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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 21 Dec 2011 10:23:37 -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/21/t13244810581ej2zhbf5zmgk63.htm/, Retrieved Thu, 31 Oct 2024 23:17:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158818, Retrieved Thu, 31 Oct 2024 23:17:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
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]
-    D    [Multiple Regression] [Multiple Regression] [2011-12-21 15:23:37] [0b94335bf72158573fe52322b9537409] [Current]
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Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=0

As an alternative you can also use a 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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
QBEFRU[t] = + 263483.769472116 -5481677.76755624PERIODE[t] + 513386.869375667PPIL[t] -923131.344192743PCOLA[t] + 520432.772549253PORA[t] -59771.7094054462PSTIM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEFRU[t] =  +  263483.769472116 -5481677.76755624PERIODE[t] +  513386.869375667PPIL[t] -923131.344192743PCOLA[t] +  520432.772549253PORA[t] -59771.7094054462PSTIM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEFRU[t] =  +  263483.769472116 -5481677.76755624PERIODE[t] +  513386.869375667PPIL[t] -923131.344192743PCOLA[t] +  520432.772549253PORA[t] -59771.7094054462PSTIM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
QBEFRU[t] = + 263483.769472116 -5481677.76755624PERIODE[t] + 513386.869375667PPIL[t] -923131.344192743PCOLA[t] + 520432.772549253PORA[t] -59771.7094054462PSTIM[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)263483.769472116374700.7575220.70320.4831610.24158
PERIODE-5481677.767556241147087.635698-4.77885e-062e-06
PPIL513386.869375667136257.7816063.76780.0002460.000123
PCOLA-923131.344192743165432.753906-5.580100
PORA520432.772549253174367.165932.98470.0033750.001687
PSTIM-59771.709405446246002.450744-1.29930.1960670.098033

\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) & 263483.769472116 & 374700.757522 & 0.7032 & 0.483161 & 0.24158 \tabularnewline
PERIODE & -5481677.76755624 & 1147087.635698 & -4.7788 & 5e-06 & 2e-06 \tabularnewline
PPIL & 513386.869375667 & 136257.781606 & 3.7678 & 0.000246 & 0.000123 \tabularnewline
PCOLA & -923131.344192743 & 165432.753906 & -5.5801 & 0 & 0 \tabularnewline
PORA & 520432.772549253 & 174367.16593 & 2.9847 & 0.003375 & 0.001687 \tabularnewline
PSTIM & -59771.7094054462 & 46002.450744 & -1.2993 & 0.196067 & 0.098033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&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]263483.769472116[/C][C]374700.757522[/C][C]0.7032[/C][C]0.483161[/C][C]0.24158[/C][/ROW]
[ROW][C]PERIODE[/C][C]-5481677.76755624[/C][C]1147087.635698[/C][C]-4.7788[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]PPIL[/C][C]513386.869375667[/C][C]136257.781606[/C][C]3.7678[/C][C]0.000246[/C][C]0.000123[/C][/ROW]
[ROW][C]PCOLA[/C][C]-923131.344192743[/C][C]165432.753906[/C][C]-5.5801[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PORA[/C][C]520432.772549253[/C][C]174367.16593[/C][C]2.9847[/C][C]0.003375[/C][C]0.001687[/C][/ROW]
[ROW][C]PSTIM[/C][C]-59771.7094054462[/C][C]46002.450744[/C][C]-1.2993[/C][C]0.196067[/C][C]0.098033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=2

As an alternative you can also use a 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)263483.769472116374700.7575220.70320.4831610.24158
PERIODE-5481677.767556241147087.635698-4.77885e-062e-06
PPIL513386.869375667136257.7816063.76780.0002460.000123
PCOLA-923131.344192743165432.753906-5.580100
PORA520432.772549253174367.165932.98470.0033750.001687
PSTIM-59771.709405446246002.450744-1.29930.1960670.098033







Multiple Linear Regression - Regression Statistics
Multiple R0.695404489114158
R-squared0.483587403480123
Adjusted R-squared0.464318276744306
F-TEST (value)25.096487770837
F-TEST (DF numerator)5
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation34190.7612000189
Sum Squared Residuals156647092292.52

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.695404489114158 \tabularnewline
R-squared & 0.483587403480123 \tabularnewline
Adjusted R-squared & 0.464318276744306 \tabularnewline
F-TEST (value) & 25.096487770837 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 34190.7612000189 \tabularnewline
Sum Squared Residuals & 156647092292.52 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.695404489114158[/C][/ROW]
[ROW][C]R-squared[/C][C]0.483587403480123[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.464318276744306[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.096487770837[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]134[/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]34190.7612000189[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]156647092292.52[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.695404489114158
R-squared0.483587403480123
Adjusted R-squared0.464318276744306
F-TEST (value)25.096487770837
F-TEST (DF numerator)5
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation34190.7612000189
Sum Squared Residuals156647092292.52







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116949899038.694823063670459.3051769364
2125451118843.0223931276607.97760687324
3140449108210.40931986832238.5906801316
414165398598.104313721443054.8956862786
5136394109463.93253549126930.0674645086
6167588126804.5074969440783.4925030598
7191807120761.87366045471045.1263395456
8149736111008.20169075238727.7983092479
9196066124002.73087456472063.2691254359
10239155118793.433340169120361.566659831
11178421123149.4890099755271.5109900296
1213987198352.589435347141518.4105646529
13118159106675.3799936111483.6200063902
14109763120547.045527182-10784.0455271815
1597415109256.55717466-11841.5571746604
16119190111065.3577379798124.64226202147
1797903115442.129099089-17539.129099089
189695394418.93160989322534.06839010678
198788864369.613884482823518.3861155172
208463783815.6044797263821.395520273686
219054996264.1160673138-5715.11606731384
2295680108203.112358682-12523.1123586817
239937195436.2198635943934.78013640601
247998498229.9436922993-18245.9436922993
258675291194.6369850227-4442.6369850227
2685733100440.227645879-14707.2276458788
278490698861.0943681829-13955.0943681829
287835699057.0201935991-20701.0201935991
29108895103807.1897977695087.81020223112
30101768108012.009160569-6244.00916056872
317328584662.3481505523-11377.3481505523
3265724102519.016647325-36795.016647325
336745789093.4659534118-21636.4659534118
346720338233.495608521728969.5043914783
356927391618.4819246339-22345.4819246339
368080789107.8517231636-8300.85172316358
377512992260.3245783452-17131.3245783452
387499196781.3814420567-21790.3814420567
3968157107634.543812212-39477.543812212
407385887990.5324932749-14132.5324932749
417134987265.9470887755-15916.9470887755
428563484309.50355058111324.49644941887
439162478573.875336745413050.1246632546
44116014113756.083099332257.91690067008
45120033109546.42389390210486.576106098
46108651122620.073262695-13969.0732626947
47105378143220.436002039-37842.4360020385
48138939149147.713413204-10208.7134132036
49132974160279.185346597-27305.1853465974
50135277167981.388657611-32704.3886576107
51152741146532.6130247766208.38697522445
52158417169657.67291025-11240.67291025
53157460160224.065158884-2764.0651588842
54193997168319.25453322125677.7454667785
55154089155895.085011007-1806.08501100665
56147570148095.618299281-525.618299281236
57162924202143.398542314-39219.3985423142
58153629174930.77917796-21301.7791779595
59155907171525.869269066-15618.8692690658
60197675170582.28676812627092.7132318743
61250708181236.95991923469471.040080766
62266652169013.28732303797638.7126769634
63209842177625.01780786132216.9821921394
64165826168303.355771725-2477.35577172503
65137152163167.689694547-26015.6896945473
66150581164714.966107385-14133.9661073849
67145973151121.247510994-5148.24751099441
68126532134627.494655119-8095.49465511871
69115437118660.999861562-3223.99986156159
70119526125720.340940566-6194.34094056571
71110856127145.344703987-16289.3447039866
7297243112059.883108239-14816.883108239
73103876118707.306776204-14831.3067762037
74116370122934.358275491-6564.35827549057
75109616120770.209440459-11154.2094404588
7698365125937.579375353-27572.5793753526
7790440121136.97701137-30696.9770113695
7888899113593.374329848-24694.3743298476
7992358125633.034638087-33275.0346380872
8088394123509.348535625-35115.3485356249
8198219139039.195127755-40820.1951277554
82113546136158.781688716-22612.7816887157
83107168135839.407770424-28671.4077704236
8477540129422.871834085-51882.8718340853
8574944124638.500636811-49694.5006368106
867564182421.6860891356-6780.68608913558
8775910148999.498850941-73089.4988509414
8887384146352.73031067-58968.7303106704
8984615134106.400508676-49491.4005086764
9080420102131.550602983-21711.5506029827
9180784102141.20747004-21357.2074700401
9279933136792.080240192-56859.0802401917
9382118119209.963717643-37091.9637176426
9491420117961.306733939-26541.3067339385
95112426126541.680286216-14115.6802862164
96114528144800.263248874-30272.2632488739
97131025153094.565727566-22069.5657275664
98116460139094.450206595-22634.4502065947
99111258137278.04923972-26020.0492397202
100155318152945.9131402272372.08685977305
101155078164658.921878492-9580.92187849228
102134794149295.775864658-14501.7758646578
103139985131698.7954165528286.20458344833
104198778144554.87149024154223.1285097587
105172436162240.60576504110195.3942349592
106169585172200.998694834-2615.99869483433
107203702174741.32164391528960.6783560849
108282392172675.033961558109716.966038442
109220658206385.69353841614272.3064615843
110194472161661.66196120232810.3380387981
111269246170459.92442107798786.0755789231
112215340171224.2839277144115.71607229
113218319183060.49541984135258.5045801592
114195724172015.35976386923708.6402361313
115174614154061.29631102820552.7036889721
116172085166719.2996225065365.70037749409
117152347192390.016939613-40043.0169396128
118189615175464.91337479914150.0866252006
119173804125458.14535558148345.8546444189
120145683120217.41235851625465.5876414835
121133550116983.02082204116566.9791779589
122121156122818.760285864-1662.76028586408
123112040126617.496191386-14577.4961913857
124120767147103.79987765-26336.7998776501
125127019112379.32533392414639.6746660758
12613629599528.654117382436766.3458826176
127113425108042.6656565675382.33434343341
12810781597668.38048756810146.619512432
129100298107461.3222738-7163.32227380042
13097048121810.484139363-24762.4841393631
13198750131062.771318128-32312.7713181282
13298235135198.725686574-36963.7256865741
133101254129946.332671146-28692.3326711464
134139589128280.24222004511308.7577799547
135134921137175.387505995-2254.38750599466
1368035587654.7628972846-7299.76289728458
1378039666196.326434152314199.6735658477
1388218346390.123189711835792.8768102882
1397970922734.036650410756974.9633495893
14090781108244.7821008-17463.7821007998

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 169498 & 99038.6948230636 & 70459.3051769364 \tabularnewline
2 & 125451 & 118843.022393127 & 6607.97760687324 \tabularnewline
3 & 140449 & 108210.409319868 & 32238.5906801316 \tabularnewline
4 & 141653 & 98598.1043137214 & 43054.8956862786 \tabularnewline
5 & 136394 & 109463.932535491 & 26930.0674645086 \tabularnewline
6 & 167588 & 126804.50749694 & 40783.4925030598 \tabularnewline
7 & 191807 & 120761.873660454 & 71045.1263395456 \tabularnewline
8 & 149736 & 111008.201690752 & 38727.7983092479 \tabularnewline
9 & 196066 & 124002.730874564 & 72063.2691254359 \tabularnewline
10 & 239155 & 118793.433340169 & 120361.566659831 \tabularnewline
11 & 178421 & 123149.48900997 & 55271.5109900296 \tabularnewline
12 & 139871 & 98352.5894353471 & 41518.4105646529 \tabularnewline
13 & 118159 & 106675.37999361 & 11483.6200063902 \tabularnewline
14 & 109763 & 120547.045527182 & -10784.0455271815 \tabularnewline
15 & 97415 & 109256.55717466 & -11841.5571746604 \tabularnewline
16 & 119190 & 111065.357737979 & 8124.64226202147 \tabularnewline
17 & 97903 & 115442.129099089 & -17539.129099089 \tabularnewline
18 & 96953 & 94418.9316098932 & 2534.06839010678 \tabularnewline
19 & 87888 & 64369.6138844828 & 23518.3861155172 \tabularnewline
20 & 84637 & 83815.6044797263 & 821.395520273686 \tabularnewline
21 & 90549 & 96264.1160673138 & -5715.11606731384 \tabularnewline
22 & 95680 & 108203.112358682 & -12523.1123586817 \tabularnewline
23 & 99371 & 95436.219863594 & 3934.78013640601 \tabularnewline
24 & 79984 & 98229.9436922993 & -18245.9436922993 \tabularnewline
25 & 86752 & 91194.6369850227 & -4442.6369850227 \tabularnewline
26 & 85733 & 100440.227645879 & -14707.2276458788 \tabularnewline
27 & 84906 & 98861.0943681829 & -13955.0943681829 \tabularnewline
28 & 78356 & 99057.0201935991 & -20701.0201935991 \tabularnewline
29 & 108895 & 103807.189797769 & 5087.81020223112 \tabularnewline
30 & 101768 & 108012.009160569 & -6244.00916056872 \tabularnewline
31 & 73285 & 84662.3481505523 & -11377.3481505523 \tabularnewline
32 & 65724 & 102519.016647325 & -36795.016647325 \tabularnewline
33 & 67457 & 89093.4659534118 & -21636.4659534118 \tabularnewline
34 & 67203 & 38233.4956085217 & 28969.5043914783 \tabularnewline
35 & 69273 & 91618.4819246339 & -22345.4819246339 \tabularnewline
36 & 80807 & 89107.8517231636 & -8300.85172316358 \tabularnewline
37 & 75129 & 92260.3245783452 & -17131.3245783452 \tabularnewline
38 & 74991 & 96781.3814420567 & -21790.3814420567 \tabularnewline
39 & 68157 & 107634.543812212 & -39477.543812212 \tabularnewline
40 & 73858 & 87990.5324932749 & -14132.5324932749 \tabularnewline
41 & 71349 & 87265.9470887755 & -15916.9470887755 \tabularnewline
42 & 85634 & 84309.5035505811 & 1324.49644941887 \tabularnewline
43 & 91624 & 78573.8753367454 & 13050.1246632546 \tabularnewline
44 & 116014 & 113756.08309933 & 2257.91690067008 \tabularnewline
45 & 120033 & 109546.423893902 & 10486.576106098 \tabularnewline
46 & 108651 & 122620.073262695 & -13969.0732626947 \tabularnewline
47 & 105378 & 143220.436002039 & -37842.4360020385 \tabularnewline
48 & 138939 & 149147.713413204 & -10208.7134132036 \tabularnewline
49 & 132974 & 160279.185346597 & -27305.1853465974 \tabularnewline
50 & 135277 & 167981.388657611 & -32704.3886576107 \tabularnewline
51 & 152741 & 146532.613024776 & 6208.38697522445 \tabularnewline
52 & 158417 & 169657.67291025 & -11240.67291025 \tabularnewline
53 & 157460 & 160224.065158884 & -2764.0651588842 \tabularnewline
54 & 193997 & 168319.254533221 & 25677.7454667785 \tabularnewline
55 & 154089 & 155895.085011007 & -1806.08501100665 \tabularnewline
56 & 147570 & 148095.618299281 & -525.618299281236 \tabularnewline
57 & 162924 & 202143.398542314 & -39219.3985423142 \tabularnewline
58 & 153629 & 174930.77917796 & -21301.7791779595 \tabularnewline
59 & 155907 & 171525.869269066 & -15618.8692690658 \tabularnewline
60 & 197675 & 170582.286768126 & 27092.7132318743 \tabularnewline
61 & 250708 & 181236.959919234 & 69471.040080766 \tabularnewline
62 & 266652 & 169013.287323037 & 97638.7126769634 \tabularnewline
63 & 209842 & 177625.017807861 & 32216.9821921394 \tabularnewline
64 & 165826 & 168303.355771725 & -2477.35577172503 \tabularnewline
65 & 137152 & 163167.689694547 & -26015.6896945473 \tabularnewline
66 & 150581 & 164714.966107385 & -14133.9661073849 \tabularnewline
67 & 145973 & 151121.247510994 & -5148.24751099441 \tabularnewline
68 & 126532 & 134627.494655119 & -8095.49465511871 \tabularnewline
69 & 115437 & 118660.999861562 & -3223.99986156159 \tabularnewline
70 & 119526 & 125720.340940566 & -6194.34094056571 \tabularnewline
71 & 110856 & 127145.344703987 & -16289.3447039866 \tabularnewline
72 & 97243 & 112059.883108239 & -14816.883108239 \tabularnewline
73 & 103876 & 118707.306776204 & -14831.3067762037 \tabularnewline
74 & 116370 & 122934.358275491 & -6564.35827549057 \tabularnewline
75 & 109616 & 120770.209440459 & -11154.2094404588 \tabularnewline
76 & 98365 & 125937.579375353 & -27572.5793753526 \tabularnewline
77 & 90440 & 121136.97701137 & -30696.9770113695 \tabularnewline
78 & 88899 & 113593.374329848 & -24694.3743298476 \tabularnewline
79 & 92358 & 125633.034638087 & -33275.0346380872 \tabularnewline
80 & 88394 & 123509.348535625 & -35115.3485356249 \tabularnewline
81 & 98219 & 139039.195127755 & -40820.1951277554 \tabularnewline
82 & 113546 & 136158.781688716 & -22612.7816887157 \tabularnewline
83 & 107168 & 135839.407770424 & -28671.4077704236 \tabularnewline
84 & 77540 & 129422.871834085 & -51882.8718340853 \tabularnewline
85 & 74944 & 124638.500636811 & -49694.5006368106 \tabularnewline
86 & 75641 & 82421.6860891356 & -6780.68608913558 \tabularnewline
87 & 75910 & 148999.498850941 & -73089.4988509414 \tabularnewline
88 & 87384 & 146352.73031067 & -58968.7303106704 \tabularnewline
89 & 84615 & 134106.400508676 & -49491.4005086764 \tabularnewline
90 & 80420 & 102131.550602983 & -21711.5506029827 \tabularnewline
91 & 80784 & 102141.20747004 & -21357.2074700401 \tabularnewline
92 & 79933 & 136792.080240192 & -56859.0802401917 \tabularnewline
93 & 82118 & 119209.963717643 & -37091.9637176426 \tabularnewline
94 & 91420 & 117961.306733939 & -26541.3067339385 \tabularnewline
95 & 112426 & 126541.680286216 & -14115.6802862164 \tabularnewline
96 & 114528 & 144800.263248874 & -30272.2632488739 \tabularnewline
97 & 131025 & 153094.565727566 & -22069.5657275664 \tabularnewline
98 & 116460 & 139094.450206595 & -22634.4502065947 \tabularnewline
99 & 111258 & 137278.04923972 & -26020.0492397202 \tabularnewline
100 & 155318 & 152945.913140227 & 2372.08685977305 \tabularnewline
101 & 155078 & 164658.921878492 & -9580.92187849228 \tabularnewline
102 & 134794 & 149295.775864658 & -14501.7758646578 \tabularnewline
103 & 139985 & 131698.795416552 & 8286.20458344833 \tabularnewline
104 & 198778 & 144554.871490241 & 54223.1285097587 \tabularnewline
105 & 172436 & 162240.605765041 & 10195.3942349592 \tabularnewline
106 & 169585 & 172200.998694834 & -2615.99869483433 \tabularnewline
107 & 203702 & 174741.321643915 & 28960.6783560849 \tabularnewline
108 & 282392 & 172675.033961558 & 109716.966038442 \tabularnewline
109 & 220658 & 206385.693538416 & 14272.3064615843 \tabularnewline
110 & 194472 & 161661.661961202 & 32810.3380387981 \tabularnewline
111 & 269246 & 170459.924421077 & 98786.0755789231 \tabularnewline
112 & 215340 & 171224.28392771 & 44115.71607229 \tabularnewline
113 & 218319 & 183060.495419841 & 35258.5045801592 \tabularnewline
114 & 195724 & 172015.359763869 & 23708.6402361313 \tabularnewline
115 & 174614 & 154061.296311028 & 20552.7036889721 \tabularnewline
116 & 172085 & 166719.299622506 & 5365.70037749409 \tabularnewline
117 & 152347 & 192390.016939613 & -40043.0169396128 \tabularnewline
118 & 189615 & 175464.913374799 & 14150.0866252006 \tabularnewline
119 & 173804 & 125458.145355581 & 48345.8546444189 \tabularnewline
120 & 145683 & 120217.412358516 & 25465.5876414835 \tabularnewline
121 & 133550 & 116983.020822041 & 16566.9791779589 \tabularnewline
122 & 121156 & 122818.760285864 & -1662.76028586408 \tabularnewline
123 & 112040 & 126617.496191386 & -14577.4961913857 \tabularnewline
124 & 120767 & 147103.79987765 & -26336.7998776501 \tabularnewline
125 & 127019 & 112379.325333924 & 14639.6746660758 \tabularnewline
126 & 136295 & 99528.6541173824 & 36766.3458826176 \tabularnewline
127 & 113425 & 108042.665656567 & 5382.33434343341 \tabularnewline
128 & 107815 & 97668.380487568 & 10146.619512432 \tabularnewline
129 & 100298 & 107461.3222738 & -7163.32227380042 \tabularnewline
130 & 97048 & 121810.484139363 & -24762.4841393631 \tabularnewline
131 & 98750 & 131062.771318128 & -32312.7713181282 \tabularnewline
132 & 98235 & 135198.725686574 & -36963.7256865741 \tabularnewline
133 & 101254 & 129946.332671146 & -28692.3326711464 \tabularnewline
134 & 139589 & 128280.242220045 & 11308.7577799547 \tabularnewline
135 & 134921 & 137175.387505995 & -2254.38750599466 \tabularnewline
136 & 80355 & 87654.7628972846 & -7299.76289728458 \tabularnewline
137 & 80396 & 66196.3264341523 & 14199.6735658477 \tabularnewline
138 & 82183 & 46390.1231897118 & 35792.8768102882 \tabularnewline
139 & 79709 & 22734.0366504107 & 56974.9633495893 \tabularnewline
140 & 90781 & 108244.7821008 & -17463.7821007998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&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]99038.6948230636[/C][C]70459.3051769364[/C][/ROW]
[ROW][C]2[/C][C]125451[/C][C]118843.022393127[/C][C]6607.97760687324[/C][/ROW]
[ROW][C]3[/C][C]140449[/C][C]108210.409319868[/C][C]32238.5906801316[/C][/ROW]
[ROW][C]4[/C][C]141653[/C][C]98598.1043137214[/C][C]43054.8956862786[/C][/ROW]
[ROW][C]5[/C][C]136394[/C][C]109463.932535491[/C][C]26930.0674645086[/C][/ROW]
[ROW][C]6[/C][C]167588[/C][C]126804.50749694[/C][C]40783.4925030598[/C][/ROW]
[ROW][C]7[/C][C]191807[/C][C]120761.873660454[/C][C]71045.1263395456[/C][/ROW]
[ROW][C]8[/C][C]149736[/C][C]111008.201690752[/C][C]38727.7983092479[/C][/ROW]
[ROW][C]9[/C][C]196066[/C][C]124002.730874564[/C][C]72063.2691254359[/C][/ROW]
[ROW][C]10[/C][C]239155[/C][C]118793.433340169[/C][C]120361.566659831[/C][/ROW]
[ROW][C]11[/C][C]178421[/C][C]123149.48900997[/C][C]55271.5109900296[/C][/ROW]
[ROW][C]12[/C][C]139871[/C][C]98352.5894353471[/C][C]41518.4105646529[/C][/ROW]
[ROW][C]13[/C][C]118159[/C][C]106675.37999361[/C][C]11483.6200063902[/C][/ROW]
[ROW][C]14[/C][C]109763[/C][C]120547.045527182[/C][C]-10784.0455271815[/C][/ROW]
[ROW][C]15[/C][C]97415[/C][C]109256.55717466[/C][C]-11841.5571746604[/C][/ROW]
[ROW][C]16[/C][C]119190[/C][C]111065.357737979[/C][C]8124.64226202147[/C][/ROW]
[ROW][C]17[/C][C]97903[/C][C]115442.129099089[/C][C]-17539.129099089[/C][/ROW]
[ROW][C]18[/C][C]96953[/C][C]94418.9316098932[/C][C]2534.06839010678[/C][/ROW]
[ROW][C]19[/C][C]87888[/C][C]64369.6138844828[/C][C]23518.3861155172[/C][/ROW]
[ROW][C]20[/C][C]84637[/C][C]83815.6044797263[/C][C]821.395520273686[/C][/ROW]
[ROW][C]21[/C][C]90549[/C][C]96264.1160673138[/C][C]-5715.11606731384[/C][/ROW]
[ROW][C]22[/C][C]95680[/C][C]108203.112358682[/C][C]-12523.1123586817[/C][/ROW]
[ROW][C]23[/C][C]99371[/C][C]95436.219863594[/C][C]3934.78013640601[/C][/ROW]
[ROW][C]24[/C][C]79984[/C][C]98229.9436922993[/C][C]-18245.9436922993[/C][/ROW]
[ROW][C]25[/C][C]86752[/C][C]91194.6369850227[/C][C]-4442.6369850227[/C][/ROW]
[ROW][C]26[/C][C]85733[/C][C]100440.227645879[/C][C]-14707.2276458788[/C][/ROW]
[ROW][C]27[/C][C]84906[/C][C]98861.0943681829[/C][C]-13955.0943681829[/C][/ROW]
[ROW][C]28[/C][C]78356[/C][C]99057.0201935991[/C][C]-20701.0201935991[/C][/ROW]
[ROW][C]29[/C][C]108895[/C][C]103807.189797769[/C][C]5087.81020223112[/C][/ROW]
[ROW][C]30[/C][C]101768[/C][C]108012.009160569[/C][C]-6244.00916056872[/C][/ROW]
[ROW][C]31[/C][C]73285[/C][C]84662.3481505523[/C][C]-11377.3481505523[/C][/ROW]
[ROW][C]32[/C][C]65724[/C][C]102519.016647325[/C][C]-36795.016647325[/C][/ROW]
[ROW][C]33[/C][C]67457[/C][C]89093.4659534118[/C][C]-21636.4659534118[/C][/ROW]
[ROW][C]34[/C][C]67203[/C][C]38233.4956085217[/C][C]28969.5043914783[/C][/ROW]
[ROW][C]35[/C][C]69273[/C][C]91618.4819246339[/C][C]-22345.4819246339[/C][/ROW]
[ROW][C]36[/C][C]80807[/C][C]89107.8517231636[/C][C]-8300.85172316358[/C][/ROW]
[ROW][C]37[/C][C]75129[/C][C]92260.3245783452[/C][C]-17131.3245783452[/C][/ROW]
[ROW][C]38[/C][C]74991[/C][C]96781.3814420567[/C][C]-21790.3814420567[/C][/ROW]
[ROW][C]39[/C][C]68157[/C][C]107634.543812212[/C][C]-39477.543812212[/C][/ROW]
[ROW][C]40[/C][C]73858[/C][C]87990.5324932749[/C][C]-14132.5324932749[/C][/ROW]
[ROW][C]41[/C][C]71349[/C][C]87265.9470887755[/C][C]-15916.9470887755[/C][/ROW]
[ROW][C]42[/C][C]85634[/C][C]84309.5035505811[/C][C]1324.49644941887[/C][/ROW]
[ROW][C]43[/C][C]91624[/C][C]78573.8753367454[/C][C]13050.1246632546[/C][/ROW]
[ROW][C]44[/C][C]116014[/C][C]113756.08309933[/C][C]2257.91690067008[/C][/ROW]
[ROW][C]45[/C][C]120033[/C][C]109546.423893902[/C][C]10486.576106098[/C][/ROW]
[ROW][C]46[/C][C]108651[/C][C]122620.073262695[/C][C]-13969.0732626947[/C][/ROW]
[ROW][C]47[/C][C]105378[/C][C]143220.436002039[/C][C]-37842.4360020385[/C][/ROW]
[ROW][C]48[/C][C]138939[/C][C]149147.713413204[/C][C]-10208.7134132036[/C][/ROW]
[ROW][C]49[/C][C]132974[/C][C]160279.185346597[/C][C]-27305.1853465974[/C][/ROW]
[ROW][C]50[/C][C]135277[/C][C]167981.388657611[/C][C]-32704.3886576107[/C][/ROW]
[ROW][C]51[/C][C]152741[/C][C]146532.613024776[/C][C]6208.38697522445[/C][/ROW]
[ROW][C]52[/C][C]158417[/C][C]169657.67291025[/C][C]-11240.67291025[/C][/ROW]
[ROW][C]53[/C][C]157460[/C][C]160224.065158884[/C][C]-2764.0651588842[/C][/ROW]
[ROW][C]54[/C][C]193997[/C][C]168319.254533221[/C][C]25677.7454667785[/C][/ROW]
[ROW][C]55[/C][C]154089[/C][C]155895.085011007[/C][C]-1806.08501100665[/C][/ROW]
[ROW][C]56[/C][C]147570[/C][C]148095.618299281[/C][C]-525.618299281236[/C][/ROW]
[ROW][C]57[/C][C]162924[/C][C]202143.398542314[/C][C]-39219.3985423142[/C][/ROW]
[ROW][C]58[/C][C]153629[/C][C]174930.77917796[/C][C]-21301.7791779595[/C][/ROW]
[ROW][C]59[/C][C]155907[/C][C]171525.869269066[/C][C]-15618.8692690658[/C][/ROW]
[ROW][C]60[/C][C]197675[/C][C]170582.286768126[/C][C]27092.7132318743[/C][/ROW]
[ROW][C]61[/C][C]250708[/C][C]181236.959919234[/C][C]69471.040080766[/C][/ROW]
[ROW][C]62[/C][C]266652[/C][C]169013.287323037[/C][C]97638.7126769634[/C][/ROW]
[ROW][C]63[/C][C]209842[/C][C]177625.017807861[/C][C]32216.9821921394[/C][/ROW]
[ROW][C]64[/C][C]165826[/C][C]168303.355771725[/C][C]-2477.35577172503[/C][/ROW]
[ROW][C]65[/C][C]137152[/C][C]163167.689694547[/C][C]-26015.6896945473[/C][/ROW]
[ROW][C]66[/C][C]150581[/C][C]164714.966107385[/C][C]-14133.9661073849[/C][/ROW]
[ROW][C]67[/C][C]145973[/C][C]151121.247510994[/C][C]-5148.24751099441[/C][/ROW]
[ROW][C]68[/C][C]126532[/C][C]134627.494655119[/C][C]-8095.49465511871[/C][/ROW]
[ROW][C]69[/C][C]115437[/C][C]118660.999861562[/C][C]-3223.99986156159[/C][/ROW]
[ROW][C]70[/C][C]119526[/C][C]125720.340940566[/C][C]-6194.34094056571[/C][/ROW]
[ROW][C]71[/C][C]110856[/C][C]127145.344703987[/C][C]-16289.3447039866[/C][/ROW]
[ROW][C]72[/C][C]97243[/C][C]112059.883108239[/C][C]-14816.883108239[/C][/ROW]
[ROW][C]73[/C][C]103876[/C][C]118707.306776204[/C][C]-14831.3067762037[/C][/ROW]
[ROW][C]74[/C][C]116370[/C][C]122934.358275491[/C][C]-6564.35827549057[/C][/ROW]
[ROW][C]75[/C][C]109616[/C][C]120770.209440459[/C][C]-11154.2094404588[/C][/ROW]
[ROW][C]76[/C][C]98365[/C][C]125937.579375353[/C][C]-27572.5793753526[/C][/ROW]
[ROW][C]77[/C][C]90440[/C][C]121136.97701137[/C][C]-30696.9770113695[/C][/ROW]
[ROW][C]78[/C][C]88899[/C][C]113593.374329848[/C][C]-24694.3743298476[/C][/ROW]
[ROW][C]79[/C][C]92358[/C][C]125633.034638087[/C][C]-33275.0346380872[/C][/ROW]
[ROW][C]80[/C][C]88394[/C][C]123509.348535625[/C][C]-35115.3485356249[/C][/ROW]
[ROW][C]81[/C][C]98219[/C][C]139039.195127755[/C][C]-40820.1951277554[/C][/ROW]
[ROW][C]82[/C][C]113546[/C][C]136158.781688716[/C][C]-22612.7816887157[/C][/ROW]
[ROW][C]83[/C][C]107168[/C][C]135839.407770424[/C][C]-28671.4077704236[/C][/ROW]
[ROW][C]84[/C][C]77540[/C][C]129422.871834085[/C][C]-51882.8718340853[/C][/ROW]
[ROW][C]85[/C][C]74944[/C][C]124638.500636811[/C][C]-49694.5006368106[/C][/ROW]
[ROW][C]86[/C][C]75641[/C][C]82421.6860891356[/C][C]-6780.68608913558[/C][/ROW]
[ROW][C]87[/C][C]75910[/C][C]148999.498850941[/C][C]-73089.4988509414[/C][/ROW]
[ROW][C]88[/C][C]87384[/C][C]146352.73031067[/C][C]-58968.7303106704[/C][/ROW]
[ROW][C]89[/C][C]84615[/C][C]134106.400508676[/C][C]-49491.4005086764[/C][/ROW]
[ROW][C]90[/C][C]80420[/C][C]102131.550602983[/C][C]-21711.5506029827[/C][/ROW]
[ROW][C]91[/C][C]80784[/C][C]102141.20747004[/C][C]-21357.2074700401[/C][/ROW]
[ROW][C]92[/C][C]79933[/C][C]136792.080240192[/C][C]-56859.0802401917[/C][/ROW]
[ROW][C]93[/C][C]82118[/C][C]119209.963717643[/C][C]-37091.9637176426[/C][/ROW]
[ROW][C]94[/C][C]91420[/C][C]117961.306733939[/C][C]-26541.3067339385[/C][/ROW]
[ROW][C]95[/C][C]112426[/C][C]126541.680286216[/C][C]-14115.6802862164[/C][/ROW]
[ROW][C]96[/C][C]114528[/C][C]144800.263248874[/C][C]-30272.2632488739[/C][/ROW]
[ROW][C]97[/C][C]131025[/C][C]153094.565727566[/C][C]-22069.5657275664[/C][/ROW]
[ROW][C]98[/C][C]116460[/C][C]139094.450206595[/C][C]-22634.4502065947[/C][/ROW]
[ROW][C]99[/C][C]111258[/C][C]137278.04923972[/C][C]-26020.0492397202[/C][/ROW]
[ROW][C]100[/C][C]155318[/C][C]152945.913140227[/C][C]2372.08685977305[/C][/ROW]
[ROW][C]101[/C][C]155078[/C][C]164658.921878492[/C][C]-9580.92187849228[/C][/ROW]
[ROW][C]102[/C][C]134794[/C][C]149295.775864658[/C][C]-14501.7758646578[/C][/ROW]
[ROW][C]103[/C][C]139985[/C][C]131698.795416552[/C][C]8286.20458344833[/C][/ROW]
[ROW][C]104[/C][C]198778[/C][C]144554.871490241[/C][C]54223.1285097587[/C][/ROW]
[ROW][C]105[/C][C]172436[/C][C]162240.605765041[/C][C]10195.3942349592[/C][/ROW]
[ROW][C]106[/C][C]169585[/C][C]172200.998694834[/C][C]-2615.99869483433[/C][/ROW]
[ROW][C]107[/C][C]203702[/C][C]174741.321643915[/C][C]28960.6783560849[/C][/ROW]
[ROW][C]108[/C][C]282392[/C][C]172675.033961558[/C][C]109716.966038442[/C][/ROW]
[ROW][C]109[/C][C]220658[/C][C]206385.693538416[/C][C]14272.3064615843[/C][/ROW]
[ROW][C]110[/C][C]194472[/C][C]161661.661961202[/C][C]32810.3380387981[/C][/ROW]
[ROW][C]111[/C][C]269246[/C][C]170459.924421077[/C][C]98786.0755789231[/C][/ROW]
[ROW][C]112[/C][C]215340[/C][C]171224.28392771[/C][C]44115.71607229[/C][/ROW]
[ROW][C]113[/C][C]218319[/C][C]183060.495419841[/C][C]35258.5045801592[/C][/ROW]
[ROW][C]114[/C][C]195724[/C][C]172015.359763869[/C][C]23708.6402361313[/C][/ROW]
[ROW][C]115[/C][C]174614[/C][C]154061.296311028[/C][C]20552.7036889721[/C][/ROW]
[ROW][C]116[/C][C]172085[/C][C]166719.299622506[/C][C]5365.70037749409[/C][/ROW]
[ROW][C]117[/C][C]152347[/C][C]192390.016939613[/C][C]-40043.0169396128[/C][/ROW]
[ROW][C]118[/C][C]189615[/C][C]175464.913374799[/C][C]14150.0866252006[/C][/ROW]
[ROW][C]119[/C][C]173804[/C][C]125458.145355581[/C][C]48345.8546444189[/C][/ROW]
[ROW][C]120[/C][C]145683[/C][C]120217.412358516[/C][C]25465.5876414835[/C][/ROW]
[ROW][C]121[/C][C]133550[/C][C]116983.020822041[/C][C]16566.9791779589[/C][/ROW]
[ROW][C]122[/C][C]121156[/C][C]122818.760285864[/C][C]-1662.76028586408[/C][/ROW]
[ROW][C]123[/C][C]112040[/C][C]126617.496191386[/C][C]-14577.4961913857[/C][/ROW]
[ROW][C]124[/C][C]120767[/C][C]147103.79987765[/C][C]-26336.7998776501[/C][/ROW]
[ROW][C]125[/C][C]127019[/C][C]112379.325333924[/C][C]14639.6746660758[/C][/ROW]
[ROW][C]126[/C][C]136295[/C][C]99528.6541173824[/C][C]36766.3458826176[/C][/ROW]
[ROW][C]127[/C][C]113425[/C][C]108042.665656567[/C][C]5382.33434343341[/C][/ROW]
[ROW][C]128[/C][C]107815[/C][C]97668.380487568[/C][C]10146.619512432[/C][/ROW]
[ROW][C]129[/C][C]100298[/C][C]107461.3222738[/C][C]-7163.32227380042[/C][/ROW]
[ROW][C]130[/C][C]97048[/C][C]121810.484139363[/C][C]-24762.4841393631[/C][/ROW]
[ROW][C]131[/C][C]98750[/C][C]131062.771318128[/C][C]-32312.7713181282[/C][/ROW]
[ROW][C]132[/C][C]98235[/C][C]135198.725686574[/C][C]-36963.7256865741[/C][/ROW]
[ROW][C]133[/C][C]101254[/C][C]129946.332671146[/C][C]-28692.3326711464[/C][/ROW]
[ROW][C]134[/C][C]139589[/C][C]128280.242220045[/C][C]11308.7577799547[/C][/ROW]
[ROW][C]135[/C][C]134921[/C][C]137175.387505995[/C][C]-2254.38750599466[/C][/ROW]
[ROW][C]136[/C][C]80355[/C][C]87654.7628972846[/C][C]-7299.76289728458[/C][/ROW]
[ROW][C]137[/C][C]80396[/C][C]66196.3264341523[/C][C]14199.6735658477[/C][/ROW]
[ROW][C]138[/C][C]82183[/C][C]46390.1231897118[/C][C]35792.8768102882[/C][/ROW]
[ROW][C]139[/C][C]79709[/C][C]22734.0366504107[/C][C]56974.9633495893[/C][/ROW]
[ROW][C]140[/C][C]90781[/C][C]108244.7821008[/C][C]-17463.7821007998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116949899038.694823063670459.3051769364
2125451118843.0223931276607.97760687324
3140449108210.40931986832238.5906801316
414165398598.104313721443054.8956862786
5136394109463.93253549126930.0674645086
6167588126804.5074969440783.4925030598
7191807120761.87366045471045.1263395456
8149736111008.20169075238727.7983092479
9196066124002.73087456472063.2691254359
10239155118793.433340169120361.566659831
11178421123149.4890099755271.5109900296
1213987198352.589435347141518.4105646529
13118159106675.3799936111483.6200063902
14109763120547.045527182-10784.0455271815
1597415109256.55717466-11841.5571746604
16119190111065.3577379798124.64226202147
1797903115442.129099089-17539.129099089
189695394418.93160989322534.06839010678
198788864369.613884482823518.3861155172
208463783815.6044797263821.395520273686
219054996264.1160673138-5715.11606731384
2295680108203.112358682-12523.1123586817
239937195436.2198635943934.78013640601
247998498229.9436922993-18245.9436922993
258675291194.6369850227-4442.6369850227
2685733100440.227645879-14707.2276458788
278490698861.0943681829-13955.0943681829
287835699057.0201935991-20701.0201935991
29108895103807.1897977695087.81020223112
30101768108012.009160569-6244.00916056872
317328584662.3481505523-11377.3481505523
3265724102519.016647325-36795.016647325
336745789093.4659534118-21636.4659534118
346720338233.495608521728969.5043914783
356927391618.4819246339-22345.4819246339
368080789107.8517231636-8300.85172316358
377512992260.3245783452-17131.3245783452
387499196781.3814420567-21790.3814420567
3968157107634.543812212-39477.543812212
407385887990.5324932749-14132.5324932749
417134987265.9470887755-15916.9470887755
428563484309.50355058111324.49644941887
439162478573.875336745413050.1246632546
44116014113756.083099332257.91690067008
45120033109546.42389390210486.576106098
46108651122620.073262695-13969.0732626947
47105378143220.436002039-37842.4360020385
48138939149147.713413204-10208.7134132036
49132974160279.185346597-27305.1853465974
50135277167981.388657611-32704.3886576107
51152741146532.6130247766208.38697522445
52158417169657.67291025-11240.67291025
53157460160224.065158884-2764.0651588842
54193997168319.25453322125677.7454667785
55154089155895.085011007-1806.08501100665
56147570148095.618299281-525.618299281236
57162924202143.398542314-39219.3985423142
58153629174930.77917796-21301.7791779595
59155907171525.869269066-15618.8692690658
60197675170582.28676812627092.7132318743
61250708181236.95991923469471.040080766
62266652169013.28732303797638.7126769634
63209842177625.01780786132216.9821921394
64165826168303.355771725-2477.35577172503
65137152163167.689694547-26015.6896945473
66150581164714.966107385-14133.9661073849
67145973151121.247510994-5148.24751099441
68126532134627.494655119-8095.49465511871
69115437118660.999861562-3223.99986156159
70119526125720.340940566-6194.34094056571
71110856127145.344703987-16289.3447039866
7297243112059.883108239-14816.883108239
73103876118707.306776204-14831.3067762037
74116370122934.358275491-6564.35827549057
75109616120770.209440459-11154.2094404588
7698365125937.579375353-27572.5793753526
7790440121136.97701137-30696.9770113695
7888899113593.374329848-24694.3743298476
7992358125633.034638087-33275.0346380872
8088394123509.348535625-35115.3485356249
8198219139039.195127755-40820.1951277554
82113546136158.781688716-22612.7816887157
83107168135839.407770424-28671.4077704236
8477540129422.871834085-51882.8718340853
8574944124638.500636811-49694.5006368106
867564182421.6860891356-6780.68608913558
8775910148999.498850941-73089.4988509414
8887384146352.73031067-58968.7303106704
8984615134106.400508676-49491.4005086764
9080420102131.550602983-21711.5506029827
9180784102141.20747004-21357.2074700401
9279933136792.080240192-56859.0802401917
9382118119209.963717643-37091.9637176426
9491420117961.306733939-26541.3067339385
95112426126541.680286216-14115.6802862164
96114528144800.263248874-30272.2632488739
97131025153094.565727566-22069.5657275664
98116460139094.450206595-22634.4502065947
99111258137278.04923972-26020.0492397202
100155318152945.9131402272372.08685977305
101155078164658.921878492-9580.92187849228
102134794149295.775864658-14501.7758646578
103139985131698.7954165528286.20458344833
104198778144554.87149024154223.1285097587
105172436162240.60576504110195.3942349592
106169585172200.998694834-2615.99869483433
107203702174741.32164391528960.6783560849
108282392172675.033961558109716.966038442
109220658206385.69353841614272.3064615843
110194472161661.66196120232810.3380387981
111269246170459.92442107798786.0755789231
112215340171224.2839277144115.71607229
113218319183060.49541984135258.5045801592
114195724172015.35976386923708.6402361313
115174614154061.29631102820552.7036889721
116172085166719.2996225065365.70037749409
117152347192390.016939613-40043.0169396128
118189615175464.91337479914150.0866252006
119173804125458.14535558148345.8546444189
120145683120217.41235851625465.5876414835
121133550116983.02082204116566.9791779589
122121156122818.760285864-1662.76028586408
123112040126617.496191386-14577.4961913857
124120767147103.79987765-26336.7998776501
125127019112379.32533392414639.6746660758
12613629599528.654117382436766.3458826176
127113425108042.6656565675382.33434343341
12810781597668.38048756810146.619512432
129100298107461.3222738-7163.32227380042
13097048121810.484139363-24762.4841393631
13198750131062.771318128-32312.7713181282
13298235135198.725686574-36963.7256865741
133101254129946.332671146-28692.3326711464
134139589128280.24222004511308.7577799547
135134921137175.387505995-2254.38750599466
1368035587654.7628972846-7299.76289728458
1378039666196.326434152314199.6735658477
1388218346390.123189711835792.8768102882
1397970922734.036650410756974.9633495893
14090781108244.7821008-17463.7821007998







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2563464296186670.5126928592373330.743653570381333
100.2098957667917560.4197915335835130.790104233208244
110.6314907060210160.7370185879579690.368509293978984
120.612331814364550.77533637127090.38766818563545
130.5636859040384170.8726281919231660.436314095961583
140.7837054903600280.4325890192799430.216294509639972
150.7140250755729880.5719498488540230.285974924427012
160.6344059801479620.7311880397040760.365594019852038
170.544343642977010.911312714045980.45565635702299
180.53260892699780.9347821460044010.4673910730022
190.5892905004949620.8214189990100760.410709499505038
200.5207339782570570.9585320434858860.479266021742943
210.4447753824852130.8895507649704260.555224617514787
220.3712825201806610.7425650403613210.628717479819339
230.3226541668832220.6453083337664440.677345833116778
240.3180167021054980.6360334042109960.681983297894502
250.3931942763831050.7863885527662110.606805723616895
260.3922791716207540.7845583432415080.607720828379246
270.4191613419723740.8383226839447480.580838658027626
280.4345603374565150.8691206749130290.565439662543485
290.4305318330758780.8610636661517550.569468166924122
300.4123123836025320.8246247672050650.587687616397468
310.3775391109966880.7550782219933750.622460889003312
320.3751851963256270.7503703926512540.624814803674373
330.3395946527409560.6791893054819130.660405347259044
340.3733440125971060.7466880251942120.626655987402894
350.4567556919619280.9135113839238550.543244308038072
360.4278754503634210.8557509007268410.572124549636579
370.3751341792210760.7502683584421510.624865820778924
380.3677452502131590.7354905004263170.632254749786841
390.377629498866360.7552589977327190.62237050113364
400.3465981866350520.6931963732701040.653401813364948
410.4299751250032380.8599502500064760.570024874996762
420.4394957041608830.8789914083217660.560504295839117
430.4504434934683340.9008869869366680.549556506531666
440.6169438647841060.7661122704317880.383056135215894
450.6810960627522250.6378078744955490.318903937247775
460.7513389046221590.4973221907556820.248661095377841
470.8128473283263160.3743053433473680.187152671673684
480.7940481326925340.4119037346149320.205951867307466
490.77706945339720.4458610932056010.2229305466028
500.7511659703405920.4976680593188160.248834029659408
510.7438778023264990.5122443953470030.256122197673501
520.7063653545662810.5872692908674390.293634645433719
530.674498142282430.651003715435140.32550185771757
540.7084299254812330.5831401490375340.291570074518767
550.6746889098515970.6506221802968070.325311090148403
560.6416439065680010.7167121868639980.358356093431999
570.6393520191184140.7212959617631730.360647980881586
580.5940931109776240.8118137780447510.405906889022376
590.5450117647883410.9099764704233170.454988235211659
600.5663986401086250.867202719782750.433601359891375
610.7573572553541430.4852854892917140.242642744645857
620.97056705958430.05886588083140030.0294329404157002
630.9739850931968060.05202981360638710.0260149068031936
640.9674364890341130.06512702193177490.0325635109658874
650.9606392089910260.07872158201794750.0393607910089738
660.9517963328399680.09640733432006380.0482036671600319
670.9407905220537850.1184189558924310.0592094779462153
680.9280469212963940.1439061574072110.0719530787036057
690.9157673785518830.1684652428962340.0842326214481169
700.9158097856479970.1683804287040050.0841902143520027
710.9088961380905410.1822077238189180.0911038619094588
720.9052577104461160.1894845791077690.0947422895538843
730.9083529636417940.1832940727164120.0916470363582061
740.9352128817361680.1295742365276630.0647871182638317
750.9563553027175680.08728939456486450.0436446972824322
760.9528638859425940.09427222811481260.0471361140574063
770.9474219326294240.1051561347411520.052578067370576
780.9438171415234690.1123657169530610.0561828584765306
790.9434275937998860.1131448124002280.0565724062001138
800.9468820544257930.1062358911484140.0531179455742068
810.9550753716040710.08984925679185820.0449246283959291
820.9575756901789350.0848486196421310.0424243098210655
830.951064733457930.09787053308413990.0489352665420699
840.9485373908008810.1029252183982380.0514626091991191
850.9531633179784840.09367336404303180.0468366820215159
860.9526508347228460.09469833055430760.0473491652771538
870.9623481267060290.07530374658794190.0376518732939709
880.9697096141146090.0605807717707820.030290385885391
890.9639692581874790.07206148362504170.0360307418125208
900.9523230856782790.09535382864344250.0476769143217212
910.938086373757220.1238272524855590.0619136262427797
920.9395260771972260.1209478456055490.0604739228027745
930.9224147416985020.1551705166029970.0775852583014985
940.9047758557304930.1904482885390140.0952241442695071
950.884813353500240.2303732929995190.11518664649976
960.8562722505168560.2874554989662880.143727749483144
970.8417165925134260.3165668149731470.158283407486574
980.8378638388605280.3242723222789430.162136161139472
990.8999931216535120.2000137566929770.100006878346488
1000.8756624374825470.2486751250349060.124337562517453
1010.8576207719349630.2847584561300740.142379228065037
1020.8342101137252480.3315797725495040.165789886274752
1030.8053385555435820.3893228889128350.194661444456418
1040.8599907142730720.2800185714538560.140009285726928
1050.8282409358876790.3435181282246420.171759064112321
1060.8178363664055910.3643272671888180.182163633594409
1070.7987893262747360.4024213474505280.201210673725264
1080.972661130950710.0546777380985790.0273388690492895
1090.9627715532533940.07445689349321290.0372284467466064
1100.9581650413184960.08366991736300770.0418349586815038
1110.996594447052880.00681110589423960.0034055529471198
1120.9970207971598330.005958405680333280.00297920284016664
1130.9993097697496980.001380460500604310.000690230250302153
1140.9991717549849360.001656490030127980.000828245015063991
1150.9986826646757620.002634670648476280.00131733532423814
1160.9984021914296670.003195617140665030.00159780857033252
1170.9971547215845950.005690556830810740.00284527841540537
1180.998723246167170.002553507665659130.00127675383282956
1190.9986600046966480.002679990606704480.00133999530335224
1200.9983751988129510.003249602374097610.00162480118704881
1210.9988455312530890.002308937493822540.00115446874691127
1220.9981121850139660.003775629972068160.00188781498603408
1230.9959147449540130.008170510091974950.00408525504598748
1240.9988600232753960.002279953449208410.0011399767246042
1250.9988068270335140.002386345932972250.00119317296648613
1260.9992113672649580.001577265470084960.00078863273504248
1270.9988339917926810.002332016414638740.00116600820731937
1280.9965134924306120.006973015138775790.0034865075693879
1290.9909419625940390.01811607481192290.00905803740596143
1300.9729317112797870.05413657744042610.0270682887202131
1310.9196366904840940.1607266190318120.0803633095159061

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.256346429618667 & 0.512692859237333 & 0.743653570381333 \tabularnewline
10 & 0.209895766791756 & 0.419791533583513 & 0.790104233208244 \tabularnewline
11 & 0.631490706021016 & 0.737018587957969 & 0.368509293978984 \tabularnewline
12 & 0.61233181436455 & 0.7753363712709 & 0.38766818563545 \tabularnewline
13 & 0.563685904038417 & 0.872628191923166 & 0.436314095961583 \tabularnewline
14 & 0.783705490360028 & 0.432589019279943 & 0.216294509639972 \tabularnewline
15 & 0.714025075572988 & 0.571949848854023 & 0.285974924427012 \tabularnewline
16 & 0.634405980147962 & 0.731188039704076 & 0.365594019852038 \tabularnewline
17 & 0.54434364297701 & 0.91131271404598 & 0.45565635702299 \tabularnewline
18 & 0.5326089269978 & 0.934782146004401 & 0.4673910730022 \tabularnewline
19 & 0.589290500494962 & 0.821418999010076 & 0.410709499505038 \tabularnewline
20 & 0.520733978257057 & 0.958532043485886 & 0.479266021742943 \tabularnewline
21 & 0.444775382485213 & 0.889550764970426 & 0.555224617514787 \tabularnewline
22 & 0.371282520180661 & 0.742565040361321 & 0.628717479819339 \tabularnewline
23 & 0.322654166883222 & 0.645308333766444 & 0.677345833116778 \tabularnewline
24 & 0.318016702105498 & 0.636033404210996 & 0.681983297894502 \tabularnewline
25 & 0.393194276383105 & 0.786388552766211 & 0.606805723616895 \tabularnewline
26 & 0.392279171620754 & 0.784558343241508 & 0.607720828379246 \tabularnewline
27 & 0.419161341972374 & 0.838322683944748 & 0.580838658027626 \tabularnewline
28 & 0.434560337456515 & 0.869120674913029 & 0.565439662543485 \tabularnewline
29 & 0.430531833075878 & 0.861063666151755 & 0.569468166924122 \tabularnewline
30 & 0.412312383602532 & 0.824624767205065 & 0.587687616397468 \tabularnewline
31 & 0.377539110996688 & 0.755078221993375 & 0.622460889003312 \tabularnewline
32 & 0.375185196325627 & 0.750370392651254 & 0.624814803674373 \tabularnewline
33 & 0.339594652740956 & 0.679189305481913 & 0.660405347259044 \tabularnewline
34 & 0.373344012597106 & 0.746688025194212 & 0.626655987402894 \tabularnewline
35 & 0.456755691961928 & 0.913511383923855 & 0.543244308038072 \tabularnewline
36 & 0.427875450363421 & 0.855750900726841 & 0.572124549636579 \tabularnewline
37 & 0.375134179221076 & 0.750268358442151 & 0.624865820778924 \tabularnewline
38 & 0.367745250213159 & 0.735490500426317 & 0.632254749786841 \tabularnewline
39 & 0.37762949886636 & 0.755258997732719 & 0.62237050113364 \tabularnewline
40 & 0.346598186635052 & 0.693196373270104 & 0.653401813364948 \tabularnewline
41 & 0.429975125003238 & 0.859950250006476 & 0.570024874996762 \tabularnewline
42 & 0.439495704160883 & 0.878991408321766 & 0.560504295839117 \tabularnewline
43 & 0.450443493468334 & 0.900886986936668 & 0.549556506531666 \tabularnewline
44 & 0.616943864784106 & 0.766112270431788 & 0.383056135215894 \tabularnewline
45 & 0.681096062752225 & 0.637807874495549 & 0.318903937247775 \tabularnewline
46 & 0.751338904622159 & 0.497322190755682 & 0.248661095377841 \tabularnewline
47 & 0.812847328326316 & 0.374305343347368 & 0.187152671673684 \tabularnewline
48 & 0.794048132692534 & 0.411903734614932 & 0.205951867307466 \tabularnewline
49 & 0.7770694533972 & 0.445861093205601 & 0.2229305466028 \tabularnewline
50 & 0.751165970340592 & 0.497668059318816 & 0.248834029659408 \tabularnewline
51 & 0.743877802326499 & 0.512244395347003 & 0.256122197673501 \tabularnewline
52 & 0.706365354566281 & 0.587269290867439 & 0.293634645433719 \tabularnewline
53 & 0.67449814228243 & 0.65100371543514 & 0.32550185771757 \tabularnewline
54 & 0.708429925481233 & 0.583140149037534 & 0.291570074518767 \tabularnewline
55 & 0.674688909851597 & 0.650622180296807 & 0.325311090148403 \tabularnewline
56 & 0.641643906568001 & 0.716712186863998 & 0.358356093431999 \tabularnewline
57 & 0.639352019118414 & 0.721295961763173 & 0.360647980881586 \tabularnewline
58 & 0.594093110977624 & 0.811813778044751 & 0.405906889022376 \tabularnewline
59 & 0.545011764788341 & 0.909976470423317 & 0.454988235211659 \tabularnewline
60 & 0.566398640108625 & 0.86720271978275 & 0.433601359891375 \tabularnewline
61 & 0.757357255354143 & 0.485285489291714 & 0.242642744645857 \tabularnewline
62 & 0.9705670595843 & 0.0588658808314003 & 0.0294329404157002 \tabularnewline
63 & 0.973985093196806 & 0.0520298136063871 & 0.0260149068031936 \tabularnewline
64 & 0.967436489034113 & 0.0651270219317749 & 0.0325635109658874 \tabularnewline
65 & 0.960639208991026 & 0.0787215820179475 & 0.0393607910089738 \tabularnewline
66 & 0.951796332839968 & 0.0964073343200638 & 0.0482036671600319 \tabularnewline
67 & 0.940790522053785 & 0.118418955892431 & 0.0592094779462153 \tabularnewline
68 & 0.928046921296394 & 0.143906157407211 & 0.0719530787036057 \tabularnewline
69 & 0.915767378551883 & 0.168465242896234 & 0.0842326214481169 \tabularnewline
70 & 0.915809785647997 & 0.168380428704005 & 0.0841902143520027 \tabularnewline
71 & 0.908896138090541 & 0.182207723818918 & 0.0911038619094588 \tabularnewline
72 & 0.905257710446116 & 0.189484579107769 & 0.0947422895538843 \tabularnewline
73 & 0.908352963641794 & 0.183294072716412 & 0.0916470363582061 \tabularnewline
74 & 0.935212881736168 & 0.129574236527663 & 0.0647871182638317 \tabularnewline
75 & 0.956355302717568 & 0.0872893945648645 & 0.0436446972824322 \tabularnewline
76 & 0.952863885942594 & 0.0942722281148126 & 0.0471361140574063 \tabularnewline
77 & 0.947421932629424 & 0.105156134741152 & 0.052578067370576 \tabularnewline
78 & 0.943817141523469 & 0.112365716953061 & 0.0561828584765306 \tabularnewline
79 & 0.943427593799886 & 0.113144812400228 & 0.0565724062001138 \tabularnewline
80 & 0.946882054425793 & 0.106235891148414 & 0.0531179455742068 \tabularnewline
81 & 0.955075371604071 & 0.0898492567918582 & 0.0449246283959291 \tabularnewline
82 & 0.957575690178935 & 0.084848619642131 & 0.0424243098210655 \tabularnewline
83 & 0.95106473345793 & 0.0978705330841399 & 0.0489352665420699 \tabularnewline
84 & 0.948537390800881 & 0.102925218398238 & 0.0514626091991191 \tabularnewline
85 & 0.953163317978484 & 0.0936733640430318 & 0.0468366820215159 \tabularnewline
86 & 0.952650834722846 & 0.0946983305543076 & 0.0473491652771538 \tabularnewline
87 & 0.962348126706029 & 0.0753037465879419 & 0.0376518732939709 \tabularnewline
88 & 0.969709614114609 & 0.060580771770782 & 0.030290385885391 \tabularnewline
89 & 0.963969258187479 & 0.0720614836250417 & 0.0360307418125208 \tabularnewline
90 & 0.952323085678279 & 0.0953538286434425 & 0.0476769143217212 \tabularnewline
91 & 0.93808637375722 & 0.123827252485559 & 0.0619136262427797 \tabularnewline
92 & 0.939526077197226 & 0.120947845605549 & 0.0604739228027745 \tabularnewline
93 & 0.922414741698502 & 0.155170516602997 & 0.0775852583014985 \tabularnewline
94 & 0.904775855730493 & 0.190448288539014 & 0.0952241442695071 \tabularnewline
95 & 0.88481335350024 & 0.230373292999519 & 0.11518664649976 \tabularnewline
96 & 0.856272250516856 & 0.287455498966288 & 0.143727749483144 \tabularnewline
97 & 0.841716592513426 & 0.316566814973147 & 0.158283407486574 \tabularnewline
98 & 0.837863838860528 & 0.324272322278943 & 0.162136161139472 \tabularnewline
99 & 0.899993121653512 & 0.200013756692977 & 0.100006878346488 \tabularnewline
100 & 0.875662437482547 & 0.248675125034906 & 0.124337562517453 \tabularnewline
101 & 0.857620771934963 & 0.284758456130074 & 0.142379228065037 \tabularnewline
102 & 0.834210113725248 & 0.331579772549504 & 0.165789886274752 \tabularnewline
103 & 0.805338555543582 & 0.389322888912835 & 0.194661444456418 \tabularnewline
104 & 0.859990714273072 & 0.280018571453856 & 0.140009285726928 \tabularnewline
105 & 0.828240935887679 & 0.343518128224642 & 0.171759064112321 \tabularnewline
106 & 0.817836366405591 & 0.364327267188818 & 0.182163633594409 \tabularnewline
107 & 0.798789326274736 & 0.402421347450528 & 0.201210673725264 \tabularnewline
108 & 0.97266113095071 & 0.054677738098579 & 0.0273388690492895 \tabularnewline
109 & 0.962771553253394 & 0.0744568934932129 & 0.0372284467466064 \tabularnewline
110 & 0.958165041318496 & 0.0836699173630077 & 0.0418349586815038 \tabularnewline
111 & 0.99659444705288 & 0.0068111058942396 & 0.0034055529471198 \tabularnewline
112 & 0.997020797159833 & 0.00595840568033328 & 0.00297920284016664 \tabularnewline
113 & 0.999309769749698 & 0.00138046050060431 & 0.000690230250302153 \tabularnewline
114 & 0.999171754984936 & 0.00165649003012798 & 0.000828245015063991 \tabularnewline
115 & 0.998682664675762 & 0.00263467064847628 & 0.00131733532423814 \tabularnewline
116 & 0.998402191429667 & 0.00319561714066503 & 0.00159780857033252 \tabularnewline
117 & 0.997154721584595 & 0.00569055683081074 & 0.00284527841540537 \tabularnewline
118 & 0.99872324616717 & 0.00255350766565913 & 0.00127675383282956 \tabularnewline
119 & 0.998660004696648 & 0.00267999060670448 & 0.00133999530335224 \tabularnewline
120 & 0.998375198812951 & 0.00324960237409761 & 0.00162480118704881 \tabularnewline
121 & 0.998845531253089 & 0.00230893749382254 & 0.00115446874691127 \tabularnewline
122 & 0.998112185013966 & 0.00377562997206816 & 0.00188781498603408 \tabularnewline
123 & 0.995914744954013 & 0.00817051009197495 & 0.00408525504598748 \tabularnewline
124 & 0.998860023275396 & 0.00227995344920841 & 0.0011399767246042 \tabularnewline
125 & 0.998806827033514 & 0.00238634593297225 & 0.00119317296648613 \tabularnewline
126 & 0.999211367264958 & 0.00157726547008496 & 0.00078863273504248 \tabularnewline
127 & 0.998833991792681 & 0.00233201641463874 & 0.00116600820731937 \tabularnewline
128 & 0.996513492430612 & 0.00697301513877579 & 0.0034865075693879 \tabularnewline
129 & 0.990941962594039 & 0.0181160748119229 & 0.00905803740596143 \tabularnewline
130 & 0.972931711279787 & 0.0541365774404261 & 0.0270682887202131 \tabularnewline
131 & 0.919636690484094 & 0.160726619031812 & 0.0803633095159061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&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]9[/C][C]0.256346429618667[/C][C]0.512692859237333[/C][C]0.743653570381333[/C][/ROW]
[ROW][C]10[/C][C]0.209895766791756[/C][C]0.419791533583513[/C][C]0.790104233208244[/C][/ROW]
[ROW][C]11[/C][C]0.631490706021016[/C][C]0.737018587957969[/C][C]0.368509293978984[/C][/ROW]
[ROW][C]12[/C][C]0.61233181436455[/C][C]0.7753363712709[/C][C]0.38766818563545[/C][/ROW]
[ROW][C]13[/C][C]0.563685904038417[/C][C]0.872628191923166[/C][C]0.436314095961583[/C][/ROW]
[ROW][C]14[/C][C]0.783705490360028[/C][C]0.432589019279943[/C][C]0.216294509639972[/C][/ROW]
[ROW][C]15[/C][C]0.714025075572988[/C][C]0.571949848854023[/C][C]0.285974924427012[/C][/ROW]
[ROW][C]16[/C][C]0.634405980147962[/C][C]0.731188039704076[/C][C]0.365594019852038[/C][/ROW]
[ROW][C]17[/C][C]0.54434364297701[/C][C]0.91131271404598[/C][C]0.45565635702299[/C][/ROW]
[ROW][C]18[/C][C]0.5326089269978[/C][C]0.934782146004401[/C][C]0.4673910730022[/C][/ROW]
[ROW][C]19[/C][C]0.589290500494962[/C][C]0.821418999010076[/C][C]0.410709499505038[/C][/ROW]
[ROW][C]20[/C][C]0.520733978257057[/C][C]0.958532043485886[/C][C]0.479266021742943[/C][/ROW]
[ROW][C]21[/C][C]0.444775382485213[/C][C]0.889550764970426[/C][C]0.555224617514787[/C][/ROW]
[ROW][C]22[/C][C]0.371282520180661[/C][C]0.742565040361321[/C][C]0.628717479819339[/C][/ROW]
[ROW][C]23[/C][C]0.322654166883222[/C][C]0.645308333766444[/C][C]0.677345833116778[/C][/ROW]
[ROW][C]24[/C][C]0.318016702105498[/C][C]0.636033404210996[/C][C]0.681983297894502[/C][/ROW]
[ROW][C]25[/C][C]0.393194276383105[/C][C]0.786388552766211[/C][C]0.606805723616895[/C][/ROW]
[ROW][C]26[/C][C]0.392279171620754[/C][C]0.784558343241508[/C][C]0.607720828379246[/C][/ROW]
[ROW][C]27[/C][C]0.419161341972374[/C][C]0.838322683944748[/C][C]0.580838658027626[/C][/ROW]
[ROW][C]28[/C][C]0.434560337456515[/C][C]0.869120674913029[/C][C]0.565439662543485[/C][/ROW]
[ROW][C]29[/C][C]0.430531833075878[/C][C]0.861063666151755[/C][C]0.569468166924122[/C][/ROW]
[ROW][C]30[/C][C]0.412312383602532[/C][C]0.824624767205065[/C][C]0.587687616397468[/C][/ROW]
[ROW][C]31[/C][C]0.377539110996688[/C][C]0.755078221993375[/C][C]0.622460889003312[/C][/ROW]
[ROW][C]32[/C][C]0.375185196325627[/C][C]0.750370392651254[/C][C]0.624814803674373[/C][/ROW]
[ROW][C]33[/C][C]0.339594652740956[/C][C]0.679189305481913[/C][C]0.660405347259044[/C][/ROW]
[ROW][C]34[/C][C]0.373344012597106[/C][C]0.746688025194212[/C][C]0.626655987402894[/C][/ROW]
[ROW][C]35[/C][C]0.456755691961928[/C][C]0.913511383923855[/C][C]0.543244308038072[/C][/ROW]
[ROW][C]36[/C][C]0.427875450363421[/C][C]0.855750900726841[/C][C]0.572124549636579[/C][/ROW]
[ROW][C]37[/C][C]0.375134179221076[/C][C]0.750268358442151[/C][C]0.624865820778924[/C][/ROW]
[ROW][C]38[/C][C]0.367745250213159[/C][C]0.735490500426317[/C][C]0.632254749786841[/C][/ROW]
[ROW][C]39[/C][C]0.37762949886636[/C][C]0.755258997732719[/C][C]0.62237050113364[/C][/ROW]
[ROW][C]40[/C][C]0.346598186635052[/C][C]0.693196373270104[/C][C]0.653401813364948[/C][/ROW]
[ROW][C]41[/C][C]0.429975125003238[/C][C]0.859950250006476[/C][C]0.570024874996762[/C][/ROW]
[ROW][C]42[/C][C]0.439495704160883[/C][C]0.878991408321766[/C][C]0.560504295839117[/C][/ROW]
[ROW][C]43[/C][C]0.450443493468334[/C][C]0.900886986936668[/C][C]0.549556506531666[/C][/ROW]
[ROW][C]44[/C][C]0.616943864784106[/C][C]0.766112270431788[/C][C]0.383056135215894[/C][/ROW]
[ROW][C]45[/C][C]0.681096062752225[/C][C]0.637807874495549[/C][C]0.318903937247775[/C][/ROW]
[ROW][C]46[/C][C]0.751338904622159[/C][C]0.497322190755682[/C][C]0.248661095377841[/C][/ROW]
[ROW][C]47[/C][C]0.812847328326316[/C][C]0.374305343347368[/C][C]0.187152671673684[/C][/ROW]
[ROW][C]48[/C][C]0.794048132692534[/C][C]0.411903734614932[/C][C]0.205951867307466[/C][/ROW]
[ROW][C]49[/C][C]0.7770694533972[/C][C]0.445861093205601[/C][C]0.2229305466028[/C][/ROW]
[ROW][C]50[/C][C]0.751165970340592[/C][C]0.497668059318816[/C][C]0.248834029659408[/C][/ROW]
[ROW][C]51[/C][C]0.743877802326499[/C][C]0.512244395347003[/C][C]0.256122197673501[/C][/ROW]
[ROW][C]52[/C][C]0.706365354566281[/C][C]0.587269290867439[/C][C]0.293634645433719[/C][/ROW]
[ROW][C]53[/C][C]0.67449814228243[/C][C]0.65100371543514[/C][C]0.32550185771757[/C][/ROW]
[ROW][C]54[/C][C]0.708429925481233[/C][C]0.583140149037534[/C][C]0.291570074518767[/C][/ROW]
[ROW][C]55[/C][C]0.674688909851597[/C][C]0.650622180296807[/C][C]0.325311090148403[/C][/ROW]
[ROW][C]56[/C][C]0.641643906568001[/C][C]0.716712186863998[/C][C]0.358356093431999[/C][/ROW]
[ROW][C]57[/C][C]0.639352019118414[/C][C]0.721295961763173[/C][C]0.360647980881586[/C][/ROW]
[ROW][C]58[/C][C]0.594093110977624[/C][C]0.811813778044751[/C][C]0.405906889022376[/C][/ROW]
[ROW][C]59[/C][C]0.545011764788341[/C][C]0.909976470423317[/C][C]0.454988235211659[/C][/ROW]
[ROW][C]60[/C][C]0.566398640108625[/C][C]0.86720271978275[/C][C]0.433601359891375[/C][/ROW]
[ROW][C]61[/C][C]0.757357255354143[/C][C]0.485285489291714[/C][C]0.242642744645857[/C][/ROW]
[ROW][C]62[/C][C]0.9705670595843[/C][C]0.0588658808314003[/C][C]0.0294329404157002[/C][/ROW]
[ROW][C]63[/C][C]0.973985093196806[/C][C]0.0520298136063871[/C][C]0.0260149068031936[/C][/ROW]
[ROW][C]64[/C][C]0.967436489034113[/C][C]0.0651270219317749[/C][C]0.0325635109658874[/C][/ROW]
[ROW][C]65[/C][C]0.960639208991026[/C][C]0.0787215820179475[/C][C]0.0393607910089738[/C][/ROW]
[ROW][C]66[/C][C]0.951796332839968[/C][C]0.0964073343200638[/C][C]0.0482036671600319[/C][/ROW]
[ROW][C]67[/C][C]0.940790522053785[/C][C]0.118418955892431[/C][C]0.0592094779462153[/C][/ROW]
[ROW][C]68[/C][C]0.928046921296394[/C][C]0.143906157407211[/C][C]0.0719530787036057[/C][/ROW]
[ROW][C]69[/C][C]0.915767378551883[/C][C]0.168465242896234[/C][C]0.0842326214481169[/C][/ROW]
[ROW][C]70[/C][C]0.915809785647997[/C][C]0.168380428704005[/C][C]0.0841902143520027[/C][/ROW]
[ROW][C]71[/C][C]0.908896138090541[/C][C]0.182207723818918[/C][C]0.0911038619094588[/C][/ROW]
[ROW][C]72[/C][C]0.905257710446116[/C][C]0.189484579107769[/C][C]0.0947422895538843[/C][/ROW]
[ROW][C]73[/C][C]0.908352963641794[/C][C]0.183294072716412[/C][C]0.0916470363582061[/C][/ROW]
[ROW][C]74[/C][C]0.935212881736168[/C][C]0.129574236527663[/C][C]0.0647871182638317[/C][/ROW]
[ROW][C]75[/C][C]0.956355302717568[/C][C]0.0872893945648645[/C][C]0.0436446972824322[/C][/ROW]
[ROW][C]76[/C][C]0.952863885942594[/C][C]0.0942722281148126[/C][C]0.0471361140574063[/C][/ROW]
[ROW][C]77[/C][C]0.947421932629424[/C][C]0.105156134741152[/C][C]0.052578067370576[/C][/ROW]
[ROW][C]78[/C][C]0.943817141523469[/C][C]0.112365716953061[/C][C]0.0561828584765306[/C][/ROW]
[ROW][C]79[/C][C]0.943427593799886[/C][C]0.113144812400228[/C][C]0.0565724062001138[/C][/ROW]
[ROW][C]80[/C][C]0.946882054425793[/C][C]0.106235891148414[/C][C]0.0531179455742068[/C][/ROW]
[ROW][C]81[/C][C]0.955075371604071[/C][C]0.0898492567918582[/C][C]0.0449246283959291[/C][/ROW]
[ROW][C]82[/C][C]0.957575690178935[/C][C]0.084848619642131[/C][C]0.0424243098210655[/C][/ROW]
[ROW][C]83[/C][C]0.95106473345793[/C][C]0.0978705330841399[/C][C]0.0489352665420699[/C][/ROW]
[ROW][C]84[/C][C]0.948537390800881[/C][C]0.102925218398238[/C][C]0.0514626091991191[/C][/ROW]
[ROW][C]85[/C][C]0.953163317978484[/C][C]0.0936733640430318[/C][C]0.0468366820215159[/C][/ROW]
[ROW][C]86[/C][C]0.952650834722846[/C][C]0.0946983305543076[/C][C]0.0473491652771538[/C][/ROW]
[ROW][C]87[/C][C]0.962348126706029[/C][C]0.0753037465879419[/C][C]0.0376518732939709[/C][/ROW]
[ROW][C]88[/C][C]0.969709614114609[/C][C]0.060580771770782[/C][C]0.030290385885391[/C][/ROW]
[ROW][C]89[/C][C]0.963969258187479[/C][C]0.0720614836250417[/C][C]0.0360307418125208[/C][/ROW]
[ROW][C]90[/C][C]0.952323085678279[/C][C]0.0953538286434425[/C][C]0.0476769143217212[/C][/ROW]
[ROW][C]91[/C][C]0.93808637375722[/C][C]0.123827252485559[/C][C]0.0619136262427797[/C][/ROW]
[ROW][C]92[/C][C]0.939526077197226[/C][C]0.120947845605549[/C][C]0.0604739228027745[/C][/ROW]
[ROW][C]93[/C][C]0.922414741698502[/C][C]0.155170516602997[/C][C]0.0775852583014985[/C][/ROW]
[ROW][C]94[/C][C]0.904775855730493[/C][C]0.190448288539014[/C][C]0.0952241442695071[/C][/ROW]
[ROW][C]95[/C][C]0.88481335350024[/C][C]0.230373292999519[/C][C]0.11518664649976[/C][/ROW]
[ROW][C]96[/C][C]0.856272250516856[/C][C]0.287455498966288[/C][C]0.143727749483144[/C][/ROW]
[ROW][C]97[/C][C]0.841716592513426[/C][C]0.316566814973147[/C][C]0.158283407486574[/C][/ROW]
[ROW][C]98[/C][C]0.837863838860528[/C][C]0.324272322278943[/C][C]0.162136161139472[/C][/ROW]
[ROW][C]99[/C][C]0.899993121653512[/C][C]0.200013756692977[/C][C]0.100006878346488[/C][/ROW]
[ROW][C]100[/C][C]0.875662437482547[/C][C]0.248675125034906[/C][C]0.124337562517453[/C][/ROW]
[ROW][C]101[/C][C]0.857620771934963[/C][C]0.284758456130074[/C][C]0.142379228065037[/C][/ROW]
[ROW][C]102[/C][C]0.834210113725248[/C][C]0.331579772549504[/C][C]0.165789886274752[/C][/ROW]
[ROW][C]103[/C][C]0.805338555543582[/C][C]0.389322888912835[/C][C]0.194661444456418[/C][/ROW]
[ROW][C]104[/C][C]0.859990714273072[/C][C]0.280018571453856[/C][C]0.140009285726928[/C][/ROW]
[ROW][C]105[/C][C]0.828240935887679[/C][C]0.343518128224642[/C][C]0.171759064112321[/C][/ROW]
[ROW][C]106[/C][C]0.817836366405591[/C][C]0.364327267188818[/C][C]0.182163633594409[/C][/ROW]
[ROW][C]107[/C][C]0.798789326274736[/C][C]0.402421347450528[/C][C]0.201210673725264[/C][/ROW]
[ROW][C]108[/C][C]0.97266113095071[/C][C]0.054677738098579[/C][C]0.0273388690492895[/C][/ROW]
[ROW][C]109[/C][C]0.962771553253394[/C][C]0.0744568934932129[/C][C]0.0372284467466064[/C][/ROW]
[ROW][C]110[/C][C]0.958165041318496[/C][C]0.0836699173630077[/C][C]0.0418349586815038[/C][/ROW]
[ROW][C]111[/C][C]0.99659444705288[/C][C]0.0068111058942396[/C][C]0.0034055529471198[/C][/ROW]
[ROW][C]112[/C][C]0.997020797159833[/C][C]0.00595840568033328[/C][C]0.00297920284016664[/C][/ROW]
[ROW][C]113[/C][C]0.999309769749698[/C][C]0.00138046050060431[/C][C]0.000690230250302153[/C][/ROW]
[ROW][C]114[/C][C]0.999171754984936[/C][C]0.00165649003012798[/C][C]0.000828245015063991[/C][/ROW]
[ROW][C]115[/C][C]0.998682664675762[/C][C]0.00263467064847628[/C][C]0.00131733532423814[/C][/ROW]
[ROW][C]116[/C][C]0.998402191429667[/C][C]0.00319561714066503[/C][C]0.00159780857033252[/C][/ROW]
[ROW][C]117[/C][C]0.997154721584595[/C][C]0.00569055683081074[/C][C]0.00284527841540537[/C][/ROW]
[ROW][C]118[/C][C]0.99872324616717[/C][C]0.00255350766565913[/C][C]0.00127675383282956[/C][/ROW]
[ROW][C]119[/C][C]0.998660004696648[/C][C]0.00267999060670448[/C][C]0.00133999530335224[/C][/ROW]
[ROW][C]120[/C][C]0.998375198812951[/C][C]0.00324960237409761[/C][C]0.00162480118704881[/C][/ROW]
[ROW][C]121[/C][C]0.998845531253089[/C][C]0.00230893749382254[/C][C]0.00115446874691127[/C][/ROW]
[ROW][C]122[/C][C]0.998112185013966[/C][C]0.00377562997206816[/C][C]0.00188781498603408[/C][/ROW]
[ROW][C]123[/C][C]0.995914744954013[/C][C]0.00817051009197495[/C][C]0.00408525504598748[/C][/ROW]
[ROW][C]124[/C][C]0.998860023275396[/C][C]0.00227995344920841[/C][C]0.0011399767246042[/C][/ROW]
[ROW][C]125[/C][C]0.998806827033514[/C][C]0.00238634593297225[/C][C]0.00119317296648613[/C][/ROW]
[ROW][C]126[/C][C]0.999211367264958[/C][C]0.00157726547008496[/C][C]0.00078863273504248[/C][/ROW]
[ROW][C]127[/C][C]0.998833991792681[/C][C]0.00233201641463874[/C][C]0.00116600820731937[/C][/ROW]
[ROW][C]128[/C][C]0.996513492430612[/C][C]0.00697301513877579[/C][C]0.0034865075693879[/C][/ROW]
[ROW][C]129[/C][C]0.990941962594039[/C][C]0.0181160748119229[/C][C]0.00905803740596143[/C][/ROW]
[ROW][C]130[/C][C]0.972931711279787[/C][C]0.0541365774404261[/C][C]0.0270682887202131[/C][/ROW]
[ROW][C]131[/C][C]0.919636690484094[/C][C]0.160726619031812[/C][C]0.0803633095159061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2563464296186670.5126928592373330.743653570381333
100.2098957667917560.4197915335835130.790104233208244
110.6314907060210160.7370185879579690.368509293978984
120.612331814364550.77533637127090.38766818563545
130.5636859040384170.8726281919231660.436314095961583
140.7837054903600280.4325890192799430.216294509639972
150.7140250755729880.5719498488540230.285974924427012
160.6344059801479620.7311880397040760.365594019852038
170.544343642977010.911312714045980.45565635702299
180.53260892699780.9347821460044010.4673910730022
190.5892905004949620.8214189990100760.410709499505038
200.5207339782570570.9585320434858860.479266021742943
210.4447753824852130.8895507649704260.555224617514787
220.3712825201806610.7425650403613210.628717479819339
230.3226541668832220.6453083337664440.677345833116778
240.3180167021054980.6360334042109960.681983297894502
250.3931942763831050.7863885527662110.606805723616895
260.3922791716207540.7845583432415080.607720828379246
270.4191613419723740.8383226839447480.580838658027626
280.4345603374565150.8691206749130290.565439662543485
290.4305318330758780.8610636661517550.569468166924122
300.4123123836025320.8246247672050650.587687616397468
310.3775391109966880.7550782219933750.622460889003312
320.3751851963256270.7503703926512540.624814803674373
330.3395946527409560.6791893054819130.660405347259044
340.3733440125971060.7466880251942120.626655987402894
350.4567556919619280.9135113839238550.543244308038072
360.4278754503634210.8557509007268410.572124549636579
370.3751341792210760.7502683584421510.624865820778924
380.3677452502131590.7354905004263170.632254749786841
390.377629498866360.7552589977327190.62237050113364
400.3465981866350520.6931963732701040.653401813364948
410.4299751250032380.8599502500064760.570024874996762
420.4394957041608830.8789914083217660.560504295839117
430.4504434934683340.9008869869366680.549556506531666
440.6169438647841060.7661122704317880.383056135215894
450.6810960627522250.6378078744955490.318903937247775
460.7513389046221590.4973221907556820.248661095377841
470.8128473283263160.3743053433473680.187152671673684
480.7940481326925340.4119037346149320.205951867307466
490.77706945339720.4458610932056010.2229305466028
500.7511659703405920.4976680593188160.248834029659408
510.7438778023264990.5122443953470030.256122197673501
520.7063653545662810.5872692908674390.293634645433719
530.674498142282430.651003715435140.32550185771757
540.7084299254812330.5831401490375340.291570074518767
550.6746889098515970.6506221802968070.325311090148403
560.6416439065680010.7167121868639980.358356093431999
570.6393520191184140.7212959617631730.360647980881586
580.5940931109776240.8118137780447510.405906889022376
590.5450117647883410.9099764704233170.454988235211659
600.5663986401086250.867202719782750.433601359891375
610.7573572553541430.4852854892917140.242642744645857
620.97056705958430.05886588083140030.0294329404157002
630.9739850931968060.05202981360638710.0260149068031936
640.9674364890341130.06512702193177490.0325635109658874
650.9606392089910260.07872158201794750.0393607910089738
660.9517963328399680.09640733432006380.0482036671600319
670.9407905220537850.1184189558924310.0592094779462153
680.9280469212963940.1439061574072110.0719530787036057
690.9157673785518830.1684652428962340.0842326214481169
700.9158097856479970.1683804287040050.0841902143520027
710.9088961380905410.1822077238189180.0911038619094588
720.9052577104461160.1894845791077690.0947422895538843
730.9083529636417940.1832940727164120.0916470363582061
740.9352128817361680.1295742365276630.0647871182638317
750.9563553027175680.08728939456486450.0436446972824322
760.9528638859425940.09427222811481260.0471361140574063
770.9474219326294240.1051561347411520.052578067370576
780.9438171415234690.1123657169530610.0561828584765306
790.9434275937998860.1131448124002280.0565724062001138
800.9468820544257930.1062358911484140.0531179455742068
810.9550753716040710.08984925679185820.0449246283959291
820.9575756901789350.0848486196421310.0424243098210655
830.951064733457930.09787053308413990.0489352665420699
840.9485373908008810.1029252183982380.0514626091991191
850.9531633179784840.09367336404303180.0468366820215159
860.9526508347228460.09469833055430760.0473491652771538
870.9623481267060290.07530374658794190.0376518732939709
880.9697096141146090.0605807717707820.030290385885391
890.9639692581874790.07206148362504170.0360307418125208
900.9523230856782790.09535382864344250.0476769143217212
910.938086373757220.1238272524855590.0619136262427797
920.9395260771972260.1209478456055490.0604739228027745
930.9224147416985020.1551705166029970.0775852583014985
940.9047758557304930.1904482885390140.0952241442695071
950.884813353500240.2303732929995190.11518664649976
960.8562722505168560.2874554989662880.143727749483144
970.8417165925134260.3165668149731470.158283407486574
980.8378638388605280.3242723222789430.162136161139472
990.8999931216535120.2000137566929770.100006878346488
1000.8756624374825470.2486751250349060.124337562517453
1010.8576207719349630.2847584561300740.142379228065037
1020.8342101137252480.3315797725495040.165789886274752
1030.8053385555435820.3893228889128350.194661444456418
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1080.972661130950710.0546777380985790.0273388690492895
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1100.9581650413184960.08366991736300770.0418349586815038
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1120.9970207971598330.005958405680333280.00297920284016664
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1200.9983751988129510.003249602374097610.00162480118704881
1210.9988455312530890.002308937493822540.00115446874691127
1220.9981121850139660.003775629972068160.00188781498603408
1230.9959147449540130.008170510091974950.00408525504598748
1240.9988600232753960.002279953449208410.0011399767246042
1250.9988068270335140.002386345932972250.00119317296648613
1260.9992113672649580.001577265470084960.00078863273504248
1270.9988339917926810.002332016414638740.00116600820731937
1280.9965134924306120.006973015138775790.0034865075693879
1290.9909419625940390.01811607481192290.00905803740596143
1300.9729317112797870.05413657744042610.0270682887202131
1310.9196366904840940.1607266190318120.0803633095159061







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.146341463414634NOK
5% type I error level190.154471544715447NOK
10% type I error level390.317073170731707NOK

\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 & 18 & 0.146341463414634 & NOK \tabularnewline
5% type I error level & 19 & 0.154471544715447 & NOK \tabularnewline
10% type I error level & 39 & 0.317073170731707 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158818&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]18[/C][C]0.146341463414634[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.154471544715447[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.317073170731707[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158818&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158818&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.146341463414634NOK
5% type I error level190.154471544715447NOK
10% type I error level390.317073170731707NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}