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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 22 Dec 2011 05:00:19 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245480478trngkm27j0dubv.htm/, Retrieved Fri, 03 May 2024 13:14:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159224, Retrieved Fri, 03 May 2024 13:14:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regressi...] [2011-12-22 10:00:19] [0b94335bf72158573fe52322b9537409] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
9/06/2002	169498	2.47	0.97	0.80	5.06
16/06/2002	125451	2.51	0.95	0.80	5.07
23/06/2002	140449	2.51	0.96	0.80	5.04
30/06/2002	141653	2.51	0.97	0.81	5.08
7/07/2002	136394	2.49	0.96	0.81	5.15
14/07/2002	167588	2.52	0.96	0.83	5.16
21/07/2002	191807	2.51	0.94	0.79	5.09
28/07/2002	149736	2.54	0.95	0.80	5.14
4/08/2002	196066	2.53	0.95	0.81	5.17
11/08/2002	239155	2.55	0.95	0.80	5.13
18/08/2002	178421	2.50	0.95	0.81	5.19
25/08/2002	139871	2.59	0.97	0.81	5.17
1/09/2002	118159	2.56	0.97	0.82	5.17
8/09/2002	109763	2.59	0.95	0.81	5.21
15/09/2002	97415	2.58	0.96	0.82	5.21
22/09/2002	119190	2.62	0.96	0.82	5.23
29/09/2002	97903	2.59	0.94	0.80	5.17
6/10/2002	96953	2.58	0.96	0.79	5.16
13/10/2002	87888	2.57	0.98	0.79	5.15
20/10/2002	84637	2.57	0.97	0.80	5.12
27/10/2002	90549	2.55	0.96	0.80	5.12
3/11/2002	95680	2.51	0.95	0.80	5.10
10/11/2002	99371	2.50	0.96	0.80	5.13
17/11/2002	79984	2.59	0.96	0.80	5.14
24/11/2002	86752	2.63	0.97	0.80	5.16
1/12/2002	85733	2.63	0.96	0.80	5.17
8/12/2002	84906	2.61	0.95	0.78	5.15
15/12/2002	78356	2.64	0.95	0.78	5.12
22/12/2002	108895	2.67	0.94	0.76	5.08
29/12/2002	101768	2.63	0.94	0.77	5.07
5/01/2003	73285	2.58	0.98	0.82	5.16
12/01/2003	65724	2.56	0.93	0.81	5.14
19/01/2003	67457	2.57	0.93	0.81	5.13
26/01/2003	67203	2.55	0.96	0.80	5.11
2/02/2003	69273	2.58	0.97	0.79	5.12
9/02/2003	80807	2.50	0.97	0.81	5.09
16/02/2003	75129	2.56	0.95	0.81	5.10
23/02/2003	74991	2.62	0.95	0.81	4.95
2/03/2003	68157	2.71	0.96	0.81	5.11
9/03/2003	73858	2.74	0.98	0.82	5.11
16/03/2003	71349	2.76	0.98	0.81	5.10
23/03/2003	85634	2.66	0.97	0.80	5.11
30/03/2003	91624	2.61	0.98	0.83	5.12
6/04/2003	116014	2.68	0.98	0.81	5.09
13/04/2003	120033	2.70	0.99	0.83	5.10
20/04/2003	108651	2.70	0.99	0.84	5.06
27/04/2003	105378	2.72	0.97	0.84	5.03
4/05/2003	138939	2.77	0.98	0.85	5.05
11/05/2003	132974	2.76	0.97	0.85	5.04
18/05/2003	135277	2.72	0.97	0.86	5.02
25/05/2003	152741	2.69	0.97	0.85	4.97
1/06/2003	158417	2.70	0.98	0.87	4.91
8/06/2003	157460	2.69	0.97	0.86	4.91
15/06/2003	193997	2.66	0.97	0.87	4.98
22/06/2003	154089	2.74	0.98	0.87	4.98
29/06/2003	147570	2.76	0.98	0.86	4.97
6/07/2003	162924	2.79	0.95	0.87	4.97
13/07/2003	153629	2.78	0.97	0.88	4.90
20/07/2003	155907	2.80	0.97	0.87	4.91
27/07/2003	197675	2.78	0.97	0.87	4.88
3/08/2003	250708	2.76	0.97	0.86	4.86
10/08/2003	266652	2.73	0.98	0.86	4.87
17/08/2003	209842	2.72	0.98	0.88	4.86
24/08/2003	165826	2.73	0.98	0.88	4.89
31/08/2003	137152	2.74	0.96	0.87	4.90
7/09/2003	150581	2.72	0.98	0.88	4.88
14/09/2003	145973	2.71	1.00	0.89	4.85
21/09/2003	126532	2.66	1.01	0.89	4.85
28/09/2003	115437	2.68	1.02	0.88	4.84
5/10/2003	119526	2.67	1.01	0.88	4.91
12/10/2003	110856	2.68	1.01	0.88	4.94
19/10/2003	97243	2.67	1.02	0.88	4.92
26/10/2003	103876	2.71	1.01	0.87	4.93
2/11/2003	116370	2.69	1.01	0.87	4.97
9/11/2003	109616	2.64	1.01	0.86	4.89
16/11/2003	98365	2.66	1.02	0.88	4.88
23/11/2003	90440	2.70	1.02	0.87	4.93
30/11/2003	88899	2.69	1.02	0.86	4.94
7/12/2003	92358	2.71	1.01	0.85	4.99
14/12/2003	88394	2.74	1.01	0.86	5.00
21/12/2003	98219	2.78	0.99	0.84	5.02
28/12/2003	113546	2.79	1.00	0.85	5.06
4/01/2004	107168	2.75	1.01	0.88	5.01
11/01/2004	77540	2.69	0.99	0.88	5.02
18/01/2004	74944	2.69	1.00	0.89	4.97
25/01/2004	75641	2.69	1.02	0.88	4.96
1/02/2004	75910	2.72	1.01	0.88	4.95
8/02/2004	87384	2.69	1.01	0.88	4.92
15/02/2004	84615	2.70	1.01	0.89	4.88
22/02/2004	80420	2.68	1.03	0.89	4.86
29/02/2004	80784	2.70	1.02	0.89	4.94
7/03/2004	79933	2.72	1.02	0.88	4.83
14/03/2004	82118	2.70	1.03	0.89	4.95
21/03/2004	91420	2.66	1.03	0.89	4.95
28/03/2004	112426	2.68	1.02	0.89	4.94
4/04/2004	114528	2.65	1.02	0.89	4.93
11/04/2004	131025	2.69	1.02	0.90	4.97
18/04/2004	116460	2.66	1.02	0.88	4.95
25/04/2004	111258	2.69	1.03	0.90	4.92
2/05/2004	155318	2.69	1.02	0.88	4.82
9/05/2004	155078	2.65	1.02	0.90	4.82
16/05/2004	134794	2.66	1.02	0.89	4.84
23/05/2004	139985	2.63	1.03	0.89	4.83
30/05/2004	198778	2.65	1.02	0.88	4.79
6/06/2004	172436	2.60	1.02	0.89	4.81
13/06/2004	169585	2.57	1.02	0.91	4.85
20/06/2004	203702	2.65	1.02	0.91	4.84
27/06/2004	282392	2.69	1.02	0.90	4.82
4/07/2004	220658	2.71	1.00	0.93	4.92
11/07/2004	194472	2.72	1.04	0.94	4.92
18/07/2004	269246	2.73	1.04	0.95	4.90
25/07/2004	215340	2.72	1.03	0.95	4.91
1/08/2004	218319	2.73	1.02	0.93	4.85
8/08/2004	195724	2.72	1.04	0.95	4.86
15/08/2004	174614	2.70	1.05	0.95	4.88
22/08/2004	172085	2.72	1.03	0.94	4.85
29/08/2004	152347	2.70	0.99	0.92	4.91
5/09/2004	189615	2.72	1.03	0.94	4.89
12/09/2004	173804	2.70	1.08	0.95	4.92
19/09/2004	145683	2.65	1.09	0.97	4.82
26/09/2004	133550	2.66	1.08	0.96	4.82
3/10/2004	121156	2.69	1.05	0.92	4.87
10/10/2004	112040	2.70	1.06	0.94	4.88
17/10/2004	120767	2.71	1.04	0.94	4.90
24/10/2004	127019	2.69	1.06	0.92	4.88
31/10/2004	136295	2.72	1.06	0.91	4.89
7/11/2004	113425	2.71	1.07	0.93	4.88
14/11/2004	107815	2.71	1.08	0.93	4.87
21/11/2004	100298	2.74	1.08	0.94	4.85
28/11/2004	97048	2.82	1.05	0.92	4.87
5/12/2004	98750	2.76	1.04	0.91	4.88
12/12/2004	98235	2.77	1.04	0.91	4.87
19/12/2004	101254	2.77	1.04	0.90	4.93
26/12/2004	139589	2.81	1.04	0.89	4.93
2/01/2005	134921	2.77	1.06	0.91	4.74
9/01/2005	80355	2.76	1.08	0.93	4.77
16/01/2005	80396	2.73	1.08	0.94	4.81
23/01/2005	82183	2.72	1.08	0.93	4.82
30/01/2005	79709	2.73	1.07	0.91	4.79
6/02/2005	90781	2.71	1.06	0.92	4.75

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
QFRU[t] = + 1493128.27150325 -5656312.50139251Tijd[t] -149096.932117663PFRU[t] -1188166.18051993PCOLA[t] + 989530.221058538PORA[t] -125882.624219891`PSTIM `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QFRU[t] =  +  1493128.27150325 -5656312.50139251Tijd[t] -149096.932117663PFRU[t] -1188166.18051993PCOLA[t] +  989530.221058538PORA[t] -125882.624219891`PSTIM
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QFRU[t] =  +  1493128.27150325 -5656312.50139251Tijd[t] -149096.932117663PFRU[t] -1188166.18051993PCOLA[t] +  989530.221058538PORA[t] -125882.624219891`PSTIM
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
QFRU[t] = + 1493128.27150325 -5656312.50139251Tijd[t] -149096.932117663PFRU[t] -1188166.18051993PCOLA[t] + 989530.221058538PORA[t] -125882.624219891`PSTIM `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1493128.27150325361692.2661614.12826.4e-053.2e-05
Tijd-5656312.501392511165954.937299-4.85123e-062e-06
PFRU-149096.93211766347759.858002-3.12180.0022020.001101
PCOLA-1188166.18051993157838.824494-7.527700
PORA989530.221058538153081.0204296.464100
`PSTIM `-125882.62421989146700.082303-2.69560.0079280.003964

\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) & 1493128.27150325 & 361692.266161 & 4.1282 & 6.4e-05 & 3.2e-05 \tabularnewline
Tijd & -5656312.50139251 & 1165954.937299 & -4.8512 & 3e-06 & 2e-06 \tabularnewline
PFRU & -149096.932117663 & 47759.858002 & -3.1218 & 0.002202 & 0.001101 \tabularnewline
PCOLA & -1188166.18051993 & 157838.824494 & -7.5277 & 0 & 0 \tabularnewline
PORA & 989530.221058538 & 153081.020429 & 6.4641 & 0 & 0 \tabularnewline
`PSTIM
` & -125882.624219891 & 46700.082303 & -2.6956 & 0.007928 & 0.003964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&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]1493128.27150325[/C][C]361692.266161[/C][C]4.1282[/C][C]6.4e-05[/C][C]3.2e-05[/C][/ROW]
[ROW][C]Tijd[/C][C]-5656312.50139251[/C][C]1165954.937299[/C][C]-4.8512[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]PFRU[/C][C]-149096.932117663[/C][C]47759.858002[/C][C]-3.1218[/C][C]0.002202[/C][C]0.001101[/C][/ROW]
[ROW][C]PCOLA[/C][C]-1188166.18051993[/C][C]157838.824494[/C][C]-7.5277[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PORA[/C][C]989530.221058538[/C][C]153081.020429[/C][C]6.4641[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`PSTIM
`[/C][C]-125882.624219891[/C][C]46700.082303[/C][C]-2.6956[/C][C]0.007928[/C][C]0.003964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1493128.27150325361692.2661614.12826.4e-053.2e-05
Tijd-5656312.501392511165954.937299-4.85123e-062e-06
PFRU-149096.93211766347759.858002-3.12180.0022020.001101
PCOLA-1188166.18051993157838.824494-7.527700
PORA989530.221058538153081.0204296.464100
`PSTIM `-125882.62421989146700.082303-2.69560.0079280.003964






Multiple Linear Regression - Regression Statistics
Multiple R0.683812302618052
R-squared0.467599265211802
Adjusted R-squared0.447733566152541
F-TEST (value)23.5380221867306
F-TEST (DF numerator)5
F-TEST (DF denominator)134
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation34715.9999639074
Sum Squared Residuals161496887568.199

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.683812302618052 \tabularnewline
R-squared & 0.467599265211802 \tabularnewline
Adjusted R-squared & 0.447733566152541 \tabularnewline
F-TEST (value) & 23.5380221867306 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 34715.9999639074 \tabularnewline
Sum Squared Residuals & 161496887568.199 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.683812302618052[/C][/ROW]
[ROW][C]R-squared[/C][C]0.467599265211802[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.447733566152541[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.5380221867306[/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]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]34715.9999639074[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]161496887568.199[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.683812302618052
R-squared0.467599265211802
Adjusted R-squared0.447733566152541
F-TEST (value)23.5380221867306
F-TEST (DF numerator)5
F-TEST (DF denominator)134
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation34715.9999639074
Sum Squared Residuals161496887568.199






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1169498122757.75598280346740.2440171971
2125451136002.156659889-10551.1566598886
3140449124600.7541748815848.2458251198
4141653114282.87020506527370.1297949353
5136394131636.0106363314757.98936366932
6167588142869.54993199624718.4500680038
7191807134529.08679684657277.913203154
8149736118950.35710793130785.6428920689
9196066136448.80813231459617.191867686
10239155126134.707693367113020.292306633
11178421133459.73450183744961.2654981628
1213987196323.174930442443547.8250695576
13118159119206.618571106-1047.61857110605
14109763121368.947434323-11605.9474343233
1597415118676.077556615-21261.0775566153
16119190107997.0681832411192.9318167598
1797903121798.173184921-23895.1731849211
189695398297.988450282-1344.98845028198
198788875306.728759415412581.2712405846
208463798882.4398579533-14245.4398579533
2190549111768.308661662-21219.3086616624
2295680138989.34892306-43309.3489230599
2399371123024.239854401-23653.2398544009
2479984106548.751863573-26564.7518635727
258675284387.62243122942364.37756877059
2685733100939.372121337-15206.3721213365
278490696881.9109289132-11975.9109289132
287835694537.3719887771-16181.3719887771
2910889585542.716674868423352.2833251316
30101768101012.612709156755.387290843688
317328591796.1681728001-18511.1681728001
3265724127041.323523975-61317.3235239751
3367457107041.73785401-39584.7378540101
346720347233.598763590719969.4012364093
356927390322.9097956032-21049.9097956032
3680807115934.02621729-35127.0262172901
3775129119608.986362936-44479.9863629364
3874991119661.842773367-44670.8427733666
3968157104812.707878923-36655.707878923
407385879882.6309852506-6024.63098525057
417134961675.0688441829673.931155818
428563470723.147878034114910.8521219659
439162488134.2655379463489.73446205396
4411601485686.677741334530327.3222586655
4512003384412.994825006935620.0051749931
4610865194401.741356641114249.2586433589
47105378114017.744403536-8639.74440353634
48138939118861.21192098120077.7880790193
49132974129539.1807713583434.81922864189
50135277143962.52423285-8685.52423285038
51152741140880.77267859211860.227321408
52158417168500.651882262-10083.6518822619
53157460168683.407032888-11223.407032888
54193997170945.25974644623051.7402535537
55154089143841.26960666910247.7303933307
56147570128928.28123076518641.7187692348
57162924181224.106174694-18300.1061746937
58153629174834.917421308-21205.9174213084
59155907157874.92995603-1967.9299560298
60197675161809.42695483935865.5730451612
61250708167247.01001703183460.9899829687
62266652156108.49960929110543.50039071
63209842176177.96926996233664.0307300376
64165826168439.590898316-2613.59089831571
65137152177086.88641088-39934.8864108799
66150581177464.765062005-26883.7650620046
67145973166667.809199855-20694.8091998551
68126532160044.611490429-33512.6114904292
69115437134348.15256438-18911.1525643804
70119526146282.569850732-26756.5698507323
71110856139038.37754386-28182.3775438601
7297243129188.593285137-31945.5932851366
73103876121975.505093746-18099.5050937464
74116370126750.891662372-10380.8916623723
75109616132584.005759717-22968.0057597171
7698365136972.79573999-38607.7957399899
7790440113022.444798159-22582.4447981591
7888899101562.245431007-12663.2454310071
7992358100326.849299167-7968.84929916652
8088394102843.130421441-14449.1304214408
819821996687.03295898221531.96704101783
8211354686527.112191813627018.8878081864
83107168111888.468131442-4720.46813144157
8477540123581.202829023-46041.2028290227
8574944108131.395847725-33187.3958477249
867564155974.017671261319666.9823287387
8775910133793.122847004-57883.122847004
8887384132163.720238292-44779.7202382915
8984615135724.568797657-51109.5687976567
9080420107582.04701517-27162.0470151699
918078496532.3709415855-15748.3709415854
9279933131842.772029752-51909.7720297521
9382118111146.576638545-29028.576638545
9491420110524.594390692-19104.5943906921
95112426114097.284263178-1671.28426317755
96114528143349.945370905-28821.9453709046
97131025137306.670678568-6281.67067856819
98116460119567.232055905-3107.23205590545
99111258121840.350785524-10582.3507855242
100155318143031.36127674412286.6387232555
101155078164834.327263086-9756.32726308611
102134794146978.887527391-12184.8875273906
103139985136877.4442083843107.55579161557
104198778136965.65440990561812.345590095
105172436165910.7068831756525.29311682533
106169585181845.9845328-12260.9845328
107203702167884.12643930635817.8735606939
108282392151249.669662132131142.330337868
109220658200217.13580802920440.8641919705
110194472158272.31024840136199.6897515989
111269246166371.784393968102874.215606032
112215340175663.07804990539676.9219500949
113218319183543.70690613434775.2930938658
114195724177333.43347117418390.566528826
115174614163446.3604992211167.6395007796
116172085175639.224658567-3554.22465856734
117152347196334.551322644-43987.5513226439
118189615176797.76377396512817.2362260355
119173804124294.93036345749509.0696365433
120145683150051.695496447-4368.69549644725
121133550148351.799259031-14801.7992590314
122121156140955.704615318-19799.7046153175
123112040144139.093808145-32099.0938081455
124120767161918.037753202-41151.0377532018
125127019121887.9429886165131.0570113842
126136295104285.14871253432009.8512874662
127113425121897.528190544-8472.52819054421
128107815109478.549118664-1663.54911866393
129100298115622.452341237-15324.4523412373
13097048115235.282772974-18187.2827729739
13198750130917.159742868-32167.1597428682
13298235129038.551780751-30803.5517807506
133101254109943.827233832-8689.82723383188
13413958992438.182855400147150.8171445999
135134921118820.27356324516100.7264367551
1368035592814.3205248326-12459.3205248326
1378039682399.5012863878-2003.5012863878
1388218352988.617711015829194.3822889842
1397970927617.460056700152091.5399432999
14090781133581.461966725-42800.4619667248

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 169498 & 122757.755982803 & 46740.2440171971 \tabularnewline
2 & 125451 & 136002.156659889 & -10551.1566598886 \tabularnewline
3 & 140449 & 124600.75417488 & 15848.2458251198 \tabularnewline
4 & 141653 & 114282.870205065 & 27370.1297949353 \tabularnewline
5 & 136394 & 131636.010636331 & 4757.98936366932 \tabularnewline
6 & 167588 & 142869.549931996 & 24718.4500680038 \tabularnewline
7 & 191807 & 134529.086796846 & 57277.913203154 \tabularnewline
8 & 149736 & 118950.357107931 & 30785.6428920689 \tabularnewline
9 & 196066 & 136448.808132314 & 59617.191867686 \tabularnewline
10 & 239155 & 126134.707693367 & 113020.292306633 \tabularnewline
11 & 178421 & 133459.734501837 & 44961.2654981628 \tabularnewline
12 & 139871 & 96323.1749304424 & 43547.8250695576 \tabularnewline
13 & 118159 & 119206.618571106 & -1047.61857110605 \tabularnewline
14 & 109763 & 121368.947434323 & -11605.9474343233 \tabularnewline
15 & 97415 & 118676.077556615 & -21261.0775566153 \tabularnewline
16 & 119190 & 107997.06818324 & 11192.9318167598 \tabularnewline
17 & 97903 & 121798.173184921 & -23895.1731849211 \tabularnewline
18 & 96953 & 98297.988450282 & -1344.98845028198 \tabularnewline
19 & 87888 & 75306.7287594154 & 12581.2712405846 \tabularnewline
20 & 84637 & 98882.4398579533 & -14245.4398579533 \tabularnewline
21 & 90549 & 111768.308661662 & -21219.3086616624 \tabularnewline
22 & 95680 & 138989.34892306 & -43309.3489230599 \tabularnewline
23 & 99371 & 123024.239854401 & -23653.2398544009 \tabularnewline
24 & 79984 & 106548.751863573 & -26564.7518635727 \tabularnewline
25 & 86752 & 84387.6224312294 & 2364.37756877059 \tabularnewline
26 & 85733 & 100939.372121337 & -15206.3721213365 \tabularnewline
27 & 84906 & 96881.9109289132 & -11975.9109289132 \tabularnewline
28 & 78356 & 94537.3719887771 & -16181.3719887771 \tabularnewline
29 & 108895 & 85542.7166748684 & 23352.2833251316 \tabularnewline
30 & 101768 & 101012.612709156 & 755.387290843688 \tabularnewline
31 & 73285 & 91796.1681728001 & -18511.1681728001 \tabularnewline
32 & 65724 & 127041.323523975 & -61317.3235239751 \tabularnewline
33 & 67457 & 107041.73785401 & -39584.7378540101 \tabularnewline
34 & 67203 & 47233.5987635907 & 19969.4012364093 \tabularnewline
35 & 69273 & 90322.9097956032 & -21049.9097956032 \tabularnewline
36 & 80807 & 115934.02621729 & -35127.0262172901 \tabularnewline
37 & 75129 & 119608.986362936 & -44479.9863629364 \tabularnewline
38 & 74991 & 119661.842773367 & -44670.8427733666 \tabularnewline
39 & 68157 & 104812.707878923 & -36655.707878923 \tabularnewline
40 & 73858 & 79882.6309852506 & -6024.63098525057 \tabularnewline
41 & 71349 & 61675.068844182 & 9673.931155818 \tabularnewline
42 & 85634 & 70723.1478780341 & 14910.8521219659 \tabularnewline
43 & 91624 & 88134.265537946 & 3489.73446205396 \tabularnewline
44 & 116014 & 85686.6777413345 & 30327.3222586655 \tabularnewline
45 & 120033 & 84412.9948250069 & 35620.0051749931 \tabularnewline
46 & 108651 & 94401.7413566411 & 14249.2586433589 \tabularnewline
47 & 105378 & 114017.744403536 & -8639.74440353634 \tabularnewline
48 & 138939 & 118861.211920981 & 20077.7880790193 \tabularnewline
49 & 132974 & 129539.180771358 & 3434.81922864189 \tabularnewline
50 & 135277 & 143962.52423285 & -8685.52423285038 \tabularnewline
51 & 152741 & 140880.772678592 & 11860.227321408 \tabularnewline
52 & 158417 & 168500.651882262 & -10083.6518822619 \tabularnewline
53 & 157460 & 168683.407032888 & -11223.407032888 \tabularnewline
54 & 193997 & 170945.259746446 & 23051.7402535537 \tabularnewline
55 & 154089 & 143841.269606669 & 10247.7303933307 \tabularnewline
56 & 147570 & 128928.281230765 & 18641.7187692348 \tabularnewline
57 & 162924 & 181224.106174694 & -18300.1061746937 \tabularnewline
58 & 153629 & 174834.917421308 & -21205.9174213084 \tabularnewline
59 & 155907 & 157874.92995603 & -1967.9299560298 \tabularnewline
60 & 197675 & 161809.426954839 & 35865.5730451612 \tabularnewline
61 & 250708 & 167247.010017031 & 83460.9899829687 \tabularnewline
62 & 266652 & 156108.49960929 & 110543.50039071 \tabularnewline
63 & 209842 & 176177.969269962 & 33664.0307300376 \tabularnewline
64 & 165826 & 168439.590898316 & -2613.59089831571 \tabularnewline
65 & 137152 & 177086.88641088 & -39934.8864108799 \tabularnewline
66 & 150581 & 177464.765062005 & -26883.7650620046 \tabularnewline
67 & 145973 & 166667.809199855 & -20694.8091998551 \tabularnewline
68 & 126532 & 160044.611490429 & -33512.6114904292 \tabularnewline
69 & 115437 & 134348.15256438 & -18911.1525643804 \tabularnewline
70 & 119526 & 146282.569850732 & -26756.5698507323 \tabularnewline
71 & 110856 & 139038.37754386 & -28182.3775438601 \tabularnewline
72 & 97243 & 129188.593285137 & -31945.5932851366 \tabularnewline
73 & 103876 & 121975.505093746 & -18099.5050937464 \tabularnewline
74 & 116370 & 126750.891662372 & -10380.8916623723 \tabularnewline
75 & 109616 & 132584.005759717 & -22968.0057597171 \tabularnewline
76 & 98365 & 136972.79573999 & -38607.7957399899 \tabularnewline
77 & 90440 & 113022.444798159 & -22582.4447981591 \tabularnewline
78 & 88899 & 101562.245431007 & -12663.2454310071 \tabularnewline
79 & 92358 & 100326.849299167 & -7968.84929916652 \tabularnewline
80 & 88394 & 102843.130421441 & -14449.1304214408 \tabularnewline
81 & 98219 & 96687.0329589822 & 1531.96704101783 \tabularnewline
82 & 113546 & 86527.1121918136 & 27018.8878081864 \tabularnewline
83 & 107168 & 111888.468131442 & -4720.46813144157 \tabularnewline
84 & 77540 & 123581.202829023 & -46041.2028290227 \tabularnewline
85 & 74944 & 108131.395847725 & -33187.3958477249 \tabularnewline
86 & 75641 & 55974.0176712613 & 19666.9823287387 \tabularnewline
87 & 75910 & 133793.122847004 & -57883.122847004 \tabularnewline
88 & 87384 & 132163.720238292 & -44779.7202382915 \tabularnewline
89 & 84615 & 135724.568797657 & -51109.5687976567 \tabularnewline
90 & 80420 & 107582.04701517 & -27162.0470151699 \tabularnewline
91 & 80784 & 96532.3709415855 & -15748.3709415854 \tabularnewline
92 & 79933 & 131842.772029752 & -51909.7720297521 \tabularnewline
93 & 82118 & 111146.576638545 & -29028.576638545 \tabularnewline
94 & 91420 & 110524.594390692 & -19104.5943906921 \tabularnewline
95 & 112426 & 114097.284263178 & -1671.28426317755 \tabularnewline
96 & 114528 & 143349.945370905 & -28821.9453709046 \tabularnewline
97 & 131025 & 137306.670678568 & -6281.67067856819 \tabularnewline
98 & 116460 & 119567.232055905 & -3107.23205590545 \tabularnewline
99 & 111258 & 121840.350785524 & -10582.3507855242 \tabularnewline
100 & 155318 & 143031.361276744 & 12286.6387232555 \tabularnewline
101 & 155078 & 164834.327263086 & -9756.32726308611 \tabularnewline
102 & 134794 & 146978.887527391 & -12184.8875273906 \tabularnewline
103 & 139985 & 136877.444208384 & 3107.55579161557 \tabularnewline
104 & 198778 & 136965.654409905 & 61812.345590095 \tabularnewline
105 & 172436 & 165910.706883175 & 6525.29311682533 \tabularnewline
106 & 169585 & 181845.9845328 & -12260.9845328 \tabularnewline
107 & 203702 & 167884.126439306 & 35817.8735606939 \tabularnewline
108 & 282392 & 151249.669662132 & 131142.330337868 \tabularnewline
109 & 220658 & 200217.135808029 & 20440.8641919705 \tabularnewline
110 & 194472 & 158272.310248401 & 36199.6897515989 \tabularnewline
111 & 269246 & 166371.784393968 & 102874.215606032 \tabularnewline
112 & 215340 & 175663.078049905 & 39676.9219500949 \tabularnewline
113 & 218319 & 183543.706906134 & 34775.2930938658 \tabularnewline
114 & 195724 & 177333.433471174 & 18390.566528826 \tabularnewline
115 & 174614 & 163446.36049922 & 11167.6395007796 \tabularnewline
116 & 172085 & 175639.224658567 & -3554.22465856734 \tabularnewline
117 & 152347 & 196334.551322644 & -43987.5513226439 \tabularnewline
118 & 189615 & 176797.763773965 & 12817.2362260355 \tabularnewline
119 & 173804 & 124294.930363457 & 49509.0696365433 \tabularnewline
120 & 145683 & 150051.695496447 & -4368.69549644725 \tabularnewline
121 & 133550 & 148351.799259031 & -14801.7992590314 \tabularnewline
122 & 121156 & 140955.704615318 & -19799.7046153175 \tabularnewline
123 & 112040 & 144139.093808145 & -32099.0938081455 \tabularnewline
124 & 120767 & 161918.037753202 & -41151.0377532018 \tabularnewline
125 & 127019 & 121887.942988616 & 5131.0570113842 \tabularnewline
126 & 136295 & 104285.148712534 & 32009.8512874662 \tabularnewline
127 & 113425 & 121897.528190544 & -8472.52819054421 \tabularnewline
128 & 107815 & 109478.549118664 & -1663.54911866393 \tabularnewline
129 & 100298 & 115622.452341237 & -15324.4523412373 \tabularnewline
130 & 97048 & 115235.282772974 & -18187.2827729739 \tabularnewline
131 & 98750 & 130917.159742868 & -32167.1597428682 \tabularnewline
132 & 98235 & 129038.551780751 & -30803.5517807506 \tabularnewline
133 & 101254 & 109943.827233832 & -8689.82723383188 \tabularnewline
134 & 139589 & 92438.1828554001 & 47150.8171445999 \tabularnewline
135 & 134921 & 118820.273563245 & 16100.7264367551 \tabularnewline
136 & 80355 & 92814.3205248326 & -12459.3205248326 \tabularnewline
137 & 80396 & 82399.5012863878 & -2003.5012863878 \tabularnewline
138 & 82183 & 52988.6177110158 & 29194.3822889842 \tabularnewline
139 & 79709 & 27617.4600567001 & 52091.5399432999 \tabularnewline
140 & 90781 & 133581.461966725 & -42800.4619667248 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&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]122757.755982803[/C][C]46740.2440171971[/C][/ROW]
[ROW][C]2[/C][C]125451[/C][C]136002.156659889[/C][C]-10551.1566598886[/C][/ROW]
[ROW][C]3[/C][C]140449[/C][C]124600.75417488[/C][C]15848.2458251198[/C][/ROW]
[ROW][C]4[/C][C]141653[/C][C]114282.870205065[/C][C]27370.1297949353[/C][/ROW]
[ROW][C]5[/C][C]136394[/C][C]131636.010636331[/C][C]4757.98936366932[/C][/ROW]
[ROW][C]6[/C][C]167588[/C][C]142869.549931996[/C][C]24718.4500680038[/C][/ROW]
[ROW][C]7[/C][C]191807[/C][C]134529.086796846[/C][C]57277.913203154[/C][/ROW]
[ROW][C]8[/C][C]149736[/C][C]118950.357107931[/C][C]30785.6428920689[/C][/ROW]
[ROW][C]9[/C][C]196066[/C][C]136448.808132314[/C][C]59617.191867686[/C][/ROW]
[ROW][C]10[/C][C]239155[/C][C]126134.707693367[/C][C]113020.292306633[/C][/ROW]
[ROW][C]11[/C][C]178421[/C][C]133459.734501837[/C][C]44961.2654981628[/C][/ROW]
[ROW][C]12[/C][C]139871[/C][C]96323.1749304424[/C][C]43547.8250695576[/C][/ROW]
[ROW][C]13[/C][C]118159[/C][C]119206.618571106[/C][C]-1047.61857110605[/C][/ROW]
[ROW][C]14[/C][C]109763[/C][C]121368.947434323[/C][C]-11605.9474343233[/C][/ROW]
[ROW][C]15[/C][C]97415[/C][C]118676.077556615[/C][C]-21261.0775566153[/C][/ROW]
[ROW][C]16[/C][C]119190[/C][C]107997.06818324[/C][C]11192.9318167598[/C][/ROW]
[ROW][C]17[/C][C]97903[/C][C]121798.173184921[/C][C]-23895.1731849211[/C][/ROW]
[ROW][C]18[/C][C]96953[/C][C]98297.988450282[/C][C]-1344.98845028198[/C][/ROW]
[ROW][C]19[/C][C]87888[/C][C]75306.7287594154[/C][C]12581.2712405846[/C][/ROW]
[ROW][C]20[/C][C]84637[/C][C]98882.4398579533[/C][C]-14245.4398579533[/C][/ROW]
[ROW][C]21[/C][C]90549[/C][C]111768.308661662[/C][C]-21219.3086616624[/C][/ROW]
[ROW][C]22[/C][C]95680[/C][C]138989.34892306[/C][C]-43309.3489230599[/C][/ROW]
[ROW][C]23[/C][C]99371[/C][C]123024.239854401[/C][C]-23653.2398544009[/C][/ROW]
[ROW][C]24[/C][C]79984[/C][C]106548.751863573[/C][C]-26564.7518635727[/C][/ROW]
[ROW][C]25[/C][C]86752[/C][C]84387.6224312294[/C][C]2364.37756877059[/C][/ROW]
[ROW][C]26[/C][C]85733[/C][C]100939.372121337[/C][C]-15206.3721213365[/C][/ROW]
[ROW][C]27[/C][C]84906[/C][C]96881.9109289132[/C][C]-11975.9109289132[/C][/ROW]
[ROW][C]28[/C][C]78356[/C][C]94537.3719887771[/C][C]-16181.3719887771[/C][/ROW]
[ROW][C]29[/C][C]108895[/C][C]85542.7166748684[/C][C]23352.2833251316[/C][/ROW]
[ROW][C]30[/C][C]101768[/C][C]101012.612709156[/C][C]755.387290843688[/C][/ROW]
[ROW][C]31[/C][C]73285[/C][C]91796.1681728001[/C][C]-18511.1681728001[/C][/ROW]
[ROW][C]32[/C][C]65724[/C][C]127041.323523975[/C][C]-61317.3235239751[/C][/ROW]
[ROW][C]33[/C][C]67457[/C][C]107041.73785401[/C][C]-39584.7378540101[/C][/ROW]
[ROW][C]34[/C][C]67203[/C][C]47233.5987635907[/C][C]19969.4012364093[/C][/ROW]
[ROW][C]35[/C][C]69273[/C][C]90322.9097956032[/C][C]-21049.9097956032[/C][/ROW]
[ROW][C]36[/C][C]80807[/C][C]115934.02621729[/C][C]-35127.0262172901[/C][/ROW]
[ROW][C]37[/C][C]75129[/C][C]119608.986362936[/C][C]-44479.9863629364[/C][/ROW]
[ROW][C]38[/C][C]74991[/C][C]119661.842773367[/C][C]-44670.8427733666[/C][/ROW]
[ROW][C]39[/C][C]68157[/C][C]104812.707878923[/C][C]-36655.707878923[/C][/ROW]
[ROW][C]40[/C][C]73858[/C][C]79882.6309852506[/C][C]-6024.63098525057[/C][/ROW]
[ROW][C]41[/C][C]71349[/C][C]61675.068844182[/C][C]9673.931155818[/C][/ROW]
[ROW][C]42[/C][C]85634[/C][C]70723.1478780341[/C][C]14910.8521219659[/C][/ROW]
[ROW][C]43[/C][C]91624[/C][C]88134.265537946[/C][C]3489.73446205396[/C][/ROW]
[ROW][C]44[/C][C]116014[/C][C]85686.6777413345[/C][C]30327.3222586655[/C][/ROW]
[ROW][C]45[/C][C]120033[/C][C]84412.9948250069[/C][C]35620.0051749931[/C][/ROW]
[ROW][C]46[/C][C]108651[/C][C]94401.7413566411[/C][C]14249.2586433589[/C][/ROW]
[ROW][C]47[/C][C]105378[/C][C]114017.744403536[/C][C]-8639.74440353634[/C][/ROW]
[ROW][C]48[/C][C]138939[/C][C]118861.211920981[/C][C]20077.7880790193[/C][/ROW]
[ROW][C]49[/C][C]132974[/C][C]129539.180771358[/C][C]3434.81922864189[/C][/ROW]
[ROW][C]50[/C][C]135277[/C][C]143962.52423285[/C][C]-8685.52423285038[/C][/ROW]
[ROW][C]51[/C][C]152741[/C][C]140880.772678592[/C][C]11860.227321408[/C][/ROW]
[ROW][C]52[/C][C]158417[/C][C]168500.651882262[/C][C]-10083.6518822619[/C][/ROW]
[ROW][C]53[/C][C]157460[/C][C]168683.407032888[/C][C]-11223.407032888[/C][/ROW]
[ROW][C]54[/C][C]193997[/C][C]170945.259746446[/C][C]23051.7402535537[/C][/ROW]
[ROW][C]55[/C][C]154089[/C][C]143841.269606669[/C][C]10247.7303933307[/C][/ROW]
[ROW][C]56[/C][C]147570[/C][C]128928.281230765[/C][C]18641.7187692348[/C][/ROW]
[ROW][C]57[/C][C]162924[/C][C]181224.106174694[/C][C]-18300.1061746937[/C][/ROW]
[ROW][C]58[/C][C]153629[/C][C]174834.917421308[/C][C]-21205.9174213084[/C][/ROW]
[ROW][C]59[/C][C]155907[/C][C]157874.92995603[/C][C]-1967.9299560298[/C][/ROW]
[ROW][C]60[/C][C]197675[/C][C]161809.426954839[/C][C]35865.5730451612[/C][/ROW]
[ROW][C]61[/C][C]250708[/C][C]167247.010017031[/C][C]83460.9899829687[/C][/ROW]
[ROW][C]62[/C][C]266652[/C][C]156108.49960929[/C][C]110543.50039071[/C][/ROW]
[ROW][C]63[/C][C]209842[/C][C]176177.969269962[/C][C]33664.0307300376[/C][/ROW]
[ROW][C]64[/C][C]165826[/C][C]168439.590898316[/C][C]-2613.59089831571[/C][/ROW]
[ROW][C]65[/C][C]137152[/C][C]177086.88641088[/C][C]-39934.8864108799[/C][/ROW]
[ROW][C]66[/C][C]150581[/C][C]177464.765062005[/C][C]-26883.7650620046[/C][/ROW]
[ROW][C]67[/C][C]145973[/C][C]166667.809199855[/C][C]-20694.8091998551[/C][/ROW]
[ROW][C]68[/C][C]126532[/C][C]160044.611490429[/C][C]-33512.6114904292[/C][/ROW]
[ROW][C]69[/C][C]115437[/C][C]134348.15256438[/C][C]-18911.1525643804[/C][/ROW]
[ROW][C]70[/C][C]119526[/C][C]146282.569850732[/C][C]-26756.5698507323[/C][/ROW]
[ROW][C]71[/C][C]110856[/C][C]139038.37754386[/C][C]-28182.3775438601[/C][/ROW]
[ROW][C]72[/C][C]97243[/C][C]129188.593285137[/C][C]-31945.5932851366[/C][/ROW]
[ROW][C]73[/C][C]103876[/C][C]121975.505093746[/C][C]-18099.5050937464[/C][/ROW]
[ROW][C]74[/C][C]116370[/C][C]126750.891662372[/C][C]-10380.8916623723[/C][/ROW]
[ROW][C]75[/C][C]109616[/C][C]132584.005759717[/C][C]-22968.0057597171[/C][/ROW]
[ROW][C]76[/C][C]98365[/C][C]136972.79573999[/C][C]-38607.7957399899[/C][/ROW]
[ROW][C]77[/C][C]90440[/C][C]113022.444798159[/C][C]-22582.4447981591[/C][/ROW]
[ROW][C]78[/C][C]88899[/C][C]101562.245431007[/C][C]-12663.2454310071[/C][/ROW]
[ROW][C]79[/C][C]92358[/C][C]100326.849299167[/C][C]-7968.84929916652[/C][/ROW]
[ROW][C]80[/C][C]88394[/C][C]102843.130421441[/C][C]-14449.1304214408[/C][/ROW]
[ROW][C]81[/C][C]98219[/C][C]96687.0329589822[/C][C]1531.96704101783[/C][/ROW]
[ROW][C]82[/C][C]113546[/C][C]86527.1121918136[/C][C]27018.8878081864[/C][/ROW]
[ROW][C]83[/C][C]107168[/C][C]111888.468131442[/C][C]-4720.46813144157[/C][/ROW]
[ROW][C]84[/C][C]77540[/C][C]123581.202829023[/C][C]-46041.2028290227[/C][/ROW]
[ROW][C]85[/C][C]74944[/C][C]108131.395847725[/C][C]-33187.3958477249[/C][/ROW]
[ROW][C]86[/C][C]75641[/C][C]55974.0176712613[/C][C]19666.9823287387[/C][/ROW]
[ROW][C]87[/C][C]75910[/C][C]133793.122847004[/C][C]-57883.122847004[/C][/ROW]
[ROW][C]88[/C][C]87384[/C][C]132163.720238292[/C][C]-44779.7202382915[/C][/ROW]
[ROW][C]89[/C][C]84615[/C][C]135724.568797657[/C][C]-51109.5687976567[/C][/ROW]
[ROW][C]90[/C][C]80420[/C][C]107582.04701517[/C][C]-27162.0470151699[/C][/ROW]
[ROW][C]91[/C][C]80784[/C][C]96532.3709415855[/C][C]-15748.3709415854[/C][/ROW]
[ROW][C]92[/C][C]79933[/C][C]131842.772029752[/C][C]-51909.7720297521[/C][/ROW]
[ROW][C]93[/C][C]82118[/C][C]111146.576638545[/C][C]-29028.576638545[/C][/ROW]
[ROW][C]94[/C][C]91420[/C][C]110524.594390692[/C][C]-19104.5943906921[/C][/ROW]
[ROW][C]95[/C][C]112426[/C][C]114097.284263178[/C][C]-1671.28426317755[/C][/ROW]
[ROW][C]96[/C][C]114528[/C][C]143349.945370905[/C][C]-28821.9453709046[/C][/ROW]
[ROW][C]97[/C][C]131025[/C][C]137306.670678568[/C][C]-6281.67067856819[/C][/ROW]
[ROW][C]98[/C][C]116460[/C][C]119567.232055905[/C][C]-3107.23205590545[/C][/ROW]
[ROW][C]99[/C][C]111258[/C][C]121840.350785524[/C][C]-10582.3507855242[/C][/ROW]
[ROW][C]100[/C][C]155318[/C][C]143031.361276744[/C][C]12286.6387232555[/C][/ROW]
[ROW][C]101[/C][C]155078[/C][C]164834.327263086[/C][C]-9756.32726308611[/C][/ROW]
[ROW][C]102[/C][C]134794[/C][C]146978.887527391[/C][C]-12184.8875273906[/C][/ROW]
[ROW][C]103[/C][C]139985[/C][C]136877.444208384[/C][C]3107.55579161557[/C][/ROW]
[ROW][C]104[/C][C]198778[/C][C]136965.654409905[/C][C]61812.345590095[/C][/ROW]
[ROW][C]105[/C][C]172436[/C][C]165910.706883175[/C][C]6525.29311682533[/C][/ROW]
[ROW][C]106[/C][C]169585[/C][C]181845.9845328[/C][C]-12260.9845328[/C][/ROW]
[ROW][C]107[/C][C]203702[/C][C]167884.126439306[/C][C]35817.8735606939[/C][/ROW]
[ROW][C]108[/C][C]282392[/C][C]151249.669662132[/C][C]131142.330337868[/C][/ROW]
[ROW][C]109[/C][C]220658[/C][C]200217.135808029[/C][C]20440.8641919705[/C][/ROW]
[ROW][C]110[/C][C]194472[/C][C]158272.310248401[/C][C]36199.6897515989[/C][/ROW]
[ROW][C]111[/C][C]269246[/C][C]166371.784393968[/C][C]102874.215606032[/C][/ROW]
[ROW][C]112[/C][C]215340[/C][C]175663.078049905[/C][C]39676.9219500949[/C][/ROW]
[ROW][C]113[/C][C]218319[/C][C]183543.706906134[/C][C]34775.2930938658[/C][/ROW]
[ROW][C]114[/C][C]195724[/C][C]177333.433471174[/C][C]18390.566528826[/C][/ROW]
[ROW][C]115[/C][C]174614[/C][C]163446.36049922[/C][C]11167.6395007796[/C][/ROW]
[ROW][C]116[/C][C]172085[/C][C]175639.224658567[/C][C]-3554.22465856734[/C][/ROW]
[ROW][C]117[/C][C]152347[/C][C]196334.551322644[/C][C]-43987.5513226439[/C][/ROW]
[ROW][C]118[/C][C]189615[/C][C]176797.763773965[/C][C]12817.2362260355[/C][/ROW]
[ROW][C]119[/C][C]173804[/C][C]124294.930363457[/C][C]49509.0696365433[/C][/ROW]
[ROW][C]120[/C][C]145683[/C][C]150051.695496447[/C][C]-4368.69549644725[/C][/ROW]
[ROW][C]121[/C][C]133550[/C][C]148351.799259031[/C][C]-14801.7992590314[/C][/ROW]
[ROW][C]122[/C][C]121156[/C][C]140955.704615318[/C][C]-19799.7046153175[/C][/ROW]
[ROW][C]123[/C][C]112040[/C][C]144139.093808145[/C][C]-32099.0938081455[/C][/ROW]
[ROW][C]124[/C][C]120767[/C][C]161918.037753202[/C][C]-41151.0377532018[/C][/ROW]
[ROW][C]125[/C][C]127019[/C][C]121887.942988616[/C][C]5131.0570113842[/C][/ROW]
[ROW][C]126[/C][C]136295[/C][C]104285.148712534[/C][C]32009.8512874662[/C][/ROW]
[ROW][C]127[/C][C]113425[/C][C]121897.528190544[/C][C]-8472.52819054421[/C][/ROW]
[ROW][C]128[/C][C]107815[/C][C]109478.549118664[/C][C]-1663.54911866393[/C][/ROW]
[ROW][C]129[/C][C]100298[/C][C]115622.452341237[/C][C]-15324.4523412373[/C][/ROW]
[ROW][C]130[/C][C]97048[/C][C]115235.282772974[/C][C]-18187.2827729739[/C][/ROW]
[ROW][C]131[/C][C]98750[/C][C]130917.159742868[/C][C]-32167.1597428682[/C][/ROW]
[ROW][C]132[/C][C]98235[/C][C]129038.551780751[/C][C]-30803.5517807506[/C][/ROW]
[ROW][C]133[/C][C]101254[/C][C]109943.827233832[/C][C]-8689.82723383188[/C][/ROW]
[ROW][C]134[/C][C]139589[/C][C]92438.1828554001[/C][C]47150.8171445999[/C][/ROW]
[ROW][C]135[/C][C]134921[/C][C]118820.273563245[/C][C]16100.7264367551[/C][/ROW]
[ROW][C]136[/C][C]80355[/C][C]92814.3205248326[/C][C]-12459.3205248326[/C][/ROW]
[ROW][C]137[/C][C]80396[/C][C]82399.5012863878[/C][C]-2003.5012863878[/C][/ROW]
[ROW][C]138[/C][C]82183[/C][C]52988.6177110158[/C][C]29194.3822889842[/C][/ROW]
[ROW][C]139[/C][C]79709[/C][C]27617.4600567001[/C][C]52091.5399432999[/C][/ROW]
[ROW][C]140[/C][C]90781[/C][C]133581.461966725[/C][C]-42800.4619667248[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1169498122757.75598280346740.2440171971
2125451136002.156659889-10551.1566598886
3140449124600.7541748815848.2458251198
4141653114282.87020506527370.1297949353
5136394131636.0106363314757.98936366932
6167588142869.54993199624718.4500680038
7191807134529.08679684657277.913203154
8149736118950.35710793130785.6428920689
9196066136448.80813231459617.191867686
10239155126134.707693367113020.292306633
11178421133459.73450183744961.2654981628
1213987196323.174930442443547.8250695576
13118159119206.618571106-1047.61857110605
14109763121368.947434323-11605.9474343233
1597415118676.077556615-21261.0775566153
16119190107997.0681832411192.9318167598
1797903121798.173184921-23895.1731849211
189695398297.988450282-1344.98845028198
198788875306.728759415412581.2712405846
208463798882.4398579533-14245.4398579533
2190549111768.308661662-21219.3086616624
2295680138989.34892306-43309.3489230599
2399371123024.239854401-23653.2398544009
2479984106548.751863573-26564.7518635727
258675284387.62243122942364.37756877059
2685733100939.372121337-15206.3721213365
278490696881.9109289132-11975.9109289132
287835694537.3719887771-16181.3719887771
2910889585542.716674868423352.2833251316
30101768101012.612709156755.387290843688
317328591796.1681728001-18511.1681728001
3265724127041.323523975-61317.3235239751
3367457107041.73785401-39584.7378540101
346720347233.598763590719969.4012364093
356927390322.9097956032-21049.9097956032
3680807115934.02621729-35127.0262172901
3775129119608.986362936-44479.9863629364
3874991119661.842773367-44670.8427733666
3968157104812.707878923-36655.707878923
407385879882.6309852506-6024.63098525057
417134961675.0688441829673.931155818
428563470723.147878034114910.8521219659
439162488134.2655379463489.73446205396
4411601485686.677741334530327.3222586655
4512003384412.994825006935620.0051749931
4610865194401.741356641114249.2586433589
47105378114017.744403536-8639.74440353634
48138939118861.21192098120077.7880790193
49132974129539.1807713583434.81922864189
50135277143962.52423285-8685.52423285038
51152741140880.77267859211860.227321408
52158417168500.651882262-10083.6518822619
53157460168683.407032888-11223.407032888
54193997170945.25974644623051.7402535537
55154089143841.26960666910247.7303933307
56147570128928.28123076518641.7187692348
57162924181224.106174694-18300.1061746937
58153629174834.917421308-21205.9174213084
59155907157874.92995603-1967.9299560298
60197675161809.42695483935865.5730451612
61250708167247.01001703183460.9899829687
62266652156108.49960929110543.50039071
63209842176177.96926996233664.0307300376
64165826168439.590898316-2613.59089831571
65137152177086.88641088-39934.8864108799
66150581177464.765062005-26883.7650620046
67145973166667.809199855-20694.8091998551
68126532160044.611490429-33512.6114904292
69115437134348.15256438-18911.1525643804
70119526146282.569850732-26756.5698507323
71110856139038.37754386-28182.3775438601
7297243129188.593285137-31945.5932851366
73103876121975.505093746-18099.5050937464
74116370126750.891662372-10380.8916623723
75109616132584.005759717-22968.0057597171
7698365136972.79573999-38607.7957399899
7790440113022.444798159-22582.4447981591
7888899101562.245431007-12663.2454310071
7992358100326.849299167-7968.84929916652
8088394102843.130421441-14449.1304214408
819821996687.03295898221531.96704101783
8211354686527.112191813627018.8878081864
83107168111888.468131442-4720.46813144157
8477540123581.202829023-46041.2028290227
8574944108131.395847725-33187.3958477249
867564155974.017671261319666.9823287387
8775910133793.122847004-57883.122847004
8887384132163.720238292-44779.7202382915
8984615135724.568797657-51109.5687976567
9080420107582.04701517-27162.0470151699
918078496532.3709415855-15748.3709415854
9279933131842.772029752-51909.7720297521
9382118111146.576638545-29028.576638545
9491420110524.594390692-19104.5943906921
95112426114097.284263178-1671.28426317755
96114528143349.945370905-28821.9453709046
97131025137306.670678568-6281.67067856819
98116460119567.232055905-3107.23205590545
99111258121840.350785524-10582.3507855242
100155318143031.36127674412286.6387232555
101155078164834.327263086-9756.32726308611
102134794146978.887527391-12184.8875273906
103139985136877.4442083843107.55579161557
104198778136965.65440990561812.345590095
105172436165910.7068831756525.29311682533
106169585181845.9845328-12260.9845328
107203702167884.12643930635817.8735606939
108282392151249.669662132131142.330337868
109220658200217.13580802920440.8641919705
110194472158272.31024840136199.6897515989
111269246166371.784393968102874.215606032
112215340175663.07804990539676.9219500949
113218319183543.70690613434775.2930938658
114195724177333.43347117418390.566528826
115174614163446.3604992211167.6395007796
116172085175639.224658567-3554.22465856734
117152347196334.551322644-43987.5513226439
118189615176797.76377396512817.2362260355
119173804124294.93036345749509.0696365433
120145683150051.695496447-4368.69549644725
121133550148351.799259031-14801.7992590314
122121156140955.704615318-19799.7046153175
123112040144139.093808145-32099.0938081455
124120767161918.037753202-41151.0377532018
125127019121887.9429886165131.0570113842
126136295104285.14871253432009.8512874662
127113425121897.528190544-8472.52819054421
128107815109478.549118664-1663.54911866393
129100298115622.452341237-15324.4523412373
13097048115235.282772974-18187.2827729739
13198750130917.159742868-32167.1597428682
13298235129038.551780751-30803.5517807506
133101254109943.827233832-8689.82723383188
13413958992438.182855400147150.8171445999
135134921118820.27356324516100.7264367551
1368035592814.3205248326-12459.3205248326
1378039682399.5012863878-2003.5012863878
1388218352988.617711015829194.3822889842
1397970927617.460056700152091.5399432999
14090781133581.461966725-42800.4619667248






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6051122223481860.7897755553036280.394887777651814
100.6155499925264260.7689000149471490.384450007473574
110.5820114706087770.8359770587824470.417988529391223
120.6525427228069790.6949145543860430.347457277193021
130.7300609603528670.5398780792942660.269939039647133
140.8502096036987690.2995807926024630.149790396301231
150.8462720036553140.3074559926893720.153727996344686
160.7896718365560390.4206563268879230.210328163443961
170.832247679662730.335504640674540.16775232033727
180.8306254500715230.3387490998569540.169374549928477
190.7840897153033210.4318205693933570.215910284696679
200.7517108703019330.4965782593961350.248289129698067
210.7551938404304470.4896123191391060.244806159569553
220.8484208187915030.3031583624169940.151579181208497
230.8667875177639150.266424964472170.133212482236085
240.8379127640611680.3241744718776640.162087235938832
250.7995818194078480.4008363611843050.200418180592152
260.7494004657484260.5011990685031470.250599534251574
270.6938255616548320.6123488766903350.306174438345168
280.6335176383632480.7329647232735030.366482361636752
290.6419644335324840.7160711329350330.358035566467516
300.5838950424727570.8322099150544850.416104957527243
310.5572336691604840.8855326616790320.442766330839516
320.6749657144035430.6500685711929150.325034285596457
330.6281643877607950.743671224478410.371835612239205
340.6405820947087530.7188358105824930.359417905291247
350.6237123788802740.7525752422394510.376287621119726
360.6434219256348830.7131561487302340.356578074365117
370.6222795765583740.7554408468832530.377720423441627
380.5925649154119770.8148701691760460.407435084588023
390.5495258492239090.9009483015521820.450474150776091
400.543699133591340.9126017328173190.45630086640866
410.5401155543789510.9197688912420980.459884445621049
420.5082155622633230.9835688754733550.491784437736677
430.4626826555818140.9253653111636290.537317344418186
440.4768340232796880.9536680465593760.523165976720312
450.5119601398705390.9760797202589220.488039860129461
460.4819134153886250.963826830777250.518086584611375
470.4301833998693280.8603667997386570.569816600130672
480.4105059880800320.8210119761600650.589494011919968
490.3640603491651860.7281206983303720.635939650834814
500.3153789709812120.6307579419624250.684621029018788
510.2810062765528480.5620125531056970.718993723447152
520.2436979676950390.4873959353900780.756302032304961
530.2059286448914250.4118572897828490.794071355108575
540.1969901064771490.3939802129542980.803009893522851
550.1667484432833810.3334968865667610.833251556716619
560.1461745278169150.2923490556338310.853825472183085
570.1235777670167480.2471555340334970.876422232983252
580.1121248757842650.2242497515685290.887875124215735
590.0928685255888120.1857370511776240.907131474411188
600.09423732263970830.1884746452794170.905762677360292
610.2104962685327670.4209925370655350.789503731467233
620.580670165977590.8386596680448190.41932983402241
630.568580911355040.862838177289920.43141908864496
640.5356954106533510.9286091786932970.464304589346649
650.5540132875050220.8919734249899570.445986712494978
660.564296191886110.871407616227780.43570380811389
670.5737053453885440.8525893092229110.426294654611456
680.6079647180886640.7840705638226710.392035281911335
690.5895156063261430.8209687873477140.410484393673857
700.5709448848581850.8581102302836310.429055115141815
710.5464133638032210.9071732723935570.453586636196779
720.5243781463486070.9512437073027860.475621853651393
730.4810342010976120.9620684021952240.518965798902388
740.4341038973367980.8682077946735960.565896102663202
750.3963015592963680.7926031185927360.603698440703632
760.3822406197149430.7644812394298870.617759380285057
770.3407003245783530.6814006491567050.659299675421647
780.2961691368011110.5923382736022230.703830863198889
790.2575899952378760.5151799904757510.742410004762124
800.2196728766768480.4393457533536950.780327123323152
810.1910099938800530.3820199877601060.808990006119947
820.2206727402527530.4413454805055060.779327259747247
830.1911096989202350.382219397840470.808890301079765
840.1811199968512780.3622399937025560.818880003148722
850.1719986634356790.3439973268713570.828001336564321
860.1749567280751550.3499134561503110.825043271924845
870.1985020717893030.3970041435786060.801497928210697
880.198867558793530.397735117587060.80113244120647
890.2381148005095740.4762296010191480.761885199490426
900.2223724482555940.4447448965111890.777627551744406
910.2029463720083520.4058927440167050.797053627991648
920.2474958298508510.4949916597017020.752504170149149
930.2242676133349860.4485352266699720.775732386665014
940.196203729888120.392407459776240.80379627011188
950.1708243162992260.3416486325984520.829175683700774
960.1564667382409770.3129334764819530.843533261759023
970.1329439777429590.2658879554859180.867056022257041
980.1123432111558540.2246864223117090.887656788844146
990.1023707576109830.2047415152219650.897629242389017
1000.08360701861807460.1672140372361490.916392981381925
1010.06856742067489920.1371348413497980.931432579325101
1020.0595631960703380.1191263921406760.940436803929662
1030.04851390535710250.0970278107142050.951486094642897
1040.07062894630864520.141257892617290.929371053691355
1050.05399057696739080.1079811539347820.946009423032609
1060.04972274001546110.09944548003092230.950277259984539
1070.04444155734735460.08888311469470920.955558442652645
1080.6782970311006620.6434059377986760.321702968899338
1090.6379610133307220.7240779733385570.362038986669278
1100.6156475331436480.7687049337127040.384352466856352
1110.9191783228033480.1616433543933050.0808216771966524
1120.9244823301070010.1510353397859980.0755176698929988
1130.9663233514764910.06735329704701910.0336766485235095
1140.9736171278930680.05276574421386450.0263828721069322
1150.9704314813892620.0591370372214760.029568518610738
1160.9708171760275540.05836564794489140.0291828239724457
1170.9584838479233350.08303230415333070.0415161520766653
1180.9918227521492850.0163544957014310.00817724785071551
1190.9978625553941840.004274889211632860.00213744460581643
1200.9985087516488330.002982496702334690.00149124835116734
1210.9990897146427870.00182057071442680.000910285357213402
1220.9978838594136160.004232281172767960.00211614058638398
1230.9955091806173970.008981638765206580.00449081938260329
1240.9989388402250750.002122319549850050.00106115977492502
1250.9986272543635690.00274549127286280.0013727456364314
1260.9980817843261520.003836431347695380.00191821567384769
1270.9966541908710580.006691618257884220.00334580912894211
1280.9899352524960.02012949500799930.0100647475039997
1290.9756865571455440.04862688570891170.0243134428544559
1300.9399953174728460.1200093650543070.0600046825271537
1310.8528366808613190.2943266382773630.147163319138681

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.605112222348186 & 0.789775555303628 & 0.394887777651814 \tabularnewline
10 & 0.615549992526426 & 0.768900014947149 & 0.384450007473574 \tabularnewline
11 & 0.582011470608777 & 0.835977058782447 & 0.417988529391223 \tabularnewline
12 & 0.652542722806979 & 0.694914554386043 & 0.347457277193021 \tabularnewline
13 & 0.730060960352867 & 0.539878079294266 & 0.269939039647133 \tabularnewline
14 & 0.850209603698769 & 0.299580792602463 & 0.149790396301231 \tabularnewline
15 & 0.846272003655314 & 0.307455992689372 & 0.153727996344686 \tabularnewline
16 & 0.789671836556039 & 0.420656326887923 & 0.210328163443961 \tabularnewline
17 & 0.83224767966273 & 0.33550464067454 & 0.16775232033727 \tabularnewline
18 & 0.830625450071523 & 0.338749099856954 & 0.169374549928477 \tabularnewline
19 & 0.784089715303321 & 0.431820569393357 & 0.215910284696679 \tabularnewline
20 & 0.751710870301933 & 0.496578259396135 & 0.248289129698067 \tabularnewline
21 & 0.755193840430447 & 0.489612319139106 & 0.244806159569553 \tabularnewline
22 & 0.848420818791503 & 0.303158362416994 & 0.151579181208497 \tabularnewline
23 & 0.866787517763915 & 0.26642496447217 & 0.133212482236085 \tabularnewline
24 & 0.837912764061168 & 0.324174471877664 & 0.162087235938832 \tabularnewline
25 & 0.799581819407848 & 0.400836361184305 & 0.200418180592152 \tabularnewline
26 & 0.749400465748426 & 0.501199068503147 & 0.250599534251574 \tabularnewline
27 & 0.693825561654832 & 0.612348876690335 & 0.306174438345168 \tabularnewline
28 & 0.633517638363248 & 0.732964723273503 & 0.366482361636752 \tabularnewline
29 & 0.641964433532484 & 0.716071132935033 & 0.358035566467516 \tabularnewline
30 & 0.583895042472757 & 0.832209915054485 & 0.416104957527243 \tabularnewline
31 & 0.557233669160484 & 0.885532661679032 & 0.442766330839516 \tabularnewline
32 & 0.674965714403543 & 0.650068571192915 & 0.325034285596457 \tabularnewline
33 & 0.628164387760795 & 0.74367122447841 & 0.371835612239205 \tabularnewline
34 & 0.640582094708753 & 0.718835810582493 & 0.359417905291247 \tabularnewline
35 & 0.623712378880274 & 0.752575242239451 & 0.376287621119726 \tabularnewline
36 & 0.643421925634883 & 0.713156148730234 & 0.356578074365117 \tabularnewline
37 & 0.622279576558374 & 0.755440846883253 & 0.377720423441627 \tabularnewline
38 & 0.592564915411977 & 0.814870169176046 & 0.407435084588023 \tabularnewline
39 & 0.549525849223909 & 0.900948301552182 & 0.450474150776091 \tabularnewline
40 & 0.54369913359134 & 0.912601732817319 & 0.45630086640866 \tabularnewline
41 & 0.540115554378951 & 0.919768891242098 & 0.459884445621049 \tabularnewline
42 & 0.508215562263323 & 0.983568875473355 & 0.491784437736677 \tabularnewline
43 & 0.462682655581814 & 0.925365311163629 & 0.537317344418186 \tabularnewline
44 & 0.476834023279688 & 0.953668046559376 & 0.523165976720312 \tabularnewline
45 & 0.511960139870539 & 0.976079720258922 & 0.488039860129461 \tabularnewline
46 & 0.481913415388625 & 0.96382683077725 & 0.518086584611375 \tabularnewline
47 & 0.430183399869328 & 0.860366799738657 & 0.569816600130672 \tabularnewline
48 & 0.410505988080032 & 0.821011976160065 & 0.589494011919968 \tabularnewline
49 & 0.364060349165186 & 0.728120698330372 & 0.635939650834814 \tabularnewline
50 & 0.315378970981212 & 0.630757941962425 & 0.684621029018788 \tabularnewline
51 & 0.281006276552848 & 0.562012553105697 & 0.718993723447152 \tabularnewline
52 & 0.243697967695039 & 0.487395935390078 & 0.756302032304961 \tabularnewline
53 & 0.205928644891425 & 0.411857289782849 & 0.794071355108575 \tabularnewline
54 & 0.196990106477149 & 0.393980212954298 & 0.803009893522851 \tabularnewline
55 & 0.166748443283381 & 0.333496886566761 & 0.833251556716619 \tabularnewline
56 & 0.146174527816915 & 0.292349055633831 & 0.853825472183085 \tabularnewline
57 & 0.123577767016748 & 0.247155534033497 & 0.876422232983252 \tabularnewline
58 & 0.112124875784265 & 0.224249751568529 & 0.887875124215735 \tabularnewline
59 & 0.092868525588812 & 0.185737051177624 & 0.907131474411188 \tabularnewline
60 & 0.0942373226397083 & 0.188474645279417 & 0.905762677360292 \tabularnewline
61 & 0.210496268532767 & 0.420992537065535 & 0.789503731467233 \tabularnewline
62 & 0.58067016597759 & 0.838659668044819 & 0.41932983402241 \tabularnewline
63 & 0.56858091135504 & 0.86283817728992 & 0.43141908864496 \tabularnewline
64 & 0.535695410653351 & 0.928609178693297 & 0.464304589346649 \tabularnewline
65 & 0.554013287505022 & 0.891973424989957 & 0.445986712494978 \tabularnewline
66 & 0.56429619188611 & 0.87140761622778 & 0.43570380811389 \tabularnewline
67 & 0.573705345388544 & 0.852589309222911 & 0.426294654611456 \tabularnewline
68 & 0.607964718088664 & 0.784070563822671 & 0.392035281911335 \tabularnewline
69 & 0.589515606326143 & 0.820968787347714 & 0.410484393673857 \tabularnewline
70 & 0.570944884858185 & 0.858110230283631 & 0.429055115141815 \tabularnewline
71 & 0.546413363803221 & 0.907173272393557 & 0.453586636196779 \tabularnewline
72 & 0.524378146348607 & 0.951243707302786 & 0.475621853651393 \tabularnewline
73 & 0.481034201097612 & 0.962068402195224 & 0.518965798902388 \tabularnewline
74 & 0.434103897336798 & 0.868207794673596 & 0.565896102663202 \tabularnewline
75 & 0.396301559296368 & 0.792603118592736 & 0.603698440703632 \tabularnewline
76 & 0.382240619714943 & 0.764481239429887 & 0.617759380285057 \tabularnewline
77 & 0.340700324578353 & 0.681400649156705 & 0.659299675421647 \tabularnewline
78 & 0.296169136801111 & 0.592338273602223 & 0.703830863198889 \tabularnewline
79 & 0.257589995237876 & 0.515179990475751 & 0.742410004762124 \tabularnewline
80 & 0.219672876676848 & 0.439345753353695 & 0.780327123323152 \tabularnewline
81 & 0.191009993880053 & 0.382019987760106 & 0.808990006119947 \tabularnewline
82 & 0.220672740252753 & 0.441345480505506 & 0.779327259747247 \tabularnewline
83 & 0.191109698920235 & 0.38221939784047 & 0.808890301079765 \tabularnewline
84 & 0.181119996851278 & 0.362239993702556 & 0.818880003148722 \tabularnewline
85 & 0.171998663435679 & 0.343997326871357 & 0.828001336564321 \tabularnewline
86 & 0.174956728075155 & 0.349913456150311 & 0.825043271924845 \tabularnewline
87 & 0.198502071789303 & 0.397004143578606 & 0.801497928210697 \tabularnewline
88 & 0.19886755879353 & 0.39773511758706 & 0.80113244120647 \tabularnewline
89 & 0.238114800509574 & 0.476229601019148 & 0.761885199490426 \tabularnewline
90 & 0.222372448255594 & 0.444744896511189 & 0.777627551744406 \tabularnewline
91 & 0.202946372008352 & 0.405892744016705 & 0.797053627991648 \tabularnewline
92 & 0.247495829850851 & 0.494991659701702 & 0.752504170149149 \tabularnewline
93 & 0.224267613334986 & 0.448535226669972 & 0.775732386665014 \tabularnewline
94 & 0.19620372988812 & 0.39240745977624 & 0.80379627011188 \tabularnewline
95 & 0.170824316299226 & 0.341648632598452 & 0.829175683700774 \tabularnewline
96 & 0.156466738240977 & 0.312933476481953 & 0.843533261759023 \tabularnewline
97 & 0.132943977742959 & 0.265887955485918 & 0.867056022257041 \tabularnewline
98 & 0.112343211155854 & 0.224686422311709 & 0.887656788844146 \tabularnewline
99 & 0.102370757610983 & 0.204741515221965 & 0.897629242389017 \tabularnewline
100 & 0.0836070186180746 & 0.167214037236149 & 0.916392981381925 \tabularnewline
101 & 0.0685674206748992 & 0.137134841349798 & 0.931432579325101 \tabularnewline
102 & 0.059563196070338 & 0.119126392140676 & 0.940436803929662 \tabularnewline
103 & 0.0485139053571025 & 0.097027810714205 & 0.951486094642897 \tabularnewline
104 & 0.0706289463086452 & 0.14125789261729 & 0.929371053691355 \tabularnewline
105 & 0.0539905769673908 & 0.107981153934782 & 0.946009423032609 \tabularnewline
106 & 0.0497227400154611 & 0.0994454800309223 & 0.950277259984539 \tabularnewline
107 & 0.0444415573473546 & 0.0888831146947092 & 0.955558442652645 \tabularnewline
108 & 0.678297031100662 & 0.643405937798676 & 0.321702968899338 \tabularnewline
109 & 0.637961013330722 & 0.724077973338557 & 0.362038986669278 \tabularnewline
110 & 0.615647533143648 & 0.768704933712704 & 0.384352466856352 \tabularnewline
111 & 0.919178322803348 & 0.161643354393305 & 0.0808216771966524 \tabularnewline
112 & 0.924482330107001 & 0.151035339785998 & 0.0755176698929988 \tabularnewline
113 & 0.966323351476491 & 0.0673532970470191 & 0.0336766485235095 \tabularnewline
114 & 0.973617127893068 & 0.0527657442138645 & 0.0263828721069322 \tabularnewline
115 & 0.970431481389262 & 0.059137037221476 & 0.029568518610738 \tabularnewline
116 & 0.970817176027554 & 0.0583656479448914 & 0.0291828239724457 \tabularnewline
117 & 0.958483847923335 & 0.0830323041533307 & 0.0415161520766653 \tabularnewline
118 & 0.991822752149285 & 0.016354495701431 & 0.00817724785071551 \tabularnewline
119 & 0.997862555394184 & 0.00427488921163286 & 0.00213744460581643 \tabularnewline
120 & 0.998508751648833 & 0.00298249670233469 & 0.00149124835116734 \tabularnewline
121 & 0.999089714642787 & 0.0018205707144268 & 0.000910285357213402 \tabularnewline
122 & 0.997883859413616 & 0.00423228117276796 & 0.00211614058638398 \tabularnewline
123 & 0.995509180617397 & 0.00898163876520658 & 0.00449081938260329 \tabularnewline
124 & 0.998938840225075 & 0.00212231954985005 & 0.00106115977492502 \tabularnewline
125 & 0.998627254363569 & 0.0027454912728628 & 0.0013727456364314 \tabularnewline
126 & 0.998081784326152 & 0.00383643134769538 & 0.00191821567384769 \tabularnewline
127 & 0.996654190871058 & 0.00669161825788422 & 0.00334580912894211 \tabularnewline
128 & 0.989935252496 & 0.0201294950079993 & 0.0100647475039997 \tabularnewline
129 & 0.975686557145544 & 0.0486268857089117 & 0.0243134428544559 \tabularnewline
130 & 0.939995317472846 & 0.120009365054307 & 0.0600046825271537 \tabularnewline
131 & 0.852836680861319 & 0.294326638277363 & 0.147163319138681 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&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]0.605112222348186[/C][C]0.789775555303628[/C][C]0.394887777651814[/C][/ROW]
[ROW][C]10[/C][C]0.615549992526426[/C][C]0.768900014947149[/C][C]0.384450007473574[/C][/ROW]
[ROW][C]11[/C][C]0.582011470608777[/C][C]0.835977058782447[/C][C]0.417988529391223[/C][/ROW]
[ROW][C]12[/C][C]0.652542722806979[/C][C]0.694914554386043[/C][C]0.347457277193021[/C][/ROW]
[ROW][C]13[/C][C]0.730060960352867[/C][C]0.539878079294266[/C][C]0.269939039647133[/C][/ROW]
[ROW][C]14[/C][C]0.850209603698769[/C][C]0.299580792602463[/C][C]0.149790396301231[/C][/ROW]
[ROW][C]15[/C][C]0.846272003655314[/C][C]0.307455992689372[/C][C]0.153727996344686[/C][/ROW]
[ROW][C]16[/C][C]0.789671836556039[/C][C]0.420656326887923[/C][C]0.210328163443961[/C][/ROW]
[ROW][C]17[/C][C]0.83224767966273[/C][C]0.33550464067454[/C][C]0.16775232033727[/C][/ROW]
[ROW][C]18[/C][C]0.830625450071523[/C][C]0.338749099856954[/C][C]0.169374549928477[/C][/ROW]
[ROW][C]19[/C][C]0.784089715303321[/C][C]0.431820569393357[/C][C]0.215910284696679[/C][/ROW]
[ROW][C]20[/C][C]0.751710870301933[/C][C]0.496578259396135[/C][C]0.248289129698067[/C][/ROW]
[ROW][C]21[/C][C]0.755193840430447[/C][C]0.489612319139106[/C][C]0.244806159569553[/C][/ROW]
[ROW][C]22[/C][C]0.848420818791503[/C][C]0.303158362416994[/C][C]0.151579181208497[/C][/ROW]
[ROW][C]23[/C][C]0.866787517763915[/C][C]0.26642496447217[/C][C]0.133212482236085[/C][/ROW]
[ROW][C]24[/C][C]0.837912764061168[/C][C]0.324174471877664[/C][C]0.162087235938832[/C][/ROW]
[ROW][C]25[/C][C]0.799581819407848[/C][C]0.400836361184305[/C][C]0.200418180592152[/C][/ROW]
[ROW][C]26[/C][C]0.749400465748426[/C][C]0.501199068503147[/C][C]0.250599534251574[/C][/ROW]
[ROW][C]27[/C][C]0.693825561654832[/C][C]0.612348876690335[/C][C]0.306174438345168[/C][/ROW]
[ROW][C]28[/C][C]0.633517638363248[/C][C]0.732964723273503[/C][C]0.366482361636752[/C][/ROW]
[ROW][C]29[/C][C]0.641964433532484[/C][C]0.716071132935033[/C][C]0.358035566467516[/C][/ROW]
[ROW][C]30[/C][C]0.583895042472757[/C][C]0.832209915054485[/C][C]0.416104957527243[/C][/ROW]
[ROW][C]31[/C][C]0.557233669160484[/C][C]0.885532661679032[/C][C]0.442766330839516[/C][/ROW]
[ROW][C]32[/C][C]0.674965714403543[/C][C]0.650068571192915[/C][C]0.325034285596457[/C][/ROW]
[ROW][C]33[/C][C]0.628164387760795[/C][C]0.74367122447841[/C][C]0.371835612239205[/C][/ROW]
[ROW][C]34[/C][C]0.640582094708753[/C][C]0.718835810582493[/C][C]0.359417905291247[/C][/ROW]
[ROW][C]35[/C][C]0.623712378880274[/C][C]0.752575242239451[/C][C]0.376287621119726[/C][/ROW]
[ROW][C]36[/C][C]0.643421925634883[/C][C]0.713156148730234[/C][C]0.356578074365117[/C][/ROW]
[ROW][C]37[/C][C]0.622279576558374[/C][C]0.755440846883253[/C][C]0.377720423441627[/C][/ROW]
[ROW][C]38[/C][C]0.592564915411977[/C][C]0.814870169176046[/C][C]0.407435084588023[/C][/ROW]
[ROW][C]39[/C][C]0.549525849223909[/C][C]0.900948301552182[/C][C]0.450474150776091[/C][/ROW]
[ROW][C]40[/C][C]0.54369913359134[/C][C]0.912601732817319[/C][C]0.45630086640866[/C][/ROW]
[ROW][C]41[/C][C]0.540115554378951[/C][C]0.919768891242098[/C][C]0.459884445621049[/C][/ROW]
[ROW][C]42[/C][C]0.508215562263323[/C][C]0.983568875473355[/C][C]0.491784437736677[/C][/ROW]
[ROW][C]43[/C][C]0.462682655581814[/C][C]0.925365311163629[/C][C]0.537317344418186[/C][/ROW]
[ROW][C]44[/C][C]0.476834023279688[/C][C]0.953668046559376[/C][C]0.523165976720312[/C][/ROW]
[ROW][C]45[/C][C]0.511960139870539[/C][C]0.976079720258922[/C][C]0.488039860129461[/C][/ROW]
[ROW][C]46[/C][C]0.481913415388625[/C][C]0.96382683077725[/C][C]0.518086584611375[/C][/ROW]
[ROW][C]47[/C][C]0.430183399869328[/C][C]0.860366799738657[/C][C]0.569816600130672[/C][/ROW]
[ROW][C]48[/C][C]0.410505988080032[/C][C]0.821011976160065[/C][C]0.589494011919968[/C][/ROW]
[ROW][C]49[/C][C]0.364060349165186[/C][C]0.728120698330372[/C][C]0.635939650834814[/C][/ROW]
[ROW][C]50[/C][C]0.315378970981212[/C][C]0.630757941962425[/C][C]0.684621029018788[/C][/ROW]
[ROW][C]51[/C][C]0.281006276552848[/C][C]0.562012553105697[/C][C]0.718993723447152[/C][/ROW]
[ROW][C]52[/C][C]0.243697967695039[/C][C]0.487395935390078[/C][C]0.756302032304961[/C][/ROW]
[ROW][C]53[/C][C]0.205928644891425[/C][C]0.411857289782849[/C][C]0.794071355108575[/C][/ROW]
[ROW][C]54[/C][C]0.196990106477149[/C][C]0.393980212954298[/C][C]0.803009893522851[/C][/ROW]
[ROW][C]55[/C][C]0.166748443283381[/C][C]0.333496886566761[/C][C]0.833251556716619[/C][/ROW]
[ROW][C]56[/C][C]0.146174527816915[/C][C]0.292349055633831[/C][C]0.853825472183085[/C][/ROW]
[ROW][C]57[/C][C]0.123577767016748[/C][C]0.247155534033497[/C][C]0.876422232983252[/C][/ROW]
[ROW][C]58[/C][C]0.112124875784265[/C][C]0.224249751568529[/C][C]0.887875124215735[/C][/ROW]
[ROW][C]59[/C][C]0.092868525588812[/C][C]0.185737051177624[/C][C]0.907131474411188[/C][/ROW]
[ROW][C]60[/C][C]0.0942373226397083[/C][C]0.188474645279417[/C][C]0.905762677360292[/C][/ROW]
[ROW][C]61[/C][C]0.210496268532767[/C][C]0.420992537065535[/C][C]0.789503731467233[/C][/ROW]
[ROW][C]62[/C][C]0.58067016597759[/C][C]0.838659668044819[/C][C]0.41932983402241[/C][/ROW]
[ROW][C]63[/C][C]0.56858091135504[/C][C]0.86283817728992[/C][C]0.43141908864496[/C][/ROW]
[ROW][C]64[/C][C]0.535695410653351[/C][C]0.928609178693297[/C][C]0.464304589346649[/C][/ROW]
[ROW][C]65[/C][C]0.554013287505022[/C][C]0.891973424989957[/C][C]0.445986712494978[/C][/ROW]
[ROW][C]66[/C][C]0.56429619188611[/C][C]0.87140761622778[/C][C]0.43570380811389[/C][/ROW]
[ROW][C]67[/C][C]0.573705345388544[/C][C]0.852589309222911[/C][C]0.426294654611456[/C][/ROW]
[ROW][C]68[/C][C]0.607964718088664[/C][C]0.784070563822671[/C][C]0.392035281911335[/C][/ROW]
[ROW][C]69[/C][C]0.589515606326143[/C][C]0.820968787347714[/C][C]0.410484393673857[/C][/ROW]
[ROW][C]70[/C][C]0.570944884858185[/C][C]0.858110230283631[/C][C]0.429055115141815[/C][/ROW]
[ROW][C]71[/C][C]0.546413363803221[/C][C]0.907173272393557[/C][C]0.453586636196779[/C][/ROW]
[ROW][C]72[/C][C]0.524378146348607[/C][C]0.951243707302786[/C][C]0.475621853651393[/C][/ROW]
[ROW][C]73[/C][C]0.481034201097612[/C][C]0.962068402195224[/C][C]0.518965798902388[/C][/ROW]
[ROW][C]74[/C][C]0.434103897336798[/C][C]0.868207794673596[/C][C]0.565896102663202[/C][/ROW]
[ROW][C]75[/C][C]0.396301559296368[/C][C]0.792603118592736[/C][C]0.603698440703632[/C][/ROW]
[ROW][C]76[/C][C]0.382240619714943[/C][C]0.764481239429887[/C][C]0.617759380285057[/C][/ROW]
[ROW][C]77[/C][C]0.340700324578353[/C][C]0.681400649156705[/C][C]0.659299675421647[/C][/ROW]
[ROW][C]78[/C][C]0.296169136801111[/C][C]0.592338273602223[/C][C]0.703830863198889[/C][/ROW]
[ROW][C]79[/C][C]0.257589995237876[/C][C]0.515179990475751[/C][C]0.742410004762124[/C][/ROW]
[ROW][C]80[/C][C]0.219672876676848[/C][C]0.439345753353695[/C][C]0.780327123323152[/C][/ROW]
[ROW][C]81[/C][C]0.191009993880053[/C][C]0.382019987760106[/C][C]0.808990006119947[/C][/ROW]
[ROW][C]82[/C][C]0.220672740252753[/C][C]0.441345480505506[/C][C]0.779327259747247[/C][/ROW]
[ROW][C]83[/C][C]0.191109698920235[/C][C]0.38221939784047[/C][C]0.808890301079765[/C][/ROW]
[ROW][C]84[/C][C]0.181119996851278[/C][C]0.362239993702556[/C][C]0.818880003148722[/C][/ROW]
[ROW][C]85[/C][C]0.171998663435679[/C][C]0.343997326871357[/C][C]0.828001336564321[/C][/ROW]
[ROW][C]86[/C][C]0.174956728075155[/C][C]0.349913456150311[/C][C]0.825043271924845[/C][/ROW]
[ROW][C]87[/C][C]0.198502071789303[/C][C]0.397004143578606[/C][C]0.801497928210697[/C][/ROW]
[ROW][C]88[/C][C]0.19886755879353[/C][C]0.39773511758706[/C][C]0.80113244120647[/C][/ROW]
[ROW][C]89[/C][C]0.238114800509574[/C][C]0.476229601019148[/C][C]0.761885199490426[/C][/ROW]
[ROW][C]90[/C][C]0.222372448255594[/C][C]0.444744896511189[/C][C]0.777627551744406[/C][/ROW]
[ROW][C]91[/C][C]0.202946372008352[/C][C]0.405892744016705[/C][C]0.797053627991648[/C][/ROW]
[ROW][C]92[/C][C]0.247495829850851[/C][C]0.494991659701702[/C][C]0.752504170149149[/C][/ROW]
[ROW][C]93[/C][C]0.224267613334986[/C][C]0.448535226669972[/C][C]0.775732386665014[/C][/ROW]
[ROW][C]94[/C][C]0.19620372988812[/C][C]0.39240745977624[/C][C]0.80379627011188[/C][/ROW]
[ROW][C]95[/C][C]0.170824316299226[/C][C]0.341648632598452[/C][C]0.829175683700774[/C][/ROW]
[ROW][C]96[/C][C]0.156466738240977[/C][C]0.312933476481953[/C][C]0.843533261759023[/C][/ROW]
[ROW][C]97[/C][C]0.132943977742959[/C][C]0.265887955485918[/C][C]0.867056022257041[/C][/ROW]
[ROW][C]98[/C][C]0.112343211155854[/C][C]0.224686422311709[/C][C]0.887656788844146[/C][/ROW]
[ROW][C]99[/C][C]0.102370757610983[/C][C]0.204741515221965[/C][C]0.897629242389017[/C][/ROW]
[ROW][C]100[/C][C]0.0836070186180746[/C][C]0.167214037236149[/C][C]0.916392981381925[/C][/ROW]
[ROW][C]101[/C][C]0.0685674206748992[/C][C]0.137134841349798[/C][C]0.931432579325101[/C][/ROW]
[ROW][C]102[/C][C]0.059563196070338[/C][C]0.119126392140676[/C][C]0.940436803929662[/C][/ROW]
[ROW][C]103[/C][C]0.0485139053571025[/C][C]0.097027810714205[/C][C]0.951486094642897[/C][/ROW]
[ROW][C]104[/C][C]0.0706289463086452[/C][C]0.14125789261729[/C][C]0.929371053691355[/C][/ROW]
[ROW][C]105[/C][C]0.0539905769673908[/C][C]0.107981153934782[/C][C]0.946009423032609[/C][/ROW]
[ROW][C]106[/C][C]0.0497227400154611[/C][C]0.0994454800309223[/C][C]0.950277259984539[/C][/ROW]
[ROW][C]107[/C][C]0.0444415573473546[/C][C]0.0888831146947092[/C][C]0.955558442652645[/C][/ROW]
[ROW][C]108[/C][C]0.678297031100662[/C][C]0.643405937798676[/C][C]0.321702968899338[/C][/ROW]
[ROW][C]109[/C][C]0.637961013330722[/C][C]0.724077973338557[/C][C]0.362038986669278[/C][/ROW]
[ROW][C]110[/C][C]0.615647533143648[/C][C]0.768704933712704[/C][C]0.384352466856352[/C][/ROW]
[ROW][C]111[/C][C]0.919178322803348[/C][C]0.161643354393305[/C][C]0.0808216771966524[/C][/ROW]
[ROW][C]112[/C][C]0.924482330107001[/C][C]0.151035339785998[/C][C]0.0755176698929988[/C][/ROW]
[ROW][C]113[/C][C]0.966323351476491[/C][C]0.0673532970470191[/C][C]0.0336766485235095[/C][/ROW]
[ROW][C]114[/C][C]0.973617127893068[/C][C]0.0527657442138645[/C][C]0.0263828721069322[/C][/ROW]
[ROW][C]115[/C][C]0.970431481389262[/C][C]0.059137037221476[/C][C]0.029568518610738[/C][/ROW]
[ROW][C]116[/C][C]0.970817176027554[/C][C]0.0583656479448914[/C][C]0.0291828239724457[/C][/ROW]
[ROW][C]117[/C][C]0.958483847923335[/C][C]0.0830323041533307[/C][C]0.0415161520766653[/C][/ROW]
[ROW][C]118[/C][C]0.991822752149285[/C][C]0.016354495701431[/C][C]0.00817724785071551[/C][/ROW]
[ROW][C]119[/C][C]0.997862555394184[/C][C]0.00427488921163286[/C][C]0.00213744460581643[/C][/ROW]
[ROW][C]120[/C][C]0.998508751648833[/C][C]0.00298249670233469[/C][C]0.00149124835116734[/C][/ROW]
[ROW][C]121[/C][C]0.999089714642787[/C][C]0.0018205707144268[/C][C]0.000910285357213402[/C][/ROW]
[ROW][C]122[/C][C]0.997883859413616[/C][C]0.00423228117276796[/C][C]0.00211614058638398[/C][/ROW]
[ROW][C]123[/C][C]0.995509180617397[/C][C]0.00898163876520658[/C][C]0.00449081938260329[/C][/ROW]
[ROW][C]124[/C][C]0.998938840225075[/C][C]0.00212231954985005[/C][C]0.00106115977492502[/C][/ROW]
[ROW][C]125[/C][C]0.998627254363569[/C][C]0.0027454912728628[/C][C]0.0013727456364314[/C][/ROW]
[ROW][C]126[/C][C]0.998081784326152[/C][C]0.00383643134769538[/C][C]0.00191821567384769[/C][/ROW]
[ROW][C]127[/C][C]0.996654190871058[/C][C]0.00669161825788422[/C][C]0.00334580912894211[/C][/ROW]
[ROW][C]128[/C][C]0.989935252496[/C][C]0.0201294950079993[/C][C]0.0100647475039997[/C][/ROW]
[ROW][C]129[/C][C]0.975686557145544[/C][C]0.0486268857089117[/C][C]0.0243134428544559[/C][/ROW]
[ROW][C]130[/C][C]0.939995317472846[/C][C]0.120009365054307[/C][C]0.0600046825271537[/C][/ROW]
[ROW][C]131[/C][C]0.852836680861319[/C][C]0.294326638277363[/C][C]0.147163319138681[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6051122223481860.7897755553036280.394887777651814
100.6155499925264260.7689000149471490.384450007473574
110.5820114706087770.8359770587824470.417988529391223
120.6525427228069790.6949145543860430.347457277193021
130.7300609603528670.5398780792942660.269939039647133
140.8502096036987690.2995807926024630.149790396301231
150.8462720036553140.3074559926893720.153727996344686
160.7896718365560390.4206563268879230.210328163443961
170.832247679662730.335504640674540.16775232033727
180.8306254500715230.3387490998569540.169374549928477
190.7840897153033210.4318205693933570.215910284696679
200.7517108703019330.4965782593961350.248289129698067
210.7551938404304470.4896123191391060.244806159569553
220.8484208187915030.3031583624169940.151579181208497
230.8667875177639150.266424964472170.133212482236085
240.8379127640611680.3241744718776640.162087235938832
250.7995818194078480.4008363611843050.200418180592152
260.7494004657484260.5011990685031470.250599534251574
270.6938255616548320.6123488766903350.306174438345168
280.6335176383632480.7329647232735030.366482361636752
290.6419644335324840.7160711329350330.358035566467516
300.5838950424727570.8322099150544850.416104957527243
310.5572336691604840.8855326616790320.442766330839516
320.6749657144035430.6500685711929150.325034285596457
330.6281643877607950.743671224478410.371835612239205
340.6405820947087530.7188358105824930.359417905291247
350.6237123788802740.7525752422394510.376287621119726
360.6434219256348830.7131561487302340.356578074365117
370.6222795765583740.7554408468832530.377720423441627
380.5925649154119770.8148701691760460.407435084588023
390.5495258492239090.9009483015521820.450474150776091
400.543699133591340.9126017328173190.45630086640866
410.5401155543789510.9197688912420980.459884445621049
420.5082155622633230.9835688754733550.491784437736677
430.4626826555818140.9253653111636290.537317344418186
440.4768340232796880.9536680465593760.523165976720312
450.5119601398705390.9760797202589220.488039860129461
460.4819134153886250.963826830777250.518086584611375
470.4301833998693280.8603667997386570.569816600130672
480.4105059880800320.8210119761600650.589494011919968
490.3640603491651860.7281206983303720.635939650834814
500.3153789709812120.6307579419624250.684621029018788
510.2810062765528480.5620125531056970.718993723447152
520.2436979676950390.4873959353900780.756302032304961
530.2059286448914250.4118572897828490.794071355108575
540.1969901064771490.3939802129542980.803009893522851
550.1667484432833810.3334968865667610.833251556716619
560.1461745278169150.2923490556338310.853825472183085
570.1235777670167480.2471555340334970.876422232983252
580.1121248757842650.2242497515685290.887875124215735
590.0928685255888120.1857370511776240.907131474411188
600.09423732263970830.1884746452794170.905762677360292
610.2104962685327670.4209925370655350.789503731467233
620.580670165977590.8386596680448190.41932983402241
630.568580911355040.862838177289920.43141908864496
640.5356954106533510.9286091786932970.464304589346649
650.5540132875050220.8919734249899570.445986712494978
660.564296191886110.871407616227780.43570380811389
670.5737053453885440.8525893092229110.426294654611456
680.6079647180886640.7840705638226710.392035281911335
690.5895156063261430.8209687873477140.410484393673857
700.5709448848581850.8581102302836310.429055115141815
710.5464133638032210.9071732723935570.453586636196779
720.5243781463486070.9512437073027860.475621853651393
730.4810342010976120.9620684021952240.518965798902388
740.4341038973367980.8682077946735960.565896102663202
750.3963015592963680.7926031185927360.603698440703632
760.3822406197149430.7644812394298870.617759380285057
770.3407003245783530.6814006491567050.659299675421647
780.2961691368011110.5923382736022230.703830863198889
790.2575899952378760.5151799904757510.742410004762124
800.2196728766768480.4393457533536950.780327123323152
810.1910099938800530.3820199877601060.808990006119947
820.2206727402527530.4413454805055060.779327259747247
830.1911096989202350.382219397840470.808890301079765
840.1811199968512780.3622399937025560.818880003148722
850.1719986634356790.3439973268713570.828001336564321
860.1749567280751550.3499134561503110.825043271924845
870.1985020717893030.3970041435786060.801497928210697
880.198867558793530.397735117587060.80113244120647
890.2381148005095740.4762296010191480.761885199490426
900.2223724482555940.4447448965111890.777627551744406
910.2029463720083520.4058927440167050.797053627991648
920.2474958298508510.4949916597017020.752504170149149
930.2242676133349860.4485352266699720.775732386665014
940.196203729888120.392407459776240.80379627011188
950.1708243162992260.3416486325984520.829175683700774
960.1564667382409770.3129334764819530.843533261759023
970.1329439777429590.2658879554859180.867056022257041
980.1123432111558540.2246864223117090.887656788844146
990.1023707576109830.2047415152219650.897629242389017
1000.08360701861807460.1672140372361490.916392981381925
1010.06856742067489920.1371348413497980.931432579325101
1020.0595631960703380.1191263921406760.940436803929662
1030.04851390535710250.0970278107142050.951486094642897
1040.07062894630864520.141257892617290.929371053691355
1050.05399057696739080.1079811539347820.946009423032609
1060.04972274001546110.09944548003092230.950277259984539
1070.04444155734735460.08888311469470920.955558442652645
1080.6782970311006620.6434059377986760.321702968899338
1090.6379610133307220.7240779733385570.362038986669278
1100.6156475331436480.7687049337127040.384352466856352
1110.9191783228033480.1616433543933050.0808216771966524
1120.9244823301070010.1510353397859980.0755176698929988
1130.9663233514764910.06735329704701910.0336766485235095
1140.9736171278930680.05276574421386450.0263828721069322
1150.9704314813892620.0591370372214760.029568518610738
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1170.9584838479233350.08303230415333070.0415161520766653
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1200.9985087516488330.002982496702334690.00149124835116734
1210.9990897146427870.00182057071442680.000910285357213402
1220.9978838594136160.004232281172767960.00211614058638398
1230.9955091806173970.008981638765206580.00449081938260329
1240.9989388402250750.002122319549850050.00106115977492502
1250.9986272543635690.00274549127286280.0013727456364314
1260.9980817843261520.003836431347695380.00191821567384769
1270.9966541908710580.006691618257884220.00334580912894211
1280.9899352524960.02012949500799930.0100647475039997
1290.9756865571455440.04862688570891170.0243134428544559
1300.9399953174728460.1200093650543070.0600046825271537
1310.8528366808613190.2943266382773630.147163319138681






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.0731707317073171NOK
5% type I error level120.0975609756097561NOK
10% type I error level200.16260162601626NOK

\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 & 9 & 0.0731707317073171 & NOK \tabularnewline
5% type I error level & 12 & 0.0975609756097561 & NOK \tabularnewline
10% type I error level & 20 & 0.16260162601626 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159224&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]9[/C][C]0.0731707317073171[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.0975609756097561[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.16260162601626[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159224&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159224&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.0731707317073171NOK
5% type I error level120.0975609756097561NOK
10% type I error level200.16260162601626NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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')
}