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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 22 Dec 2011 05:35:01 -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/t1324550166nv8hkwre43jr64n.htm/, Retrieved Fri, 03 May 2024 14:27:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159277, Retrieved Fri, 03 May 2024 14:27:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

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] [Lineaire trend + ...] [2011-12-22 10:35:01] [0b94335bf72158573fe52322b9537409] [Current]
-    D        [Multiple Regression] [Lineair trend + r...] [2011-12-22 10:44:53] [eb6e95800005ec22b7fd76eead8d8a59]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9/06/2002	169498	1,21	0,97	0,80	5,06
16/06/2002	125451	1,22	0,95	0,80	5,07
23/06/2002	140449	1,22	0,96	0,80	5,04
30/06/2002	141653	1,22	0,97	0,81	5,08
7/07/2002	136394	1,21	0,96	0,81	5,15
14/07/2002	167588	1,23	0,96	0,83	5,16
21/07/2002	191807	1,22	0,94	0,79	5,09
28/07/2002	149736	1,22	0,95	0,80	5,14
4/08/2002	196066	1,22	0,95	0,81	5,17
11/08/2002	239155	1,22	0,95	0,80	5,13
18/08/2002	178421	1,23	0,95	0,81	5,19
25/08/2002	139871	1,22	0,97	0,81	5,17
1/09/2002	118159	1,21	0,97	0,82	5,17
8/09/2002	109763	1,22	0,95	0,81	5,21
15/09/2002	97415	1,21	0,96	0,82	5,21
22/09/2002	119190	1,22	0,96	0,82	5,23
29/09/2002	97903	1,21	0,94	0,80	5,17
6/10/2002	96953	1,20	0,96	0,79	5,16
13/10/2002	87888	1,18	0,98	0,79	5,15
20/10/2002	84637	1,19	0,97	0,80	5,12
27/10/2002	90549	1,20	0,96	0,80	5,12
3/11/2002	95680	1,19	0,95	0,80	5,10
10/11/2002	99371	1,19	0,96	0,80	5,13
17/11/2002	79984	1,20	0,96	0,80	5,14
24/11/2002	86752	1,21	0,97	0,80	5,16
1/12/2002	85733	1,20	0,96	0,80	5,17
8/12/2002	84906	1,20	0,95	0,78	5,15
15/12/2002	78356	1,20	0,95	0,78	5,12
22/12/2002	108895	1,21	0,94	0,76	5,08
29/12/2002	101768	1,21	0,94	0,77	5,07
5/01/2003	73285	1,21	0,98	0,82	5,16
12/01/2003	65724	1,20	0,93	0,81	5,14
19/01/2003	67457	1,21	0,93	0,81	5,13
26/01/2003	67203	1,21	0,96	0,80	5,11
2/02/2003	69273	1,21	0,97	0,79	5,12
9/02/2003	80807	1,20	0,97	0,81	5,09
16/02/2003	75129	1,19	0,95	0,81	5,10
23/02/2003	74991	1,20	0,95	0,81	4,95
2/03/2003	68157	1,20	0,96	0,81	5,11
9/03/2003	73858	1,20	0,98	0,82	5,11
16/03/2003	71349	1,22	0,98	0,81	5,10
23/03/2003	85634	1,22	0,97	0,80	5,11
30/03/2003	91624	1,21	0,98	0,83	5,12
6/04/2003	116014	1,25	0,98	0,81	5,09
13/04/2003	120033	1,25	0,99	0,83	5,10
20/04/2003	108651	1,27	0,99	0,84	5,06
27/04/2003	105378	1,28	0,97	0,84	5,03
4/05/2003	138939	1,27	0,98	0,85	5,05
11/05/2003	132974	1,28	0,97	0,85	5,04
18/05/2003	135277	1,29	0,97	0,86	5,02
25/05/2003	152741	1,26	0,97	0,85	4,97
1/06/2003	158417	1,27	0,98	0,87	4,91
8/06/2003	157460	1,25	0,97	0,86	4,91
15/06/2003	193997	1,27	0,97	0,87	4,98
22/06/2003	154089	1,27	0,98	0,87	4,98
29/06/2003	147570	1,27	0,98	0,86	4,97
6/07/2003	162924	1,29	0,95	0,87	4,97
13/07/2003	153629	1,26	0,97	0,88	4,90
20/07/2003	155907	1,27	0,97	0,87	4,91
27/07/2003	197675	1,27	0,97	0,87	4,88
3/08/2003	250708	1,28	0,97	0,86	4,86
10/08/2003	266652	1,28	0,98	0,86	4,87
17/08/2003	209842	1,28	0,98	0,88	4,86
24/08/2003	165826	1,27	0,98	0,88	4,89
31/08/2003	137152	1,24	0,96	0,87	4,90
7/09/2003	150581	1,25	0,98	0,88	4,88
14/09/2003	145973	1,25	1,00	0,89	4,85
21/09/2003	126532	1,24	1,01	0,89	4,85
28/09/2003	115437	1,24	1,02	0,88	4,84
5/10/2003	119526	1,23	1,01	0,88	4,91
12/10/2003	110856	1,24	1,01	0,88	4,94
19/10/2003	97243	1,23	1,02	0,88	4,92
26/10/2003	103876	1,24	1,01	0,87	4,93
2/11/2003	116370	1,24	1,01	0,87	4,97
9/11/2003	109616	1,24	1,01	0,86	4,89
16/11/2003	98365	1,25	1,02	0,88	4,88
23/11/2003	90440	1,26	1,02	0,87	4,93
30/11/2003	88899	1,26	1,02	0,86	4,94
7/12/2003	92358	1,27	1,01	0,85	4,99
14/12/2003	88394	1,26	1,01	0,86	5,00
21/12/2003	98219	1,28	0,99	0,84	5,02
28/12/2003	113546	1,29	1,00	0,85	5,06
4/01/2004	107168	1,28	1,01	0,88	5,01
11/01/2004	77540	1,27	0,99	0,88	5,02
18/01/2004	74944	1,30	1,00	0,89	4,97
25/01/2004	75641	1,30	1,02	0,88	4,96
1/02/2004	75910	1,28	1,01	0,88	4,95
8/02/2004	87384	1,29	1,01	0,88	4,92
15/02/2004	84615	1,27	1,01	0,89	4,88
22/02/2004	80420	1,26	1,03	0,89	4,86
29/02/2004	80784	1,27	1,02	0,89	4,94
7/03/2004	79933	1,27	1,02	0,88	4,83
14/03/2004	82118	1,27	1,03	0,89	4,95
21/03/2004	91420	1,28	1,03	0,89	4,95
28/03/2004	112426	1,29	1,02	0,89	4,94
4/04/2004	114528	1,28	1,02	0,89	4,93
11/04/2004	131025	1,30	1,02	0,90	4,97
18/04/2004	116460	1,30	1,02	0,88	4,95
25/04/2004	111258	1,30	1,03	0,90	4,92
2/05/2004	155318	1,29	1,02	0,88	4,82
9/05/2004	155078	1,30	1,02	0,90	4,82
16/05/2004	134794	1,29	1,02	0,89	4,84
23/05/2004	139985	1,28	1,03	0,89	4,83
30/05/2004	198778	1,30	1,02	0,88	4,79
6/06/2004	172436	1,30	1,02	0,89	4,81
13/06/2004	169585	1,31	1,02	0,91	4,85
20/06/2004	203702	1,32	1,02	0,91	4,84
27/06/2004	282392	1,33	1,02	0,90	4,82
4/07/2004	220658	1,32	1,00	0,93	4,92
11/07/2004	194472	1,30	1,04	0,94	4,92
18/07/2004	269246	1,31	1,04	0,95	4,90
25/07/2004	215340	1,30	1,03	0,95	4,91
1/08/2004	218319	1,30	1,02	0,93	4,85
8/08/2004	195724	1,30	1,04	0,95	4,86
15/08/2004	174614	1,29	1,05	0,95	4,88
22/08/2004	172085	1,29	1,03	0,94	4,85
29/08/2004	152347	1,30	0,99	0,92	4,91
5/09/2004	189615	1,30	1,03	0,94	4,89
12/09/2004	173804	1,29	1,08	0,95	4,92
19/09/2004	145683	1,27	1,09	0,97	4,82
26/09/2004	133550	1,26	1,08	0,96	4,82
3/10/2004	121156	1,25	1,05	0,92	4,87
10/10/2004	112040	1,26	1,06	0,94	4,88
17/10/2004	120767	1,27	1,04	0,94	4,90
24/10/2004	127019	1,26	1,06	0,92	4,88
31/10/2004	136295	1,25	1,06	0,91	4,89
7/11/2004	113425	1,25	1,07	0,93	4,88
14/11/2004	107815	1,25	1,08	0,93	4,87
21/11/2004	100298	1,26	1,08	0,94	4,85
28/11/2004	97048	1,26	1,05	0,92	4,87
5/12/2004	98750	1,26	1,04	0,91	4,88
12/12/2004	98235	1,27	1,04	0,91	4,87
19/12/2004	101254	1,28	1,04	0,90	4,93
26/12/2004	139589	1,29	1,04	0,89	4,93
2/01/2005	134921	1,30	1,06	0,91	4,74
9/01/2005	80355	1,26	1,08	0,93	4,77
16/01/2005	80396	1,25	1,08	0,94	4,81
23/01/2005	82183	1,26	1,08	0,93	4,82
30/01/2005	79709	1,25	1,07	0,91	4,79
6/02/2005	90781	1,24	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'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
QBEFRU[t] = -293092.810506366 -4611258.26706543Tijd[t] + 701009.524429788PBEPIL[t] -350578.685700115PSOCOLA[t] + 705127.992995622PSOORA[t] -127005.372071326PSTIM[t] -1106.08248538532t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEFRU[t] =  -293092.810506366 -4611258.26706543Tijd[t] +  701009.524429788PBEPIL[t] -350578.685700115PSOCOLA[t] +  705127.992995622PSOORA[t] -127005.372071326PSTIM[t] -1106.08248538532t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEFRU[t] =  -293092.810506366 -4611258.26706543Tijd[t] +  701009.524429788PBEPIL[t] -350578.685700115PSOCOLA[t] +  705127.992995622PSOORA[t] -127005.372071326PSTIM[t] -1106.08248538532t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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] = -293092.810506366 -4611258.26706543Tijd[t] + 701009.524429788PBEPIL[t] -350578.685700115PSOCOLA[t] + 705127.992995622PSOORA[t] -127005.372071326PSTIM[t] -1106.08248538532t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-293092.810506366366167.395209-0.80040.4248870.212443
Tijd-4611258.267065431078539.578349-4.27553.6e-051.8e-05
PBEPIL701009.524429788132210.3518855.302200
PSOCOLA-350578.685700115194336.399735-1.8040.0734980.036749
PSOORA705127.992995622166135.2714024.24434.1e-052e-05
PSTIM-127005.37207132644882.746465-2.82970.0053820.002691
t-1106.08248538532230.668868-4.79514e-062e-06

\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) & -293092.810506366 & 366167.395209 & -0.8004 & 0.424887 & 0.212443 \tabularnewline
Tijd & -4611258.26706543 & 1078539.578349 & -4.2755 & 3.6e-05 & 1.8e-05 \tabularnewline
PBEPIL & 701009.524429788 & 132210.351885 & 5.3022 & 0 & 0 \tabularnewline
PSOCOLA & -350578.685700115 & 194336.399735 & -1.804 & 0.073498 & 0.036749 \tabularnewline
PSOORA & 705127.992995622 & 166135.271402 & 4.2443 & 4.1e-05 & 2e-05 \tabularnewline
PSTIM & -127005.372071326 & 44882.746465 & -2.8297 & 0.005382 & 0.002691 \tabularnewline
t & -1106.08248538532 & 230.668868 & -4.7951 & 4e-06 & 2e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&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]-293092.810506366[/C][C]366167.395209[/C][C]-0.8004[/C][C]0.424887[/C][C]0.212443[/C][/ROW]
[ROW][C]Tijd[/C][C]-4611258.26706543[/C][C]1078539.578349[/C][C]-4.2755[/C][C]3.6e-05[/C][C]1.8e-05[/C][/ROW]
[ROW][C]PBEPIL[/C][C]701009.524429788[/C][C]132210.351885[/C][C]5.3022[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PSOCOLA[/C][C]-350578.685700115[/C][C]194336.399735[/C][C]-1.804[/C][C]0.073498[/C][C]0.036749[/C][/ROW]
[ROW][C]PSOORA[/C][C]705127.992995622[/C][C]166135.271402[/C][C]4.2443[/C][C]4.1e-05[/C][C]2e-05[/C][/ROW]
[ROW][C]PSTIM[/C][C]-127005.372071326[/C][C]44882.746465[/C][C]-2.8297[/C][C]0.005382[/C][C]0.002691[/C][/ROW]
[ROW][C]t[/C][C]-1106.08248538532[/C][C]230.668868[/C][C]-4.7951[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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)-293092.810506366366167.395209-0.80040.4248870.212443
Tijd-4611258.267065431078539.578349-4.27553.6e-051.8e-05
PBEPIL701009.524429788132210.3518855.302200
PSOCOLA-350578.685700115194336.399735-1.8040.0734980.036749
PSOORA705127.992995622166135.2714024.24434.1e-052e-05
PSTIM-127005.37207132644882.746465-2.82970.0053820.002691
t-1106.08248538532230.668868-4.79514e-062e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.74813475641361
R-squared0.559705613754051
Adjusted R-squared0.539842709111377
F-TEST (value)28.1784373344649
F-TEST (DF numerator)6
F-TEST (DF denominator)133
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31688.9981362752
Sum Squared Residuals133557616183.154

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.74813475641361 \tabularnewline
R-squared & 0.559705613754051 \tabularnewline
Adjusted R-squared & 0.539842709111377 \tabularnewline
F-TEST (value) & 28.1784373344649 \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 & 31688.9981362752 \tabularnewline
Sum Squared Residuals & 133557616183.154 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.74813475641361[/C][/ROW]
[ROW][C]R-squared[/C][C]0.559705613754051[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.539842709111377[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.1784373344649[/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]31688.9981362752[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]133557616183.154[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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 R0.74813475641361
R-squared0.559705613754051
Adjusted R-squared0.539842709111377
F-TEST (value)28.1784373344649
F-TEST (DF numerator)6
F-TEST (DF denominator)133
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31688.9981362752
Sum Squared Residuals133557616183.154






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1169498131961.52944317937536.4705568207
2125451140919.848753037-15468.8487530367
3140449137430.9271304443018.0728695558
4141653132102.9093928159550.09060718486
5136394127815.445706048578.55429395978
6167588151258.73404072516329.2659592752
7191807128536.06054248763270.939457513
8149736122321.87671876527414.1232812346
9196066132518.55332823463547.446671766
10239155127425.995713986111729.004286014
11178421130745.55599681647675.444003184
12139871116142.50191279823728.4980872023
13118159122019.572172464-3860.57217246397
14109763121012.188204339-11249.1882043391
1597415114650.02791938-17235.0279193804
16119190116222.4576086362967.54239136418
1797903106844.140429092-8941.14042909163
189695391974.99489419754978.00510580247
198788869494.873861519918393.1261384801
208463788153.7865041221-3516.78650412214
219054995951.2580546284-5402.25805462837
229568099471.7745384872-3791.77453848719
239937189583.99124722689787.00875277316
247998492752.1974986921-12768.1974986921
258675291144.5631724428-4392.56317244284
268573390097.6128876826-4364.61288768258
278490679591.25811963955314.74188036047
287835680951.7300752211-2595.7300752211
2910889579995.577992902528899.4220070975
3010176885867.222437013815900.7775629862
317328588619.400040347-15334.400040347
326572477405.7530190431-11681.7530190431
336745768464.5884105721-1007.58841057207
346720336214.741777556730988.2582224433
356927380835.9355277038-11562.9355277038
368080782574.8632760244-1767.86327602443
377512972142.58999558192986.4100044181
387499189039.7930211449-14048.7930211448
396815789047.3027360675-20890.3027360676
407385882609.1827706038-8751.18277060377
417134984370.3208685391-13021.3208685391
428563473076.947893453112557.0521065469
439162475967.025679891915656.9743201081
44116014112177.4203666153836.57963338547
45120033116369.2493914033663.75060859701
46108651137386.044435398-28735.0444353985
47105378150082.984298429-44704.9842984289
48138939156670.138625157-17731.1386251566
49132974164126.945744164-31152.9457441642
50135277176399.29965684-41122.2996568402
51152741150338.8738945522402.12610544812
52158417185587.165351008-27170.1653510078
53157460164229.527456056-6769.52745605553
54193997172618.66749621321378.3325037868
55154089165320.926305811-11231.9263058106
56147570155747.745763166-8177.74576316628
57162924195384.384035065-32460.3840350649
58153629179875.922208004-26246.9222080045
59155907175156.425446519-19249.4254465194
60197675175558.32825354622116.6717464542
61250708184967.67378458965740.326215411
62266652177071.34683547789580.6531645228
63209842189323.47404470520518.5259552945
64165826175382.731266871-9556.7312668705
65137152149922.199225912-12770.1992259122
66150581165536.375905391-14955.3759053909
67145973166489.579566089-20516.5795660886
68126532153077.033747393-26545.0337473935
69115437140893.356963753-25456.3569637532
70119526133403.827039257-13877.8270392573
71110856133886.15552722-23030.1555272204
7297243123192.775273153-25949.7752731529
73103876122669.718129588-18793.7181295875
74116370122050.500591783-5680.50059178263
75109616122588.546934138-12972.5469341384
7698365138894.365408667-40529.3654086666
7790440129931.808626048-39491.8086260479
7888899119039.371481984-30140.3714819842
7992358119983.348103262-27625.3481032623
8088394116305.460658814-27911.460658814
8198219118245.53915068-20026.5391506798
82113546121271.894175686-7725.8941756863
83107168133321.673360973-26153.6733609733
8477540114839.826068961-37299.8260689614
8574944128552.601437373-53608.6014373733
867564198546.529473125-22905.529473125
8775910144571.26052152-68661.2605215204
8887384146231.839664764-58847.8396647639
8984615135183.466725783-50568.4667257832
9080420114542.227945715-34122.2279457153
9180784105738.003018114-24954.0030181141
9279933139547.060996335-59614.060996335
9382118121376.76375014-39258.7637501397
9491420121911.713323846-30491.7133238464
95112426127222.503475267-14796.5034752674
96114528139551.605127747-25023.6051277472
97131025150409.980789156-19384.9807891564
98116460133714.648496381-17254.6484963807
99111258142988.702787142-31730.7027871421
100155318150437.2976760184880.7023239822
101155078167222.432383719-12144.4323837193
102134794146293.42937153-11499.4293715298
103139985132720.0805944357264.91940556485
104198778143947.4724964254830.52750358
105172436158857.69789643413578.3021035657
106169585171099.524039803-1514.52403980332
107203702175589.05892682628112.9410731739
108282392174297.367604606108094.632395394
109220658190685.81541205929972.184587941
110194472166286.64786065528185.3521393445
111269246179481.02091157789764.9790884232
112215340171299.54923880844040.4507611925
113218319175147.34154477643171.6584552242
114195724177848.79279013517875.207209865
115174614161673.32206757212940.677932428
116172085162324.295833929760.70416607965
117152347158515.175142193-6168.17514219335
118189615167091.48731544322523.512684557
119173804142897.80634016830906.1936598315
120145683149279.155847829-3596.15584782923
121133550135827.797316789-2277.79731678898
122121156109630.69527354211525.3047264581
123112040123250.708359091-11210.7083590907
124120767132015.468435017-11248.4684350172
125127019103714.54561728423304.4543827159
12613629585666.315281369750628.6847186303
127113425102095.95350606711329.0464939334
12810781597289.847924791710525.1520752083
129100298111320.958095485-11022.9580954854
13097048102625.278920163-5577.27892016297
13198750101602.048196443-2852.04819644334
13298235107433.848879768-9198.84887976765
13310125497323.9935881433930.00641185699
13413958994834.460620797944754.5393792021
135134921132346.2800212592574.71997874135
1368035590381.4854978348-10026.4854978348
1378039668137.216770637312258.7832293627
1388218349620.739834262932562.2601657371
1397970918618.794219190861090.2057808092
1409078188236.64290284242544.35709715758

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 169498 & 131961.529443179 & 37536.4705568207 \tabularnewline
2 & 125451 & 140919.848753037 & -15468.8487530367 \tabularnewline
3 & 140449 & 137430.927130444 & 3018.0728695558 \tabularnewline
4 & 141653 & 132102.909392815 & 9550.09060718486 \tabularnewline
5 & 136394 & 127815.44570604 & 8578.55429395978 \tabularnewline
6 & 167588 & 151258.734040725 & 16329.2659592752 \tabularnewline
7 & 191807 & 128536.060542487 & 63270.939457513 \tabularnewline
8 & 149736 & 122321.876718765 & 27414.1232812346 \tabularnewline
9 & 196066 & 132518.553328234 & 63547.446671766 \tabularnewline
10 & 239155 & 127425.995713986 & 111729.004286014 \tabularnewline
11 & 178421 & 130745.555996816 & 47675.444003184 \tabularnewline
12 & 139871 & 116142.501912798 & 23728.4980872023 \tabularnewline
13 & 118159 & 122019.572172464 & -3860.57217246397 \tabularnewline
14 & 109763 & 121012.188204339 & -11249.1882043391 \tabularnewline
15 & 97415 & 114650.02791938 & -17235.0279193804 \tabularnewline
16 & 119190 & 116222.457608636 & 2967.54239136418 \tabularnewline
17 & 97903 & 106844.140429092 & -8941.14042909163 \tabularnewline
18 & 96953 & 91974.9948941975 & 4978.00510580247 \tabularnewline
19 & 87888 & 69494.8738615199 & 18393.1261384801 \tabularnewline
20 & 84637 & 88153.7865041221 & -3516.78650412214 \tabularnewline
21 & 90549 & 95951.2580546284 & -5402.25805462837 \tabularnewline
22 & 95680 & 99471.7745384872 & -3791.77453848719 \tabularnewline
23 & 99371 & 89583.9912472268 & 9787.00875277316 \tabularnewline
24 & 79984 & 92752.1974986921 & -12768.1974986921 \tabularnewline
25 & 86752 & 91144.5631724428 & -4392.56317244284 \tabularnewline
26 & 85733 & 90097.6128876826 & -4364.61288768258 \tabularnewline
27 & 84906 & 79591.2581196395 & 5314.74188036047 \tabularnewline
28 & 78356 & 80951.7300752211 & -2595.7300752211 \tabularnewline
29 & 108895 & 79995.5779929025 & 28899.4220070975 \tabularnewline
30 & 101768 & 85867.2224370138 & 15900.7775629862 \tabularnewline
31 & 73285 & 88619.400040347 & -15334.400040347 \tabularnewline
32 & 65724 & 77405.7530190431 & -11681.7530190431 \tabularnewline
33 & 67457 & 68464.5884105721 & -1007.58841057207 \tabularnewline
34 & 67203 & 36214.7417775567 & 30988.2582224433 \tabularnewline
35 & 69273 & 80835.9355277038 & -11562.9355277038 \tabularnewline
36 & 80807 & 82574.8632760244 & -1767.86327602443 \tabularnewline
37 & 75129 & 72142.5899955819 & 2986.4100044181 \tabularnewline
38 & 74991 & 89039.7930211449 & -14048.7930211448 \tabularnewline
39 & 68157 & 89047.3027360675 & -20890.3027360676 \tabularnewline
40 & 73858 & 82609.1827706038 & -8751.18277060377 \tabularnewline
41 & 71349 & 84370.3208685391 & -13021.3208685391 \tabularnewline
42 & 85634 & 73076.9478934531 & 12557.0521065469 \tabularnewline
43 & 91624 & 75967.0256798919 & 15656.9743201081 \tabularnewline
44 & 116014 & 112177.420366615 & 3836.57963338547 \tabularnewline
45 & 120033 & 116369.249391403 & 3663.75060859701 \tabularnewline
46 & 108651 & 137386.044435398 & -28735.0444353985 \tabularnewline
47 & 105378 & 150082.984298429 & -44704.9842984289 \tabularnewline
48 & 138939 & 156670.138625157 & -17731.1386251566 \tabularnewline
49 & 132974 & 164126.945744164 & -31152.9457441642 \tabularnewline
50 & 135277 & 176399.29965684 & -41122.2996568402 \tabularnewline
51 & 152741 & 150338.873894552 & 2402.12610544812 \tabularnewline
52 & 158417 & 185587.165351008 & -27170.1653510078 \tabularnewline
53 & 157460 & 164229.527456056 & -6769.52745605553 \tabularnewline
54 & 193997 & 172618.667496213 & 21378.3325037868 \tabularnewline
55 & 154089 & 165320.926305811 & -11231.9263058106 \tabularnewline
56 & 147570 & 155747.745763166 & -8177.74576316628 \tabularnewline
57 & 162924 & 195384.384035065 & -32460.3840350649 \tabularnewline
58 & 153629 & 179875.922208004 & -26246.9222080045 \tabularnewline
59 & 155907 & 175156.425446519 & -19249.4254465194 \tabularnewline
60 & 197675 & 175558.328253546 & 22116.6717464542 \tabularnewline
61 & 250708 & 184967.673784589 & 65740.326215411 \tabularnewline
62 & 266652 & 177071.346835477 & 89580.6531645228 \tabularnewline
63 & 209842 & 189323.474044705 & 20518.5259552945 \tabularnewline
64 & 165826 & 175382.731266871 & -9556.7312668705 \tabularnewline
65 & 137152 & 149922.199225912 & -12770.1992259122 \tabularnewline
66 & 150581 & 165536.375905391 & -14955.3759053909 \tabularnewline
67 & 145973 & 166489.579566089 & -20516.5795660886 \tabularnewline
68 & 126532 & 153077.033747393 & -26545.0337473935 \tabularnewline
69 & 115437 & 140893.356963753 & -25456.3569637532 \tabularnewline
70 & 119526 & 133403.827039257 & -13877.8270392573 \tabularnewline
71 & 110856 & 133886.15552722 & -23030.1555272204 \tabularnewline
72 & 97243 & 123192.775273153 & -25949.7752731529 \tabularnewline
73 & 103876 & 122669.718129588 & -18793.7181295875 \tabularnewline
74 & 116370 & 122050.500591783 & -5680.50059178263 \tabularnewline
75 & 109616 & 122588.546934138 & -12972.5469341384 \tabularnewline
76 & 98365 & 138894.365408667 & -40529.3654086666 \tabularnewline
77 & 90440 & 129931.808626048 & -39491.8086260479 \tabularnewline
78 & 88899 & 119039.371481984 & -30140.3714819842 \tabularnewline
79 & 92358 & 119983.348103262 & -27625.3481032623 \tabularnewline
80 & 88394 & 116305.460658814 & -27911.460658814 \tabularnewline
81 & 98219 & 118245.53915068 & -20026.5391506798 \tabularnewline
82 & 113546 & 121271.894175686 & -7725.8941756863 \tabularnewline
83 & 107168 & 133321.673360973 & -26153.6733609733 \tabularnewline
84 & 77540 & 114839.826068961 & -37299.8260689614 \tabularnewline
85 & 74944 & 128552.601437373 & -53608.6014373733 \tabularnewline
86 & 75641 & 98546.529473125 & -22905.529473125 \tabularnewline
87 & 75910 & 144571.26052152 & -68661.2605215204 \tabularnewline
88 & 87384 & 146231.839664764 & -58847.8396647639 \tabularnewline
89 & 84615 & 135183.466725783 & -50568.4667257832 \tabularnewline
90 & 80420 & 114542.227945715 & -34122.2279457153 \tabularnewline
91 & 80784 & 105738.003018114 & -24954.0030181141 \tabularnewline
92 & 79933 & 139547.060996335 & -59614.060996335 \tabularnewline
93 & 82118 & 121376.76375014 & -39258.7637501397 \tabularnewline
94 & 91420 & 121911.713323846 & -30491.7133238464 \tabularnewline
95 & 112426 & 127222.503475267 & -14796.5034752674 \tabularnewline
96 & 114528 & 139551.605127747 & -25023.6051277472 \tabularnewline
97 & 131025 & 150409.980789156 & -19384.9807891564 \tabularnewline
98 & 116460 & 133714.648496381 & -17254.6484963807 \tabularnewline
99 & 111258 & 142988.702787142 & -31730.7027871421 \tabularnewline
100 & 155318 & 150437.297676018 & 4880.7023239822 \tabularnewline
101 & 155078 & 167222.432383719 & -12144.4323837193 \tabularnewline
102 & 134794 & 146293.42937153 & -11499.4293715298 \tabularnewline
103 & 139985 & 132720.080594435 & 7264.91940556485 \tabularnewline
104 & 198778 & 143947.47249642 & 54830.52750358 \tabularnewline
105 & 172436 & 158857.697896434 & 13578.3021035657 \tabularnewline
106 & 169585 & 171099.524039803 & -1514.52403980332 \tabularnewline
107 & 203702 & 175589.058926826 & 28112.9410731739 \tabularnewline
108 & 282392 & 174297.367604606 & 108094.632395394 \tabularnewline
109 & 220658 & 190685.815412059 & 29972.184587941 \tabularnewline
110 & 194472 & 166286.647860655 & 28185.3521393445 \tabularnewline
111 & 269246 & 179481.020911577 & 89764.9790884232 \tabularnewline
112 & 215340 & 171299.549238808 & 44040.4507611925 \tabularnewline
113 & 218319 & 175147.341544776 & 43171.6584552242 \tabularnewline
114 & 195724 & 177848.792790135 & 17875.207209865 \tabularnewline
115 & 174614 & 161673.322067572 & 12940.677932428 \tabularnewline
116 & 172085 & 162324.29583392 & 9760.70416607965 \tabularnewline
117 & 152347 & 158515.175142193 & -6168.17514219335 \tabularnewline
118 & 189615 & 167091.487315443 & 22523.512684557 \tabularnewline
119 & 173804 & 142897.806340168 & 30906.1936598315 \tabularnewline
120 & 145683 & 149279.155847829 & -3596.15584782923 \tabularnewline
121 & 133550 & 135827.797316789 & -2277.79731678898 \tabularnewline
122 & 121156 & 109630.695273542 & 11525.3047264581 \tabularnewline
123 & 112040 & 123250.708359091 & -11210.7083590907 \tabularnewline
124 & 120767 & 132015.468435017 & -11248.4684350172 \tabularnewline
125 & 127019 & 103714.545617284 & 23304.4543827159 \tabularnewline
126 & 136295 & 85666.3152813697 & 50628.6847186303 \tabularnewline
127 & 113425 & 102095.953506067 & 11329.0464939334 \tabularnewline
128 & 107815 & 97289.8479247917 & 10525.1520752083 \tabularnewline
129 & 100298 & 111320.958095485 & -11022.9580954854 \tabularnewline
130 & 97048 & 102625.278920163 & -5577.27892016297 \tabularnewline
131 & 98750 & 101602.048196443 & -2852.04819644334 \tabularnewline
132 & 98235 & 107433.848879768 & -9198.84887976765 \tabularnewline
133 & 101254 & 97323.993588143 & 3930.00641185699 \tabularnewline
134 & 139589 & 94834.4606207979 & 44754.5393792021 \tabularnewline
135 & 134921 & 132346.280021259 & 2574.71997874135 \tabularnewline
136 & 80355 & 90381.4854978348 & -10026.4854978348 \tabularnewline
137 & 80396 & 68137.2167706373 & 12258.7832293627 \tabularnewline
138 & 82183 & 49620.7398342629 & 32562.2601657371 \tabularnewline
139 & 79709 & 18618.7942191908 & 61090.2057808092 \tabularnewline
140 & 90781 & 88236.6429028424 & 2544.35709715758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&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]131961.529443179[/C][C]37536.4705568207[/C][/ROW]
[ROW][C]2[/C][C]125451[/C][C]140919.848753037[/C][C]-15468.8487530367[/C][/ROW]
[ROW][C]3[/C][C]140449[/C][C]137430.927130444[/C][C]3018.0728695558[/C][/ROW]
[ROW][C]4[/C][C]141653[/C][C]132102.909392815[/C][C]9550.09060718486[/C][/ROW]
[ROW][C]5[/C][C]136394[/C][C]127815.44570604[/C][C]8578.55429395978[/C][/ROW]
[ROW][C]6[/C][C]167588[/C][C]151258.734040725[/C][C]16329.2659592752[/C][/ROW]
[ROW][C]7[/C][C]191807[/C][C]128536.060542487[/C][C]63270.939457513[/C][/ROW]
[ROW][C]8[/C][C]149736[/C][C]122321.876718765[/C][C]27414.1232812346[/C][/ROW]
[ROW][C]9[/C][C]196066[/C][C]132518.553328234[/C][C]63547.446671766[/C][/ROW]
[ROW][C]10[/C][C]239155[/C][C]127425.995713986[/C][C]111729.004286014[/C][/ROW]
[ROW][C]11[/C][C]178421[/C][C]130745.555996816[/C][C]47675.444003184[/C][/ROW]
[ROW][C]12[/C][C]139871[/C][C]116142.501912798[/C][C]23728.4980872023[/C][/ROW]
[ROW][C]13[/C][C]118159[/C][C]122019.572172464[/C][C]-3860.57217246397[/C][/ROW]
[ROW][C]14[/C][C]109763[/C][C]121012.188204339[/C][C]-11249.1882043391[/C][/ROW]
[ROW][C]15[/C][C]97415[/C][C]114650.02791938[/C][C]-17235.0279193804[/C][/ROW]
[ROW][C]16[/C][C]119190[/C][C]116222.457608636[/C][C]2967.54239136418[/C][/ROW]
[ROW][C]17[/C][C]97903[/C][C]106844.140429092[/C][C]-8941.14042909163[/C][/ROW]
[ROW][C]18[/C][C]96953[/C][C]91974.9948941975[/C][C]4978.00510580247[/C][/ROW]
[ROW][C]19[/C][C]87888[/C][C]69494.8738615199[/C][C]18393.1261384801[/C][/ROW]
[ROW][C]20[/C][C]84637[/C][C]88153.7865041221[/C][C]-3516.78650412214[/C][/ROW]
[ROW][C]21[/C][C]90549[/C][C]95951.2580546284[/C][C]-5402.25805462837[/C][/ROW]
[ROW][C]22[/C][C]95680[/C][C]99471.7745384872[/C][C]-3791.77453848719[/C][/ROW]
[ROW][C]23[/C][C]99371[/C][C]89583.9912472268[/C][C]9787.00875277316[/C][/ROW]
[ROW][C]24[/C][C]79984[/C][C]92752.1974986921[/C][C]-12768.1974986921[/C][/ROW]
[ROW][C]25[/C][C]86752[/C][C]91144.5631724428[/C][C]-4392.56317244284[/C][/ROW]
[ROW][C]26[/C][C]85733[/C][C]90097.6128876826[/C][C]-4364.61288768258[/C][/ROW]
[ROW][C]27[/C][C]84906[/C][C]79591.2581196395[/C][C]5314.74188036047[/C][/ROW]
[ROW][C]28[/C][C]78356[/C][C]80951.7300752211[/C][C]-2595.7300752211[/C][/ROW]
[ROW][C]29[/C][C]108895[/C][C]79995.5779929025[/C][C]28899.4220070975[/C][/ROW]
[ROW][C]30[/C][C]101768[/C][C]85867.2224370138[/C][C]15900.7775629862[/C][/ROW]
[ROW][C]31[/C][C]73285[/C][C]88619.400040347[/C][C]-15334.400040347[/C][/ROW]
[ROW][C]32[/C][C]65724[/C][C]77405.7530190431[/C][C]-11681.7530190431[/C][/ROW]
[ROW][C]33[/C][C]67457[/C][C]68464.5884105721[/C][C]-1007.58841057207[/C][/ROW]
[ROW][C]34[/C][C]67203[/C][C]36214.7417775567[/C][C]30988.2582224433[/C][/ROW]
[ROW][C]35[/C][C]69273[/C][C]80835.9355277038[/C][C]-11562.9355277038[/C][/ROW]
[ROW][C]36[/C][C]80807[/C][C]82574.8632760244[/C][C]-1767.86327602443[/C][/ROW]
[ROW][C]37[/C][C]75129[/C][C]72142.5899955819[/C][C]2986.4100044181[/C][/ROW]
[ROW][C]38[/C][C]74991[/C][C]89039.7930211449[/C][C]-14048.7930211448[/C][/ROW]
[ROW][C]39[/C][C]68157[/C][C]89047.3027360675[/C][C]-20890.3027360676[/C][/ROW]
[ROW][C]40[/C][C]73858[/C][C]82609.1827706038[/C][C]-8751.18277060377[/C][/ROW]
[ROW][C]41[/C][C]71349[/C][C]84370.3208685391[/C][C]-13021.3208685391[/C][/ROW]
[ROW][C]42[/C][C]85634[/C][C]73076.9478934531[/C][C]12557.0521065469[/C][/ROW]
[ROW][C]43[/C][C]91624[/C][C]75967.0256798919[/C][C]15656.9743201081[/C][/ROW]
[ROW][C]44[/C][C]116014[/C][C]112177.420366615[/C][C]3836.57963338547[/C][/ROW]
[ROW][C]45[/C][C]120033[/C][C]116369.249391403[/C][C]3663.75060859701[/C][/ROW]
[ROW][C]46[/C][C]108651[/C][C]137386.044435398[/C][C]-28735.0444353985[/C][/ROW]
[ROW][C]47[/C][C]105378[/C][C]150082.984298429[/C][C]-44704.9842984289[/C][/ROW]
[ROW][C]48[/C][C]138939[/C][C]156670.138625157[/C][C]-17731.1386251566[/C][/ROW]
[ROW][C]49[/C][C]132974[/C][C]164126.945744164[/C][C]-31152.9457441642[/C][/ROW]
[ROW][C]50[/C][C]135277[/C][C]176399.29965684[/C][C]-41122.2996568402[/C][/ROW]
[ROW][C]51[/C][C]152741[/C][C]150338.873894552[/C][C]2402.12610544812[/C][/ROW]
[ROW][C]52[/C][C]158417[/C][C]185587.165351008[/C][C]-27170.1653510078[/C][/ROW]
[ROW][C]53[/C][C]157460[/C][C]164229.527456056[/C][C]-6769.52745605553[/C][/ROW]
[ROW][C]54[/C][C]193997[/C][C]172618.667496213[/C][C]21378.3325037868[/C][/ROW]
[ROW][C]55[/C][C]154089[/C][C]165320.926305811[/C][C]-11231.9263058106[/C][/ROW]
[ROW][C]56[/C][C]147570[/C][C]155747.745763166[/C][C]-8177.74576316628[/C][/ROW]
[ROW][C]57[/C][C]162924[/C][C]195384.384035065[/C][C]-32460.3840350649[/C][/ROW]
[ROW][C]58[/C][C]153629[/C][C]179875.922208004[/C][C]-26246.9222080045[/C][/ROW]
[ROW][C]59[/C][C]155907[/C][C]175156.425446519[/C][C]-19249.4254465194[/C][/ROW]
[ROW][C]60[/C][C]197675[/C][C]175558.328253546[/C][C]22116.6717464542[/C][/ROW]
[ROW][C]61[/C][C]250708[/C][C]184967.673784589[/C][C]65740.326215411[/C][/ROW]
[ROW][C]62[/C][C]266652[/C][C]177071.346835477[/C][C]89580.6531645228[/C][/ROW]
[ROW][C]63[/C][C]209842[/C][C]189323.474044705[/C][C]20518.5259552945[/C][/ROW]
[ROW][C]64[/C][C]165826[/C][C]175382.731266871[/C][C]-9556.7312668705[/C][/ROW]
[ROW][C]65[/C][C]137152[/C][C]149922.199225912[/C][C]-12770.1992259122[/C][/ROW]
[ROW][C]66[/C][C]150581[/C][C]165536.375905391[/C][C]-14955.3759053909[/C][/ROW]
[ROW][C]67[/C][C]145973[/C][C]166489.579566089[/C][C]-20516.5795660886[/C][/ROW]
[ROW][C]68[/C][C]126532[/C][C]153077.033747393[/C][C]-26545.0337473935[/C][/ROW]
[ROW][C]69[/C][C]115437[/C][C]140893.356963753[/C][C]-25456.3569637532[/C][/ROW]
[ROW][C]70[/C][C]119526[/C][C]133403.827039257[/C][C]-13877.8270392573[/C][/ROW]
[ROW][C]71[/C][C]110856[/C][C]133886.15552722[/C][C]-23030.1555272204[/C][/ROW]
[ROW][C]72[/C][C]97243[/C][C]123192.775273153[/C][C]-25949.7752731529[/C][/ROW]
[ROW][C]73[/C][C]103876[/C][C]122669.718129588[/C][C]-18793.7181295875[/C][/ROW]
[ROW][C]74[/C][C]116370[/C][C]122050.500591783[/C][C]-5680.50059178263[/C][/ROW]
[ROW][C]75[/C][C]109616[/C][C]122588.546934138[/C][C]-12972.5469341384[/C][/ROW]
[ROW][C]76[/C][C]98365[/C][C]138894.365408667[/C][C]-40529.3654086666[/C][/ROW]
[ROW][C]77[/C][C]90440[/C][C]129931.808626048[/C][C]-39491.8086260479[/C][/ROW]
[ROW][C]78[/C][C]88899[/C][C]119039.371481984[/C][C]-30140.3714819842[/C][/ROW]
[ROW][C]79[/C][C]92358[/C][C]119983.348103262[/C][C]-27625.3481032623[/C][/ROW]
[ROW][C]80[/C][C]88394[/C][C]116305.460658814[/C][C]-27911.460658814[/C][/ROW]
[ROW][C]81[/C][C]98219[/C][C]118245.53915068[/C][C]-20026.5391506798[/C][/ROW]
[ROW][C]82[/C][C]113546[/C][C]121271.894175686[/C][C]-7725.8941756863[/C][/ROW]
[ROW][C]83[/C][C]107168[/C][C]133321.673360973[/C][C]-26153.6733609733[/C][/ROW]
[ROW][C]84[/C][C]77540[/C][C]114839.826068961[/C][C]-37299.8260689614[/C][/ROW]
[ROW][C]85[/C][C]74944[/C][C]128552.601437373[/C][C]-53608.6014373733[/C][/ROW]
[ROW][C]86[/C][C]75641[/C][C]98546.529473125[/C][C]-22905.529473125[/C][/ROW]
[ROW][C]87[/C][C]75910[/C][C]144571.26052152[/C][C]-68661.2605215204[/C][/ROW]
[ROW][C]88[/C][C]87384[/C][C]146231.839664764[/C][C]-58847.8396647639[/C][/ROW]
[ROW][C]89[/C][C]84615[/C][C]135183.466725783[/C][C]-50568.4667257832[/C][/ROW]
[ROW][C]90[/C][C]80420[/C][C]114542.227945715[/C][C]-34122.2279457153[/C][/ROW]
[ROW][C]91[/C][C]80784[/C][C]105738.003018114[/C][C]-24954.0030181141[/C][/ROW]
[ROW][C]92[/C][C]79933[/C][C]139547.060996335[/C][C]-59614.060996335[/C][/ROW]
[ROW][C]93[/C][C]82118[/C][C]121376.76375014[/C][C]-39258.7637501397[/C][/ROW]
[ROW][C]94[/C][C]91420[/C][C]121911.713323846[/C][C]-30491.7133238464[/C][/ROW]
[ROW][C]95[/C][C]112426[/C][C]127222.503475267[/C][C]-14796.5034752674[/C][/ROW]
[ROW][C]96[/C][C]114528[/C][C]139551.605127747[/C][C]-25023.6051277472[/C][/ROW]
[ROW][C]97[/C][C]131025[/C][C]150409.980789156[/C][C]-19384.9807891564[/C][/ROW]
[ROW][C]98[/C][C]116460[/C][C]133714.648496381[/C][C]-17254.6484963807[/C][/ROW]
[ROW][C]99[/C][C]111258[/C][C]142988.702787142[/C][C]-31730.7027871421[/C][/ROW]
[ROW][C]100[/C][C]155318[/C][C]150437.297676018[/C][C]4880.7023239822[/C][/ROW]
[ROW][C]101[/C][C]155078[/C][C]167222.432383719[/C][C]-12144.4323837193[/C][/ROW]
[ROW][C]102[/C][C]134794[/C][C]146293.42937153[/C][C]-11499.4293715298[/C][/ROW]
[ROW][C]103[/C][C]139985[/C][C]132720.080594435[/C][C]7264.91940556485[/C][/ROW]
[ROW][C]104[/C][C]198778[/C][C]143947.47249642[/C][C]54830.52750358[/C][/ROW]
[ROW][C]105[/C][C]172436[/C][C]158857.697896434[/C][C]13578.3021035657[/C][/ROW]
[ROW][C]106[/C][C]169585[/C][C]171099.524039803[/C][C]-1514.52403980332[/C][/ROW]
[ROW][C]107[/C][C]203702[/C][C]175589.058926826[/C][C]28112.9410731739[/C][/ROW]
[ROW][C]108[/C][C]282392[/C][C]174297.367604606[/C][C]108094.632395394[/C][/ROW]
[ROW][C]109[/C][C]220658[/C][C]190685.815412059[/C][C]29972.184587941[/C][/ROW]
[ROW][C]110[/C][C]194472[/C][C]166286.647860655[/C][C]28185.3521393445[/C][/ROW]
[ROW][C]111[/C][C]269246[/C][C]179481.020911577[/C][C]89764.9790884232[/C][/ROW]
[ROW][C]112[/C][C]215340[/C][C]171299.549238808[/C][C]44040.4507611925[/C][/ROW]
[ROW][C]113[/C][C]218319[/C][C]175147.341544776[/C][C]43171.6584552242[/C][/ROW]
[ROW][C]114[/C][C]195724[/C][C]177848.792790135[/C][C]17875.207209865[/C][/ROW]
[ROW][C]115[/C][C]174614[/C][C]161673.322067572[/C][C]12940.677932428[/C][/ROW]
[ROW][C]116[/C][C]172085[/C][C]162324.29583392[/C][C]9760.70416607965[/C][/ROW]
[ROW][C]117[/C][C]152347[/C][C]158515.175142193[/C][C]-6168.17514219335[/C][/ROW]
[ROW][C]118[/C][C]189615[/C][C]167091.487315443[/C][C]22523.512684557[/C][/ROW]
[ROW][C]119[/C][C]173804[/C][C]142897.806340168[/C][C]30906.1936598315[/C][/ROW]
[ROW][C]120[/C][C]145683[/C][C]149279.155847829[/C][C]-3596.15584782923[/C][/ROW]
[ROW][C]121[/C][C]133550[/C][C]135827.797316789[/C][C]-2277.79731678898[/C][/ROW]
[ROW][C]122[/C][C]121156[/C][C]109630.695273542[/C][C]11525.3047264581[/C][/ROW]
[ROW][C]123[/C][C]112040[/C][C]123250.708359091[/C][C]-11210.7083590907[/C][/ROW]
[ROW][C]124[/C][C]120767[/C][C]132015.468435017[/C][C]-11248.4684350172[/C][/ROW]
[ROW][C]125[/C][C]127019[/C][C]103714.545617284[/C][C]23304.4543827159[/C][/ROW]
[ROW][C]126[/C][C]136295[/C][C]85666.3152813697[/C][C]50628.6847186303[/C][/ROW]
[ROW][C]127[/C][C]113425[/C][C]102095.953506067[/C][C]11329.0464939334[/C][/ROW]
[ROW][C]128[/C][C]107815[/C][C]97289.8479247917[/C][C]10525.1520752083[/C][/ROW]
[ROW][C]129[/C][C]100298[/C][C]111320.958095485[/C][C]-11022.9580954854[/C][/ROW]
[ROW][C]130[/C][C]97048[/C][C]102625.278920163[/C][C]-5577.27892016297[/C][/ROW]
[ROW][C]131[/C][C]98750[/C][C]101602.048196443[/C][C]-2852.04819644334[/C][/ROW]
[ROW][C]132[/C][C]98235[/C][C]107433.848879768[/C][C]-9198.84887976765[/C][/ROW]
[ROW][C]133[/C][C]101254[/C][C]97323.993588143[/C][C]3930.00641185699[/C][/ROW]
[ROW][C]134[/C][C]139589[/C][C]94834.4606207979[/C][C]44754.5393792021[/C][/ROW]
[ROW][C]135[/C][C]134921[/C][C]132346.280021259[/C][C]2574.71997874135[/C][/ROW]
[ROW][C]136[/C][C]80355[/C][C]90381.4854978348[/C][C]-10026.4854978348[/C][/ROW]
[ROW][C]137[/C][C]80396[/C][C]68137.2167706373[/C][C]12258.7832293627[/C][/ROW]
[ROW][C]138[/C][C]82183[/C][C]49620.7398342629[/C][C]32562.2601657371[/C][/ROW]
[ROW][C]139[/C][C]79709[/C][C]18618.7942191908[/C][C]61090.2057808092[/C][/ROW]
[ROW][C]140[/C][C]90781[/C][C]88236.6429028424[/C][C]2544.35709715758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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
1169498131961.52944317937536.4705568207
2125451140919.848753037-15468.8487530367
3140449137430.9271304443018.0728695558
4141653132102.9093928159550.09060718486
5136394127815.445706048578.55429395978
6167588151258.73404072516329.2659592752
7191807128536.06054248763270.939457513
8149736122321.87671876527414.1232812346
9196066132518.55332823463547.446671766
10239155127425.995713986111729.004286014
11178421130745.55599681647675.444003184
12139871116142.50191279823728.4980872023
13118159122019.572172464-3860.57217246397
14109763121012.188204339-11249.1882043391
1597415114650.02791938-17235.0279193804
16119190116222.4576086362967.54239136418
1797903106844.140429092-8941.14042909163
189695391974.99489419754978.00510580247
198788869494.873861519918393.1261384801
208463788153.7865041221-3516.78650412214
219054995951.2580546284-5402.25805462837
229568099471.7745384872-3791.77453848719
239937189583.99124722689787.00875277316
247998492752.1974986921-12768.1974986921
258675291144.5631724428-4392.56317244284
268573390097.6128876826-4364.61288768258
278490679591.25811963955314.74188036047
287835680951.7300752211-2595.7300752211
2910889579995.577992902528899.4220070975
3010176885867.222437013815900.7775629862
317328588619.400040347-15334.400040347
326572477405.7530190431-11681.7530190431
336745768464.5884105721-1007.58841057207
346720336214.741777556730988.2582224433
356927380835.9355277038-11562.9355277038
368080782574.8632760244-1767.86327602443
377512972142.58999558192986.4100044181
387499189039.7930211449-14048.7930211448
396815789047.3027360675-20890.3027360676
407385882609.1827706038-8751.18277060377
417134984370.3208685391-13021.3208685391
428563473076.947893453112557.0521065469
439162475967.025679891915656.9743201081
44116014112177.4203666153836.57963338547
45120033116369.2493914033663.75060859701
46108651137386.044435398-28735.0444353985
47105378150082.984298429-44704.9842984289
48138939156670.138625157-17731.1386251566
49132974164126.945744164-31152.9457441642
50135277176399.29965684-41122.2996568402
51152741150338.8738945522402.12610544812
52158417185587.165351008-27170.1653510078
53157460164229.527456056-6769.52745605553
54193997172618.66749621321378.3325037868
55154089165320.926305811-11231.9263058106
56147570155747.745763166-8177.74576316628
57162924195384.384035065-32460.3840350649
58153629179875.922208004-26246.9222080045
59155907175156.425446519-19249.4254465194
60197675175558.32825354622116.6717464542
61250708184967.67378458965740.326215411
62266652177071.34683547789580.6531645228
63209842189323.47404470520518.5259552945
64165826175382.731266871-9556.7312668705
65137152149922.199225912-12770.1992259122
66150581165536.375905391-14955.3759053909
67145973166489.579566089-20516.5795660886
68126532153077.033747393-26545.0337473935
69115437140893.356963753-25456.3569637532
70119526133403.827039257-13877.8270392573
71110856133886.15552722-23030.1555272204
7297243123192.775273153-25949.7752731529
73103876122669.718129588-18793.7181295875
74116370122050.500591783-5680.50059178263
75109616122588.546934138-12972.5469341384
7698365138894.365408667-40529.3654086666
7790440129931.808626048-39491.8086260479
7888899119039.371481984-30140.3714819842
7992358119983.348103262-27625.3481032623
8088394116305.460658814-27911.460658814
8198219118245.53915068-20026.5391506798
82113546121271.894175686-7725.8941756863
83107168133321.673360973-26153.6733609733
8477540114839.826068961-37299.8260689614
8574944128552.601437373-53608.6014373733
867564198546.529473125-22905.529473125
8775910144571.26052152-68661.2605215204
8887384146231.839664764-58847.8396647639
8984615135183.466725783-50568.4667257832
9080420114542.227945715-34122.2279457153
9180784105738.003018114-24954.0030181141
9279933139547.060996335-59614.060996335
9382118121376.76375014-39258.7637501397
9491420121911.713323846-30491.7133238464
95112426127222.503475267-14796.5034752674
96114528139551.605127747-25023.6051277472
97131025150409.980789156-19384.9807891564
98116460133714.648496381-17254.6484963807
99111258142988.702787142-31730.7027871421
100155318150437.2976760184880.7023239822
101155078167222.432383719-12144.4323837193
102134794146293.42937153-11499.4293715298
103139985132720.0805944357264.91940556485
104198778143947.4724964254830.52750358
105172436158857.69789643413578.3021035657
106169585171099.524039803-1514.52403980332
107203702175589.05892682628112.9410731739
108282392174297.367604606108094.632395394
109220658190685.81541205929972.184587941
110194472166286.64786065528185.3521393445
111269246179481.02091157789764.9790884232
112215340171299.54923880844040.4507611925
113218319175147.34154477643171.6584552242
114195724177848.79279013517875.207209865
115174614161673.32206757212940.677932428
116172085162324.295833929760.70416607965
117152347158515.175142193-6168.17514219335
118189615167091.48731544322523.512684557
119173804142897.80634016830906.1936598315
120145683149279.155847829-3596.15584782923
121133550135827.797316789-2277.79731678898
122121156109630.69527354211525.3047264581
123112040123250.708359091-11210.7083590907
124120767132015.468435017-11248.4684350172
125127019103714.54561728423304.4543827159
12613629585666.315281369750628.6847186303
127113425102095.95350606711329.0464939334
12810781597289.847924791710525.1520752083
129100298111320.958095485-11022.9580954854
13097048102625.278920163-5577.27892016297
13198750101602.048196443-2852.04819644334
13298235107433.848879768-9198.84887976765
13310125497323.9935881433930.00641185699
13413958994834.460620797944754.5393792021
135134921132346.2800212592574.71997874135
1368035590381.4854978348-10026.4854978348
1378039668137.216770637312258.7832293627
1388218349620.739834262932562.2601657371
1397970918618.794219190861090.2057808092
1409078188236.64290284242544.35709715758






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1855999447481880.3711998894963770.814400055251812
110.2041472494371510.4082944988743020.795852750562849
120.5069225933572410.9861548132855170.493077406642759
130.6936189082777220.6127621834445560.306381091722278
140.8667431461431620.2665137077136760.133256853856838
150.8110677737839510.3778644524320970.188932226216049
160.7403616002605230.5192767994789530.259638399739477
170.6574612655081880.6850774689836240.342538734491812
180.6362536919202110.7274926161595790.363746308079789
190.6797557493278910.6404885013442180.320244250672109
200.6192326046716420.7615347906567150.380767395328358
210.540476997894990.919046004210020.45952300210501
220.4649811146382970.9299622292765930.535018885361703
230.4115025481504730.8230050963009450.588497451849528
240.3897768478610940.7795536957221880.610223152138906
250.3730892480541470.7461784961082940.626910751945853
260.3275025866556010.6550051733112020.672497413344399
270.3104437349759970.6208874699519940.689556265024003
280.2699354570724710.5398709141449420.730064542927529
290.2336450922046070.4672901844092150.766354907795393
300.1929857530446790.3859715060893580.807014246955321
310.1845287832215210.3690575664430420.815471216778479
320.1822254621523390.3644509243046780.817774537847661
330.1494158785572260.2988317571144510.850584121442774
340.1456950462168650.291390092433730.854304953783135
350.1153040651976850.2306081303953690.884695934802315
360.1145311010445280.2290622020890560.885468898955472
370.1126282014361730.2252564028723460.887371798563827
380.08613378209111370.1722675641822270.913866217908886
390.06508413018245950.1301682603649190.93491586981754
400.05682318907634020.113646378152680.94317681092366
410.04433268418071850.08866536836143690.955667315819282
420.04256277043764430.08512554087528850.957437229562356
430.06730007156521870.1346001431304370.932699928434781
440.06475259586223480.129505191724470.935247404137765
450.06656853321556870.1331370664311370.933431466784431
460.06776836908225070.1355367381645010.932231630917749
470.07402526268279390.1480505253655880.925974737317206
480.0624733050662550.124946610132510.937526694933745
490.04734647796216210.09469295592432420.952653522037838
500.03718735486873730.07437470973747450.962812645131263
510.04745168878474230.09490337756948460.952548311215258
520.03874539565402590.07749079130805180.961254604345974
530.03775999355626050.07551998711252090.96224000644374
540.07115938502950310.1423187700590060.928840614970497
550.05866818175841330.1173363635168270.941331818241587
560.04836098446342190.09672196892684390.951639015536578
570.04023352980775560.08046705961551130.959766470192244
580.03096709233564080.06193418467128160.969032907664359
590.02323596029839050.0464719205967810.97676403970161
600.03201991533942090.06403983067884180.967980084660579
610.1525692344121160.3051384688242330.847430765587884
620.6674411264588580.6651177470822840.332558873541142
630.6743444223033190.6513111553933630.325655577696681
640.6334002727989990.7331994544020010.366599727201001
650.5995083328034630.8009833343930740.400491667196537
660.5604096779026940.8791806441946110.439590322097306
670.515894982717190.9682100345656190.48410501728281
680.4708413207847580.9416826415695160.529158679215242
690.4285483493345440.8570966986690870.571451650665456
700.4233199115618350.846639823123670.576680088438165
710.3961167406914850.792233481382970.603883259308515
720.3743172075418980.7486344150837960.625682792458102
730.3701307331429270.7402614662858550.629869266857073
740.4521369620339610.9042739240679210.547863037966039
750.5171464509032620.9657070981934760.482853549096738
760.5029046237003380.9941907525993250.497095376299662
770.4718072893752870.9436145787505750.528192710624713
780.4502621935340280.9005243870680550.549737806465972
790.4209714098531160.8419428197062310.579028590146884
800.4218814557721510.8437629115443010.578118544227849
810.4103361981178350.8206723962356710.589663801882164
820.4291516699169310.8583033398338610.570848330083069
830.4068648526209820.8137297052419630.593135147379018
840.3824550388236170.7649100776472340.617544961176383
850.4128025472860330.8256050945720660.587197452713967
860.3923500191111220.7847000382222450.607649980888878
870.4137262854358240.8274525708716480.586273714564176
880.4504742465417930.9009484930835860.549525753458207
890.4180870465341070.8361740930682150.581912953465893
900.3675070319832960.7350140639665910.632492968016704
910.3272511583781080.6545023167562160.672748841621892
920.3398724264172030.6797448528344070.660127573582797
930.298676964350.59735392870.70132303565
940.27171472031380.54342944062760.7282852796862
950.2548636931607920.5097273863215840.745136306839208
960.2237421242776080.4474842485552150.776257875722392
970.2323051135432930.4646102270865860.767694886456707
980.2633466699707560.5266933399415130.736653330029244
990.5234647283982780.9530705432034440.476535271601722
1000.4924296004756070.9848592009512150.507570399524393
1010.5293898878597640.9412202242804720.470610112140236
1020.6137217746997820.7725564506004350.386278225300218
1030.6675812865255610.6648374269488780.332418713474439
1040.7035098520490110.5929802959019780.296490147950989
1050.7367724087093470.5264551825813060.263227591290653
1060.9243578979265270.1512842041469450.0756421020734726
1070.9883120086123890.02337598277522120.0116879913876106
1080.9951538802844920.009692239431015290.00484611971550765
1090.9950050840935020.0099898318129960.004994915906498
1100.9969130580915320.006173883816935650.00308694190846783
1110.9999687127310936.25745378144836e-053.12872689072418e-05
1120.9999866224319442.67551361114354e-051.33775680557177e-05
1130.9999906446449141.87107101711808e-059.35535508559038e-06
1140.9999834555850943.30888298116061e-051.65444149058031e-05
1150.9999602245445787.95509108441661e-053.9775455422083e-05
1160.9999062717514440.0001874564971122949.37282485561469e-05
1170.999868862915720.0002622741685595440.000131137084279772
1180.9999210893202610.0001578213594783967.89106797391979e-05
1190.999919157275190.000161685449619418.08427248097048e-05
1200.9998650296124420.0002699407751155070.000134970387557754
1210.9997880849919880.0004238300160233270.000211915008011663
1220.9994931165184830.001013766963033540.000506883481516769
1230.9986839972678560.002632005464289010.0013160027321445
1240.9990506357165140.001898728566972070.000949364283486037
1250.9977211030109390.004557793978122720.00227889698906136
1260.9967957377996780.0064085244006430.0032042622003215
1270.9941975051537540.01160498969249150.00580249484624577
1280.9835818816747640.03283623665047240.0164181183252362
1290.9644917153397770.07101656932044690.0355082846602235
1300.923141368633750.1537172627325010.0768586313662503

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.185599944748188 & 0.371199889496377 & 0.814400055251812 \tabularnewline
11 & 0.204147249437151 & 0.408294498874302 & 0.795852750562849 \tabularnewline
12 & 0.506922593357241 & 0.986154813285517 & 0.493077406642759 \tabularnewline
13 & 0.693618908277722 & 0.612762183444556 & 0.306381091722278 \tabularnewline
14 & 0.866743146143162 & 0.266513707713676 & 0.133256853856838 \tabularnewline
15 & 0.811067773783951 & 0.377864452432097 & 0.188932226216049 \tabularnewline
16 & 0.740361600260523 & 0.519276799478953 & 0.259638399739477 \tabularnewline
17 & 0.657461265508188 & 0.685077468983624 & 0.342538734491812 \tabularnewline
18 & 0.636253691920211 & 0.727492616159579 & 0.363746308079789 \tabularnewline
19 & 0.679755749327891 & 0.640488501344218 & 0.320244250672109 \tabularnewline
20 & 0.619232604671642 & 0.761534790656715 & 0.380767395328358 \tabularnewline
21 & 0.54047699789499 & 0.91904600421002 & 0.45952300210501 \tabularnewline
22 & 0.464981114638297 & 0.929962229276593 & 0.535018885361703 \tabularnewline
23 & 0.411502548150473 & 0.823005096300945 & 0.588497451849528 \tabularnewline
24 & 0.389776847861094 & 0.779553695722188 & 0.610223152138906 \tabularnewline
25 & 0.373089248054147 & 0.746178496108294 & 0.626910751945853 \tabularnewline
26 & 0.327502586655601 & 0.655005173311202 & 0.672497413344399 \tabularnewline
27 & 0.310443734975997 & 0.620887469951994 & 0.689556265024003 \tabularnewline
28 & 0.269935457072471 & 0.539870914144942 & 0.730064542927529 \tabularnewline
29 & 0.233645092204607 & 0.467290184409215 & 0.766354907795393 \tabularnewline
30 & 0.192985753044679 & 0.385971506089358 & 0.807014246955321 \tabularnewline
31 & 0.184528783221521 & 0.369057566443042 & 0.815471216778479 \tabularnewline
32 & 0.182225462152339 & 0.364450924304678 & 0.817774537847661 \tabularnewline
33 & 0.149415878557226 & 0.298831757114451 & 0.850584121442774 \tabularnewline
34 & 0.145695046216865 & 0.29139009243373 & 0.854304953783135 \tabularnewline
35 & 0.115304065197685 & 0.230608130395369 & 0.884695934802315 \tabularnewline
36 & 0.114531101044528 & 0.229062202089056 & 0.885468898955472 \tabularnewline
37 & 0.112628201436173 & 0.225256402872346 & 0.887371798563827 \tabularnewline
38 & 0.0861337820911137 & 0.172267564182227 & 0.913866217908886 \tabularnewline
39 & 0.0650841301824595 & 0.130168260364919 & 0.93491586981754 \tabularnewline
40 & 0.0568231890763402 & 0.11364637815268 & 0.94317681092366 \tabularnewline
41 & 0.0443326841807185 & 0.0886653683614369 & 0.955667315819282 \tabularnewline
42 & 0.0425627704376443 & 0.0851255408752885 & 0.957437229562356 \tabularnewline
43 & 0.0673000715652187 & 0.134600143130437 & 0.932699928434781 \tabularnewline
44 & 0.0647525958622348 & 0.12950519172447 & 0.935247404137765 \tabularnewline
45 & 0.0665685332155687 & 0.133137066431137 & 0.933431466784431 \tabularnewline
46 & 0.0677683690822507 & 0.135536738164501 & 0.932231630917749 \tabularnewline
47 & 0.0740252626827939 & 0.148050525365588 & 0.925974737317206 \tabularnewline
48 & 0.062473305066255 & 0.12494661013251 & 0.937526694933745 \tabularnewline
49 & 0.0473464779621621 & 0.0946929559243242 & 0.952653522037838 \tabularnewline
50 & 0.0371873548687373 & 0.0743747097374745 & 0.962812645131263 \tabularnewline
51 & 0.0474516887847423 & 0.0949033775694846 & 0.952548311215258 \tabularnewline
52 & 0.0387453956540259 & 0.0774907913080518 & 0.961254604345974 \tabularnewline
53 & 0.0377599935562605 & 0.0755199871125209 & 0.96224000644374 \tabularnewline
54 & 0.0711593850295031 & 0.142318770059006 & 0.928840614970497 \tabularnewline
55 & 0.0586681817584133 & 0.117336363516827 & 0.941331818241587 \tabularnewline
56 & 0.0483609844634219 & 0.0967219689268439 & 0.951639015536578 \tabularnewline
57 & 0.0402335298077556 & 0.0804670596155113 & 0.959766470192244 \tabularnewline
58 & 0.0309670923356408 & 0.0619341846712816 & 0.969032907664359 \tabularnewline
59 & 0.0232359602983905 & 0.046471920596781 & 0.97676403970161 \tabularnewline
60 & 0.0320199153394209 & 0.0640398306788418 & 0.967980084660579 \tabularnewline
61 & 0.152569234412116 & 0.305138468824233 & 0.847430765587884 \tabularnewline
62 & 0.667441126458858 & 0.665117747082284 & 0.332558873541142 \tabularnewline
63 & 0.674344422303319 & 0.651311155393363 & 0.325655577696681 \tabularnewline
64 & 0.633400272798999 & 0.733199454402001 & 0.366599727201001 \tabularnewline
65 & 0.599508332803463 & 0.800983334393074 & 0.400491667196537 \tabularnewline
66 & 0.560409677902694 & 0.879180644194611 & 0.439590322097306 \tabularnewline
67 & 0.51589498271719 & 0.968210034565619 & 0.48410501728281 \tabularnewline
68 & 0.470841320784758 & 0.941682641569516 & 0.529158679215242 \tabularnewline
69 & 0.428548349334544 & 0.857096698669087 & 0.571451650665456 \tabularnewline
70 & 0.423319911561835 & 0.84663982312367 & 0.576680088438165 \tabularnewline
71 & 0.396116740691485 & 0.79223348138297 & 0.603883259308515 \tabularnewline
72 & 0.374317207541898 & 0.748634415083796 & 0.625682792458102 \tabularnewline
73 & 0.370130733142927 & 0.740261466285855 & 0.629869266857073 \tabularnewline
74 & 0.452136962033961 & 0.904273924067921 & 0.547863037966039 \tabularnewline
75 & 0.517146450903262 & 0.965707098193476 & 0.482853549096738 \tabularnewline
76 & 0.502904623700338 & 0.994190752599325 & 0.497095376299662 \tabularnewline
77 & 0.471807289375287 & 0.943614578750575 & 0.528192710624713 \tabularnewline
78 & 0.450262193534028 & 0.900524387068055 & 0.549737806465972 \tabularnewline
79 & 0.420971409853116 & 0.841942819706231 & 0.579028590146884 \tabularnewline
80 & 0.421881455772151 & 0.843762911544301 & 0.578118544227849 \tabularnewline
81 & 0.410336198117835 & 0.820672396235671 & 0.589663801882164 \tabularnewline
82 & 0.429151669916931 & 0.858303339833861 & 0.570848330083069 \tabularnewline
83 & 0.406864852620982 & 0.813729705241963 & 0.593135147379018 \tabularnewline
84 & 0.382455038823617 & 0.764910077647234 & 0.617544961176383 \tabularnewline
85 & 0.412802547286033 & 0.825605094572066 & 0.587197452713967 \tabularnewline
86 & 0.392350019111122 & 0.784700038222245 & 0.607649980888878 \tabularnewline
87 & 0.413726285435824 & 0.827452570871648 & 0.586273714564176 \tabularnewline
88 & 0.450474246541793 & 0.900948493083586 & 0.549525753458207 \tabularnewline
89 & 0.418087046534107 & 0.836174093068215 & 0.581912953465893 \tabularnewline
90 & 0.367507031983296 & 0.735014063966591 & 0.632492968016704 \tabularnewline
91 & 0.327251158378108 & 0.654502316756216 & 0.672748841621892 \tabularnewline
92 & 0.339872426417203 & 0.679744852834407 & 0.660127573582797 \tabularnewline
93 & 0.29867696435 & 0.5973539287 & 0.70132303565 \tabularnewline
94 & 0.2717147203138 & 0.5434294406276 & 0.7282852796862 \tabularnewline
95 & 0.254863693160792 & 0.509727386321584 & 0.745136306839208 \tabularnewline
96 & 0.223742124277608 & 0.447484248555215 & 0.776257875722392 \tabularnewline
97 & 0.232305113543293 & 0.464610227086586 & 0.767694886456707 \tabularnewline
98 & 0.263346669970756 & 0.526693339941513 & 0.736653330029244 \tabularnewline
99 & 0.523464728398278 & 0.953070543203444 & 0.476535271601722 \tabularnewline
100 & 0.492429600475607 & 0.984859200951215 & 0.507570399524393 \tabularnewline
101 & 0.529389887859764 & 0.941220224280472 & 0.470610112140236 \tabularnewline
102 & 0.613721774699782 & 0.772556450600435 & 0.386278225300218 \tabularnewline
103 & 0.667581286525561 & 0.664837426948878 & 0.332418713474439 \tabularnewline
104 & 0.703509852049011 & 0.592980295901978 & 0.296490147950989 \tabularnewline
105 & 0.736772408709347 & 0.526455182581306 & 0.263227591290653 \tabularnewline
106 & 0.924357897926527 & 0.151284204146945 & 0.0756421020734726 \tabularnewline
107 & 0.988312008612389 & 0.0233759827752212 & 0.0116879913876106 \tabularnewline
108 & 0.995153880284492 & 0.00969223943101529 & 0.00484611971550765 \tabularnewline
109 & 0.995005084093502 & 0.009989831812996 & 0.004994915906498 \tabularnewline
110 & 0.996913058091532 & 0.00617388381693565 & 0.00308694190846783 \tabularnewline
111 & 0.999968712731093 & 6.25745378144836e-05 & 3.12872689072418e-05 \tabularnewline
112 & 0.999986622431944 & 2.67551361114354e-05 & 1.33775680557177e-05 \tabularnewline
113 & 0.999990644644914 & 1.87107101711808e-05 & 9.35535508559038e-06 \tabularnewline
114 & 0.999983455585094 & 3.30888298116061e-05 & 1.65444149058031e-05 \tabularnewline
115 & 0.999960224544578 & 7.95509108441661e-05 & 3.9775455422083e-05 \tabularnewline
116 & 0.999906271751444 & 0.000187456497112294 & 9.37282485561469e-05 \tabularnewline
117 & 0.99986886291572 & 0.000262274168559544 & 0.000131137084279772 \tabularnewline
118 & 0.999921089320261 & 0.000157821359478396 & 7.89106797391979e-05 \tabularnewline
119 & 0.99991915727519 & 0.00016168544961941 & 8.08427248097048e-05 \tabularnewline
120 & 0.999865029612442 & 0.000269940775115507 & 0.000134970387557754 \tabularnewline
121 & 0.999788084991988 & 0.000423830016023327 & 0.000211915008011663 \tabularnewline
122 & 0.999493116518483 & 0.00101376696303354 & 0.000506883481516769 \tabularnewline
123 & 0.998683997267856 & 0.00263200546428901 & 0.0013160027321445 \tabularnewline
124 & 0.999050635716514 & 0.00189872856697207 & 0.000949364283486037 \tabularnewline
125 & 0.997721103010939 & 0.00455779397812272 & 0.00227889698906136 \tabularnewline
126 & 0.996795737799678 & 0.006408524400643 & 0.0032042622003215 \tabularnewline
127 & 0.994197505153754 & 0.0116049896924915 & 0.00580249484624577 \tabularnewline
128 & 0.983581881674764 & 0.0328362366504724 & 0.0164181183252362 \tabularnewline
129 & 0.964491715339777 & 0.0710165693204469 & 0.0355082846602235 \tabularnewline
130 & 0.92314136863375 & 0.153717262732501 & 0.0768586313662503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&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]0.185599944748188[/C][C]0.371199889496377[/C][C]0.814400055251812[/C][/ROW]
[ROW][C]11[/C][C]0.204147249437151[/C][C]0.408294498874302[/C][C]0.795852750562849[/C][/ROW]
[ROW][C]12[/C][C]0.506922593357241[/C][C]0.986154813285517[/C][C]0.493077406642759[/C][/ROW]
[ROW][C]13[/C][C]0.693618908277722[/C][C]0.612762183444556[/C][C]0.306381091722278[/C][/ROW]
[ROW][C]14[/C][C]0.866743146143162[/C][C]0.266513707713676[/C][C]0.133256853856838[/C][/ROW]
[ROW][C]15[/C][C]0.811067773783951[/C][C]0.377864452432097[/C][C]0.188932226216049[/C][/ROW]
[ROW][C]16[/C][C]0.740361600260523[/C][C]0.519276799478953[/C][C]0.259638399739477[/C][/ROW]
[ROW][C]17[/C][C]0.657461265508188[/C][C]0.685077468983624[/C][C]0.342538734491812[/C][/ROW]
[ROW][C]18[/C][C]0.636253691920211[/C][C]0.727492616159579[/C][C]0.363746308079789[/C][/ROW]
[ROW][C]19[/C][C]0.679755749327891[/C][C]0.640488501344218[/C][C]0.320244250672109[/C][/ROW]
[ROW][C]20[/C][C]0.619232604671642[/C][C]0.761534790656715[/C][C]0.380767395328358[/C][/ROW]
[ROW][C]21[/C][C]0.54047699789499[/C][C]0.91904600421002[/C][C]0.45952300210501[/C][/ROW]
[ROW][C]22[/C][C]0.464981114638297[/C][C]0.929962229276593[/C][C]0.535018885361703[/C][/ROW]
[ROW][C]23[/C][C]0.411502548150473[/C][C]0.823005096300945[/C][C]0.588497451849528[/C][/ROW]
[ROW][C]24[/C][C]0.389776847861094[/C][C]0.779553695722188[/C][C]0.610223152138906[/C][/ROW]
[ROW][C]25[/C][C]0.373089248054147[/C][C]0.746178496108294[/C][C]0.626910751945853[/C][/ROW]
[ROW][C]26[/C][C]0.327502586655601[/C][C]0.655005173311202[/C][C]0.672497413344399[/C][/ROW]
[ROW][C]27[/C][C]0.310443734975997[/C][C]0.620887469951994[/C][C]0.689556265024003[/C][/ROW]
[ROW][C]28[/C][C]0.269935457072471[/C][C]0.539870914144942[/C][C]0.730064542927529[/C][/ROW]
[ROW][C]29[/C][C]0.233645092204607[/C][C]0.467290184409215[/C][C]0.766354907795393[/C][/ROW]
[ROW][C]30[/C][C]0.192985753044679[/C][C]0.385971506089358[/C][C]0.807014246955321[/C][/ROW]
[ROW][C]31[/C][C]0.184528783221521[/C][C]0.369057566443042[/C][C]0.815471216778479[/C][/ROW]
[ROW][C]32[/C][C]0.182225462152339[/C][C]0.364450924304678[/C][C]0.817774537847661[/C][/ROW]
[ROW][C]33[/C][C]0.149415878557226[/C][C]0.298831757114451[/C][C]0.850584121442774[/C][/ROW]
[ROW][C]34[/C][C]0.145695046216865[/C][C]0.29139009243373[/C][C]0.854304953783135[/C][/ROW]
[ROW][C]35[/C][C]0.115304065197685[/C][C]0.230608130395369[/C][C]0.884695934802315[/C][/ROW]
[ROW][C]36[/C][C]0.114531101044528[/C][C]0.229062202089056[/C][C]0.885468898955472[/C][/ROW]
[ROW][C]37[/C][C]0.112628201436173[/C][C]0.225256402872346[/C][C]0.887371798563827[/C][/ROW]
[ROW][C]38[/C][C]0.0861337820911137[/C][C]0.172267564182227[/C][C]0.913866217908886[/C][/ROW]
[ROW][C]39[/C][C]0.0650841301824595[/C][C]0.130168260364919[/C][C]0.93491586981754[/C][/ROW]
[ROW][C]40[/C][C]0.0568231890763402[/C][C]0.11364637815268[/C][C]0.94317681092366[/C][/ROW]
[ROW][C]41[/C][C]0.0443326841807185[/C][C]0.0886653683614369[/C][C]0.955667315819282[/C][/ROW]
[ROW][C]42[/C][C]0.0425627704376443[/C][C]0.0851255408752885[/C][C]0.957437229562356[/C][/ROW]
[ROW][C]43[/C][C]0.0673000715652187[/C][C]0.134600143130437[/C][C]0.932699928434781[/C][/ROW]
[ROW][C]44[/C][C]0.0647525958622348[/C][C]0.12950519172447[/C][C]0.935247404137765[/C][/ROW]
[ROW][C]45[/C][C]0.0665685332155687[/C][C]0.133137066431137[/C][C]0.933431466784431[/C][/ROW]
[ROW][C]46[/C][C]0.0677683690822507[/C][C]0.135536738164501[/C][C]0.932231630917749[/C][/ROW]
[ROW][C]47[/C][C]0.0740252626827939[/C][C]0.148050525365588[/C][C]0.925974737317206[/C][/ROW]
[ROW][C]48[/C][C]0.062473305066255[/C][C]0.12494661013251[/C][C]0.937526694933745[/C][/ROW]
[ROW][C]49[/C][C]0.0473464779621621[/C][C]0.0946929559243242[/C][C]0.952653522037838[/C][/ROW]
[ROW][C]50[/C][C]0.0371873548687373[/C][C]0.0743747097374745[/C][C]0.962812645131263[/C][/ROW]
[ROW][C]51[/C][C]0.0474516887847423[/C][C]0.0949033775694846[/C][C]0.952548311215258[/C][/ROW]
[ROW][C]52[/C][C]0.0387453956540259[/C][C]0.0774907913080518[/C][C]0.961254604345974[/C][/ROW]
[ROW][C]53[/C][C]0.0377599935562605[/C][C]0.0755199871125209[/C][C]0.96224000644374[/C][/ROW]
[ROW][C]54[/C][C]0.0711593850295031[/C][C]0.142318770059006[/C][C]0.928840614970497[/C][/ROW]
[ROW][C]55[/C][C]0.0586681817584133[/C][C]0.117336363516827[/C][C]0.941331818241587[/C][/ROW]
[ROW][C]56[/C][C]0.0483609844634219[/C][C]0.0967219689268439[/C][C]0.951639015536578[/C][/ROW]
[ROW][C]57[/C][C]0.0402335298077556[/C][C]0.0804670596155113[/C][C]0.959766470192244[/C][/ROW]
[ROW][C]58[/C][C]0.0309670923356408[/C][C]0.0619341846712816[/C][C]0.969032907664359[/C][/ROW]
[ROW][C]59[/C][C]0.0232359602983905[/C][C]0.046471920596781[/C][C]0.97676403970161[/C][/ROW]
[ROW][C]60[/C][C]0.0320199153394209[/C][C]0.0640398306788418[/C][C]0.967980084660579[/C][/ROW]
[ROW][C]61[/C][C]0.152569234412116[/C][C]0.305138468824233[/C][C]0.847430765587884[/C][/ROW]
[ROW][C]62[/C][C]0.667441126458858[/C][C]0.665117747082284[/C][C]0.332558873541142[/C][/ROW]
[ROW][C]63[/C][C]0.674344422303319[/C][C]0.651311155393363[/C][C]0.325655577696681[/C][/ROW]
[ROW][C]64[/C][C]0.633400272798999[/C][C]0.733199454402001[/C][C]0.366599727201001[/C][/ROW]
[ROW][C]65[/C][C]0.599508332803463[/C][C]0.800983334393074[/C][C]0.400491667196537[/C][/ROW]
[ROW][C]66[/C][C]0.560409677902694[/C][C]0.879180644194611[/C][C]0.439590322097306[/C][/ROW]
[ROW][C]67[/C][C]0.51589498271719[/C][C]0.968210034565619[/C][C]0.48410501728281[/C][/ROW]
[ROW][C]68[/C][C]0.470841320784758[/C][C]0.941682641569516[/C][C]0.529158679215242[/C][/ROW]
[ROW][C]69[/C][C]0.428548349334544[/C][C]0.857096698669087[/C][C]0.571451650665456[/C][/ROW]
[ROW][C]70[/C][C]0.423319911561835[/C][C]0.84663982312367[/C][C]0.576680088438165[/C][/ROW]
[ROW][C]71[/C][C]0.396116740691485[/C][C]0.79223348138297[/C][C]0.603883259308515[/C][/ROW]
[ROW][C]72[/C][C]0.374317207541898[/C][C]0.748634415083796[/C][C]0.625682792458102[/C][/ROW]
[ROW][C]73[/C][C]0.370130733142927[/C][C]0.740261466285855[/C][C]0.629869266857073[/C][/ROW]
[ROW][C]74[/C][C]0.452136962033961[/C][C]0.904273924067921[/C][C]0.547863037966039[/C][/ROW]
[ROW][C]75[/C][C]0.517146450903262[/C][C]0.965707098193476[/C][C]0.482853549096738[/C][/ROW]
[ROW][C]76[/C][C]0.502904623700338[/C][C]0.994190752599325[/C][C]0.497095376299662[/C][/ROW]
[ROW][C]77[/C][C]0.471807289375287[/C][C]0.943614578750575[/C][C]0.528192710624713[/C][/ROW]
[ROW][C]78[/C][C]0.450262193534028[/C][C]0.900524387068055[/C][C]0.549737806465972[/C][/ROW]
[ROW][C]79[/C][C]0.420971409853116[/C][C]0.841942819706231[/C][C]0.579028590146884[/C][/ROW]
[ROW][C]80[/C][C]0.421881455772151[/C][C]0.843762911544301[/C][C]0.578118544227849[/C][/ROW]
[ROW][C]81[/C][C]0.410336198117835[/C][C]0.820672396235671[/C][C]0.589663801882164[/C][/ROW]
[ROW][C]82[/C][C]0.429151669916931[/C][C]0.858303339833861[/C][C]0.570848330083069[/C][/ROW]
[ROW][C]83[/C][C]0.406864852620982[/C][C]0.813729705241963[/C][C]0.593135147379018[/C][/ROW]
[ROW][C]84[/C][C]0.382455038823617[/C][C]0.764910077647234[/C][C]0.617544961176383[/C][/ROW]
[ROW][C]85[/C][C]0.412802547286033[/C][C]0.825605094572066[/C][C]0.587197452713967[/C][/ROW]
[ROW][C]86[/C][C]0.392350019111122[/C][C]0.784700038222245[/C][C]0.607649980888878[/C][/ROW]
[ROW][C]87[/C][C]0.413726285435824[/C][C]0.827452570871648[/C][C]0.586273714564176[/C][/ROW]
[ROW][C]88[/C][C]0.450474246541793[/C][C]0.900948493083586[/C][C]0.549525753458207[/C][/ROW]
[ROW][C]89[/C][C]0.418087046534107[/C][C]0.836174093068215[/C][C]0.581912953465893[/C][/ROW]
[ROW][C]90[/C][C]0.367507031983296[/C][C]0.735014063966591[/C][C]0.632492968016704[/C][/ROW]
[ROW][C]91[/C][C]0.327251158378108[/C][C]0.654502316756216[/C][C]0.672748841621892[/C][/ROW]
[ROW][C]92[/C][C]0.339872426417203[/C][C]0.679744852834407[/C][C]0.660127573582797[/C][/ROW]
[ROW][C]93[/C][C]0.29867696435[/C][C]0.5973539287[/C][C]0.70132303565[/C][/ROW]
[ROW][C]94[/C][C]0.2717147203138[/C][C]0.5434294406276[/C][C]0.7282852796862[/C][/ROW]
[ROW][C]95[/C][C]0.254863693160792[/C][C]0.509727386321584[/C][C]0.745136306839208[/C][/ROW]
[ROW][C]96[/C][C]0.223742124277608[/C][C]0.447484248555215[/C][C]0.776257875722392[/C][/ROW]
[ROW][C]97[/C][C]0.232305113543293[/C][C]0.464610227086586[/C][C]0.767694886456707[/C][/ROW]
[ROW][C]98[/C][C]0.263346669970756[/C][C]0.526693339941513[/C][C]0.736653330029244[/C][/ROW]
[ROW][C]99[/C][C]0.523464728398278[/C][C]0.953070543203444[/C][C]0.476535271601722[/C][/ROW]
[ROW][C]100[/C][C]0.492429600475607[/C][C]0.984859200951215[/C][C]0.507570399524393[/C][/ROW]
[ROW][C]101[/C][C]0.529389887859764[/C][C]0.941220224280472[/C][C]0.470610112140236[/C][/ROW]
[ROW][C]102[/C][C]0.613721774699782[/C][C]0.772556450600435[/C][C]0.386278225300218[/C][/ROW]
[ROW][C]103[/C][C]0.667581286525561[/C][C]0.664837426948878[/C][C]0.332418713474439[/C][/ROW]
[ROW][C]104[/C][C]0.703509852049011[/C][C]0.592980295901978[/C][C]0.296490147950989[/C][/ROW]
[ROW][C]105[/C][C]0.736772408709347[/C][C]0.526455182581306[/C][C]0.263227591290653[/C][/ROW]
[ROW][C]106[/C][C]0.924357897926527[/C][C]0.151284204146945[/C][C]0.0756421020734726[/C][/ROW]
[ROW][C]107[/C][C]0.988312008612389[/C][C]0.0233759827752212[/C][C]0.0116879913876106[/C][/ROW]
[ROW][C]108[/C][C]0.995153880284492[/C][C]0.00969223943101529[/C][C]0.00484611971550765[/C][/ROW]
[ROW][C]109[/C][C]0.995005084093502[/C][C]0.009989831812996[/C][C]0.004994915906498[/C][/ROW]
[ROW][C]110[/C][C]0.996913058091532[/C][C]0.00617388381693565[/C][C]0.00308694190846783[/C][/ROW]
[ROW][C]111[/C][C]0.999968712731093[/C][C]6.25745378144836e-05[/C][C]3.12872689072418e-05[/C][/ROW]
[ROW][C]112[/C][C]0.999986622431944[/C][C]2.67551361114354e-05[/C][C]1.33775680557177e-05[/C][/ROW]
[ROW][C]113[/C][C]0.999990644644914[/C][C]1.87107101711808e-05[/C][C]9.35535508559038e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999983455585094[/C][C]3.30888298116061e-05[/C][C]1.65444149058031e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999960224544578[/C][C]7.95509108441661e-05[/C][C]3.9775455422083e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999906271751444[/C][C]0.000187456497112294[/C][C]9.37282485561469e-05[/C][/ROW]
[ROW][C]117[/C][C]0.99986886291572[/C][C]0.000262274168559544[/C][C]0.000131137084279772[/C][/ROW]
[ROW][C]118[/C][C]0.999921089320261[/C][C]0.000157821359478396[/C][C]7.89106797391979e-05[/C][/ROW]
[ROW][C]119[/C][C]0.99991915727519[/C][C]0.00016168544961941[/C][C]8.08427248097048e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999865029612442[/C][C]0.000269940775115507[/C][C]0.000134970387557754[/C][/ROW]
[ROW][C]121[/C][C]0.999788084991988[/C][C]0.000423830016023327[/C][C]0.000211915008011663[/C][/ROW]
[ROW][C]122[/C][C]0.999493116518483[/C][C]0.00101376696303354[/C][C]0.000506883481516769[/C][/ROW]
[ROW][C]123[/C][C]0.998683997267856[/C][C]0.00263200546428901[/C][C]0.0013160027321445[/C][/ROW]
[ROW][C]124[/C][C]0.999050635716514[/C][C]0.00189872856697207[/C][C]0.000949364283486037[/C][/ROW]
[ROW][C]125[/C][C]0.997721103010939[/C][C]0.00455779397812272[/C][C]0.00227889698906136[/C][/ROW]
[ROW][C]126[/C][C]0.996795737799678[/C][C]0.006408524400643[/C][C]0.0032042622003215[/C][/ROW]
[ROW][C]127[/C][C]0.994197505153754[/C][C]0.0116049896924915[/C][C]0.00580249484624577[/C][/ROW]
[ROW][C]128[/C][C]0.983581881674764[/C][C]0.0328362366504724[/C][C]0.0164181183252362[/C][/ROW]
[ROW][C]129[/C][C]0.964491715339777[/C][C]0.0710165693204469[/C][C]0.0355082846602235[/C][/ROW]
[ROW][C]130[/C][C]0.92314136863375[/C][C]0.153717262732501[/C][C]0.0768586313662503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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
100.1855999447481880.3711998894963770.814400055251812
110.2041472494371510.4082944988743020.795852750562849
120.5069225933572410.9861548132855170.493077406642759
130.6936189082777220.6127621834445560.306381091722278
140.8667431461431620.2665137077136760.133256853856838
150.8110677737839510.3778644524320970.188932226216049
160.7403616002605230.5192767994789530.259638399739477
170.6574612655081880.6850774689836240.342538734491812
180.6362536919202110.7274926161595790.363746308079789
190.6797557493278910.6404885013442180.320244250672109
200.6192326046716420.7615347906567150.380767395328358
210.540476997894990.919046004210020.45952300210501
220.4649811146382970.9299622292765930.535018885361703
230.4115025481504730.8230050963009450.588497451849528
240.3897768478610940.7795536957221880.610223152138906
250.3730892480541470.7461784961082940.626910751945853
260.3275025866556010.6550051733112020.672497413344399
270.3104437349759970.6208874699519940.689556265024003
280.2699354570724710.5398709141449420.730064542927529
290.2336450922046070.4672901844092150.766354907795393
300.1929857530446790.3859715060893580.807014246955321
310.1845287832215210.3690575664430420.815471216778479
320.1822254621523390.3644509243046780.817774537847661
330.1494158785572260.2988317571144510.850584121442774
340.1456950462168650.291390092433730.854304953783135
350.1153040651976850.2306081303953690.884695934802315
360.1145311010445280.2290622020890560.885468898955472
370.1126282014361730.2252564028723460.887371798563827
380.08613378209111370.1722675641822270.913866217908886
390.06508413018245950.1301682603649190.93491586981754
400.05682318907634020.113646378152680.94317681092366
410.04433268418071850.08866536836143690.955667315819282
420.04256277043764430.08512554087528850.957437229562356
430.06730007156521870.1346001431304370.932699928434781
440.06475259586223480.129505191724470.935247404137765
450.06656853321556870.1331370664311370.933431466784431
460.06776836908225070.1355367381645010.932231630917749
470.07402526268279390.1480505253655880.925974737317206
480.0624733050662550.124946610132510.937526694933745
490.04734647796216210.09469295592432420.952653522037838
500.03718735486873730.07437470973747450.962812645131263
510.04745168878474230.09490337756948460.952548311215258
520.03874539565402590.07749079130805180.961254604345974
530.03775999355626050.07551998711252090.96224000644374
540.07115938502950310.1423187700590060.928840614970497
550.05866818175841330.1173363635168270.941331818241587
560.04836098446342190.09672196892684390.951639015536578
570.04023352980775560.08046705961551130.959766470192244
580.03096709233564080.06193418467128160.969032907664359
590.02323596029839050.0464719205967810.97676403970161
600.03201991533942090.06403983067884180.967980084660579
610.1525692344121160.3051384688242330.847430765587884
620.6674411264588580.6651177470822840.332558873541142
630.6743444223033190.6513111553933630.325655577696681
640.6334002727989990.7331994544020010.366599727201001
650.5995083328034630.8009833343930740.400491667196537
660.5604096779026940.8791806441946110.439590322097306
670.515894982717190.9682100345656190.48410501728281
680.4708413207847580.9416826415695160.529158679215242
690.4285483493345440.8570966986690870.571451650665456
700.4233199115618350.846639823123670.576680088438165
710.3961167406914850.792233481382970.603883259308515
720.3743172075418980.7486344150837960.625682792458102
730.3701307331429270.7402614662858550.629869266857073
740.4521369620339610.9042739240679210.547863037966039
750.5171464509032620.9657070981934760.482853549096738
760.5029046237003380.9941907525993250.497095376299662
770.4718072893752870.9436145787505750.528192710624713
780.4502621935340280.9005243870680550.549737806465972
790.4209714098531160.8419428197062310.579028590146884
800.4218814557721510.8437629115443010.578118544227849
810.4103361981178350.8206723962356710.589663801882164
820.4291516699169310.8583033398338610.570848330083069
830.4068648526209820.8137297052419630.593135147379018
840.3824550388236170.7649100776472340.617544961176383
850.4128025472860330.8256050945720660.587197452713967
860.3923500191111220.7847000382222450.607649980888878
870.4137262854358240.8274525708716480.586273714564176
880.4504742465417930.9009484930835860.549525753458207
890.4180870465341070.8361740930682150.581912953465893
900.3675070319832960.7350140639665910.632492968016704
910.3272511583781080.6545023167562160.672748841621892
920.3398724264172030.6797448528344070.660127573582797
930.298676964350.59735392870.70132303565
940.27171472031380.54342944062760.7282852796862
950.2548636931607920.5097273863215840.745136306839208
960.2237421242776080.4474842485552150.776257875722392
970.2323051135432930.4646102270865860.767694886456707
980.2633466699707560.5266933399415130.736653330029244
990.5234647283982780.9530705432034440.476535271601722
1000.4924296004756070.9848592009512150.507570399524393
1010.5293898878597640.9412202242804720.470610112140236
1020.6137217746997820.7725564506004350.386278225300218
1030.6675812865255610.6648374269488780.332418713474439
1040.7035098520490110.5929802959019780.296490147950989
1050.7367724087093470.5264551825813060.263227591290653
1060.9243578979265270.1512842041469450.0756421020734726
1070.9883120086123890.02337598277522120.0116879913876106
1080.9951538802844920.009692239431015290.00484611971550765
1090.9950050840935020.0099898318129960.004994915906498
1100.9969130580915320.006173883816935650.00308694190846783
1110.9999687127310936.25745378144836e-053.12872689072418e-05
1120.9999866224319442.67551361114354e-051.33775680557177e-05
1130.9999906446449141.87107101711808e-059.35535508559038e-06
1140.9999834555850943.30888298116061e-051.65444149058031e-05
1150.9999602245445787.95509108441661e-053.9775455422083e-05
1160.9999062717514440.0001874564971122949.37282485561469e-05
1170.999868862915720.0002622741685595440.000131137084279772
1180.9999210893202610.0001578213594783967.89106797391979e-05
1190.999919157275190.000161685449619418.08427248097048e-05
1200.9998650296124420.0002699407751155070.000134970387557754
1210.9997880849919880.0004238300160233270.000211915008011663
1220.9994931165184830.001013766963033540.000506883481516769
1230.9986839972678560.002632005464289010.0013160027321445
1240.9990506357165140.001898728566972070.000949364283486037
1250.9977211030109390.004557793978122720.00227889698906136
1260.9967957377996780.0064085244006430.0032042622003215
1270.9941975051537540.01160498969249150.00580249484624577
1280.9835818816747640.03283623665047240.0164181183252362
1290.9644917153397770.07101656932044690.0355082846602235
1300.923141368633750.1537172627325010.0768586313662503






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.15702479338843NOK
5% type I error level230.190082644628099NOK
10% type I error level350.289256198347107NOK

\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 & 19 & 0.15702479338843 & NOK \tabularnewline
5% type I error level & 23 & 0.190082644628099 & NOK \tabularnewline
10% type I error level & 35 & 0.289256198347107 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159277&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]19[/C][C]0.15702479338843[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.190082644628099[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.289256198347107[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159277&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159277&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 level190.15702479338843NOK
5% type I error level230.190082644628099NOK
10% type I error level350.289256198347107NOK


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')
}