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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 17 Dec 2009 04:25:28 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/17/t1261049189g6m3zi8l04aejh6.htm/, Retrieved Tue, 30 Apr 2024 07:05:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68760, Retrieved Tue, 30 Apr 2024 07:05:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Partial Correlation] [CVM Paper: Partia...] [2009-12-17 09:09:51] [03d5b865e91ca35b5a5d21b8d6da5aba]
- RMPD    [Multiple Regression] [CVM Paper: Multip...] [2009-12-17 11:25:28] [a5ada8bd39e806b5b90f09589c89554a] [Current]
-   PD      [Multiple Regression] [CVM Paper: Multip...] [2009-12-17 11:28:53] [03d5b865e91ca35b5a5d21b8d6da5aba]
-   P         [Multiple Regression] [CVM Paper: Multip...] [2009-12-17 13:14:43] [03d5b865e91ca35b5a5d21b8d6da5aba]
-   P         [Multiple Regression] [vraag3_EliendeHaas] [2010-01-26 08:05:02] [2663058f2a5dda519058ac6b2228468f]
-   P         [Multiple Regression] [Examenvraag 3] [2010-01-26 08:06:14] [1433a524809eda02c3198b3ae6eebb69]
-   P         [Multiple Regression] [] [2010-01-26 08:06:15] [1e83ffa964db6f7ea6ccc4e7b5acbbff]
-   P         [Multiple Regression] [Vraag 3- computer...] [2010-01-26 08:06:02] [12f02da0296cb21dc23d82ae014a8b71]
-   P         [Multiple Regression] [vraag 3 0900909] [2010-01-26 08:07:43] [5e6d255681a7853beaa91b62357037a7]
-   P         [Multiple Regression] [examen Kim De Vos] [2010-01-26 08:07:57] [005293453b571dbccb80b45226e44173]
-   P         [Multiple Regression] [examen statistiek] [2010-01-26 08:08:57] [f1a50df816abcbb519e7637ff6b72fa0]
-             [Multiple Regression] [examen comp] [2010-01-26 08:08:33] [134dc66689e3d457a82860db6471d419]
-   P         [Multiple Regression] [examen] [2010-01-26 08:05:05] [eff6c0799cbd248896739259ce57a08e]
-   P         [Multiple Regression] [examen statistiek] [2010-01-26 08:09:32] [309ee52d0058ff0a6f7eec15e07b2d9f]
-             [Multiple Regression] [] [2010-01-26 08:10:08] [4fe1472705bb0a32f118ba3ca90ffa8e]
-   P         [Multiple Regression] [vraag3] [2010-01-26 08:10:26] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [lineair trend] [2010-01-26 08:13:01] [dff692ae32125bdbbfc005d665e23b83]
-   P         [Multiple Regression] [Examenvraag3 bloggen] [2010-01-26 08:11:35] [f15cf5036ae52d4243ad71d4fb151dbe]
-   P         [Multiple Regression] [] [2010-01-26 08:17:34] [4d237c8ec53dd43f33fce99b7dc91018]
-   P         [Multiple Regression] [] [2010-01-26 08:18:46] [3af9fa3d2c04a43d660a9a466bdfbaa0]
-   P         [Multiple Regression] [vraag 3] [2010-01-26 08:25:14] [eaf42bcf5162b5692bb3c7f9d4636222]
-   P         [Multiple Regression] [] [2010-01-26 08:25:50] [e0a128c302a1ec9189220a385b8da313]
-   P         [Multiple Regression] [examen bloggen] [2010-01-26 08:25:42] [03c44f58d7d4de05d4cfabfda8c46d2c]
-   P         [Multiple Regression] [Trend examen] [2010-01-26 08:26:05] [a94022e7c2399c0f4d62eea578db3411]
-   P         [Multiple Regression] [examenvraag] [2010-01-26 08:25:55] [b5ed7ca2d893a9c0b68720eb2614783c]
-   P         [Multiple Regression] [sdws examen] [2010-01-26 08:26:44] [f7fc9270f813d017f9fa5b506fdc7682]
-   P         [Multiple Regression] [] [2010-01-26 08:27:37] [445b292c553470d9fed8bc2796fd3a00]
-   P         [Multiple Regression] [0.313] [2010-01-26 08:27:05] [f5d341d4bbba73282fc6e80153a6d315]
-   P         [Multiple Regression] [] [2010-01-26 08:26:32] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-   P         [Multiple Regression] [parameter] [2010-01-26 08:27:43] [24c4941ee50deadff4640c9c09cc70cb]
-   P         [Multiple Regression] [] [2010-01-26 08:28:46] [b5908418e3090fddbd22f5f0f774653d]
-   P         [Multiple Regression] [Examen testshw1, ...] [2010-01-26 08:37:56] [96e597a9107bfe8c07649cce3d4f6fec]
-   P         [Multiple Regression] [examan] [2010-01-26 08:40:29] [b8b64ced21f32e31669b267b64eede7f]
-   P         [Multiple Regression] [Examen Bram Hutten] [2010-01-26 08:42:31] [786e067c4f7cec17385c4742b96b6dfa]
-   P         [Multiple Regression] [examen statistiek] [2010-01-26 08:44:04] [28d531aeb5ea2ff1b676cbab66947a19]
-             [Multiple Regression] [] [2010-01-26 08:45:05] [830e13ac5e5ac1e5b21c6af0c149b21d]
-   P         [Multiple Regression] [] [2010-01-26 08:58:12] [0750c128064677e728c9436fc3f45ae7]
-   P         [Multiple Regression] [ExamenStatistiek] [2010-01-26 08:59:03] [a66d3a79ef9e5308cd94a469bc5ca464]
-   P         [Multiple Regression] [Vraag 3] [2010-01-26 08:57:15] [626f1d98f4a7f05bcb9f17666b672c60]
-   P         [Multiple Regression] [Examen Statistisc...] [2010-01-26 08:59:14] [1646a2766cb8c4a6f9d3b2fffef409b3]
-   P         [Multiple Regression] [Examenvraag3] [2010-01-26 08:59:45] [2b2cfeea2f5ac2a1bcb842baaf1415ef]
-   P         [Multiple Regression] [Examenvraag 3] [2010-01-26 09:01:00] [253127ae8da904b75450fbd69fe4eb21]
-   P         [Multiple Regression] [s0901624] [2010-01-26 08:57:55] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [Examen] [2010-01-26 09:02:59] [b6394cb5c2dcec6d17418d3cdf42d699]
-   P         [Multiple Regression] [] [2010-01-26 09:00:18] [21a312bc02d33649b9f78fd202ca0963]
-   P         [Multiple Regression] [Lode Merckx computer] [2010-01-26 09:03:20] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [] [2010-01-26 09:02:32] [ebd107afac1bd6180acb277edd05815b]
-   P         [Multiple Regression] [3e examenvraag co...] [2010-01-26 09:01:42] [8cf9233b7464ea02e32be3b30fdac052]
-   P         [Multiple Regression] [Computeropdracht ...] [2010-01-26 09:04:39] [2f17fb7f9ce5412e0690130b6ae01587]
-   P         [Multiple Regression] [Vraag 3 examen Fa...] [2010-01-26 09:03:13] [9717cb857c153ca3061376906953b329]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
25,6	7,4	1,8
23,7	7,1	2,7
22	6,8	2,3
21,3	6,9	1,9
20,7	7,2	2
20,4	7,4	2,3
20,3	7,3	2,8
20,4	6,9	2,4
19,8	6,9	2,3
19,5	6,8	2,7
23,1	7,1	2,7
23,5	7,2	2,9
23,5	7,1	3
22,9	7	2,2
21,9	6,9	2,3
21,5	7,1	2,8
20,5	7,3	2,8
20,2	7,5	2,8
19,4	7,5	2,2
19,2	7,5	2,6
18,8	7,3	2,8
18,8	7	2,5
22,6	6,7	2,4
23,3	6,5	2,3
23	6,5	1,9
21,4	6,5	1,7
19,9	6,6	2
18,8	6,8	2,1
18,6	6,9	1,7
18,4	6,9	1,8
18,6	6,8	1,8
19,9	6,8	1,8
19,2	6,5	1,3
18,4	6,1	1,3
21,1	6,1	1,3
20,5	5,9	1,2
19,1	5,7	1,4
18,1	5,9	2,2
17	5,9	2,9
17,1	6,1	3,1
17,4	6,3	3,5
16,8	6,2	3,6
15,3	5,9	4,4
14,3	5,7	4,1
13,4	5,4	5,1
15,3	5,6	5,8
22,1	6,2	5,9
23,7	6,3	5,4
22,2	6	5,5
19,5	5,6	4,8
16,6	5,5	3,2
17,3	5,9	2,7
19,8	6,5	2,1
21,2	6,8	1,9
21,5	6,8	0,6
20,6	6,5	0,7
19,1	6,2	-0,2
19,6	6,2	-1
23,5	6,5	-1,7
24	6,7	-0,7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
W<25j[t] = + 6.48209224673715 + 2.17628821832016`W>25j`[t] -0.302658180265085Inflatie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
W<25j[t] =  +  6.48209224673715 +  2.17628821832016`W>25j`[t] -0.302658180265085Inflatie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]W<25j[t] =  +  6.48209224673715 +  2.17628821832016`W>25j`[t] -0.302658180265085Inflatie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
W<25j[t] = + 6.48209224673715 + 2.17628821832016`W>25j`[t] -0.302658180265085Inflatie[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.482092246737153.5364781.83290.0720380.036019
`W>25j`2.176288218320160.5123664.24758.1e-054e-05
Inflatie-0.3026581802650850.195452-1.54850.1270360.063518

\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) & 6.48209224673715 & 3.536478 & 1.8329 & 0.072038 & 0.036019 \tabularnewline
`W>25j` & 2.17628821832016 & 0.512366 & 4.2475 & 8.1e-05 & 4e-05 \tabularnewline
Inflatie & -0.302658180265085 & 0.195452 & -1.5485 & 0.127036 & 0.063518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&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]6.48209224673715[/C][C]3.536478[/C][C]1.8329[/C][C]0.072038[/C][C]0.036019[/C][/ROW]
[ROW][C]`W>25j`[/C][C]2.17628821832016[/C][C]0.512366[/C][C]4.2475[/C][C]8.1e-05[/C][C]4e-05[/C][/ROW]
[ROW][C]Inflatie[/C][C]-0.302658180265085[/C][C]0.195452[/C][C]-1.5485[/C][C]0.127036[/C][C]0.063518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.482092246737153.5364781.83290.0720380.036019
`W>25j`2.176288218320160.5123664.24758.1e-054e-05
Inflatie-0.3026581802650850.195452-1.54850.1270360.063518






Multiple Linear Regression - Regression Statistics
Multiple R0.556061678884002
R-squared0.309204590723295
Adjusted R-squared0.284966155310077
F-TEST (value)12.7567883591479
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value2.63911988551691e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.14500650880961
Sum Squared Residuals262.260016601628

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.556061678884002 \tabularnewline
R-squared & 0.309204590723295 \tabularnewline
Adjusted R-squared & 0.284966155310077 \tabularnewline
F-TEST (value) & 12.7567883591479 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 2.63911988551691e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.14500650880961 \tabularnewline
Sum Squared Residuals & 262.260016601628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.556061678884002[/C][/ROW]
[ROW][C]R-squared[/C][C]0.309204590723295[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.284966155310077[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.7567883591479[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]2.63911988551691e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.14500650880961[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]262.260016601628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.556061678884002
R-squared0.309204590723295
Adjusted R-squared0.284966155310077
F-TEST (value)12.7567883591479
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value2.63911988551691e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.14500650880961
Sum Squared Residuals262.260016601628






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.622.04184033782923.55815966217077
223.721.11656151009462.58343848990541
32220.58473831670461.41526168329543
421.320.92343041064260.37656958935738
520.721.5460510581122-0.846051058112162
620.421.8905112476967-1.49051124769667
720.321.5215533357321-1.22155333573211
820.420.7721013205101-0.372101320510080
919.820.8023671385366-1.00236713853659
1019.520.4636750445985-0.963675044598535
1123.121.11656151009461.98343848990542
1223.521.27365869587362.22634130412641
1323.521.02576405601512.47423594398494
1422.921.05026177839511.84973822160489
1521.920.80236713853661.09763286146341
1621.521.08629569206810.413704307931924
1720.521.5215533357321-1.02155333573211
1820.221.9568109793961-1.75681097939614
1919.422.1384058875552-2.73840588755519
2019.222.0173426154492-2.81734261544916
2118.821.5215533357321-2.72155333573211
2218.820.9594643243156-2.15946432431558
2322.620.33684367684602.26315632315396
2423.319.93185185120853.36814814879148
252320.05291512331462.94708487668545
2621.420.11344675936761.28655324063243
2719.920.2402781271201-0.340278127120063
2818.820.6452699527576-1.84526995275758
2918.620.9839620466956-2.38396204669564
3018.420.9536962286691-2.55369622866913
3118.620.7360674068371-2.13606740683711
3219.920.7360674068371-0.836067406837113
3319.220.2345100314736-1.03451003147361
3418.419.3639947441455-0.96399474414554
3521.119.36399474414551.73600525585446
3620.518.95900291850801.54099708149198
3719.118.46321363879100.636786361209036
3818.118.6563447382429-0.556344738242929
391718.4444840120574-1.44448401205737
4017.118.8192100196684-1.71921001966838
4117.419.1334043912264-1.73340439122639
4216.818.8855097513679-2.08550975136786
4315.317.9904967416597-2.69049674165974
4414.317.6460365520752-3.34603655207523
4513.416.6904919063141-3.2904919063141
4615.316.9138888237926-1.61388882379257
4722.118.18939593675823.91060406324184
4823.718.55835384872275.14164615127728
4922.217.87520156520024.32479843479983
5019.517.21654700405772.28345299594234
5116.617.4831712706498-0.883171270649777
5217.318.5050156481104-1.20501564811039
5319.819.9923834872615-0.192383487261536
5421.220.70580158881060.494198411189396
5521.521.09925722315520.400742776844786
5620.620.41610493963270.183895060367345
5719.120.0356108363752-0.935610836375183
5819.620.2777373805873-0.677737380587251
5923.521.14248457226892.35751542773114
602421.27508403566782.72491596433219

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25.6 & 22.0418403378292 & 3.55815966217077 \tabularnewline
2 & 23.7 & 21.1165615100946 & 2.58343848990541 \tabularnewline
3 & 22 & 20.5847383167046 & 1.41526168329543 \tabularnewline
4 & 21.3 & 20.9234304106426 & 0.37656958935738 \tabularnewline
5 & 20.7 & 21.5460510581122 & -0.846051058112162 \tabularnewline
6 & 20.4 & 21.8905112476967 & -1.49051124769667 \tabularnewline
7 & 20.3 & 21.5215533357321 & -1.22155333573211 \tabularnewline
8 & 20.4 & 20.7721013205101 & -0.372101320510080 \tabularnewline
9 & 19.8 & 20.8023671385366 & -1.00236713853659 \tabularnewline
10 & 19.5 & 20.4636750445985 & -0.963675044598535 \tabularnewline
11 & 23.1 & 21.1165615100946 & 1.98343848990542 \tabularnewline
12 & 23.5 & 21.2736586958736 & 2.22634130412641 \tabularnewline
13 & 23.5 & 21.0257640560151 & 2.47423594398494 \tabularnewline
14 & 22.9 & 21.0502617783951 & 1.84973822160489 \tabularnewline
15 & 21.9 & 20.8023671385366 & 1.09763286146341 \tabularnewline
16 & 21.5 & 21.0862956920681 & 0.413704307931924 \tabularnewline
17 & 20.5 & 21.5215533357321 & -1.02155333573211 \tabularnewline
18 & 20.2 & 21.9568109793961 & -1.75681097939614 \tabularnewline
19 & 19.4 & 22.1384058875552 & -2.73840588755519 \tabularnewline
20 & 19.2 & 22.0173426154492 & -2.81734261544916 \tabularnewline
21 & 18.8 & 21.5215533357321 & -2.72155333573211 \tabularnewline
22 & 18.8 & 20.9594643243156 & -2.15946432431558 \tabularnewline
23 & 22.6 & 20.3368436768460 & 2.26315632315396 \tabularnewline
24 & 23.3 & 19.9318518512085 & 3.36814814879148 \tabularnewline
25 & 23 & 20.0529151233146 & 2.94708487668545 \tabularnewline
26 & 21.4 & 20.1134467593676 & 1.28655324063243 \tabularnewline
27 & 19.9 & 20.2402781271201 & -0.340278127120063 \tabularnewline
28 & 18.8 & 20.6452699527576 & -1.84526995275758 \tabularnewline
29 & 18.6 & 20.9839620466956 & -2.38396204669564 \tabularnewline
30 & 18.4 & 20.9536962286691 & -2.55369622866913 \tabularnewline
31 & 18.6 & 20.7360674068371 & -2.13606740683711 \tabularnewline
32 & 19.9 & 20.7360674068371 & -0.836067406837113 \tabularnewline
33 & 19.2 & 20.2345100314736 & -1.03451003147361 \tabularnewline
34 & 18.4 & 19.3639947441455 & -0.96399474414554 \tabularnewline
35 & 21.1 & 19.3639947441455 & 1.73600525585446 \tabularnewline
36 & 20.5 & 18.9590029185080 & 1.54099708149198 \tabularnewline
37 & 19.1 & 18.4632136387910 & 0.636786361209036 \tabularnewline
38 & 18.1 & 18.6563447382429 & -0.556344738242929 \tabularnewline
39 & 17 & 18.4444840120574 & -1.44448401205737 \tabularnewline
40 & 17.1 & 18.8192100196684 & -1.71921001966838 \tabularnewline
41 & 17.4 & 19.1334043912264 & -1.73340439122639 \tabularnewline
42 & 16.8 & 18.8855097513679 & -2.08550975136786 \tabularnewline
43 & 15.3 & 17.9904967416597 & -2.69049674165974 \tabularnewline
44 & 14.3 & 17.6460365520752 & -3.34603655207523 \tabularnewline
45 & 13.4 & 16.6904919063141 & -3.2904919063141 \tabularnewline
46 & 15.3 & 16.9138888237926 & -1.61388882379257 \tabularnewline
47 & 22.1 & 18.1893959367582 & 3.91060406324184 \tabularnewline
48 & 23.7 & 18.5583538487227 & 5.14164615127728 \tabularnewline
49 & 22.2 & 17.8752015652002 & 4.32479843479983 \tabularnewline
50 & 19.5 & 17.2165470040577 & 2.28345299594234 \tabularnewline
51 & 16.6 & 17.4831712706498 & -0.883171270649777 \tabularnewline
52 & 17.3 & 18.5050156481104 & -1.20501564811039 \tabularnewline
53 & 19.8 & 19.9923834872615 & -0.192383487261536 \tabularnewline
54 & 21.2 & 20.7058015888106 & 0.494198411189396 \tabularnewline
55 & 21.5 & 21.0992572231552 & 0.400742776844786 \tabularnewline
56 & 20.6 & 20.4161049396327 & 0.183895060367345 \tabularnewline
57 & 19.1 & 20.0356108363752 & -0.935610836375183 \tabularnewline
58 & 19.6 & 20.2777373805873 & -0.677737380587251 \tabularnewline
59 & 23.5 & 21.1424845722689 & 2.35751542773114 \tabularnewline
60 & 24 & 21.2750840356678 & 2.72491596433219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&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]25.6[/C][C]22.0418403378292[/C][C]3.55815966217077[/C][/ROW]
[ROW][C]2[/C][C]23.7[/C][C]21.1165615100946[/C][C]2.58343848990541[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]20.5847383167046[/C][C]1.41526168329543[/C][/ROW]
[ROW][C]4[/C][C]21.3[/C][C]20.9234304106426[/C][C]0.37656958935738[/C][/ROW]
[ROW][C]5[/C][C]20.7[/C][C]21.5460510581122[/C][C]-0.846051058112162[/C][/ROW]
[ROW][C]6[/C][C]20.4[/C][C]21.8905112476967[/C][C]-1.49051124769667[/C][/ROW]
[ROW][C]7[/C][C]20.3[/C][C]21.5215533357321[/C][C]-1.22155333573211[/C][/ROW]
[ROW][C]8[/C][C]20.4[/C][C]20.7721013205101[/C][C]-0.372101320510080[/C][/ROW]
[ROW][C]9[/C][C]19.8[/C][C]20.8023671385366[/C][C]-1.00236713853659[/C][/ROW]
[ROW][C]10[/C][C]19.5[/C][C]20.4636750445985[/C][C]-0.963675044598535[/C][/ROW]
[ROW][C]11[/C][C]23.1[/C][C]21.1165615100946[/C][C]1.98343848990542[/C][/ROW]
[ROW][C]12[/C][C]23.5[/C][C]21.2736586958736[/C][C]2.22634130412641[/C][/ROW]
[ROW][C]13[/C][C]23.5[/C][C]21.0257640560151[/C][C]2.47423594398494[/C][/ROW]
[ROW][C]14[/C][C]22.9[/C][C]21.0502617783951[/C][C]1.84973822160489[/C][/ROW]
[ROW][C]15[/C][C]21.9[/C][C]20.8023671385366[/C][C]1.09763286146341[/C][/ROW]
[ROW][C]16[/C][C]21.5[/C][C]21.0862956920681[/C][C]0.413704307931924[/C][/ROW]
[ROW][C]17[/C][C]20.5[/C][C]21.5215533357321[/C][C]-1.02155333573211[/C][/ROW]
[ROW][C]18[/C][C]20.2[/C][C]21.9568109793961[/C][C]-1.75681097939614[/C][/ROW]
[ROW][C]19[/C][C]19.4[/C][C]22.1384058875552[/C][C]-2.73840588755519[/C][/ROW]
[ROW][C]20[/C][C]19.2[/C][C]22.0173426154492[/C][C]-2.81734261544916[/C][/ROW]
[ROW][C]21[/C][C]18.8[/C][C]21.5215533357321[/C][C]-2.72155333573211[/C][/ROW]
[ROW][C]22[/C][C]18.8[/C][C]20.9594643243156[/C][C]-2.15946432431558[/C][/ROW]
[ROW][C]23[/C][C]22.6[/C][C]20.3368436768460[/C][C]2.26315632315396[/C][/ROW]
[ROW][C]24[/C][C]23.3[/C][C]19.9318518512085[/C][C]3.36814814879148[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]20.0529151233146[/C][C]2.94708487668545[/C][/ROW]
[ROW][C]26[/C][C]21.4[/C][C]20.1134467593676[/C][C]1.28655324063243[/C][/ROW]
[ROW][C]27[/C][C]19.9[/C][C]20.2402781271201[/C][C]-0.340278127120063[/C][/ROW]
[ROW][C]28[/C][C]18.8[/C][C]20.6452699527576[/C][C]-1.84526995275758[/C][/ROW]
[ROW][C]29[/C][C]18.6[/C][C]20.9839620466956[/C][C]-2.38396204669564[/C][/ROW]
[ROW][C]30[/C][C]18.4[/C][C]20.9536962286691[/C][C]-2.55369622866913[/C][/ROW]
[ROW][C]31[/C][C]18.6[/C][C]20.7360674068371[/C][C]-2.13606740683711[/C][/ROW]
[ROW][C]32[/C][C]19.9[/C][C]20.7360674068371[/C][C]-0.836067406837113[/C][/ROW]
[ROW][C]33[/C][C]19.2[/C][C]20.2345100314736[/C][C]-1.03451003147361[/C][/ROW]
[ROW][C]34[/C][C]18.4[/C][C]19.3639947441455[/C][C]-0.96399474414554[/C][/ROW]
[ROW][C]35[/C][C]21.1[/C][C]19.3639947441455[/C][C]1.73600525585446[/C][/ROW]
[ROW][C]36[/C][C]20.5[/C][C]18.9590029185080[/C][C]1.54099708149198[/C][/ROW]
[ROW][C]37[/C][C]19.1[/C][C]18.4632136387910[/C][C]0.636786361209036[/C][/ROW]
[ROW][C]38[/C][C]18.1[/C][C]18.6563447382429[/C][C]-0.556344738242929[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]18.4444840120574[/C][C]-1.44448401205737[/C][/ROW]
[ROW][C]40[/C][C]17.1[/C][C]18.8192100196684[/C][C]-1.71921001966838[/C][/ROW]
[ROW][C]41[/C][C]17.4[/C][C]19.1334043912264[/C][C]-1.73340439122639[/C][/ROW]
[ROW][C]42[/C][C]16.8[/C][C]18.8855097513679[/C][C]-2.08550975136786[/C][/ROW]
[ROW][C]43[/C][C]15.3[/C][C]17.9904967416597[/C][C]-2.69049674165974[/C][/ROW]
[ROW][C]44[/C][C]14.3[/C][C]17.6460365520752[/C][C]-3.34603655207523[/C][/ROW]
[ROW][C]45[/C][C]13.4[/C][C]16.6904919063141[/C][C]-3.2904919063141[/C][/ROW]
[ROW][C]46[/C][C]15.3[/C][C]16.9138888237926[/C][C]-1.61388882379257[/C][/ROW]
[ROW][C]47[/C][C]22.1[/C][C]18.1893959367582[/C][C]3.91060406324184[/C][/ROW]
[ROW][C]48[/C][C]23.7[/C][C]18.5583538487227[/C][C]5.14164615127728[/C][/ROW]
[ROW][C]49[/C][C]22.2[/C][C]17.8752015652002[/C][C]4.32479843479983[/C][/ROW]
[ROW][C]50[/C][C]19.5[/C][C]17.2165470040577[/C][C]2.28345299594234[/C][/ROW]
[ROW][C]51[/C][C]16.6[/C][C]17.4831712706498[/C][C]-0.883171270649777[/C][/ROW]
[ROW][C]52[/C][C]17.3[/C][C]18.5050156481104[/C][C]-1.20501564811039[/C][/ROW]
[ROW][C]53[/C][C]19.8[/C][C]19.9923834872615[/C][C]-0.192383487261536[/C][/ROW]
[ROW][C]54[/C][C]21.2[/C][C]20.7058015888106[/C][C]0.494198411189396[/C][/ROW]
[ROW][C]55[/C][C]21.5[/C][C]21.0992572231552[/C][C]0.400742776844786[/C][/ROW]
[ROW][C]56[/C][C]20.6[/C][C]20.4161049396327[/C][C]0.183895060367345[/C][/ROW]
[ROW][C]57[/C][C]19.1[/C][C]20.0356108363752[/C][C]-0.935610836375183[/C][/ROW]
[ROW][C]58[/C][C]19.6[/C][C]20.2777373805873[/C][C]-0.677737380587251[/C][/ROW]
[ROW][C]59[/C][C]23.5[/C][C]21.1424845722689[/C][C]2.35751542773114[/C][/ROW]
[ROW][C]60[/C][C]24[/C][C]21.2750840356678[/C][C]2.72491596433219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.622.04184033782923.55815966217077
223.721.11656151009462.58343848990541
32220.58473831670461.41526168329543
421.320.92343041064260.37656958935738
520.721.5460510581122-0.846051058112162
620.421.8905112476967-1.49051124769667
720.321.5215533357321-1.22155333573211
820.420.7721013205101-0.372101320510080
919.820.8023671385366-1.00236713853659
1019.520.4636750445985-0.963675044598535
1123.121.11656151009461.98343848990542
1223.521.27365869587362.22634130412641
1323.521.02576405601512.47423594398494
1422.921.05026177839511.84973822160489
1521.920.80236713853661.09763286146341
1621.521.08629569206810.413704307931924
1720.521.5215533357321-1.02155333573211
1820.221.9568109793961-1.75681097939614
1919.422.1384058875552-2.73840588755519
2019.222.0173426154492-2.81734261544916
2118.821.5215533357321-2.72155333573211
2218.820.9594643243156-2.15946432431558
2322.620.33684367684602.26315632315396
2423.319.93185185120853.36814814879148
252320.05291512331462.94708487668545
2621.420.11344675936761.28655324063243
2719.920.2402781271201-0.340278127120063
2818.820.6452699527576-1.84526995275758
2918.620.9839620466956-2.38396204669564
3018.420.9536962286691-2.55369622866913
3118.620.7360674068371-2.13606740683711
3219.920.7360674068371-0.836067406837113
3319.220.2345100314736-1.03451003147361
3418.419.3639947441455-0.96399474414554
3521.119.36399474414551.73600525585446
3620.518.95900291850801.54099708149198
3719.118.46321363879100.636786361209036
3818.118.6563447382429-0.556344738242929
391718.4444840120574-1.44448401205737
4017.118.8192100196684-1.71921001966838
4117.419.1334043912264-1.73340439122639
4216.818.8855097513679-2.08550975136786
4315.317.9904967416597-2.69049674165974
4414.317.6460365520752-3.34603655207523
4513.416.6904919063141-3.2904919063141
4615.316.9138888237926-1.61388882379257
4722.118.18939593675823.91060406324184
4823.718.55835384872275.14164615127728
4922.217.87520156520024.32479843479983
5019.517.21654700405772.28345299594234
5116.617.4831712706498-0.883171270649777
5217.318.5050156481104-1.20501564811039
5319.819.9923834872615-0.192383487261536
5421.220.70580158881060.494198411189396
5521.521.09925722315520.400742776844786
5620.620.41610493963270.183895060367345
5719.120.0356108363752-0.935610836375183
5819.620.2777373805873-0.677737380587251
5923.521.14248457226892.35751542773114
602421.27508403566782.72491596433219






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.7385684764984450.522863047003110.261431523501555
70.6478084789168790.7043830421662430.352191521083121
80.5295809955847780.9408380088304440.470419004415222
90.4486380930972590.8972761861945170.551361906902741
100.3363092495174220.6726184990348440.663690750482578
110.3369642489485030.6739284978970060.663035751051497
120.324397800284260.648795600568520.67560219971574
130.3093552634782110.6187105269564220.690644736521789
140.2698246202153460.5396492404306920.730175379784654
150.2051302042352690.4102604084705370.794869795764731
160.1485954508144430.2971909016288860.851404549185557
170.1354920834338730.2709841668677470.864507916566126
180.1329304574895680.2658609149791360.867069542510432
190.1574128768406850.3148257536813690.842587123159315
200.1658482526854230.3316965053708460.834151747314577
210.1897158084255850.379431616851170.810284191574415
220.2235132277506340.4470264555012670.776486772249366
230.1867698711705870.3735397423411740.813230128829413
240.1839347889593140.3678695779186290.816065211040686
250.1685691537999620.3371383075999240.831430846200038
260.1416796210130230.2833592420260470.858320378986977
270.1318902843596230.2637805687192450.868109715640377
280.154282412863240.308564825726480.84571758713676
290.1863280423468730.3726560846937460.813671957653127
300.2372802311862360.4745604623724720.762719768813764
310.2803329017362350.5606658034724710.719667098263765
320.2585331664952060.5170663329904130.741466833504794
330.2306834863187000.4613669726373990.7693165136813
340.2015357777186290.4030715554372570.798464222281371
350.1777808231558050.355561646311610.822219176844195
360.1668193656345780.3336387312691570.833180634365422
370.1695132094522440.3390264189044890.830486790547756
380.1621411140916930.3242822281833860.837858885908307
390.1677006526897190.3354013053794370.832299347310281
400.1651795356850070.3303590713700140.834820464314993
410.1856678766549850.3713357533099710.814332123345014
420.2240019967080770.4480039934161540.775998003291923
430.2707008915954400.5414017831908810.729299108404559
440.3379290372628940.6758580745257880.662070962737106
450.4076685225179070.8153370450358150.592331477482093
460.5405201229235090.9189597541529820.459479877076491
470.6129353706574370.7741292586851270.387064629342563
480.7382836065638060.5234327868723870.261716393436194
490.880611336650470.2387773266990610.119388663349530
500.9784743124716650.04305137505667060.0215256875283353
510.9790688803687680.04186223926246470.0209311196312323
520.9861592440643150.02768151187136990.0138407559356850
530.9813140300492330.03737193990153320.0186859699507666
540.9471283991090840.1057432017818310.0528716008909157

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.738568476498445 & 0.52286304700311 & 0.261431523501555 \tabularnewline
7 & 0.647808478916879 & 0.704383042166243 & 0.352191521083121 \tabularnewline
8 & 0.529580995584778 & 0.940838008830444 & 0.470419004415222 \tabularnewline
9 & 0.448638093097259 & 0.897276186194517 & 0.551361906902741 \tabularnewline
10 & 0.336309249517422 & 0.672618499034844 & 0.663690750482578 \tabularnewline
11 & 0.336964248948503 & 0.673928497897006 & 0.663035751051497 \tabularnewline
12 & 0.32439780028426 & 0.64879560056852 & 0.67560219971574 \tabularnewline
13 & 0.309355263478211 & 0.618710526956422 & 0.690644736521789 \tabularnewline
14 & 0.269824620215346 & 0.539649240430692 & 0.730175379784654 \tabularnewline
15 & 0.205130204235269 & 0.410260408470537 & 0.794869795764731 \tabularnewline
16 & 0.148595450814443 & 0.297190901628886 & 0.851404549185557 \tabularnewline
17 & 0.135492083433873 & 0.270984166867747 & 0.864507916566126 \tabularnewline
18 & 0.132930457489568 & 0.265860914979136 & 0.867069542510432 \tabularnewline
19 & 0.157412876840685 & 0.314825753681369 & 0.842587123159315 \tabularnewline
20 & 0.165848252685423 & 0.331696505370846 & 0.834151747314577 \tabularnewline
21 & 0.189715808425585 & 0.37943161685117 & 0.810284191574415 \tabularnewline
22 & 0.223513227750634 & 0.447026455501267 & 0.776486772249366 \tabularnewline
23 & 0.186769871170587 & 0.373539742341174 & 0.813230128829413 \tabularnewline
24 & 0.183934788959314 & 0.367869577918629 & 0.816065211040686 \tabularnewline
25 & 0.168569153799962 & 0.337138307599924 & 0.831430846200038 \tabularnewline
26 & 0.141679621013023 & 0.283359242026047 & 0.858320378986977 \tabularnewline
27 & 0.131890284359623 & 0.263780568719245 & 0.868109715640377 \tabularnewline
28 & 0.15428241286324 & 0.30856482572648 & 0.84571758713676 \tabularnewline
29 & 0.186328042346873 & 0.372656084693746 & 0.813671957653127 \tabularnewline
30 & 0.237280231186236 & 0.474560462372472 & 0.762719768813764 \tabularnewline
31 & 0.280332901736235 & 0.560665803472471 & 0.719667098263765 \tabularnewline
32 & 0.258533166495206 & 0.517066332990413 & 0.741466833504794 \tabularnewline
33 & 0.230683486318700 & 0.461366972637399 & 0.7693165136813 \tabularnewline
34 & 0.201535777718629 & 0.403071555437257 & 0.798464222281371 \tabularnewline
35 & 0.177780823155805 & 0.35556164631161 & 0.822219176844195 \tabularnewline
36 & 0.166819365634578 & 0.333638731269157 & 0.833180634365422 \tabularnewline
37 & 0.169513209452244 & 0.339026418904489 & 0.830486790547756 \tabularnewline
38 & 0.162141114091693 & 0.324282228183386 & 0.837858885908307 \tabularnewline
39 & 0.167700652689719 & 0.335401305379437 & 0.832299347310281 \tabularnewline
40 & 0.165179535685007 & 0.330359071370014 & 0.834820464314993 \tabularnewline
41 & 0.185667876654985 & 0.371335753309971 & 0.814332123345014 \tabularnewline
42 & 0.224001996708077 & 0.448003993416154 & 0.775998003291923 \tabularnewline
43 & 0.270700891595440 & 0.541401783190881 & 0.729299108404559 \tabularnewline
44 & 0.337929037262894 & 0.675858074525788 & 0.662070962737106 \tabularnewline
45 & 0.407668522517907 & 0.815337045035815 & 0.592331477482093 \tabularnewline
46 & 0.540520122923509 & 0.918959754152982 & 0.459479877076491 \tabularnewline
47 & 0.612935370657437 & 0.774129258685127 & 0.387064629342563 \tabularnewline
48 & 0.738283606563806 & 0.523432786872387 & 0.261716393436194 \tabularnewline
49 & 0.88061133665047 & 0.238777326699061 & 0.119388663349530 \tabularnewline
50 & 0.978474312471665 & 0.0430513750566706 & 0.0215256875283353 \tabularnewline
51 & 0.979068880368768 & 0.0418622392624647 & 0.0209311196312323 \tabularnewline
52 & 0.986159244064315 & 0.0276815118713699 & 0.0138407559356850 \tabularnewline
53 & 0.981314030049233 & 0.0373719399015332 & 0.0186859699507666 \tabularnewline
54 & 0.947128399109084 & 0.105743201781831 & 0.0528716008909157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&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]6[/C][C]0.738568476498445[/C][C]0.52286304700311[/C][C]0.261431523501555[/C][/ROW]
[ROW][C]7[/C][C]0.647808478916879[/C][C]0.704383042166243[/C][C]0.352191521083121[/C][/ROW]
[ROW][C]8[/C][C]0.529580995584778[/C][C]0.940838008830444[/C][C]0.470419004415222[/C][/ROW]
[ROW][C]9[/C][C]0.448638093097259[/C][C]0.897276186194517[/C][C]0.551361906902741[/C][/ROW]
[ROW][C]10[/C][C]0.336309249517422[/C][C]0.672618499034844[/C][C]0.663690750482578[/C][/ROW]
[ROW][C]11[/C][C]0.336964248948503[/C][C]0.673928497897006[/C][C]0.663035751051497[/C][/ROW]
[ROW][C]12[/C][C]0.32439780028426[/C][C]0.64879560056852[/C][C]0.67560219971574[/C][/ROW]
[ROW][C]13[/C][C]0.309355263478211[/C][C]0.618710526956422[/C][C]0.690644736521789[/C][/ROW]
[ROW][C]14[/C][C]0.269824620215346[/C][C]0.539649240430692[/C][C]0.730175379784654[/C][/ROW]
[ROW][C]15[/C][C]0.205130204235269[/C][C]0.410260408470537[/C][C]0.794869795764731[/C][/ROW]
[ROW][C]16[/C][C]0.148595450814443[/C][C]0.297190901628886[/C][C]0.851404549185557[/C][/ROW]
[ROW][C]17[/C][C]0.135492083433873[/C][C]0.270984166867747[/C][C]0.864507916566126[/C][/ROW]
[ROW][C]18[/C][C]0.132930457489568[/C][C]0.265860914979136[/C][C]0.867069542510432[/C][/ROW]
[ROW][C]19[/C][C]0.157412876840685[/C][C]0.314825753681369[/C][C]0.842587123159315[/C][/ROW]
[ROW][C]20[/C][C]0.165848252685423[/C][C]0.331696505370846[/C][C]0.834151747314577[/C][/ROW]
[ROW][C]21[/C][C]0.189715808425585[/C][C]0.37943161685117[/C][C]0.810284191574415[/C][/ROW]
[ROW][C]22[/C][C]0.223513227750634[/C][C]0.447026455501267[/C][C]0.776486772249366[/C][/ROW]
[ROW][C]23[/C][C]0.186769871170587[/C][C]0.373539742341174[/C][C]0.813230128829413[/C][/ROW]
[ROW][C]24[/C][C]0.183934788959314[/C][C]0.367869577918629[/C][C]0.816065211040686[/C][/ROW]
[ROW][C]25[/C][C]0.168569153799962[/C][C]0.337138307599924[/C][C]0.831430846200038[/C][/ROW]
[ROW][C]26[/C][C]0.141679621013023[/C][C]0.283359242026047[/C][C]0.858320378986977[/C][/ROW]
[ROW][C]27[/C][C]0.131890284359623[/C][C]0.263780568719245[/C][C]0.868109715640377[/C][/ROW]
[ROW][C]28[/C][C]0.15428241286324[/C][C]0.30856482572648[/C][C]0.84571758713676[/C][/ROW]
[ROW][C]29[/C][C]0.186328042346873[/C][C]0.372656084693746[/C][C]0.813671957653127[/C][/ROW]
[ROW][C]30[/C][C]0.237280231186236[/C][C]0.474560462372472[/C][C]0.762719768813764[/C][/ROW]
[ROW][C]31[/C][C]0.280332901736235[/C][C]0.560665803472471[/C][C]0.719667098263765[/C][/ROW]
[ROW][C]32[/C][C]0.258533166495206[/C][C]0.517066332990413[/C][C]0.741466833504794[/C][/ROW]
[ROW][C]33[/C][C]0.230683486318700[/C][C]0.461366972637399[/C][C]0.7693165136813[/C][/ROW]
[ROW][C]34[/C][C]0.201535777718629[/C][C]0.403071555437257[/C][C]0.798464222281371[/C][/ROW]
[ROW][C]35[/C][C]0.177780823155805[/C][C]0.35556164631161[/C][C]0.822219176844195[/C][/ROW]
[ROW][C]36[/C][C]0.166819365634578[/C][C]0.333638731269157[/C][C]0.833180634365422[/C][/ROW]
[ROW][C]37[/C][C]0.169513209452244[/C][C]0.339026418904489[/C][C]0.830486790547756[/C][/ROW]
[ROW][C]38[/C][C]0.162141114091693[/C][C]0.324282228183386[/C][C]0.837858885908307[/C][/ROW]
[ROW][C]39[/C][C]0.167700652689719[/C][C]0.335401305379437[/C][C]0.832299347310281[/C][/ROW]
[ROW][C]40[/C][C]0.165179535685007[/C][C]0.330359071370014[/C][C]0.834820464314993[/C][/ROW]
[ROW][C]41[/C][C]0.185667876654985[/C][C]0.371335753309971[/C][C]0.814332123345014[/C][/ROW]
[ROW][C]42[/C][C]0.224001996708077[/C][C]0.448003993416154[/C][C]0.775998003291923[/C][/ROW]
[ROW][C]43[/C][C]0.270700891595440[/C][C]0.541401783190881[/C][C]0.729299108404559[/C][/ROW]
[ROW][C]44[/C][C]0.337929037262894[/C][C]0.675858074525788[/C][C]0.662070962737106[/C][/ROW]
[ROW][C]45[/C][C]0.407668522517907[/C][C]0.815337045035815[/C][C]0.592331477482093[/C][/ROW]
[ROW][C]46[/C][C]0.540520122923509[/C][C]0.918959754152982[/C][C]0.459479877076491[/C][/ROW]
[ROW][C]47[/C][C]0.612935370657437[/C][C]0.774129258685127[/C][C]0.387064629342563[/C][/ROW]
[ROW][C]48[/C][C]0.738283606563806[/C][C]0.523432786872387[/C][C]0.261716393436194[/C][/ROW]
[ROW][C]49[/C][C]0.88061133665047[/C][C]0.238777326699061[/C][C]0.119388663349530[/C][/ROW]
[ROW][C]50[/C][C]0.978474312471665[/C][C]0.0430513750566706[/C][C]0.0215256875283353[/C][/ROW]
[ROW][C]51[/C][C]0.979068880368768[/C][C]0.0418622392624647[/C][C]0.0209311196312323[/C][/ROW]
[ROW][C]52[/C][C]0.986159244064315[/C][C]0.0276815118713699[/C][C]0.0138407559356850[/C][/ROW]
[ROW][C]53[/C][C]0.981314030049233[/C][C]0.0373719399015332[/C][C]0.0186859699507666[/C][/ROW]
[ROW][C]54[/C][C]0.947128399109084[/C][C]0.105743201781831[/C][C]0.0528716008909157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.7385684764984450.522863047003110.261431523501555
70.6478084789168790.7043830421662430.352191521083121
80.5295809955847780.9408380088304440.470419004415222
90.4486380930972590.8972761861945170.551361906902741
100.3363092495174220.6726184990348440.663690750482578
110.3369642489485030.6739284978970060.663035751051497
120.324397800284260.648795600568520.67560219971574
130.3093552634782110.6187105269564220.690644736521789
140.2698246202153460.5396492404306920.730175379784654
150.2051302042352690.4102604084705370.794869795764731
160.1485954508144430.2971909016288860.851404549185557
170.1354920834338730.2709841668677470.864507916566126
180.1329304574895680.2658609149791360.867069542510432
190.1574128768406850.3148257536813690.842587123159315
200.1658482526854230.3316965053708460.834151747314577
210.1897158084255850.379431616851170.810284191574415
220.2235132277506340.4470264555012670.776486772249366
230.1867698711705870.3735397423411740.813230128829413
240.1839347889593140.3678695779186290.816065211040686
250.1685691537999620.3371383075999240.831430846200038
260.1416796210130230.2833592420260470.858320378986977
270.1318902843596230.2637805687192450.868109715640377
280.154282412863240.308564825726480.84571758713676
290.1863280423468730.3726560846937460.813671957653127
300.2372802311862360.4745604623724720.762719768813764
310.2803329017362350.5606658034724710.719667098263765
320.2585331664952060.5170663329904130.741466833504794
330.2306834863187000.4613669726373990.7693165136813
340.2015357777186290.4030715554372570.798464222281371
350.1777808231558050.355561646311610.822219176844195
360.1668193656345780.3336387312691570.833180634365422
370.1695132094522440.3390264189044890.830486790547756
380.1621411140916930.3242822281833860.837858885908307
390.1677006526897190.3354013053794370.832299347310281
400.1651795356850070.3303590713700140.834820464314993
410.1856678766549850.3713357533099710.814332123345014
420.2240019967080770.4480039934161540.775998003291923
430.2707008915954400.5414017831908810.729299108404559
440.3379290372628940.6758580745257880.662070962737106
450.4076685225179070.8153370450358150.592331477482093
460.5405201229235090.9189597541529820.459479877076491
470.6129353706574370.7741292586851270.387064629342563
480.7382836065638060.5234327868723870.261716393436194
490.880611336650470.2387773266990610.119388663349530
500.9784743124716650.04305137505667060.0215256875283353
510.9790688803687680.04186223926246470.0209311196312323
520.9861592440643150.02768151187136990.0138407559356850
530.9813140300492330.03737193990153320.0186859699507666
540.9471283991090840.1057432017818310.0528716008909157






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0816326530612245NOK
10% type I error level40.0816326530612245OK

\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 & 4 & 0.0816326530612245 & NOK \tabularnewline
10% type I error level & 4 & 0.0816326530612245 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68760&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]4[/C][C]0.0816326530612245[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0816326530612245[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68760&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68760&T=6

As an alternative you can also use a QR Code:  
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 level40.0816326530612245NOK
10% type I error level40.0816326530612245OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}