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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 08:49:08 -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/2012/Nov/05/t1352123421kg0vc7vh6l5quu2.htm/, Retrieved Sun, 05 Feb 2023 23:32:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186058, Retrieved Sun, 05 Feb 2023 23:32:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
30/09/2000	501	134	368	6.7	8.5	8.7
31/10/2000	485	124	361	6.8	8.4	8.6
30/11/2000	464	113	351	6.7	8.4	8.6
31/12/2000	460	109	351	6.6	8.3	8.5
31/01/2001	467	109	358	6.4	8.2	8.5
28/02/2001	460	106	354	6.3	8.2	8.5
31/03/2001	448	101	347	6.3	8.1	8.5
30/04/2001	443	98	345	6.5	8.1	8.5
31/05/2001	436	93	343	6.5	8.1	8.5
30/06/2001	431	91	340	6.4	8.1	8.5
31/07/2001	484	122	362	6.2	8.1	8.5
31/08/2001	510	139	370	6.2	8.1	8.6
30/09/2001	513	140	373	6.5	8.1	8.6
31/10/2001	503	132	371	7	8.2	8.6
30/11/2001	471	117	354	7.2	8.2	8.7
31/12/2001	471	114	357	7.3	8.3	8.7
31/01/2002	476	113	363	7.4	8.2	8.7
28/02/2002	475	110	364	7.4	8.3	8.8
31/03/2002	470	107	363	7.4	8.3	8.8
30/04/2002	461	103	358	7.3	8.4	8.9
31/05/2002	455	98	357	7.4	8.5	8.9
30/06/2002	456	98	357	7.4	8.5	8.9
31/07/2002	517	137	380	7.6	8.6	9
31/08/2002	525	148	378	7.6	8.6	9
30/09/2002	523	147	376	7.7	8.7	9
31/10/2002	519	139	380	7.7	8.7	9
30/11/2002	509	130	379	7.8	8.8	9
31/12/2002	512	128	384	7.8	8.8	9
31/01/2003	519	127	392	8	8.9	9.1
28/02/2003	517	123	394	8.1	9	9.1
31/03/2003	510	118	392	8.1	9	9.1
30/04/2003	509	114	396	8.2	9	9.1
31/05/2003	501	108	392	8.1	9	9.1
30/06/2003	507	111	396	8.1	9.1	9.1
31/07/2003	569	151	419	8.1	9.1	9.1
31/08/2003	580	159	421	8.1	9	9.1
30/09/2003	578	158	420	8.2	9.1	9.1
31/10/2003	565	148	418	8.2	9	9.1
30/11/2003	547	138	410	8.3	9.1	9.1
31/12/2003	555	137	418	8.4	9.1	9.2
31/01/2004	562	136	426	8.6	9.2	9.3
29/02/2004	561	133	428	8.6	9.2	9.3
31/03/2004	555	126	430	8.4	9.2	9.3
30/04/2004	544	120	424	8	9.2	9.2
31/05/2004	537	114	423	7.9	9.2	9.2
30/06/2004	543	116	427	8.1	9.3	9.2
31/07/2004	594	153	441	8.5	9.3	9.2
31/08/2004	611	162	449	8.8	9.3	9.2
30/09/2004	613	161	452	8.8	9.3	9.2
31/10/2004	611	149	462	8.5	9.3	9.2
30/11/2004	594	139	455	8.3	9.4	9.2
31/12/2004	595	135	461	8.3	9.4	9.2
31/01/2005	591	130	461	8.3	9.3	9.2
28/02/2005	589	127	463	8.4	9.3	9.2
31/03/2005	584	122	462	8.5	9.3	9.2
30/04/2005	573	117	456	8.5	9.3	9.2
31/05/2005	567	112	455	8.6	9.2	9.1
30/06/2005	569	113	456	8.5	9.2	9.1
31/07/2005	621	149	472	8.6	9.2	9
31/08/2005	629	157	472	8.6	9.1	8.9
30/09/2005	628	157	471	8.6	9.1	8.9
31/10/2005	612	147	465	8.5	9.1	9
30/11/2005	595	137	459	8.4	9.1	8.9
31/12/2005	597	132	465	8.4	9	8.8
31/01/2006	593	125	468	8.5	8.9	8.7
28/02/2006	590	123	467	8.5	8.8	8.6
31/03/2006	580	117	463	8.5	8.7	8.5
30/04/2006	574	114	460	8.6	8.6	8.5
31/05/2006	573	111	462	8.6	8.6	8.4
30/06/2006	573	112	461	8.4	8.5	8.3
31/07/2006	620	144	476	8.2	8.4	8.2
31/08/2006	626	150	476	8	8.4	8.2
30/09/2006	620	149	471	8	8.3	8.1
31/10/2006	588	134	453	8	8.2	8
30/11/2006	566	123	443	8	8.2	7.9
31/12/2006	557	116	442	7.9	8	7.8
31/01/2007	561	117	444	7.9	7.9	7.6
28/02/2007	549	111	438	7.9	7.8	7.5
31/03/2007	532	105	427	7.9	7.7	7.4
30/04/2007	526	102	424	8	7.6	7.3
31/05/2007	511	95	416	7.9	7.6	7.3
30/06/2007	499	93	406	7.4	7.6	7.2
31/07/2007	555	124	431	7.2	7.6	7.2
31/08/2007	565	130	434	7	7.6	7.2
30/09/2007	542	124	418	6.9	7.5	7.1
31/10/2007	527	115	412	7.1	7.5	7
30/11/2007	510	106	404	7.2	7.4	7
31/12/2007	514	105	409	7.2	7.4	6.9
31/01/2008	517	105	412	7.1	7.4	6.9
29/02/2008	508	101	406	6.9	7.3	6.8
31/03/2008	493	95	398	6.8	7.3	6.8
30/04/2008	490	93	397	6.8	7.4	6.8
31/05/2008	469	84	385	6.8	7.5	6.9
30/06/2008	478	87	390	6.9	7.6	7
31/07/2008	528	116	413	7.1	7.6	7
31/08/2008	534	120	413	7.2	7.7	7.1
30/09/2008	518	117	401	7.2	7.7	7.2
31/10/2008	506	109	397	7.1	7.9	7.3
30/11/2008	502	105	397	7.1	8.1	7.5
31/12/2008	516	107	409	7.2	8.4	7.7
31/01/2009	528	109	419	7.5	8.7	8.1
28/02/2009	533	109	424	7.7	9	8.4
31/03/2009	536	108	428	7.8	9.3	8.6
30/04/2009	537	107	430	7.7	9.4	8.8
31/05/2009	524	99	424	7.7	9.5	8.9
30/06/2009	536	103	433	7.8	9.6	9.1
31/07/2009	587	131	456	8	9.8	9.2
31/08/2009	597	137	459	8.1	9.8	9.3
30/09/2009	581	135	446	8.1	9.9	9.4
31/10/2009	564	124	441	8	10	9.4
30/11/2009	558	118	439	8.1	10	9.5
31/12/2009	575	121	454	8.2	10.1	9.5
31/01/2010	580	121	460	8.4	10.1	9.7
28/02/2010	575	118	457	8.5	10.1	9.7
31/03/2010	563	113	451	8.5	10.1	9.7
30/04/2010	552	107	444	8.5	10.2	9.7
31/05/2010	537	100	437	8.5	10.2	9.7
30/06/2010	545	102	443	8.5	10.1	9.6
31/07/2010	601	130	471	8.4	10.1	9.6
31/08/2010	604	136	469	8.3	10.1	9.6
30/09/2010	586	133	454	8.2	10.1	9.6
31/10/2010	564	120	444	8.1	10.1	9.6
30/11/2010	549	112	436	7.9	10.1	9.6
31/12/2010	551	109	442	7.6	10.1	9.6
31/01/2011	556	110	446	7.3	10	9.5
28/02/2011	548	106	442	7.1	9.9	9.5
31/03/2011	540	102	438	7	9.9	9.4
30/04/2011	531	98	433	7.1	9.9	9.4
31/05/2011	521	92	428	7.1	9.9	9.5
30/06/2011	519	92	426	7.1	10	9.5
31/07/2011	572	120	452	7.3	10.1	9.6
31/08/2011	581	127	455	7.3	10.2	9.7
30/09/2011	563	124	439	7.3	10.3	9.8
31/10/2011	548	114	434	7.2	10.5	9.9
30/11/2011	539	108	431	7.2	10.6	10
31/12/2011	541	106	435	7.1	10.7	10
31/01/2012	562	111	450	7.1	10.8	10.1
29/02/2012	559	110	449	7.1	10.9	10.2
31/03/2012	546	104	442	7.2	11	10.3
30/04/2012	536	100	437	7.3	11.2	10.3
31/05/2012	528	96	431	7.4	11.3	10.4
30/06/2012	530	98	433	7.4	11.4	10.5
31/07/2012	582	122	460	7.5	11.5	10.5
31/08/2012	599	134	465	7.4	11.5	10.6
30/09/2012	584	133	451	7.4	11.6	10.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.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=186058&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.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=186058&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.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
Totaal[t] = + 1.2493258273714 -1.50811131221509maand[t] + 0.993701290329437jongerdan25jaar[t] + 1.00184142927099vanaf25jaar[t] -0.120094481746231`Belgi\353`[t] -0.0961650138034609Eurogebied[t] + 0.0579016126729738`eu27\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  +  1.2493258273714 -1.50811131221509maand[t] +  0.993701290329437jongerdan25jaar[t] +  1.00184142927099vanaf25jaar[t] -0.120094481746231`Belgi\353`[t] -0.0961650138034609Eurogebied[t] +  0.0579016126729738`eu27\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186058&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  +  1.2493258273714 -1.50811131221509maand[t] +  0.993701290329437jongerdan25jaar[t] +  1.00184142927099vanaf25jaar[t] -0.120094481746231`Belgi\353`[t] -0.0961650138034609Eurogebied[t] +  0.0579016126729738`eu27\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186058&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = + 1.2493258273714 -1.50811131221509maand[t] + 0.993701290329437jongerdan25jaar[t] + 1.00184142927099vanaf25jaar[t] -0.120094481746231`Belgi\353`[t] -0.0961650138034609Eurogebied[t] + 0.0579016126729738`eu27\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.24932582737140.6549231.90760.0585220.029261
maand-1.5081113122150911.405643-0.13220.8949990.447499
jongerdan25jaar0.9937012903294370.003313299.922100
vanaf25jaar1.001841429270990.002804357.313500
`Belgi\353`-0.1200944817462310.108289-1.1090.269350.134675
Eurogebied-0.09616501380346090.208858-0.46040.6459310.322966
`eu27\r`0.05790161267297380.213170.27160.7863190.393159

\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) & 1.2493258273714 & 0.654923 & 1.9076 & 0.058522 & 0.029261 \tabularnewline
maand & -1.50811131221509 & 11.405643 & -0.1322 & 0.894999 & 0.447499 \tabularnewline
jongerdan25jaar & 0.993701290329437 & 0.003313 & 299.9221 & 0 & 0 \tabularnewline
vanaf25jaar & 1.00184142927099 & 0.002804 & 357.3135 & 0 & 0 \tabularnewline
`Belgi\353` & -0.120094481746231 & 0.108289 & -1.109 & 0.26935 & 0.134675 \tabularnewline
Eurogebied & -0.0961650138034609 & 0.208858 & -0.4604 & 0.645931 & 0.322966 \tabularnewline
`eu27\r` & 0.0579016126729738 & 0.21317 & 0.2716 & 0.786319 & 0.393159 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186058&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]1.2493258273714[/C][C]0.654923[/C][C]1.9076[/C][C]0.058522[/C][C]0.029261[/C][/ROW]
[ROW][C]maand[/C][C]-1.50811131221509[/C][C]11.405643[/C][C]-0.1322[/C][C]0.894999[/C][C]0.447499[/C][/ROW]
[ROW][C]jongerdan25jaar[/C][C]0.993701290329437[/C][C]0.003313[/C][C]299.9221[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]vanaf25jaar[/C][C]1.00184142927099[/C][C]0.002804[/C][C]357.3135[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Belgi\353`[/C][C]-0.120094481746231[/C][C]0.108289[/C][C]-1.109[/C][C]0.26935[/C][C]0.134675[/C][/ROW]
[ROW][C]Eurogebied[/C][C]-0.0961650138034609[/C][C]0.208858[/C][C]-0.4604[/C][C]0.645931[/C][C]0.322966[/C][/ROW]
[ROW][C]`eu27\r`[/C][C]0.0579016126729738[/C][C]0.21317[/C][C]0.2716[/C][C]0.786319[/C][C]0.393159[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186058&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.24932582737140.6549231.90760.0585220.029261
maand-1.5081113122150911.405643-0.13220.8949990.447499
jongerdan25jaar0.9937012903294370.003313299.922100
vanaf25jaar1.001841429270990.002804357.313500
`Belgi\353`-0.1200944817462310.108289-1.1090.269350.134675
Eurogebied-0.09616501380346090.208858-0.46040.6459310.322966
`eu27\r`0.05790161267297380.213170.27160.7863190.393159







Multiple Linear Regression - Regression Statistics
Multiple R0.999940430839775
R-squared0.999880865228035
Adjusted R-squared0.99987568545534
F-TEST (value)193035.6647419
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.505202895278616
Sum Squared Residuals35.2217352249097

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999940430839775 \tabularnewline
R-squared & 0.999880865228035 \tabularnewline
Adjusted R-squared & 0.99987568545534 \tabularnewline
F-TEST (value) & 193035.6647419 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.505202895278616 \tabularnewline
Sum Squared Residuals & 35.2217352249097 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186058&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999940430839775[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999880865228035[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99987568545534[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]193035.6647419[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.505202895278616[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.2217352249097[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186058&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.999940430839775
R-squared0.999880865228035
Adjusted R-squared0.99987568545534
F-TEST (value)193035.6647419
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.505202895278616
Sum Squared Residuals35.2217352249097







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.962139569612-0.962139569611563
2485485.004229499679-0.00422949967874279
3464464.067391518628-0.0673915186278362
4460460.108530683912-0.108530683911865
5467467.133640020332-0.133640020332371
6460460.169992420319-0.169992420318592
7448448.200975954149-0.200975954149393
8443443.194305751584-0.194305751583852
9436436.223096223856-0.223096223856405
10431431.243083218139-0.243083218139219
11484484.112784232272-0.11278423227231
12510509.0266449783670.973355021632801
13513512.9902504546780.00974954532188549
14503502.9674693896380.032530610362263
15471471.012697918657-0.0126979186572813
16471471.015600869999-0.0156008699990741
17476476.009149839145-0.00914983914472393
18475474.0388671973280.961132802671825
19470470.058684005699-0.0586840056992987
20461461.084989162757-0.08498916275716
21455455.093994625344-0.0939946253438961
22456455.0948985881680.905101411831608
23517516.8641870062750.135812993725241
24525525.791635348017-0.791635348016697
25523522.7730332883650.226966711634739
26519518.8309644533630.169035546637068
27509508.8644662379040.135533762096104
28512511.8863792334840.113620766515773
29519518.8581694576730.141830542326999
30517516.8782209518760.121779048123707
31510509.9087923713320.0912076286682704
32509509.931476770012-0.931476770012311
33501499.9748915632731.02510843672692
34507506.9546481614850.0453518385149517
35569569.745482891476-0.745482891476256
36580579.7088093725030.291190627496867
37578577.6920485384090.30795146159112
38565565.761144960748-0.761144960748441
39547547.788055309885-0.788055309885143
40555554.8029745425670.197025457433191
41562561.774775442260.22522455773961
42561560.8097715139720.190228486028481
43555555.884699863869-0.884699863869176
44544543.955823404270.0441765957296952
45537537.004761996919-0.00476199691881437
46543542.9667979575980.0332020424019507
47594593.7119179457710.288082054228857
48611610.6343492388660.365650761134085
49613612.64657986790.353420132099924
50611610.776782616310.223217383689906
51594593.8415625992040.158437400796348
52595595.877914335183-0.87791433518264
53591590.897651042090.102348957910397
54589589.921007560176-0.921007560175712
55584583.9414082068820.0585917931175211
56573572.9639843427270.0360156572728019
57567566.9864311815310.0135688184690598
58569568.9948859595680.00511404043234077
59621620.7802254847320.219774515268385
60629628.734078530190.265921469809843
61628627.7326445291620.267355470837712
62612611.8025581672350.197441832765203
63595595.860996331439-0.860996331439446
64597596.9074730631760.0925269368237261
65593592.9275425220190.072457477980658
66590589.9549054565430.0450945434566615
67580579.9919149385320.00808506146793227
68574574.00502393366-0.00502393365997469
69573573.022790100278-0.0227901002782863
70573573.043397358105-0.0433973581054384
71620619.8977349243210.102265075679538
72626625.8843777377880.115622262212326
73620619.8857028663550.114297133645344
74588586.9510395447081.04896045529207
75566565.9964011135660.00359888643425442
76557558.06421115533-1.06421115532983
77561561.038279438052-0.0382794380520848
78549549.081623696889-0.0816236968892597
79532532.105741800488-0.10574180048781
80526526.113059573013-0.113059573012738
81511511.15540540808-0.155405408079553
82499499.204747325116-0.204747325116133
83555555.079971339548-0.0799713395476721
84565564.0721382334660.927861766533948
85542542.096710433677-0.0967104336771727
86527527.112716520125-0.112716520125224
87510510.152560603037-0.152560603037352
88514514.162384457551-0.162384457550556
89517517.15857678155-0.158576781549861
90508507.2129606299990.787039370001466
91493493.251160283072-0.251160283071557
92490490.254427750871-0.254427750870548
93469469.286169013426-0.286169013426099
94478477.2615455042220.738454495777952
95528529.097646072917-1.09764607291655
96534533.0570312065710.942968793429101
97518518.060027165134-0.0600271651336005
98506506.101792977434-0.101792977433776
99502502.119615073249-0.119615073248677
100516516.099944281269-0.0999442812689379
101528528.042713161475-0.0427131614754326
102533533.029183910459-0.0291839104588868
103536536.016322191991-0.0163221919908904
104537537.042403949418-0.0424039494184516
105524523.0788945919330.921105408067088
106536536.061127802797-0.0611278027974426
107587586.8900840260440.109915973955714
108597595.8520123226021.14798767739821
109581580.8372514383440.162748561655939
110564564.899898203268-0.899898203267866
111558556.9280681138591.07193188614095
112575574.9152755264370.0847244735630021
113580580.892565350534-0.892565350534369
114575574.9066829138610.0933170861392107
115563563.929879001751-0.929879001750915
116552550.9472906152141.05270938478593
117537536.9794669733870.0205330266128501
118545544.9826448347470.0173551652524783
119601600.8702791769540.129720823045671
120604604.841228855496-0.841228855495691
121586586.844919408357-0.844919408357205
122564563.9205728605040.0794271394958079
123549547.9805296588641.01947034113586
124551551.046610706348-0.0466107063481602
125556556.066222821984-0.0662228219838701
126548548.130436168914-0.130436168913769
127540540.157234324549-0.157234324549236
128531531.163337373302-0.163337373302256
129521520.1986875565840.801312443415977
130519518.1862881139040.813711886095802
131572572.030384699732-0.0303846997320887
132581581.988406820133-0.988406820132677
133563562.974419953340.0255800466603253
134548548.02694149428-0.0269414942798479
135539539.055662644127-0.0556626441267679
136541541.078126671965-0.0781266719648638
137562561.0491292369110.950870763089048
138559559.062127889251-0.0621278892509336
139546546.074317513699-0.0743175136991713
140536537.061186503626-1.0611865036256
141528527.0604714041920.93952859580761
142530531.048629973248-1.04862997324771
143582581.9259819009480.0740180990519022
144599598.8778190747610.122180925239395
145584583.8491272840060.150872715993801

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.962139569612 & -0.962139569611563 \tabularnewline
2 & 485 & 485.004229499679 & -0.00422949967874279 \tabularnewline
3 & 464 & 464.067391518628 & -0.0673915186278362 \tabularnewline
4 & 460 & 460.108530683912 & -0.108530683911865 \tabularnewline
5 & 467 & 467.133640020332 & -0.133640020332371 \tabularnewline
6 & 460 & 460.169992420319 & -0.169992420318592 \tabularnewline
7 & 448 & 448.200975954149 & -0.200975954149393 \tabularnewline
8 & 443 & 443.194305751584 & -0.194305751583852 \tabularnewline
9 & 436 & 436.223096223856 & -0.223096223856405 \tabularnewline
10 & 431 & 431.243083218139 & -0.243083218139219 \tabularnewline
11 & 484 & 484.112784232272 & -0.11278423227231 \tabularnewline
12 & 510 & 509.026644978367 & 0.973355021632801 \tabularnewline
13 & 513 & 512.990250454678 & 0.00974954532188549 \tabularnewline
14 & 503 & 502.967469389638 & 0.032530610362263 \tabularnewline
15 & 471 & 471.012697918657 & -0.0126979186572813 \tabularnewline
16 & 471 & 471.015600869999 & -0.0156008699990741 \tabularnewline
17 & 476 & 476.009149839145 & -0.00914983914472393 \tabularnewline
18 & 475 & 474.038867197328 & 0.961132802671825 \tabularnewline
19 & 470 & 470.058684005699 & -0.0586840056992987 \tabularnewline
20 & 461 & 461.084989162757 & -0.08498916275716 \tabularnewline
21 & 455 & 455.093994625344 & -0.0939946253438961 \tabularnewline
22 & 456 & 455.094898588168 & 0.905101411831608 \tabularnewline
23 & 517 & 516.864187006275 & 0.135812993725241 \tabularnewline
24 & 525 & 525.791635348017 & -0.791635348016697 \tabularnewline
25 & 523 & 522.773033288365 & 0.226966711634739 \tabularnewline
26 & 519 & 518.830964453363 & 0.169035546637068 \tabularnewline
27 & 509 & 508.864466237904 & 0.135533762096104 \tabularnewline
28 & 512 & 511.886379233484 & 0.113620766515773 \tabularnewline
29 & 519 & 518.858169457673 & 0.141830542326999 \tabularnewline
30 & 517 & 516.878220951876 & 0.121779048123707 \tabularnewline
31 & 510 & 509.908792371332 & 0.0912076286682704 \tabularnewline
32 & 509 & 509.931476770012 & -0.931476770012311 \tabularnewline
33 & 501 & 499.974891563273 & 1.02510843672692 \tabularnewline
34 & 507 & 506.954648161485 & 0.0453518385149517 \tabularnewline
35 & 569 & 569.745482891476 & -0.745482891476256 \tabularnewline
36 & 580 & 579.708809372503 & 0.291190627496867 \tabularnewline
37 & 578 & 577.692048538409 & 0.30795146159112 \tabularnewline
38 & 565 & 565.761144960748 & -0.761144960748441 \tabularnewline
39 & 547 & 547.788055309885 & -0.788055309885143 \tabularnewline
40 & 555 & 554.802974542567 & 0.197025457433191 \tabularnewline
41 & 562 & 561.77477544226 & 0.22522455773961 \tabularnewline
42 & 561 & 560.809771513972 & 0.190228486028481 \tabularnewline
43 & 555 & 555.884699863869 & -0.884699863869176 \tabularnewline
44 & 544 & 543.95582340427 & 0.0441765957296952 \tabularnewline
45 & 537 & 537.004761996919 & -0.00476199691881437 \tabularnewline
46 & 543 & 542.966797957598 & 0.0332020424019507 \tabularnewline
47 & 594 & 593.711917945771 & 0.288082054228857 \tabularnewline
48 & 611 & 610.634349238866 & 0.365650761134085 \tabularnewline
49 & 613 & 612.6465798679 & 0.353420132099924 \tabularnewline
50 & 611 & 610.77678261631 & 0.223217383689906 \tabularnewline
51 & 594 & 593.841562599204 & 0.158437400796348 \tabularnewline
52 & 595 & 595.877914335183 & -0.87791433518264 \tabularnewline
53 & 591 & 590.89765104209 & 0.102348957910397 \tabularnewline
54 & 589 & 589.921007560176 & -0.921007560175712 \tabularnewline
55 & 584 & 583.941408206882 & 0.0585917931175211 \tabularnewline
56 & 573 & 572.963984342727 & 0.0360156572728019 \tabularnewline
57 & 567 & 566.986431181531 & 0.0135688184690598 \tabularnewline
58 & 569 & 568.994885959568 & 0.00511404043234077 \tabularnewline
59 & 621 & 620.780225484732 & 0.219774515268385 \tabularnewline
60 & 629 & 628.73407853019 & 0.265921469809843 \tabularnewline
61 & 628 & 627.732644529162 & 0.267355470837712 \tabularnewline
62 & 612 & 611.802558167235 & 0.197441832765203 \tabularnewline
63 & 595 & 595.860996331439 & -0.860996331439446 \tabularnewline
64 & 597 & 596.907473063176 & 0.0925269368237261 \tabularnewline
65 & 593 & 592.927542522019 & 0.072457477980658 \tabularnewline
66 & 590 & 589.954905456543 & 0.0450945434566615 \tabularnewline
67 & 580 & 579.991914938532 & 0.00808506146793227 \tabularnewline
68 & 574 & 574.00502393366 & -0.00502393365997469 \tabularnewline
69 & 573 & 573.022790100278 & -0.0227901002782863 \tabularnewline
70 & 573 & 573.043397358105 & -0.0433973581054384 \tabularnewline
71 & 620 & 619.897734924321 & 0.102265075679538 \tabularnewline
72 & 626 & 625.884377737788 & 0.115622262212326 \tabularnewline
73 & 620 & 619.885702866355 & 0.114297133645344 \tabularnewline
74 & 588 & 586.951039544708 & 1.04896045529207 \tabularnewline
75 & 566 & 565.996401113566 & 0.00359888643425442 \tabularnewline
76 & 557 & 558.06421115533 & -1.06421115532983 \tabularnewline
77 & 561 & 561.038279438052 & -0.0382794380520848 \tabularnewline
78 & 549 & 549.081623696889 & -0.0816236968892597 \tabularnewline
79 & 532 & 532.105741800488 & -0.10574180048781 \tabularnewline
80 & 526 & 526.113059573013 & -0.113059573012738 \tabularnewline
81 & 511 & 511.15540540808 & -0.155405408079553 \tabularnewline
82 & 499 & 499.204747325116 & -0.204747325116133 \tabularnewline
83 & 555 & 555.079971339548 & -0.0799713395476721 \tabularnewline
84 & 565 & 564.072138233466 & 0.927861766533948 \tabularnewline
85 & 542 & 542.096710433677 & -0.0967104336771727 \tabularnewline
86 & 527 & 527.112716520125 & -0.112716520125224 \tabularnewline
87 & 510 & 510.152560603037 & -0.152560603037352 \tabularnewline
88 & 514 & 514.162384457551 & -0.162384457550556 \tabularnewline
89 & 517 & 517.15857678155 & -0.158576781549861 \tabularnewline
90 & 508 & 507.212960629999 & 0.787039370001466 \tabularnewline
91 & 493 & 493.251160283072 & -0.251160283071557 \tabularnewline
92 & 490 & 490.254427750871 & -0.254427750870548 \tabularnewline
93 & 469 & 469.286169013426 & -0.286169013426099 \tabularnewline
94 & 478 & 477.261545504222 & 0.738454495777952 \tabularnewline
95 & 528 & 529.097646072917 & -1.09764607291655 \tabularnewline
96 & 534 & 533.057031206571 & 0.942968793429101 \tabularnewline
97 & 518 & 518.060027165134 & -0.0600271651336005 \tabularnewline
98 & 506 & 506.101792977434 & -0.101792977433776 \tabularnewline
99 & 502 & 502.119615073249 & -0.119615073248677 \tabularnewline
100 & 516 & 516.099944281269 & -0.0999442812689379 \tabularnewline
101 & 528 & 528.042713161475 & -0.0427131614754326 \tabularnewline
102 & 533 & 533.029183910459 & -0.0291839104588868 \tabularnewline
103 & 536 & 536.016322191991 & -0.0163221919908904 \tabularnewline
104 & 537 & 537.042403949418 & -0.0424039494184516 \tabularnewline
105 & 524 & 523.078894591933 & 0.921105408067088 \tabularnewline
106 & 536 & 536.061127802797 & -0.0611278027974426 \tabularnewline
107 & 587 & 586.890084026044 & 0.109915973955714 \tabularnewline
108 & 597 & 595.852012322602 & 1.14798767739821 \tabularnewline
109 & 581 & 580.837251438344 & 0.162748561655939 \tabularnewline
110 & 564 & 564.899898203268 & -0.899898203267866 \tabularnewline
111 & 558 & 556.928068113859 & 1.07193188614095 \tabularnewline
112 & 575 & 574.915275526437 & 0.0847244735630021 \tabularnewline
113 & 580 & 580.892565350534 & -0.892565350534369 \tabularnewline
114 & 575 & 574.906682913861 & 0.0933170861392107 \tabularnewline
115 & 563 & 563.929879001751 & -0.929879001750915 \tabularnewline
116 & 552 & 550.947290615214 & 1.05270938478593 \tabularnewline
117 & 537 & 536.979466973387 & 0.0205330266128501 \tabularnewline
118 & 545 & 544.982644834747 & 0.0173551652524783 \tabularnewline
119 & 601 & 600.870279176954 & 0.129720823045671 \tabularnewline
120 & 604 & 604.841228855496 & -0.841228855495691 \tabularnewline
121 & 586 & 586.844919408357 & -0.844919408357205 \tabularnewline
122 & 564 & 563.920572860504 & 0.0794271394958079 \tabularnewline
123 & 549 & 547.980529658864 & 1.01947034113586 \tabularnewline
124 & 551 & 551.046610706348 & -0.0466107063481602 \tabularnewline
125 & 556 & 556.066222821984 & -0.0662228219838701 \tabularnewline
126 & 548 & 548.130436168914 & -0.130436168913769 \tabularnewline
127 & 540 & 540.157234324549 & -0.157234324549236 \tabularnewline
128 & 531 & 531.163337373302 & -0.163337373302256 \tabularnewline
129 & 521 & 520.198687556584 & 0.801312443415977 \tabularnewline
130 & 519 & 518.186288113904 & 0.813711886095802 \tabularnewline
131 & 572 & 572.030384699732 & -0.0303846997320887 \tabularnewline
132 & 581 & 581.988406820133 & -0.988406820132677 \tabularnewline
133 & 563 & 562.97441995334 & 0.0255800466603253 \tabularnewline
134 & 548 & 548.02694149428 & -0.0269414942798479 \tabularnewline
135 & 539 & 539.055662644127 & -0.0556626441267679 \tabularnewline
136 & 541 & 541.078126671965 & -0.0781266719648638 \tabularnewline
137 & 562 & 561.049129236911 & 0.950870763089048 \tabularnewline
138 & 559 & 559.062127889251 & -0.0621278892509336 \tabularnewline
139 & 546 & 546.074317513699 & -0.0743175136991713 \tabularnewline
140 & 536 & 537.061186503626 & -1.0611865036256 \tabularnewline
141 & 528 & 527.060471404192 & 0.93952859580761 \tabularnewline
142 & 530 & 531.048629973248 & -1.04862997324771 \tabularnewline
143 & 582 & 581.925981900948 & 0.0740180990519022 \tabularnewline
144 & 599 & 598.877819074761 & 0.122180925239395 \tabularnewline
145 & 584 & 583.849127284006 & 0.150872715993801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186058&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]501[/C][C]501.962139569612[/C][C]-0.962139569611563[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]485.004229499679[/C][C]-0.00422949967874279[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]464.067391518628[/C][C]-0.0673915186278362[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]460.108530683912[/C][C]-0.108530683911865[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.133640020332[/C][C]-0.133640020332371[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.169992420319[/C][C]-0.169992420318592[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.200975954149[/C][C]-0.200975954149393[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.194305751584[/C][C]-0.194305751583852[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.223096223856[/C][C]-0.223096223856405[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.243083218139[/C][C]-0.243083218139219[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.112784232272[/C][C]-0.11278423227231[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.026644978367[/C][C]0.973355021632801[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]512.990250454678[/C][C]0.00974954532188549[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.967469389638[/C][C]0.032530610362263[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.012697918657[/C][C]-0.0126979186572813[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.015600869999[/C][C]-0.0156008699990741[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.009149839145[/C][C]-0.00914983914472393[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.038867197328[/C][C]0.961132802671825[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.058684005699[/C][C]-0.0586840056992987[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.084989162757[/C][C]-0.08498916275716[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.093994625344[/C][C]-0.0939946253438961[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.094898588168[/C][C]0.905101411831608[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.864187006275[/C][C]0.135812993725241[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.791635348017[/C][C]-0.791635348016697[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.773033288365[/C][C]0.226966711634739[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.830964453363[/C][C]0.169035546637068[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.864466237904[/C][C]0.135533762096104[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.886379233484[/C][C]0.113620766515773[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.858169457673[/C][C]0.141830542326999[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.878220951876[/C][C]0.121779048123707[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.908792371332[/C][C]0.0912076286682704[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.931476770012[/C][C]-0.931476770012311[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]499.974891563273[/C][C]1.02510843672692[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]506.954648161485[/C][C]0.0453518385149517[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.745482891476[/C][C]-0.745482891476256[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.708809372503[/C][C]0.291190627496867[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.692048538409[/C][C]0.30795146159112[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.761144960748[/C][C]-0.761144960748441[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.788055309885[/C][C]-0.788055309885143[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.802974542567[/C][C]0.197025457433191[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.77477544226[/C][C]0.22522455773961[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.809771513972[/C][C]0.190228486028481[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.884699863869[/C][C]-0.884699863869176[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.95582340427[/C][C]0.0441765957296952[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]537.004761996919[/C][C]-0.00476199691881437[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.966797957598[/C][C]0.0332020424019507[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.711917945771[/C][C]0.288082054228857[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.634349238866[/C][C]0.365650761134085[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.6465798679[/C][C]0.353420132099924[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.77678261631[/C][C]0.223217383689906[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.841562599204[/C][C]0.158437400796348[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.877914335183[/C][C]-0.87791433518264[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.89765104209[/C][C]0.102348957910397[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.921007560176[/C][C]-0.921007560175712[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.941408206882[/C][C]0.0585917931175211[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.963984342727[/C][C]0.0360156572728019[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.986431181531[/C][C]0.0135688184690598[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.994885959568[/C][C]0.00511404043234077[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.780225484732[/C][C]0.219774515268385[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.73407853019[/C][C]0.265921469809843[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.732644529162[/C][C]0.267355470837712[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.802558167235[/C][C]0.197441832765203[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.860996331439[/C][C]-0.860996331439446[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.907473063176[/C][C]0.0925269368237261[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.927542522019[/C][C]0.072457477980658[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.954905456543[/C][C]0.0450945434566615[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.991914938532[/C][C]0.00808506146793227[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.00502393366[/C][C]-0.00502393365997469[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]573.022790100278[/C][C]-0.0227901002782863[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.043397358105[/C][C]-0.0433973581054384[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.897734924321[/C][C]0.102265075679538[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.884377737788[/C][C]0.115622262212326[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.885702866355[/C][C]0.114297133645344[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.951039544708[/C][C]1.04896045529207[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.996401113566[/C][C]0.00359888643425442[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.06421115533[/C][C]-1.06421115532983[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.038279438052[/C][C]-0.0382794380520848[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.081623696889[/C][C]-0.0816236968892597[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.105741800488[/C][C]-0.10574180048781[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.113059573013[/C][C]-0.113059573012738[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.15540540808[/C][C]-0.155405408079553[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.204747325116[/C][C]-0.204747325116133[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.079971339548[/C][C]-0.0799713395476721[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.072138233466[/C][C]0.927861766533948[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.096710433677[/C][C]-0.0967104336771727[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.112716520125[/C][C]-0.112716520125224[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.152560603037[/C][C]-0.152560603037352[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.162384457551[/C][C]-0.162384457550556[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.15857678155[/C][C]-0.158576781549861[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.212960629999[/C][C]0.787039370001466[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.251160283072[/C][C]-0.251160283071557[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.254427750871[/C][C]-0.254427750870548[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.286169013426[/C][C]-0.286169013426099[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.261545504222[/C][C]0.738454495777952[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.097646072917[/C][C]-1.09764607291655[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.057031206571[/C][C]0.942968793429101[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.060027165134[/C][C]-0.0600271651336005[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.101792977434[/C][C]-0.101792977433776[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.119615073249[/C][C]-0.119615073248677[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.099944281269[/C][C]-0.0999442812689379[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.042713161475[/C][C]-0.0427131614754326[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.029183910459[/C][C]-0.0291839104588868[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]536.016322191991[/C][C]-0.0163221919908904[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.042403949418[/C][C]-0.0424039494184516[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.078894591933[/C][C]0.921105408067088[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.061127802797[/C][C]-0.0611278027974426[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.890084026044[/C][C]0.109915973955714[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.852012322602[/C][C]1.14798767739821[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.837251438344[/C][C]0.162748561655939[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.899898203268[/C][C]-0.899898203267866[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.928068113859[/C][C]1.07193188614095[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.915275526437[/C][C]0.0847244735630021[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.892565350534[/C][C]-0.892565350534369[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.906682913861[/C][C]0.0933170861392107[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]563.929879001751[/C][C]-0.929879001750915[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.947290615214[/C][C]1.05270938478593[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]536.979466973387[/C][C]0.0205330266128501[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]544.982644834747[/C][C]0.0173551652524783[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.870279176954[/C][C]0.129720823045671[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.841228855496[/C][C]-0.841228855495691[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.844919408357[/C][C]-0.844919408357205[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.920572860504[/C][C]0.0794271394958079[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]547.980529658864[/C][C]1.01947034113586[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.046610706348[/C][C]-0.0466107063481602[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.066222821984[/C][C]-0.0662228219838701[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.130436168914[/C][C]-0.130436168913769[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.157234324549[/C][C]-0.157234324549236[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.163337373302[/C][C]-0.163337373302256[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.198687556584[/C][C]0.801312443415977[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.186288113904[/C][C]0.813711886095802[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.030384699732[/C][C]-0.0303846997320887[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]581.988406820133[/C][C]-0.988406820132677[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]562.97441995334[/C][C]0.0255800466603253[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.02694149428[/C][C]-0.0269414942798479[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.055662644127[/C][C]-0.0556626441267679[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.078126671965[/C][C]-0.0781266719648638[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.049129236911[/C][C]0.950870763089048[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.062127889251[/C][C]-0.0621278892509336[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.074317513699[/C][C]-0.0743175136991713[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]537.061186503626[/C][C]-1.0611865036256[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]527.060471404192[/C][C]0.93952859580761[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]531.048629973248[/C][C]-1.04862997324771[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.925981900948[/C][C]0.0740180990519022[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.877819074761[/C][C]0.122180925239395[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.849127284006[/C][C]0.150872715993801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186058&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.962139569612-0.962139569611563
2485485.004229499679-0.00422949967874279
3464464.067391518628-0.0673915186278362
4460460.108530683912-0.108530683911865
5467467.133640020332-0.133640020332371
6460460.169992420319-0.169992420318592
7448448.200975954149-0.200975954149393
8443443.194305751584-0.194305751583852
9436436.223096223856-0.223096223856405
10431431.243083218139-0.243083218139219
11484484.112784232272-0.11278423227231
12510509.0266449783670.973355021632801
13513512.9902504546780.00974954532188549
14503502.9674693896380.032530610362263
15471471.012697918657-0.0126979186572813
16471471.015600869999-0.0156008699990741
17476476.009149839145-0.00914983914472393
18475474.0388671973280.961132802671825
19470470.058684005699-0.0586840056992987
20461461.084989162757-0.08498916275716
21455455.093994625344-0.0939946253438961
22456455.0948985881680.905101411831608
23517516.8641870062750.135812993725241
24525525.791635348017-0.791635348016697
25523522.7730332883650.226966711634739
26519518.8309644533630.169035546637068
27509508.8644662379040.135533762096104
28512511.8863792334840.113620766515773
29519518.8581694576730.141830542326999
30517516.8782209518760.121779048123707
31510509.9087923713320.0912076286682704
32509509.931476770012-0.931476770012311
33501499.9748915632731.02510843672692
34507506.9546481614850.0453518385149517
35569569.745482891476-0.745482891476256
36580579.7088093725030.291190627496867
37578577.6920485384090.30795146159112
38565565.761144960748-0.761144960748441
39547547.788055309885-0.788055309885143
40555554.8029745425670.197025457433191
41562561.774775442260.22522455773961
42561560.8097715139720.190228486028481
43555555.884699863869-0.884699863869176
44544543.955823404270.0441765957296952
45537537.004761996919-0.00476199691881437
46543542.9667979575980.0332020424019507
47594593.7119179457710.288082054228857
48611610.6343492388660.365650761134085
49613612.64657986790.353420132099924
50611610.776782616310.223217383689906
51594593.8415625992040.158437400796348
52595595.877914335183-0.87791433518264
53591590.897651042090.102348957910397
54589589.921007560176-0.921007560175712
55584583.9414082068820.0585917931175211
56573572.9639843427270.0360156572728019
57567566.9864311815310.0135688184690598
58569568.9948859595680.00511404043234077
59621620.7802254847320.219774515268385
60629628.734078530190.265921469809843
61628627.7326445291620.267355470837712
62612611.8025581672350.197441832765203
63595595.860996331439-0.860996331439446
64597596.9074730631760.0925269368237261
65593592.9275425220190.072457477980658
66590589.9549054565430.0450945434566615
67580579.9919149385320.00808506146793227
68574574.00502393366-0.00502393365997469
69573573.022790100278-0.0227901002782863
70573573.043397358105-0.0433973581054384
71620619.8977349243210.102265075679538
72626625.8843777377880.115622262212326
73620619.8857028663550.114297133645344
74588586.9510395447081.04896045529207
75566565.9964011135660.00359888643425442
76557558.06421115533-1.06421115532983
77561561.038279438052-0.0382794380520848
78549549.081623696889-0.0816236968892597
79532532.105741800488-0.10574180048781
80526526.113059573013-0.113059573012738
81511511.15540540808-0.155405408079553
82499499.204747325116-0.204747325116133
83555555.079971339548-0.0799713395476721
84565564.0721382334660.927861766533948
85542542.096710433677-0.0967104336771727
86527527.112716520125-0.112716520125224
87510510.152560603037-0.152560603037352
88514514.162384457551-0.162384457550556
89517517.15857678155-0.158576781549861
90508507.2129606299990.787039370001466
91493493.251160283072-0.251160283071557
92490490.254427750871-0.254427750870548
93469469.286169013426-0.286169013426099
94478477.2615455042220.738454495777952
95528529.097646072917-1.09764607291655
96534533.0570312065710.942968793429101
97518518.060027165134-0.0600271651336005
98506506.101792977434-0.101792977433776
99502502.119615073249-0.119615073248677
100516516.099944281269-0.0999442812689379
101528528.042713161475-0.0427131614754326
102533533.029183910459-0.0291839104588868
103536536.016322191991-0.0163221919908904
104537537.042403949418-0.0424039494184516
105524523.0788945919330.921105408067088
106536536.061127802797-0.0611278027974426
107587586.8900840260440.109915973955714
108597595.8520123226021.14798767739821
109581580.8372514383440.162748561655939
110564564.899898203268-0.899898203267866
111558556.9280681138591.07193188614095
112575574.9152755264370.0847244735630021
113580580.892565350534-0.892565350534369
114575574.9066829138610.0933170861392107
115563563.929879001751-0.929879001750915
116552550.9472906152141.05270938478593
117537536.9794669733870.0205330266128501
118545544.9826448347470.0173551652524783
119601600.8702791769540.129720823045671
120604604.841228855496-0.841228855495691
121586586.844919408357-0.844919408357205
122564563.9205728605040.0794271394958079
123549547.9805296588641.01947034113586
124551551.046610706348-0.0466107063481602
125556556.066222821984-0.0662228219838701
126548548.130436168914-0.130436168913769
127540540.157234324549-0.157234324549236
128531531.163337373302-0.163337373302256
129521520.1986875565840.801312443415977
130519518.1862881139040.813711886095802
131572572.030384699732-0.0303846997320887
132581581.988406820133-0.988406820132677
133563562.974419953340.0255800466603253
134548548.02694149428-0.0269414942798479
135539539.055662644127-0.0556626441267679
136541541.078126671965-0.0781266719648638
137562561.0491292369110.950870763089048
138559559.062127889251-0.0621278892509336
139546546.074317513699-0.0743175136991713
140536537.061186503626-1.0611865036256
141528527.0604714041920.93952859580761
142530531.048629973248-1.04862997324771
143582581.9259819009480.0740180990519022
144599598.8778190747610.122180925239395
145584583.8491272840060.150872715993801







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09986815080493980.199736301609880.90013184919506
110.04129508819431920.08259017638863830.958704911805681
120.1434425059003750.2868850118007510.856557494099625
130.1138927540633050.227785508126610.886107245936695
140.09642137925116740.1928427585023350.903578620748833
150.05411855951940010.10823711903880.9458814404806
160.08098039588511010.161960791770220.91901960411489
170.04862283298250010.09724566596500020.9513771670175
180.2113850766992550.422770153398510.788614923300745
190.2062787687407550.4125575374815090.793721231259245
200.1601332826035130.3202665652070260.839866717396487
210.1123144286000750.2246288572001510.887685571399925
220.229611799888710.459223599777420.77038820011129
230.1775795751715290.3551591503430580.822420424828471
240.2220013778879470.4440027557758940.777998622112053
250.2500615133789720.5001230267579440.749938486621028
260.1980962774775870.3961925549551740.801903722522413
270.15150925997150.3030185199430010.8484907400285
280.1167779655422620.2335559310845250.883222034457738
290.08605834647283570.1721166929456710.913941653527164
300.0642283981136720.1284567962273440.935771601886328
310.04724015993605120.09448031987210230.952759840063949
320.1425825308526440.2851650617052880.857417469147356
330.2839413421124140.5678826842248290.716058657887586
340.233986223469080.467972446938160.76601377653092
350.2736275089719850.547255017943970.726372491028015
360.2490031728613190.4980063457226380.750996827138681
370.2335970308147280.4671940616294570.766402969185272
380.3064955158988520.6129910317977040.693504484101148
390.3425944634650180.6851889269300360.657405536534982
400.2957127638571550.591425527714310.704287236142845
410.2493008486314480.4986016972628970.750699151368552
420.2069768775138190.4139537550276380.793023122486181
430.345180911369580.690361822739160.65481908863042
440.3006030048265870.6012060096531750.699396995173413
450.25580761199720.5116152239944010.7441923880028
460.2191457263732970.4382914527465940.780854273626703
470.2144601622035080.4289203244070170.785539837796492
480.2061303412831760.4122606825663510.793869658716824
490.1845432443182270.3690864886364550.815456755681773
500.1532714319521140.3065428639042280.846728568047886
510.1265323943820090.2530647887640180.873467605617991
520.1873929145943230.3747858291886470.812607085405677
530.1549470090454210.3098940180908410.84505299095458
540.2303941226026880.4607882452053770.769605877397312
550.1957960035096880.3915920070193760.804203996490312
560.1634626159932980.3269252319865970.836537384006702
570.1338532977880150.2677065955760290.866146702211985
580.1082243477745750.2164486955491490.891775652225425
590.09086353263571570.1817270652714310.909136467364284
600.07463967147270080.1492793429454020.925360328527299
610.06031891531442520.120637830628850.939681084685575
620.04718822626883120.09437645253766240.952811773731169
630.07832844030651010.156656880613020.92167155969349
640.06189480661113060.1237896132222610.938105193388869
650.04810957992202720.09621915984405450.951890420077973
660.03686959830819730.07373919661639460.963130401691803
670.02794866050496070.05589732100992140.972051339495039
680.02124037740833850.0424807548166770.978759622591661
690.0156907813714790.0313815627429580.984309218628521
700.01140701807847850.02281403615695710.988592981921521
710.00822244666533180.01644489333066360.991777553334668
720.005877071854103250.01175414370820650.994122928145897
730.004141206082932520.008282412165865040.995858793917067
740.01164208223983340.02328416447966690.988357917760167
750.00853299397768720.01706598795537440.991467006022313
760.02763826927724050.05527653855448110.972361730722759
770.02068875318187070.04137750636374140.979311246818129
780.01523320020635950.03046640041271890.984766799793641
790.01111343526986170.02222687053972340.988886564730138
800.008036125167583540.01607225033516710.991963874832416
810.005858305241550360.01171661048310070.99414169475845
820.004386726356866830.008773452713733670.995613273643133
830.003072387454097450.006144774908194890.996927612545903
840.009441040400552490.0188820808011050.990558959599448
850.006706451508808890.01341290301761780.993293548491191
860.004697184859831930.009394369719663860.995302815140168
870.003340224685655510.006680449371311020.996659775314344
880.002315417870466120.004630835740932230.997684582129534
890.001563191052214950.003126382104429910.998436808947785
900.003250387022007340.006500774044014670.996749612977993
910.002321904235002750.004643808470005510.997678095764997
920.001636976011172680.003273952022345350.998363023988827
930.001306550158089210.002613100316178430.998693449841911
940.00218594515188150.004371890303762990.997814054848118
950.007194433705986020.0143888674119720.992805566294014
960.01619132152313660.03238264304627310.983808678476863
970.01168057721292980.02336115442585950.98831942278707
980.008489701766749590.01697940353349920.99151029823325
990.006455675126068990.0129113502521380.993544324873931
1000.004925142119820710.009850284239641430.995074857880179
1010.003479916761648790.006959833523297570.996520083238351
1020.002590254174791450.005180508349582890.997409745825209
1030.002061848245438690.004123696490877380.997938151754561
1040.001805071840313870.003610143680627730.998194928159686
1050.002593258885812580.005186517771625170.997406741114187
1060.001943188201517170.003886376403034340.998056811798483
1070.001256984594529970.002513969189059930.99874301540547
1080.006369225798633240.01273845159726650.993630774201367
1090.004541961844439350.00908392368887870.995458038155561
1100.01273554636674640.02547109273349270.987264453633254
1110.02604606165162760.05209212330325510.973953938348372
1120.01862309059807970.03724618119615950.98137690940192
1130.03213600310280080.06427200620560150.967863996897199
1140.02317240549925010.04634481099850020.97682759450075
1150.05321920490748070.1064384098149610.946780795092519
1160.09200972706581570.1840194541316310.907990272934184
1170.0699015971232170.1398031942464340.930098402876783
1180.05101916567110720.1020383313422140.948980834328893
1190.05060446854991540.1012089370998310.949395531450085
1200.04711312454747160.09422624909494320.952886875452528
1210.07627267827503170.1525453565500630.923727321724968
1220.06120095160390840.1224019032078170.938799048396092
1230.08973530337700120.1794706067540020.910264696622999
1240.06753023841175010.13506047682350.93246976158825
1250.05848653816947990.116973076338960.94151346183052
1260.0443404110971120.08868082219422410.955659588902888
1270.03248932060262570.06497864120525150.967510679397374
1280.02651969382317290.05303938764634580.973480306176827
1290.03540952445718850.07081904891437690.964590475542812
1300.07569283274238860.1513856654847770.924307167257611
1310.06713356678150510.134267133563010.932866433218495
1320.08358148120447870.1671629624089570.916418518795521
1330.06275489653755020.12550979307510.93724510346245
1340.04065881627757640.08131763255515280.959341183722424
1350.100405878181380.200811756362760.89959412181862

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0998681508049398 & 0.19973630160988 & 0.90013184919506 \tabularnewline
11 & 0.0412950881943192 & 0.0825901763886383 & 0.958704911805681 \tabularnewline
12 & 0.143442505900375 & 0.286885011800751 & 0.856557494099625 \tabularnewline
13 & 0.113892754063305 & 0.22778550812661 & 0.886107245936695 \tabularnewline
14 & 0.0964213792511674 & 0.192842758502335 & 0.903578620748833 \tabularnewline
15 & 0.0541185595194001 & 0.1082371190388 & 0.9458814404806 \tabularnewline
16 & 0.0809803958851101 & 0.16196079177022 & 0.91901960411489 \tabularnewline
17 & 0.0486228329825001 & 0.0972456659650002 & 0.9513771670175 \tabularnewline
18 & 0.211385076699255 & 0.42277015339851 & 0.788614923300745 \tabularnewline
19 & 0.206278768740755 & 0.412557537481509 & 0.793721231259245 \tabularnewline
20 & 0.160133282603513 & 0.320266565207026 & 0.839866717396487 \tabularnewline
21 & 0.112314428600075 & 0.224628857200151 & 0.887685571399925 \tabularnewline
22 & 0.22961179988871 & 0.45922359977742 & 0.77038820011129 \tabularnewline
23 & 0.177579575171529 & 0.355159150343058 & 0.822420424828471 \tabularnewline
24 & 0.222001377887947 & 0.444002755775894 & 0.777998622112053 \tabularnewline
25 & 0.250061513378972 & 0.500123026757944 & 0.749938486621028 \tabularnewline
26 & 0.198096277477587 & 0.396192554955174 & 0.801903722522413 \tabularnewline
27 & 0.1515092599715 & 0.303018519943001 & 0.8484907400285 \tabularnewline
28 & 0.116777965542262 & 0.233555931084525 & 0.883222034457738 \tabularnewline
29 & 0.0860583464728357 & 0.172116692945671 & 0.913941653527164 \tabularnewline
30 & 0.064228398113672 & 0.128456796227344 & 0.935771601886328 \tabularnewline
31 & 0.0472401599360512 & 0.0944803198721023 & 0.952759840063949 \tabularnewline
32 & 0.142582530852644 & 0.285165061705288 & 0.857417469147356 \tabularnewline
33 & 0.283941342112414 & 0.567882684224829 & 0.716058657887586 \tabularnewline
34 & 0.23398622346908 & 0.46797244693816 & 0.76601377653092 \tabularnewline
35 & 0.273627508971985 & 0.54725501794397 & 0.726372491028015 \tabularnewline
36 & 0.249003172861319 & 0.498006345722638 & 0.750996827138681 \tabularnewline
37 & 0.233597030814728 & 0.467194061629457 & 0.766402969185272 \tabularnewline
38 & 0.306495515898852 & 0.612991031797704 & 0.693504484101148 \tabularnewline
39 & 0.342594463465018 & 0.685188926930036 & 0.657405536534982 \tabularnewline
40 & 0.295712763857155 & 0.59142552771431 & 0.704287236142845 \tabularnewline
41 & 0.249300848631448 & 0.498601697262897 & 0.750699151368552 \tabularnewline
42 & 0.206976877513819 & 0.413953755027638 & 0.793023122486181 \tabularnewline
43 & 0.34518091136958 & 0.69036182273916 & 0.65481908863042 \tabularnewline
44 & 0.300603004826587 & 0.601206009653175 & 0.699396995173413 \tabularnewline
45 & 0.2558076119972 & 0.511615223994401 & 0.7441923880028 \tabularnewline
46 & 0.219145726373297 & 0.438291452746594 & 0.780854273626703 \tabularnewline
47 & 0.214460162203508 & 0.428920324407017 & 0.785539837796492 \tabularnewline
48 & 0.206130341283176 & 0.412260682566351 & 0.793869658716824 \tabularnewline
49 & 0.184543244318227 & 0.369086488636455 & 0.815456755681773 \tabularnewline
50 & 0.153271431952114 & 0.306542863904228 & 0.846728568047886 \tabularnewline
51 & 0.126532394382009 & 0.253064788764018 & 0.873467605617991 \tabularnewline
52 & 0.187392914594323 & 0.374785829188647 & 0.812607085405677 \tabularnewline
53 & 0.154947009045421 & 0.309894018090841 & 0.84505299095458 \tabularnewline
54 & 0.230394122602688 & 0.460788245205377 & 0.769605877397312 \tabularnewline
55 & 0.195796003509688 & 0.391592007019376 & 0.804203996490312 \tabularnewline
56 & 0.163462615993298 & 0.326925231986597 & 0.836537384006702 \tabularnewline
57 & 0.133853297788015 & 0.267706595576029 & 0.866146702211985 \tabularnewline
58 & 0.108224347774575 & 0.216448695549149 & 0.891775652225425 \tabularnewline
59 & 0.0908635326357157 & 0.181727065271431 & 0.909136467364284 \tabularnewline
60 & 0.0746396714727008 & 0.149279342945402 & 0.925360328527299 \tabularnewline
61 & 0.0603189153144252 & 0.12063783062885 & 0.939681084685575 \tabularnewline
62 & 0.0471882262688312 & 0.0943764525376624 & 0.952811773731169 \tabularnewline
63 & 0.0783284403065101 & 0.15665688061302 & 0.92167155969349 \tabularnewline
64 & 0.0618948066111306 & 0.123789613222261 & 0.938105193388869 \tabularnewline
65 & 0.0481095799220272 & 0.0962191598440545 & 0.951890420077973 \tabularnewline
66 & 0.0368695983081973 & 0.0737391966163946 & 0.963130401691803 \tabularnewline
67 & 0.0279486605049607 & 0.0558973210099214 & 0.972051339495039 \tabularnewline
68 & 0.0212403774083385 & 0.042480754816677 & 0.978759622591661 \tabularnewline
69 & 0.015690781371479 & 0.031381562742958 & 0.984309218628521 \tabularnewline
70 & 0.0114070180784785 & 0.0228140361569571 & 0.988592981921521 \tabularnewline
71 & 0.0082224466653318 & 0.0164448933306636 & 0.991777553334668 \tabularnewline
72 & 0.00587707185410325 & 0.0117541437082065 & 0.994122928145897 \tabularnewline
73 & 0.00414120608293252 & 0.00828241216586504 & 0.995858793917067 \tabularnewline
74 & 0.0116420822398334 & 0.0232841644796669 & 0.988357917760167 \tabularnewline
75 & 0.0085329939776872 & 0.0170659879553744 & 0.991467006022313 \tabularnewline
76 & 0.0276382692772405 & 0.0552765385544811 & 0.972361730722759 \tabularnewline
77 & 0.0206887531818707 & 0.0413775063637414 & 0.979311246818129 \tabularnewline
78 & 0.0152332002063595 & 0.0304664004127189 & 0.984766799793641 \tabularnewline
79 & 0.0111134352698617 & 0.0222268705397234 & 0.988886564730138 \tabularnewline
80 & 0.00803612516758354 & 0.0160722503351671 & 0.991963874832416 \tabularnewline
81 & 0.00585830524155036 & 0.0117166104831007 & 0.99414169475845 \tabularnewline
82 & 0.00438672635686683 & 0.00877345271373367 & 0.995613273643133 \tabularnewline
83 & 0.00307238745409745 & 0.00614477490819489 & 0.996927612545903 \tabularnewline
84 & 0.00944104040055249 & 0.018882080801105 & 0.990558959599448 \tabularnewline
85 & 0.00670645150880889 & 0.0134129030176178 & 0.993293548491191 \tabularnewline
86 & 0.00469718485983193 & 0.00939436971966386 & 0.995302815140168 \tabularnewline
87 & 0.00334022468565551 & 0.00668044937131102 & 0.996659775314344 \tabularnewline
88 & 0.00231541787046612 & 0.00463083574093223 & 0.997684582129534 \tabularnewline
89 & 0.00156319105221495 & 0.00312638210442991 & 0.998436808947785 \tabularnewline
90 & 0.00325038702200734 & 0.00650077404401467 & 0.996749612977993 \tabularnewline
91 & 0.00232190423500275 & 0.00464380847000551 & 0.997678095764997 \tabularnewline
92 & 0.00163697601117268 & 0.00327395202234535 & 0.998363023988827 \tabularnewline
93 & 0.00130655015808921 & 0.00261310031617843 & 0.998693449841911 \tabularnewline
94 & 0.0021859451518815 & 0.00437189030376299 & 0.997814054848118 \tabularnewline
95 & 0.00719443370598602 & 0.014388867411972 & 0.992805566294014 \tabularnewline
96 & 0.0161913215231366 & 0.0323826430462731 & 0.983808678476863 \tabularnewline
97 & 0.0116805772129298 & 0.0233611544258595 & 0.98831942278707 \tabularnewline
98 & 0.00848970176674959 & 0.0169794035334992 & 0.99151029823325 \tabularnewline
99 & 0.00645567512606899 & 0.012911350252138 & 0.993544324873931 \tabularnewline
100 & 0.00492514211982071 & 0.00985028423964143 & 0.995074857880179 \tabularnewline
101 & 0.00347991676164879 & 0.00695983352329757 & 0.996520083238351 \tabularnewline
102 & 0.00259025417479145 & 0.00518050834958289 & 0.997409745825209 \tabularnewline
103 & 0.00206184824543869 & 0.00412369649087738 & 0.997938151754561 \tabularnewline
104 & 0.00180507184031387 & 0.00361014368062773 & 0.998194928159686 \tabularnewline
105 & 0.00259325888581258 & 0.00518651777162517 & 0.997406741114187 \tabularnewline
106 & 0.00194318820151717 & 0.00388637640303434 & 0.998056811798483 \tabularnewline
107 & 0.00125698459452997 & 0.00251396918905993 & 0.99874301540547 \tabularnewline
108 & 0.00636922579863324 & 0.0127384515972665 & 0.993630774201367 \tabularnewline
109 & 0.00454196184443935 & 0.0090839236888787 & 0.995458038155561 \tabularnewline
110 & 0.0127355463667464 & 0.0254710927334927 & 0.987264453633254 \tabularnewline
111 & 0.0260460616516276 & 0.0520921233032551 & 0.973953938348372 \tabularnewline
112 & 0.0186230905980797 & 0.0372461811961595 & 0.98137690940192 \tabularnewline
113 & 0.0321360031028008 & 0.0642720062056015 & 0.967863996897199 \tabularnewline
114 & 0.0231724054992501 & 0.0463448109985002 & 0.97682759450075 \tabularnewline
115 & 0.0532192049074807 & 0.106438409814961 & 0.946780795092519 \tabularnewline
116 & 0.0920097270658157 & 0.184019454131631 & 0.907990272934184 \tabularnewline
117 & 0.069901597123217 & 0.139803194246434 & 0.930098402876783 \tabularnewline
118 & 0.0510191656711072 & 0.102038331342214 & 0.948980834328893 \tabularnewline
119 & 0.0506044685499154 & 0.101208937099831 & 0.949395531450085 \tabularnewline
120 & 0.0471131245474716 & 0.0942262490949432 & 0.952886875452528 \tabularnewline
121 & 0.0762726782750317 & 0.152545356550063 & 0.923727321724968 \tabularnewline
122 & 0.0612009516039084 & 0.122401903207817 & 0.938799048396092 \tabularnewline
123 & 0.0897353033770012 & 0.179470606754002 & 0.910264696622999 \tabularnewline
124 & 0.0675302384117501 & 0.1350604768235 & 0.93246976158825 \tabularnewline
125 & 0.0584865381694799 & 0.11697307633896 & 0.94151346183052 \tabularnewline
126 & 0.044340411097112 & 0.0886808221942241 & 0.955659588902888 \tabularnewline
127 & 0.0324893206026257 & 0.0649786412052515 & 0.967510679397374 \tabularnewline
128 & 0.0265196938231729 & 0.0530393876463458 & 0.973480306176827 \tabularnewline
129 & 0.0354095244571885 & 0.0708190489143769 & 0.964590475542812 \tabularnewline
130 & 0.0756928327423886 & 0.151385665484777 & 0.924307167257611 \tabularnewline
131 & 0.0671335667815051 & 0.13426713356301 & 0.932866433218495 \tabularnewline
132 & 0.0835814812044787 & 0.167162962408957 & 0.916418518795521 \tabularnewline
133 & 0.0627548965375502 & 0.1255097930751 & 0.93724510346245 \tabularnewline
134 & 0.0406588162775764 & 0.0813176325551528 & 0.959341183722424 \tabularnewline
135 & 0.10040587818138 & 0.20081175636276 & 0.89959412181862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186058&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0998681508049398[/C][C]0.19973630160988[/C][C]0.90013184919506[/C][/ROW]
[ROW][C]11[/C][C]0.0412950881943192[/C][C]0.0825901763886383[/C][C]0.958704911805681[/C][/ROW]
[ROW][C]12[/C][C]0.143442505900375[/C][C]0.286885011800751[/C][C]0.856557494099625[/C][/ROW]
[ROW][C]13[/C][C]0.113892754063305[/C][C]0.22778550812661[/C][C]0.886107245936695[/C][/ROW]
[ROW][C]14[/C][C]0.0964213792511674[/C][C]0.192842758502335[/C][C]0.903578620748833[/C][/ROW]
[ROW][C]15[/C][C]0.0541185595194001[/C][C]0.1082371190388[/C][C]0.9458814404806[/C][/ROW]
[ROW][C]16[/C][C]0.0809803958851101[/C][C]0.16196079177022[/C][C]0.91901960411489[/C][/ROW]
[ROW][C]17[/C][C]0.0486228329825001[/C][C]0.0972456659650002[/C][C]0.9513771670175[/C][/ROW]
[ROW][C]18[/C][C]0.211385076699255[/C][C]0.42277015339851[/C][C]0.788614923300745[/C][/ROW]
[ROW][C]19[/C][C]0.206278768740755[/C][C]0.412557537481509[/C][C]0.793721231259245[/C][/ROW]
[ROW][C]20[/C][C]0.160133282603513[/C][C]0.320266565207026[/C][C]0.839866717396487[/C][/ROW]
[ROW][C]21[/C][C]0.112314428600075[/C][C]0.224628857200151[/C][C]0.887685571399925[/C][/ROW]
[ROW][C]22[/C][C]0.22961179988871[/C][C]0.45922359977742[/C][C]0.77038820011129[/C][/ROW]
[ROW][C]23[/C][C]0.177579575171529[/C][C]0.355159150343058[/C][C]0.822420424828471[/C][/ROW]
[ROW][C]24[/C][C]0.222001377887947[/C][C]0.444002755775894[/C][C]0.777998622112053[/C][/ROW]
[ROW][C]25[/C][C]0.250061513378972[/C][C]0.500123026757944[/C][C]0.749938486621028[/C][/ROW]
[ROW][C]26[/C][C]0.198096277477587[/C][C]0.396192554955174[/C][C]0.801903722522413[/C][/ROW]
[ROW][C]27[/C][C]0.1515092599715[/C][C]0.303018519943001[/C][C]0.8484907400285[/C][/ROW]
[ROW][C]28[/C][C]0.116777965542262[/C][C]0.233555931084525[/C][C]0.883222034457738[/C][/ROW]
[ROW][C]29[/C][C]0.0860583464728357[/C][C]0.172116692945671[/C][C]0.913941653527164[/C][/ROW]
[ROW][C]30[/C][C]0.064228398113672[/C][C]0.128456796227344[/C][C]0.935771601886328[/C][/ROW]
[ROW][C]31[/C][C]0.0472401599360512[/C][C]0.0944803198721023[/C][C]0.952759840063949[/C][/ROW]
[ROW][C]32[/C][C]0.142582530852644[/C][C]0.285165061705288[/C][C]0.857417469147356[/C][/ROW]
[ROW][C]33[/C][C]0.283941342112414[/C][C]0.567882684224829[/C][C]0.716058657887586[/C][/ROW]
[ROW][C]34[/C][C]0.23398622346908[/C][C]0.46797244693816[/C][C]0.76601377653092[/C][/ROW]
[ROW][C]35[/C][C]0.273627508971985[/C][C]0.54725501794397[/C][C]0.726372491028015[/C][/ROW]
[ROW][C]36[/C][C]0.249003172861319[/C][C]0.498006345722638[/C][C]0.750996827138681[/C][/ROW]
[ROW][C]37[/C][C]0.233597030814728[/C][C]0.467194061629457[/C][C]0.766402969185272[/C][/ROW]
[ROW][C]38[/C][C]0.306495515898852[/C][C]0.612991031797704[/C][C]0.693504484101148[/C][/ROW]
[ROW][C]39[/C][C]0.342594463465018[/C][C]0.685188926930036[/C][C]0.657405536534982[/C][/ROW]
[ROW][C]40[/C][C]0.295712763857155[/C][C]0.59142552771431[/C][C]0.704287236142845[/C][/ROW]
[ROW][C]41[/C][C]0.249300848631448[/C][C]0.498601697262897[/C][C]0.750699151368552[/C][/ROW]
[ROW][C]42[/C][C]0.206976877513819[/C][C]0.413953755027638[/C][C]0.793023122486181[/C][/ROW]
[ROW][C]43[/C][C]0.34518091136958[/C][C]0.69036182273916[/C][C]0.65481908863042[/C][/ROW]
[ROW][C]44[/C][C]0.300603004826587[/C][C]0.601206009653175[/C][C]0.699396995173413[/C][/ROW]
[ROW][C]45[/C][C]0.2558076119972[/C][C]0.511615223994401[/C][C]0.7441923880028[/C][/ROW]
[ROW][C]46[/C][C]0.219145726373297[/C][C]0.438291452746594[/C][C]0.780854273626703[/C][/ROW]
[ROW][C]47[/C][C]0.214460162203508[/C][C]0.428920324407017[/C][C]0.785539837796492[/C][/ROW]
[ROW][C]48[/C][C]0.206130341283176[/C][C]0.412260682566351[/C][C]0.793869658716824[/C][/ROW]
[ROW][C]49[/C][C]0.184543244318227[/C][C]0.369086488636455[/C][C]0.815456755681773[/C][/ROW]
[ROW][C]50[/C][C]0.153271431952114[/C][C]0.306542863904228[/C][C]0.846728568047886[/C][/ROW]
[ROW][C]51[/C][C]0.126532394382009[/C][C]0.253064788764018[/C][C]0.873467605617991[/C][/ROW]
[ROW][C]52[/C][C]0.187392914594323[/C][C]0.374785829188647[/C][C]0.812607085405677[/C][/ROW]
[ROW][C]53[/C][C]0.154947009045421[/C][C]0.309894018090841[/C][C]0.84505299095458[/C][/ROW]
[ROW][C]54[/C][C]0.230394122602688[/C][C]0.460788245205377[/C][C]0.769605877397312[/C][/ROW]
[ROW][C]55[/C][C]0.195796003509688[/C][C]0.391592007019376[/C][C]0.804203996490312[/C][/ROW]
[ROW][C]56[/C][C]0.163462615993298[/C][C]0.326925231986597[/C][C]0.836537384006702[/C][/ROW]
[ROW][C]57[/C][C]0.133853297788015[/C][C]0.267706595576029[/C][C]0.866146702211985[/C][/ROW]
[ROW][C]58[/C][C]0.108224347774575[/C][C]0.216448695549149[/C][C]0.891775652225425[/C][/ROW]
[ROW][C]59[/C][C]0.0908635326357157[/C][C]0.181727065271431[/C][C]0.909136467364284[/C][/ROW]
[ROW][C]60[/C][C]0.0746396714727008[/C][C]0.149279342945402[/C][C]0.925360328527299[/C][/ROW]
[ROW][C]61[/C][C]0.0603189153144252[/C][C]0.12063783062885[/C][C]0.939681084685575[/C][/ROW]
[ROW][C]62[/C][C]0.0471882262688312[/C][C]0.0943764525376624[/C][C]0.952811773731169[/C][/ROW]
[ROW][C]63[/C][C]0.0783284403065101[/C][C]0.15665688061302[/C][C]0.92167155969349[/C][/ROW]
[ROW][C]64[/C][C]0.0618948066111306[/C][C]0.123789613222261[/C][C]0.938105193388869[/C][/ROW]
[ROW][C]65[/C][C]0.0481095799220272[/C][C]0.0962191598440545[/C][C]0.951890420077973[/C][/ROW]
[ROW][C]66[/C][C]0.0368695983081973[/C][C]0.0737391966163946[/C][C]0.963130401691803[/C][/ROW]
[ROW][C]67[/C][C]0.0279486605049607[/C][C]0.0558973210099214[/C][C]0.972051339495039[/C][/ROW]
[ROW][C]68[/C][C]0.0212403774083385[/C][C]0.042480754816677[/C][C]0.978759622591661[/C][/ROW]
[ROW][C]69[/C][C]0.015690781371479[/C][C]0.031381562742958[/C][C]0.984309218628521[/C][/ROW]
[ROW][C]70[/C][C]0.0114070180784785[/C][C]0.0228140361569571[/C][C]0.988592981921521[/C][/ROW]
[ROW][C]71[/C][C]0.0082224466653318[/C][C]0.0164448933306636[/C][C]0.991777553334668[/C][/ROW]
[ROW][C]72[/C][C]0.00587707185410325[/C][C]0.0117541437082065[/C][C]0.994122928145897[/C][/ROW]
[ROW][C]73[/C][C]0.00414120608293252[/C][C]0.00828241216586504[/C][C]0.995858793917067[/C][/ROW]
[ROW][C]74[/C][C]0.0116420822398334[/C][C]0.0232841644796669[/C][C]0.988357917760167[/C][/ROW]
[ROW][C]75[/C][C]0.0085329939776872[/C][C]0.0170659879553744[/C][C]0.991467006022313[/C][/ROW]
[ROW][C]76[/C][C]0.0276382692772405[/C][C]0.0552765385544811[/C][C]0.972361730722759[/C][/ROW]
[ROW][C]77[/C][C]0.0206887531818707[/C][C]0.0413775063637414[/C][C]0.979311246818129[/C][/ROW]
[ROW][C]78[/C][C]0.0152332002063595[/C][C]0.0304664004127189[/C][C]0.984766799793641[/C][/ROW]
[ROW][C]79[/C][C]0.0111134352698617[/C][C]0.0222268705397234[/C][C]0.988886564730138[/C][/ROW]
[ROW][C]80[/C][C]0.00803612516758354[/C][C]0.0160722503351671[/C][C]0.991963874832416[/C][/ROW]
[ROW][C]81[/C][C]0.00585830524155036[/C][C]0.0117166104831007[/C][C]0.99414169475845[/C][/ROW]
[ROW][C]82[/C][C]0.00438672635686683[/C][C]0.00877345271373367[/C][C]0.995613273643133[/C][/ROW]
[ROW][C]83[/C][C]0.00307238745409745[/C][C]0.00614477490819489[/C][C]0.996927612545903[/C][/ROW]
[ROW][C]84[/C][C]0.00944104040055249[/C][C]0.018882080801105[/C][C]0.990558959599448[/C][/ROW]
[ROW][C]85[/C][C]0.00670645150880889[/C][C]0.0134129030176178[/C][C]0.993293548491191[/C][/ROW]
[ROW][C]86[/C][C]0.00469718485983193[/C][C]0.00939436971966386[/C][C]0.995302815140168[/C][/ROW]
[ROW][C]87[/C][C]0.00334022468565551[/C][C]0.00668044937131102[/C][C]0.996659775314344[/C][/ROW]
[ROW][C]88[/C][C]0.00231541787046612[/C][C]0.00463083574093223[/C][C]0.997684582129534[/C][/ROW]
[ROW][C]89[/C][C]0.00156319105221495[/C][C]0.00312638210442991[/C][C]0.998436808947785[/C][/ROW]
[ROW][C]90[/C][C]0.00325038702200734[/C][C]0.00650077404401467[/C][C]0.996749612977993[/C][/ROW]
[ROW][C]91[/C][C]0.00232190423500275[/C][C]0.00464380847000551[/C][C]0.997678095764997[/C][/ROW]
[ROW][C]92[/C][C]0.00163697601117268[/C][C]0.00327395202234535[/C][C]0.998363023988827[/C][/ROW]
[ROW][C]93[/C][C]0.00130655015808921[/C][C]0.00261310031617843[/C][C]0.998693449841911[/C][/ROW]
[ROW][C]94[/C][C]0.0021859451518815[/C][C]0.00437189030376299[/C][C]0.997814054848118[/C][/ROW]
[ROW][C]95[/C][C]0.00719443370598602[/C][C]0.014388867411972[/C][C]0.992805566294014[/C][/ROW]
[ROW][C]96[/C][C]0.0161913215231366[/C][C]0.0323826430462731[/C][C]0.983808678476863[/C][/ROW]
[ROW][C]97[/C][C]0.0116805772129298[/C][C]0.0233611544258595[/C][C]0.98831942278707[/C][/ROW]
[ROW][C]98[/C][C]0.00848970176674959[/C][C]0.0169794035334992[/C][C]0.99151029823325[/C][/ROW]
[ROW][C]99[/C][C]0.00645567512606899[/C][C]0.012911350252138[/C][C]0.993544324873931[/C][/ROW]
[ROW][C]100[/C][C]0.00492514211982071[/C][C]0.00985028423964143[/C][C]0.995074857880179[/C][/ROW]
[ROW][C]101[/C][C]0.00347991676164879[/C][C]0.00695983352329757[/C][C]0.996520083238351[/C][/ROW]
[ROW][C]102[/C][C]0.00259025417479145[/C][C]0.00518050834958289[/C][C]0.997409745825209[/C][/ROW]
[ROW][C]103[/C][C]0.00206184824543869[/C][C]0.00412369649087738[/C][C]0.997938151754561[/C][/ROW]
[ROW][C]104[/C][C]0.00180507184031387[/C][C]0.00361014368062773[/C][C]0.998194928159686[/C][/ROW]
[ROW][C]105[/C][C]0.00259325888581258[/C][C]0.00518651777162517[/C][C]0.997406741114187[/C][/ROW]
[ROW][C]106[/C][C]0.00194318820151717[/C][C]0.00388637640303434[/C][C]0.998056811798483[/C][/ROW]
[ROW][C]107[/C][C]0.00125698459452997[/C][C]0.00251396918905993[/C][C]0.99874301540547[/C][/ROW]
[ROW][C]108[/C][C]0.00636922579863324[/C][C]0.0127384515972665[/C][C]0.993630774201367[/C][/ROW]
[ROW][C]109[/C][C]0.00454196184443935[/C][C]0.0090839236888787[/C][C]0.995458038155561[/C][/ROW]
[ROW][C]110[/C][C]0.0127355463667464[/C][C]0.0254710927334927[/C][C]0.987264453633254[/C][/ROW]
[ROW][C]111[/C][C]0.0260460616516276[/C][C]0.0520921233032551[/C][C]0.973953938348372[/C][/ROW]
[ROW][C]112[/C][C]0.0186230905980797[/C][C]0.0372461811961595[/C][C]0.98137690940192[/C][/ROW]
[ROW][C]113[/C][C]0.0321360031028008[/C][C]0.0642720062056015[/C][C]0.967863996897199[/C][/ROW]
[ROW][C]114[/C][C]0.0231724054992501[/C][C]0.0463448109985002[/C][C]0.97682759450075[/C][/ROW]
[ROW][C]115[/C][C]0.0532192049074807[/C][C]0.106438409814961[/C][C]0.946780795092519[/C][/ROW]
[ROW][C]116[/C][C]0.0920097270658157[/C][C]0.184019454131631[/C][C]0.907990272934184[/C][/ROW]
[ROW][C]117[/C][C]0.069901597123217[/C][C]0.139803194246434[/C][C]0.930098402876783[/C][/ROW]
[ROW][C]118[/C][C]0.0510191656711072[/C][C]0.102038331342214[/C][C]0.948980834328893[/C][/ROW]
[ROW][C]119[/C][C]0.0506044685499154[/C][C]0.101208937099831[/C][C]0.949395531450085[/C][/ROW]
[ROW][C]120[/C][C]0.0471131245474716[/C][C]0.0942262490949432[/C][C]0.952886875452528[/C][/ROW]
[ROW][C]121[/C][C]0.0762726782750317[/C][C]0.152545356550063[/C][C]0.923727321724968[/C][/ROW]
[ROW][C]122[/C][C]0.0612009516039084[/C][C]0.122401903207817[/C][C]0.938799048396092[/C][/ROW]
[ROW][C]123[/C][C]0.0897353033770012[/C][C]0.179470606754002[/C][C]0.910264696622999[/C][/ROW]
[ROW][C]124[/C][C]0.0675302384117501[/C][C]0.1350604768235[/C][C]0.93246976158825[/C][/ROW]
[ROW][C]125[/C][C]0.0584865381694799[/C][C]0.11697307633896[/C][C]0.94151346183052[/C][/ROW]
[ROW][C]126[/C][C]0.044340411097112[/C][C]0.0886808221942241[/C][C]0.955659588902888[/C][/ROW]
[ROW][C]127[/C][C]0.0324893206026257[/C][C]0.0649786412052515[/C][C]0.967510679397374[/C][/ROW]
[ROW][C]128[/C][C]0.0265196938231729[/C][C]0.0530393876463458[/C][C]0.973480306176827[/C][/ROW]
[ROW][C]129[/C][C]0.0354095244571885[/C][C]0.0708190489143769[/C][C]0.964590475542812[/C][/ROW]
[ROW][C]130[/C][C]0.0756928327423886[/C][C]0.151385665484777[/C][C]0.924307167257611[/C][/ROW]
[ROW][C]131[/C][C]0.0671335667815051[/C][C]0.13426713356301[/C][C]0.932866433218495[/C][/ROW]
[ROW][C]132[/C][C]0.0835814812044787[/C][C]0.167162962408957[/C][C]0.916418518795521[/C][/ROW]
[ROW][C]133[/C][C]0.0627548965375502[/C][C]0.1255097930751[/C][C]0.93724510346245[/C][/ROW]
[ROW][C]134[/C][C]0.0406588162775764[/C][C]0.0813176325551528[/C][C]0.959341183722424[/C][/ROW]
[ROW][C]135[/C][C]0.10040587818138[/C][C]0.20081175636276[/C][C]0.89959412181862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186058&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09986815080493980.199736301609880.90013184919506
110.04129508819431920.08259017638863830.958704911805681
120.1434425059003750.2868850118007510.856557494099625
130.1138927540633050.227785508126610.886107245936695
140.09642137925116740.1928427585023350.903578620748833
150.05411855951940010.10823711903880.9458814404806
160.08098039588511010.161960791770220.91901960411489
170.04862283298250010.09724566596500020.9513771670175
180.2113850766992550.422770153398510.788614923300745
190.2062787687407550.4125575374815090.793721231259245
200.1601332826035130.3202665652070260.839866717396487
210.1123144286000750.2246288572001510.887685571399925
220.229611799888710.459223599777420.77038820011129
230.1775795751715290.3551591503430580.822420424828471
240.2220013778879470.4440027557758940.777998622112053
250.2500615133789720.5001230267579440.749938486621028
260.1980962774775870.3961925549551740.801903722522413
270.15150925997150.3030185199430010.8484907400285
280.1167779655422620.2335559310845250.883222034457738
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310.04724015993605120.09448031987210230.952759840063949
320.1425825308526440.2851650617052880.857417469147356
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340.233986223469080.467972446938160.76601377653092
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390.3425944634650180.6851889269300360.657405536534982
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430.345180911369580.690361822739160.65481908863042
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450.25580761199720.5116152239944010.7441923880028
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480.2061303412831760.4122606825663510.793869658716824
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530.1549470090454210.3098940180908410.84505299095458
540.2303941226026880.4607882452053770.769605877397312
550.1957960035096880.3915920070193760.804203996490312
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700.01140701807847850.02281403615695710.988592981921521
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750.00853299397768720.01706598795537440.991467006022313
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800.008036125167583540.01607225033516710.991963874832416
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840.009441040400552490.0188820808011050.990558959599448
850.006706451508808890.01341290301761780.993293548491191
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870.003340224685655510.006680449371311020.996659775314344
880.002315417870466120.004630835740932230.997684582129534
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900.003250387022007340.006500774044014670.996749612977993
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920.001636976011172680.003273952022345350.998363023988827
930.001306550158089210.002613100316178430.998693449841911
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950.007194433705986020.0143888674119720.992805566294014
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980.008489701766749590.01697940353349920.99151029823325
990.006455675126068990.0129113502521380.993544324873931
1000.004925142119820710.009850284239641430.995074857880179
1010.003479916761648790.006959833523297570.996520083238351
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1340.04065881627757640.08131763255515280.959341183722424
1350.100405878181380.200811756362760.89959412181862







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.166666666666667NOK
5% type I error level440.349206349206349NOK
10% type I error level600.476190476190476NOK

\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 & 21 & 0.166666666666667 & NOK \tabularnewline
5% type I error level & 44 & 0.349206349206349 & NOK \tabularnewline
10% type I error level & 60 & 0.476190476190476 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186058&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]21[/C][C]0.166666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.349206349206349[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]60[/C][C]0.476190476190476[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186058&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186058&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.166666666666667NOK
5% type I error level440.349206349206349NOK
10% type I error level600.476190476190476NOK



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')
}