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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 18 Nov 2012 13:27:42 -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/18/t1353263680ufhrdpqn2kg93u3.htm/, Retrieved Thu, 31 Oct 2024 23:38:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190309, Retrieved Thu, 31 Oct 2024 23:38:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [WS7-1] [2012-11-18 18:27:42] [b0d74e5516a9792d73894039244f6765] [Current]
-   P       [Multiple Regression] [MultipleDummy] [2012-12-16 20:57:54] [d2e282a9ac132a80b8d48efc3a3dfb6b]
-           [Multiple Regression] [] [2012-12-21 23:40:13] [13a5ed2ca96ae4a72a1110d56328629c]
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Dataseries X:
39411	50149	82368	86371	111549	61484	70774	50982	40320
29520	58186	77795	90085	110697	61165	71212	51083	40329
31187	56275	62827	97462	108155	61172	71222	51161	40338
27463	52302	67197	98688	107545	59987	70806	50403	39814
28454	50332	66848	97734	107665	59999	70973	50853	39917
37250	47451	66421	94153	106314	59725	70852	50925	39851
69891	56251	60643	96705	111233	60989	72216	51460	40179
44435	91027	59071	93928	106930	66174	72229	51834	40181
52881	82777	58746	84753	108570	66616	73494	52045	40034
37948	73833	68515	76817	99293	68211	71846	51561	39601
28454	70024	68998	73779	96278	67105	70240	51626	39238
26285	54075	77614	75180	96179	66070	70588	51950	39333
36510	44376	73469	79710	98383	65933	70348	52599	39248
28179	49188	67145	80768	97265	64796	69876	52666	38971
29811	50930	51109	84924	92909	62341	68633	52416	38600
26553	47574	51130	88760	91516	61741	68081	52217	38347
26844	44963	49544	83140	89132	60415	66758	52339	37903
37692	42243	50730	74597	83006	57218	64609	51327	37240
74285	52678	49710	77269	87435	58594	65469	52572	37350
43479	92780	50059	75494	88227	54904	69288	53103	37257
51359	77386	49681	66254	85180	54053	67793	53106	36845
39988	67733	65773	61533	83531	49856	68855	53373	36428
28764	68127	66129	60383	80735	48894	66599	54023	36192
27567	56378	78039	65317	80067	49807	66295	54628	36160
39367	44420	71278	75500	79288	50475	65336	55135	36123
30110	51304	65862	77400	77580	50067	64382	55005	35851
28281	52963	51540	83048	75286	48500	62741	54838	35425
29968	45032	51513	88294	74919	47827	62331	55083	35276
24942	44353	49740	82431	72120	46114	60506	54321	34830
37122	43362	50980	77941	72916	45840	60182	54532	34705
66852	52722	51294	78948	80984	47138	60574	55167	34700
40973	86193	49719	77560	82160	46694	60386	55298	34607
55967	68245	50673	68186	80492	45419	59413	55248	34302
41569	69196	59191	64398	80240	44489	58195	54917	33979
30936	74491	61807	63494	80373	43776	58143	54943	33903
35059	60455	77687	69750	81710	43422	58594	55558	33906
43354	53798	77227	76441	85125	43096	59386	55887	33908
36918	62933	75594	79363	86198	42897	58887	56048	33800
40761	63956	64158	90780	85910	42681	57940	56485	33651
33552	62346	64551	97287	87804	42818	57676	56913	33588
29219	58923	65143	94922	86309	42214	56738	56688	33441
41201	52204	69958	94710	88113	42889	56552	57052	33535
70480	60898	68154	99073	91819	47416	57320	57741	33669
43943	96693	64628	100853	93407	48210	54838	60372	33650
59389	77922	61690	92333	94296	47881	53709	59892	33411
40877	77626	71412	86620	94697	47839	50993	61114	33300
32805	79173	73606	84634	94858	47972	50391	60891	33230
30211	65251	91586	92309	100812	49424	50777	61394	33329
43514	54488	85299	96796	102621	50974	51163	61766	33491
34397	62042	81752	96349	103623	51210	50467	61432	33489
38403	61147	63479	102177	103635	50787	49380	60918	33324
31352	58698	62470	103298	102282	51027	48509	60783	33112
28815	56236	60452	99765	99824	50307	48100	60447	33088
39825	49879	65593	95187	100879	51061	48507	60583	33172
68608	61076	64223	99110	108320	52409	52335	61451	33459
48668	92317	61466	96585	106920	51928	51952	61110	33432
59004	79439	58471	85981	104997	52302	51628	60920	33369
39263	79951	67261	79250	100786	52255	51480	60251	33171
31014	76304	71826	76175	98170	51683	50582	59828	33022
30275	59409	84695	81079	98420	53376	50793	60055	33072
42170	51241	80558	85030	98477	54110	50982	60184	32902
33765	59166	73755	87331	96166	54198	50986	59812	32791
34792	60574	57786	94717	94833	54486	50979	59315	32842
30210	55326	59266	96502	92590	53976	51039	58857	32811
33898	50832	58815	92301	90143	53123	50438	58330	32699
36051	50871	60945	86797	89674	52825	50647	58100	32744
66049	59889	58520	92556	95661	55079	52947	58614	32958
49577	85822	59747	89949	97152	54666	53212	58067	33110
59983	75463	56401	78975	94976	53757	53250	57454	33021
40278	80245	64773	73253	92623	52516	53768	56975	33181
33392	77079	68026	74037	90840	52057	53869	56148	33264
31009	61815	84288	76990	91044	51688	54773	55889	33239
46860	54153	84174	83195	94331	53106	56384	55975	33471
36298	63818	78618	87766	93923	52466	56926	55345	33525
33765	65730	61185	96059	91718	51795	57312	54606	33562
30808	56908	63612	98893	90124	51068	57378	54045	33516
31481	53264	62673	96403	89408	50413	56852	53579	33603
38165	51470	64549	93436	88884	50051	56897	53454	33549
63960	63334	61103	100409	94542	51953	57484	55154	33805
50949	91894	61047	98369	96969	53147	57615	55012	33712
58751	81410	61589	86173	97164	52773	57792	54362	33761
46894	81247	71233	80295	95079	51670	57262	53916	33881




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=190309&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=190309&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&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
3m-6m[t] = + 63118.5189426679 -0.368636038951893`-1m`[t] -0.358566065099581`1m-3m`[t] -0.530356988456578`6m-1j`[t] + 1.61876120754997`1j-2j`[t] -0.414167498768261`2j-3j`[t] + 3.26747548659959`3j-5j`[t] + 1.75857453526117`5j-10j`[t] -9.57819990720055`10j+`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
3m-6m[t] =  +  63118.5189426679 -0.368636038951893`-1m`[t] -0.358566065099581`1m-3m`[t] -0.530356988456578`6m-1j`[t] +  1.61876120754997`1j-2j`[t] -0.414167498768261`2j-3j`[t] +  3.26747548659959`3j-5j`[t] +  1.75857453526117`5j-10j`[t] -9.57819990720055`10j+`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190309&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]3m-6m[t] =  +  63118.5189426679 -0.368636038951893`-1m`[t] -0.358566065099581`1m-3m`[t] -0.530356988456578`6m-1j`[t] +  1.61876120754997`1j-2j`[t] -0.414167498768261`2j-3j`[t] +  3.26747548659959`3j-5j`[t] +  1.75857453526117`5j-10j`[t] -9.57819990720055`10j+`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190309&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&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
3m-6m[t] = + 63118.5189426679 -0.368636038951893`-1m`[t] -0.358566065099581`1m-3m`[t] -0.530356988456578`6m-1j`[t] + 1.61876120754997`1j-2j`[t] -0.414167498768261`2j-3j`[t] + 3.26747548659959`3j-5j`[t] + 1.75857453526117`5j-10j`[t] -9.57819990720055`10j+`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)63118.518942667944240.3500661.42670.1579240.078962
`-1m`-0.3686360389518930.067654-5.44881e-060
`1m-3m`-0.3585660650995810.067475-5.31411e-061e-06
`6m-1j`-0.5303569884565780.094051-5.63900
`1j-2j`1.618761207549970.1741389.295900
`2j-3j`-0.4141674987682610.226284-1.83030.0712880.035644
`3j-5j`3.267475486599590.6553454.98594e-062e-06
`5j-10j`1.758574535261170.6043822.90970.0047920.002396
`10j+`-9.578199907200551.836882-5.21442e-061e-06

\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) & 63118.5189426679 & 44240.350066 & 1.4267 & 0.157924 & 0.078962 \tabularnewline
`-1m` & -0.368636038951893 & 0.067654 & -5.4488 & 1e-06 & 0 \tabularnewline
`1m-3m` & -0.358566065099581 & 0.067475 & -5.3141 & 1e-06 & 1e-06 \tabularnewline
`6m-1j` & -0.530356988456578 & 0.094051 & -5.639 & 0 & 0 \tabularnewline
`1j-2j` & 1.61876120754997 & 0.174138 & 9.2959 & 0 & 0 \tabularnewline
`2j-3j` & -0.414167498768261 & 0.226284 & -1.8303 & 0.071288 & 0.035644 \tabularnewline
`3j-5j` & 3.26747548659959 & 0.655345 & 4.9859 & 4e-06 & 2e-06 \tabularnewline
`5j-10j` & 1.75857453526117 & 0.604382 & 2.9097 & 0.004792 & 0.002396 \tabularnewline
`10j+` & -9.57819990720055 & 1.836882 & -5.2144 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190309&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]63118.5189426679[/C][C]44240.350066[/C][C]1.4267[/C][C]0.157924[/C][C]0.078962[/C][/ROW]
[ROW][C]`-1m`[/C][C]-0.368636038951893[/C][C]0.067654[/C][C]-5.4488[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]`1m-3m`[/C][C]-0.358566065099581[/C][C]0.067475[/C][C]-5.3141[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]`6m-1j`[/C][C]-0.530356988456578[/C][C]0.094051[/C][C]-5.639[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`1j-2j`[/C][C]1.61876120754997[/C][C]0.174138[/C][C]9.2959[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`2j-3j`[/C][C]-0.414167498768261[/C][C]0.226284[/C][C]-1.8303[/C][C]0.071288[/C][C]0.035644[/C][/ROW]
[ROW][C]`3j-5j`[/C][C]3.26747548659959[/C][C]0.655345[/C][C]4.9859[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]`5j-10j`[/C][C]1.75857453526117[/C][C]0.604382[/C][C]2.9097[/C][C]0.004792[/C][C]0.002396[/C][/ROW]
[ROW][C]`10j+`[/C][C]-9.57819990720055[/C][C]1.836882[/C][C]-5.2144[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190309&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&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)63118.518942667944240.3500661.42670.1579240.078962
`-1m`-0.3686360389518930.067654-5.44881e-060
`1m-3m`-0.3585660650995810.067475-5.31411e-061e-06
`6m-1j`-0.5303569884565780.094051-5.63900
`1j-2j`1.618761207549970.1741389.295900
`2j-3j`-0.4141674987682610.226284-1.83030.0712880.035644
`3j-5j`3.267475486599590.6553454.98594e-062e-06
`5j-10j`1.758574535261170.6043822.90970.0047920.002396
`10j+`-9.578199907200551.836882-5.21442e-061e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.786441667023317
R-squared0.618490495630414
Adjusted R-squared0.576681234877582
F-TEST (value)14.7931459321134
F-TEST (DF numerator)8
F-TEST (DF denominator)73
p-value1.25444099552396e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6538.25176027942
Sum Squared Residuals3120657733.89818

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.786441667023317 \tabularnewline
R-squared & 0.618490495630414 \tabularnewline
Adjusted R-squared & 0.576681234877582 \tabularnewline
F-TEST (value) & 14.7931459321134 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 73 \tabularnewline
p-value & 1.25444099552396e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6538.25176027942 \tabularnewline
Sum Squared Residuals & 3120657733.89818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190309&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.786441667023317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.618490495630414[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.576681234877582[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.7931459321134[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]73[/C][/ROW]
[ROW][C]p-value[/C][C]1.25444099552396e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6538.25176027942[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3120657733.89818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190309&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.786441667023317
R-squared0.618490495630414
Adjusted R-squared0.576681234877582
F-TEST (value)14.7931459321134
F-TEST (DF numerator)8
F-TEST (DF denominator)73
p-value1.25444099552396e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6538.25176027942
Sum Squared Residuals3120657733.89818







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18236874622.46719655467745.53280344537
27779573692.60631279664102.39368720344
36282765816.715889793-2989.71588979305
46719769793.9334082974-2596.93340829737
56684871180.7045005386-4332.7045005386
66642169160.3686409306-2739.3686409306
76064362314.0703181044-1671.07031810436
85907152269.41690320286801.58309679723
95874665364.2297741492-6618.22977414916
106851560518.56569031877996.43430968132
116899860916.53140952478081.46859047529
127761467757.17963871189856.82036128816
137346969858.84964560533610.15035439469
146714570533.289741608-3388.28974160797
155110960120.7438879107-9011.7438879107
165113058753.9063221164-7623.90632211638
174954459397.9116737776-9853.91167377758
185073049861.513996881868.486003118999
194971041758.71579606177951.28420393827
205005956790.4864236524-6731.48642365239
214968158792.6083911462-9111.60839114625
226577375952.0538490446-10179.0538490446
236612972462.7369830722-6333.73698307223
247803973417.66566942624621.33433057379
257127864529.67102723756748.32897276245
266586261129.70803756294732.29196243711
275154053573.8921800221-2033.89218002208
285151353216.524844478-1703.52484447804
294974051569.4958629296-1829.49586292964
305098051727.8387307335-747.838730733493
315129451846.0536652717-552.053665271739
324971952714.9704658774-2995.97046587744
335067356076.8903072399-5403.89030723989
345919161561.641146161-2370.64114616099
356180765176.5376239857-3369.53762398569
367768770208.89041831917478.10958168089
377722774799.75349687312427.24650312688
387559473853.54684891021740.45315108977
396415864739.5862536443-581.586253644256
406455168026.0176459233-3475.01764592335
416514367882.5167947007-2739.51679470069
426995867759.86290473832198.13709526174
436815458097.040418848810056.9595811512
446462853041.292291159111586.7077088409
456169057928.05918027383761.94081972621
467141262892.52769303298519.47230696712
477360664883.57298210558722.42701789452
489158676995.581498850814590.4185011492
498529976221.29730548119077.7026945189
508175275792.4983573035959.50164269702
516347968865.1062909006-5386.10629090057
526247068405.5726666379-5935.57266663793
536045266719.2270208123-6267.22702081229
546559369527.8936484749-3934.89364847486
556422375594.2876856704-11371.2876856704
566146669422.5225043883-7956.52250438832
575847171796.6786258653-13325.6786258653
586726175899.4430077533-8638.4430077533
597182675630.1661331253-3804.16613312531
608469579672.91964266675022.08035733328
618055878382.29452864992175.70547135005
627375574063.3494136768-308.349413676755
635778665600.2214682651-7814.22146826507
645926664492.2689089971-5226.26890899706
655881561546.5778460581-2731.57784605808
666094562869.639388178-1924.63938817801
675852060650.7767776501-2130.77677765007
685974759839.5749986594-92.5749986593588
695640162290.7433758831-5889.74337588315
706477366897.4761471953-2124.47614719526
716802665539.85913463432486.14086536573
728428873546.164172543410741.8358274566
738417475085.95901297419088.04098702586
747861872840.14963102655777.85036897354
756118565005.8793846796-3820.87938467964
766361265146.6589432181-1534.6589432181
776267363266.5259872611-593.525987261129
786454962765.53454522211783.46545477792
796110356131.1308176044971.86918239601
806104756272.0475109164774.95248908396
816158963059.8845905009-1470.88459050091
827123364022.92614502397210.07385497614

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 82368 & 74622.4671965546 & 7745.53280344537 \tabularnewline
2 & 77795 & 73692.6063127966 & 4102.39368720344 \tabularnewline
3 & 62827 & 65816.715889793 & -2989.71588979305 \tabularnewline
4 & 67197 & 69793.9334082974 & -2596.93340829737 \tabularnewline
5 & 66848 & 71180.7045005386 & -4332.7045005386 \tabularnewline
6 & 66421 & 69160.3686409306 & -2739.3686409306 \tabularnewline
7 & 60643 & 62314.0703181044 & -1671.07031810436 \tabularnewline
8 & 59071 & 52269.4169032028 & 6801.58309679723 \tabularnewline
9 & 58746 & 65364.2297741492 & -6618.22977414916 \tabularnewline
10 & 68515 & 60518.5656903187 & 7996.43430968132 \tabularnewline
11 & 68998 & 60916.5314095247 & 8081.46859047529 \tabularnewline
12 & 77614 & 67757.1796387118 & 9856.82036128816 \tabularnewline
13 & 73469 & 69858.8496456053 & 3610.15035439469 \tabularnewline
14 & 67145 & 70533.289741608 & -3388.28974160797 \tabularnewline
15 & 51109 & 60120.7438879107 & -9011.7438879107 \tabularnewline
16 & 51130 & 58753.9063221164 & -7623.90632211638 \tabularnewline
17 & 49544 & 59397.9116737776 & -9853.91167377758 \tabularnewline
18 & 50730 & 49861.513996881 & 868.486003118999 \tabularnewline
19 & 49710 & 41758.7157960617 & 7951.28420393827 \tabularnewline
20 & 50059 & 56790.4864236524 & -6731.48642365239 \tabularnewline
21 & 49681 & 58792.6083911462 & -9111.60839114625 \tabularnewline
22 & 65773 & 75952.0538490446 & -10179.0538490446 \tabularnewline
23 & 66129 & 72462.7369830722 & -6333.73698307223 \tabularnewline
24 & 78039 & 73417.6656694262 & 4621.33433057379 \tabularnewline
25 & 71278 & 64529.6710272375 & 6748.32897276245 \tabularnewline
26 & 65862 & 61129.7080375629 & 4732.29196243711 \tabularnewline
27 & 51540 & 53573.8921800221 & -2033.89218002208 \tabularnewline
28 & 51513 & 53216.524844478 & -1703.52484447804 \tabularnewline
29 & 49740 & 51569.4958629296 & -1829.49586292964 \tabularnewline
30 & 50980 & 51727.8387307335 & -747.838730733493 \tabularnewline
31 & 51294 & 51846.0536652717 & -552.053665271739 \tabularnewline
32 & 49719 & 52714.9704658774 & -2995.97046587744 \tabularnewline
33 & 50673 & 56076.8903072399 & -5403.89030723989 \tabularnewline
34 & 59191 & 61561.641146161 & -2370.64114616099 \tabularnewline
35 & 61807 & 65176.5376239857 & -3369.53762398569 \tabularnewline
36 & 77687 & 70208.8904183191 & 7478.10958168089 \tabularnewline
37 & 77227 & 74799.7534968731 & 2427.24650312688 \tabularnewline
38 & 75594 & 73853.5468489102 & 1740.45315108977 \tabularnewline
39 & 64158 & 64739.5862536443 & -581.586253644256 \tabularnewline
40 & 64551 & 68026.0176459233 & -3475.01764592335 \tabularnewline
41 & 65143 & 67882.5167947007 & -2739.51679470069 \tabularnewline
42 & 69958 & 67759.8629047383 & 2198.13709526174 \tabularnewline
43 & 68154 & 58097.0404188488 & 10056.9595811512 \tabularnewline
44 & 64628 & 53041.2922911591 & 11586.7077088409 \tabularnewline
45 & 61690 & 57928.0591802738 & 3761.94081972621 \tabularnewline
46 & 71412 & 62892.5276930329 & 8519.47230696712 \tabularnewline
47 & 73606 & 64883.5729821055 & 8722.42701789452 \tabularnewline
48 & 91586 & 76995.5814988508 & 14590.4185011492 \tabularnewline
49 & 85299 & 76221.2973054811 & 9077.7026945189 \tabularnewline
50 & 81752 & 75792.498357303 & 5959.50164269702 \tabularnewline
51 & 63479 & 68865.1062909006 & -5386.10629090057 \tabularnewline
52 & 62470 & 68405.5726666379 & -5935.57266663793 \tabularnewline
53 & 60452 & 66719.2270208123 & -6267.22702081229 \tabularnewline
54 & 65593 & 69527.8936484749 & -3934.89364847486 \tabularnewline
55 & 64223 & 75594.2876856704 & -11371.2876856704 \tabularnewline
56 & 61466 & 69422.5225043883 & -7956.52250438832 \tabularnewline
57 & 58471 & 71796.6786258653 & -13325.6786258653 \tabularnewline
58 & 67261 & 75899.4430077533 & -8638.4430077533 \tabularnewline
59 & 71826 & 75630.1661331253 & -3804.16613312531 \tabularnewline
60 & 84695 & 79672.9196426667 & 5022.08035733328 \tabularnewline
61 & 80558 & 78382.2945286499 & 2175.70547135005 \tabularnewline
62 & 73755 & 74063.3494136768 & -308.349413676755 \tabularnewline
63 & 57786 & 65600.2214682651 & -7814.22146826507 \tabularnewline
64 & 59266 & 64492.2689089971 & -5226.26890899706 \tabularnewline
65 & 58815 & 61546.5778460581 & -2731.57784605808 \tabularnewline
66 & 60945 & 62869.639388178 & -1924.63938817801 \tabularnewline
67 & 58520 & 60650.7767776501 & -2130.77677765007 \tabularnewline
68 & 59747 & 59839.5749986594 & -92.5749986593588 \tabularnewline
69 & 56401 & 62290.7433758831 & -5889.74337588315 \tabularnewline
70 & 64773 & 66897.4761471953 & -2124.47614719526 \tabularnewline
71 & 68026 & 65539.8591346343 & 2486.14086536573 \tabularnewline
72 & 84288 & 73546.1641725434 & 10741.8358274566 \tabularnewline
73 & 84174 & 75085.9590129741 & 9088.04098702586 \tabularnewline
74 & 78618 & 72840.1496310265 & 5777.85036897354 \tabularnewline
75 & 61185 & 65005.8793846796 & -3820.87938467964 \tabularnewline
76 & 63612 & 65146.6589432181 & -1534.6589432181 \tabularnewline
77 & 62673 & 63266.5259872611 & -593.525987261129 \tabularnewline
78 & 64549 & 62765.5345452221 & 1783.46545477792 \tabularnewline
79 & 61103 & 56131.130817604 & 4971.86918239601 \tabularnewline
80 & 61047 & 56272.047510916 & 4774.95248908396 \tabularnewline
81 & 61589 & 63059.8845905009 & -1470.88459050091 \tabularnewline
82 & 71233 & 64022.9261450239 & 7210.07385497614 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190309&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]82368[/C][C]74622.4671965546[/C][C]7745.53280344537[/C][/ROW]
[ROW][C]2[/C][C]77795[/C][C]73692.6063127966[/C][C]4102.39368720344[/C][/ROW]
[ROW][C]3[/C][C]62827[/C][C]65816.715889793[/C][C]-2989.71588979305[/C][/ROW]
[ROW][C]4[/C][C]67197[/C][C]69793.9334082974[/C][C]-2596.93340829737[/C][/ROW]
[ROW][C]5[/C][C]66848[/C][C]71180.7045005386[/C][C]-4332.7045005386[/C][/ROW]
[ROW][C]6[/C][C]66421[/C][C]69160.3686409306[/C][C]-2739.3686409306[/C][/ROW]
[ROW][C]7[/C][C]60643[/C][C]62314.0703181044[/C][C]-1671.07031810436[/C][/ROW]
[ROW][C]8[/C][C]59071[/C][C]52269.4169032028[/C][C]6801.58309679723[/C][/ROW]
[ROW][C]9[/C][C]58746[/C][C]65364.2297741492[/C][C]-6618.22977414916[/C][/ROW]
[ROW][C]10[/C][C]68515[/C][C]60518.5656903187[/C][C]7996.43430968132[/C][/ROW]
[ROW][C]11[/C][C]68998[/C][C]60916.5314095247[/C][C]8081.46859047529[/C][/ROW]
[ROW][C]12[/C][C]77614[/C][C]67757.1796387118[/C][C]9856.82036128816[/C][/ROW]
[ROW][C]13[/C][C]73469[/C][C]69858.8496456053[/C][C]3610.15035439469[/C][/ROW]
[ROW][C]14[/C][C]67145[/C][C]70533.289741608[/C][C]-3388.28974160797[/C][/ROW]
[ROW][C]15[/C][C]51109[/C][C]60120.7438879107[/C][C]-9011.7438879107[/C][/ROW]
[ROW][C]16[/C][C]51130[/C][C]58753.9063221164[/C][C]-7623.90632211638[/C][/ROW]
[ROW][C]17[/C][C]49544[/C][C]59397.9116737776[/C][C]-9853.91167377758[/C][/ROW]
[ROW][C]18[/C][C]50730[/C][C]49861.513996881[/C][C]868.486003118999[/C][/ROW]
[ROW][C]19[/C][C]49710[/C][C]41758.7157960617[/C][C]7951.28420393827[/C][/ROW]
[ROW][C]20[/C][C]50059[/C][C]56790.4864236524[/C][C]-6731.48642365239[/C][/ROW]
[ROW][C]21[/C][C]49681[/C][C]58792.6083911462[/C][C]-9111.60839114625[/C][/ROW]
[ROW][C]22[/C][C]65773[/C][C]75952.0538490446[/C][C]-10179.0538490446[/C][/ROW]
[ROW][C]23[/C][C]66129[/C][C]72462.7369830722[/C][C]-6333.73698307223[/C][/ROW]
[ROW][C]24[/C][C]78039[/C][C]73417.6656694262[/C][C]4621.33433057379[/C][/ROW]
[ROW][C]25[/C][C]71278[/C][C]64529.6710272375[/C][C]6748.32897276245[/C][/ROW]
[ROW][C]26[/C][C]65862[/C][C]61129.7080375629[/C][C]4732.29196243711[/C][/ROW]
[ROW][C]27[/C][C]51540[/C][C]53573.8921800221[/C][C]-2033.89218002208[/C][/ROW]
[ROW][C]28[/C][C]51513[/C][C]53216.524844478[/C][C]-1703.52484447804[/C][/ROW]
[ROW][C]29[/C][C]49740[/C][C]51569.4958629296[/C][C]-1829.49586292964[/C][/ROW]
[ROW][C]30[/C][C]50980[/C][C]51727.8387307335[/C][C]-747.838730733493[/C][/ROW]
[ROW][C]31[/C][C]51294[/C][C]51846.0536652717[/C][C]-552.053665271739[/C][/ROW]
[ROW][C]32[/C][C]49719[/C][C]52714.9704658774[/C][C]-2995.97046587744[/C][/ROW]
[ROW][C]33[/C][C]50673[/C][C]56076.8903072399[/C][C]-5403.89030723989[/C][/ROW]
[ROW][C]34[/C][C]59191[/C][C]61561.641146161[/C][C]-2370.64114616099[/C][/ROW]
[ROW][C]35[/C][C]61807[/C][C]65176.5376239857[/C][C]-3369.53762398569[/C][/ROW]
[ROW][C]36[/C][C]77687[/C][C]70208.8904183191[/C][C]7478.10958168089[/C][/ROW]
[ROW][C]37[/C][C]77227[/C][C]74799.7534968731[/C][C]2427.24650312688[/C][/ROW]
[ROW][C]38[/C][C]75594[/C][C]73853.5468489102[/C][C]1740.45315108977[/C][/ROW]
[ROW][C]39[/C][C]64158[/C][C]64739.5862536443[/C][C]-581.586253644256[/C][/ROW]
[ROW][C]40[/C][C]64551[/C][C]68026.0176459233[/C][C]-3475.01764592335[/C][/ROW]
[ROW][C]41[/C][C]65143[/C][C]67882.5167947007[/C][C]-2739.51679470069[/C][/ROW]
[ROW][C]42[/C][C]69958[/C][C]67759.8629047383[/C][C]2198.13709526174[/C][/ROW]
[ROW][C]43[/C][C]68154[/C][C]58097.0404188488[/C][C]10056.9595811512[/C][/ROW]
[ROW][C]44[/C][C]64628[/C][C]53041.2922911591[/C][C]11586.7077088409[/C][/ROW]
[ROW][C]45[/C][C]61690[/C][C]57928.0591802738[/C][C]3761.94081972621[/C][/ROW]
[ROW][C]46[/C][C]71412[/C][C]62892.5276930329[/C][C]8519.47230696712[/C][/ROW]
[ROW][C]47[/C][C]73606[/C][C]64883.5729821055[/C][C]8722.42701789452[/C][/ROW]
[ROW][C]48[/C][C]91586[/C][C]76995.5814988508[/C][C]14590.4185011492[/C][/ROW]
[ROW][C]49[/C][C]85299[/C][C]76221.2973054811[/C][C]9077.7026945189[/C][/ROW]
[ROW][C]50[/C][C]81752[/C][C]75792.498357303[/C][C]5959.50164269702[/C][/ROW]
[ROW][C]51[/C][C]63479[/C][C]68865.1062909006[/C][C]-5386.10629090057[/C][/ROW]
[ROW][C]52[/C][C]62470[/C][C]68405.5726666379[/C][C]-5935.57266663793[/C][/ROW]
[ROW][C]53[/C][C]60452[/C][C]66719.2270208123[/C][C]-6267.22702081229[/C][/ROW]
[ROW][C]54[/C][C]65593[/C][C]69527.8936484749[/C][C]-3934.89364847486[/C][/ROW]
[ROW][C]55[/C][C]64223[/C][C]75594.2876856704[/C][C]-11371.2876856704[/C][/ROW]
[ROW][C]56[/C][C]61466[/C][C]69422.5225043883[/C][C]-7956.52250438832[/C][/ROW]
[ROW][C]57[/C][C]58471[/C][C]71796.6786258653[/C][C]-13325.6786258653[/C][/ROW]
[ROW][C]58[/C][C]67261[/C][C]75899.4430077533[/C][C]-8638.4430077533[/C][/ROW]
[ROW][C]59[/C][C]71826[/C][C]75630.1661331253[/C][C]-3804.16613312531[/C][/ROW]
[ROW][C]60[/C][C]84695[/C][C]79672.9196426667[/C][C]5022.08035733328[/C][/ROW]
[ROW][C]61[/C][C]80558[/C][C]78382.2945286499[/C][C]2175.70547135005[/C][/ROW]
[ROW][C]62[/C][C]73755[/C][C]74063.3494136768[/C][C]-308.349413676755[/C][/ROW]
[ROW][C]63[/C][C]57786[/C][C]65600.2214682651[/C][C]-7814.22146826507[/C][/ROW]
[ROW][C]64[/C][C]59266[/C][C]64492.2689089971[/C][C]-5226.26890899706[/C][/ROW]
[ROW][C]65[/C][C]58815[/C][C]61546.5778460581[/C][C]-2731.57784605808[/C][/ROW]
[ROW][C]66[/C][C]60945[/C][C]62869.639388178[/C][C]-1924.63938817801[/C][/ROW]
[ROW][C]67[/C][C]58520[/C][C]60650.7767776501[/C][C]-2130.77677765007[/C][/ROW]
[ROW][C]68[/C][C]59747[/C][C]59839.5749986594[/C][C]-92.5749986593588[/C][/ROW]
[ROW][C]69[/C][C]56401[/C][C]62290.7433758831[/C][C]-5889.74337588315[/C][/ROW]
[ROW][C]70[/C][C]64773[/C][C]66897.4761471953[/C][C]-2124.47614719526[/C][/ROW]
[ROW][C]71[/C][C]68026[/C][C]65539.8591346343[/C][C]2486.14086536573[/C][/ROW]
[ROW][C]72[/C][C]84288[/C][C]73546.1641725434[/C][C]10741.8358274566[/C][/ROW]
[ROW][C]73[/C][C]84174[/C][C]75085.9590129741[/C][C]9088.04098702586[/C][/ROW]
[ROW][C]74[/C][C]78618[/C][C]72840.1496310265[/C][C]5777.85036897354[/C][/ROW]
[ROW][C]75[/C][C]61185[/C][C]65005.8793846796[/C][C]-3820.87938467964[/C][/ROW]
[ROW][C]76[/C][C]63612[/C][C]65146.6589432181[/C][C]-1534.6589432181[/C][/ROW]
[ROW][C]77[/C][C]62673[/C][C]63266.5259872611[/C][C]-593.525987261129[/C][/ROW]
[ROW][C]78[/C][C]64549[/C][C]62765.5345452221[/C][C]1783.46545477792[/C][/ROW]
[ROW][C]79[/C][C]61103[/C][C]56131.130817604[/C][C]4971.86918239601[/C][/ROW]
[ROW][C]80[/C][C]61047[/C][C]56272.047510916[/C][C]4774.95248908396[/C][/ROW]
[ROW][C]81[/C][C]61589[/C][C]63059.8845905009[/C][C]-1470.88459050091[/C][/ROW]
[ROW][C]82[/C][C]71233[/C][C]64022.9261450239[/C][C]7210.07385497614[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190309&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&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
18236874622.46719655467745.53280344537
27779573692.60631279664102.39368720344
36282765816.715889793-2989.71588979305
46719769793.9334082974-2596.93340829737
56684871180.7045005386-4332.7045005386
66642169160.3686409306-2739.3686409306
76064362314.0703181044-1671.07031810436
85907152269.41690320286801.58309679723
95874665364.2297741492-6618.22977414916
106851560518.56569031877996.43430968132
116899860916.53140952478081.46859047529
127761467757.17963871189856.82036128816
137346969858.84964560533610.15035439469
146714570533.289741608-3388.28974160797
155110960120.7438879107-9011.7438879107
165113058753.9063221164-7623.90632211638
174954459397.9116737776-9853.91167377758
185073049861.513996881868.486003118999
194971041758.71579606177951.28420393827
205005956790.4864236524-6731.48642365239
214968158792.6083911462-9111.60839114625
226577375952.0538490446-10179.0538490446
236612972462.7369830722-6333.73698307223
247803973417.66566942624621.33433057379
257127864529.67102723756748.32897276245
266586261129.70803756294732.29196243711
275154053573.8921800221-2033.89218002208
285151353216.524844478-1703.52484447804
294974051569.4958629296-1829.49586292964
305098051727.8387307335-747.838730733493
315129451846.0536652717-552.053665271739
324971952714.9704658774-2995.97046587744
335067356076.8903072399-5403.89030723989
345919161561.641146161-2370.64114616099
356180765176.5376239857-3369.53762398569
367768770208.89041831917478.10958168089
377722774799.75349687312427.24650312688
387559473853.54684891021740.45315108977
396415864739.5862536443-581.586253644256
406455168026.0176459233-3475.01764592335
416514367882.5167947007-2739.51679470069
426995867759.86290473832198.13709526174
436815458097.040418848810056.9595811512
446462853041.292291159111586.7077088409
456169057928.05918027383761.94081972621
467141262892.52769303298519.47230696712
477360664883.57298210558722.42701789452
489158676995.581498850814590.4185011492
498529976221.29730548119077.7026945189
508175275792.4983573035959.50164269702
516347968865.1062909006-5386.10629090057
526247068405.5726666379-5935.57266663793
536045266719.2270208123-6267.22702081229
546559369527.8936484749-3934.89364847486
556422375594.2876856704-11371.2876856704
566146669422.5225043883-7956.52250438832
575847171796.6786258653-13325.6786258653
586726175899.4430077533-8638.4430077533
597182675630.1661331253-3804.16613312531
608469579672.91964266675022.08035733328
618055878382.29452864992175.70547135005
627375574063.3494136768-308.349413676755
635778665600.2214682651-7814.22146826507
645926664492.2689089971-5226.26890899706
655881561546.5778460581-2731.57784605808
666094562869.639388178-1924.63938817801
675852060650.7767776501-2130.77677765007
685974759839.5749986594-92.5749986593588
695640162290.7433758831-5889.74337588315
706477366897.4761471953-2124.47614719526
716802665539.85913463432486.14086536573
728428873546.164172543410741.8358274566
738417475085.95901297419088.04098702586
747861872840.14963102655777.85036897354
756118565005.8793846796-3820.87938467964
766361265146.6589432181-1534.6589432181
776267363266.5259872611-593.525987261129
786454962765.53454522211783.46545477792
796110356131.1308176044971.86918239601
806104756272.0475109164774.95248908396
816158963059.8845905009-1470.88459050091
827123364022.92614502397210.07385497614







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3038249233864780.6076498467729560.696175076613522
130.191856406893460.3837128137869210.80814359310654
140.1123050389677560.2246100779355130.887694961032244
150.07705198436836260.1541039687367250.922948015631637
160.03851578100702210.07703156201404410.961484218992978
170.01886207466030370.03772414932060740.981137925339696
180.01233400420462370.02466800840924750.987665995795376
190.0307330740413250.061466148082650.969266925958675
200.09579369652915760.1915873930583150.904206303470842
210.08460445077074580.1692089015414920.915395549229254
220.08161801424638220.1632360284927640.918381985753618
230.06883723267052590.1376744653410520.931162767329474
240.2755558597394920.5511117194789840.724444140260508
250.3887666374339640.7775332748679270.611233362566036
260.3802789655197650.760557931039530.619721034480235
270.3112116911920690.6224233823841380.688788308807931
280.2633819791148760.5267639582297510.736618020885125
290.2147314640893710.4294629281787430.785268535910629
300.1682171212388430.3364342424776870.831782878761157
310.1269548325263860.2539096650527720.873045167473614
320.1353661858166680.2707323716333370.864633814183332
330.2178293782053720.4356587564107430.782170621794628
340.2434707702009360.4869415404018710.756529229799064
350.3901464967980480.7802929935960970.609853503201952
360.641832884772230.716334230455540.35816711522777
370.7799403493411930.4401193013176130.220059650658807
380.820791074825160.358417850349680.17920892517484
390.8085196276474850.382960744705030.191480372352515
400.7900310671991490.4199378656017020.209968932800851
410.7484140263520480.5031719472959030.251585973647952
420.6965680053236820.6068639893526360.303431994676318
430.6739973888143590.6520052223712830.326002611185641
440.6640781263445870.6718437473108270.335921873655414
450.7254507753244090.5490984493511820.274549224675591
460.7125687441248290.5748625117503410.287431255875171
470.6646115717607410.6707768564785170.335388428239259
480.9026810940081830.1946378119836340.0973189059918171
490.9248796199468530.1502407601062940.0751203800531468
500.9387056862283560.1225886275432880.061294313771644
510.9621155549943370.07576889001132530.0378844450056626
520.9669021690012490.06619566199750190.0330978309987509
530.9667538417993660.06649231640126830.0332461582006342
540.9617365214049750.07652695719005080.0382634785950254
550.9693568133696820.06128637326063670.0306431866303184
560.9778249037737270.04435019245254570.0221750962262729
570.9840846664609030.03183066707819460.0159153335390973
580.986440047859170.02711990428166050.0135599521408303
590.9792745680552370.04145086388952690.0207254319447634
600.9690506886277720.06189862274445580.0309493113722279
610.9505253673639440.09894926527211140.0494746326360557
620.9195139765303380.1609720469393240.0804860234696619
630.9351493831678870.1297012336642270.0648506168321134
640.950010181641090.09997963671782020.0499898183589101
650.9130086143589090.1739827712821820.086991385641091
660.8574004093964940.2851991812070120.142599590603506
670.7851730125431870.4296539749136270.214826987456813
680.6876145608477850.624770878304430.312385439152215
690.5971746512000270.8056506975999450.402825348799973
700.7224175619152660.5551648761694670.277582438084734

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.303824923386478 & 0.607649846772956 & 0.696175076613522 \tabularnewline
13 & 0.19185640689346 & 0.383712813786921 & 0.80814359310654 \tabularnewline
14 & 0.112305038967756 & 0.224610077935513 & 0.887694961032244 \tabularnewline
15 & 0.0770519843683626 & 0.154103968736725 & 0.922948015631637 \tabularnewline
16 & 0.0385157810070221 & 0.0770315620140441 & 0.961484218992978 \tabularnewline
17 & 0.0188620746603037 & 0.0377241493206074 & 0.981137925339696 \tabularnewline
18 & 0.0123340042046237 & 0.0246680084092475 & 0.987665995795376 \tabularnewline
19 & 0.030733074041325 & 0.06146614808265 & 0.969266925958675 \tabularnewline
20 & 0.0957936965291576 & 0.191587393058315 & 0.904206303470842 \tabularnewline
21 & 0.0846044507707458 & 0.169208901541492 & 0.915395549229254 \tabularnewline
22 & 0.0816180142463822 & 0.163236028492764 & 0.918381985753618 \tabularnewline
23 & 0.0688372326705259 & 0.137674465341052 & 0.931162767329474 \tabularnewline
24 & 0.275555859739492 & 0.551111719478984 & 0.724444140260508 \tabularnewline
25 & 0.388766637433964 & 0.777533274867927 & 0.611233362566036 \tabularnewline
26 & 0.380278965519765 & 0.76055793103953 & 0.619721034480235 \tabularnewline
27 & 0.311211691192069 & 0.622423382384138 & 0.688788308807931 \tabularnewline
28 & 0.263381979114876 & 0.526763958229751 & 0.736618020885125 \tabularnewline
29 & 0.214731464089371 & 0.429462928178743 & 0.785268535910629 \tabularnewline
30 & 0.168217121238843 & 0.336434242477687 & 0.831782878761157 \tabularnewline
31 & 0.126954832526386 & 0.253909665052772 & 0.873045167473614 \tabularnewline
32 & 0.135366185816668 & 0.270732371633337 & 0.864633814183332 \tabularnewline
33 & 0.217829378205372 & 0.435658756410743 & 0.782170621794628 \tabularnewline
34 & 0.243470770200936 & 0.486941540401871 & 0.756529229799064 \tabularnewline
35 & 0.390146496798048 & 0.780292993596097 & 0.609853503201952 \tabularnewline
36 & 0.64183288477223 & 0.71633423045554 & 0.35816711522777 \tabularnewline
37 & 0.779940349341193 & 0.440119301317613 & 0.220059650658807 \tabularnewline
38 & 0.82079107482516 & 0.35841785034968 & 0.17920892517484 \tabularnewline
39 & 0.808519627647485 & 0.38296074470503 & 0.191480372352515 \tabularnewline
40 & 0.790031067199149 & 0.419937865601702 & 0.209968932800851 \tabularnewline
41 & 0.748414026352048 & 0.503171947295903 & 0.251585973647952 \tabularnewline
42 & 0.696568005323682 & 0.606863989352636 & 0.303431994676318 \tabularnewline
43 & 0.673997388814359 & 0.652005222371283 & 0.326002611185641 \tabularnewline
44 & 0.664078126344587 & 0.671843747310827 & 0.335921873655414 \tabularnewline
45 & 0.725450775324409 & 0.549098449351182 & 0.274549224675591 \tabularnewline
46 & 0.712568744124829 & 0.574862511750341 & 0.287431255875171 \tabularnewline
47 & 0.664611571760741 & 0.670776856478517 & 0.335388428239259 \tabularnewline
48 & 0.902681094008183 & 0.194637811983634 & 0.0973189059918171 \tabularnewline
49 & 0.924879619946853 & 0.150240760106294 & 0.0751203800531468 \tabularnewline
50 & 0.938705686228356 & 0.122588627543288 & 0.061294313771644 \tabularnewline
51 & 0.962115554994337 & 0.0757688900113253 & 0.0378844450056626 \tabularnewline
52 & 0.966902169001249 & 0.0661956619975019 & 0.0330978309987509 \tabularnewline
53 & 0.966753841799366 & 0.0664923164012683 & 0.0332461582006342 \tabularnewline
54 & 0.961736521404975 & 0.0765269571900508 & 0.0382634785950254 \tabularnewline
55 & 0.969356813369682 & 0.0612863732606367 & 0.0306431866303184 \tabularnewline
56 & 0.977824903773727 & 0.0443501924525457 & 0.0221750962262729 \tabularnewline
57 & 0.984084666460903 & 0.0318306670781946 & 0.0159153335390973 \tabularnewline
58 & 0.98644004785917 & 0.0271199042816605 & 0.0135599521408303 \tabularnewline
59 & 0.979274568055237 & 0.0414508638895269 & 0.0207254319447634 \tabularnewline
60 & 0.969050688627772 & 0.0618986227444558 & 0.0309493113722279 \tabularnewline
61 & 0.950525367363944 & 0.0989492652721114 & 0.0494746326360557 \tabularnewline
62 & 0.919513976530338 & 0.160972046939324 & 0.0804860234696619 \tabularnewline
63 & 0.935149383167887 & 0.129701233664227 & 0.0648506168321134 \tabularnewline
64 & 0.95001018164109 & 0.0999796367178202 & 0.0499898183589101 \tabularnewline
65 & 0.913008614358909 & 0.173982771282182 & 0.086991385641091 \tabularnewline
66 & 0.857400409396494 & 0.285199181207012 & 0.142599590603506 \tabularnewline
67 & 0.785173012543187 & 0.429653974913627 & 0.214826987456813 \tabularnewline
68 & 0.687614560847785 & 0.62477087830443 & 0.312385439152215 \tabularnewline
69 & 0.597174651200027 & 0.805650697599945 & 0.402825348799973 \tabularnewline
70 & 0.722417561915266 & 0.555164876169467 & 0.277582438084734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190309&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]12[/C][C]0.303824923386478[/C][C]0.607649846772956[/C][C]0.696175076613522[/C][/ROW]
[ROW][C]13[/C][C]0.19185640689346[/C][C]0.383712813786921[/C][C]0.80814359310654[/C][/ROW]
[ROW][C]14[/C][C]0.112305038967756[/C][C]0.224610077935513[/C][C]0.887694961032244[/C][/ROW]
[ROW][C]15[/C][C]0.0770519843683626[/C][C]0.154103968736725[/C][C]0.922948015631637[/C][/ROW]
[ROW][C]16[/C][C]0.0385157810070221[/C][C]0.0770315620140441[/C][C]0.961484218992978[/C][/ROW]
[ROW][C]17[/C][C]0.0188620746603037[/C][C]0.0377241493206074[/C][C]0.981137925339696[/C][/ROW]
[ROW][C]18[/C][C]0.0123340042046237[/C][C]0.0246680084092475[/C][C]0.987665995795376[/C][/ROW]
[ROW][C]19[/C][C]0.030733074041325[/C][C]0.06146614808265[/C][C]0.969266925958675[/C][/ROW]
[ROW][C]20[/C][C]0.0957936965291576[/C][C]0.191587393058315[/C][C]0.904206303470842[/C][/ROW]
[ROW][C]21[/C][C]0.0846044507707458[/C][C]0.169208901541492[/C][C]0.915395549229254[/C][/ROW]
[ROW][C]22[/C][C]0.0816180142463822[/C][C]0.163236028492764[/C][C]0.918381985753618[/C][/ROW]
[ROW][C]23[/C][C]0.0688372326705259[/C][C]0.137674465341052[/C][C]0.931162767329474[/C][/ROW]
[ROW][C]24[/C][C]0.275555859739492[/C][C]0.551111719478984[/C][C]0.724444140260508[/C][/ROW]
[ROW][C]25[/C][C]0.388766637433964[/C][C]0.777533274867927[/C][C]0.611233362566036[/C][/ROW]
[ROW][C]26[/C][C]0.380278965519765[/C][C]0.76055793103953[/C][C]0.619721034480235[/C][/ROW]
[ROW][C]27[/C][C]0.311211691192069[/C][C]0.622423382384138[/C][C]0.688788308807931[/C][/ROW]
[ROW][C]28[/C][C]0.263381979114876[/C][C]0.526763958229751[/C][C]0.736618020885125[/C][/ROW]
[ROW][C]29[/C][C]0.214731464089371[/C][C]0.429462928178743[/C][C]0.785268535910629[/C][/ROW]
[ROW][C]30[/C][C]0.168217121238843[/C][C]0.336434242477687[/C][C]0.831782878761157[/C][/ROW]
[ROW][C]31[/C][C]0.126954832526386[/C][C]0.253909665052772[/C][C]0.873045167473614[/C][/ROW]
[ROW][C]32[/C][C]0.135366185816668[/C][C]0.270732371633337[/C][C]0.864633814183332[/C][/ROW]
[ROW][C]33[/C][C]0.217829378205372[/C][C]0.435658756410743[/C][C]0.782170621794628[/C][/ROW]
[ROW][C]34[/C][C]0.243470770200936[/C][C]0.486941540401871[/C][C]0.756529229799064[/C][/ROW]
[ROW][C]35[/C][C]0.390146496798048[/C][C]0.780292993596097[/C][C]0.609853503201952[/C][/ROW]
[ROW][C]36[/C][C]0.64183288477223[/C][C]0.71633423045554[/C][C]0.35816711522777[/C][/ROW]
[ROW][C]37[/C][C]0.779940349341193[/C][C]0.440119301317613[/C][C]0.220059650658807[/C][/ROW]
[ROW][C]38[/C][C]0.82079107482516[/C][C]0.35841785034968[/C][C]0.17920892517484[/C][/ROW]
[ROW][C]39[/C][C]0.808519627647485[/C][C]0.38296074470503[/C][C]0.191480372352515[/C][/ROW]
[ROW][C]40[/C][C]0.790031067199149[/C][C]0.419937865601702[/C][C]0.209968932800851[/C][/ROW]
[ROW][C]41[/C][C]0.748414026352048[/C][C]0.503171947295903[/C][C]0.251585973647952[/C][/ROW]
[ROW][C]42[/C][C]0.696568005323682[/C][C]0.606863989352636[/C][C]0.303431994676318[/C][/ROW]
[ROW][C]43[/C][C]0.673997388814359[/C][C]0.652005222371283[/C][C]0.326002611185641[/C][/ROW]
[ROW][C]44[/C][C]0.664078126344587[/C][C]0.671843747310827[/C][C]0.335921873655414[/C][/ROW]
[ROW][C]45[/C][C]0.725450775324409[/C][C]0.549098449351182[/C][C]0.274549224675591[/C][/ROW]
[ROW][C]46[/C][C]0.712568744124829[/C][C]0.574862511750341[/C][C]0.287431255875171[/C][/ROW]
[ROW][C]47[/C][C]0.664611571760741[/C][C]0.670776856478517[/C][C]0.335388428239259[/C][/ROW]
[ROW][C]48[/C][C]0.902681094008183[/C][C]0.194637811983634[/C][C]0.0973189059918171[/C][/ROW]
[ROW][C]49[/C][C]0.924879619946853[/C][C]0.150240760106294[/C][C]0.0751203800531468[/C][/ROW]
[ROW][C]50[/C][C]0.938705686228356[/C][C]0.122588627543288[/C][C]0.061294313771644[/C][/ROW]
[ROW][C]51[/C][C]0.962115554994337[/C][C]0.0757688900113253[/C][C]0.0378844450056626[/C][/ROW]
[ROW][C]52[/C][C]0.966902169001249[/C][C]0.0661956619975019[/C][C]0.0330978309987509[/C][/ROW]
[ROW][C]53[/C][C]0.966753841799366[/C][C]0.0664923164012683[/C][C]0.0332461582006342[/C][/ROW]
[ROW][C]54[/C][C]0.961736521404975[/C][C]0.0765269571900508[/C][C]0.0382634785950254[/C][/ROW]
[ROW][C]55[/C][C]0.969356813369682[/C][C]0.0612863732606367[/C][C]0.0306431866303184[/C][/ROW]
[ROW][C]56[/C][C]0.977824903773727[/C][C]0.0443501924525457[/C][C]0.0221750962262729[/C][/ROW]
[ROW][C]57[/C][C]0.984084666460903[/C][C]0.0318306670781946[/C][C]0.0159153335390973[/C][/ROW]
[ROW][C]58[/C][C]0.98644004785917[/C][C]0.0271199042816605[/C][C]0.0135599521408303[/C][/ROW]
[ROW][C]59[/C][C]0.979274568055237[/C][C]0.0414508638895269[/C][C]0.0207254319447634[/C][/ROW]
[ROW][C]60[/C][C]0.969050688627772[/C][C]0.0618986227444558[/C][C]0.0309493113722279[/C][/ROW]
[ROW][C]61[/C][C]0.950525367363944[/C][C]0.0989492652721114[/C][C]0.0494746326360557[/C][/ROW]
[ROW][C]62[/C][C]0.919513976530338[/C][C]0.160972046939324[/C][C]0.0804860234696619[/C][/ROW]
[ROW][C]63[/C][C]0.935149383167887[/C][C]0.129701233664227[/C][C]0.0648506168321134[/C][/ROW]
[ROW][C]64[/C][C]0.95001018164109[/C][C]0.0999796367178202[/C][C]0.0499898183589101[/C][/ROW]
[ROW][C]65[/C][C]0.913008614358909[/C][C]0.173982771282182[/C][C]0.086991385641091[/C][/ROW]
[ROW][C]66[/C][C]0.857400409396494[/C][C]0.285199181207012[/C][C]0.142599590603506[/C][/ROW]
[ROW][C]67[/C][C]0.785173012543187[/C][C]0.429653974913627[/C][C]0.214826987456813[/C][/ROW]
[ROW][C]68[/C][C]0.687614560847785[/C][C]0.62477087830443[/C][C]0.312385439152215[/C][/ROW]
[ROW][C]69[/C][C]0.597174651200027[/C][C]0.805650697599945[/C][C]0.402825348799973[/C][/ROW]
[ROW][C]70[/C][C]0.722417561915266[/C][C]0.555164876169467[/C][C]0.277582438084734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190309&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&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
120.3038249233864780.6076498467729560.696175076613522
130.191856406893460.3837128137869210.80814359310654
140.1123050389677560.2246100779355130.887694961032244
150.07705198436836260.1541039687367250.922948015631637
160.03851578100702210.07703156201404410.961484218992978
170.01886207466030370.03772414932060740.981137925339696
180.01233400420462370.02466800840924750.987665995795376
190.0307330740413250.061466148082650.969266925958675
200.09579369652915760.1915873930583150.904206303470842
210.08460445077074580.1692089015414920.915395549229254
220.08161801424638220.1632360284927640.918381985753618
230.06883723267052590.1376744653410520.931162767329474
240.2755558597394920.5511117194789840.724444140260508
250.3887666374339640.7775332748679270.611233362566036
260.3802789655197650.760557931039530.619721034480235
270.3112116911920690.6224233823841380.688788308807931
280.2633819791148760.5267639582297510.736618020885125
290.2147314640893710.4294629281787430.785268535910629
300.1682171212388430.3364342424776870.831782878761157
310.1269548325263860.2539096650527720.873045167473614
320.1353661858166680.2707323716333370.864633814183332
330.2178293782053720.4356587564107430.782170621794628
340.2434707702009360.4869415404018710.756529229799064
350.3901464967980480.7802929935960970.609853503201952
360.641832884772230.716334230455540.35816711522777
370.7799403493411930.4401193013176130.220059650658807
380.820791074825160.358417850349680.17920892517484
390.8085196276474850.382960744705030.191480372352515
400.7900310671991490.4199378656017020.209968932800851
410.7484140263520480.5031719472959030.251585973647952
420.6965680053236820.6068639893526360.303431994676318
430.6739973888143590.6520052223712830.326002611185641
440.6640781263445870.6718437473108270.335921873655414
450.7254507753244090.5490984493511820.274549224675591
460.7125687441248290.5748625117503410.287431255875171
470.6646115717607410.6707768564785170.335388428239259
480.9026810940081830.1946378119836340.0973189059918171
490.9248796199468530.1502407601062940.0751203800531468
500.9387056862283560.1225886275432880.061294313771644
510.9621155549943370.07576889001132530.0378844450056626
520.9669021690012490.06619566199750190.0330978309987509
530.9667538417993660.06649231640126830.0332461582006342
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550.9693568133696820.06128637326063670.0306431866303184
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570.9840846664609030.03183066707819460.0159153335390973
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600.9690506886277720.06189862274445580.0309493113722279
610.9505253673639440.09894926527211140.0494746326360557
620.9195139765303380.1609720469393240.0804860234696619
630.9351493831678870.1297012336642270.0648506168321134
640.950010181641090.09997963671782020.0499898183589101
650.9130086143589090.1739827712821820.086991385641091
660.8574004093964940.2851991812070120.142599590603506
670.7851730125431870.4296539749136270.214826987456813
680.6876145608477850.624770878304430.312385439152215
690.5971746512000270.8056506975999450.402825348799973
700.7224175619152660.5551648761694670.277582438084734







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.101694915254237NOK
10% type I error level160.271186440677966NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 6 & 0.101694915254237 & NOK \tabularnewline
10% type I error level & 16 & 0.271186440677966 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190309&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.101694915254237[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.271186440677966[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190309&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190309&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 level00OK
5% type I error level60.101694915254237NOK
10% type I error level160.271186440677966NOK



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