FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationWed, 01 Dec 2010 17:31:46 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/01/t1291224596nni4yngi7iif5la.htm/, Retrieved Sun, 05 May 2024 14:51:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104131, Retrieved Sun, 05 May 2024 14:51:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [multiple regressi...] [2010-12-01 16:37:19] [96348ef82925ade81ab3c243141d80f1]
-    D      [Multiple Regression] [multiple regressi...] [2010-12-01 17:31:46] [03bcd8c83ef1a42b4029a16ba47a4880] [Current]
-             [Multiple Regression] [] [2010-12-03 15:31:57] [30b3e197115d238a51c18bcedc33a6a5]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	13	14	13	3
1	12	12	8	13	5
0	15	10	12	16	6
3	12	9	7	12	6
3	10	10	10	11	5
1	12	12	7	12	3
3	15	13	16	18	8
1	9	12	11	11	4
4	12	12	14	14	4
0	11	6	6	9	4
3	11	5	16	14	6
2	11	12	11	12	6
4	15	11	16	11	5
3	7	14	12	12	4
1	11	14	7	13	6
1	11	12	13	11	4
2	10	12	11	12	6
3	14	11	15	16	6
1	10	11	7	9	4
1	6	7	9	11	4
2	11	9	7	13	2
3	15	11	14	15	7
4	11	11	15	10	5
2	12	12	7	11	4
1	14	12	15	13	6
2	15	11	17	16	6
2	9	11	15	15	7
4	13	8	14	14	5
2	13	9	14	14	6
3	16	12	8	14	4
3	13	10	8	8	4
3	12	10	14	13	7
4	14	12	14	15	7
2	11	8	8	13	4
2	9	12	11	11	4
4	16	11	16	15	6
3	12	12	10	15	6
4	10	7	8	9	5
2	13	11	14	13	6
5	16	11	16	16	7
3	14	12	13	13	6
1	15	9	5	11	3
1	5	15	8	12	3
1	8	11	10	12	4
2	11	11	8	12	6
3	16	11	13	14	7
9	17	11	15	14	5
0	9	15	6	8	4
0	9	11	12	13	5
2	13	12	16	16	6
2	10	12	5	13	6
3	6	9	15	11	6
1	12	12	12	14	5
2	8	12	8	13	4
0	14	13	13	13	5
5	12	11	14	13	5
2	11	9	12	12	4
4	16	9	16	16	6
3	8	11	10	15	2
0	15	11	15	15	8
0	7	12	8	12	3
4	16	12	16	14	6
1	14	9	19	12	6
1	16	11	14	15	6
4	9	9	6	12	5
2	14	12	13	13	5
4	11	12	15	12	6
1	13	12	7	12	5
4	15	12	13	13	6
2	5	14	4	5	2
5	15	11	14	13	5
4	13	12	13	13	5
4	11	11	11	14	5
4	11	6	14	17	6
4	12	10	12	13	6
3	12	12	15	13	6
3	12	13	14	12	5
3	12	8	13	13	5
2	14	12	8	14	4
1	6	12	6	11	2
1	7	12	7	12	4
5	14	6	13	12	6
4	14	11	13	16	6
2	10	10	11	12	5
3	13	12	5	12	3
2	12	13	12	12	6
2	9	11	8	10	4
2	12	7	11	15	5
2	16	11	14	15	8
3	10	11	9	12	4
2	14	11	10	16	6
3	10	11	13	15	6
4	16	12	16	16	7
3	15	10	16	13	6
3	12	11	11	12	5
0	10	12	8	11	4
1	8	7	4	13	6
2	8	13	7	10	3
2	11	8	14	15	5
3	13	12	11	13	6
4	16	11	17	16	7
4	16	12	15	15	7
1	14	14	17	18	6
2	11	10	5	13	3
2	4	10	4	10	2
3	14	13	10	16	8
3	9	10	11	13	3
3	14	11	15	15	8
1	8	10	10	14	3
1	8	7	9	15	4
1	11	10	12	14	5
1	12	8	15	13	7
0	11	12	7	13	6
1	14	12	13	15	6
3	15	12	12	16	7
3	16	11	14	14	6
0	16	12	14	14	6
2	11	12	8	16	6
5	14	12	15	14	6
2	14	11	12	12	4
3	12	12	12	13	4
3	14	11	16	12	5
5	8	11	9	12	4
4	13	13	15	14	6
4	16	12	15	14	6
0	12	12	6	14	5
3	16	12	14	16	8
0	12	12	15	13	6
2	11	8	10	14	5
0	4	8	6	4	4
6	16	12	14	16	8
3	15	11	12	13	6
1	10	12	8	16	4
6	13	13	11	15	6
2	15	12	13	14	6
1	12	12	9	13	4
3	14	11	15	14	6
1	7	12	13	12	3
2	19	12	15	15	6
4	12	10	14	14	5
1	12	11	16	13	4
2	13	12	14	14	6
0	15	12	14	16	4
5	8	10	10	6	4
2	12	12	10	13	4
1	10	13	4	13	6
1	8	12	8	14	5
4	10	15	15	15	6
3	15	11	16	14	6
0	16	12	12	15	8
3	13	11	12	13	7
3	16	12	15	16	7
0	9	11	9	12	4
2	14	10	12	15	6
5	14	11	14	12	6
2	12	11	11	14	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
aantalVrienden[t] = + 1.23744674639409 + 0.0963613878771712Popularity[t] -0.0644187764141667FindingFriends[t] + 0.128803686232339KnowingPeople[t] -0.0747737175493632Liked[t] + 0.0387444743481176`Celebrity `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
aantalVrienden[t] =  +  1.23744674639409 +  0.0963613878771712Popularity[t] -0.0644187764141667FindingFriends[t] +  0.128803686232339KnowingPeople[t] -0.0747737175493632Liked[t] +  0.0387444743481176`Celebrity
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]aantalVrienden[t] =  +  1.23744674639409 +  0.0963613878771712Popularity[t] -0.0644187764141667FindingFriends[t] +  0.128803686232339KnowingPeople[t] -0.0747737175493632Liked[t] +  0.0387444743481176`Celebrity
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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
aantalVrienden[t] = + 1.23744674639409 + 0.0963613878771712Popularity[t] -0.0644187764141667FindingFriends[t] + 0.128803686232339KnowingPeople[t] -0.0747737175493632Liked[t] + 0.0387444743481176`Celebrity `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.237446746394090.9528711.29870.1960560.098028
Popularity0.09636138787717120.0544041.77120.0785540.039277
FindingFriends-0.06441877641416670.064356-1.0010.3184510.159225
KnowingPeople0.1288036862323390.0431212.9870.0032910.001646
Liked-0.07477371754936320.067234-1.11210.2678560.133928
`Celebrity `0.03874447434811760.1097490.3530.7245630.362281

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.23744674639409 & 0.952871 & 1.2987 & 0.196056 & 0.098028 \tabularnewline
Popularity & 0.0963613878771712 & 0.054404 & 1.7712 & 0.078554 & 0.039277 \tabularnewline
FindingFriends & -0.0644187764141667 & 0.064356 & -1.001 & 0.318451 & 0.159225 \tabularnewline
KnowingPeople & 0.128803686232339 & 0.043121 & 2.987 & 0.003291 & 0.001646 \tabularnewline
Liked & -0.0747737175493632 & 0.067234 & -1.1121 & 0.267856 & 0.133928 \tabularnewline
`Celebrity
` & 0.0387444743481176 & 0.109749 & 0.353 & 0.724563 & 0.362281 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.23744674639409[/C][C]0.952871[/C][C]1.2987[/C][C]0.196056[/C][C]0.098028[/C][/ROW]
[ROW][C]Popularity[/C][C]0.0963613878771712[/C][C]0.054404[/C][C]1.7712[/C][C]0.078554[/C][C]0.039277[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0644187764141667[/C][C]0.064356[/C][C]-1.001[/C][C]0.318451[/C][C]0.159225[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.128803686232339[/C][C]0.043121[/C][C]2.987[/C][C]0.003291[/C][C]0.001646[/C][/ROW]
[ROW][C]Liked[/C][C]-0.0747737175493632[/C][C]0.067234[/C][C]-1.1121[/C][C]0.267856[/C][C]0.133928[/C][/ROW]
[ROW][C]`Celebrity
`[/C][C]0.0387444743481176[/C][C]0.109749[/C][C]0.353[/C][C]0.724563[/C][C]0.362281[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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)1.237446746394090.9528711.29870.1960560.098028
Popularity0.09636138787717120.0544041.77120.0785540.039277
FindingFriends-0.06441877641416670.064356-1.0010.3184510.159225
KnowingPeople0.1288036862323390.0431212.9870.0032910.001646
Liked-0.07477371754936320.067234-1.11210.2678560.133928
`Celebrity `0.03874447434811760.1097490.3530.7245630.362281






Multiple Linear Regression - Regression Statistics
Multiple R0.404565543716048
R-squared0.163673279162262
Adjusted R-squared0.135795721801004
F-TEST (value)5.8711485027638
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value5.51936533799147e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.40758944238227
Sum Squared Residuals297.196205745903

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.404565543716048 \tabularnewline
R-squared & 0.163673279162262 \tabularnewline
Adjusted R-squared & 0.135795721801004 \tabularnewline
F-TEST (value) & 5.8711485027638 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 5.51936533799147e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.40758944238227 \tabularnewline
Sum Squared Residuals & 297.196205745903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.404565543716048[/C][/ROW]
[ROW][C]R-squared[/C][C]0.163673279162262[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.135795721801004[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.8711485027638[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]5.51936533799147e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.40758944238227[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]297.196205745903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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 R0.404565543716048
R-squared0.163673279162262
Adjusted R-squared0.135795721801004
F-TEST (value)5.8711485027638
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value5.51936533799147e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.40758944238227
Sum Squared Residuals297.196205745903






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
122.60012739756854-0.600127397568545
211.87285161740772-0.872851617407716
302.62041140049695-2.62041140049695
432.050822452315360.94917754768464
532.216121202045110.783878797954886
611.74133270002851-0.741332700028511
732.870311329781310.129688670218689
812.08098147322383-1.08098147322383
942.532155542904271.46784445709573
1002.16574591140021-2.16574591140021
1133.22182191108718-0.221821911087177
1222.27641948012504-0.276419480125044
1343.406331482410830.593668517589166
1431.813451113324131.18654888667587
1511.55759346481799-0.557593464817993
1612.53131162144285-1.53131162144285
1722.18005809224787-0.180058092247873
1832.846042294902630.153957705097374
1911.87609432768454-0.87609432768454
2011.85638381919847-0.856383819198472
2121.724709449496360.275290550503643
2232.927118188444940.0728818115550605
2342.966855962219171.03314403778083
2421.854850891925990.145149108074011
2513.00594467113655-2.00594467113655
2623.20001105524447-1.20001105524447
2722.47775354741425-0.47775354741425
2842.924936510786231.07506348921377
2922.89926220872018-0.899262208720176
3032.144778977018920.855221022981077
3132.433174671511920.566825328488078
3232.852000236326320.147999763673682
3342.76633802415361.23366197584640
3421.995420860839100.00457913916090297
3522.08098147322383-0.0809814732238296
3643.242342474438670.75765752556133
3732.019656029121790.980343970878213
3842.301317593921661.69868240607834
3922.84519837344121-0.845198373441206
4053.206313231237421.79368676876258
4132.748337298671870.251662701328128
4212.04083953798721-1.04083953798721
4311.00235034187815-0.00235034187814842
4411.84546145797912-0.845461457979123
4521.954427197842190.0455728021578049
4632.969449607639140.0305503923608648
4793.245929419284755.75407058071525
4801.46802786546773-1.46802786546773
4902.16340097511973-2.16340097511973
5022.81406581684363-0.814065816843627
5121.332462257304480.667537742695522
5232.577857332460400.422142667539595
5312.31329264478771-1.31329264478771
5421.448661591550920.551338408449084
5502.64517404790959-2.64517404790959
5652.710092511215922.28990748878408
5722.52099054690365-0.520990546903648
5843.296406309717640.70359369028236
5931.543651356634801.45634864336520
6003.09466634902540-3.09466634902540
6101.38832944687499-1.38832944687499
6243.252697415573870.747302584426133
6313.78918946285777-2.78918946285777
6412.98473510197399-1.98473510197399
6541.594190128103392.40580987189661
6622.70959282432375-0.709592824323754
6742.79163422505441.20836577494560
6811.91518303660192-0.915183036601915
6942.844698686549041.15530131345096
7021.036225921860390.963774078139615
7152.999176674847432.00082332515257
7242.613231436446581.38676856355342
7342.152546347092371.84745365290763
7442.675474609560241.32452539043976
7542.555648389513521.44435161048648
7632.813221895382210.186778104617794
7732.656028675936950.343971324063053
7832.774545154226080.225454845773921
7921.952056201264580.0479437987354192
8011.07038992973439-0.0703899297343883
8111.29827023499077-0.29827023499077
8253.209623674706241.79037632529376
8342.588434922437951.41156507756205
8422.27015117072809-0.270151170728089
8531.580086715441001.41991328455900
8622.43716577782039-0.437165777820387
8721.833762908490340.166237091509656
8822.43180912307684-0.431809123076842
8923.06222405067023-1.06222405067023
9031.909380547501131.09061945249887
9122.20202386374093-0.202023863740933
9232.277763088478630.722236911521373
9343.141894454823260.858105545176742
9433.35994729807439-0.359947298074392
9532.398455170068260.601544829931735
9601.79093180240399-1.79093180240399
9711.33302967738863-0.333029677388631
9821.441015807204380.558984192795617
9922.65744001748252-0.65744001748252
10032.394368538330020.605631461669977
10143.335116917469760.664883082530237
10243.087864486140280.912135513859718
10312.76084590302608-1.76084590302608
10421.441427774965630.558572225034369
10520.8236710518930651.17632894810694
10632.150675259608830.849324740391165
10732.021527116605320.97847288339468
10832.998304961148220.00169503885177585
10911.72158832494645-0.721588324946446
11011.75001172475536-0.750011724755362
11112.34576880973887-1.34576880973887
11213.10964147538699-2.10964147538699
11301.68643101764633-1.68643101764633
11412.59878986357315-1.59878986357315
11532.530318322016730.469681677983268
11633.05950881952336-0.0595088195233563
11702.99509004310919-2.99509004310919
11821.590913551230580.409086448769424
11952.931170953587192.06882904641281
12022.68123715770683-0.681237157706828
12132.349321887988960.650678112011044
12233.2351963769843-0.235196376984300
12351.716657771746783.28334222825322
12442.770390789295851.22960921070415
12543.123893729341530.876106270658472
12601.54047052739368-1.54047052739368
12732.92303155670670.0769684432933017
12802.81322189538221-2.81322189538221
12922.21699899010253-0.216998990102528
13001.73624723117849-1.73624723117849
13162.92303155670673.0769684432933
13232.780313776730870.219686223269129
13311.41706321465717-0.41706321465717
13462.180402326817133.81959767318287
13522.76992496899968-0.76992496899968
13611.96291082929194-0.96291082929194
13732.995589730001350.00441026999864769
13812.03234787803668-1.03234787803668
13923.33820417542368-1.33820417542368
14042.699737570080721.30026242991928
14112.92895540933248-1.92895540933248
14222.70600587947768-0.706005879477676
14302.67169227143706-2.67169227143706
14452.358522539689472.64147746031053
14522.09171451552428-0.0917145155242786
14611.13923979465797-0.139239794657973
14711.41263234834967-0.412632348349671
14842.277695355286641.72230464471336
14933.22075480411086-0.220754804110862
15002.74019790179138-2.74019790179138
15132.626335475324650.373664524675354
15233.01309076859092-0.0130907685909193
15301.81301915962396-1.81301915962396
15422.59882373016914-0.598823730169140
15553.016333478867741.98366652113226
15622.13267431192519-0.132674311925186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 2.60012739756854 & -0.600127397568545 \tabularnewline
2 & 1 & 1.87285161740772 & -0.872851617407716 \tabularnewline
3 & 0 & 2.62041140049695 & -2.62041140049695 \tabularnewline
4 & 3 & 2.05082245231536 & 0.94917754768464 \tabularnewline
5 & 3 & 2.21612120204511 & 0.783878797954886 \tabularnewline
6 & 1 & 1.74133270002851 & -0.741332700028511 \tabularnewline
7 & 3 & 2.87031132978131 & 0.129688670218689 \tabularnewline
8 & 1 & 2.08098147322383 & -1.08098147322383 \tabularnewline
9 & 4 & 2.53215554290427 & 1.46784445709573 \tabularnewline
10 & 0 & 2.16574591140021 & -2.16574591140021 \tabularnewline
11 & 3 & 3.22182191108718 & -0.221821911087177 \tabularnewline
12 & 2 & 2.27641948012504 & -0.276419480125044 \tabularnewline
13 & 4 & 3.40633148241083 & 0.593668517589166 \tabularnewline
14 & 3 & 1.81345111332413 & 1.18654888667587 \tabularnewline
15 & 1 & 1.55759346481799 & -0.557593464817993 \tabularnewline
16 & 1 & 2.53131162144285 & -1.53131162144285 \tabularnewline
17 & 2 & 2.18005809224787 & -0.180058092247873 \tabularnewline
18 & 3 & 2.84604229490263 & 0.153957705097374 \tabularnewline
19 & 1 & 1.87609432768454 & -0.87609432768454 \tabularnewline
20 & 1 & 1.85638381919847 & -0.856383819198472 \tabularnewline
21 & 2 & 1.72470944949636 & 0.275290550503643 \tabularnewline
22 & 3 & 2.92711818844494 & 0.0728818115550605 \tabularnewline
23 & 4 & 2.96685596221917 & 1.03314403778083 \tabularnewline
24 & 2 & 1.85485089192599 & 0.145149108074011 \tabularnewline
25 & 1 & 3.00594467113655 & -2.00594467113655 \tabularnewline
26 & 2 & 3.20001105524447 & -1.20001105524447 \tabularnewline
27 & 2 & 2.47775354741425 & -0.47775354741425 \tabularnewline
28 & 4 & 2.92493651078623 & 1.07506348921377 \tabularnewline
29 & 2 & 2.89926220872018 & -0.899262208720176 \tabularnewline
30 & 3 & 2.14477897701892 & 0.855221022981077 \tabularnewline
31 & 3 & 2.43317467151192 & 0.566825328488078 \tabularnewline
32 & 3 & 2.85200023632632 & 0.147999763673682 \tabularnewline
33 & 4 & 2.7663380241536 & 1.23366197584640 \tabularnewline
34 & 2 & 1.99542086083910 & 0.00457913916090297 \tabularnewline
35 & 2 & 2.08098147322383 & -0.0809814732238296 \tabularnewline
36 & 4 & 3.24234247443867 & 0.75765752556133 \tabularnewline
37 & 3 & 2.01965602912179 & 0.980343970878213 \tabularnewline
38 & 4 & 2.30131759392166 & 1.69868240607834 \tabularnewline
39 & 2 & 2.84519837344121 & -0.845198373441206 \tabularnewline
40 & 5 & 3.20631323123742 & 1.79368676876258 \tabularnewline
41 & 3 & 2.74833729867187 & 0.251662701328128 \tabularnewline
42 & 1 & 2.04083953798721 & -1.04083953798721 \tabularnewline
43 & 1 & 1.00235034187815 & -0.00235034187814842 \tabularnewline
44 & 1 & 1.84546145797912 & -0.845461457979123 \tabularnewline
45 & 2 & 1.95442719784219 & 0.0455728021578049 \tabularnewline
46 & 3 & 2.96944960763914 & 0.0305503923608648 \tabularnewline
47 & 9 & 3.24592941928475 & 5.75407058071525 \tabularnewline
48 & 0 & 1.46802786546773 & -1.46802786546773 \tabularnewline
49 & 0 & 2.16340097511973 & -2.16340097511973 \tabularnewline
50 & 2 & 2.81406581684363 & -0.814065816843627 \tabularnewline
51 & 2 & 1.33246225730448 & 0.667537742695522 \tabularnewline
52 & 3 & 2.57785733246040 & 0.422142667539595 \tabularnewline
53 & 1 & 2.31329264478771 & -1.31329264478771 \tabularnewline
54 & 2 & 1.44866159155092 & 0.551338408449084 \tabularnewline
55 & 0 & 2.64517404790959 & -2.64517404790959 \tabularnewline
56 & 5 & 2.71009251121592 & 2.28990748878408 \tabularnewline
57 & 2 & 2.52099054690365 & -0.520990546903648 \tabularnewline
58 & 4 & 3.29640630971764 & 0.70359369028236 \tabularnewline
59 & 3 & 1.54365135663480 & 1.45634864336520 \tabularnewline
60 & 0 & 3.09466634902540 & -3.09466634902540 \tabularnewline
61 & 0 & 1.38832944687499 & -1.38832944687499 \tabularnewline
62 & 4 & 3.25269741557387 & 0.747302584426133 \tabularnewline
63 & 1 & 3.78918946285777 & -2.78918946285777 \tabularnewline
64 & 1 & 2.98473510197399 & -1.98473510197399 \tabularnewline
65 & 4 & 1.59419012810339 & 2.40580987189661 \tabularnewline
66 & 2 & 2.70959282432375 & -0.709592824323754 \tabularnewline
67 & 4 & 2.7916342250544 & 1.20836577494560 \tabularnewline
68 & 1 & 1.91518303660192 & -0.915183036601915 \tabularnewline
69 & 4 & 2.84469868654904 & 1.15530131345096 \tabularnewline
70 & 2 & 1.03622592186039 & 0.963774078139615 \tabularnewline
71 & 5 & 2.99917667484743 & 2.00082332515257 \tabularnewline
72 & 4 & 2.61323143644658 & 1.38676856355342 \tabularnewline
73 & 4 & 2.15254634709237 & 1.84745365290763 \tabularnewline
74 & 4 & 2.67547460956024 & 1.32452539043976 \tabularnewline
75 & 4 & 2.55564838951352 & 1.44435161048648 \tabularnewline
76 & 3 & 2.81322189538221 & 0.186778104617794 \tabularnewline
77 & 3 & 2.65602867593695 & 0.343971324063053 \tabularnewline
78 & 3 & 2.77454515422608 & 0.225454845773921 \tabularnewline
79 & 2 & 1.95205620126458 & 0.0479437987354192 \tabularnewline
80 & 1 & 1.07038992973439 & -0.0703899297343883 \tabularnewline
81 & 1 & 1.29827023499077 & -0.29827023499077 \tabularnewline
82 & 5 & 3.20962367470624 & 1.79037632529376 \tabularnewline
83 & 4 & 2.58843492243795 & 1.41156507756205 \tabularnewline
84 & 2 & 2.27015117072809 & -0.270151170728089 \tabularnewline
85 & 3 & 1.58008671544100 & 1.41991328455900 \tabularnewline
86 & 2 & 2.43716577782039 & -0.437165777820387 \tabularnewline
87 & 2 & 1.83376290849034 & 0.166237091509656 \tabularnewline
88 & 2 & 2.43180912307684 & -0.431809123076842 \tabularnewline
89 & 2 & 3.06222405067023 & -1.06222405067023 \tabularnewline
90 & 3 & 1.90938054750113 & 1.09061945249887 \tabularnewline
91 & 2 & 2.20202386374093 & -0.202023863740933 \tabularnewline
92 & 3 & 2.27776308847863 & 0.722236911521373 \tabularnewline
93 & 4 & 3.14189445482326 & 0.858105545176742 \tabularnewline
94 & 3 & 3.35994729807439 & -0.359947298074392 \tabularnewline
95 & 3 & 2.39845517006826 & 0.601544829931735 \tabularnewline
96 & 0 & 1.79093180240399 & -1.79093180240399 \tabularnewline
97 & 1 & 1.33302967738863 & -0.333029677388631 \tabularnewline
98 & 2 & 1.44101580720438 & 0.558984192795617 \tabularnewline
99 & 2 & 2.65744001748252 & -0.65744001748252 \tabularnewline
100 & 3 & 2.39436853833002 & 0.605631461669977 \tabularnewline
101 & 4 & 3.33511691746976 & 0.664883082530237 \tabularnewline
102 & 4 & 3.08786448614028 & 0.912135513859718 \tabularnewline
103 & 1 & 2.76084590302608 & -1.76084590302608 \tabularnewline
104 & 2 & 1.44142777496563 & 0.558572225034369 \tabularnewline
105 & 2 & 0.823671051893065 & 1.17632894810694 \tabularnewline
106 & 3 & 2.15067525960883 & 0.849324740391165 \tabularnewline
107 & 3 & 2.02152711660532 & 0.97847288339468 \tabularnewline
108 & 3 & 2.99830496114822 & 0.00169503885177585 \tabularnewline
109 & 1 & 1.72158832494645 & -0.721588324946446 \tabularnewline
110 & 1 & 1.75001172475536 & -0.750011724755362 \tabularnewline
111 & 1 & 2.34576880973887 & -1.34576880973887 \tabularnewline
112 & 1 & 3.10964147538699 & -2.10964147538699 \tabularnewline
113 & 0 & 1.68643101764633 & -1.68643101764633 \tabularnewline
114 & 1 & 2.59878986357315 & -1.59878986357315 \tabularnewline
115 & 3 & 2.53031832201673 & 0.469681677983268 \tabularnewline
116 & 3 & 3.05950881952336 & -0.0595088195233563 \tabularnewline
117 & 0 & 2.99509004310919 & -2.99509004310919 \tabularnewline
118 & 2 & 1.59091355123058 & 0.409086448769424 \tabularnewline
119 & 5 & 2.93117095358719 & 2.06882904641281 \tabularnewline
120 & 2 & 2.68123715770683 & -0.681237157706828 \tabularnewline
121 & 3 & 2.34932188798896 & 0.650678112011044 \tabularnewline
122 & 3 & 3.2351963769843 & -0.235196376984300 \tabularnewline
123 & 5 & 1.71665777174678 & 3.28334222825322 \tabularnewline
124 & 4 & 2.77039078929585 & 1.22960921070415 \tabularnewline
125 & 4 & 3.12389372934153 & 0.876106270658472 \tabularnewline
126 & 0 & 1.54047052739368 & -1.54047052739368 \tabularnewline
127 & 3 & 2.9230315567067 & 0.0769684432933017 \tabularnewline
128 & 0 & 2.81322189538221 & -2.81322189538221 \tabularnewline
129 & 2 & 2.21699899010253 & -0.216998990102528 \tabularnewline
130 & 0 & 1.73624723117849 & -1.73624723117849 \tabularnewline
131 & 6 & 2.9230315567067 & 3.0769684432933 \tabularnewline
132 & 3 & 2.78031377673087 & 0.219686223269129 \tabularnewline
133 & 1 & 1.41706321465717 & -0.41706321465717 \tabularnewline
134 & 6 & 2.18040232681713 & 3.81959767318287 \tabularnewline
135 & 2 & 2.76992496899968 & -0.76992496899968 \tabularnewline
136 & 1 & 1.96291082929194 & -0.96291082929194 \tabularnewline
137 & 3 & 2.99558973000135 & 0.00441026999864769 \tabularnewline
138 & 1 & 2.03234787803668 & -1.03234787803668 \tabularnewline
139 & 2 & 3.33820417542368 & -1.33820417542368 \tabularnewline
140 & 4 & 2.69973757008072 & 1.30026242991928 \tabularnewline
141 & 1 & 2.92895540933248 & -1.92895540933248 \tabularnewline
142 & 2 & 2.70600587947768 & -0.706005879477676 \tabularnewline
143 & 0 & 2.67169227143706 & -2.67169227143706 \tabularnewline
144 & 5 & 2.35852253968947 & 2.64147746031053 \tabularnewline
145 & 2 & 2.09171451552428 & -0.0917145155242786 \tabularnewline
146 & 1 & 1.13923979465797 & -0.139239794657973 \tabularnewline
147 & 1 & 1.41263234834967 & -0.412632348349671 \tabularnewline
148 & 4 & 2.27769535528664 & 1.72230464471336 \tabularnewline
149 & 3 & 3.22075480411086 & -0.220754804110862 \tabularnewline
150 & 0 & 2.74019790179138 & -2.74019790179138 \tabularnewline
151 & 3 & 2.62633547532465 & 0.373664524675354 \tabularnewline
152 & 3 & 3.01309076859092 & -0.0130907685909193 \tabularnewline
153 & 0 & 1.81301915962396 & -1.81301915962396 \tabularnewline
154 & 2 & 2.59882373016914 & -0.598823730169140 \tabularnewline
155 & 5 & 3.01633347886774 & 1.98366652113226 \tabularnewline
156 & 2 & 2.13267431192519 & -0.132674311925186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&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]2[/C][C]2.60012739756854[/C][C]-0.600127397568545[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.87285161740772[/C][C]-0.872851617407716[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]2.62041140049695[/C][C]-2.62041140049695[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]2.05082245231536[/C][C]0.94917754768464[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.21612120204511[/C][C]0.783878797954886[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.74133270002851[/C][C]-0.741332700028511[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]2.87031132978131[/C][C]0.129688670218689[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]2.08098147322383[/C][C]-1.08098147322383[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]2.53215554290427[/C][C]1.46784445709573[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]2.16574591140021[/C][C]-2.16574591140021[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]3.22182191108718[/C][C]-0.221821911087177[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]2.27641948012504[/C][C]-0.276419480125044[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]3.40633148241083[/C][C]0.593668517589166[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]1.81345111332413[/C][C]1.18654888667587[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.55759346481799[/C][C]-0.557593464817993[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]2.53131162144285[/C][C]-1.53131162144285[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.18005809224787[/C][C]-0.180058092247873[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.84604229490263[/C][C]0.153957705097374[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.87609432768454[/C][C]-0.87609432768454[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.85638381919847[/C][C]-0.856383819198472[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.72470944949636[/C][C]0.275290550503643[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.92711818844494[/C][C]0.0728818115550605[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]2.96685596221917[/C][C]1.03314403778083[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.85485089192599[/C][C]0.145149108074011[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]3.00594467113655[/C][C]-2.00594467113655[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]3.20001105524447[/C][C]-1.20001105524447[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]2.47775354741425[/C][C]-0.47775354741425[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]2.92493651078623[/C][C]1.07506348921377[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]2.89926220872018[/C][C]-0.899262208720176[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]2.14477897701892[/C][C]0.855221022981077[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]2.43317467151192[/C][C]0.566825328488078[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]2.85200023632632[/C][C]0.147999763673682[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]2.7663380241536[/C][C]1.23366197584640[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.99542086083910[/C][C]0.00457913916090297[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.08098147322383[/C][C]-0.0809814732238296[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.24234247443867[/C][C]0.75765752556133[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]2.01965602912179[/C][C]0.980343970878213[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]2.30131759392166[/C][C]1.69868240607834[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.84519837344121[/C][C]-0.845198373441206[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]3.20631323123742[/C][C]1.79368676876258[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]2.74833729867187[/C][C]0.251662701328128[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]2.04083953798721[/C][C]-1.04083953798721[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.00235034187815[/C][C]-0.00235034187814842[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.84546145797912[/C][C]-0.845461457979123[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.95442719784219[/C][C]0.0455728021578049[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]2.96944960763914[/C][C]0.0305503923608648[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]3.24592941928475[/C][C]5.75407058071525[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]1.46802786546773[/C][C]-1.46802786546773[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]2.16340097511973[/C][C]-2.16340097511973[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.81406581684363[/C][C]-0.814065816843627[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.33246225730448[/C][C]0.667537742695522[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.57785733246040[/C][C]0.422142667539595[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]2.31329264478771[/C][C]-1.31329264478771[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.44866159155092[/C][C]0.551338408449084[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]2.64517404790959[/C][C]-2.64517404790959[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]2.71009251121592[/C][C]2.28990748878408[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]2.52099054690365[/C][C]-0.520990546903648[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]3.29640630971764[/C][C]0.70359369028236[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]1.54365135663480[/C][C]1.45634864336520[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]3.09466634902540[/C][C]-3.09466634902540[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]1.38832944687499[/C][C]-1.38832944687499[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.25269741557387[/C][C]0.747302584426133[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]3.78918946285777[/C][C]-2.78918946285777[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]2.98473510197399[/C][C]-1.98473510197399[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]1.59419012810339[/C][C]2.40580987189661[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]2.70959282432375[/C][C]-0.709592824323754[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]2.7916342250544[/C][C]1.20836577494560[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.91518303660192[/C][C]-0.915183036601915[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]2.84469868654904[/C][C]1.15530131345096[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]1.03622592186039[/C][C]0.963774078139615[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]2.99917667484743[/C][C]2.00082332515257[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]2.61323143644658[/C][C]1.38676856355342[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]2.15254634709237[/C][C]1.84745365290763[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]2.67547460956024[/C][C]1.32452539043976[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]2.55564838951352[/C][C]1.44435161048648[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.81322189538221[/C][C]0.186778104617794[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.65602867593695[/C][C]0.343971324063053[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]2.77454515422608[/C][C]0.225454845773921[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.95205620126458[/C][C]0.0479437987354192[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.07038992973439[/C][C]-0.0703899297343883[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.29827023499077[/C][C]-0.29827023499077[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]3.20962367470624[/C][C]1.79037632529376[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]2.58843492243795[/C][C]1.41156507756205[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]2.27015117072809[/C][C]-0.270151170728089[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]1.58008671544100[/C][C]1.41991328455900[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]2.43716577782039[/C][C]-0.437165777820387[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.83376290849034[/C][C]0.166237091509656[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]2.43180912307684[/C][C]-0.431809123076842[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]3.06222405067023[/C][C]-1.06222405067023[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]1.90938054750113[/C][C]1.09061945249887[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]2.20202386374093[/C][C]-0.202023863740933[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.27776308847863[/C][C]0.722236911521373[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.14189445482326[/C][C]0.858105545176742[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]3.35994729807439[/C][C]-0.359947298074392[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]2.39845517006826[/C][C]0.601544829931735[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]1.79093180240399[/C][C]-1.79093180240399[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.33302967738863[/C][C]-0.333029677388631[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]1.44101580720438[/C][C]0.558984192795617[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]2.65744001748252[/C][C]-0.65744001748252[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]2.39436853833002[/C][C]0.605631461669977[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.33511691746976[/C][C]0.664883082530237[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.08786448614028[/C][C]0.912135513859718[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]2.76084590302608[/C][C]-1.76084590302608[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.44142777496563[/C][C]0.558572225034369[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]0.823671051893065[/C][C]1.17632894810694[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]2.15067525960883[/C][C]0.849324740391165[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]2.02152711660532[/C][C]0.97847288339468[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]2.99830496114822[/C][C]0.00169503885177585[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.72158832494645[/C][C]-0.721588324946446[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.75001172475536[/C][C]-0.750011724755362[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]2.34576880973887[/C][C]-1.34576880973887[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]3.10964147538699[/C][C]-2.10964147538699[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]1.68643101764633[/C][C]-1.68643101764633[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]2.59878986357315[/C][C]-1.59878986357315[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]2.53031832201673[/C][C]0.469681677983268[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]3.05950881952336[/C][C]-0.0595088195233563[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]2.99509004310919[/C][C]-2.99509004310919[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.59091355123058[/C][C]0.409086448769424[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]2.93117095358719[/C][C]2.06882904641281[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.68123715770683[/C][C]-0.681237157706828[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.34932188798896[/C][C]0.650678112011044[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]3.2351963769843[/C][C]-0.235196376984300[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]1.71665777174678[/C][C]3.28334222825322[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]2.77039078929585[/C][C]1.22960921070415[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]3.12389372934153[/C][C]0.876106270658472[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]1.54047052739368[/C][C]-1.54047052739368[/C][/ROW]
[ROW][C]127[/C][C]3[/C][C]2.9230315567067[/C][C]0.0769684432933017[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]2.81322189538221[/C][C]-2.81322189538221[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]2.21699899010253[/C][C]-0.216998990102528[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]1.73624723117849[/C][C]-1.73624723117849[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]2.9230315567067[/C][C]3.0769684432933[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]2.78031377673087[/C][C]0.219686223269129[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.41706321465717[/C][C]-0.41706321465717[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]2.18040232681713[/C][C]3.81959767318287[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]2.76992496899968[/C][C]-0.76992496899968[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.96291082929194[/C][C]-0.96291082929194[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]2.99558973000135[/C][C]0.00441026999864769[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]2.03234787803668[/C][C]-1.03234787803668[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]3.33820417542368[/C][C]-1.33820417542368[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]2.69973757008072[/C][C]1.30026242991928[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]2.92895540933248[/C][C]-1.92895540933248[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]2.70600587947768[/C][C]-0.706005879477676[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]2.67169227143706[/C][C]-2.67169227143706[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]2.35852253968947[/C][C]2.64147746031053[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]2.09171451552428[/C][C]-0.0917145155242786[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.13923979465797[/C][C]-0.139239794657973[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]1.41263234834967[/C][C]-0.412632348349671[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]2.27769535528664[/C][C]1.72230464471336[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]3.22075480411086[/C][C]-0.220754804110862[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]2.74019790179138[/C][C]-2.74019790179138[/C][/ROW]
[ROW][C]151[/C][C]3[/C][C]2.62633547532465[/C][C]0.373664524675354[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]3.01309076859092[/C][C]-0.0130907685909193[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]1.81301915962396[/C][C]-1.81301915962396[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]2.59882373016914[/C][C]-0.598823730169140[/C][/ROW]
[ROW][C]155[/C][C]5[/C][C]3.01633347886774[/C][C]1.98366652113226[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]2.13267431192519[/C][C]-0.132674311925186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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
122.60012739756854-0.600127397568545
211.87285161740772-0.872851617407716
302.62041140049695-2.62041140049695
432.050822452315360.94917754768464
532.216121202045110.783878797954886
611.74133270002851-0.741332700028511
732.870311329781310.129688670218689
812.08098147322383-1.08098147322383
942.532155542904271.46784445709573
1002.16574591140021-2.16574591140021
1133.22182191108718-0.221821911087177
1222.27641948012504-0.276419480125044
1343.406331482410830.593668517589166
1431.813451113324131.18654888667587
1511.55759346481799-0.557593464817993
1612.53131162144285-1.53131162144285
1722.18005809224787-0.180058092247873
1832.846042294902630.153957705097374
1911.87609432768454-0.87609432768454
2011.85638381919847-0.856383819198472
2121.724709449496360.275290550503643
2232.927118188444940.0728818115550605
2342.966855962219171.03314403778083
2421.854850891925990.145149108074011
2513.00594467113655-2.00594467113655
2623.20001105524447-1.20001105524447
2722.47775354741425-0.47775354741425
2842.924936510786231.07506348921377
2922.89926220872018-0.899262208720176
3032.144778977018920.855221022981077
3132.433174671511920.566825328488078
3232.852000236326320.147999763673682
3342.76633802415361.23366197584640
3421.995420860839100.00457913916090297
3522.08098147322383-0.0809814732238296
3643.242342474438670.75765752556133
3732.019656029121790.980343970878213
3842.301317593921661.69868240607834
3922.84519837344121-0.845198373441206
4053.206313231237421.79368676876258
4132.748337298671870.251662701328128
4212.04083953798721-1.04083953798721
4311.00235034187815-0.00235034187814842
4411.84546145797912-0.845461457979123
4521.954427197842190.0455728021578049
4632.969449607639140.0305503923608648
4793.245929419284755.75407058071525
4801.46802786546773-1.46802786546773
4902.16340097511973-2.16340097511973
5022.81406581684363-0.814065816843627
5121.332462257304480.667537742695522
5232.577857332460400.422142667539595
5312.31329264478771-1.31329264478771
5421.448661591550920.551338408449084
5502.64517404790959-2.64517404790959
5652.710092511215922.28990748878408
5722.52099054690365-0.520990546903648
5843.296406309717640.70359369028236
5931.543651356634801.45634864336520
6003.09466634902540-3.09466634902540
6101.38832944687499-1.38832944687499
6243.252697415573870.747302584426133
6313.78918946285777-2.78918946285777
6412.98473510197399-1.98473510197399
6541.594190128103392.40580987189661
6622.70959282432375-0.709592824323754
6742.79163422505441.20836577494560
6811.91518303660192-0.915183036601915
6942.844698686549041.15530131345096
7021.036225921860390.963774078139615
7152.999176674847432.00082332515257
7242.613231436446581.38676856355342
7342.152546347092371.84745365290763
7442.675474609560241.32452539043976
7542.555648389513521.44435161048648
7632.813221895382210.186778104617794
7732.656028675936950.343971324063053
7832.774545154226080.225454845773921
7921.952056201264580.0479437987354192
8011.07038992973439-0.0703899297343883
8111.29827023499077-0.29827023499077
8253.209623674706241.79037632529376
8342.588434922437951.41156507756205
8422.27015117072809-0.270151170728089
8531.580086715441001.41991328455900
8622.43716577782039-0.437165777820387
8721.833762908490340.166237091509656
8822.43180912307684-0.431809123076842
8923.06222405067023-1.06222405067023
9031.909380547501131.09061945249887
9122.20202386374093-0.202023863740933
9232.277763088478630.722236911521373
9343.141894454823260.858105545176742
9433.35994729807439-0.359947298074392
9532.398455170068260.601544829931735
9601.79093180240399-1.79093180240399
9711.33302967738863-0.333029677388631
9821.441015807204380.558984192795617
9922.65744001748252-0.65744001748252
10032.394368538330020.605631461669977
10143.335116917469760.664883082530237
10243.087864486140280.912135513859718
10312.76084590302608-1.76084590302608
10421.441427774965630.558572225034369
10520.8236710518930651.17632894810694
10632.150675259608830.849324740391165
10732.021527116605320.97847288339468
10832.998304961148220.00169503885177585
10911.72158832494645-0.721588324946446
11011.75001172475536-0.750011724755362
11112.34576880973887-1.34576880973887
11213.10964147538699-2.10964147538699
11301.68643101764633-1.68643101764633
11412.59878986357315-1.59878986357315
11532.530318322016730.469681677983268
11633.05950881952336-0.0595088195233563
11702.99509004310919-2.99509004310919
11821.590913551230580.409086448769424
11952.931170953587192.06882904641281
12022.68123715770683-0.681237157706828
12132.349321887988960.650678112011044
12233.2351963769843-0.235196376984300
12351.716657771746783.28334222825322
12442.770390789295851.22960921070415
12543.123893729341530.876106270658472
12601.54047052739368-1.54047052739368
12732.92303155670670.0769684432933017
12802.81322189538221-2.81322189538221
12922.21699899010253-0.216998990102528
13001.73624723117849-1.73624723117849
13162.92303155670673.0769684432933
13232.780313776730870.219686223269129
13311.41706321465717-0.41706321465717
13462.180402326817133.81959767318287
13522.76992496899968-0.76992496899968
13611.96291082929194-0.96291082929194
13732.995589730001350.00441026999864769
13812.03234787803668-1.03234787803668
13923.33820417542368-1.33820417542368
14042.699737570080721.30026242991928
14112.92895540933248-1.92895540933248
14222.70600587947768-0.706005879477676
14302.67169227143706-2.67169227143706
14452.358522539689472.64147746031053
14522.09171451552428-0.0917145155242786
14611.13923979465797-0.139239794657973
14711.41263234834967-0.412632348349671
14842.277695355286641.72230464471336
14933.22075480411086-0.220754804110862
15002.74019790179138-2.74019790179138
15132.626335475324650.373664524675354
15233.01309076859092-0.0130907685909193
15301.81301915962396-1.81301915962396
15422.59882373016914-0.598823730169140
15553.016333478867741.98366652113226
15622.13267431192519-0.132674311925186






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8234240773885430.3531518452229130.176575922611457
100.7795588050183980.4408823899632040.220441194981602
110.6690695529090640.6618608941818730.330930447090936
120.5670464128136740.8659071743726520.432953587186326
130.4790198657162550.958039731432510.520980134283745
140.3747561545706110.7495123091412220.625243845429389
150.2877050197896960.5754100395793920.712294980210304
160.3467360427034640.6934720854069290.653263957296536
170.270953208171010.541906416342020.72904679182899
180.2061739825559630.4123479651119260.793826017444037
190.1511275945752100.3022551891504210.84887240542479
200.1111744523983030.2223489047966070.888825547601697
210.1205110460243510.2410220920487020.87948895397565
220.0866829725952140.1733659451904280.913317027404786
230.07218587956100020.1443717591220000.927814120439
240.05661156560293180.1132231312058640.943388434397068
250.09224208311513720.1844841662302740.907757916884863
260.07901472124474040.1580294424894810.92098527875526
270.06084052451975840.1216810490395170.939159475480242
280.0701610262516310.1403220525032620.929838973748369
290.05304310826281180.1060862165256240.946956891737188
300.05667703408679320.1133540681735860.943322965913207
310.04823508415471620.09647016830943250.951764915845284
320.03543390889850670.07086781779701350.964566091101493
330.03836160782563090.07672321565126170.961638392174369
340.02799494126410240.05598988252820480.972005058735898
350.01918433142708020.03836866285416030.98081566857292
360.01515721319552140.03031442639104280.984842786804479
370.01391719025082640.02783438050165280.986082809749174
380.02267547150108250.04535094300216510.977324528498917
390.0182697023964860.0365394047929720.981730297603514
400.02495050614538370.04990101229076750.975049493854616
410.01763490380506220.03526980761012440.982365096194938
420.01434287491638640.02868574983277290.985657125083613
430.009890614703985320.01978122940797060.990109385296015
440.007506627625004990.015013255250010.992493372374995
450.005032721343297130.01006544268659430.994967278656703
460.003314256603173170.006628513206346330.996685743396827
470.2800933986491870.5601867972983730.719906601350813
480.2731143279125380.5462286558250760.726885672087462
490.3190137432875470.6380274865750950.680986256712453
500.2948679267112490.5897358534224980.705132073288751
510.2797539253152520.5595078506305040.720246074684748
520.2507411730732870.5014823461465730.749258826926713
530.2445736892965390.4891473785930790.75542631070346
540.2240776697030870.4481553394061750.775922330296913
550.3431982775513880.6863965551027760.656801722448612
560.4162537384841220.8325074769682430.583746261515878
570.3741676608648890.7483353217297780.625832339135111
580.3363265065809660.6726530131619320.663673493419034
590.3474139741496470.6948279482992930.652586025850353
600.5290185109727930.9419629780544150.470981489027207
610.5222154716123160.9555690567753680.477784528387684
620.4844773902128550.968954780425710.515522609787145
630.6272234913279320.7455530173441370.372776508672069
640.6706990946556750.6586018106886510.329300905344325
650.7498086714041050.500382657191790.250191328595895
660.7192854454245930.5614291091508130.280714554575407
670.7118840754235490.5762318491529010.288115924576451
680.685822660170540.628354679658920.31417733982946
690.6726053475628280.6547893048743440.327394652437172
700.6524468057048550.695106388590290.347553194295145
710.6928008456121820.6143983087756360.307199154387818
720.6894795210648060.6210409578703880.310520478935194
730.7155179030802080.5689641938395830.284482096919792
740.7108912070354440.5782175859291110.289108792964556
750.7121237382969760.5757525234060490.287876261703024
760.6711192680698920.6577614638602160.328880731930108
770.629380140314760.741239719370480.37061985968524
780.5864628351939430.8270743296121150.413537164806057
790.5404124611090370.9191750777819260.459587538890963
800.4936090071913210.9872180143826430.506390992808679
810.4496230485460830.8992460970921660.550376951453917
820.4967508773990500.9935017547981010.503249122600950
830.4998819603016220.9997639206032440.500118039698378
840.4540130427517270.9080260855034540.545986957248273
850.4588314245952880.9176628491905770.541168575404712
860.420489465580420.840978931160840.57951053441958
870.3751060163836930.7502120327673870.624893983616307
880.3398153125833160.6796306251666320.660184687416684
890.3176052707956980.6352105415913950.682394729204302
900.3013755320358610.6027510640717230.698624467964139
910.2621909766408370.5243819532816730.737809023359163
920.2319253353302220.4638506706604440.768074664669778
930.2101346374983600.4202692749967210.78986536250164
940.1786994602479860.3573989204959710.821300539752014
950.1549622153582590.3099244307165170.845037784641741
960.1749491305767660.3498982611535320.825050869423234
970.1458794528367300.2917589056734600.85412054716327
980.1217701553167520.2435403106335030.878229844683248
990.1029148116914590.2058296233829190.89708518830854
1000.0856182087595450.171236417519090.914381791240455
1010.07399696232666010.1479939246533200.92600303767334
1020.06538748444208250.1307749688841650.934612515557917
1030.07841620675914670.1568324135182930.921583793240853
1040.07113559373137350.1422711874627470.928864406268627
1050.06726664722457920.1345332944491580.93273335277542
1060.05620749499186570.1124149899837310.943792505008134
1070.053005539601220.106011079202440.94699446039878
1080.04076427271907690.08152854543815390.959235727280923
1090.03237008668199350.06474017336398690.967629913318007
1100.02636927882873410.05273855765746810.973630721171266
1110.02307680766882200.04615361533764410.976923192331178
1120.03001073630819310.06002147261638620.969989263691807
1130.03253551425369720.06507102850739440.967464485746303
1140.03443319004066530.06886638008133060.965566809959335
1150.02614568331400870.05229136662801730.97385431668599
1160.01930724050010160.03861448100020330.980692759499898
1170.04544272844261750.09088545688523510.954557271557382
1180.03451391066090130.06902782132180260.965486089339099
1190.04176553691712980.08353107383425970.95823446308287
1200.03170783463096320.06341566926192630.968292165369037
1210.02538826236955840.05077652473911680.974611737630442
1220.01815724129294280.03631448258588560.981842758707057
1230.06884807184556440.1376961436911290.931151928154436
1240.0581036167069060.1162072334138120.941896383293094
1250.0481013313938130.0962026627876260.951898668606187
1260.04331846242380620.08663692484761240.956681537576194
1270.03142506096846910.06285012193693820.96857493903153
1280.08711177335255930.1742235467051190.91288822664744
1290.0717388387861840.1434776775723680.928261161213816
1300.1007572771216480.2015145542432950.899242722878352
1310.2206580469815730.4413160939631460.779341953018427
1320.1760065527454570.3520131054909130.823993447254543
1330.1444330567446240.2888661134892470.855566943255377
1340.6481281393481920.7037437213036160.351871860651808
1350.5784497983437330.8431004033125330.421550201656267
1360.5043237131309480.9913525737381040.495676286869052
1370.4239221804177310.8478443608354620.576077819582269
1380.4546731543625460.9093463087250930.545326845637454
1390.3774776686169220.7549553372338440.622522331383078
1400.4385417860888080.8770835721776160.561458213911192
1410.5824362387783610.8351275224432780.417563761221639
1420.5220754263106230.9558491473787540.477924573689377
1430.6052576125515430.7894847748969150.394742387448458
1440.5046891827434590.9906216345130820.495310817256541
1450.3789415102940710.7578830205881420.621058489705929
1460.432917191836450.86583438367290.56708280816355
1470.3397342618224370.6794685236448740.660265738177563

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.823424077388543 & 0.353151845222913 & 0.176575922611457 \tabularnewline
10 & 0.779558805018398 & 0.440882389963204 & 0.220441194981602 \tabularnewline
11 & 0.669069552909064 & 0.661860894181873 & 0.330930447090936 \tabularnewline
12 & 0.567046412813674 & 0.865907174372652 & 0.432953587186326 \tabularnewline
13 & 0.479019865716255 & 0.95803973143251 & 0.520980134283745 \tabularnewline
14 & 0.374756154570611 & 0.749512309141222 & 0.625243845429389 \tabularnewline
15 & 0.287705019789696 & 0.575410039579392 & 0.712294980210304 \tabularnewline
16 & 0.346736042703464 & 0.693472085406929 & 0.653263957296536 \tabularnewline
17 & 0.27095320817101 & 0.54190641634202 & 0.72904679182899 \tabularnewline
18 & 0.206173982555963 & 0.412347965111926 & 0.793826017444037 \tabularnewline
19 & 0.151127594575210 & 0.302255189150421 & 0.84887240542479 \tabularnewline
20 & 0.111174452398303 & 0.222348904796607 & 0.888825547601697 \tabularnewline
21 & 0.120511046024351 & 0.241022092048702 & 0.87948895397565 \tabularnewline
22 & 0.086682972595214 & 0.173365945190428 & 0.913317027404786 \tabularnewline
23 & 0.0721858795610002 & 0.144371759122000 & 0.927814120439 \tabularnewline
24 & 0.0566115656029318 & 0.113223131205864 & 0.943388434397068 \tabularnewline
25 & 0.0922420831151372 & 0.184484166230274 & 0.907757916884863 \tabularnewline
26 & 0.0790147212447404 & 0.158029442489481 & 0.92098527875526 \tabularnewline
27 & 0.0608405245197584 & 0.121681049039517 & 0.939159475480242 \tabularnewline
28 & 0.070161026251631 & 0.140322052503262 & 0.929838973748369 \tabularnewline
29 & 0.0530431082628118 & 0.106086216525624 & 0.946956891737188 \tabularnewline
30 & 0.0566770340867932 & 0.113354068173586 & 0.943322965913207 \tabularnewline
31 & 0.0482350841547162 & 0.0964701683094325 & 0.951764915845284 \tabularnewline
32 & 0.0354339088985067 & 0.0708678177970135 & 0.964566091101493 \tabularnewline
33 & 0.0383616078256309 & 0.0767232156512617 & 0.961638392174369 \tabularnewline
34 & 0.0279949412641024 & 0.0559898825282048 & 0.972005058735898 \tabularnewline
35 & 0.0191843314270802 & 0.0383686628541603 & 0.98081566857292 \tabularnewline
36 & 0.0151572131955214 & 0.0303144263910428 & 0.984842786804479 \tabularnewline
37 & 0.0139171902508264 & 0.0278343805016528 & 0.986082809749174 \tabularnewline
38 & 0.0226754715010825 & 0.0453509430021651 & 0.977324528498917 \tabularnewline
39 & 0.018269702396486 & 0.036539404792972 & 0.981730297603514 \tabularnewline
40 & 0.0249505061453837 & 0.0499010122907675 & 0.975049493854616 \tabularnewline
41 & 0.0176349038050622 & 0.0352698076101244 & 0.982365096194938 \tabularnewline
42 & 0.0143428749163864 & 0.0286857498327729 & 0.985657125083613 \tabularnewline
43 & 0.00989061470398532 & 0.0197812294079706 & 0.990109385296015 \tabularnewline
44 & 0.00750662762500499 & 0.01501325525001 & 0.992493372374995 \tabularnewline
45 & 0.00503272134329713 & 0.0100654426865943 & 0.994967278656703 \tabularnewline
46 & 0.00331425660317317 & 0.00662851320634633 & 0.996685743396827 \tabularnewline
47 & 0.280093398649187 & 0.560186797298373 & 0.719906601350813 \tabularnewline
48 & 0.273114327912538 & 0.546228655825076 & 0.726885672087462 \tabularnewline
49 & 0.319013743287547 & 0.638027486575095 & 0.680986256712453 \tabularnewline
50 & 0.294867926711249 & 0.589735853422498 & 0.705132073288751 \tabularnewline
51 & 0.279753925315252 & 0.559507850630504 & 0.720246074684748 \tabularnewline
52 & 0.250741173073287 & 0.501482346146573 & 0.749258826926713 \tabularnewline
53 & 0.244573689296539 & 0.489147378593079 & 0.75542631070346 \tabularnewline
54 & 0.224077669703087 & 0.448155339406175 & 0.775922330296913 \tabularnewline
55 & 0.343198277551388 & 0.686396555102776 & 0.656801722448612 \tabularnewline
56 & 0.416253738484122 & 0.832507476968243 & 0.583746261515878 \tabularnewline
57 & 0.374167660864889 & 0.748335321729778 & 0.625832339135111 \tabularnewline
58 & 0.336326506580966 & 0.672653013161932 & 0.663673493419034 \tabularnewline
59 & 0.347413974149647 & 0.694827948299293 & 0.652586025850353 \tabularnewline
60 & 0.529018510972793 & 0.941962978054415 & 0.470981489027207 \tabularnewline
61 & 0.522215471612316 & 0.955569056775368 & 0.477784528387684 \tabularnewline
62 & 0.484477390212855 & 0.96895478042571 & 0.515522609787145 \tabularnewline
63 & 0.627223491327932 & 0.745553017344137 & 0.372776508672069 \tabularnewline
64 & 0.670699094655675 & 0.658601810688651 & 0.329300905344325 \tabularnewline
65 & 0.749808671404105 & 0.50038265719179 & 0.250191328595895 \tabularnewline
66 & 0.719285445424593 & 0.561429109150813 & 0.280714554575407 \tabularnewline
67 & 0.711884075423549 & 0.576231849152901 & 0.288115924576451 \tabularnewline
68 & 0.68582266017054 & 0.62835467965892 & 0.31417733982946 \tabularnewline
69 & 0.672605347562828 & 0.654789304874344 & 0.327394652437172 \tabularnewline
70 & 0.652446805704855 & 0.69510638859029 & 0.347553194295145 \tabularnewline
71 & 0.692800845612182 & 0.614398308775636 & 0.307199154387818 \tabularnewline
72 & 0.689479521064806 & 0.621040957870388 & 0.310520478935194 \tabularnewline
73 & 0.715517903080208 & 0.568964193839583 & 0.284482096919792 \tabularnewline
74 & 0.710891207035444 & 0.578217585929111 & 0.289108792964556 \tabularnewline
75 & 0.712123738296976 & 0.575752523406049 & 0.287876261703024 \tabularnewline
76 & 0.671119268069892 & 0.657761463860216 & 0.328880731930108 \tabularnewline
77 & 0.62938014031476 & 0.74123971937048 & 0.37061985968524 \tabularnewline
78 & 0.586462835193943 & 0.827074329612115 & 0.413537164806057 \tabularnewline
79 & 0.540412461109037 & 0.919175077781926 & 0.459587538890963 \tabularnewline
80 & 0.493609007191321 & 0.987218014382643 & 0.506390992808679 \tabularnewline
81 & 0.449623048546083 & 0.899246097092166 & 0.550376951453917 \tabularnewline
82 & 0.496750877399050 & 0.993501754798101 & 0.503249122600950 \tabularnewline
83 & 0.499881960301622 & 0.999763920603244 & 0.500118039698378 \tabularnewline
84 & 0.454013042751727 & 0.908026085503454 & 0.545986957248273 \tabularnewline
85 & 0.458831424595288 & 0.917662849190577 & 0.541168575404712 \tabularnewline
86 & 0.42048946558042 & 0.84097893116084 & 0.57951053441958 \tabularnewline
87 & 0.375106016383693 & 0.750212032767387 & 0.624893983616307 \tabularnewline
88 & 0.339815312583316 & 0.679630625166632 & 0.660184687416684 \tabularnewline
89 & 0.317605270795698 & 0.635210541591395 & 0.682394729204302 \tabularnewline
90 & 0.301375532035861 & 0.602751064071723 & 0.698624467964139 \tabularnewline
91 & 0.262190976640837 & 0.524381953281673 & 0.737809023359163 \tabularnewline
92 & 0.231925335330222 & 0.463850670660444 & 0.768074664669778 \tabularnewline
93 & 0.210134637498360 & 0.420269274996721 & 0.78986536250164 \tabularnewline
94 & 0.178699460247986 & 0.357398920495971 & 0.821300539752014 \tabularnewline
95 & 0.154962215358259 & 0.309924430716517 & 0.845037784641741 \tabularnewline
96 & 0.174949130576766 & 0.349898261153532 & 0.825050869423234 \tabularnewline
97 & 0.145879452836730 & 0.291758905673460 & 0.85412054716327 \tabularnewline
98 & 0.121770155316752 & 0.243540310633503 & 0.878229844683248 \tabularnewline
99 & 0.102914811691459 & 0.205829623382919 & 0.89708518830854 \tabularnewline
100 & 0.085618208759545 & 0.17123641751909 & 0.914381791240455 \tabularnewline
101 & 0.0739969623266601 & 0.147993924653320 & 0.92600303767334 \tabularnewline
102 & 0.0653874844420825 & 0.130774968884165 & 0.934612515557917 \tabularnewline
103 & 0.0784162067591467 & 0.156832413518293 & 0.921583793240853 \tabularnewline
104 & 0.0711355937313735 & 0.142271187462747 & 0.928864406268627 \tabularnewline
105 & 0.0672666472245792 & 0.134533294449158 & 0.93273335277542 \tabularnewline
106 & 0.0562074949918657 & 0.112414989983731 & 0.943792505008134 \tabularnewline
107 & 0.05300553960122 & 0.10601107920244 & 0.94699446039878 \tabularnewline
108 & 0.0407642727190769 & 0.0815285454381539 & 0.959235727280923 \tabularnewline
109 & 0.0323700866819935 & 0.0647401733639869 & 0.967629913318007 \tabularnewline
110 & 0.0263692788287341 & 0.0527385576574681 & 0.973630721171266 \tabularnewline
111 & 0.0230768076688220 & 0.0461536153376441 & 0.976923192331178 \tabularnewline
112 & 0.0300107363081931 & 0.0600214726163862 & 0.969989263691807 \tabularnewline
113 & 0.0325355142536972 & 0.0650710285073944 & 0.967464485746303 \tabularnewline
114 & 0.0344331900406653 & 0.0688663800813306 & 0.965566809959335 \tabularnewline
115 & 0.0261456833140087 & 0.0522913666280173 & 0.97385431668599 \tabularnewline
116 & 0.0193072405001016 & 0.0386144810002033 & 0.980692759499898 \tabularnewline
117 & 0.0454427284426175 & 0.0908854568852351 & 0.954557271557382 \tabularnewline
118 & 0.0345139106609013 & 0.0690278213218026 & 0.965486089339099 \tabularnewline
119 & 0.0417655369171298 & 0.0835310738342597 & 0.95823446308287 \tabularnewline
120 & 0.0317078346309632 & 0.0634156692619263 & 0.968292165369037 \tabularnewline
121 & 0.0253882623695584 & 0.0507765247391168 & 0.974611737630442 \tabularnewline
122 & 0.0181572412929428 & 0.0363144825858856 & 0.981842758707057 \tabularnewline
123 & 0.0688480718455644 & 0.137696143691129 & 0.931151928154436 \tabularnewline
124 & 0.058103616706906 & 0.116207233413812 & 0.941896383293094 \tabularnewline
125 & 0.048101331393813 & 0.096202662787626 & 0.951898668606187 \tabularnewline
126 & 0.0433184624238062 & 0.0866369248476124 & 0.956681537576194 \tabularnewline
127 & 0.0314250609684691 & 0.0628501219369382 & 0.96857493903153 \tabularnewline
128 & 0.0871117733525593 & 0.174223546705119 & 0.91288822664744 \tabularnewline
129 & 0.071738838786184 & 0.143477677572368 & 0.928261161213816 \tabularnewline
130 & 0.100757277121648 & 0.201514554243295 & 0.899242722878352 \tabularnewline
131 & 0.220658046981573 & 0.441316093963146 & 0.779341953018427 \tabularnewline
132 & 0.176006552745457 & 0.352013105490913 & 0.823993447254543 \tabularnewline
133 & 0.144433056744624 & 0.288866113489247 & 0.855566943255377 \tabularnewline
134 & 0.648128139348192 & 0.703743721303616 & 0.351871860651808 \tabularnewline
135 & 0.578449798343733 & 0.843100403312533 & 0.421550201656267 \tabularnewline
136 & 0.504323713130948 & 0.991352573738104 & 0.495676286869052 \tabularnewline
137 & 0.423922180417731 & 0.847844360835462 & 0.576077819582269 \tabularnewline
138 & 0.454673154362546 & 0.909346308725093 & 0.545326845637454 \tabularnewline
139 & 0.377477668616922 & 0.754955337233844 & 0.622522331383078 \tabularnewline
140 & 0.438541786088808 & 0.877083572177616 & 0.561458213911192 \tabularnewline
141 & 0.582436238778361 & 0.835127522443278 & 0.417563761221639 \tabularnewline
142 & 0.522075426310623 & 0.955849147378754 & 0.477924573689377 \tabularnewline
143 & 0.605257612551543 & 0.789484774896915 & 0.394742387448458 \tabularnewline
144 & 0.504689182743459 & 0.990621634513082 & 0.495310817256541 \tabularnewline
145 & 0.378941510294071 & 0.757883020588142 & 0.621058489705929 \tabularnewline
146 & 0.43291719183645 & 0.8658343836729 & 0.56708280816355 \tabularnewline
147 & 0.339734261822437 & 0.679468523644874 & 0.660265738177563 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&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]9[/C][C]0.823424077388543[/C][C]0.353151845222913[/C][C]0.176575922611457[/C][/ROW]
[ROW][C]10[/C][C]0.779558805018398[/C][C]0.440882389963204[/C][C]0.220441194981602[/C][/ROW]
[ROW][C]11[/C][C]0.669069552909064[/C][C]0.661860894181873[/C][C]0.330930447090936[/C][/ROW]
[ROW][C]12[/C][C]0.567046412813674[/C][C]0.865907174372652[/C][C]0.432953587186326[/C][/ROW]
[ROW][C]13[/C][C]0.479019865716255[/C][C]0.95803973143251[/C][C]0.520980134283745[/C][/ROW]
[ROW][C]14[/C][C]0.374756154570611[/C][C]0.749512309141222[/C][C]0.625243845429389[/C][/ROW]
[ROW][C]15[/C][C]0.287705019789696[/C][C]0.575410039579392[/C][C]0.712294980210304[/C][/ROW]
[ROW][C]16[/C][C]0.346736042703464[/C][C]0.693472085406929[/C][C]0.653263957296536[/C][/ROW]
[ROW][C]17[/C][C]0.27095320817101[/C][C]0.54190641634202[/C][C]0.72904679182899[/C][/ROW]
[ROW][C]18[/C][C]0.206173982555963[/C][C]0.412347965111926[/C][C]0.793826017444037[/C][/ROW]
[ROW][C]19[/C][C]0.151127594575210[/C][C]0.302255189150421[/C][C]0.84887240542479[/C][/ROW]
[ROW][C]20[/C][C]0.111174452398303[/C][C]0.222348904796607[/C][C]0.888825547601697[/C][/ROW]
[ROW][C]21[/C][C]0.120511046024351[/C][C]0.241022092048702[/C][C]0.87948895397565[/C][/ROW]
[ROW][C]22[/C][C]0.086682972595214[/C][C]0.173365945190428[/C][C]0.913317027404786[/C][/ROW]
[ROW][C]23[/C][C]0.0721858795610002[/C][C]0.144371759122000[/C][C]0.927814120439[/C][/ROW]
[ROW][C]24[/C][C]0.0566115656029318[/C][C]0.113223131205864[/C][C]0.943388434397068[/C][/ROW]
[ROW][C]25[/C][C]0.0922420831151372[/C][C]0.184484166230274[/C][C]0.907757916884863[/C][/ROW]
[ROW][C]26[/C][C]0.0790147212447404[/C][C]0.158029442489481[/C][C]0.92098527875526[/C][/ROW]
[ROW][C]27[/C][C]0.0608405245197584[/C][C]0.121681049039517[/C][C]0.939159475480242[/C][/ROW]
[ROW][C]28[/C][C]0.070161026251631[/C][C]0.140322052503262[/C][C]0.929838973748369[/C][/ROW]
[ROW][C]29[/C][C]0.0530431082628118[/C][C]0.106086216525624[/C][C]0.946956891737188[/C][/ROW]
[ROW][C]30[/C][C]0.0566770340867932[/C][C]0.113354068173586[/C][C]0.943322965913207[/C][/ROW]
[ROW][C]31[/C][C]0.0482350841547162[/C][C]0.0964701683094325[/C][C]0.951764915845284[/C][/ROW]
[ROW][C]32[/C][C]0.0354339088985067[/C][C]0.0708678177970135[/C][C]0.964566091101493[/C][/ROW]
[ROW][C]33[/C][C]0.0383616078256309[/C][C]0.0767232156512617[/C][C]0.961638392174369[/C][/ROW]
[ROW][C]34[/C][C]0.0279949412641024[/C][C]0.0559898825282048[/C][C]0.972005058735898[/C][/ROW]
[ROW][C]35[/C][C]0.0191843314270802[/C][C]0.0383686628541603[/C][C]0.98081566857292[/C][/ROW]
[ROW][C]36[/C][C]0.0151572131955214[/C][C]0.0303144263910428[/C][C]0.984842786804479[/C][/ROW]
[ROW][C]37[/C][C]0.0139171902508264[/C][C]0.0278343805016528[/C][C]0.986082809749174[/C][/ROW]
[ROW][C]38[/C][C]0.0226754715010825[/C][C]0.0453509430021651[/C][C]0.977324528498917[/C][/ROW]
[ROW][C]39[/C][C]0.018269702396486[/C][C]0.036539404792972[/C][C]0.981730297603514[/C][/ROW]
[ROW][C]40[/C][C]0.0249505061453837[/C][C]0.0499010122907675[/C][C]0.975049493854616[/C][/ROW]
[ROW][C]41[/C][C]0.0176349038050622[/C][C]0.0352698076101244[/C][C]0.982365096194938[/C][/ROW]
[ROW][C]42[/C][C]0.0143428749163864[/C][C]0.0286857498327729[/C][C]0.985657125083613[/C][/ROW]
[ROW][C]43[/C][C]0.00989061470398532[/C][C]0.0197812294079706[/C][C]0.990109385296015[/C][/ROW]
[ROW][C]44[/C][C]0.00750662762500499[/C][C]0.01501325525001[/C][C]0.992493372374995[/C][/ROW]
[ROW][C]45[/C][C]0.00503272134329713[/C][C]0.0100654426865943[/C][C]0.994967278656703[/C][/ROW]
[ROW][C]46[/C][C]0.00331425660317317[/C][C]0.00662851320634633[/C][C]0.996685743396827[/C][/ROW]
[ROW][C]47[/C][C]0.280093398649187[/C][C]0.560186797298373[/C][C]0.719906601350813[/C][/ROW]
[ROW][C]48[/C][C]0.273114327912538[/C][C]0.546228655825076[/C][C]0.726885672087462[/C][/ROW]
[ROW][C]49[/C][C]0.319013743287547[/C][C]0.638027486575095[/C][C]0.680986256712453[/C][/ROW]
[ROW][C]50[/C][C]0.294867926711249[/C][C]0.589735853422498[/C][C]0.705132073288751[/C][/ROW]
[ROW][C]51[/C][C]0.279753925315252[/C][C]0.559507850630504[/C][C]0.720246074684748[/C][/ROW]
[ROW][C]52[/C][C]0.250741173073287[/C][C]0.501482346146573[/C][C]0.749258826926713[/C][/ROW]
[ROW][C]53[/C][C]0.244573689296539[/C][C]0.489147378593079[/C][C]0.75542631070346[/C][/ROW]
[ROW][C]54[/C][C]0.224077669703087[/C][C]0.448155339406175[/C][C]0.775922330296913[/C][/ROW]
[ROW][C]55[/C][C]0.343198277551388[/C][C]0.686396555102776[/C][C]0.656801722448612[/C][/ROW]
[ROW][C]56[/C][C]0.416253738484122[/C][C]0.832507476968243[/C][C]0.583746261515878[/C][/ROW]
[ROW][C]57[/C][C]0.374167660864889[/C][C]0.748335321729778[/C][C]0.625832339135111[/C][/ROW]
[ROW][C]58[/C][C]0.336326506580966[/C][C]0.672653013161932[/C][C]0.663673493419034[/C][/ROW]
[ROW][C]59[/C][C]0.347413974149647[/C][C]0.694827948299293[/C][C]0.652586025850353[/C][/ROW]
[ROW][C]60[/C][C]0.529018510972793[/C][C]0.941962978054415[/C][C]0.470981489027207[/C][/ROW]
[ROW][C]61[/C][C]0.522215471612316[/C][C]0.955569056775368[/C][C]0.477784528387684[/C][/ROW]
[ROW][C]62[/C][C]0.484477390212855[/C][C]0.96895478042571[/C][C]0.515522609787145[/C][/ROW]
[ROW][C]63[/C][C]0.627223491327932[/C][C]0.745553017344137[/C][C]0.372776508672069[/C][/ROW]
[ROW][C]64[/C][C]0.670699094655675[/C][C]0.658601810688651[/C][C]0.329300905344325[/C][/ROW]
[ROW][C]65[/C][C]0.749808671404105[/C][C]0.50038265719179[/C][C]0.250191328595895[/C][/ROW]
[ROW][C]66[/C][C]0.719285445424593[/C][C]0.561429109150813[/C][C]0.280714554575407[/C][/ROW]
[ROW][C]67[/C][C]0.711884075423549[/C][C]0.576231849152901[/C][C]0.288115924576451[/C][/ROW]
[ROW][C]68[/C][C]0.68582266017054[/C][C]0.62835467965892[/C][C]0.31417733982946[/C][/ROW]
[ROW][C]69[/C][C]0.672605347562828[/C][C]0.654789304874344[/C][C]0.327394652437172[/C][/ROW]
[ROW][C]70[/C][C]0.652446805704855[/C][C]0.69510638859029[/C][C]0.347553194295145[/C][/ROW]
[ROW][C]71[/C][C]0.692800845612182[/C][C]0.614398308775636[/C][C]0.307199154387818[/C][/ROW]
[ROW][C]72[/C][C]0.689479521064806[/C][C]0.621040957870388[/C][C]0.310520478935194[/C][/ROW]
[ROW][C]73[/C][C]0.715517903080208[/C][C]0.568964193839583[/C][C]0.284482096919792[/C][/ROW]
[ROW][C]74[/C][C]0.710891207035444[/C][C]0.578217585929111[/C][C]0.289108792964556[/C][/ROW]
[ROW][C]75[/C][C]0.712123738296976[/C][C]0.575752523406049[/C][C]0.287876261703024[/C][/ROW]
[ROW][C]76[/C][C]0.671119268069892[/C][C]0.657761463860216[/C][C]0.328880731930108[/C][/ROW]
[ROW][C]77[/C][C]0.62938014031476[/C][C]0.74123971937048[/C][C]0.37061985968524[/C][/ROW]
[ROW][C]78[/C][C]0.586462835193943[/C][C]0.827074329612115[/C][C]0.413537164806057[/C][/ROW]
[ROW][C]79[/C][C]0.540412461109037[/C][C]0.919175077781926[/C][C]0.459587538890963[/C][/ROW]
[ROW][C]80[/C][C]0.493609007191321[/C][C]0.987218014382643[/C][C]0.506390992808679[/C][/ROW]
[ROW][C]81[/C][C]0.449623048546083[/C][C]0.899246097092166[/C][C]0.550376951453917[/C][/ROW]
[ROW][C]82[/C][C]0.496750877399050[/C][C]0.993501754798101[/C][C]0.503249122600950[/C][/ROW]
[ROW][C]83[/C][C]0.499881960301622[/C][C]0.999763920603244[/C][C]0.500118039698378[/C][/ROW]
[ROW][C]84[/C][C]0.454013042751727[/C][C]0.908026085503454[/C][C]0.545986957248273[/C][/ROW]
[ROW][C]85[/C][C]0.458831424595288[/C][C]0.917662849190577[/C][C]0.541168575404712[/C][/ROW]
[ROW][C]86[/C][C]0.42048946558042[/C][C]0.84097893116084[/C][C]0.57951053441958[/C][/ROW]
[ROW][C]87[/C][C]0.375106016383693[/C][C]0.750212032767387[/C][C]0.624893983616307[/C][/ROW]
[ROW][C]88[/C][C]0.339815312583316[/C][C]0.679630625166632[/C][C]0.660184687416684[/C][/ROW]
[ROW][C]89[/C][C]0.317605270795698[/C][C]0.635210541591395[/C][C]0.682394729204302[/C][/ROW]
[ROW][C]90[/C][C]0.301375532035861[/C][C]0.602751064071723[/C][C]0.698624467964139[/C][/ROW]
[ROW][C]91[/C][C]0.262190976640837[/C][C]0.524381953281673[/C][C]0.737809023359163[/C][/ROW]
[ROW][C]92[/C][C]0.231925335330222[/C][C]0.463850670660444[/C][C]0.768074664669778[/C][/ROW]
[ROW][C]93[/C][C]0.210134637498360[/C][C]0.420269274996721[/C][C]0.78986536250164[/C][/ROW]
[ROW][C]94[/C][C]0.178699460247986[/C][C]0.357398920495971[/C][C]0.821300539752014[/C][/ROW]
[ROW][C]95[/C][C]0.154962215358259[/C][C]0.309924430716517[/C][C]0.845037784641741[/C][/ROW]
[ROW][C]96[/C][C]0.174949130576766[/C][C]0.349898261153532[/C][C]0.825050869423234[/C][/ROW]
[ROW][C]97[/C][C]0.145879452836730[/C][C]0.291758905673460[/C][C]0.85412054716327[/C][/ROW]
[ROW][C]98[/C][C]0.121770155316752[/C][C]0.243540310633503[/C][C]0.878229844683248[/C][/ROW]
[ROW][C]99[/C][C]0.102914811691459[/C][C]0.205829623382919[/C][C]0.89708518830854[/C][/ROW]
[ROW][C]100[/C][C]0.085618208759545[/C][C]0.17123641751909[/C][C]0.914381791240455[/C][/ROW]
[ROW][C]101[/C][C]0.0739969623266601[/C][C]0.147993924653320[/C][C]0.92600303767334[/C][/ROW]
[ROW][C]102[/C][C]0.0653874844420825[/C][C]0.130774968884165[/C][C]0.934612515557917[/C][/ROW]
[ROW][C]103[/C][C]0.0784162067591467[/C][C]0.156832413518293[/C][C]0.921583793240853[/C][/ROW]
[ROW][C]104[/C][C]0.0711355937313735[/C][C]0.142271187462747[/C][C]0.928864406268627[/C][/ROW]
[ROW][C]105[/C][C]0.0672666472245792[/C][C]0.134533294449158[/C][C]0.93273335277542[/C][/ROW]
[ROW][C]106[/C][C]0.0562074949918657[/C][C]0.112414989983731[/C][C]0.943792505008134[/C][/ROW]
[ROW][C]107[/C][C]0.05300553960122[/C][C]0.10601107920244[/C][C]0.94699446039878[/C][/ROW]
[ROW][C]108[/C][C]0.0407642727190769[/C][C]0.0815285454381539[/C][C]0.959235727280923[/C][/ROW]
[ROW][C]109[/C][C]0.0323700866819935[/C][C]0.0647401733639869[/C][C]0.967629913318007[/C][/ROW]
[ROW][C]110[/C][C]0.0263692788287341[/C][C]0.0527385576574681[/C][C]0.973630721171266[/C][/ROW]
[ROW][C]111[/C][C]0.0230768076688220[/C][C]0.0461536153376441[/C][C]0.976923192331178[/C][/ROW]
[ROW][C]112[/C][C]0.0300107363081931[/C][C]0.0600214726163862[/C][C]0.969989263691807[/C][/ROW]
[ROW][C]113[/C][C]0.0325355142536972[/C][C]0.0650710285073944[/C][C]0.967464485746303[/C][/ROW]
[ROW][C]114[/C][C]0.0344331900406653[/C][C]0.0688663800813306[/C][C]0.965566809959335[/C][/ROW]
[ROW][C]115[/C][C]0.0261456833140087[/C][C]0.0522913666280173[/C][C]0.97385431668599[/C][/ROW]
[ROW][C]116[/C][C]0.0193072405001016[/C][C]0.0386144810002033[/C][C]0.980692759499898[/C][/ROW]
[ROW][C]117[/C][C]0.0454427284426175[/C][C]0.0908854568852351[/C][C]0.954557271557382[/C][/ROW]
[ROW][C]118[/C][C]0.0345139106609013[/C][C]0.0690278213218026[/C][C]0.965486089339099[/C][/ROW]
[ROW][C]119[/C][C]0.0417655369171298[/C][C]0.0835310738342597[/C][C]0.95823446308287[/C][/ROW]
[ROW][C]120[/C][C]0.0317078346309632[/C][C]0.0634156692619263[/C][C]0.968292165369037[/C][/ROW]
[ROW][C]121[/C][C]0.0253882623695584[/C][C]0.0507765247391168[/C][C]0.974611737630442[/C][/ROW]
[ROW][C]122[/C][C]0.0181572412929428[/C][C]0.0363144825858856[/C][C]0.981842758707057[/C][/ROW]
[ROW][C]123[/C][C]0.0688480718455644[/C][C]0.137696143691129[/C][C]0.931151928154436[/C][/ROW]
[ROW][C]124[/C][C]0.058103616706906[/C][C]0.116207233413812[/C][C]0.941896383293094[/C][/ROW]
[ROW][C]125[/C][C]0.048101331393813[/C][C]0.096202662787626[/C][C]0.951898668606187[/C][/ROW]
[ROW][C]126[/C][C]0.0433184624238062[/C][C]0.0866369248476124[/C][C]0.956681537576194[/C][/ROW]
[ROW][C]127[/C][C]0.0314250609684691[/C][C]0.0628501219369382[/C][C]0.96857493903153[/C][/ROW]
[ROW][C]128[/C][C]0.0871117733525593[/C][C]0.174223546705119[/C][C]0.91288822664744[/C][/ROW]
[ROW][C]129[/C][C]0.071738838786184[/C][C]0.143477677572368[/C][C]0.928261161213816[/C][/ROW]
[ROW][C]130[/C][C]0.100757277121648[/C][C]0.201514554243295[/C][C]0.899242722878352[/C][/ROW]
[ROW][C]131[/C][C]0.220658046981573[/C][C]0.441316093963146[/C][C]0.779341953018427[/C][/ROW]
[ROW][C]132[/C][C]0.176006552745457[/C][C]0.352013105490913[/C][C]0.823993447254543[/C][/ROW]
[ROW][C]133[/C][C]0.144433056744624[/C][C]0.288866113489247[/C][C]0.855566943255377[/C][/ROW]
[ROW][C]134[/C][C]0.648128139348192[/C][C]0.703743721303616[/C][C]0.351871860651808[/C][/ROW]
[ROW][C]135[/C][C]0.578449798343733[/C][C]0.843100403312533[/C][C]0.421550201656267[/C][/ROW]
[ROW][C]136[/C][C]0.504323713130948[/C][C]0.991352573738104[/C][C]0.495676286869052[/C][/ROW]
[ROW][C]137[/C][C]0.423922180417731[/C][C]0.847844360835462[/C][C]0.576077819582269[/C][/ROW]
[ROW][C]138[/C][C]0.454673154362546[/C][C]0.909346308725093[/C][C]0.545326845637454[/C][/ROW]
[ROW][C]139[/C][C]0.377477668616922[/C][C]0.754955337233844[/C][C]0.622522331383078[/C][/ROW]
[ROW][C]140[/C][C]0.438541786088808[/C][C]0.877083572177616[/C][C]0.561458213911192[/C][/ROW]
[ROW][C]141[/C][C]0.582436238778361[/C][C]0.835127522443278[/C][C]0.417563761221639[/C][/ROW]
[ROW][C]142[/C][C]0.522075426310623[/C][C]0.955849147378754[/C][C]0.477924573689377[/C][/ROW]
[ROW][C]143[/C][C]0.605257612551543[/C][C]0.789484774896915[/C][C]0.394742387448458[/C][/ROW]
[ROW][C]144[/C][C]0.504689182743459[/C][C]0.990621634513082[/C][C]0.495310817256541[/C][/ROW]
[ROW][C]145[/C][C]0.378941510294071[/C][C]0.757883020588142[/C][C]0.621058489705929[/C][/ROW]
[ROW][C]146[/C][C]0.43291719183645[/C][C]0.8658343836729[/C][C]0.56708280816355[/C][/ROW]
[ROW][C]147[/C][C]0.339734261822437[/C][C]0.679468523644874[/C][C]0.660265738177563[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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
90.8234240773885430.3531518452229130.176575922611457
100.7795588050183980.4408823899632040.220441194981602
110.6690695529090640.6618608941818730.330930447090936
120.5670464128136740.8659071743726520.432953587186326
130.4790198657162550.958039731432510.520980134283745
140.3747561545706110.7495123091412220.625243845429389
150.2877050197896960.5754100395793920.712294980210304
160.3467360427034640.6934720854069290.653263957296536
170.270953208171010.541906416342020.72904679182899
180.2061739825559630.4123479651119260.793826017444037
190.1511275945752100.3022551891504210.84887240542479
200.1111744523983030.2223489047966070.888825547601697
210.1205110460243510.2410220920487020.87948895397565
220.0866829725952140.1733659451904280.913317027404786
230.07218587956100020.1443717591220000.927814120439
240.05661156560293180.1132231312058640.943388434397068
250.09224208311513720.1844841662302740.907757916884863
260.07901472124474040.1580294424894810.92098527875526
270.06084052451975840.1216810490395170.939159475480242
280.0701610262516310.1403220525032620.929838973748369
290.05304310826281180.1060862165256240.946956891737188
300.05667703408679320.1133540681735860.943322965913207
310.04823508415471620.09647016830943250.951764915845284
320.03543390889850670.07086781779701350.964566091101493
330.03836160782563090.07672321565126170.961638392174369
340.02799494126410240.05598988252820480.972005058735898
350.01918433142708020.03836866285416030.98081566857292
360.01515721319552140.03031442639104280.984842786804479
370.01391719025082640.02783438050165280.986082809749174
380.02267547150108250.04535094300216510.977324528498917
390.0182697023964860.0365394047929720.981730297603514
400.02495050614538370.04990101229076750.975049493854616
410.01763490380506220.03526980761012440.982365096194938
420.01434287491638640.02868574983277290.985657125083613
430.009890614703985320.01978122940797060.990109385296015
440.007506627625004990.015013255250010.992493372374995
450.005032721343297130.01006544268659430.994967278656703
460.003314256603173170.006628513206346330.996685743396827
470.2800933986491870.5601867972983730.719906601350813
480.2731143279125380.5462286558250760.726885672087462
490.3190137432875470.6380274865750950.680986256712453
500.2948679267112490.5897358534224980.705132073288751
510.2797539253152520.5595078506305040.720246074684748
520.2507411730732870.5014823461465730.749258826926713
530.2445736892965390.4891473785930790.75542631070346
540.2240776697030870.4481553394061750.775922330296913
550.3431982775513880.6863965551027760.656801722448612
560.4162537384841220.8325074769682430.583746261515878
570.3741676608648890.7483353217297780.625832339135111
580.3363265065809660.6726530131619320.663673493419034
590.3474139741496470.6948279482992930.652586025850353
600.5290185109727930.9419629780544150.470981489027207
610.5222154716123160.9555690567753680.477784528387684
620.4844773902128550.968954780425710.515522609787145
630.6272234913279320.7455530173441370.372776508672069
640.6706990946556750.6586018106886510.329300905344325
650.7498086714041050.500382657191790.250191328595895
660.7192854454245930.5614291091508130.280714554575407
670.7118840754235490.5762318491529010.288115924576451
680.685822660170540.628354679658920.31417733982946
690.6726053475628280.6547893048743440.327394652437172
700.6524468057048550.695106388590290.347553194295145
710.6928008456121820.6143983087756360.307199154387818
720.6894795210648060.6210409578703880.310520478935194
730.7155179030802080.5689641938395830.284482096919792
740.7108912070354440.5782175859291110.289108792964556
750.7121237382969760.5757525234060490.287876261703024
760.6711192680698920.6577614638602160.328880731930108
770.629380140314760.741239719370480.37061985968524
780.5864628351939430.8270743296121150.413537164806057
790.5404124611090370.9191750777819260.459587538890963
800.4936090071913210.9872180143826430.506390992808679
810.4496230485460830.8992460970921660.550376951453917
820.4967508773990500.9935017547981010.503249122600950
830.4998819603016220.9997639206032440.500118039698378
840.4540130427517270.9080260855034540.545986957248273
850.4588314245952880.9176628491905770.541168575404712
860.420489465580420.840978931160840.57951053441958
870.3751060163836930.7502120327673870.624893983616307
880.3398153125833160.6796306251666320.660184687416684
890.3176052707956980.6352105415913950.682394729204302
900.3013755320358610.6027510640717230.698624467964139
910.2621909766408370.5243819532816730.737809023359163
920.2319253353302220.4638506706604440.768074664669778
930.2101346374983600.4202692749967210.78986536250164
940.1786994602479860.3573989204959710.821300539752014
950.1549622153582590.3099244307165170.845037784641741
960.1749491305767660.3498982611535320.825050869423234
970.1458794528367300.2917589056734600.85412054716327
980.1217701553167520.2435403106335030.878229844683248
990.1029148116914590.2058296233829190.89708518830854
1000.0856182087595450.171236417519090.914381791240455
1010.07399696232666010.1479939246533200.92600303767334
1020.06538748444208250.1307749688841650.934612515557917
1030.07841620675914670.1568324135182930.921583793240853
1040.07113559373137350.1422711874627470.928864406268627
1050.06726664722457920.1345332944491580.93273335277542
1060.05620749499186570.1124149899837310.943792505008134
1070.053005539601220.106011079202440.94699446039878
1080.04076427271907690.08152854543815390.959235727280923
1090.03237008668199350.06474017336398690.967629913318007
1100.02636927882873410.05273855765746810.973630721171266
1110.02307680766882200.04615361533764410.976923192331178
1120.03001073630819310.06002147261638620.969989263691807
1130.03253551425369720.06507102850739440.967464485746303
1140.03443319004066530.06886638008133060.965566809959335
1150.02614568331400870.05229136662801730.97385431668599
1160.01930724050010160.03861448100020330.980692759499898
1170.04544272844261750.09088545688523510.954557271557382
1180.03451391066090130.06902782132180260.965486089339099
1190.04176553691712980.08353107383425970.95823446308287
1200.03170783463096320.06341566926192630.968292165369037
1210.02538826236955840.05077652473911680.974611737630442
1220.01815724129294280.03631448258588560.981842758707057
1230.06884807184556440.1376961436911290.931151928154436
1240.0581036167069060.1162072334138120.941896383293094
1250.0481013313938130.0962026627876260.951898668606187
1260.04331846242380620.08663692484761240.956681537576194
1270.03142506096846910.06285012193693820.96857493903153
1280.08711177335255930.1742235467051190.91288822664744
1290.0717388387861840.1434776775723680.928261161213816
1300.1007572771216480.2015145542432950.899242722878352
1310.2206580469815730.4413160939631460.779341953018427
1320.1760065527454570.3520131054909130.823993447254543
1330.1444330567446240.2888661134892470.855566943255377
1340.6481281393481920.7037437213036160.351871860651808
1350.5784497983437330.8431004033125330.421550201656267
1360.5043237131309480.9913525737381040.495676286869052
1370.4239221804177310.8478443608354620.576077819582269
1380.4546731543625460.9093463087250930.545326845637454
1390.3774776686169220.7549553372338440.622522331383078
1400.4385417860888080.8770835721776160.561458213911192
1410.5824362387783610.8351275224432780.417563761221639
1420.5220754263106230.9558491473787540.477924573689377
1430.6052576125515430.7894847748969150.394742387448458
1440.5046891827434590.9906216345130820.495310817256541
1450.3789415102940710.7578830205881420.621058489705929
1460.432917191836450.86583438367290.56708280816355
1470.3397342618224370.6794685236448740.660265738177563






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00719424460431655OK
5% type I error level150.107913669064748NOK
10% type I error level340.244604316546763NOK

\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 & 1 & 0.00719424460431655 & OK \tabularnewline
5% type I error level & 15 & 0.107913669064748 & NOK \tabularnewline
10% type I error level & 34 & 0.244604316546763 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104131&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]1[/C][C]0.00719424460431655[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.107913669064748[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.244604316546763[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104131&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104131&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 level10.00719424460431655OK
5% type I error level150.107913669064748NOK
10% type I error level340.244604316546763NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')
}