Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 20:18:20 -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/Dec/20/t1356052722zlndy1obmupfcge.htm/, Retrieved Fri, 29 Mar 2024 07:07:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203237, Retrieved Fri, 29 Mar 2024 07:07:11 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
- R  D  [Univariate Explorative Data Analysis] [Workshop 7 (1)] [2012-11-17 15:28:24] [e31fe164d58995c48777312ee804d655]
- R       [Univariate Explorative Data Analysis] [workshop 7 b] [2012-11-18 11:29:34] [dbae308bdff61c0f4902cc85498d0d35]
- RMPD      [Multiple Regression] [Workshop 7 met ma...] [2012-11-19 15:00:24] [8c30f4dd45e15fd207e4faf2fdf6253e]
- RMP           [Multiple Regression] [] [2012-12-21 01:18:20] [ab4290de075ebbfc5b460761b0110080] [Current]
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Dataseries X:
1	593408	280190	313218	44148	125326	223560
2	590072	280408	309664	42065	122716	223789
3	579799	276836	302963	38546	116615	223893
4	574205	275216	298989	35324	113719	221010
5	572775	274352	298423	26599	110737	221742
6	572942	271311	301631	24935	112093	221353
7	619567	289802	329765	51349	143565	224844
8	625809	290726	335083	58672	149946	230418
9	619916	292300	327616	61271	149147	232189
10	587625	278506	309119	53145	134339	231219
11	565742	269826	295916	46211	122683	228209
12	557274	265861	291413	40744	115614	227941
1	560576	269034	291542	41248	116566	228128
2	548854	264176	284678	39032	111272	226309
3	531673	255198	276475	35907	104609	221990
4	525919	253353	272566	33335	101802	220386
5	511038	246057	264981	23988	94542	217415
6	498662	235372	263290	23099	93051	210394
7	555362	258556	296806	46390	124129	213985
8	564591	260993	303598	51588	130374	214552
9	541657	254663	286994	51579	123946	211797
10	527070	250643	276427	45390	114971	208512
11	509846	243422	266424	39215	105531	205708
12	514258	247105	267153	38433	104919	206890
1	516922	248541	268381	37676	104782	207069
2	507561	245039	262522	36055	101281	205305
3	492622	237080	255542	32986	94545	201504
4	490243	237085	253158	30953	93248	200517
5	469357	225554	243803	23558	84031	195771
6	477580	226839	250741	22487	87486	195259
7	528379	247934	280445	43528	115867	197579
8	533590	248333	285257	47913	120327	196985
9	517945	246969	270976	48621	117008	194382
10	506174	245098	261076	42169	108811	191580
11	501866	246263	255603	38444	104519	190765
12	516141	255765	260376	38692	106758	191480
1	528222	264319	263903	38124	109337	192277
2	532638	268347	264291	37886	109078	191632
3	536322	273046	263276	37310	108293	190757
4	536535	273963	262572	34689	106534	190995
5	523597	267430	256167	26450	99197	189081
6	536214	271993	264221	25565	103493	190028
7	586570	292710	293860	46562	130676	196146
8	596594	295881	300713	52653	137448	197070
9	580523	293299	287224	54807	134704	194893
10	564478	288576	275902	47534	123725	193246
11	557560	286445	271115	43565	118277	192484
12	575093	297584	277509	44051	121225	194924
1	580112	300431	279681	42622	120528	197394
2	574761	298522	276239	41761	118240	196598
3	563250	292213	271037	39086	112514	194409
4	551531	285383	266148	35438	107304	193431
5	537034	277537	259497	27356	100001	191942
6	544686	277891	266795	26149	102082	193323
7	600991	302686	298305	47034	130455	199654
8	604378	300653	303725	53091	135574	198422
9	586111	296369	289742	55718	132540	198219
10	563668	287224	276444	47637	119920	197157
11	548604	279998	268606	43237	112454	195115
12	551174	283495	267679	40597	109415	197296
1	555654	285775	269879	39884	109843	198178
2	547970	282329	265641	38504	106365	197787
3	540324	277799	262525	36393	102304	197622
4	530577	271980	258597	33740	97968	196683
5	520579	266730	253849	26131	92462	194590
6	518654	262433	256221	23885	92286	194316
7	572273	285378	286895	43899	120092	199598
8	581302	286692	294610	49871	126656	199055
9	563280	282917	280363	52292	124144	197482
10	547612	277686	269926	45493	114045	196440
11	538712	274371	264341	41124	108120	195338
12	540735	277466	263269	39385	105698	195589
1	561649	290604	271045	41472	111203	198936
2	558685	290770	267915	41688	110030	198262
3	545732	283654	262078	38711	104009	197275
4	536352	278601	257751	36840	99772	196007
5	527676	274405	253271	35141	96301	194447
6	530455	272817	257638	37443	97680	193951
7	581744	294292	287452	51905	121563	198396
8	598714	300562	298152	60016	134210	199486
9	583775	298982	284793	58611	133111	198688
10	571477	296917	274560	52097	124527	196729




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Werkzoekenden[t] = + 9.4137083078822e-13 -1.47595426624539e-13Maand[t] + 1Mannen[t] + 1Vrouwen[t] -2.52926783767913e-17Beroepsinschakelingstijd[t] + 1.32309862647938e-17`<25jaar`[t] -2.18953975484347e-17`inactiviteitsduur>=2jaar`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkzoekenden[t] =  +  9.4137083078822e-13 -1.47595426624539e-13Maand[t] +  1Mannen[t] +  1Vrouwen[t] -2.52926783767913e-17Beroepsinschakelingstijd[t] +  1.32309862647938e-17`<25jaar`[t] -2.18953975484347e-17`inactiviteitsduur>=2jaar`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkzoekenden[t] =  +  9.4137083078822e-13 -1.47595426624539e-13Maand[t] +  1Mannen[t] +  1Vrouwen[t] -2.52926783767913e-17Beroepsinschakelingstijd[t] +  1.32309862647938e-17`<25jaar`[t] -2.18953975484347e-17`inactiviteitsduur>=2jaar`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&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
Werkzoekenden[t] = + 9.4137083078822e-13 -1.47595426624539e-13Maand[t] + 1Mannen[t] + 1Vrouwen[t] -2.52926783767913e-17Beroepsinschakelingstijd[t] + 1.32309862647938e-17`<25jaar`[t] -2.18953975484347e-17`inactiviteitsduur>=2jaar`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.4137083078822e-1300.10320.9181160.459058
Maand-1.47595426624539e-130-1.30380.1963060.098153
Mannen103857787412404649600
Vrouwen101122036161690600600
Beroepsinschakelingstijd-2.52926783767913e-170-0.22390.8234230.411712
`<25jaar`1.32309862647938e-1700.08520.9322960.466148
`inactiviteitsduur>=2jaar`-2.18953975484347e-170-0.35510.7234840.361742

\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) & 9.4137083078822e-13 & 0 & 0.1032 & 0.918116 & 0.459058 \tabularnewline
Maand & -1.47595426624539e-13 & 0 & -1.3038 & 0.196306 & 0.098153 \tabularnewline
Mannen & 1 & 0 & 38577874124046496 & 0 & 0 \tabularnewline
Vrouwen & 1 & 0 & 11220361616906006 & 0 & 0 \tabularnewline
Beroepsinschakelingstijd & -2.52926783767913e-17 & 0 & -0.2239 & 0.823423 & 0.411712 \tabularnewline
`<25jaar` & 1.32309862647938e-17 & 0 & 0.0852 & 0.932296 & 0.466148 \tabularnewline
`inactiviteitsduur>=2jaar` & -2.18953975484347e-17 & 0 & -0.3551 & 0.723484 & 0.361742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&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]9.4137083078822e-13[/C][C]0[/C][C]0.1032[/C][C]0.918116[/C][C]0.459058[/C][/ROW]
[ROW][C]Maand[/C][C]-1.47595426624539e-13[/C][C]0[/C][C]-1.3038[/C][C]0.196306[/C][C]0.098153[/C][/ROW]
[ROW][C]Mannen[/C][C]1[/C][C]0[/C][C]38577874124046496[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vrouwen[/C][C]1[/C][C]0[/C][C]11220361616906006[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Beroepsinschakelingstijd[/C][C]-2.52926783767913e-17[/C][C]0[/C][C]-0.2239[/C][C]0.823423[/C][C]0.411712[/C][/ROW]
[ROW][C]`<25jaar`[/C][C]1.32309862647938e-17[/C][C]0[/C][C]0.0852[/C][C]0.932296[/C][C]0.466148[/C][/ROW]
[ROW][C]`inactiviteitsduur>=2jaar`[/C][C]-2.18953975484347e-17[/C][C]0[/C][C]-0.3551[/C][C]0.723484[/C][C]0.361742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.4137083078822e-1300.10320.9181160.459058
Maand-1.47595426624539e-130-1.30380.1963060.098153
Mannen103857787412404649600
Vrouwen101122036161690600600
Beroepsinschakelingstijd-2.52926783767913e-170-0.22390.8234230.411712
`<25jaar`1.32309862647938e-1700.08520.9322960.466148
`inactiviteitsduur>=2jaar`-2.18953975484347e-170-0.35510.7234840.361742







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)1.68082351317658e+33
F-TEST (DF numerator)6
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.94684506667045e-12
Sum Squared Residuals6.51292188521997e-22

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 1.68082351317658e+33 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.94684506667045e-12 \tabularnewline
Sum Squared Residuals & 6.51292188521997e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.68082351317658e+33[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/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]2.94684506667045e-12[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6.51292188521997e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)1.68082351317658e+33
F-TEST (DF numerator)6
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.94684506667045e-12
Sum Squared Residuals6.51292188521997e-22







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15934085934082.44201154238717e-12
2590072590072-3.783616384487e-12
3579799579799-6.90220221687566e-12
45742055742059.87085419307986e-13
55727755727756.6801760186297e-13
65729425729422.13504398037157e-12
7619567619567-2.13948946713613e-12
86258096258094.81925904508278e-12
96199166199167.30470064178225e-13
10587625587625-7.57211439050367e-13
115657425657422.06034501084624e-12
125572745572743.06441005997693e-12
13560576560576-2.9316208706919e-12
145488545488544.61977932897679e-13
155316735316735.55157000539042e-12
165259195259192.32478579098379e-12
17511038511038-4.85698494972428e-12
18498662498662-8.52230848284974e-13
19555362555362-1.25105026286967e-12
205645915645911.6789962096871e-12
215416575416571.1142629687916e-12
22527070527070-5.8856383834731e-13
23509846509846-1.17308671715353e-12
24514258514258-3.24853427028823e-12
25516922516922-1.88010367452053e-12
26507561507561-6.45968796656386e-12
274926224926223.85054773607235e-12
28490243490243-1.54748540890199e-12
294693574693575.662356835887e-13
304775804775804.7834893298684e-12
315283795283793.98142122353893e-13
325335905335905.27486060189218e-12
33517945517945-2.6365431034682e-12
34506174506174-4.50779550609321e-12
355018665018662.29993609485324e-12
36516141516141-5.89731737624105e-12
375282225282221.3863129109491e-12
38532638532638-1.06000690378972e-12
395363225363224.25580186387998e-12
405365355365353.57545375803029e-12
41523597523597-1.35236353610983e-13
425362145362142.84848182384854e-12
435865705865705.50956475785104e-13
44596594596594-3.92833061378487e-12
455805235805232.73156621753421e-12
465644785644781.7190047852878e-13
475575605575602.53082119358923e-13
485750935750938.42284500221758e-13
495801125801121.32241451024824e-12
505747615747612.12970667444904e-13
515632505632503.65781616680844e-12
52551531551531-4.55045513349082e-12
535370345370343.32248055058485e-12
54544686544686-3.39855549755375e-12
55600991600991-3.18638230762246e-12
56604378604378-2.50493053399709e-12
575861115861111.58026845949619e-12
585636685636681.42517197815163e-13
59548604548604-5.66006614443802e-13
605511745511741.12506180175244e-12
61555654555654-4.01024680432628e-12
62547970547970-3.02059356837157e-12
63540324540324-1.79251772706498e-12
645305775305772.64183477965689e-12
65520579520579-1.63331379655198e-12
66518654518654-2.24084659928743e-12
675722735722731.46789172096475e-12
685813025813021.81388036445779e-12
695632805632801.34313730107964e-13
70547612547612-1.16747944925959e-12
71538712538712-3.25230456579049e-12
725407355407351.18356009376666e-12
735616495616494.99525653332162e-13
74558685558685-2.47351691182456e-12
755457325457323.49803140277316e-12
765363525363521.99215872721911e-12
775276765276762.77434248909365e-12
785304555304553.23642794713553e-12
795817445817442.59910051547538e-13
80598714598714-2.24554095834726e-12
815837755837752.56683416718498e-12
82571477571477-2.67973919010478e-12

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 593408 & 593408 & 2.44201154238717e-12 \tabularnewline
2 & 590072 & 590072 & -3.783616384487e-12 \tabularnewline
3 & 579799 & 579799 & -6.90220221687566e-12 \tabularnewline
4 & 574205 & 574205 & 9.87085419307986e-13 \tabularnewline
5 & 572775 & 572775 & 6.6801760186297e-13 \tabularnewline
6 & 572942 & 572942 & 2.13504398037157e-12 \tabularnewline
7 & 619567 & 619567 & -2.13948946713613e-12 \tabularnewline
8 & 625809 & 625809 & 4.81925904508278e-12 \tabularnewline
9 & 619916 & 619916 & 7.30470064178225e-13 \tabularnewline
10 & 587625 & 587625 & -7.57211439050367e-13 \tabularnewline
11 & 565742 & 565742 & 2.06034501084624e-12 \tabularnewline
12 & 557274 & 557274 & 3.06441005997693e-12 \tabularnewline
13 & 560576 & 560576 & -2.9316208706919e-12 \tabularnewline
14 & 548854 & 548854 & 4.61977932897679e-13 \tabularnewline
15 & 531673 & 531673 & 5.55157000539042e-12 \tabularnewline
16 & 525919 & 525919 & 2.32478579098379e-12 \tabularnewline
17 & 511038 & 511038 & -4.85698494972428e-12 \tabularnewline
18 & 498662 & 498662 & -8.52230848284974e-13 \tabularnewline
19 & 555362 & 555362 & -1.25105026286967e-12 \tabularnewline
20 & 564591 & 564591 & 1.6789962096871e-12 \tabularnewline
21 & 541657 & 541657 & 1.1142629687916e-12 \tabularnewline
22 & 527070 & 527070 & -5.8856383834731e-13 \tabularnewline
23 & 509846 & 509846 & -1.17308671715353e-12 \tabularnewline
24 & 514258 & 514258 & -3.24853427028823e-12 \tabularnewline
25 & 516922 & 516922 & -1.88010367452053e-12 \tabularnewline
26 & 507561 & 507561 & -6.45968796656386e-12 \tabularnewline
27 & 492622 & 492622 & 3.85054773607235e-12 \tabularnewline
28 & 490243 & 490243 & -1.54748540890199e-12 \tabularnewline
29 & 469357 & 469357 & 5.662356835887e-13 \tabularnewline
30 & 477580 & 477580 & 4.7834893298684e-12 \tabularnewline
31 & 528379 & 528379 & 3.98142122353893e-13 \tabularnewline
32 & 533590 & 533590 & 5.27486060189218e-12 \tabularnewline
33 & 517945 & 517945 & -2.6365431034682e-12 \tabularnewline
34 & 506174 & 506174 & -4.50779550609321e-12 \tabularnewline
35 & 501866 & 501866 & 2.29993609485324e-12 \tabularnewline
36 & 516141 & 516141 & -5.89731737624105e-12 \tabularnewline
37 & 528222 & 528222 & 1.3863129109491e-12 \tabularnewline
38 & 532638 & 532638 & -1.06000690378972e-12 \tabularnewline
39 & 536322 & 536322 & 4.25580186387998e-12 \tabularnewline
40 & 536535 & 536535 & 3.57545375803029e-12 \tabularnewline
41 & 523597 & 523597 & -1.35236353610983e-13 \tabularnewline
42 & 536214 & 536214 & 2.84848182384854e-12 \tabularnewline
43 & 586570 & 586570 & 5.50956475785104e-13 \tabularnewline
44 & 596594 & 596594 & -3.92833061378487e-12 \tabularnewline
45 & 580523 & 580523 & 2.73156621753421e-12 \tabularnewline
46 & 564478 & 564478 & 1.7190047852878e-13 \tabularnewline
47 & 557560 & 557560 & 2.53082119358923e-13 \tabularnewline
48 & 575093 & 575093 & 8.42284500221758e-13 \tabularnewline
49 & 580112 & 580112 & 1.32241451024824e-12 \tabularnewline
50 & 574761 & 574761 & 2.12970667444904e-13 \tabularnewline
51 & 563250 & 563250 & 3.65781616680844e-12 \tabularnewline
52 & 551531 & 551531 & -4.55045513349082e-12 \tabularnewline
53 & 537034 & 537034 & 3.32248055058485e-12 \tabularnewline
54 & 544686 & 544686 & -3.39855549755375e-12 \tabularnewline
55 & 600991 & 600991 & -3.18638230762246e-12 \tabularnewline
56 & 604378 & 604378 & -2.50493053399709e-12 \tabularnewline
57 & 586111 & 586111 & 1.58026845949619e-12 \tabularnewline
58 & 563668 & 563668 & 1.42517197815163e-13 \tabularnewline
59 & 548604 & 548604 & -5.66006614443802e-13 \tabularnewline
60 & 551174 & 551174 & 1.12506180175244e-12 \tabularnewline
61 & 555654 & 555654 & -4.01024680432628e-12 \tabularnewline
62 & 547970 & 547970 & -3.02059356837157e-12 \tabularnewline
63 & 540324 & 540324 & -1.79251772706498e-12 \tabularnewline
64 & 530577 & 530577 & 2.64183477965689e-12 \tabularnewline
65 & 520579 & 520579 & -1.63331379655198e-12 \tabularnewline
66 & 518654 & 518654 & -2.24084659928743e-12 \tabularnewline
67 & 572273 & 572273 & 1.46789172096475e-12 \tabularnewline
68 & 581302 & 581302 & 1.81388036445779e-12 \tabularnewline
69 & 563280 & 563280 & 1.34313730107964e-13 \tabularnewline
70 & 547612 & 547612 & -1.16747944925959e-12 \tabularnewline
71 & 538712 & 538712 & -3.25230456579049e-12 \tabularnewline
72 & 540735 & 540735 & 1.18356009376666e-12 \tabularnewline
73 & 561649 & 561649 & 4.99525653332162e-13 \tabularnewline
74 & 558685 & 558685 & -2.47351691182456e-12 \tabularnewline
75 & 545732 & 545732 & 3.49803140277316e-12 \tabularnewline
76 & 536352 & 536352 & 1.99215872721911e-12 \tabularnewline
77 & 527676 & 527676 & 2.77434248909365e-12 \tabularnewline
78 & 530455 & 530455 & 3.23642794713553e-12 \tabularnewline
79 & 581744 & 581744 & 2.59910051547538e-13 \tabularnewline
80 & 598714 & 598714 & -2.24554095834726e-12 \tabularnewline
81 & 583775 & 583775 & 2.56683416718498e-12 \tabularnewline
82 & 571477 & 571477 & -2.67973919010478e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&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]593408[/C][C]593408[/C][C]2.44201154238717e-12[/C][/ROW]
[ROW][C]2[/C][C]590072[/C][C]590072[/C][C]-3.783616384487e-12[/C][/ROW]
[ROW][C]3[/C][C]579799[/C][C]579799[/C][C]-6.90220221687566e-12[/C][/ROW]
[ROW][C]4[/C][C]574205[/C][C]574205[/C][C]9.87085419307986e-13[/C][/ROW]
[ROW][C]5[/C][C]572775[/C][C]572775[/C][C]6.6801760186297e-13[/C][/ROW]
[ROW][C]6[/C][C]572942[/C][C]572942[/C][C]2.13504398037157e-12[/C][/ROW]
[ROW][C]7[/C][C]619567[/C][C]619567[/C][C]-2.13948946713613e-12[/C][/ROW]
[ROW][C]8[/C][C]625809[/C][C]625809[/C][C]4.81925904508278e-12[/C][/ROW]
[ROW][C]9[/C][C]619916[/C][C]619916[/C][C]7.30470064178225e-13[/C][/ROW]
[ROW][C]10[/C][C]587625[/C][C]587625[/C][C]-7.57211439050367e-13[/C][/ROW]
[ROW][C]11[/C][C]565742[/C][C]565742[/C][C]2.06034501084624e-12[/C][/ROW]
[ROW][C]12[/C][C]557274[/C][C]557274[/C][C]3.06441005997693e-12[/C][/ROW]
[ROW][C]13[/C][C]560576[/C][C]560576[/C][C]-2.9316208706919e-12[/C][/ROW]
[ROW][C]14[/C][C]548854[/C][C]548854[/C][C]4.61977932897679e-13[/C][/ROW]
[ROW][C]15[/C][C]531673[/C][C]531673[/C][C]5.55157000539042e-12[/C][/ROW]
[ROW][C]16[/C][C]525919[/C][C]525919[/C][C]2.32478579098379e-12[/C][/ROW]
[ROW][C]17[/C][C]511038[/C][C]511038[/C][C]-4.85698494972428e-12[/C][/ROW]
[ROW][C]18[/C][C]498662[/C][C]498662[/C][C]-8.52230848284974e-13[/C][/ROW]
[ROW][C]19[/C][C]555362[/C][C]555362[/C][C]-1.25105026286967e-12[/C][/ROW]
[ROW][C]20[/C][C]564591[/C][C]564591[/C][C]1.6789962096871e-12[/C][/ROW]
[ROW][C]21[/C][C]541657[/C][C]541657[/C][C]1.1142629687916e-12[/C][/ROW]
[ROW][C]22[/C][C]527070[/C][C]527070[/C][C]-5.8856383834731e-13[/C][/ROW]
[ROW][C]23[/C][C]509846[/C][C]509846[/C][C]-1.17308671715353e-12[/C][/ROW]
[ROW][C]24[/C][C]514258[/C][C]514258[/C][C]-3.24853427028823e-12[/C][/ROW]
[ROW][C]25[/C][C]516922[/C][C]516922[/C][C]-1.88010367452053e-12[/C][/ROW]
[ROW][C]26[/C][C]507561[/C][C]507561[/C][C]-6.45968796656386e-12[/C][/ROW]
[ROW][C]27[/C][C]492622[/C][C]492622[/C][C]3.85054773607235e-12[/C][/ROW]
[ROW][C]28[/C][C]490243[/C][C]490243[/C][C]-1.54748540890199e-12[/C][/ROW]
[ROW][C]29[/C][C]469357[/C][C]469357[/C][C]5.662356835887e-13[/C][/ROW]
[ROW][C]30[/C][C]477580[/C][C]477580[/C][C]4.7834893298684e-12[/C][/ROW]
[ROW][C]31[/C][C]528379[/C][C]528379[/C][C]3.98142122353893e-13[/C][/ROW]
[ROW][C]32[/C][C]533590[/C][C]533590[/C][C]5.27486060189218e-12[/C][/ROW]
[ROW][C]33[/C][C]517945[/C][C]517945[/C][C]-2.6365431034682e-12[/C][/ROW]
[ROW][C]34[/C][C]506174[/C][C]506174[/C][C]-4.50779550609321e-12[/C][/ROW]
[ROW][C]35[/C][C]501866[/C][C]501866[/C][C]2.29993609485324e-12[/C][/ROW]
[ROW][C]36[/C][C]516141[/C][C]516141[/C][C]-5.89731737624105e-12[/C][/ROW]
[ROW][C]37[/C][C]528222[/C][C]528222[/C][C]1.3863129109491e-12[/C][/ROW]
[ROW][C]38[/C][C]532638[/C][C]532638[/C][C]-1.06000690378972e-12[/C][/ROW]
[ROW][C]39[/C][C]536322[/C][C]536322[/C][C]4.25580186387998e-12[/C][/ROW]
[ROW][C]40[/C][C]536535[/C][C]536535[/C][C]3.57545375803029e-12[/C][/ROW]
[ROW][C]41[/C][C]523597[/C][C]523597[/C][C]-1.35236353610983e-13[/C][/ROW]
[ROW][C]42[/C][C]536214[/C][C]536214[/C][C]2.84848182384854e-12[/C][/ROW]
[ROW][C]43[/C][C]586570[/C][C]586570[/C][C]5.50956475785104e-13[/C][/ROW]
[ROW][C]44[/C][C]596594[/C][C]596594[/C][C]-3.92833061378487e-12[/C][/ROW]
[ROW][C]45[/C][C]580523[/C][C]580523[/C][C]2.73156621753421e-12[/C][/ROW]
[ROW][C]46[/C][C]564478[/C][C]564478[/C][C]1.7190047852878e-13[/C][/ROW]
[ROW][C]47[/C][C]557560[/C][C]557560[/C][C]2.53082119358923e-13[/C][/ROW]
[ROW][C]48[/C][C]575093[/C][C]575093[/C][C]8.42284500221758e-13[/C][/ROW]
[ROW][C]49[/C][C]580112[/C][C]580112[/C][C]1.32241451024824e-12[/C][/ROW]
[ROW][C]50[/C][C]574761[/C][C]574761[/C][C]2.12970667444904e-13[/C][/ROW]
[ROW][C]51[/C][C]563250[/C][C]563250[/C][C]3.65781616680844e-12[/C][/ROW]
[ROW][C]52[/C][C]551531[/C][C]551531[/C][C]-4.55045513349082e-12[/C][/ROW]
[ROW][C]53[/C][C]537034[/C][C]537034[/C][C]3.32248055058485e-12[/C][/ROW]
[ROW][C]54[/C][C]544686[/C][C]544686[/C][C]-3.39855549755375e-12[/C][/ROW]
[ROW][C]55[/C][C]600991[/C][C]600991[/C][C]-3.18638230762246e-12[/C][/ROW]
[ROW][C]56[/C][C]604378[/C][C]604378[/C][C]-2.50493053399709e-12[/C][/ROW]
[ROW][C]57[/C][C]586111[/C][C]586111[/C][C]1.58026845949619e-12[/C][/ROW]
[ROW][C]58[/C][C]563668[/C][C]563668[/C][C]1.42517197815163e-13[/C][/ROW]
[ROW][C]59[/C][C]548604[/C][C]548604[/C][C]-5.66006614443802e-13[/C][/ROW]
[ROW][C]60[/C][C]551174[/C][C]551174[/C][C]1.12506180175244e-12[/C][/ROW]
[ROW][C]61[/C][C]555654[/C][C]555654[/C][C]-4.01024680432628e-12[/C][/ROW]
[ROW][C]62[/C][C]547970[/C][C]547970[/C][C]-3.02059356837157e-12[/C][/ROW]
[ROW][C]63[/C][C]540324[/C][C]540324[/C][C]-1.79251772706498e-12[/C][/ROW]
[ROW][C]64[/C][C]530577[/C][C]530577[/C][C]2.64183477965689e-12[/C][/ROW]
[ROW][C]65[/C][C]520579[/C][C]520579[/C][C]-1.63331379655198e-12[/C][/ROW]
[ROW][C]66[/C][C]518654[/C][C]518654[/C][C]-2.24084659928743e-12[/C][/ROW]
[ROW][C]67[/C][C]572273[/C][C]572273[/C][C]1.46789172096475e-12[/C][/ROW]
[ROW][C]68[/C][C]581302[/C][C]581302[/C][C]1.81388036445779e-12[/C][/ROW]
[ROW][C]69[/C][C]563280[/C][C]563280[/C][C]1.34313730107964e-13[/C][/ROW]
[ROW][C]70[/C][C]547612[/C][C]547612[/C][C]-1.16747944925959e-12[/C][/ROW]
[ROW][C]71[/C][C]538712[/C][C]538712[/C][C]-3.25230456579049e-12[/C][/ROW]
[ROW][C]72[/C][C]540735[/C][C]540735[/C][C]1.18356009376666e-12[/C][/ROW]
[ROW][C]73[/C][C]561649[/C][C]561649[/C][C]4.99525653332162e-13[/C][/ROW]
[ROW][C]74[/C][C]558685[/C][C]558685[/C][C]-2.47351691182456e-12[/C][/ROW]
[ROW][C]75[/C][C]545732[/C][C]545732[/C][C]3.49803140277316e-12[/C][/ROW]
[ROW][C]76[/C][C]536352[/C][C]536352[/C][C]1.99215872721911e-12[/C][/ROW]
[ROW][C]77[/C][C]527676[/C][C]527676[/C][C]2.77434248909365e-12[/C][/ROW]
[ROW][C]78[/C][C]530455[/C][C]530455[/C][C]3.23642794713553e-12[/C][/ROW]
[ROW][C]79[/C][C]581744[/C][C]581744[/C][C]2.59910051547538e-13[/C][/ROW]
[ROW][C]80[/C][C]598714[/C][C]598714[/C][C]-2.24554095834726e-12[/C][/ROW]
[ROW][C]81[/C][C]583775[/C][C]583775[/C][C]2.56683416718498e-12[/C][/ROW]
[ROW][C]82[/C][C]571477[/C][C]571477[/C][C]-2.67973919010478e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15934085934082.44201154238717e-12
2590072590072-3.783616384487e-12
3579799579799-6.90220221687566e-12
45742055742059.87085419307986e-13
55727755727756.6801760186297e-13
65729425729422.13504398037157e-12
7619567619567-2.13948946713613e-12
86258096258094.81925904508278e-12
96199166199167.30470064178225e-13
10587625587625-7.57211439050367e-13
115657425657422.06034501084624e-12
125572745572743.06441005997693e-12
13560576560576-2.9316208706919e-12
145488545488544.61977932897679e-13
155316735316735.55157000539042e-12
165259195259192.32478579098379e-12
17511038511038-4.85698494972428e-12
18498662498662-8.52230848284974e-13
19555362555362-1.25105026286967e-12
205645915645911.6789962096871e-12
215416575416571.1142629687916e-12
22527070527070-5.8856383834731e-13
23509846509846-1.17308671715353e-12
24514258514258-3.24853427028823e-12
25516922516922-1.88010367452053e-12
26507561507561-6.45968796656386e-12
274926224926223.85054773607235e-12
28490243490243-1.54748540890199e-12
294693574693575.662356835887e-13
304775804775804.7834893298684e-12
315283795283793.98142122353893e-13
325335905335905.27486060189218e-12
33517945517945-2.6365431034682e-12
34506174506174-4.50779550609321e-12
355018665018662.29993609485324e-12
36516141516141-5.89731737624105e-12
375282225282221.3863129109491e-12
38532638532638-1.06000690378972e-12
395363225363224.25580186387998e-12
405365355365353.57545375803029e-12
41523597523597-1.35236353610983e-13
425362145362142.84848182384854e-12
435865705865705.50956475785104e-13
44596594596594-3.92833061378487e-12
455805235805232.73156621753421e-12
465644785644781.7190047852878e-13
475575605575602.53082119358923e-13
485750935750938.42284500221758e-13
495801125801121.32241451024824e-12
505747615747612.12970667444904e-13
515632505632503.65781616680844e-12
52551531551531-4.55045513349082e-12
535370345370343.32248055058485e-12
54544686544686-3.39855549755375e-12
55600991600991-3.18638230762246e-12
56604378604378-2.50493053399709e-12
575861115861111.58026845949619e-12
585636685636681.42517197815163e-13
59548604548604-5.66006614443802e-13
605511745511741.12506180175244e-12
61555654555654-4.01024680432628e-12
62547970547970-3.02059356837157e-12
63540324540324-1.79251772706498e-12
645305775305772.64183477965689e-12
65520579520579-1.63331379655198e-12
66518654518654-2.24084659928743e-12
675722735722731.46789172096475e-12
685813025813021.81388036445779e-12
695632805632801.34313730107964e-13
70547612547612-1.16747944925959e-12
71538712538712-3.25230456579049e-12
725407355407351.18356009376666e-12
735616495616494.99525653332162e-13
74558685558685-2.47351691182456e-12
755457325457323.49803140277316e-12
765363525363521.99215872721911e-12
775276765276762.77434248909365e-12
785304555304553.23642794713553e-12
795817445817442.59910051547538e-13
80598714598714-2.24554095834726e-12
815837755837752.56683416718498e-12
82571477571477-2.67973919010478e-12







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2350026092940290.4700052185880580.764997390705971
110.2122074113934220.4244148227868450.787792588606578
120.1498592270275060.2997184540550110.850140772972494
132.22136745112394e-064.44273490224787e-060.999997778632549
1413.69050430449422e-501.84525215224711e-50
153.40216954259887e-086.80433908519774e-080.999999965978305
160.03870585290571970.07741170581143950.96129414709428
171.10575789643416e-132.21151579286832e-130.999999999999889
181.08847505891665e-112.17695011783331e-110.999999999989115
192.34292399303289e-134.68584798606579e-130.999999999999766
204.00313626150567e-138.00627252301134e-130.9999999999996
2115.13589689945679e-432.56794844972839e-43
220.9995349573068960.0009300853862087890.000465042693104394
232.40961487592163e-114.81922975184327e-110.999999999975904
240.9725710262657330.0548579474685330.0274289737342665
254.35358912680297e-148.70717825360594e-140.999999999999956
261.2875576196119e-102.5751152392238e-100.999999999871244
270.9980981082267920.003803783546415570.00190189177320779
283.72514706757559e-147.45029413515119e-140.999999999999963
295.03470994934791e-181.00694198986958e-171
300.9754885610026060.04902287799478750.0245114389973937
3114.78188121890544e-362.39094060945272e-36
325.49751398800981e-151.09950279760196e-140.999999999999994
3315.19867825503317e-302.59933912751658e-30
340.8586535518749880.2826928962500230.141346448125012
350.8903490616420990.2193018767158020.109650938357901
360.8947882093895710.2104235812208570.105211790610429
370.9999895688512032.08622975944714e-051.04311487972357e-05
3812.95103753901585e-361.47551876950793e-36
3911.74004291540708e-348.70021457703538e-35
4013.2044263883829e-251.60221319419145e-25
4114.398159657315e-342.1990798286575e-34
420.3504413966421730.7008827932843470.649558603357827
436.19109713937958e-251.23821942787592e-241
440.4845992948587940.9691985897175890.515400705141206
450.999927810072910.0001443798541796017.21899270898006e-05
460.9999995455087669.0898246751592e-074.5449123375796e-07
470.9999999999344541.31091089262182e-106.55455446310909e-11
4812.26094473263793e-261.13047236631897e-26
490.9999659511998776.8097600245251e-053.40488001226255e-05
5014.65531947453127e-272.32765973726564e-27
5111.15842504624254e-215.7921252312127e-22
526.92051324940418e-281.38410264988084e-271
530.8885256600317370.2229486799365250.111474339968263
540.1709920939875140.3419841879750280.829007906012486
550.1732640938412730.3465281876825460.826735906158727
561.39394033795451e-262.78788067590902e-261
579.20647337998573e-401.84129467599715e-391
581.59665228129596e-373.19330456259192e-371
5915.96263942916188e-232.98131971458094e-23
600.0001756310628775330.0003512621257550660.999824368937122
610.9999999999999976.85725565023932e-153.42862782511966e-15
6213.37410863209243e-181.68705431604622e-18
630.8968008816312260.2063982367375490.103199118368775
640.3348843821094750.6697687642189510.665115617890525
650.9352282567828530.1295434864342930.0647717432171465
660.9301977641802150.139604471639570.0698022358197851
670.969181024510160.06163795097967950.0308189754898398
681.15760652742108e-152.31521305484216e-150.999999999999999
699.70874379496008e-241.94174875899202e-231
709.0583395693504e-131.81166791387008e-120.999999999999094
710.9998194193507930.0003611612984134720.000180580649206736
720.9996456122522610.0007087754954784370.000354387747739219

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.235002609294029 & 0.470005218588058 & 0.764997390705971 \tabularnewline
11 & 0.212207411393422 & 0.424414822786845 & 0.787792588606578 \tabularnewline
12 & 0.149859227027506 & 0.299718454055011 & 0.850140772972494 \tabularnewline
13 & 2.22136745112394e-06 & 4.44273490224787e-06 & 0.999997778632549 \tabularnewline
14 & 1 & 3.69050430449422e-50 & 1.84525215224711e-50 \tabularnewline
15 & 3.40216954259887e-08 & 6.80433908519774e-08 & 0.999999965978305 \tabularnewline
16 & 0.0387058529057197 & 0.0774117058114395 & 0.96129414709428 \tabularnewline
17 & 1.10575789643416e-13 & 2.21151579286832e-13 & 0.999999999999889 \tabularnewline
18 & 1.08847505891665e-11 & 2.17695011783331e-11 & 0.999999999989115 \tabularnewline
19 & 2.34292399303289e-13 & 4.68584798606579e-13 & 0.999999999999766 \tabularnewline
20 & 4.00313626150567e-13 & 8.00627252301134e-13 & 0.9999999999996 \tabularnewline
21 & 1 & 5.13589689945679e-43 & 2.56794844972839e-43 \tabularnewline
22 & 0.999534957306896 & 0.000930085386208789 & 0.000465042693104394 \tabularnewline
23 & 2.40961487592163e-11 & 4.81922975184327e-11 & 0.999999999975904 \tabularnewline
24 & 0.972571026265733 & 0.054857947468533 & 0.0274289737342665 \tabularnewline
25 & 4.35358912680297e-14 & 8.70717825360594e-14 & 0.999999999999956 \tabularnewline
26 & 1.2875576196119e-10 & 2.5751152392238e-10 & 0.999999999871244 \tabularnewline
27 & 0.998098108226792 & 0.00380378354641557 & 0.00190189177320779 \tabularnewline
28 & 3.72514706757559e-14 & 7.45029413515119e-14 & 0.999999999999963 \tabularnewline
29 & 5.03470994934791e-18 & 1.00694198986958e-17 & 1 \tabularnewline
30 & 0.975488561002606 & 0.0490228779947875 & 0.0245114389973937 \tabularnewline
31 & 1 & 4.78188121890544e-36 & 2.39094060945272e-36 \tabularnewline
32 & 5.49751398800981e-15 & 1.09950279760196e-14 & 0.999999999999994 \tabularnewline
33 & 1 & 5.19867825503317e-30 & 2.59933912751658e-30 \tabularnewline
34 & 0.858653551874988 & 0.282692896250023 & 0.141346448125012 \tabularnewline
35 & 0.890349061642099 & 0.219301876715802 & 0.109650938357901 \tabularnewline
36 & 0.894788209389571 & 0.210423581220857 & 0.105211790610429 \tabularnewline
37 & 0.999989568851203 & 2.08622975944714e-05 & 1.04311487972357e-05 \tabularnewline
38 & 1 & 2.95103753901585e-36 & 1.47551876950793e-36 \tabularnewline
39 & 1 & 1.74004291540708e-34 & 8.70021457703538e-35 \tabularnewline
40 & 1 & 3.2044263883829e-25 & 1.60221319419145e-25 \tabularnewline
41 & 1 & 4.398159657315e-34 & 2.1990798286575e-34 \tabularnewline
42 & 0.350441396642173 & 0.700882793284347 & 0.649558603357827 \tabularnewline
43 & 6.19109713937958e-25 & 1.23821942787592e-24 & 1 \tabularnewline
44 & 0.484599294858794 & 0.969198589717589 & 0.515400705141206 \tabularnewline
45 & 0.99992781007291 & 0.000144379854179601 & 7.21899270898006e-05 \tabularnewline
46 & 0.999999545508766 & 9.0898246751592e-07 & 4.5449123375796e-07 \tabularnewline
47 & 0.999999999934454 & 1.31091089262182e-10 & 6.55455446310909e-11 \tabularnewline
48 & 1 & 2.26094473263793e-26 & 1.13047236631897e-26 \tabularnewline
49 & 0.999965951199877 & 6.8097600245251e-05 & 3.40488001226255e-05 \tabularnewline
50 & 1 & 4.65531947453127e-27 & 2.32765973726564e-27 \tabularnewline
51 & 1 & 1.15842504624254e-21 & 5.7921252312127e-22 \tabularnewline
52 & 6.92051324940418e-28 & 1.38410264988084e-27 & 1 \tabularnewline
53 & 0.888525660031737 & 0.222948679936525 & 0.111474339968263 \tabularnewline
54 & 0.170992093987514 & 0.341984187975028 & 0.829007906012486 \tabularnewline
55 & 0.173264093841273 & 0.346528187682546 & 0.826735906158727 \tabularnewline
56 & 1.39394033795451e-26 & 2.78788067590902e-26 & 1 \tabularnewline
57 & 9.20647337998573e-40 & 1.84129467599715e-39 & 1 \tabularnewline
58 & 1.59665228129596e-37 & 3.19330456259192e-37 & 1 \tabularnewline
59 & 1 & 5.96263942916188e-23 & 2.98131971458094e-23 \tabularnewline
60 & 0.000175631062877533 & 0.000351262125755066 & 0.999824368937122 \tabularnewline
61 & 0.999999999999997 & 6.85725565023932e-15 & 3.42862782511966e-15 \tabularnewline
62 & 1 & 3.37410863209243e-18 & 1.68705431604622e-18 \tabularnewline
63 & 0.896800881631226 & 0.206398236737549 & 0.103199118368775 \tabularnewline
64 & 0.334884382109475 & 0.669768764218951 & 0.665115617890525 \tabularnewline
65 & 0.935228256782853 & 0.129543486434293 & 0.0647717432171465 \tabularnewline
66 & 0.930197764180215 & 0.13960447163957 & 0.0698022358197851 \tabularnewline
67 & 0.96918102451016 & 0.0616379509796795 & 0.0308189754898398 \tabularnewline
68 & 1.15760652742108e-15 & 2.31521305484216e-15 & 0.999999999999999 \tabularnewline
69 & 9.70874379496008e-24 & 1.94174875899202e-23 & 1 \tabularnewline
70 & 9.0583395693504e-13 & 1.81166791387008e-12 & 0.999999999999094 \tabularnewline
71 & 0.999819419350793 & 0.000361161298413472 & 0.000180580649206736 \tabularnewline
72 & 0.999645612252261 & 0.000708775495478437 & 0.000354387747739219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&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]0.235002609294029[/C][C]0.470005218588058[/C][C]0.764997390705971[/C][/ROW]
[ROW][C]11[/C][C]0.212207411393422[/C][C]0.424414822786845[/C][C]0.787792588606578[/C][/ROW]
[ROW][C]12[/C][C]0.149859227027506[/C][C]0.299718454055011[/C][C]0.850140772972494[/C][/ROW]
[ROW][C]13[/C][C]2.22136745112394e-06[/C][C]4.44273490224787e-06[/C][C]0.999997778632549[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]3.69050430449422e-50[/C][C]1.84525215224711e-50[/C][/ROW]
[ROW][C]15[/C][C]3.40216954259887e-08[/C][C]6.80433908519774e-08[/C][C]0.999999965978305[/C][/ROW]
[ROW][C]16[/C][C]0.0387058529057197[/C][C]0.0774117058114395[/C][C]0.96129414709428[/C][/ROW]
[ROW][C]17[/C][C]1.10575789643416e-13[/C][C]2.21151579286832e-13[/C][C]0.999999999999889[/C][/ROW]
[ROW][C]18[/C][C]1.08847505891665e-11[/C][C]2.17695011783331e-11[/C][C]0.999999999989115[/C][/ROW]
[ROW][C]19[/C][C]2.34292399303289e-13[/C][C]4.68584798606579e-13[/C][C]0.999999999999766[/C][/ROW]
[ROW][C]20[/C][C]4.00313626150567e-13[/C][C]8.00627252301134e-13[/C][C]0.9999999999996[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]5.13589689945679e-43[/C][C]2.56794844972839e-43[/C][/ROW]
[ROW][C]22[/C][C]0.999534957306896[/C][C]0.000930085386208789[/C][C]0.000465042693104394[/C][/ROW]
[ROW][C]23[/C][C]2.40961487592163e-11[/C][C]4.81922975184327e-11[/C][C]0.999999999975904[/C][/ROW]
[ROW][C]24[/C][C]0.972571026265733[/C][C]0.054857947468533[/C][C]0.0274289737342665[/C][/ROW]
[ROW][C]25[/C][C]4.35358912680297e-14[/C][C]8.70717825360594e-14[/C][C]0.999999999999956[/C][/ROW]
[ROW][C]26[/C][C]1.2875576196119e-10[/C][C]2.5751152392238e-10[/C][C]0.999999999871244[/C][/ROW]
[ROW][C]27[/C][C]0.998098108226792[/C][C]0.00380378354641557[/C][C]0.00190189177320779[/C][/ROW]
[ROW][C]28[/C][C]3.72514706757559e-14[/C][C]7.45029413515119e-14[/C][C]0.999999999999963[/C][/ROW]
[ROW][C]29[/C][C]5.03470994934791e-18[/C][C]1.00694198986958e-17[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0.975488561002606[/C][C]0.0490228779947875[/C][C]0.0245114389973937[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]4.78188121890544e-36[/C][C]2.39094060945272e-36[/C][/ROW]
[ROW][C]32[/C][C]5.49751398800981e-15[/C][C]1.09950279760196e-14[/C][C]0.999999999999994[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]5.19867825503317e-30[/C][C]2.59933912751658e-30[/C][/ROW]
[ROW][C]34[/C][C]0.858653551874988[/C][C]0.282692896250023[/C][C]0.141346448125012[/C][/ROW]
[ROW][C]35[/C][C]0.890349061642099[/C][C]0.219301876715802[/C][C]0.109650938357901[/C][/ROW]
[ROW][C]36[/C][C]0.894788209389571[/C][C]0.210423581220857[/C][C]0.105211790610429[/C][/ROW]
[ROW][C]37[/C][C]0.999989568851203[/C][C]2.08622975944714e-05[/C][C]1.04311487972357e-05[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]2.95103753901585e-36[/C][C]1.47551876950793e-36[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.74004291540708e-34[/C][C]8.70021457703538e-35[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]3.2044263883829e-25[/C][C]1.60221319419145e-25[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]4.398159657315e-34[/C][C]2.1990798286575e-34[/C][/ROW]
[ROW][C]42[/C][C]0.350441396642173[/C][C]0.700882793284347[/C][C]0.649558603357827[/C][/ROW]
[ROW][C]43[/C][C]6.19109713937958e-25[/C][C]1.23821942787592e-24[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0.484599294858794[/C][C]0.969198589717589[/C][C]0.515400705141206[/C][/ROW]
[ROW][C]45[/C][C]0.99992781007291[/C][C]0.000144379854179601[/C][C]7.21899270898006e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999999545508766[/C][C]9.0898246751592e-07[/C][C]4.5449123375796e-07[/C][/ROW]
[ROW][C]47[/C][C]0.999999999934454[/C][C]1.31091089262182e-10[/C][C]6.55455446310909e-11[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]2.26094473263793e-26[/C][C]1.13047236631897e-26[/C][/ROW]
[ROW][C]49[/C][C]0.999965951199877[/C][C]6.8097600245251e-05[/C][C]3.40488001226255e-05[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]4.65531947453127e-27[/C][C]2.32765973726564e-27[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.15842504624254e-21[/C][C]5.7921252312127e-22[/C][/ROW]
[ROW][C]52[/C][C]6.92051324940418e-28[/C][C]1.38410264988084e-27[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0.888525660031737[/C][C]0.222948679936525[/C][C]0.111474339968263[/C][/ROW]
[ROW][C]54[/C][C]0.170992093987514[/C][C]0.341984187975028[/C][C]0.829007906012486[/C][/ROW]
[ROW][C]55[/C][C]0.173264093841273[/C][C]0.346528187682546[/C][C]0.826735906158727[/C][/ROW]
[ROW][C]56[/C][C]1.39394033795451e-26[/C][C]2.78788067590902e-26[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]9.20647337998573e-40[/C][C]1.84129467599715e-39[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]1.59665228129596e-37[/C][C]3.19330456259192e-37[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]5.96263942916188e-23[/C][C]2.98131971458094e-23[/C][/ROW]
[ROW][C]60[/C][C]0.000175631062877533[/C][C]0.000351262125755066[/C][C]0.999824368937122[/C][/ROW]
[ROW][C]61[/C][C]0.999999999999997[/C][C]6.85725565023932e-15[/C][C]3.42862782511966e-15[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]3.37410863209243e-18[/C][C]1.68705431604622e-18[/C][/ROW]
[ROW][C]63[/C][C]0.896800881631226[/C][C]0.206398236737549[/C][C]0.103199118368775[/C][/ROW]
[ROW][C]64[/C][C]0.334884382109475[/C][C]0.669768764218951[/C][C]0.665115617890525[/C][/ROW]
[ROW][C]65[/C][C]0.935228256782853[/C][C]0.129543486434293[/C][C]0.0647717432171465[/C][/ROW]
[ROW][C]66[/C][C]0.930197764180215[/C][C]0.13960447163957[/C][C]0.0698022358197851[/C][/ROW]
[ROW][C]67[/C][C]0.96918102451016[/C][C]0.0616379509796795[/C][C]0.0308189754898398[/C][/ROW]
[ROW][C]68[/C][C]1.15760652742108e-15[/C][C]2.31521305484216e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]69[/C][C]9.70874379496008e-24[/C][C]1.94174875899202e-23[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]9.0583395693504e-13[/C][C]1.81166791387008e-12[/C][C]0.999999999999094[/C][/ROW]
[ROW][C]71[/C][C]0.999819419350793[/C][C]0.000361161298413472[/C][C]0.000180580649206736[/C][/ROW]
[ROW][C]72[/C][C]0.999645612252261[/C][C]0.000708775495478437[/C][C]0.000354387747739219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2350026092940290.4700052185880580.764997390705971
110.2122074113934220.4244148227868450.787792588606578
120.1498592270275060.2997184540550110.850140772972494
132.22136745112394e-064.44273490224787e-060.999997778632549
1413.69050430449422e-501.84525215224711e-50
153.40216954259887e-086.80433908519774e-080.999999965978305
160.03870585290571970.07741170581143950.96129414709428
171.10575789643416e-132.21151579286832e-130.999999999999889
181.08847505891665e-112.17695011783331e-110.999999999989115
192.34292399303289e-134.68584798606579e-130.999999999999766
204.00313626150567e-138.00627252301134e-130.9999999999996
2115.13589689945679e-432.56794844972839e-43
220.9995349573068960.0009300853862087890.000465042693104394
232.40961487592163e-114.81922975184327e-110.999999999975904
240.9725710262657330.0548579474685330.0274289737342665
254.35358912680297e-148.70717825360594e-140.999999999999956
261.2875576196119e-102.5751152392238e-100.999999999871244
270.9980981082267920.003803783546415570.00190189177320779
283.72514706757559e-147.45029413515119e-140.999999999999963
295.03470994934791e-181.00694198986958e-171
300.9754885610026060.04902287799478750.0245114389973937
3114.78188121890544e-362.39094060945272e-36
325.49751398800981e-151.09950279760196e-140.999999999999994
3315.19867825503317e-302.59933912751658e-30
340.8586535518749880.2826928962500230.141346448125012
350.8903490616420990.2193018767158020.109650938357901
360.8947882093895710.2104235812208570.105211790610429
370.9999895688512032.08622975944714e-051.04311487972357e-05
3812.95103753901585e-361.47551876950793e-36
3911.74004291540708e-348.70021457703538e-35
4013.2044263883829e-251.60221319419145e-25
4114.398159657315e-342.1990798286575e-34
420.3504413966421730.7008827932843470.649558603357827
436.19109713937958e-251.23821942787592e-241
440.4845992948587940.9691985897175890.515400705141206
450.999927810072910.0001443798541796017.21899270898006e-05
460.9999995455087669.0898246751592e-074.5449123375796e-07
470.9999999999344541.31091089262182e-106.55455446310909e-11
4812.26094473263793e-261.13047236631897e-26
490.9999659511998776.8097600245251e-053.40488001226255e-05
5014.65531947453127e-272.32765973726564e-27
5111.15842504624254e-215.7921252312127e-22
526.92051324940418e-281.38410264988084e-271
530.8885256600317370.2229486799365250.111474339968263
540.1709920939875140.3419841879750280.829007906012486
550.1732640938412730.3465281876825460.826735906158727
561.39394033795451e-262.78788067590902e-261
579.20647337998573e-401.84129467599715e-391
581.59665228129596e-373.19330456259192e-371
5915.96263942916188e-232.98131971458094e-23
600.0001756310628775330.0003512621257550660.999824368937122
610.9999999999999976.85725565023932e-153.42862782511966e-15
6213.37410863209243e-181.68705431604622e-18
630.8968008816312260.2063982367375490.103199118368775
640.3348843821094750.6697687642189510.665115617890525
650.9352282567828530.1295434864342930.0647717432171465
660.9301977641802150.139604471639570.0698022358197851
670.969181024510160.06163795097967950.0308189754898398
681.15760652742108e-152.31521305484216e-150.999999999999999
699.70874379496008e-241.94174875899202e-231
709.0583395693504e-131.81166791387008e-120.999999999999094
710.9998194193507930.0003611612984134720.000180580649206736
720.9996456122522610.0007087754954784370.000354387747739219







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level440.698412698412698NOK
5% type I error level450.714285714285714NOK
10% type I error level480.761904761904762NOK

\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 & 44 & 0.698412698412698 & NOK \tabularnewline
5% type I error level & 45 & 0.714285714285714 & NOK \tabularnewline
10% type I error level & 48 & 0.761904761904762 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203237&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]44[/C][C]0.698412698412698[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]45[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.761904761904762[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203237&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203237&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level440.698412698412698NOK
5% type I error level450.714285714285714NOK
10% type I error level480.761904761904762NOK



Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}