## Free Statistics

of Irreproducible Research!

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 13:47:09 -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/t13521412640a0n647u6plnakp.htm/, Retrieved Wed, 01 Feb 2023 15:34:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186216, Retrieved Wed, 01 Feb 2023 15:34:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7] [2012-11-05 18:47:09] [9fce0523ac0e7dfdcafaec3da59cfa0a] [Current]
- R PD    [Multiple Regression] [] [2012-11-05 21:39:19] [15cbcfe738d59edaf37329746b028204]
Feedback Forum

Post a new message
Dataseries X:
2000	501	134	368	6.70	8.50	8.70
2000	485	124	361	6.80	8.40	8.60
2000	464	113	351	6.70	8.40	8.60
2000	460	109	351	6.60	8.30	8.50
2001	467	109	358	6.40	8.20	8.50
2001	460	106	354	6.30	8.20	8.50
2001	448	101	347	6.30	8.10	8.50
2001	443	98	345	6.50	8.10	8.50
2001	436	93	343	6.50	8.10	8.50
2001	431	91	340	6.40	8.10	8.50
2001	484	122	362	6.20	8.10	8.50
2001	510	139	370	6.20	8.10	8.60
2001	513	140	373	6.50	8.10	8.60
2001	503	132	371	7.00	8.20	8.60
2001	471	117	354	7.20	8.20	8.70
2001	471	114	357	7.30	8.30	8.70
2002	476	113	363	7.40	8.20	8.70
2002	475	110	364	7.40	8.30	8.80
2002	470	107	363	7.40	8.30	8.80
2002	461	103	358	7.30	8.40	8.90
2002	455	98	357	7.40	8.50	8.90
2002	456	98	357	7.40	8.50	8.90
2002	517	137	380	7.60	8.60	9.00
2002	525	148	378	7.60	8.60	9.00
2002	523	147	376	7.70	8.70	9.00
2002	519	139	380	7.70	8.70	9.00
2002	509	130	379	7.80	8.80	9.00
2002	512	128	384	7.80	8.80	9.00
2003	519	127	392	8.00	8.90	9.10
2003	517	123	394	8.10	9.00	9.10
2003	510	118	392	8.10	9.00	9.10
2003	509	114	396	8.20	9.00	9.10
2003	501	108	392	8.10	9.00	9.10
2003	507	111	396	8.10	9.10	9.10
2003	569	151	419	8.10	9.10	9.10
2003	580	159	421	8.10	9.00	9.10
2003	578	158	420	8.20	9.10	9.10
2003	565	148	418	8.20	9.00	9.10
2003	547	138	410	8.30	9.10	9.10
2003	555	137	418	8.40	9.10	9.20
2004	562	136	426	8.60	9.20	9.30
2004	561	133	428	8.60	9.20	9.30
2004	555	126	430	8.40	9.20	9.30
2004	544	120	424	8.00	9.20	9.20
2004	537	114	423	7.90	9.20	9.20
2004	543	116	427	8.10	9.30	9.20
2004	594	153	441	8.50	9.30	9.20
2004	611	162	449	8.80	9.30	9.20
2004	613	161	452	8.80	9.30	9.20
2004	611	149	462	8.50	9.30	9.20
2004	594	139	455	8.30	9.40	9.20
2004	595	135	461	8.30	9.40	9.20
2005	591	130	461	8.30	9.30	9.20
2005	589	127	463	8.40	9.30	9.20
2005	584	122	462	8.50	9.30	9.20
2005	573	117	456	8.50	9.30	9.20
2005	567	112	455	8.60	9.20	9.10
2005	569	113	456	8.50	9.20	9.10
2005	621	149	472	8.60	9.20	9.00
2005	629	157	472	8.60	9.10	8.90
2005	628	157	471	8.60	9.10	8.90
2005	612	147	465	8.50	9.10	9.00
2005	595	137	459	8.40	9.10	8.90
2005	597	132	465	8.40	9.00	8.80
2006	593	125	468	8.50	8.90	8.70
2006	590	123	467	8.50	8.80	8.60
2006	580	117	463	8.50	8.70	8.50
2006	574	114	460	8.60	8.60	8.50
2006	573	111	462	8.60	8.60	8.40
2006	573	112	461	8.40	8.50	8.30
2006	620	144	476	8.20	8.40	8.20
2006	626	150	476	8.00	8.40	8.20
2006	620	149	471	8.00	8.30	8.10
2006	588	134	453	8.00	8.20	8.00
2006	566	123	443	8.00	8.20	7.90
2006	557	116	442	7.90	8.00	7.80
2007	561	117	444	7.90	7.90	7.60
2007	549	111	438	7.90	7.80	7.50
2007	532	105	427	7.90	7.70	7.40
2007	526	102	424	8.00	7.60	7.30
2007	511	95	416	7.90	7.60	7.30
2007	499	93	406	7.40	7.60	7.20
2007	555	124	431	7.20	7.60	7.20
2007	565	130	434	7.00	7.60	7.20
2007	542	124	418	6.90	7.50	7.10
2007	527	115	412	7.10	7.50	7.00
2007	510	106	404	7.20	7.40	7.00
2007	514	105	409	7.20	7.40	6.90
2008	517	105	412	7.10	7.40	6.90
2008	508	101	406	6.90	7.30	6.80
2008	493	95	398	6.80	7.30	6.80
2008	490	93	397	6.80	7.40	6.80
2008	469	84	385	6.80	7.50	6.90
2008	478	87	390	6.90	7.60	7.00
2008	528	116	413	7.10	7.60	7.00
2008	534	120	413	7.20	7.70	7.10
2008	518	117	401	7.20	7.70	7.20
2008	506	109	397	7.10	7.90	7.30
2008	502	105	397	7.10	8.10	7.50
2008	516	107	409	7.20	8.40	7.70
2009	528	109	419	7.50	8.70	8.10
2009	533	109	424	7.70	9.00	8.40
2009	536	108	428	7.80	9.30	8.60
2009	537	107	430	7.70	9.40	8.80
2009	524	99	424	7.70	9.50	8.90
2009	536	103	433	7.80	9.60	9.10
2009	587	131	456	8.00	9.80	9.20
2009	597	137	459	8.10	9.80	9.30
2009	581	135	446	8.10	9.90	9.40
2009	564	124	441	8.00	10.00	9.40
2009	558	118	439	8.10	10.00	9.50
2010	575	121	454	8.20	10.10	9.50
2010	580	121	460	8.40	10.10	9.70
2010	575	118	457	8.50	10.10	9.70
2010	563	113	451	8.50	10.10	9.70
2010	552	107	444	8.50	10.20	9.70
2010	537	100	437	8.50	10.20	9.70
2010	545	102	443	8.50	10.10	9.60
2010	601	130	471	8.40	10.10	9.60
2010	604	136	469	8.30	10.10	9.60
2010	586	133	454	8.20	10.10	9.60
2010	564	120	444	8.10	10.10	9.60
2010	549	112	436	7.90	10.10	9.60
2010	551	109	442	7.60	10.10	9.60
2011	556	110	446	7.30	10.00	9.50
2011	548	106	442	7.10	9.90	9.50
2011	540	102	438	7.00	9.90	9.40
2011	531	98	433	7.10	9.90	9.40
2011	521	92	428	7.10	9.90	9.50
2011	519	92	426	7.10	10.00	9.50
2011	572	120	452	7.30	10.10	9.60
2011	581	127	455	7.30	10.20	9.70
2011	563	124	439	7.30	10.30	9.80
2011	548	114	434	7.20	10.50	9.90
2011	539	108	431	7.20	10.60	10.00
2011	541	106	435	7.10	10.70	10.00
2012	562	111	450	7.10	10.80	10.10
2012	559	110	449	7.10	10.90	10.20
2012	546	104	442	7.20	11.00	10.30
2012	536	100	437	7.30	11.20	10.30
2012	528	96	431	7.40	11.30	10.40
2012	530	98	433	7.40	11.40	10.50
2012	582	122	460	7.50	11.50	10.50
2012	599	134	465	7.40	11.50	10.60
2012	584	133	451	7.40	11.60	10.60


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Totale_werkloosheid[t] = -110.139290815839 + 0.0557688214312652Jaartal[t] + 0.995998265315216Jonger_dan_25[t] + 1.00020013681646Vanaf_25[t] -0.0773667171300518Belgi\303\253[t] -0.45287714325954Euroraad[t] + 0.376606852518923EU-27[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totale_werkloosheid[t] =  -110.139290815839 +  0.0557688214312652Jaartal[t] +  0.995998265315216Jonger_dan_25[t] +  1.00020013681646Vanaf_25[t] -0.0773667171300518Belgi\303\253[t] -0.45287714325954Euroraad[t] +  0.376606852518923EU-27[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totale_werkloosheid[t] =  -110.139290815839 +  0.0557688214312652Jaartal[t] +  0.995998265315216Jonger_dan_25[t] +  1.00020013681646Vanaf_25[t] -0.0773667171300518Belgi\303\253[t] -0.45287714325954Euroraad[t] +  0.376606852518923EU-27[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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 Totale_werkloosheid[t] = -110.139290815839 + 0.0557688214312652Jaartal[t] + 0.995998265315216Jonger_dan_25[t] + 1.00020013681646Vanaf_25[t] -0.0773667171300518Belgi\303\253[t] -0.45287714325954Euroraad[t] + 0.376606852518923EU-27[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -110.139290815839 88.54661 -1.2439 0.215661 0.107831 Jaartal 0.0557688214312652 0.044331 1.258 0.210509 0.105255 Jonger_dan_25 0.995998265315216 0.003604 276.3903 0 0 Vanaf_25 1.00020013681646 0.003015 331.711 0 0 Belgi\303\253 -0.0773667171300518 0.11269 -0.6865 0.493521 0.24676 Euroraad -0.45287714325954 0.352526 -1.2847 0.201062 0.100531 EU-27 0.376606852518923 0.331026 1.1377 0.257219 0.128609

\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) & -110.139290815839 & 88.54661 & -1.2439 & 0.215661 & 0.107831 \tabularnewline
Jaartal & 0.0557688214312652 & 0.044331 & 1.258 & 0.210509 & 0.105255 \tabularnewline
Jonger_dan_25 & 0.995998265315216 & 0.003604 & 276.3903 & 0 & 0 \tabularnewline
Vanaf_25 & 1.00020013681646 & 0.003015 & 331.711 & 0 & 0 \tabularnewline
Belgi\303\253 & -0.0773667171300518 & 0.11269 & -0.6865 & 0.493521 & 0.24676 \tabularnewline
Euroraad & -0.45287714325954 & 0.352526 & -1.2847 & 0.201062 & 0.100531 \tabularnewline
EU-27 & 0.376606852518923 & 0.331026 & 1.1377 & 0.257219 & 0.128609 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&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]-110.139290815839[/C][C]88.54661[/C][C]-1.2439[/C][C]0.215661[/C][C]0.107831[/C][/ROW]
[ROW][C]Jaartal[/C][C]0.0557688214312652[/C][C]0.044331[/C][C]1.258[/C][C]0.210509[/C][C]0.105255[/C][/ROW]
[ROW][C]Jonger_dan_25[/C][C]0.995998265315216[/C][C]0.003604[/C][C]276.3903[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vanaf_25[/C][C]1.00020013681646[/C][C]0.003015[/C][C]331.711[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belgi\303\253[/C][C]-0.0773667171300518[/C][C]0.11269[/C][C]-0.6865[/C][C]0.493521[/C][C]0.24676[/C][/ROW]
[ROW][C]EU-27[/C][C]0.376606852518923[/C][C]0.331026[/C][C]1.1377[/C][C]0.257219[/C][C]0.128609[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -110.139290815839 88.54661 -1.2439 0.215661 0.107831 Jaartal 0.0557688214312652 0.044331 1.258 0.210509 0.105255 Jonger_dan_25 0.995998265315216 0.003604 276.3903 0 0 Vanaf_25 1.00020013681646 0.003015 331.711 0 0 Belgi\303\253 -0.0773667171300518 0.11269 -0.6865 0.493521 0.24676 Euroraad -0.45287714325954 0.352526 -1.2847 0.201062 0.100531 EU-27 0.376606852518923 0.331026 1.1377 0.257219 0.128609

 Multiple Linear Regression - Regression Statistics Multiple R 0.99994109880369 R-squared 0.999882201076731 Adjusted R-squared 0.999877079384415 F-TEST (value) 195224.96459656 F-TEST (DF numerator) 6 F-TEST (DF denominator) 138 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.502362510695439 Sum Squared Residuals 34.8267967170071

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99994109880369 \tabularnewline
R-squared & 0.999882201076731 \tabularnewline
F-TEST (value) & 195224.96459656 \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 & 0.502362510695439 \tabularnewline
Sum Squared Residuals & 34.8267967170071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99994109880369[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999882201076731[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]195224.96459656[/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]0.502362510695439[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.8267967170071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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 R 0.99994109880369 R-squared 0.999882201076731 Adjusted R-squared 0.999877079384415 F-TEST (value) 195224.96459656 F-TEST (DF numerator) 6 F-TEST (DF denominator) 138 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.502362510695439 Sum Squared Residuals 34.8267967170071

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 501 501.844436841823 -0.844436841823466 2 485 484.882943588317 0.117056411682668 3 464 463.932697973398 0.0673020266015946 4 460 459.964068612925 0.0359313870753726 5 467 467.081999449823 -0.0819994498230227 6 460 460.100940778325 -0.100940778324548 7 448 448.164836208359 -0.164836208359217 8 443 443.160967795355 -0.160967795354641 9 436 436.180576195146 -0.180576195145646 10 431 431.195715925779 -0.195715925778848 11 484 484.091538503939 -0.0915385039386273 12 510 509.062770794081 0.937229205919149 13 513 513.036159454706 -0.0361594547064236 14 503 502.983801985661 0.0161980143392029 15 471 471.062613021879 -0.0626130218786618 16 471 471.022194250343 -0.0221942503434258 17 476 476.120716669971 -0.120716669971165 18 475 474.125294981768 0.87470501823209 19 470 470.137100049006 -0.137100049005804 20 461 461.152215946302 -0.152215946301601 21 455 455.11900009687 -0.119000096870104 22 456 455.11900009687 0.880999903129895 23 517 516.944435218442 0.0555647815580166 24 525 525.900015863276 -0.900015863276438 25 523 522.850592938289 0.149407061710655 26 519 518.883407363033 0.116592636966544 27 509 508.866198452341 0.133801547658903 28 512 511.875202605793 0.124797394207046 29 519 518.913473883941 0.0865261160594088 30 517 516.876856710274 0.123143289726316 31 510 509.896465110065 0.103534889935311 32 509 509.905535924357 -0.905535924356648 33 501 499.936482456913 1.06351754308748 34 507 506.879990085798 0.120009914201947 35 569 569.724523845185 -0.72452384518521 36 580 579.738197955666 0.261802044334196 37 578 577.688975167495 0.311024832504827 38 565 565.773879955036 -0.773879955036055 39 547 547.759271821313 -0.759271821313276 40 555 554.794798664069 0.20520133593139 41 562 561.833069942216 0.166930057783753 42 561 560.845475419904 0.154524580096485 43 555 555.889361179756 -0.889361179755928 44 544 543.905456768566 0.0945432314339837 45 537 536.937003711571 0.062996288428732 46 543 542.869039731716 0.130960268284434 47 594 593.692830776957 0.307169223043061 48 611 610.635206244187 0.364793755813476 49 613 612.639808389321 0.360191610679318 50 611 610.713040588842 0.286959411158317 51 594 593.721842607074 0.278157392925617 52 595 595.739050366712 -0.739050366712259 53 591 590.860115575893 0.139884424106596 54 589 589.864784381868 -0.864784381867655 55 584 583.876856246762 0.123143753237886 56 573 572.895664099287 0.104335900712707 57 567 566.915362993256 0.0846370067441861 58 569 568.9192980671 0.0807019328995088 59 621 620.733040450547 0.266959549453318 60 629 628.708653602142 0.291346397857532 61 628 627.708453465326 0.291546534673988 62 612 611.79266734824 0.207332651759988 63 595 595.80155986065 -0.801559860650222 64 597 596.830396384047 0.169603615953054 65 593 592.914668116082 0.0853318839178652 66 590 589.930098477709 0.0699015222906932 67 580 579.960935367626 0.0390646323737611 68 574 574.009891203844 -0.00989120384416442 69 573 572.98463599628 0.0153640037204581 70 573 573.003534497278 -0.00353449727837724 71 620 619.901581412112 0.0984185878877877 72 626 625.89304434743 0.106955652570485 73 620 619.903672427106 0.0963275728939233 74 588 586.967723013756 1.03227698624434 75 566 565.972080041872 0.0279199581281656 76 557 558.060543462962 -1.06054346296188 77 561 561.082677167163 -0.0826771671634487 78 549 549.113113783447 -0.113113783447465 79 532 532.142549715649 -0.142549715649194 80 526 526.153844866615 -0.153844866615228 81 511 511.18799258659 -0.187992586590061 82 499 499.195017361108 -0.19501736110819 83 555 555.091440349717 -0.0914403497173359 84 565 564.083503695484 0.916496304515982 85 542 542.119675615317 -0.119675615316462 86 527 527.101356377903 -0.10135637790287 87 510 510.173321938147 -0.173321938147215 88 514 514.140663671662 -0.140663671662397 89 517 517.204769575256 -0.204769575256036 90 508 507.242676065596 0.757323934403502 91 493 493.272822050887 -0.272822050886545 92 490 490.235337669114 -0.235337669113703 93 469 469.261324610405 -0.261324610405201 94 478 477.234956389646 0.765043610353934 95 528 529.10803588714 -1.10803588713985 96 534 533.076665247614 0.923334752386349 97 518 518.123929495122 -0.123929495122407 98 506 506.109964753648 -0.109964753647833 99 502 502.110717634239 -0.110717634238847 100 516 516.03683736248 -0.0368373624796877 101 528 528.078173665597 -0.0781736655966503 102 533 533.040819919031 -0.0408199190307438 103 536 535.977343756794 0.0226562432057249 104 537 537.019516093003 -0.01951609300281 105 524 523.042702120508 0.957297879491723 106 536 536.050793397582 -0.0507933975820834 107 587 586.874959886361 0.125040113639369 108 597 595.88147390224 1.11852609775981 109 581 580.879248563922 0.12075143607825 110 564 564.884715918759 -0.884715918759135 111 558 556.938250066774 1.06174993322619 112 575 574.931991350359 0.0680086496413746 113 580 580.993040198335 -0.993040198335143 114 575 574.996708320227 0.00329167977288161 115 563 564.015516172752 -1.0155161727523 116 552 550.99283790882 1.00716209118016 117 537 537.019449093898 -0.0194490938981244 118 545 545.020273474501 -0.0202734745013668 119 601 600.921565405901 0.0784345940987712 120 604 604.904891395873 -0.904891395872614 121 586 586.921631219393 -0.921631219393104 122 564 563.979389073844 0.0206109261562677 123 549 548.025275200216 0.974724799783648 124 551 551.061691240309 -0.0616912403084659 125 556 556.145095918534 -0.145095918533856 126 548 548.221063367759 -0.221063367759126 127 540 540.206345745694 -0.206345745693543 128 531 531.213615328637 -0.213615328637379 129 521 520.274285737916 0.725714262084305 130 519 518.228597749957 0.771402250043174 131 572 572.098652363511 -0.0986523635106971 132 581 582.063613602093 -1.06361360209252 133 563 563.064789588009 -0.0647895880094908 134 548 548.058628179088 -0.0586281790880352 135 539 539.074411147673 -0.0744111476733008 136 541 541.045664121696 -0.0456641216957534 137 562 561.076799292876 0.923200707124103 138 559 559.07297386167 -0.0729738616701632 139 546 546.080219611277 -0.080219611276598 140 536 536.996913765568 -0.996913765568534 141 528 526.996356182622 1.00364381737815 142 530 530.981125957811 -0.981125957811138 143 582 581.837463633382 0.162536366618282 144 599 598.835840858211 0.16415914178851 145 584 583.79175296314 0.208247036860084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.844436841823 & -0.844436841823466 \tabularnewline
2 & 485 & 484.882943588317 & 0.117056411682668 \tabularnewline
3 & 464 & 463.932697973398 & 0.0673020266015946 \tabularnewline
4 & 460 & 459.964068612925 & 0.0359313870753726 \tabularnewline
5 & 467 & 467.081999449823 & -0.0819994498230227 \tabularnewline
6 & 460 & 460.100940778325 & -0.100940778324548 \tabularnewline
7 & 448 & 448.164836208359 & -0.164836208359217 \tabularnewline
8 & 443 & 443.160967795355 & -0.160967795354641 \tabularnewline
9 & 436 & 436.180576195146 & -0.180576195145646 \tabularnewline
10 & 431 & 431.195715925779 & -0.195715925778848 \tabularnewline
11 & 484 & 484.091538503939 & -0.0915385039386273 \tabularnewline
12 & 510 & 509.062770794081 & 0.937229205919149 \tabularnewline
13 & 513 & 513.036159454706 & -0.0361594547064236 \tabularnewline
14 & 503 & 502.983801985661 & 0.0161980143392029 \tabularnewline
15 & 471 & 471.062613021879 & -0.0626130218786618 \tabularnewline
16 & 471 & 471.022194250343 & -0.0221942503434258 \tabularnewline
17 & 476 & 476.120716669971 & -0.120716669971165 \tabularnewline
18 & 475 & 474.125294981768 & 0.87470501823209 \tabularnewline
19 & 470 & 470.137100049006 & -0.137100049005804 \tabularnewline
20 & 461 & 461.152215946302 & -0.152215946301601 \tabularnewline
21 & 455 & 455.11900009687 & -0.119000096870104 \tabularnewline
22 & 456 & 455.11900009687 & 0.880999903129895 \tabularnewline
23 & 517 & 516.944435218442 & 0.0555647815580166 \tabularnewline
24 & 525 & 525.900015863276 & -0.900015863276438 \tabularnewline
25 & 523 & 522.850592938289 & 0.149407061710655 \tabularnewline
26 & 519 & 518.883407363033 & 0.116592636966544 \tabularnewline
27 & 509 & 508.866198452341 & 0.133801547658903 \tabularnewline
28 & 512 & 511.875202605793 & 0.124797394207046 \tabularnewline
29 & 519 & 518.913473883941 & 0.0865261160594088 \tabularnewline
30 & 517 & 516.876856710274 & 0.123143289726316 \tabularnewline
31 & 510 & 509.896465110065 & 0.103534889935311 \tabularnewline
32 & 509 & 509.905535924357 & -0.905535924356648 \tabularnewline
33 & 501 & 499.936482456913 & 1.06351754308748 \tabularnewline
34 & 507 & 506.879990085798 & 0.120009914201947 \tabularnewline
35 & 569 & 569.724523845185 & -0.72452384518521 \tabularnewline
36 & 580 & 579.738197955666 & 0.261802044334196 \tabularnewline
37 & 578 & 577.688975167495 & 0.311024832504827 \tabularnewline
38 & 565 & 565.773879955036 & -0.773879955036055 \tabularnewline
39 & 547 & 547.759271821313 & -0.759271821313276 \tabularnewline
40 & 555 & 554.794798664069 & 0.20520133593139 \tabularnewline
41 & 562 & 561.833069942216 & 0.166930057783753 \tabularnewline
42 & 561 & 560.845475419904 & 0.154524580096485 \tabularnewline
43 & 555 & 555.889361179756 & -0.889361179755928 \tabularnewline
44 & 544 & 543.905456768566 & 0.0945432314339837 \tabularnewline
45 & 537 & 536.937003711571 & 0.062996288428732 \tabularnewline
46 & 543 & 542.869039731716 & 0.130960268284434 \tabularnewline
47 & 594 & 593.692830776957 & 0.307169223043061 \tabularnewline
48 & 611 & 610.635206244187 & 0.364793755813476 \tabularnewline
49 & 613 & 612.639808389321 & 0.360191610679318 \tabularnewline
50 & 611 & 610.713040588842 & 0.286959411158317 \tabularnewline
51 & 594 & 593.721842607074 & 0.278157392925617 \tabularnewline
52 & 595 & 595.739050366712 & -0.739050366712259 \tabularnewline
53 & 591 & 590.860115575893 & 0.139884424106596 \tabularnewline
54 & 589 & 589.864784381868 & -0.864784381867655 \tabularnewline
55 & 584 & 583.876856246762 & 0.123143753237886 \tabularnewline
56 & 573 & 572.895664099287 & 0.104335900712707 \tabularnewline
57 & 567 & 566.915362993256 & 0.0846370067441861 \tabularnewline
58 & 569 & 568.9192980671 & 0.0807019328995088 \tabularnewline
59 & 621 & 620.733040450547 & 0.266959549453318 \tabularnewline
60 & 629 & 628.708653602142 & 0.291346397857532 \tabularnewline
61 & 628 & 627.708453465326 & 0.291546534673988 \tabularnewline
62 & 612 & 611.79266734824 & 0.207332651759988 \tabularnewline
63 & 595 & 595.80155986065 & -0.801559860650222 \tabularnewline
64 & 597 & 596.830396384047 & 0.169603615953054 \tabularnewline
65 & 593 & 592.914668116082 & 0.0853318839178652 \tabularnewline
66 & 590 & 589.930098477709 & 0.0699015222906932 \tabularnewline
67 & 580 & 579.960935367626 & 0.0390646323737611 \tabularnewline
68 & 574 & 574.009891203844 & -0.00989120384416442 \tabularnewline
69 & 573 & 572.98463599628 & 0.0153640037204581 \tabularnewline
70 & 573 & 573.003534497278 & -0.00353449727837724 \tabularnewline
71 & 620 & 619.901581412112 & 0.0984185878877877 \tabularnewline
72 & 626 & 625.89304434743 & 0.106955652570485 \tabularnewline
73 & 620 & 619.903672427106 & 0.0963275728939233 \tabularnewline
74 & 588 & 586.967723013756 & 1.03227698624434 \tabularnewline
75 & 566 & 565.972080041872 & 0.0279199581281656 \tabularnewline
76 & 557 & 558.060543462962 & -1.06054346296188 \tabularnewline
77 & 561 & 561.082677167163 & -0.0826771671634487 \tabularnewline
78 & 549 & 549.113113783447 & -0.113113783447465 \tabularnewline
79 & 532 & 532.142549715649 & -0.142549715649194 \tabularnewline
80 & 526 & 526.153844866615 & -0.153844866615228 \tabularnewline
81 & 511 & 511.18799258659 & -0.187992586590061 \tabularnewline
82 & 499 & 499.195017361108 & -0.19501736110819 \tabularnewline
83 & 555 & 555.091440349717 & -0.0914403497173359 \tabularnewline
84 & 565 & 564.083503695484 & 0.916496304515982 \tabularnewline
85 & 542 & 542.119675615317 & -0.119675615316462 \tabularnewline
86 & 527 & 527.101356377903 & -0.10135637790287 \tabularnewline
87 & 510 & 510.173321938147 & -0.173321938147215 \tabularnewline
88 & 514 & 514.140663671662 & -0.140663671662397 \tabularnewline
89 & 517 & 517.204769575256 & -0.204769575256036 \tabularnewline
90 & 508 & 507.242676065596 & 0.757323934403502 \tabularnewline
91 & 493 & 493.272822050887 & -0.272822050886545 \tabularnewline
92 & 490 & 490.235337669114 & -0.235337669113703 \tabularnewline
93 & 469 & 469.261324610405 & -0.261324610405201 \tabularnewline
94 & 478 & 477.234956389646 & 0.765043610353934 \tabularnewline
95 & 528 & 529.10803588714 & -1.10803588713985 \tabularnewline
96 & 534 & 533.076665247614 & 0.923334752386349 \tabularnewline
97 & 518 & 518.123929495122 & -0.123929495122407 \tabularnewline
98 & 506 & 506.109964753648 & -0.109964753647833 \tabularnewline
99 & 502 & 502.110717634239 & -0.110717634238847 \tabularnewline
100 & 516 & 516.03683736248 & -0.0368373624796877 \tabularnewline
101 & 528 & 528.078173665597 & -0.0781736655966503 \tabularnewline
102 & 533 & 533.040819919031 & -0.0408199190307438 \tabularnewline
103 & 536 & 535.977343756794 & 0.0226562432057249 \tabularnewline
104 & 537 & 537.019516093003 & -0.01951609300281 \tabularnewline
105 & 524 & 523.042702120508 & 0.957297879491723 \tabularnewline
106 & 536 & 536.050793397582 & -0.0507933975820834 \tabularnewline
107 & 587 & 586.874959886361 & 0.125040113639369 \tabularnewline
108 & 597 & 595.88147390224 & 1.11852609775981 \tabularnewline
109 & 581 & 580.879248563922 & 0.12075143607825 \tabularnewline
110 & 564 & 564.884715918759 & -0.884715918759135 \tabularnewline
111 & 558 & 556.938250066774 & 1.06174993322619 \tabularnewline
112 & 575 & 574.931991350359 & 0.0680086496413746 \tabularnewline
113 & 580 & 580.993040198335 & -0.993040198335143 \tabularnewline
114 & 575 & 574.996708320227 & 0.00329167977288161 \tabularnewline
115 & 563 & 564.015516172752 & -1.0155161727523 \tabularnewline
116 & 552 & 550.99283790882 & 1.00716209118016 \tabularnewline
117 & 537 & 537.019449093898 & -0.0194490938981244 \tabularnewline
118 & 545 & 545.020273474501 & -0.0202734745013668 \tabularnewline
119 & 601 & 600.921565405901 & 0.0784345940987712 \tabularnewline
120 & 604 & 604.904891395873 & -0.904891395872614 \tabularnewline
121 & 586 & 586.921631219393 & -0.921631219393104 \tabularnewline
122 & 564 & 563.979389073844 & 0.0206109261562677 \tabularnewline
123 & 549 & 548.025275200216 & 0.974724799783648 \tabularnewline
124 & 551 & 551.061691240309 & -0.0616912403084659 \tabularnewline
125 & 556 & 556.145095918534 & -0.145095918533856 \tabularnewline
126 & 548 & 548.221063367759 & -0.221063367759126 \tabularnewline
127 & 540 & 540.206345745694 & -0.206345745693543 \tabularnewline
128 & 531 & 531.213615328637 & -0.213615328637379 \tabularnewline
129 & 521 & 520.274285737916 & 0.725714262084305 \tabularnewline
130 & 519 & 518.228597749957 & 0.771402250043174 \tabularnewline
131 & 572 & 572.098652363511 & -0.0986523635106971 \tabularnewline
132 & 581 & 582.063613602093 & -1.06361360209252 \tabularnewline
133 & 563 & 563.064789588009 & -0.0647895880094908 \tabularnewline
134 & 548 & 548.058628179088 & -0.0586281790880352 \tabularnewline
135 & 539 & 539.074411147673 & -0.0744111476733008 \tabularnewline
136 & 541 & 541.045664121696 & -0.0456641216957534 \tabularnewline
137 & 562 & 561.076799292876 & 0.923200707124103 \tabularnewline
138 & 559 & 559.07297386167 & -0.0729738616701632 \tabularnewline
139 & 546 & 546.080219611277 & -0.080219611276598 \tabularnewline
140 & 536 & 536.996913765568 & -0.996913765568534 \tabularnewline
141 & 528 & 526.996356182622 & 1.00364381737815 \tabularnewline
142 & 530 & 530.981125957811 & -0.981125957811138 \tabularnewline
143 & 582 & 581.837463633382 & 0.162536366618282 \tabularnewline
144 & 599 & 598.835840858211 & 0.16415914178851 \tabularnewline
145 & 584 & 583.79175296314 & 0.208247036860084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&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]501.844436841823[/C][C]-0.844436841823466[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.882943588317[/C][C]0.117056411682668[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.932697973398[/C][C]0.0673020266015946[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.964068612925[/C][C]0.0359313870753726[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.081999449823[/C][C]-0.0819994498230227[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.100940778325[/C][C]-0.100940778324548[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.164836208359[/C][C]-0.164836208359217[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.160967795355[/C][C]-0.160967795354641[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.180576195146[/C][C]-0.180576195145646[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.195715925779[/C][C]-0.195715925778848[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.091538503939[/C][C]-0.0915385039386273[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.062770794081[/C][C]0.937229205919149[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.036159454706[/C][C]-0.0361594547064236[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.983801985661[/C][C]0.0161980143392029[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.062613021879[/C][C]-0.0626130218786618[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.022194250343[/C][C]-0.0221942503434258[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.120716669971[/C][C]-0.120716669971165[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.125294981768[/C][C]0.87470501823209[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.137100049006[/C][C]-0.137100049005804[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.152215946302[/C][C]-0.152215946301601[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.11900009687[/C][C]-0.119000096870104[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.11900009687[/C][C]0.880999903129895[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.944435218442[/C][C]0.0555647815580166[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.900015863276[/C][C]-0.900015863276438[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.850592938289[/C][C]0.149407061710655[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.883407363033[/C][C]0.116592636966544[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.866198452341[/C][C]0.133801547658903[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.875202605793[/C][C]0.124797394207046[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.913473883941[/C][C]0.0865261160594088[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.876856710274[/C][C]0.123143289726316[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.896465110065[/C][C]0.103534889935311[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.905535924357[/C][C]-0.905535924356648[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]499.936482456913[/C][C]1.06351754308748[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]506.879990085798[/C][C]0.120009914201947[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.724523845185[/C][C]-0.72452384518521[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.738197955666[/C][C]0.261802044334196[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.688975167495[/C][C]0.311024832504827[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.773879955036[/C][C]-0.773879955036055[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.759271821313[/C][C]-0.759271821313276[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.794798664069[/C][C]0.20520133593139[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.833069942216[/C][C]0.166930057783753[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.845475419904[/C][C]0.154524580096485[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.889361179756[/C][C]-0.889361179755928[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.905456768566[/C][C]0.0945432314339837[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]536.937003711571[/C][C]0.062996288428732[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.869039731716[/C][C]0.130960268284434[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.692830776957[/C][C]0.307169223043061[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.635206244187[/C][C]0.364793755813476[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.639808389321[/C][C]0.360191610679318[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.713040588842[/C][C]0.286959411158317[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.721842607074[/C][C]0.278157392925617[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.739050366712[/C][C]-0.739050366712259[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.860115575893[/C][C]0.139884424106596[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.864784381868[/C][C]-0.864784381867655[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.876856246762[/C][C]0.123143753237886[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.895664099287[/C][C]0.104335900712707[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.915362993256[/C][C]0.0846370067441861[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.9192980671[/C][C]0.0807019328995088[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.733040450547[/C][C]0.266959549453318[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.708653602142[/C][C]0.291346397857532[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.708453465326[/C][C]0.291546534673988[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.79266734824[/C][C]0.207332651759988[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.80155986065[/C][C]-0.801559860650222[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.830396384047[/C][C]0.169603615953054[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.914668116082[/C][C]0.0853318839178652[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.930098477709[/C][C]0.0699015222906932[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.960935367626[/C][C]0.0390646323737611[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.009891203844[/C][C]-0.00989120384416442[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]572.98463599628[/C][C]0.0153640037204581[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.003534497278[/C][C]-0.00353449727837724[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.901581412112[/C][C]0.0984185878877877[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.89304434743[/C][C]0.106955652570485[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.903672427106[/C][C]0.0963275728939233[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.967723013756[/C][C]1.03227698624434[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.972080041872[/C][C]0.0279199581281656[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.060543462962[/C][C]-1.06054346296188[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.082677167163[/C][C]-0.0826771671634487[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.113113783447[/C][C]-0.113113783447465[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.142549715649[/C][C]-0.142549715649194[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.153844866615[/C][C]-0.153844866615228[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.18799258659[/C][C]-0.187992586590061[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.195017361108[/C][C]-0.19501736110819[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.091440349717[/C][C]-0.0914403497173359[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.083503695484[/C][C]0.916496304515982[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.119675615317[/C][C]-0.119675615316462[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.101356377903[/C][C]-0.10135637790287[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.173321938147[/C][C]-0.173321938147215[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.140663671662[/C][C]-0.140663671662397[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.204769575256[/C][C]-0.204769575256036[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.242676065596[/C][C]0.757323934403502[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.272822050887[/C][C]-0.272822050886545[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.235337669114[/C][C]-0.235337669113703[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.261324610405[/C][C]-0.261324610405201[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.234956389646[/C][C]0.765043610353934[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.10803588714[/C][C]-1.10803588713985[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.076665247614[/C][C]0.923334752386349[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.123929495122[/C][C]-0.123929495122407[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.109964753648[/C][C]-0.109964753647833[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.110717634239[/C][C]-0.110717634238847[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.03683736248[/C][C]-0.0368373624796877[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.078173665597[/C][C]-0.0781736655966503[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.040819919031[/C][C]-0.0408199190307438[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.977343756794[/C][C]0.0226562432057249[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.019516093003[/C][C]-0.01951609300281[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.042702120508[/C][C]0.957297879491723[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.050793397582[/C][C]-0.0507933975820834[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.874959886361[/C][C]0.125040113639369[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.88147390224[/C][C]1.11852609775981[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.879248563922[/C][C]0.12075143607825[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.884715918759[/C][C]-0.884715918759135[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.938250066774[/C][C]1.06174993322619[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.931991350359[/C][C]0.0680086496413746[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.993040198335[/C][C]-0.993040198335143[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.996708320227[/C][C]0.00329167977288161[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]564.015516172752[/C][C]-1.0155161727523[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.99283790882[/C][C]1.00716209118016[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.019449093898[/C][C]-0.0194490938981244[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.020273474501[/C][C]-0.0202734745013668[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.921565405901[/C][C]0.0784345940987712[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.904891395873[/C][C]-0.904891395872614[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.921631219393[/C][C]-0.921631219393104[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.979389073844[/C][C]0.0206109261562677[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.025275200216[/C][C]0.974724799783648[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.061691240309[/C][C]-0.0616912403084659[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.145095918534[/C][C]-0.145095918533856[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.221063367759[/C][C]-0.221063367759126[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.206345745694[/C][C]-0.206345745693543[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.213615328637[/C][C]-0.213615328637379[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.274285737916[/C][C]0.725714262084305[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.228597749957[/C][C]0.771402250043174[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.098652363511[/C][C]-0.0986523635106971[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.063613602093[/C][C]-1.06361360209252[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.064789588009[/C][C]-0.0647895880094908[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.058628179088[/C][C]-0.0586281790880352[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.074411147673[/C][C]-0.0744111476733008[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.045664121696[/C][C]-0.0456641216957534[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.076799292876[/C][C]0.923200707124103[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.07297386167[/C][C]-0.0729738616701632[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.080219611277[/C][C]-0.080219611276598[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.996913765568[/C][C]-0.996913765568534[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]526.996356182622[/C][C]1.00364381737815[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]530.981125957811[/C][C]-0.981125957811138[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.837463633382[/C][C]0.162536366618282[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.835840858211[/C][C]0.16415914178851[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.79175296314[/C][C]0.208247036860084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 501 501.844436841823 -0.844436841823466 2 485 484.882943588317 0.117056411682668 3 464 463.932697973398 0.0673020266015946 4 460 459.964068612925 0.0359313870753726 5 467 467.081999449823 -0.0819994498230227 6 460 460.100940778325 -0.100940778324548 7 448 448.164836208359 -0.164836208359217 8 443 443.160967795355 -0.160967795354641 9 436 436.180576195146 -0.180576195145646 10 431 431.195715925779 -0.195715925778848 11 484 484.091538503939 -0.0915385039386273 12 510 509.062770794081 0.937229205919149 13 513 513.036159454706 -0.0361594547064236 14 503 502.983801985661 0.0161980143392029 15 471 471.062613021879 -0.0626130218786618 16 471 471.022194250343 -0.0221942503434258 17 476 476.120716669971 -0.120716669971165 18 475 474.125294981768 0.87470501823209 19 470 470.137100049006 -0.137100049005804 20 461 461.152215946302 -0.152215946301601 21 455 455.11900009687 -0.119000096870104 22 456 455.11900009687 0.880999903129895 23 517 516.944435218442 0.0555647815580166 24 525 525.900015863276 -0.900015863276438 25 523 522.850592938289 0.149407061710655 26 519 518.883407363033 0.116592636966544 27 509 508.866198452341 0.133801547658903 28 512 511.875202605793 0.124797394207046 29 519 518.913473883941 0.0865261160594088 30 517 516.876856710274 0.123143289726316 31 510 509.896465110065 0.103534889935311 32 509 509.905535924357 -0.905535924356648 33 501 499.936482456913 1.06351754308748 34 507 506.879990085798 0.120009914201947 35 569 569.724523845185 -0.72452384518521 36 580 579.738197955666 0.261802044334196 37 578 577.688975167495 0.311024832504827 38 565 565.773879955036 -0.773879955036055 39 547 547.759271821313 -0.759271821313276 40 555 554.794798664069 0.20520133593139 41 562 561.833069942216 0.166930057783753 42 561 560.845475419904 0.154524580096485 43 555 555.889361179756 -0.889361179755928 44 544 543.905456768566 0.0945432314339837 45 537 536.937003711571 0.062996288428732 46 543 542.869039731716 0.130960268284434 47 594 593.692830776957 0.307169223043061 48 611 610.635206244187 0.364793755813476 49 613 612.639808389321 0.360191610679318 50 611 610.713040588842 0.286959411158317 51 594 593.721842607074 0.278157392925617 52 595 595.739050366712 -0.739050366712259 53 591 590.860115575893 0.139884424106596 54 589 589.864784381868 -0.864784381867655 55 584 583.876856246762 0.123143753237886 56 573 572.895664099287 0.104335900712707 57 567 566.915362993256 0.0846370067441861 58 569 568.9192980671 0.0807019328995088 59 621 620.733040450547 0.266959549453318 60 629 628.708653602142 0.291346397857532 61 628 627.708453465326 0.291546534673988 62 612 611.79266734824 0.207332651759988 63 595 595.80155986065 -0.801559860650222 64 597 596.830396384047 0.169603615953054 65 593 592.914668116082 0.0853318839178652 66 590 589.930098477709 0.0699015222906932 67 580 579.960935367626 0.0390646323737611 68 574 574.009891203844 -0.00989120384416442 69 573 572.98463599628 0.0153640037204581 70 573 573.003534497278 -0.00353449727837724 71 620 619.901581412112 0.0984185878877877 72 626 625.89304434743 0.106955652570485 73 620 619.903672427106 0.0963275728939233 74 588 586.967723013756 1.03227698624434 75 566 565.972080041872 0.0279199581281656 76 557 558.060543462962 -1.06054346296188 77 561 561.082677167163 -0.0826771671634487 78 549 549.113113783447 -0.113113783447465 79 532 532.142549715649 -0.142549715649194 80 526 526.153844866615 -0.153844866615228 81 511 511.18799258659 -0.187992586590061 82 499 499.195017361108 -0.19501736110819 83 555 555.091440349717 -0.0914403497173359 84 565 564.083503695484 0.916496304515982 85 542 542.119675615317 -0.119675615316462 86 527 527.101356377903 -0.10135637790287 87 510 510.173321938147 -0.173321938147215 88 514 514.140663671662 -0.140663671662397 89 517 517.204769575256 -0.204769575256036 90 508 507.242676065596 0.757323934403502 91 493 493.272822050887 -0.272822050886545 92 490 490.235337669114 -0.235337669113703 93 469 469.261324610405 -0.261324610405201 94 478 477.234956389646 0.765043610353934 95 528 529.10803588714 -1.10803588713985 96 534 533.076665247614 0.923334752386349 97 518 518.123929495122 -0.123929495122407 98 506 506.109964753648 -0.109964753647833 99 502 502.110717634239 -0.110717634238847 100 516 516.03683736248 -0.0368373624796877 101 528 528.078173665597 -0.0781736655966503 102 533 533.040819919031 -0.0408199190307438 103 536 535.977343756794 0.0226562432057249 104 537 537.019516093003 -0.01951609300281 105 524 523.042702120508 0.957297879491723 106 536 536.050793397582 -0.0507933975820834 107 587 586.874959886361 0.125040113639369 108 597 595.88147390224 1.11852609775981 109 581 580.879248563922 0.12075143607825 110 564 564.884715918759 -0.884715918759135 111 558 556.938250066774 1.06174993322619 112 575 574.931991350359 0.0680086496413746 113 580 580.993040198335 -0.993040198335143 114 575 574.996708320227 0.00329167977288161 115 563 564.015516172752 -1.0155161727523 116 552 550.99283790882 1.00716209118016 117 537 537.019449093898 -0.0194490938981244 118 545 545.020273474501 -0.0202734745013668 119 601 600.921565405901 0.0784345940987712 120 604 604.904891395873 -0.904891395872614 121 586 586.921631219393 -0.921631219393104 122 564 563.979389073844 0.0206109261562677 123 549 548.025275200216 0.974724799783648 124 551 551.061691240309 -0.0616912403084659 125 556 556.145095918534 -0.145095918533856 126 548 548.221063367759 -0.221063367759126 127 540 540.206345745694 -0.206345745693543 128 531 531.213615328637 -0.213615328637379 129 521 520.274285737916 0.725714262084305 130 519 518.228597749957 0.771402250043174 131 572 572.098652363511 -0.0986523635106971 132 581 582.063613602093 -1.06361360209252 133 563 563.064789588009 -0.0647895880094908 134 548 548.058628179088 -0.0586281790880352 135 539 539.074411147673 -0.0744111476733008 136 541 541.045664121696 -0.0456641216957534 137 562 561.076799292876 0.923200707124103 138 559 559.07297386167 -0.0729738616701632 139 546 546.080219611277 -0.080219611276598 140 536 536.996913765568 -0.996913765568534 141 528 526.996356182622 1.00364381737815 142 530 530.981125957811 -0.981125957811138 143 582 581.837463633382 0.162536366618282 144 599 598.835840858211 0.16415914178851 145 584 583.79175296314 0.208247036860084

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0424116850511485 0.0848233701022969 0.957588314948852 11 0.0120418085042438 0.0240836170084877 0.987958191495756 12 0.230118028286516 0.460236056573032 0.769881971713484 13 0.205851003359324 0.411702006718648 0.794148996640676 14 0.125055701172913 0.250111402345826 0.874944298827087 15 0.0718306984319717 0.143661396863943 0.928169301568028 16 0.0706378897761419 0.141275779552284 0.929362110223858 17 0.0418707922622472 0.0837415845244945 0.958129207737753 18 0.223135617698231 0.446271235396463 0.776864382301769 19 0.207607280201702 0.415214560403405 0.792392719798298 20 0.160899391843438 0.321798783686875 0.839100608156562 21 0.112928828445675 0.22585765689135 0.887071171554325 22 0.227741463395236 0.455482926790472 0.772258536604764 23 0.186135932067842 0.372271864135684 0.813864067932158 24 0.192342481207849 0.384684962415698 0.807657518792151 25 0.270495022856046 0.540990045712092 0.729504977143954 26 0.219435065203837 0.438870130407675 0.780564934796163 27 0.172147593210472 0.344295186420944 0.827852406789528 28 0.131159608183739 0.262319216367478 0.868840391816261 29 0.10102361911694 0.20204723823388 0.89897638088306 30 0.0741822192032969 0.148364438406594 0.925817780796703 31 0.0535496094659077 0.107099218931815 0.946450390534092 32 0.142328224230387 0.284656448460774 0.857671775769613 33 0.292910830885605 0.58582166177121 0.707089169114395 34 0.242180488659813 0.484360977319626 0.757819511340187 35 0.275326168254246 0.550652336508492 0.724673831745754 36 0.252392311760705 0.50478462352141 0.747607688239295 37 0.239898969190142 0.479797938380285 0.760101030809858 38 0.308199894871551 0.616399789743101 0.691800105128449 39 0.344235184403218 0.688470368806437 0.655764815596782 40 0.297580212225149 0.595160424450298 0.702419787774851 41 0.250149715205781 0.500299430411562 0.749850284794219 42 0.207271890144174 0.414543780288348 0.792728109855826 43 0.342503992201715 0.685007984403429 0.657496007798285 44 0.296427656615708 0.592855313231417 0.703572343384292 45 0.251306976989507 0.502613953979014 0.748693023010493 46 0.213814223186912 0.427628446373824 0.786185776813088 47 0.201703861239361 0.403407722478722 0.798296138760639 48 0.190429929690068 0.380859859380136 0.809570070309932 49 0.17031817292244 0.340636345844879 0.82968182707756 50 0.144758770380447 0.289517540760895 0.855241229619553 51 0.121196067832655 0.24239213566531 0.878803932167345 52 0.162161904288763 0.324323808577526 0.837838095711237 53 0.131837669474582 0.263675338949163 0.868162330525418 54 0.207716516730773 0.415433033461547 0.792283483269227 55 0.17565189523608 0.351303790472161 0.82434810476392 56 0.145477887883538 0.290955775767075 0.854522112116462 57 0.118202066047292 0.236404132094584 0.881797933952708 58 0.0950123076244034 0.190024615248807 0.904987692375597 59 0.0759891205703684 0.151978241140737 0.924010879429632 60 0.0598607109538731 0.119721421907746 0.940139289046127 61 0.0467652215171398 0.0935304430342796 0.95323477848286 62 0.0357460102809748 0.0714920205619496 0.964253989719025 63 0.0768165804709118 0.153633160941824 0.923183419529088 64 0.06100330310173 0.12200660620346 0.93899669689827 65 0.0476470282911067 0.0952940565822134 0.952352971708893 66 0.0367597074540065 0.0735194149080129 0.963240292545994 67 0.0281011430560903 0.0562022861121806 0.97189885694391 68 0.0214990650079275 0.0429981300158549 0.978500934992073 69 0.0159269984457967 0.0318539968915933 0.984073001554203 70 0.0117204969179674 0.0234409938359347 0.988279503082033 71 0.00839269326250215 0.0167853865250043 0.991607306737498 72 0.00592255870616449 0.011845117412329 0.994077441293835 73 0.00412567029003812 0.00825134058007624 0.995874329709962 74 0.0087979009791719 0.0175958019583438 0.991202099020828 75 0.00671810347019132 0.0134362069403826 0.993281896529809 76 0.02984757375343 0.05969514750686 0.97015242624657 77 0.0226690809602933 0.0453381619205866 0.977330919039707 78 0.0170189582377244 0.0340379164754489 0.982981041762276 79 0.0126769435821974 0.0253538871643947 0.987323056417803 80 0.0092708695421448 0.0185417390842896 0.990729130457855 81 0.00691919911705063 0.0138383982341013 0.993080800882949 82 0.00554950995505966 0.0110990199101193 0.99445049004494 83 0.00399413957889545 0.00798827915779089 0.996005860421105 84 0.00795242393848406 0.0159048478769681 0.992047576061516 85 0.00591621949197728 0.0118324389839546 0.994083780508023 86 0.00421548705227017 0.00843097410454034 0.99578451294773 87 0.00314111969878888 0.00628223939757777 0.996858880301211 88 0.00228864395355509 0.00457728790711018 0.997711356046445 89 0.0015873839120243 0.00317476782404859 0.998412616087976 90 0.00283458382798409 0.00566916765596818 0.997165416172016 91 0.00215641724988731 0.00431283449977461 0.997843582750113 92 0.00155609030825072 0.00311218061650144 0.998443909691749 93 0.00126192100242366 0.00252384200484733 0.998738078997576 94 0.00178600976914501 0.00357201953829003 0.998213990230855 95 0.00632430107665823 0.0126486021533165 0.993675698923342 96 0.0161386128754129 0.0322772257508257 0.983861387124587 97 0.012221301688765 0.0244426033775301 0.987778698311235 98 0.00877288435160158 0.0175457687032032 0.991227115648398 99 0.00634412761559509 0.0126882552311902 0.993655872384405 100 0.00467865732445724 0.00935731464891448 0.995321342675543 101 0.00323659185102116 0.00647318370204232 0.996763408148979 102 0.0021937320494383 0.0043874640988766 0.997806267950562 103 0.00151961229092335 0.0030392245818467 0.998480387709077 104 0.00122662700705127 0.00245325401410254 0.998773372992949 105 0.00141311782917599 0.00282623565835198 0.998586882170824 106 0.00118847225685683 0.00237694451371365 0.998811527743143 107 0.000762856411816489 0.00152571282363298 0.999237143588183 108 0.00276189869450956 0.00552379738901912 0.99723810130549 109 0.00199526044923736 0.00399052089847471 0.998004739550763 110 0.00890939105255597 0.0178187821051119 0.991090608947444 111 0.0140016659193766 0.0280033318387532 0.985998334080623 112 0.0100031307636843 0.0200062615273686 0.989996869236316 113 0.0199279186838727 0.0398558373677454 0.980072081316127 114 0.0144551435229988 0.0289102870459975 0.985544856477001 115 0.0360755067554291 0.0721510135108582 0.963924493244571 116 0.0667600176617655 0.133520035323531 0.933239982338234 117 0.0489702268053778 0.0979404536107555 0.951029773194622 118 0.0348393123289475 0.0696786246578951 0.965160687671052 119 0.0312362004990866 0.0624724009981733 0.968763799500913 120 0.0326501338985087 0.0653002677970173 0.967349866101491 121 0.0549799359629178 0.109959871925836 0.945020064037082 122 0.0431362994646097 0.0862725989292195 0.95686370053539 123 0.070396602367123 0.140793204734246 0.929603397632877 124 0.0604947198195074 0.120989439639015 0.939505280180493 125 0.041207442492292 0.082414884984584 0.958792557507708 126 0.0281631437122644 0.0563262874245289 0.971836856287736 127 0.0217265260700611 0.0434530521401222 0.978273473929939 128 0.0199285701490022 0.0398571402980044 0.980071429850998 129 0.021703782709705 0.04340756541941 0.978296217290295 130 0.0274939588142561 0.0549879176285122 0.972506041185744 131 0.0194106802649508 0.0388213605299016 0.980589319735049 132 0.0368030383721838 0.0736060767443676 0.963196961627816 133 0.0264860579745862 0.0529721159491725 0.973513942025414 134 0.0216776889770303 0.0433553779540607 0.97832231102297 135 0.295666214716471 0.591332429432942 0.704333785283529

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0424116850511485 & 0.0848233701022969 & 0.957588314948852 \tabularnewline
11 & 0.0120418085042438 & 0.0240836170084877 & 0.987958191495756 \tabularnewline
12 & 0.230118028286516 & 0.460236056573032 & 0.769881971713484 \tabularnewline
13 & 0.205851003359324 & 0.411702006718648 & 0.794148996640676 \tabularnewline
14 & 0.125055701172913 & 0.250111402345826 & 0.874944298827087 \tabularnewline
15 & 0.0718306984319717 & 0.143661396863943 & 0.928169301568028 \tabularnewline
16 & 0.0706378897761419 & 0.141275779552284 & 0.929362110223858 \tabularnewline
17 & 0.0418707922622472 & 0.0837415845244945 & 0.958129207737753 \tabularnewline
18 & 0.223135617698231 & 0.446271235396463 & 0.776864382301769 \tabularnewline
19 & 0.207607280201702 & 0.415214560403405 & 0.792392719798298 \tabularnewline
20 & 0.160899391843438 & 0.321798783686875 & 0.839100608156562 \tabularnewline
21 & 0.112928828445675 & 0.22585765689135 & 0.887071171554325 \tabularnewline
22 & 0.227741463395236 & 0.455482926790472 & 0.772258536604764 \tabularnewline
23 & 0.186135932067842 & 0.372271864135684 & 0.813864067932158 \tabularnewline
24 & 0.192342481207849 & 0.384684962415698 & 0.807657518792151 \tabularnewline
25 & 0.270495022856046 & 0.540990045712092 & 0.729504977143954 \tabularnewline
26 & 0.219435065203837 & 0.438870130407675 & 0.780564934796163 \tabularnewline
27 & 0.172147593210472 & 0.344295186420944 & 0.827852406789528 \tabularnewline
28 & 0.131159608183739 & 0.262319216367478 & 0.868840391816261 \tabularnewline
29 & 0.10102361911694 & 0.20204723823388 & 0.89897638088306 \tabularnewline
30 & 0.0741822192032969 & 0.148364438406594 & 0.925817780796703 \tabularnewline
31 & 0.0535496094659077 & 0.107099218931815 & 0.946450390534092 \tabularnewline
32 & 0.142328224230387 & 0.284656448460774 & 0.857671775769613 \tabularnewline
33 & 0.292910830885605 & 0.58582166177121 & 0.707089169114395 \tabularnewline
34 & 0.242180488659813 & 0.484360977319626 & 0.757819511340187 \tabularnewline
35 & 0.275326168254246 & 0.550652336508492 & 0.724673831745754 \tabularnewline
36 & 0.252392311760705 & 0.50478462352141 & 0.747607688239295 \tabularnewline
37 & 0.239898969190142 & 0.479797938380285 & 0.760101030809858 \tabularnewline
38 & 0.308199894871551 & 0.616399789743101 & 0.691800105128449 \tabularnewline
39 & 0.344235184403218 & 0.688470368806437 & 0.655764815596782 \tabularnewline
40 & 0.297580212225149 & 0.595160424450298 & 0.702419787774851 \tabularnewline
41 & 0.250149715205781 & 0.500299430411562 & 0.749850284794219 \tabularnewline
42 & 0.207271890144174 & 0.414543780288348 & 0.792728109855826 \tabularnewline
43 & 0.342503992201715 & 0.685007984403429 & 0.657496007798285 \tabularnewline
44 & 0.296427656615708 & 0.592855313231417 & 0.703572343384292 \tabularnewline
45 & 0.251306976989507 & 0.502613953979014 & 0.748693023010493 \tabularnewline
46 & 0.213814223186912 & 0.427628446373824 & 0.786185776813088 \tabularnewline
47 & 0.201703861239361 & 0.403407722478722 & 0.798296138760639 \tabularnewline
48 & 0.190429929690068 & 0.380859859380136 & 0.809570070309932 \tabularnewline
49 & 0.17031817292244 & 0.340636345844879 & 0.82968182707756 \tabularnewline
50 & 0.144758770380447 & 0.289517540760895 & 0.855241229619553 \tabularnewline
51 & 0.121196067832655 & 0.24239213566531 & 0.878803932167345 \tabularnewline
52 & 0.162161904288763 & 0.324323808577526 & 0.837838095711237 \tabularnewline
53 & 0.131837669474582 & 0.263675338949163 & 0.868162330525418 \tabularnewline
54 & 0.207716516730773 & 0.415433033461547 & 0.792283483269227 \tabularnewline
55 & 0.17565189523608 & 0.351303790472161 & 0.82434810476392 \tabularnewline
56 & 0.145477887883538 & 0.290955775767075 & 0.854522112116462 \tabularnewline
57 & 0.118202066047292 & 0.236404132094584 & 0.881797933952708 \tabularnewline
58 & 0.0950123076244034 & 0.190024615248807 & 0.904987692375597 \tabularnewline
59 & 0.0759891205703684 & 0.151978241140737 & 0.924010879429632 \tabularnewline
60 & 0.0598607109538731 & 0.119721421907746 & 0.940139289046127 \tabularnewline
61 & 0.0467652215171398 & 0.0935304430342796 & 0.95323477848286 \tabularnewline
62 & 0.0357460102809748 & 0.0714920205619496 & 0.964253989719025 \tabularnewline
63 & 0.0768165804709118 & 0.153633160941824 & 0.923183419529088 \tabularnewline
64 & 0.06100330310173 & 0.12200660620346 & 0.93899669689827 \tabularnewline
65 & 0.0476470282911067 & 0.0952940565822134 & 0.952352971708893 \tabularnewline
66 & 0.0367597074540065 & 0.0735194149080129 & 0.963240292545994 \tabularnewline
67 & 0.0281011430560903 & 0.0562022861121806 & 0.97189885694391 \tabularnewline
68 & 0.0214990650079275 & 0.0429981300158549 & 0.978500934992073 \tabularnewline
69 & 0.0159269984457967 & 0.0318539968915933 & 0.984073001554203 \tabularnewline
70 & 0.0117204969179674 & 0.0234409938359347 & 0.988279503082033 \tabularnewline
71 & 0.00839269326250215 & 0.0167853865250043 & 0.991607306737498 \tabularnewline
72 & 0.00592255870616449 & 0.011845117412329 & 0.994077441293835 \tabularnewline
73 & 0.00412567029003812 & 0.00825134058007624 & 0.995874329709962 \tabularnewline
74 & 0.0087979009791719 & 0.0175958019583438 & 0.991202099020828 \tabularnewline
75 & 0.00671810347019132 & 0.0134362069403826 & 0.993281896529809 \tabularnewline
76 & 0.02984757375343 & 0.05969514750686 & 0.97015242624657 \tabularnewline
77 & 0.0226690809602933 & 0.0453381619205866 & 0.977330919039707 \tabularnewline
78 & 0.0170189582377244 & 0.0340379164754489 & 0.982981041762276 \tabularnewline
79 & 0.0126769435821974 & 0.0253538871643947 & 0.987323056417803 \tabularnewline
80 & 0.0092708695421448 & 0.0185417390842896 & 0.990729130457855 \tabularnewline
81 & 0.00691919911705063 & 0.0138383982341013 & 0.993080800882949 \tabularnewline
82 & 0.00554950995505966 & 0.0110990199101193 & 0.99445049004494 \tabularnewline
83 & 0.00399413957889545 & 0.00798827915779089 & 0.996005860421105 \tabularnewline
84 & 0.00795242393848406 & 0.0159048478769681 & 0.992047576061516 \tabularnewline
85 & 0.00591621949197728 & 0.0118324389839546 & 0.994083780508023 \tabularnewline
86 & 0.00421548705227017 & 0.00843097410454034 & 0.99578451294773 \tabularnewline
87 & 0.00314111969878888 & 0.00628223939757777 & 0.996858880301211 \tabularnewline
88 & 0.00228864395355509 & 0.00457728790711018 & 0.997711356046445 \tabularnewline
89 & 0.0015873839120243 & 0.00317476782404859 & 0.998412616087976 \tabularnewline
90 & 0.00283458382798409 & 0.00566916765596818 & 0.997165416172016 \tabularnewline
91 & 0.00215641724988731 & 0.00431283449977461 & 0.997843582750113 \tabularnewline
92 & 0.00155609030825072 & 0.00311218061650144 & 0.998443909691749 \tabularnewline
93 & 0.00126192100242366 & 0.00252384200484733 & 0.998738078997576 \tabularnewline
94 & 0.00178600976914501 & 0.00357201953829003 & 0.998213990230855 \tabularnewline
95 & 0.00632430107665823 & 0.0126486021533165 & 0.993675698923342 \tabularnewline
96 & 0.0161386128754129 & 0.0322772257508257 & 0.983861387124587 \tabularnewline
97 & 0.012221301688765 & 0.0244426033775301 & 0.987778698311235 \tabularnewline
98 & 0.00877288435160158 & 0.0175457687032032 & 0.991227115648398 \tabularnewline
99 & 0.00634412761559509 & 0.0126882552311902 & 0.993655872384405 \tabularnewline
100 & 0.00467865732445724 & 0.00935731464891448 & 0.995321342675543 \tabularnewline
101 & 0.00323659185102116 & 0.00647318370204232 & 0.996763408148979 \tabularnewline
102 & 0.0021937320494383 & 0.0043874640988766 & 0.997806267950562 \tabularnewline
103 & 0.00151961229092335 & 0.0030392245818467 & 0.998480387709077 \tabularnewline
104 & 0.00122662700705127 & 0.00245325401410254 & 0.998773372992949 \tabularnewline
105 & 0.00141311782917599 & 0.00282623565835198 & 0.998586882170824 \tabularnewline
106 & 0.00118847225685683 & 0.00237694451371365 & 0.998811527743143 \tabularnewline
107 & 0.000762856411816489 & 0.00152571282363298 & 0.999237143588183 \tabularnewline
108 & 0.00276189869450956 & 0.00552379738901912 & 0.99723810130549 \tabularnewline
109 & 0.00199526044923736 & 0.00399052089847471 & 0.998004739550763 \tabularnewline
110 & 0.00890939105255597 & 0.0178187821051119 & 0.991090608947444 \tabularnewline
111 & 0.0140016659193766 & 0.0280033318387532 & 0.985998334080623 \tabularnewline
112 & 0.0100031307636843 & 0.0200062615273686 & 0.989996869236316 \tabularnewline
113 & 0.0199279186838727 & 0.0398558373677454 & 0.980072081316127 \tabularnewline
114 & 0.0144551435229988 & 0.0289102870459975 & 0.985544856477001 \tabularnewline
115 & 0.0360755067554291 & 0.0721510135108582 & 0.963924493244571 \tabularnewline
116 & 0.0667600176617655 & 0.133520035323531 & 0.933239982338234 \tabularnewline
117 & 0.0489702268053778 & 0.0979404536107555 & 0.951029773194622 \tabularnewline
118 & 0.0348393123289475 & 0.0696786246578951 & 0.965160687671052 \tabularnewline
119 & 0.0312362004990866 & 0.0624724009981733 & 0.968763799500913 \tabularnewline
120 & 0.0326501338985087 & 0.0653002677970173 & 0.967349866101491 \tabularnewline
121 & 0.0549799359629178 & 0.109959871925836 & 0.945020064037082 \tabularnewline
122 & 0.0431362994646097 & 0.0862725989292195 & 0.95686370053539 \tabularnewline
123 & 0.070396602367123 & 0.140793204734246 & 0.929603397632877 \tabularnewline
124 & 0.0604947198195074 & 0.120989439639015 & 0.939505280180493 \tabularnewline
125 & 0.041207442492292 & 0.082414884984584 & 0.958792557507708 \tabularnewline
126 & 0.0281631437122644 & 0.0563262874245289 & 0.971836856287736 \tabularnewline
127 & 0.0217265260700611 & 0.0434530521401222 & 0.978273473929939 \tabularnewline
128 & 0.0199285701490022 & 0.0398571402980044 & 0.980071429850998 \tabularnewline
129 & 0.021703782709705 & 0.04340756541941 & 0.978296217290295 \tabularnewline
130 & 0.0274939588142561 & 0.0549879176285122 & 0.972506041185744 \tabularnewline
131 & 0.0194106802649508 & 0.0388213605299016 & 0.980589319735049 \tabularnewline
132 & 0.0368030383721838 & 0.0736060767443676 & 0.963196961627816 \tabularnewline
133 & 0.0264860579745862 & 0.0529721159491725 & 0.973513942025414 \tabularnewline
134 & 0.0216776889770303 & 0.0433553779540607 & 0.97832231102297 \tabularnewline
135 & 0.295666214716471 & 0.591332429432942 & 0.704333785283529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&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]0.0424116850511485[/C][C]0.0848233701022969[/C][C]0.957588314948852[/C][/ROW]
[ROW][C]11[/C][C]0.0120418085042438[/C][C]0.0240836170084877[/C][C]0.987958191495756[/C][/ROW]
[ROW][C]12[/C][C]0.230118028286516[/C][C]0.460236056573032[/C][C]0.769881971713484[/C][/ROW]
[ROW][C]13[/C][C]0.205851003359324[/C][C]0.411702006718648[/C][C]0.794148996640676[/C][/ROW]
[ROW][C]14[/C][C]0.125055701172913[/C][C]0.250111402345826[/C][C]0.874944298827087[/C][/ROW]
[ROW][C]15[/C][C]0.0718306984319717[/C][C]0.143661396863943[/C][C]0.928169301568028[/C][/ROW]
[ROW][C]16[/C][C]0.0706378897761419[/C][C]0.141275779552284[/C][C]0.929362110223858[/C][/ROW]
[ROW][C]17[/C][C]0.0418707922622472[/C][C]0.0837415845244945[/C][C]0.958129207737753[/C][/ROW]
[ROW][C]18[/C][C]0.223135617698231[/C][C]0.446271235396463[/C][C]0.776864382301769[/C][/ROW]
[ROW][C]19[/C][C]0.207607280201702[/C][C]0.415214560403405[/C][C]0.792392719798298[/C][/ROW]
[ROW][C]20[/C][C]0.160899391843438[/C][C]0.321798783686875[/C][C]0.839100608156562[/C][/ROW]
[ROW][C]21[/C][C]0.112928828445675[/C][C]0.22585765689135[/C][C]0.887071171554325[/C][/ROW]
[ROW][C]22[/C][C]0.227741463395236[/C][C]0.455482926790472[/C][C]0.772258536604764[/C][/ROW]
[ROW][C]23[/C][C]0.186135932067842[/C][C]0.372271864135684[/C][C]0.813864067932158[/C][/ROW]
[ROW][C]24[/C][C]0.192342481207849[/C][C]0.384684962415698[/C][C]0.807657518792151[/C][/ROW]
[ROW][C]25[/C][C]0.270495022856046[/C][C]0.540990045712092[/C][C]0.729504977143954[/C][/ROW]
[ROW][C]26[/C][C]0.219435065203837[/C][C]0.438870130407675[/C][C]0.780564934796163[/C][/ROW]
[ROW][C]27[/C][C]0.172147593210472[/C][C]0.344295186420944[/C][C]0.827852406789528[/C][/ROW]
[ROW][C]28[/C][C]0.131159608183739[/C][C]0.262319216367478[/C][C]0.868840391816261[/C][/ROW]
[ROW][C]29[/C][C]0.10102361911694[/C][C]0.20204723823388[/C][C]0.89897638088306[/C][/ROW]
[ROW][C]30[/C][C]0.0741822192032969[/C][C]0.148364438406594[/C][C]0.925817780796703[/C][/ROW]
[ROW][C]31[/C][C]0.0535496094659077[/C][C]0.107099218931815[/C][C]0.946450390534092[/C][/ROW]
[ROW][C]32[/C][C]0.142328224230387[/C][C]0.284656448460774[/C][C]0.857671775769613[/C][/ROW]
[ROW][C]33[/C][C]0.292910830885605[/C][C]0.58582166177121[/C][C]0.707089169114395[/C][/ROW]
[ROW][C]34[/C][C]0.242180488659813[/C][C]0.484360977319626[/C][C]0.757819511340187[/C][/ROW]
[ROW][C]35[/C][C]0.275326168254246[/C][C]0.550652336508492[/C][C]0.724673831745754[/C][/ROW]
[ROW][C]36[/C][C]0.252392311760705[/C][C]0.50478462352141[/C][C]0.747607688239295[/C][/ROW]
[ROW][C]37[/C][C]0.239898969190142[/C][C]0.479797938380285[/C][C]0.760101030809858[/C][/ROW]
[ROW][C]38[/C][C]0.308199894871551[/C][C]0.616399789743101[/C][C]0.691800105128449[/C][/ROW]
[ROW][C]39[/C][C]0.344235184403218[/C][C]0.688470368806437[/C][C]0.655764815596782[/C][/ROW]
[ROW][C]40[/C][C]0.297580212225149[/C][C]0.595160424450298[/C][C]0.702419787774851[/C][/ROW]
[ROW][C]41[/C][C]0.250149715205781[/C][C]0.500299430411562[/C][C]0.749850284794219[/C][/ROW]
[ROW][C]42[/C][C]0.207271890144174[/C][C]0.414543780288348[/C][C]0.792728109855826[/C][/ROW]
[ROW][C]43[/C][C]0.342503992201715[/C][C]0.685007984403429[/C][C]0.657496007798285[/C][/ROW]
[ROW][C]44[/C][C]0.296427656615708[/C][C]0.592855313231417[/C][C]0.703572343384292[/C][/ROW]
[ROW][C]45[/C][C]0.251306976989507[/C][C]0.502613953979014[/C][C]0.748693023010493[/C][/ROW]
[ROW][C]46[/C][C]0.213814223186912[/C][C]0.427628446373824[/C][C]0.786185776813088[/C][/ROW]
[ROW][C]47[/C][C]0.201703861239361[/C][C]0.403407722478722[/C][C]0.798296138760639[/C][/ROW]
[ROW][C]48[/C][C]0.190429929690068[/C][C]0.380859859380136[/C][C]0.809570070309932[/C][/ROW]
[ROW][C]49[/C][C]0.17031817292244[/C][C]0.340636345844879[/C][C]0.82968182707756[/C][/ROW]
[ROW][C]50[/C][C]0.144758770380447[/C][C]0.289517540760895[/C][C]0.855241229619553[/C][/ROW]
[ROW][C]51[/C][C]0.121196067832655[/C][C]0.24239213566531[/C][C]0.878803932167345[/C][/ROW]
[ROW][C]52[/C][C]0.162161904288763[/C][C]0.324323808577526[/C][C]0.837838095711237[/C][/ROW]
[ROW][C]53[/C][C]0.131837669474582[/C][C]0.263675338949163[/C][C]0.868162330525418[/C][/ROW]
[ROW][C]54[/C][C]0.207716516730773[/C][C]0.415433033461547[/C][C]0.792283483269227[/C][/ROW]
[ROW][C]55[/C][C]0.17565189523608[/C][C]0.351303790472161[/C][C]0.82434810476392[/C][/ROW]
[ROW][C]56[/C][C]0.145477887883538[/C][C]0.290955775767075[/C][C]0.854522112116462[/C][/ROW]
[ROW][C]57[/C][C]0.118202066047292[/C][C]0.236404132094584[/C][C]0.881797933952708[/C][/ROW]
[ROW][C]58[/C][C]0.0950123076244034[/C][C]0.190024615248807[/C][C]0.904987692375597[/C][/ROW]
[ROW][C]59[/C][C]0.0759891205703684[/C][C]0.151978241140737[/C][C]0.924010879429632[/C][/ROW]
[ROW][C]60[/C][C]0.0598607109538731[/C][C]0.119721421907746[/C][C]0.940139289046127[/C][/ROW]
[ROW][C]61[/C][C]0.0467652215171398[/C][C]0.0935304430342796[/C][C]0.95323477848286[/C][/ROW]
[ROW][C]62[/C][C]0.0357460102809748[/C][C]0.0714920205619496[/C][C]0.964253989719025[/C][/ROW]
[ROW][C]63[/C][C]0.0768165804709118[/C][C]0.153633160941824[/C][C]0.923183419529088[/C][/ROW]
[ROW][C]64[/C][C]0.06100330310173[/C][C]0.12200660620346[/C][C]0.93899669689827[/C][/ROW]
[ROW][C]65[/C][C]0.0476470282911067[/C][C]0.0952940565822134[/C][C]0.952352971708893[/C][/ROW]
[ROW][C]66[/C][C]0.0367597074540065[/C][C]0.0735194149080129[/C][C]0.963240292545994[/C][/ROW]
[ROW][C]67[/C][C]0.0281011430560903[/C][C]0.0562022861121806[/C][C]0.97189885694391[/C][/ROW]
[ROW][C]68[/C][C]0.0214990650079275[/C][C]0.0429981300158549[/C][C]0.978500934992073[/C][/ROW]
[ROW][C]69[/C][C]0.0159269984457967[/C][C]0.0318539968915933[/C][C]0.984073001554203[/C][/ROW]
[ROW][C]70[/C][C]0.0117204969179674[/C][C]0.0234409938359347[/C][C]0.988279503082033[/C][/ROW]
[ROW][C]71[/C][C]0.00839269326250215[/C][C]0.0167853865250043[/C][C]0.991607306737498[/C][/ROW]
[ROW][C]72[/C][C]0.00592255870616449[/C][C]0.011845117412329[/C][C]0.994077441293835[/C][/ROW]
[ROW][C]73[/C][C]0.00412567029003812[/C][C]0.00825134058007624[/C][C]0.995874329709962[/C][/ROW]
[ROW][C]74[/C][C]0.0087979009791719[/C][C]0.0175958019583438[/C][C]0.991202099020828[/C][/ROW]
[ROW][C]75[/C][C]0.00671810347019132[/C][C]0.0134362069403826[/C][C]0.993281896529809[/C][/ROW]
[ROW][C]76[/C][C]0.02984757375343[/C][C]0.05969514750686[/C][C]0.97015242624657[/C][/ROW]
[ROW][C]77[/C][C]0.0226690809602933[/C][C]0.0453381619205866[/C][C]0.977330919039707[/C][/ROW]
[ROW][C]78[/C][C]0.0170189582377244[/C][C]0.0340379164754489[/C][C]0.982981041762276[/C][/ROW]
[ROW][C]79[/C][C]0.0126769435821974[/C][C]0.0253538871643947[/C][C]0.987323056417803[/C][/ROW]
[ROW][C]80[/C][C]0.0092708695421448[/C][C]0.0185417390842896[/C][C]0.990729130457855[/C][/ROW]
[ROW][C]81[/C][C]0.00691919911705063[/C][C]0.0138383982341013[/C][C]0.993080800882949[/C][/ROW]
[ROW][C]82[/C][C]0.00554950995505966[/C][C]0.0110990199101193[/C][C]0.99445049004494[/C][/ROW]
[ROW][C]83[/C][C]0.00399413957889545[/C][C]0.00798827915779089[/C][C]0.996005860421105[/C][/ROW]
[ROW][C]84[/C][C]0.00795242393848406[/C][C]0.0159048478769681[/C][C]0.992047576061516[/C][/ROW]
[ROW][C]85[/C][C]0.00591621949197728[/C][C]0.0118324389839546[/C][C]0.994083780508023[/C][/ROW]
[ROW][C]86[/C][C]0.00421548705227017[/C][C]0.00843097410454034[/C][C]0.99578451294773[/C][/ROW]
[ROW][C]87[/C][C]0.00314111969878888[/C][C]0.00628223939757777[/C][C]0.996858880301211[/C][/ROW]
[ROW][C]88[/C][C]0.00228864395355509[/C][C]0.00457728790711018[/C][C]0.997711356046445[/C][/ROW]
[ROW][C]89[/C][C]0.0015873839120243[/C][C]0.00317476782404859[/C][C]0.998412616087976[/C][/ROW]
[ROW][C]90[/C][C]0.00283458382798409[/C][C]0.00566916765596818[/C][C]0.997165416172016[/C][/ROW]
[ROW][C]91[/C][C]0.00215641724988731[/C][C]0.00431283449977461[/C][C]0.997843582750113[/C][/ROW]
[ROW][C]92[/C][C]0.00155609030825072[/C][C]0.00311218061650144[/C][C]0.998443909691749[/C][/ROW]
[ROW][C]93[/C][C]0.00126192100242366[/C][C]0.00252384200484733[/C][C]0.998738078997576[/C][/ROW]
[ROW][C]94[/C][C]0.00178600976914501[/C][C]0.00357201953829003[/C][C]0.998213990230855[/C][/ROW]
[ROW][C]95[/C][C]0.00632430107665823[/C][C]0.0126486021533165[/C][C]0.993675698923342[/C][/ROW]
[ROW][C]96[/C][C]0.0161386128754129[/C][C]0.0322772257508257[/C][C]0.983861387124587[/C][/ROW]
[ROW][C]97[/C][C]0.012221301688765[/C][C]0.0244426033775301[/C][C]0.987778698311235[/C][/ROW]
[ROW][C]98[/C][C]0.00877288435160158[/C][C]0.0175457687032032[/C][C]0.991227115648398[/C][/ROW]
[ROW][C]99[/C][C]0.00634412761559509[/C][C]0.0126882552311902[/C][C]0.993655872384405[/C][/ROW]
[ROW][C]100[/C][C]0.00467865732445724[/C][C]0.00935731464891448[/C][C]0.995321342675543[/C][/ROW]
[ROW][C]101[/C][C]0.00323659185102116[/C][C]0.00647318370204232[/C][C]0.996763408148979[/C][/ROW]
[ROW][C]102[/C][C]0.0021937320494383[/C][C]0.0043874640988766[/C][C]0.997806267950562[/C][/ROW]
[ROW][C]103[/C][C]0.00151961229092335[/C][C]0.0030392245818467[/C][C]0.998480387709077[/C][/ROW]
[ROW][C]104[/C][C]0.00122662700705127[/C][C]0.00245325401410254[/C][C]0.998773372992949[/C][/ROW]
[ROW][C]105[/C][C]0.00141311782917599[/C][C]0.00282623565835198[/C][C]0.998586882170824[/C][/ROW]
[ROW][C]106[/C][C]0.00118847225685683[/C][C]0.00237694451371365[/C][C]0.998811527743143[/C][/ROW]
[ROW][C]107[/C][C]0.000762856411816489[/C][C]0.00152571282363298[/C][C]0.999237143588183[/C][/ROW]
[ROW][C]108[/C][C]0.00276189869450956[/C][C]0.00552379738901912[/C][C]0.99723810130549[/C][/ROW]
[ROW][C]109[/C][C]0.00199526044923736[/C][C]0.00399052089847471[/C][C]0.998004739550763[/C][/ROW]
[ROW][C]110[/C][C]0.00890939105255597[/C][C]0.0178187821051119[/C][C]0.991090608947444[/C][/ROW]
[ROW][C]111[/C][C]0.0140016659193766[/C][C]0.0280033318387532[/C][C]0.985998334080623[/C][/ROW]
[ROW][C]112[/C][C]0.0100031307636843[/C][C]0.0200062615273686[/C][C]0.989996869236316[/C][/ROW]
[ROW][C]113[/C][C]0.0199279186838727[/C][C]0.0398558373677454[/C][C]0.980072081316127[/C][/ROW]
[ROW][C]114[/C][C]0.0144551435229988[/C][C]0.0289102870459975[/C][C]0.985544856477001[/C][/ROW]
[ROW][C]115[/C][C]0.0360755067554291[/C][C]0.0721510135108582[/C][C]0.963924493244571[/C][/ROW]
[ROW][C]116[/C][C]0.0667600176617655[/C][C]0.133520035323531[/C][C]0.933239982338234[/C][/ROW]
[ROW][C]117[/C][C]0.0489702268053778[/C][C]0.0979404536107555[/C][C]0.951029773194622[/C][/ROW]
[ROW][C]118[/C][C]0.0348393123289475[/C][C]0.0696786246578951[/C][C]0.965160687671052[/C][/ROW]
[ROW][C]119[/C][C]0.0312362004990866[/C][C]0.0624724009981733[/C][C]0.968763799500913[/C][/ROW]
[ROW][C]120[/C][C]0.0326501338985087[/C][C]0.0653002677970173[/C][C]0.967349866101491[/C][/ROW]
[ROW][C]121[/C][C]0.0549799359629178[/C][C]0.109959871925836[/C][C]0.945020064037082[/C][/ROW]
[ROW][C]122[/C][C]0.0431362994646097[/C][C]0.0862725989292195[/C][C]0.95686370053539[/C][/ROW]
[ROW][C]123[/C][C]0.070396602367123[/C][C]0.140793204734246[/C][C]0.929603397632877[/C][/ROW]
[ROW][C]124[/C][C]0.0604947198195074[/C][C]0.120989439639015[/C][C]0.939505280180493[/C][/ROW]
[ROW][C]125[/C][C]0.041207442492292[/C][C]0.082414884984584[/C][C]0.958792557507708[/C][/ROW]
[ROW][C]126[/C][C]0.0281631437122644[/C][C]0.0563262874245289[/C][C]0.971836856287736[/C][/ROW]
[ROW][C]127[/C][C]0.0217265260700611[/C][C]0.0434530521401222[/C][C]0.978273473929939[/C][/ROW]
[ROW][C]128[/C][C]0.0199285701490022[/C][C]0.0398571402980044[/C][C]0.980071429850998[/C][/ROW]
[ROW][C]129[/C][C]0.021703782709705[/C][C]0.04340756541941[/C][C]0.978296217290295[/C][/ROW]
[ROW][C]130[/C][C]0.0274939588142561[/C][C]0.0549879176285122[/C][C]0.972506041185744[/C][/ROW]
[ROW][C]131[/C][C]0.0194106802649508[/C][C]0.0388213605299016[/C][C]0.980589319735049[/C][/ROW]
[ROW][C]132[/C][C]0.0368030383721838[/C][C]0.0736060767443676[/C][C]0.963196961627816[/C][/ROW]
[ROW][C]133[/C][C]0.0264860579745862[/C][C]0.0529721159491725[/C][C]0.973513942025414[/C][/ROW]
[ROW][C]134[/C][C]0.0216776889770303[/C][C]0.0433553779540607[/C][C]0.97832231102297[/C][/ROW]
[ROW][C]135[/C][C]0.295666214716471[/C][C]0.591332429432942[/C][C]0.704333785283529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0424116850511485 0.0848233701022969 0.957588314948852 11 0.0120418085042438 0.0240836170084877 0.987958191495756 12 0.230118028286516 0.460236056573032 0.769881971713484 13 0.205851003359324 0.411702006718648 0.794148996640676 14 0.125055701172913 0.250111402345826 0.874944298827087 15 0.0718306984319717 0.143661396863943 0.928169301568028 16 0.0706378897761419 0.141275779552284 0.929362110223858 17 0.0418707922622472 0.0837415845244945 0.958129207737753 18 0.223135617698231 0.446271235396463 0.776864382301769 19 0.207607280201702 0.415214560403405 0.792392719798298 20 0.160899391843438 0.321798783686875 0.839100608156562 21 0.112928828445675 0.22585765689135 0.887071171554325 22 0.227741463395236 0.455482926790472 0.772258536604764 23 0.186135932067842 0.372271864135684 0.813864067932158 24 0.192342481207849 0.384684962415698 0.807657518792151 25 0.270495022856046 0.540990045712092 0.729504977143954 26 0.219435065203837 0.438870130407675 0.780564934796163 27 0.172147593210472 0.344295186420944 0.827852406789528 28 0.131159608183739 0.262319216367478 0.868840391816261 29 0.10102361911694 0.20204723823388 0.89897638088306 30 0.0741822192032969 0.148364438406594 0.925817780796703 31 0.0535496094659077 0.107099218931815 0.946450390534092 32 0.142328224230387 0.284656448460774 0.857671775769613 33 0.292910830885605 0.58582166177121 0.707089169114395 34 0.242180488659813 0.484360977319626 0.757819511340187 35 0.275326168254246 0.550652336508492 0.724673831745754 36 0.252392311760705 0.50478462352141 0.747607688239295 37 0.239898969190142 0.479797938380285 0.760101030809858 38 0.308199894871551 0.616399789743101 0.691800105128449 39 0.344235184403218 0.688470368806437 0.655764815596782 40 0.297580212225149 0.595160424450298 0.702419787774851 41 0.250149715205781 0.500299430411562 0.749850284794219 42 0.207271890144174 0.414543780288348 0.792728109855826 43 0.342503992201715 0.685007984403429 0.657496007798285 44 0.296427656615708 0.592855313231417 0.703572343384292 45 0.251306976989507 0.502613953979014 0.748693023010493 46 0.213814223186912 0.427628446373824 0.786185776813088 47 0.201703861239361 0.403407722478722 0.798296138760639 48 0.190429929690068 0.380859859380136 0.809570070309932 49 0.17031817292244 0.340636345844879 0.82968182707756 50 0.144758770380447 0.289517540760895 0.855241229619553 51 0.121196067832655 0.24239213566531 0.878803932167345 52 0.162161904288763 0.324323808577526 0.837838095711237 53 0.131837669474582 0.263675338949163 0.868162330525418 54 0.207716516730773 0.415433033461547 0.792283483269227 55 0.17565189523608 0.351303790472161 0.82434810476392 56 0.145477887883538 0.290955775767075 0.854522112116462 57 0.118202066047292 0.236404132094584 0.881797933952708 58 0.0950123076244034 0.190024615248807 0.904987692375597 59 0.0759891205703684 0.151978241140737 0.924010879429632 60 0.0598607109538731 0.119721421907746 0.940139289046127 61 0.0467652215171398 0.0935304430342796 0.95323477848286 62 0.0357460102809748 0.0714920205619496 0.964253989719025 63 0.0768165804709118 0.153633160941824 0.923183419529088 64 0.06100330310173 0.12200660620346 0.93899669689827 65 0.0476470282911067 0.0952940565822134 0.952352971708893 66 0.0367597074540065 0.0735194149080129 0.963240292545994 67 0.0281011430560903 0.0562022861121806 0.97189885694391 68 0.0214990650079275 0.0429981300158549 0.978500934992073 69 0.0159269984457967 0.0318539968915933 0.984073001554203 70 0.0117204969179674 0.0234409938359347 0.988279503082033 71 0.00839269326250215 0.0167853865250043 0.991607306737498 72 0.00592255870616449 0.011845117412329 0.994077441293835 73 0.00412567029003812 0.00825134058007624 0.995874329709962 74 0.0087979009791719 0.0175958019583438 0.991202099020828 75 0.00671810347019132 0.0134362069403826 0.993281896529809 76 0.02984757375343 0.05969514750686 0.97015242624657 77 0.0226690809602933 0.0453381619205866 0.977330919039707 78 0.0170189582377244 0.0340379164754489 0.982981041762276 79 0.0126769435821974 0.0253538871643947 0.987323056417803 80 0.0092708695421448 0.0185417390842896 0.990729130457855 81 0.00691919911705063 0.0138383982341013 0.993080800882949 82 0.00554950995505966 0.0110990199101193 0.99445049004494 83 0.00399413957889545 0.00798827915779089 0.996005860421105 84 0.00795242393848406 0.0159048478769681 0.992047576061516 85 0.00591621949197728 0.0118324389839546 0.994083780508023 86 0.00421548705227017 0.00843097410454034 0.99578451294773 87 0.00314111969878888 0.00628223939757777 0.996858880301211 88 0.00228864395355509 0.00457728790711018 0.997711356046445 89 0.0015873839120243 0.00317476782404859 0.998412616087976 90 0.00283458382798409 0.00566916765596818 0.997165416172016 91 0.00215641724988731 0.00431283449977461 0.997843582750113 92 0.00155609030825072 0.00311218061650144 0.998443909691749 93 0.00126192100242366 0.00252384200484733 0.998738078997576 94 0.00178600976914501 0.00357201953829003 0.998213990230855 95 0.00632430107665823 0.0126486021533165 0.993675698923342 96 0.0161386128754129 0.0322772257508257 0.983861387124587 97 0.012221301688765 0.0244426033775301 0.987778698311235 98 0.00877288435160158 0.0175457687032032 0.991227115648398 99 0.00634412761559509 0.0126882552311902 0.993655872384405 100 0.00467865732445724 0.00935731464891448 0.995321342675543 101 0.00323659185102116 0.00647318370204232 0.996763408148979 102 0.0021937320494383 0.0043874640988766 0.997806267950562 103 0.00151961229092335 0.0030392245818467 0.998480387709077 104 0.00122662700705127 0.00245325401410254 0.998773372992949 105 0.00141311782917599 0.00282623565835198 0.998586882170824 106 0.00118847225685683 0.00237694451371365 0.998811527743143 107 0.000762856411816489 0.00152571282363298 0.999237143588183 108 0.00276189869450956 0.00552379738901912 0.99723810130549 109 0.00199526044923736 0.00399052089847471 0.998004739550763 110 0.00890939105255597 0.0178187821051119 0.991090608947444 111 0.0140016659193766 0.0280033318387532 0.985998334080623 112 0.0100031307636843 0.0200062615273686 0.989996869236316 113 0.0199279186838727 0.0398558373677454 0.980072081316127 114 0.0144551435229988 0.0289102870459975 0.985544856477001 115 0.0360755067554291 0.0721510135108582 0.963924493244571 116 0.0667600176617655 0.133520035323531 0.933239982338234 117 0.0489702268053778 0.0979404536107555 0.951029773194622 118 0.0348393123289475 0.0696786246578951 0.965160687671052 119 0.0312362004990866 0.0624724009981733 0.968763799500913 120 0.0326501338985087 0.0653002677970173 0.967349866101491 121 0.0549799359629178 0.109959871925836 0.945020064037082 122 0.0431362994646097 0.0862725989292195 0.95686370053539 123 0.070396602367123 0.140793204734246 0.929603397632877 124 0.0604947198195074 0.120989439639015 0.939505280180493 125 0.041207442492292 0.082414884984584 0.958792557507708 126 0.0281631437122644 0.0563262874245289 0.971836856287736 127 0.0217265260700611 0.0434530521401222 0.978273473929939 128 0.0199285701490022 0.0398571402980044 0.980071429850998 129 0.021703782709705 0.04340756541941 0.978296217290295 130 0.0274939588142561 0.0549879176285122 0.972506041185744 131 0.0194106802649508 0.0388213605299016 0.980589319735049 132 0.0368030383721838 0.0736060767443676 0.963196961627816 133 0.0264860579745862 0.0529721159491725 0.973513942025414 134 0.0216776889770303 0.0433553779540607 0.97832231102297 135 0.295666214716471 0.591332429432942 0.704333785283529

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.166666666666667 NOK 5% type I error level 52 0.412698412698413 NOK 10% type I error level 71 0.563492063492063 NOK

\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 & 52 & 0.412698412698413 & NOK \tabularnewline
10% type I error level & 71 & 0.563492063492063 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186216&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]52[/C][C]0.412698412698413[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]71[/C][C]0.563492063492063[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186216&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186216&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 tests OK/NOK 1% type I error level 21 0.166666666666667 NOK 5% type I error level 52 0.412698412698413 NOK 10% type I error level 71 0.563492063492063 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}