449429704-34265.4494297038
407385884798.2633249442-10940.2633249442
417134966644.02914179654704.97085820346
428563467457.996439800318176.0035601997
439162488194.41043878383429.58956121616
4411601484998.46834984431015.531650156
4512003388881.75991266331151.2400873371
46108651101016.7896397297634.2103602714
47105378117411.552505706-12033.5525057055
48138939125583.52632636913355.4736736314
49132974133487.43768271-513.437682709789
50135277147643.197470245-12366.1974702449
51152741143331.4395390399409.56046096077
52158417176165.930048432-17748.9300484316
53157460172109.762069256-14649.7620692562
54193997171936.74567734622060.254322654
55154089150535.6843984253553.31560157462
56147570135412.17916072812157.8208392724
57162924181490.071760511-18566.0717605113
58153629182326.70800532-28697.7080053196
59155907164550.470545685-8643.47054568496
60197675168166.20333713729508.7966628632
61250708170859.27679951279848.7232004881
62266652160215.364417929106436.635582071
63209842182067.58019814627774.4198018543
64165826173603.049790945-7777.04979094462
65137152176125.395210788-38973.3952107883
66150581180940.730340693-30359.7303406927
67145973176183.26128968-30210.2612896799
68126532169433.358841827-42901.358841827
69115437145857.816235014-30420.8162350136
70119526151838.888347231-32312.8883472308
71110856143828.967281847-32972.967281847
7297243136169.602053523-38926.6020535231
73103876126536.372806733-22660.3728067328
74116370128231.820557688-11861.8205576876
75109616132630.955169819-23014.9551698193
7698365142492.908771335-44127.9087713352
7790440117391.32466183-26951.3246618299
7888899103707.633647907-14808.6336479068
799235897389.513645733-5031.51364573296
8088394101693.134911084-13299.1349110836
819821989259.03378516398959.96621483608
8211354681570.436888141331975.5631118587
83107168112251.660444217-5083.66044421724
8477540117240.239848097-39700.2398480969
8574944107826.629758393-32882.6297583934
867564160732.595568686414908.4044313136
8775910131689.725501669-55779.7255016689
8887384129864.877362852-42480.877362852
8984615136409.049471949-51794.0494719488
9080420113067.492662165-32647.4926621652
918078498487.1045291943-17703.1045291943
9279933133413.197480945-53480.1974809449
9382118111986.66150342-29868.6615034201
9491420109611.790939646-18191.7909396458
95112426111946.240161694479.759838306303
96114528137760.609808967-23232.6098089667
97131025133357.929169597-2332.92916959655
98116460112487.3024399813972.69756001926
99111258121494.948293033-10236.9482930329
100155318139014.6547159716303.3452840298
101155078161109.914186076-6031.91418607628
102134794141802.838936426-7008.838936426
103139985132882.6068370017102.39316299901
104198778131245.59121868467532.4087813157
105172436156898.49684160315537.5031583973
106169585172378.91141395-2793.91141395046
107203702161984.48957568441717.5104243159
108282392146306.740989362136085.259010638
109220658190461.14791241530196.8520875854
110194472159475.9900587634996.0099412403
111269246169425.64710869299820.3528913084
112215340175284.26832343740055.7316765628
113218319179342.48211236238976.5178876384
114195724179185.577945716538.4220542999
115174614165950.651849868663.34815013975
116172085173596.217305092-1511.21730509221
117152347179454.549214842-27107.5492148416
118189615172095.358033917519.6419661
119173804130744.27687621843059.723123782
120145683161470.60690354-15787.6069035398
121133550156327.087694363-22777.0876943635
122121156135966.611508494-14810.6115084943
123112040143627.767745267-31587.7677452673
124120767156086.161535553-35319.1615355528
125127019117967.9678299919051.03217000899
12613629599922.666855317636372.3331446824
127113425120982.410293427-7557.41029342713
128107815110897.034496549-3082.03449654892
129100298119692.26051769-19394.2605176899
13097048112386.299372013-15338.2993720128
13198750120591.856477663-21841.8564776629
13298235119057.151476437-20822.1514764371
13310125496748.44207020334505.55792979667
13413958979499.608286851660089.3917131484
135134921116054.790456318866.2095437004
1368035596917.8705974303-16562.8705974303
1378039686237.8887541648-5841.88875416484
1388218356041.087972985626141.9120270144
1397970928355.973729431351353.0262705687
14090781126667.741872236-35886.741872236






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2233126109689680.4466252219379360.776687389031032
110.1506221548976360.3012443097952710.849377845102364
120.2817599431323260.5635198862646530.718240056867674
130.7891135407850980.4217729184298030.210886459214902
140.8620449904731090.2759100190537830.137955009526891
150.8770533577024160.2458932845951680.122946642297584
160.8262960340010020.3474079319979950.173703965998998
170.8718929659403670.2562140681192650.128107034059633
180.8534137316436590.2931725367126820.146586268356341
190.8051130494887140.3897739010225710.194886950511286
200.7431647637161290.5136704725677420.256835236283871
210.6740122240773060.6519755518453890.325987775922694
220.607517559426980.784964881146040.39248244057302
230.5325961866613740.9348076266772520.467403813338626
240.4579236154031180.9158472308062350.542076384596882
250.4252697845189690.8505395690379380.574730215481031
260.3573400409631270.7146800819262550.642659959036873
270.2941596174052860.5883192348105710.705840382594714
280.2366438867909670.4732877735819340.763356113209033
290.2204783575458180.4409567150916360.779521642454182
300.1891014486731280.3782028973462570.810898551326872
310.1952691920327270.3905383840654540.804730807967273
320.1700353647468710.3400707294937420.829964635253129
330.1366753022957680.2733506045915370.863324697704232
340.1219855421519870.2439710843039740.878014457848013
350.1005331590802830.2010663181605660.899466840919717
360.09201623045160520.184032460903210.907983769548395
370.07464040206710930.1492808041342190.92535959793289
380.06712503991082940.1342500798216590.93287496008917
390.05600878296239830.1120175659247970.943991217037602
400.05251844799676390.1050368959935280.947481552003236
410.04466130187490660.08932260374981330.955338698125093
420.05034825506895180.1006965101379040.949651744931048
430.05842399828870180.1168479965774040.941576001711298
440.09908969833514330.1981793966702870.900910301664857
450.1391134033380480.2782268066760960.860886596661952
460.1262116506405920.2524233012811840.873788349359408
470.1005706949235060.2011413898470130.899429305076494
480.09317569083663270.1863513816732650.906824309163367
490.07541651847541830.1508330369508370.924583481524582
500.05840631302906470.1168126260581290.941593686970935
510.05309912897658740.1061982579531750.946900871023413
520.04024297236557790.08048594473115570.959757027634422
530.03006848588118620.06013697176237240.969931514118814
540.04050969808835810.08101939617671610.959490301911642
550.03118788253210750.0623757650642150.968812117467893
560.02477104272264560.04954208544529120.975228957277354
570.01918472608269350.0383694521653870.980815273917306
580.01798242882490050.03596485764980110.9820175711751
590.01353250465453640.02706500930907280.986467495345464
600.01315747372864610.02631494745729210.986842526271354
610.0601274751684530.1202549503369060.939872524831547
620.3801816679710830.7603633359421670.619818332028916
630.3824728259455080.7649456518910160.617527174054492
640.3464064814461530.6928129628923060.653593518553847
650.3411481389172210.6822962778344430.658851861082778
660.3231071772588250.6462143545176510.676892822741174
670.3154207310050040.6308414620100080.684579268994996
680.3134834674871990.6269669349743970.686516532512801
690.2876408623476470.5752817246952930.712359137652353
700.2489045359625230.4978090719250460.751095464037477
710.2127500053828220.4255000107656440.787249994617178
720.1832436230424430.3664872460848860.816756376957557
730.1517658775855940.3035317551711870.848234122414406
740.1315649743558760.2631299487117520.868435025644124
750.1071141473775150.2142282947550290.892885852622485
760.0937601803044650.187520360608930.906239819695535
770.07492025164275640.1498405032855130.925079748357244
780.0603451714736340.1206903429472680.939654828526366
790.05273868549387520.105477370987750.947261314506125
800.04192526368903740.08385052737807490.958074736310963
810.03915318051488730.07830636102977470.960846819485113
820.06413063402377780.1282612680475560.935869365976222
830.05546931656211920.1109386331242380.94453068343788
840.04828548617046070.09657097234092140.95171451382954
850.04476777717566870.08953555435133740.955232222824331
860.0420088655130330.0840177310260660.957991134486967
870.04160010474771220.08320020949542440.958399895252288
880.03761245044556750.0752249008911350.962387549554433
890.04821816343093770.09643632686187550.951781836569062
900.04656266930668070.09312533861336140.95343733069332
910.04441830252225110.08883660504450220.955581697477749
920.09032161434146740.1806432286829350.909678385658533
930.1017769587746130.2035539175492270.898223041225387
940.1044598260866080.2089196521732170.895540173913392
950.1178060789321530.2356121578643050.882193921067847
960.1278001073102340.2556002146204690.872199892689766
970.1455765450468110.2911530900936210.85442345495319
980.1623252144107540.3246504288215080.837674785589246
990.3162830421458090.6325660842916180.683716957854191
1000.383025886279340.766051772558680.61697411372066
1010.4668046292034840.9336092584069690.533195370796516
1020.714794976931280.570410046137440.28520502306872
1030.8893619770832820.2212760458334350.110638022916718
1040.9418321786566760.1163356426866490.0581678213433243
1050.9623110343861880.0753779312276230.0376889656138115
1060.9647726651267010.07045466974659730.0352273348732987
1070.9824029440062490.03519411198750310.0175970559937515
1080.9945085621767750.01098287564644940.00549143782322469
1090.9920600860280030.01587982794399460.00793991397199732
1100.992093021018380.01581395796324130.00790697898162064
1110.999470279236970.001059441526058590.000529720763029297
1120.999470423666620.00105915266676120.000529576333380602
1130.9992693622810920.001461275437815840.00073063771890792
1140.9988839641105290.00223207177894240.0011160358894712
1150.9979191017246640.004161796550672320.00208089827533616
1160.996023844483730.007952311032539430.00397615551626971
1170.9937629948595790.01247401028084260.00623700514042128
1180.9978233388777930.004353322244413840.00217666112220692
1190.9994291277879560.001141744424088250.000570872212044124
1200.9995736688978840.000852662204232350.000426331102116175
1210.9995701838488780.0008596323022449370.000429816151122469
1220.999256919130930.001486161738138450.000743080869069224
1230.9981308661549760.003738267690047530.00186913384502376
1240.9992322350533940.001535529893211050.000767764946605523
1250.9981142166330460.003771566733907840.00188578336695392
1260.995494172330890.009011655338218280.00450582766910914
1270.990281299648370.01943740070325860.00971870035162928
1280.9731301205952260.0537397588095480.026869879404774
1290.9341905351853190.1316189296293620.0658094648146809
1300.8547468266061750.290506346787650.145253173393825

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.223312610968968 & 0.446625221937936 & 0.776687389031032 \tabularnewline
11 & 0.150622154897636 & 0.301244309795271 & 0.849377845102364 \tabularnewline
12 & 0.281759943132326 & 0.563519886264653 & 0.718240056867674 \tabularnewline
13 & 0.789113540785098 & 0.421772918429803 & 0.210886459214902 \tabularnewline
14 & 0.862044990473109 & 0.275910019053783 & 0.137955009526891 \tabularnewline
15 & 0.877053357702416 & 0.245893284595168 & 0.122946642297584 \tabularnewline
16 & 0.826296034001002 & 0.347407931997995 & 0.173703965998998 \tabularnewline
17 & 0.871892965940367 & 0.256214068119265 & 0.128107034059633 \tabularnewline
18 & 0.853413731643659 & 0.293172536712682 & 0.146586268356341 \tabularnewline
19 & 0.805113049488714 & 0.389773901022571 & 0.194886950511286 \tabularnewline
20 & 0.743164763716129 & 0.513670472567742 & 0.256835236283871 \tabularnewline
21 & 0.674012224077306 & 0.651975551845389 & 0.325987775922694 \tabularnewline
22 & 0.60751755942698 & 0.78496488114604 & 0.39248244057302 \tabularnewline
23 & 0.532596186661374 & 0.934807626677252 & 0.467403813338626 \tabularnewline
24 & 0.457923615403118 & 0.915847230806235 & 0.542076384596882 \tabularnewline
25 & 0.425269784518969 & 0.850539569037938 & 0.574730215481031 \tabularnewline
26 & 0.357340040963127 & 0.714680081926255 & 0.642659959036873 \tabularnewline
27 & 0.294159617405286 & 0.588319234810571 & 0.705840382594714 \tabularnewline
28 & 0.236643886790967 & 0.473287773581934 & 0.763356113209033 \tabularnewline
29 & 0.220478357545818 & 0.440956715091636 & 0.779521642454182 \tabularnewline
30 & 0.189101448673128 & 0.378202897346257 & 0.810898551326872 \tabularnewline
31 & 0.195269192032727 & 0.390538384065454 & 0.804730807967273 \tabularnewline
32 & 0.170035364746871 & 0.340070729493742 & 0.829964635253129 \tabularnewline
33 & 0.136675302295768 & 0.273350604591537 & 0.863324697704232 \tabularnewline
34 & 0.121985542151987 & 0.243971084303974 & 0.878014457848013 \tabularnewline
35 & 0.100533159080283 & 0.201066318160566 & 0.899466840919717 \tabularnewline
36 & 0.0920162304516052 & 0.18403246090321 & 0.907983769548395 \tabularnewline
37 & 0.0746404020671093 & 0.149280804134219 & 0.92535959793289 \tabularnewline
38 & 0.0671250399108294 & 0.134250079821659 & 0.93287496008917 \tabularnewline
39 & 0.0560087829623983 & 0.112017565924797 & 0.943991217037602 \tabularnewline
40 & 0.0525184479967639 & 0.105036895993528 & 0.947481552003236 \tabularnewline
41 & 0.0446613018749066 & 0.0893226037498133 & 0.955338698125093 \tabularnewline
42 & 0.0503482550689518 & 0.100696510137904 & 0.949651744931048 \tabularnewline
43 & 0.0584239982887018 & 0.116847996577404 & 0.941576001711298 \tabularnewline
44 & 0.0990896983351433 & 0.198179396670287 & 0.900910301664857 \tabularnewline
45 & 0.139113403338048 & 0.278226806676096 & 0.860886596661952 \tabularnewline
46 & 0.126211650640592 & 0.252423301281184 & 0.873788349359408 \tabularnewline
47 & 0.100570694923506 & 0.201141389847013 & 0.899429305076494 \tabularnewline
48 & 0.0931756908366327 & 0.186351381673265 & 0.906824309163367 \tabularnewline
49 & 0.0754165184754183 & 0.150833036950837 & 0.924583481524582 \tabularnewline
50 & 0.0584063130290647 & 0.116812626058129 & 0.941593686970935 \tabularnewline
51 & 0.0530991289765874 & 0.106198257953175 & 0.946900871023413 \tabularnewline
52 & 0.0402429723655779 & 0.0804859447311557 & 0.959757027634422 \tabularnewline
53 & 0.0300684858811862 & 0.0601369717623724 & 0.969931514118814 \tabularnewline
54 & 0.0405096980883581 & 0.0810193961767161 & 0.959490301911642 \tabularnewline
55 & 0.0311878825321075 & 0.062375765064215 & 0.968812117467893 \tabularnewline
56 & 0.0247710427226456 & 0.0495420854452912 & 0.975228957277354 \tabularnewline
57 & 0.0191847260826935 & 0.038369452165387 & 0.980815273917306 \tabularnewline
58 & 0.0179824288249005 & 0.0359648576498011 & 0.9820175711751 \tabularnewline
59 & 0.0135325046545364 & 0.0270650093090728 & 0.986467495345464 \tabularnewline
60 & 0.0131574737286461 & 0.0263149474572921 & 0.986842526271354 \tabularnewline
61 & 0.060127475168453 & 0.120254950336906 & 0.939872524831547 \tabularnewline
62 & 0.380181667971083 & 0.760363335942167 & 0.619818332028916 \tabularnewline
63 & 0.382472825945508 & 0.764945651891016 & 0.617527174054492 \tabularnewline
64 & 0.346406481446153 & 0.692812962892306 & 0.653593518553847 \tabularnewline
65 & 0.341148138917221 & 0.682296277834443 & 0.658851861082778 \tabularnewline
66 & 0.323107177258825 & 0.646214354517651 & 0.676892822741174 \tabularnewline
67 & 0.315420731005004 & 0.630841462010008 & 0.684579268994996 \tabularnewline
68 & 0.313483467487199 & 0.626966934974397 & 0.686516532512801 \tabularnewline
69 & 0.287640862347647 & 0.575281724695293 & 0.712359137652353 \tabularnewline
70 & 0.248904535962523 & 0.497809071925046 & 0.751095464037477 \tabularnewline
71 & 0.212750005382822 & 0.425500010765644 & 0.787249994617178 \tabularnewline
72 & 0.183243623042443 & 0.366487246084886 & 0.816756376957557 \tabularnewline
73 & 0.151765877585594 & 0.303531755171187 & 0.848234122414406 \tabularnewline
74 & 0.131564974355876 & 0.263129948711752 & 0.868435025644124 \tabularnewline
75 & 0.107114147377515 & 0.214228294755029 & 0.892885852622485 \tabularnewline
76 & 0.093760180304465 & 0.18752036060893 & 0.906239819695535 \tabularnewline
77 & 0.0749202516427564 & 0.149840503285513 & 0.925079748357244 \tabularnewline
78 & 0.060345171473634 & 0.120690342947268 & 0.939654828526366 \tabularnewline
79 & 0.0527386854938752 & 0.10547737098775 & 0.947261314506125 \tabularnewline
80 & 0.0419252636890374 & 0.0838505273780749 & 0.958074736310963 \tabularnewline
81 & 0.0391531805148873 & 0.0783063610297747 & 0.960846819485113 \tabularnewline
82 & 0.0641306340237778 & 0.128261268047556 & 0.935869365976222 \tabularnewline
83 & 0.0554693165621192 & 0.110938633124238 & 0.94453068343788 \tabularnewline
84 & 0.0482854861704607 & 0.0965709723409214 & 0.95171451382954 \tabularnewline
85 & 0.0447677771756687 & 0.0895355543513374 & 0.955232222824331 \tabularnewline
86 & 0.042008865513033 & 0.084017731026066 & 0.957991134486967 \tabularnewline
87 & 0.0416001047477122 & 0.0832002094954244 & 0.958399895252288 \tabularnewline
88 & 0.0376124504455675 & 0.075224900891135 & 0.962387549554433 \tabularnewline
89 & 0.0482181634309377 & 0.0964363268618755 & 0.951781836569062 \tabularnewline
90 & 0.0465626693066807 & 0.0931253386133614 & 0.95343733069332 \tabularnewline
91 & 0.0444183025222511 & 0.0888366050445022 & 0.955581697477749 \tabularnewline
92 & 0.0903216143414674 & 0.180643228682935 & 0.909678385658533 \tabularnewline
93 & 0.101776958774613 & 0.203553917549227 & 0.898223041225387 \tabularnewline
94 & 0.104459826086608 & 0.208919652173217 & 0.895540173913392 \tabularnewline
95 & 0.117806078932153 & 0.235612157864305 & 0.882193921067847 \tabularnewline
96 & 0.127800107310234 & 0.255600214620469 & 0.872199892689766 \tabularnewline
97 & 0.145576545046811 & 0.291153090093621 & 0.85442345495319 \tabularnewline
98 & 0.162325214410754 & 0.324650428821508 & 0.837674785589246 \tabularnewline
99 & 0.316283042145809 & 0.632566084291618 & 0.683716957854191 \tabularnewline
100 & 0.38302588627934 & 0.76605177255868 & 0.61697411372066 \tabularnewline
101 & 0.466804629203484 & 0.933609258406969 & 0.533195370796516 \tabularnewline
102 & 0.71479497693128 & 0.57041004613744 & 0.28520502306872 \tabularnewline
103 & 0.889361977083282 & 0.221276045833435 & 0.110638022916718 \tabularnewline
104 & 0.941832178656676 & 0.116335642686649 & 0.0581678213433243 \tabularnewline
105 & 0.962311034386188 & 0.075377931227623 & 0.0376889656138115 \tabularnewline
106 & 0.964772665126701 & 0.0704546697465973 & 0.0352273348732987 \tabularnewline
107 & 0.982402944006249 & 0.0351941119875031 & 0.0175970559937515 \tabularnewline
108 & 0.994508562176775 & 0.0109828756464494 & 0.00549143782322469 \tabularnewline
109 & 0.992060086028003 & 0.0158798279439946 & 0.00793991397199732 \tabularnewline
110 & 0.99209302101838 & 0.0158139579632413 & 0.00790697898162064 \tabularnewline
111 & 0.99947027923697 & 0.00105944152605859 & 0.000529720763029297 \tabularnewline
112 & 0.99947042366662 & 0.0010591526667612 & 0.000529576333380602 \tabularnewline
113 & 0.999269362281092 & 0.00146127543781584 & 0.00073063771890792 \tabularnewline
114 & 0.998883964110529 & 0.0022320717789424 & 0.0011160358894712 \tabularnewline
115 & 0.997919101724664 & 0.00416179655067232 & 0.00208089827533616 \tabularnewline
116 & 0.99602384448373 & 0.00795231103253943 & 0.00397615551626971 \tabularnewline
117 & 0.993762994859579 & 0.0124740102808426 & 0.00623700514042128 \tabularnewline
118 & 0.997823338877793 & 0.00435332224441384 & 0.00217666112220692 \tabularnewline
119 & 0.999429127787956 & 0.00114174442408825 & 0.000570872212044124 \tabularnewline
120 & 0.999573668897884 & 0.00085266220423235 & 0.000426331102116175 \tabularnewline
121 & 0.999570183848878 & 0.000859632302244937 & 0.000429816151122469 \tabularnewline
122 & 0.99925691913093 & 0.00148616173813845 & 0.000743080869069224 \tabularnewline
123 & 0.998130866154976 & 0.00373826769004753 & 0.00186913384502376 \tabularnewline
124 & 0.999232235053394 & 0.00153552989321105 & 0.000767764946605523 \tabularnewline
125 & 0.998114216633046 & 0.00377156673390784 & 0.00188578336695392 \tabularnewline
126 & 0.99549417233089 & 0.00901165533821828 & 0.00450582766910914 \tabularnewline
127 & 0.99028129964837 & 0.0194374007032586 & 0.00971870035162928 \tabularnewline
128 & 0.973130120595226 & 0.053739758809548 & 0.026869879404774 \tabularnewline
129 & 0.934190535185319 & 0.131618929629362 & 0.0658094648146809 \tabularnewline
130 & 0.854746826606175 & 0.29050634678765 & 0.145253173393825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&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]10[/C][C]0.223312610968968[/C][C]0.446625221937936[/C][C]0.776687389031032[/C][/ROW]
[ROW][C]11[/C][C]0.150622154897636[/C][C]0.301244309795271[/C][C]0.849377845102364[/C][/ROW]
[ROW][C]12[/C][C]0.281759943132326[/C][C]0.563519886264653[/C][C]0.718240056867674[/C][/ROW]
[ROW][C]13[/C][C]0.789113540785098[/C][C]0.421772918429803[/C][C]0.210886459214902[/C][/ROW]
[ROW][C]14[/C][C]0.862044990473109[/C][C]0.275910019053783[/C][C]0.137955009526891[/C][/ROW]
[ROW][C]15[/C][C]0.877053357702416[/C][C]0.245893284595168[/C][C]0.122946642297584[/C][/ROW]
[ROW][C]16[/C][C]0.826296034001002[/C][C]0.347407931997995[/C][C]0.173703965998998[/C][/ROW]
[ROW][C]17[/C][C]0.871892965940367[/C][C]0.256214068119265[/C][C]0.128107034059633[/C][/ROW]
[ROW][C]18[/C][C]0.853413731643659[/C][C]0.293172536712682[/C][C]0.146586268356341[/C][/ROW]
[ROW][C]19[/C][C]0.805113049488714[/C][C]0.389773901022571[/C][C]0.194886950511286[/C][/ROW]
[ROW][C]20[/C][C]0.743164763716129[/C][C]0.513670472567742[/C][C]0.256835236283871[/C][/ROW]
[ROW][C]21[/C][C]0.674012224077306[/C][C]0.651975551845389[/C][C]0.325987775922694[/C][/ROW]
[ROW][C]22[/C][C]0.60751755942698[/C][C]0.78496488114604[/C][C]0.39248244057302[/C][/ROW]
[ROW][C]23[/C][C]0.532596186661374[/C][C]0.934807626677252[/C][C]0.467403813338626[/C][/ROW]
[ROW][C]24[/C][C]0.457923615403118[/C][C]0.915847230806235[/C][C]0.542076384596882[/C][/ROW]
[ROW][C]25[/C][C]0.425269784518969[/C][C]0.850539569037938[/C][C]0.574730215481031[/C][/ROW]
[ROW][C]26[/C][C]0.357340040963127[/C][C]0.714680081926255[/C][C]0.642659959036873[/C][/ROW]
[ROW][C]27[/C][C]0.294159617405286[/C][C]0.588319234810571[/C][C]0.705840382594714[/C][/ROW]
[ROW][C]28[/C][C]0.236643886790967[/C][C]0.473287773581934[/C][C]0.763356113209033[/C][/ROW]
[ROW][C]29[/C][C]0.220478357545818[/C][C]0.440956715091636[/C][C]0.779521642454182[/C][/ROW]
[ROW][C]30[/C][C]0.189101448673128[/C][C]0.378202897346257[/C][C]0.810898551326872[/C][/ROW]
[ROW][C]31[/C][C]0.195269192032727[/C][C]0.390538384065454[/C][C]0.804730807967273[/C][/ROW]
[ROW][C]32[/C][C]0.170035364746871[/C][C]0.340070729493742[/C][C]0.829964635253129[/C][/ROW]
[ROW][C]33[/C][C]0.136675302295768[/C][C]0.273350604591537[/C][C]0.863324697704232[/C][/ROW]
[ROW][C]34[/C][C]0.121985542151987[/C][C]0.243971084303974[/C][C]0.878014457848013[/C][/ROW]
[ROW][C]35[/C][C]0.100533159080283[/C][C]0.201066318160566[/C][C]0.899466840919717[/C][/ROW]
[ROW][C]36[/C][C]0.0920162304516052[/C][C]0.18403246090321[/C][C]0.907983769548395[/C][/ROW]
[ROW][C]37[/C][C]0.0746404020671093[/C][C]0.149280804134219[/C][C]0.92535959793289[/C][/ROW]
[ROW][C]38[/C][C]0.0671250399108294[/C][C]0.134250079821659[/C][C]0.93287496008917[/C][/ROW]
[ROW][C]39[/C][C]0.0560087829623983[/C][C]0.112017565924797[/C][C]0.943991217037602[/C][/ROW]
[ROW][C]40[/C][C]0.0525184479967639[/C][C]0.105036895993528[/C][C]0.947481552003236[/C][/ROW]
[ROW][C]41[/C][C]0.0446613018749066[/C][C]0.0893226037498133[/C][C]0.955338698125093[/C][/ROW]
[ROW][C]42[/C][C]0.0503482550689518[/C][C]0.100696510137904[/C][C]0.949651744931048[/C][/ROW]
[ROW][C]43[/C][C]0.0584239982887018[/C][C]0.116847996577404[/C][C]0.941576001711298[/C][/ROW]
[ROW][C]44[/C][C]0.0990896983351433[/C][C]0.198179396670287[/C][C]0.900910301664857[/C][/ROW]
[ROW][C]45[/C][C]0.139113403338048[/C][C]0.278226806676096[/C][C]0.860886596661952[/C][/ROW]
[ROW][C]46[/C][C]0.126211650640592[/C][C]0.252423301281184[/C][C]0.873788349359408[/C][/ROW]
[ROW][C]47[/C][C]0.100570694923506[/C][C]0.201141389847013[/C][C]0.899429305076494[/C][/ROW]
[ROW][C]48[/C][C]0.0931756908366327[/C][C]0.186351381673265[/C][C]0.906824309163367[/C][/ROW]
[ROW][C]49[/C][C]0.0754165184754183[/C][C]0.150833036950837[/C][C]0.924583481524582[/C][/ROW]
[ROW][C]50[/C][C]0.0584063130290647[/C][C]0.116812626058129[/C][C]0.941593686970935[/C][/ROW]
[ROW][C]51[/C][C]0.0530991289765874[/C][C]0.106198257953175[/C][C]0.946900871023413[/C][/ROW]
[ROW][C]52[/C][C]0.0402429723655779[/C][C]0.0804859447311557[/C][C]0.959757027634422[/C][/ROW]
[ROW][C]53[/C][C]0.0300684858811862[/C][C]0.0601369717623724[/C][C]0.969931514118814[/C][/ROW]
[ROW][C]54[/C][C]0.0405096980883581[/C][C]0.0810193961767161[/C][C]0.959490301911642[/C][/ROW]
[ROW][C]55[/C][C]0.0311878825321075[/C][C]0.062375765064215[/C][C]0.968812117467893[/C][/ROW]
[ROW][C]56[/C][C]0.0247710427226456[/C][C]0.0495420854452912[/C][C]0.975228957277354[/C][/ROW]
[ROW][C]57[/C][C]0.0191847260826935[/C][C]0.038369452165387[/C][C]0.980815273917306[/C][/ROW]
[ROW][C]58[/C][C]0.0179824288249005[/C][C]0.0359648576498011[/C][C]0.9820175711751[/C][/ROW]
[ROW][C]59[/C][C]0.0135325046545364[/C][C]0.0270650093090728[/C][C]0.986467495345464[/C][/ROW]
[ROW][C]60[/C][C]0.0131574737286461[/C][C]0.0263149474572921[/C][C]0.986842526271354[/C][/ROW]
[ROW][C]61[/C][C]0.060127475168453[/C][C]0.120254950336906[/C][C]0.939872524831547[/C][/ROW]
[ROW][C]62[/C][C]0.380181667971083[/C][C]0.760363335942167[/C][C]0.619818332028916[/C][/ROW]
[ROW][C]63[/C][C]0.382472825945508[/C][C]0.764945651891016[/C][C]0.617527174054492[/C][/ROW]
[ROW][C]64[/C][C]0.346406481446153[/C][C]0.692812962892306[/C][C]0.653593518553847[/C][/ROW]
[ROW][C]65[/C][C]0.341148138917221[/C][C]0.682296277834443[/C][C]0.658851861082778[/C][/ROW]
[ROW][C]66[/C][C]0.323107177258825[/C][C]0.646214354517651[/C][C]0.676892822741174[/C][/ROW]
[ROW][C]67[/C][C]0.315420731005004[/C][C]0.630841462010008[/C][C]0.684579268994996[/C][/ROW]
[ROW][C]68[/C][C]0.313483467487199[/C][C]0.626966934974397[/C][C]0.686516532512801[/C][/ROW]
[ROW][C]69[/C][C]0.287640862347647[/C][C]0.575281724695293[/C][C]0.712359137652353[/C][/ROW]
[ROW][C]70[/C][C]0.248904535962523[/C][C]0.497809071925046[/C][C]0.751095464037477[/C][/ROW]
[ROW][C]71[/C][C]0.212750005382822[/C][C]0.425500010765644[/C][C]0.787249994617178[/C][/ROW]
[ROW][C]72[/C][C]0.183243623042443[/C][C]0.366487246084886[/C][C]0.816756376957557[/C][/ROW]
[ROW][C]73[/C][C]0.151765877585594[/C][C]0.303531755171187[/C][C]0.848234122414406[/C][/ROW]
[ROW][C]74[/C][C]0.131564974355876[/C][C]0.263129948711752[/C][C]0.868435025644124[/C][/ROW]
[ROW][C]75[/C][C]0.107114147377515[/C][C]0.214228294755029[/C][C]0.892885852622485[/C][/ROW]
[ROW][C]76[/C][C]0.093760180304465[/C][C]0.18752036060893[/C][C]0.906239819695535[/C][/ROW]
[ROW][C]77[/C][C]0.0749202516427564[/C][C]0.149840503285513[/C][C]0.925079748357244[/C][/ROW]
[ROW][C]78[/C][C]0.060345171473634[/C][C]0.120690342947268[/C][C]0.939654828526366[/C][/ROW]
[ROW][C]79[/C][C]0.0527386854938752[/C][C]0.10547737098775[/C][C]0.947261314506125[/C][/ROW]
[ROW][C]80[/C][C]0.0419252636890374[/C][C]0.0838505273780749[/C][C]0.958074736310963[/C][/ROW]
[ROW][C]81[/C][C]0.0391531805148873[/C][C]0.0783063610297747[/C][C]0.960846819485113[/C][/ROW]
[ROW][C]82[/C][C]0.0641306340237778[/C][C]0.128261268047556[/C][C]0.935869365976222[/C][/ROW]
[ROW][C]83[/C][C]0.0554693165621192[/C][C]0.110938633124238[/C][C]0.94453068343788[/C][/ROW]
[ROW][C]84[/C][C]0.0482854861704607[/C][C]0.0965709723409214[/C][C]0.95171451382954[/C][/ROW]
[ROW][C]85[/C][C]0.0447677771756687[/C][C]0.0895355543513374[/C][C]0.955232222824331[/C][/ROW]
[ROW][C]86[/C][C]0.042008865513033[/C][C]0.084017731026066[/C][C]0.957991134486967[/C][/ROW]
[ROW][C]87[/C][C]0.0416001047477122[/C][C]0.0832002094954244[/C][C]0.958399895252288[/C][/ROW]
[ROW][C]88[/C][C]0.0376124504455675[/C][C]0.075224900891135[/C][C]0.962387549554433[/C][/ROW]
[ROW][C]89[/C][C]0.0482181634309377[/C][C]0.0964363268618755[/C][C]0.951781836569062[/C][/ROW]
[ROW][C]90[/C][C]0.0465626693066807[/C][C]0.0931253386133614[/C][C]0.95343733069332[/C][/ROW]
[ROW][C]91[/C][C]0.0444183025222511[/C][C]0.0888366050445022[/C][C]0.955581697477749[/C][/ROW]
[ROW][C]92[/C][C]0.0903216143414674[/C][C]0.180643228682935[/C][C]0.909678385658533[/C][/ROW]
[ROW][C]93[/C][C]0.101776958774613[/C][C]0.203553917549227[/C][C]0.898223041225387[/C][/ROW]
[ROW][C]94[/C][C]0.104459826086608[/C][C]0.208919652173217[/C][C]0.895540173913392[/C][/ROW]
[ROW][C]95[/C][C]0.117806078932153[/C][C]0.235612157864305[/C][C]0.882193921067847[/C][/ROW]
[ROW][C]96[/C][C]0.127800107310234[/C][C]0.255600214620469[/C][C]0.872199892689766[/C][/ROW]
[ROW][C]97[/C][C]0.145576545046811[/C][C]0.291153090093621[/C][C]0.85442345495319[/C][/ROW]
[ROW][C]98[/C][C]0.162325214410754[/C][C]0.324650428821508[/C][C]0.837674785589246[/C][/ROW]
[ROW][C]99[/C][C]0.316283042145809[/C][C]0.632566084291618[/C][C]0.683716957854191[/C][/ROW]
[ROW][C]100[/C][C]0.38302588627934[/C][C]0.76605177255868[/C][C]0.61697411372066[/C][/ROW]
[ROW][C]101[/C][C]0.466804629203484[/C][C]0.933609258406969[/C][C]0.533195370796516[/C][/ROW]
[ROW][C]102[/C][C]0.71479497693128[/C][C]0.57041004613744[/C][C]0.28520502306872[/C][/ROW]
[ROW][C]103[/C][C]0.889361977083282[/C][C]0.221276045833435[/C][C]0.110638022916718[/C][/ROW]
[ROW][C]104[/C][C]0.941832178656676[/C][C]0.116335642686649[/C][C]0.0581678213433243[/C][/ROW]
[ROW][C]105[/C][C]0.962311034386188[/C][C]0.075377931227623[/C][C]0.0376889656138115[/C][/ROW]
[ROW][C]106[/C][C]0.964772665126701[/C][C]0.0704546697465973[/C][C]0.0352273348732987[/C][/ROW]
[ROW][C]107[/C][C]0.982402944006249[/C][C]0.0351941119875031[/C][C]0.0175970559937515[/C][/ROW]
[ROW][C]108[/C][C]0.994508562176775[/C][C]0.0109828756464494[/C][C]0.00549143782322469[/C][/ROW]
[ROW][C]109[/C][C]0.992060086028003[/C][C]0.0158798279439946[/C][C]0.00793991397199732[/C][/ROW]
[ROW][C]110[/C][C]0.99209302101838[/C][C]0.0158139579632413[/C][C]0.00790697898162064[/C][/ROW]
[ROW][C]111[/C][C]0.99947027923697[/C][C]0.00105944152605859[/C][C]0.000529720763029297[/C][/ROW]
[ROW][C]112[/C][C]0.99947042366662[/C][C]0.0010591526667612[/C][C]0.000529576333380602[/C][/ROW]
[ROW][C]113[/C][C]0.999269362281092[/C][C]0.00146127543781584[/C][C]0.00073063771890792[/C][/ROW]
[ROW][C]114[/C][C]0.998883964110529[/C][C]0.0022320717789424[/C][C]0.0011160358894712[/C][/ROW]
[ROW][C]115[/C][C]0.997919101724664[/C][C]0.00416179655067232[/C][C]0.00208089827533616[/C][/ROW]
[ROW][C]116[/C][C]0.99602384448373[/C][C]0.00795231103253943[/C][C]0.00397615551626971[/C][/ROW]
[ROW][C]117[/C][C]0.993762994859579[/C][C]0.0124740102808426[/C][C]0.00623700514042128[/C][/ROW]
[ROW][C]118[/C][C]0.997823338877793[/C][C]0.00435332224441384[/C][C]0.00217666112220692[/C][/ROW]
[ROW][C]119[/C][C]0.999429127787956[/C][C]0.00114174442408825[/C][C]0.000570872212044124[/C][/ROW]
[ROW][C]120[/C][C]0.999573668897884[/C][C]0.00085266220423235[/C][C]0.000426331102116175[/C][/ROW]
[ROW][C]121[/C][C]0.999570183848878[/C][C]0.000859632302244937[/C][C]0.000429816151122469[/C][/ROW]
[ROW][C]122[/C][C]0.99925691913093[/C][C]0.00148616173813845[/C][C]0.000743080869069224[/C][/ROW]
[ROW][C]123[/C][C]0.998130866154976[/C][C]0.00373826769004753[/C][C]0.00186913384502376[/C][/ROW]
[ROW][C]124[/C][C]0.999232235053394[/C][C]0.00153552989321105[/C][C]0.000767764946605523[/C][/ROW]
[ROW][C]125[/C][C]0.998114216633046[/C][C]0.00377156673390784[/C][C]0.00188578336695392[/C][/ROW]
[ROW][C]126[/C][C]0.99549417233089[/C][C]0.00901165533821828[/C][C]0.00450582766910914[/C][/ROW]
[ROW][C]127[/C][C]0.99028129964837[/C][C]0.0194374007032586[/C][C]0.00971870035162928[/C][/ROW]
[ROW][C]128[/C][C]0.973130120595226[/C][C]0.053739758809548[/C][C]0.026869879404774[/C][/ROW]
[ROW][C]129[/C][C]0.934190535185319[/C][C]0.131618929629362[/C][C]0.0658094648146809[/C][/ROW]
[ROW][C]130[/C][C]0.854746826606175[/C][C]0.29050634678765[/C][C]0.145253173393825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&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
100.2233126109689680.4466252219379360.776687389031032
110.1506221548976360.3012443097952710.849377845102364
120.2817599431323260.5635198862646530.718240056867674
130.7891135407850980.4217729184298030.210886459214902
140.8620449904731090.2759100190537830.137955009526891
150.8770533577024160.2458932845951680.122946642297584
160.8262960340010020.3474079319979950.173703965998998
170.8718929659403670.2562140681192650.128107034059633
180.8534137316436590.2931725367126820.146586268356341
190.8051130494887140.3897739010225710.194886950511286
200.7431647637161290.5136704725677420.256835236283871
210.6740122240773060.6519755518453890.325987775922694
220.607517559426980.784964881146040.39248244057302
230.5325961866613740.9348076266772520.467403813338626
240.4579236154031180.9158472308062350.542076384596882
250.4252697845189690.8505395690379380.574730215481031
260.3573400409631270.7146800819262550.642659959036873
270.2941596174052860.5883192348105710.705840382594714
280.2366438867909670.4732877735819340.763356113209033
290.2204783575458180.4409567150916360.779521642454182
300.1891014486731280.3782028973462570.810898551326872
310.1952691920327270.3905383840654540.804730807967273
320.1700353647468710.3400707294937420.829964635253129
330.1366753022957680.2733506045915370.863324697704232
340.1219855421519870.2439710843039740.878014457848013
350.1005331590802830.2010663181605660.899466840919717
360.09201623045160520.184032460903210.907983769548395
370.07464040206710930.1492808041342190.92535959793289
380.06712503991082940.1342500798216590.93287496008917
390.05600878296239830.1120175659247970.943991217037602
400.05251844799676390.1050368959935280.947481552003236
410.04466130187490660.08932260374981330.955338698125093
420.05034825506895180.1006965101379040.949651744931048
430.05842399828870180.1168479965774040.941576001711298
440.09908969833514330.1981793966702870.900910301664857
450.1391134033380480.2782268066760960.860886596661952
460.1262116506405920.2524233012811840.873788349359408
470.1005706949235060.2011413898470130.899429305076494
480.09317569083663270.1863513816732650.906824309163367
490.07541651847541830.1508330369508370.924583481524582
500.05840631302906470.1168126260581290.941593686970935
510.05309912897658740.1061982579531750.946900871023413
520.04024297236557790.08048594473115570.959757027634422
530.03006848588118620.06013697176237240.969931514118814
540.04050969808835810.08101939617671610.959490301911642
550.03118788253210750.0623757650642150.968812117467893
560.02477104272264560.04954208544529120.975228957277354
570.01918472608269350.0383694521653870.980815273917306
580.01798242882490050.03596485764980110.9820175711751
590.01353250465453640.02706500930907280.986467495345464
600.01315747372864610.02631494745729210.986842526271354
610.0601274751684530.1202549503369060.939872524831547
620.3801816679710830.7603633359421670.619818332028916
630.3824728259455080.7649456518910160.617527174054492
640.3464064814461530.6928129628923060.653593518553847
650.3411481389172210.6822962778344430.658851861082778
660.3231071772588250.6462143545176510.676892822741174
670.3154207310050040.6308414620100080.684579268994996
680.3134834674871990.6269669349743970.686516532512801
690.2876408623476470.5752817246952930.712359137652353
700.2489045359625230.4978090719250460.751095464037477
710.2127500053828220.4255000107656440.787249994617178
720.1832436230424430.3664872460848860.816756376957557
730.1517658775855940.3035317551711870.848234122414406
740.1315649743558760.2631299487117520.868435025644124
750.1071141473775150.2142282947550290.892885852622485
760.0937601803044650.187520360608930.906239819695535
770.07492025164275640.1498405032855130.925079748357244
780.0603451714736340.1206903429472680.939654828526366
790.05273868549387520.105477370987750.947261314506125
800.04192526368903740.08385052737807490.958074736310963
810.03915318051488730.07830636102977470.960846819485113
820.06413063402377780.1282612680475560.935869365976222
830.05546931656211920.1109386331242380.94453068343788
840.04828548617046070.09657097234092140.95171451382954
850.04476777717566870.08953555435133740.955232222824331
860.0420088655130330.0840177310260660.957991134486967
870.04160010474771220.08320020949542440.958399895252288
880.03761245044556750.0752249008911350.962387549554433
890.04821816343093770.09643632686187550.951781836569062
900.04656266930668070.09312533861336140.95343733069332
910.04441830252225110.08883660504450220.955581697477749
920.09032161434146740.1806432286829350.909678385658533
930.1017769587746130.2035539175492270.898223041225387
940.1044598260866080.2089196521732170.895540173913392
950.1178060789321530.2356121578643050.882193921067847
960.1278001073102340.2556002146204690.872199892689766
970.1455765450468110.2911530900936210.85442345495319
980.1623252144107540.3246504288215080.837674785589246
990.3162830421458090.6325660842916180.683716957854191
1000.383025886279340.766051772558680.61697411372066
1010.4668046292034840.9336092584069690.533195370796516
1020.714794976931280.570410046137440.28520502306872
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1040.9418321786566760.1163356426866490.0581678213433243
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1080.9945085621767750.01098287564644940.00549143782322469
1090.9920600860280030.01587982794399460.00793991397199732
1100.992093021018380.01581395796324130.00790697898162064
1110.999470279236970.001059441526058590.000529720763029297
1120.999470423666620.00105915266676120.000529576333380602
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1200.9995736688978840.000852662204232350.000426331102116175
1210.9995701838488780.0008596323022449370.000429816151122469
1220.999256919130930.001486161738138450.000743080869069224
1230.9981308661549760.003738267690047530.00186913384502376
1240.9992322350533940.001535529893211050.000767764946605523
1250.9981142166330460.003771566733907840.00188578336695392
1260.995494172330890.009011655338218280.00450582766910914
1270.990281299648370.01943740070325860.00971870035162928
1280.9731301205952260.0537397588095480.026869879404774
1290.9341905351853190.1316189296293620.0658094648146809
1300.8547468266061750.290506346787650.145253173393825






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.12396694214876NOK
5% type I error level260.214876033057851NOK
10% type I error level440.363636363636364NOK

\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 & 15 & 0.12396694214876 & NOK \tabularnewline
5% type I error level & 26 & 0.214876033057851 & NOK \tabularnewline
10% type I error level & 44 & 0.363636363636364 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159358&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]15[/C][C]0.12396694214876[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.214876033057851[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.363636363636364[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159358&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159358&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 level150.12396694214876NOK
5% type I error level260.214876033057851NOK
10% type I error level440.363636363636364NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal 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')
}