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 computationMon, 19 Nov 2012 04:50:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/19/t13533187280k7g3tgethug1o5.htm/, Retrieved Sun, 28 Apr 2024 03:48:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190421, Retrieved Sun, 28 Apr 2024 03:48:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7] [2012-11-19 09:50:59] [e2ccb4f6662abf6b355cbcf28b97e404] [Current]
-  M        [Multiple Regression] [Workshop 7] [2013-11-20 14:38:51] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:27:21] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:45:43] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:46:30] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:47:22] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:47:57] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:48:46] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2013-11-21 01:49:24] [74be16979710d4c4e7c6647856088456]
-  M        [Multiple Regression] [] [2014-11-12 18:58:24] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:03:13] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:07:42] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:10:56] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:12:43] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:14:21] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:16:47] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:19:12] [9922f47a08b670aeeb7c38448acbfea1]
-  M        [Multiple Regression] [] [2014-11-12 19:20:36] [9922f47a08b670aeeb7c38448acbfea1]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	7	7	5
5	5	5	5
6	5	4	4
4	5	5	5
5	5	5	5
6	6	6	7
7	4	7	7
6	5	5	6
6	7	7	6
6	5	5	6
5	2	4	6
5	5	6	6
4	5	4	6
6	6	6	6
6	6	7	7
5	5	6	5
3	3	4	3
7	6	6	7
3	5	6	6
5	5	6	6
3	3	4	4
5	5	5	6
2	1	2	2
6	5	6	6
3	4	5	5
6	6	6	7
6	6	6	7
5	4	5	5
5	4	5	6
7	5	5	6
6	4	6	6
5	5	6	6
5	6	5	5
4	3	4	4
4	5	6	5
6	5	5	6
5	3	5	5
5	5	5	5
7	7	7	7
5	6	5	6
5	4	5	4
6	5	7	5
5	5	5	5
6	6	7	6
7	7	6	6
5	3	3	4
5	4	4	4
5	6	6	6
6	5	5	5
2	2	4	5
4	4	4	6
4	4	6	5
6	5	5	6
3	4	4	5
6	6	6	6
6	2	5	5
5	4	5	6
6	6	6	6
1	4	6	3
5	5	6	6
7	6	5	6
4	4	5	5
5	6	5	5
6	6	5	6
4	5	4	4
6	6	5	5
6	6	6	6
5	4	6	6
5	6	5	6
3	3	5	5
5	5	5	6
6	5	6	6
5	5	6	6
6	6	6	6
6	6	6	6
4	4	4	4
4	4	4	4
6	5	5	5
7	6	6	7
4	3	3	5
5	6	7	7
6	2	5	5
6	5	6	6
5	3	6	6
3	4	5	5
7	6	6	6
6	5	6	7
4	4	4	5
4	5	6	4
5	5	5	5
3	4	4	4
7	7	7	6
6	4	6	6
6	6	6	5
4	4	4	4
5	4	5	5
6	6	6	7
5	3	5	6
6	4	4	5
6	7	7	6
4	5	6	6
5	5	5	5
6	6	6	6
5	5	6	6
5	5	5	5
4	4	5	5
4	5	5	4
6	6	5	7
5	5	7	7
6	5	6	5
5	4	7	7
6	4	6	6
5	5	5	5
4	5	4	5
6	6	6	7
4	5	4	5
5	3	3	5
5	5	5	5
6	3	5	5
3	4	5	3
5	4	5	5
4	5	5	5
5	2	5	4
5	5	3	4
7	7	7	7
5	6	6	6
7	6	6	6
5	5	4	6
4	4	4	5
6	6	6	5
4	3	4	5
4	7	6	6
4	3	2	2
4	4	5	5
6	6	5	7
6	5	6	6
5	6	5	5
3	4	4	5
6	6	6	6
5	6	6	6
4	4	5	5
5	5	5	5
2	3	2	2
5	6	6	7
7	5	6	7
4	6	5	5
4	5	6	6
7	6	7	5
6	5	5	6
5	6	5	5
5	5	5	6
5	5	6	6
7	6	7	7
6	5	7	6
6	6	6	6
5	5	5	6
2	4	6	6
4	4	4	4
6	4	6	6
5	5	6	4
5	4	4	5
5	5	5	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Q2[t] = + 0.644149847447203 + 0.275369724984056Q9[t] + 0.135943712711935Q16[t] + 0.438581205726035Q23[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Q2[t] =  +  0.644149847447203 +  0.275369724984056Q9[t] +  0.135943712711935Q16[t] +  0.438581205726035Q23[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Q2[t] =  +  0.644149847447203 +  0.275369724984056Q9[t] +  0.135943712711935Q16[t] +  0.438581205726035Q23[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&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
Q2[t] = + 0.644149847447203 + 0.275369724984056Q9[t] + 0.135943712711935Q16[t] + 0.438581205726035Q23[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6441498474472030.4154121.55060.1229920.061496
Q90.2753697249840560.0776393.54680.0005130.000257
Q160.1359437127119350.0991381.37130.1722390.086119
Q230.4385812057260350.0954294.59599e-064e-06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.644149847447203 & 0.415412 & 1.5506 & 0.122992 & 0.061496 \tabularnewline
Q9 & 0.275369724984056 & 0.077639 & 3.5468 & 0.000513 & 0.000257 \tabularnewline
Q16 & 0.135943712711935 & 0.099138 & 1.3713 & 0.172239 & 0.086119 \tabularnewline
Q23 & 0.438581205726035 & 0.095429 & 4.5959 & 9e-06 & 4e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&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]0.644149847447203[/C][C]0.415412[/C][C]1.5506[/C][C]0.122992[/C][C]0.061496[/C][/ROW]
[ROW][C]Q9[/C][C]0.275369724984056[/C][C]0.077639[/C][C]3.5468[/C][C]0.000513[/C][C]0.000257[/C][/ROW]
[ROW][C]Q16[/C][C]0.135943712711935[/C][C]0.099138[/C][C]1.3713[/C][C]0.172239[/C][C]0.086119[/C][/ROW]
[ROW][C]Q23[/C][C]0.438581205726035[/C][C]0.095429[/C][C]4.5959[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&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)0.6441498474472030.4154121.55060.1229920.061496
Q90.2753697249840560.0776393.54680.0005130.000257
Q160.1359437127119350.0991381.37130.1722390.086119
Q230.4385812057260350.0954294.59599e-064e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.660729320281224
R-squared0.436563234679289
Adjusted R-squared0.425865068249148
F-TEST (value)40.8072951126796
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.901613309503443
Sum Squared Residuals128.439236460053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.660729320281224 \tabularnewline
R-squared & 0.436563234679289 \tabularnewline
Adjusted R-squared & 0.425865068249148 \tabularnewline
F-TEST (value) & 40.8072951126796 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.901613309503443 \tabularnewline
Sum Squared Residuals & 128.439236460053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.660729320281224[/C][/ROW]
[ROW][C]R-squared[/C][C]0.436563234679289[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.425865068249148[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]40.8072951126796[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.901613309503443[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]128.439236460053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&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 R0.660729320281224
R-squared0.436563234679289
Adjusted R-squared0.425865068249148
F-TEST (value)40.8072951126796
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.901613309503443
Sum Squared Residuals128.439236460053






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.716249939949321.28375006005068
254.893623064557330.106376935442666
364.319098146119361.68090185388064
444.89362306455733-0.893623064557333
554.893623064557330.106376935442667
666.18209891370539-0.182098913705394
775.767303176449221.23269682355078
865.332204270283370.667795729716632
966.15483114567535-0.15483114567535
1065.332204270283370.667795729716632
1154.370151382619270.629848617380735
1255.4681479829953-0.468147982995303
1345.19626055757143-1.19626055757143
1465.743517707979360.256482292020641
1566.31804262641733-0.318042626417329
1655.02956677726927-0.029566777269268
1733.32977749042522-0.329777490425216
1876.182098913705390.817901086294606
1935.4681479829953-2.4681479829953
2055.4681479829953-0.468147982995303
2133.76835869615125-0.768358696151251
2255.33220427028337-0.332204270283368
2322.0685694093072-0.0685694093071992
2465.46814798299530.531852017004697
2534.61825333957328-1.61825333957328
2666.18209891370539-0.182098913705394
2766.18209891370539-0.182098913705394
2854.618253339573280.381746660426723
2955.05683454529931-0.056834545299312
3075.332204270283371.66779572971663
3165.192778258011250.807221741988753
3255.4681479829953-0.468147982995303
3355.16899278954139-0.168992789541389
3443.768358696151250.231641303848749
3545.02956677726927-1.02956677726927
3665.332204270283370.667795729716632
3754.342883614589220.657116385410779
3854.893623064557330.106376935442667
3976.593412351401390.406587648598615
4055.60757399526742-0.607573995267424
4154.179672133847240.820327866152758
4265.16551048998120.834489510018797
4354.893623064557330.106376935442667
4465.879461420691290.120538579308706
4576.018887432963420.981112567036585
4653.632414983439321.36758501656068
4754.043728421135310.956271578864692
4855.74351770797936-0.743517707979359
4964.893623064557331.10637693544267
5023.93157017689323-1.93157017689323
5144.92089083258738-0.920890832587377
5244.75419705228521-0.754197052285212
5365.332204270283370.667795729716632
5434.48230962686134-1.48230962686134
5565.743517707979360.256482292020641
5664.067513889605161.93248611039484
5755.05683454529931-0.056834545299312
5865.743517707979360.256482292020641
5913.87703464083314-2.87703464083314
6055.4681479829953-0.468147982995303
6175.607573995267421.39242600473258
6244.61825333957328-0.618253339573277
6355.16899278954139-0.168992789541389
6465.607573995267420.392426004732576
6544.31909814611936-0.319098146119363
6665.168992789541390.83100721045861
6765.743517707979360.256482292020641
6855.19277825801125-0.192778258011247
6955.60757399526742-0.607573995267424
7034.34288361458922-1.34288361458922
7155.33220427028337-0.332204270283368
7265.46814798299530.531852017004697
7355.4681479829953-0.468147982995303
7465.743517707979360.256482292020641
7565.743517707979360.256482292020641
7644.04372842113531-0.0437284211353072
7744.04372842113531-0.0437284211353072
7864.893623064557331.10637693544267
7976.182098913705390.817901086294606
8044.07099618916535-0.0709961891653513
8156.31804262641733-1.31804262641733
8264.067513889605161.93248611039484
8365.46814798299530.531852017004697
8454.917408533027190.0825914669728096
8534.61825333957328-1.61825333957328
8675.743517707979361.25648229202064
8765.906729188721340.0932708112786621
8844.48230962686134-0.482309626861342
8944.59098557154323-0.590985571543233
9054.893623064557330.106376935442667
9134.04372842113531-1.04372842113531
9276.154831145675350.84516885432465
9365.192778258011250.807221741988753
9465.304936502253320.695063497746676
9544.04372842113531-0.0437284211353072
9654.618253339573280.381746660426723
9766.18209891370539-0.182098913705394
9854.781464820315260.218535179684744
9964.482309626861341.51769037313866
10066.15483114567535-0.15483114567535
10145.4681479829953-1.4681479829953
10254.893623064557330.106376935442667
10365.743517707979360.256482292020641
10455.4681479829953-0.468147982995303
10554.893623064557330.106376935442667
10644.61825333957328-0.618253339573277
10744.4550418588313-0.455041858831298
10866.04615520099346-0.0461552009934594
10956.04267290143327-1.04267290143327
11065.029566777269270.970433222730732
11155.76730317644922-0.767303176449217
11265.192778258011250.807221741988753
11354.893623064557330.106376935442667
11444.7576793518454-0.757679351845399
11566.18209891370539-0.182098913705394
11644.7576793518454-0.757679351845399
11754.070996189165350.929003810834649
11854.893623064557330.106376935442667
11964.342883614589221.65711638541078
12033.74109092812121-0.741090928121207
12154.618253339573280.381746660426723
12244.89362306455733-0.893623064557333
12353.628932683879131.37106731612087
12454.183154433407430.816845566592571
12576.593412351401390.406587648598615
12655.74351770797936-0.743517707979359
12775.743517707979361.25648229202064
12855.19626055757143-0.196260557571433
12944.48230962686134-0.482309626861342
13065.304936502253320.695063497746676
13144.20693990187729-0.206939901877286
13246.01888743296341-2.01888743296341
13342.619308859275311.38069114072469
13444.61825333957328-0.618253339573277
13566.04615520099346-0.0461552009934594
13665.46814798299530.531852017004697
13755.16899278954139-0.168992789541389
13834.48230962686134-1.48230962686134
13965.743517707979360.256482292020641
14055.74351770797936-0.743517707979359
14144.61825333957328-0.618253339573277
14254.893623064557330.106376935442667
14322.61930885927531-0.619308859275312
14456.18209891370539-1.18209891370539
14575.906729188721341.09327081127866
14645.16899278954139-1.16899278954139
14745.4681479829953-1.4681479829953
14875.440880214965261.55911978503474
14965.332204270283370.667795729716632
15055.16899278954139-0.168992789541389
15155.33220427028337-0.332204270283368
15255.4681479829953-0.468147982995303
15376.318042626417330.681957373582671
15465.604091695707240.395908304292762
15565.743517707979360.256482292020641
15655.33220427028337-0.332204270283368
15725.19277825801125-3.19277825801125
15844.04372842113531-0.0437284211353072
15965.192778258011250.807221741988753
16054.590985571543230.409014428456767
16154.482309626861340.517690373138658
16254.893623064557330.106376935442667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7 & 5.71624993994932 & 1.28375006005068 \tabularnewline
2 & 5 & 4.89362306455733 & 0.106376935442666 \tabularnewline
3 & 6 & 4.31909814611936 & 1.68090185388064 \tabularnewline
4 & 4 & 4.89362306455733 & -0.893623064557333 \tabularnewline
5 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
6 & 6 & 6.18209891370539 & -0.182098913705394 \tabularnewline
7 & 7 & 5.76730317644922 & 1.23269682355078 \tabularnewline
8 & 6 & 5.33220427028337 & 0.667795729716632 \tabularnewline
9 & 6 & 6.15483114567535 & -0.15483114567535 \tabularnewline
10 & 6 & 5.33220427028337 & 0.667795729716632 \tabularnewline
11 & 5 & 4.37015138261927 & 0.629848617380735 \tabularnewline
12 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
13 & 4 & 5.19626055757143 & -1.19626055757143 \tabularnewline
14 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
15 & 6 & 6.31804262641733 & -0.318042626417329 \tabularnewline
16 & 5 & 5.02956677726927 & -0.029566777269268 \tabularnewline
17 & 3 & 3.32977749042522 & -0.329777490425216 \tabularnewline
18 & 7 & 6.18209891370539 & 0.817901086294606 \tabularnewline
19 & 3 & 5.4681479829953 & -2.4681479829953 \tabularnewline
20 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
21 & 3 & 3.76835869615125 & -0.768358696151251 \tabularnewline
22 & 5 & 5.33220427028337 & -0.332204270283368 \tabularnewline
23 & 2 & 2.0685694093072 & -0.0685694093071992 \tabularnewline
24 & 6 & 5.4681479829953 & 0.531852017004697 \tabularnewline
25 & 3 & 4.61825333957328 & -1.61825333957328 \tabularnewline
26 & 6 & 6.18209891370539 & -0.182098913705394 \tabularnewline
27 & 6 & 6.18209891370539 & -0.182098913705394 \tabularnewline
28 & 5 & 4.61825333957328 & 0.381746660426723 \tabularnewline
29 & 5 & 5.05683454529931 & -0.056834545299312 \tabularnewline
30 & 7 & 5.33220427028337 & 1.66779572971663 \tabularnewline
31 & 6 & 5.19277825801125 & 0.807221741988753 \tabularnewline
32 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
33 & 5 & 5.16899278954139 & -0.168992789541389 \tabularnewline
34 & 4 & 3.76835869615125 & 0.231641303848749 \tabularnewline
35 & 4 & 5.02956677726927 & -1.02956677726927 \tabularnewline
36 & 6 & 5.33220427028337 & 0.667795729716632 \tabularnewline
37 & 5 & 4.34288361458922 & 0.657116385410779 \tabularnewline
38 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
39 & 7 & 6.59341235140139 & 0.406587648598615 \tabularnewline
40 & 5 & 5.60757399526742 & -0.607573995267424 \tabularnewline
41 & 5 & 4.17967213384724 & 0.820327866152758 \tabularnewline
42 & 6 & 5.1655104899812 & 0.834489510018797 \tabularnewline
43 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
44 & 6 & 5.87946142069129 & 0.120538579308706 \tabularnewline
45 & 7 & 6.01888743296342 & 0.981112567036585 \tabularnewline
46 & 5 & 3.63241498343932 & 1.36758501656068 \tabularnewline
47 & 5 & 4.04372842113531 & 0.956271578864692 \tabularnewline
48 & 5 & 5.74351770797936 & -0.743517707979359 \tabularnewline
49 & 6 & 4.89362306455733 & 1.10637693544267 \tabularnewline
50 & 2 & 3.93157017689323 & -1.93157017689323 \tabularnewline
51 & 4 & 4.92089083258738 & -0.920890832587377 \tabularnewline
52 & 4 & 4.75419705228521 & -0.754197052285212 \tabularnewline
53 & 6 & 5.33220427028337 & 0.667795729716632 \tabularnewline
54 & 3 & 4.48230962686134 & -1.48230962686134 \tabularnewline
55 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
56 & 6 & 4.06751388960516 & 1.93248611039484 \tabularnewline
57 & 5 & 5.05683454529931 & -0.056834545299312 \tabularnewline
58 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
59 & 1 & 3.87703464083314 & -2.87703464083314 \tabularnewline
60 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
61 & 7 & 5.60757399526742 & 1.39242600473258 \tabularnewline
62 & 4 & 4.61825333957328 & -0.618253339573277 \tabularnewline
63 & 5 & 5.16899278954139 & -0.168992789541389 \tabularnewline
64 & 6 & 5.60757399526742 & 0.392426004732576 \tabularnewline
65 & 4 & 4.31909814611936 & -0.319098146119363 \tabularnewline
66 & 6 & 5.16899278954139 & 0.83100721045861 \tabularnewline
67 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
68 & 5 & 5.19277825801125 & -0.192778258011247 \tabularnewline
69 & 5 & 5.60757399526742 & -0.607573995267424 \tabularnewline
70 & 3 & 4.34288361458922 & -1.34288361458922 \tabularnewline
71 & 5 & 5.33220427028337 & -0.332204270283368 \tabularnewline
72 & 6 & 5.4681479829953 & 0.531852017004697 \tabularnewline
73 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
74 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
75 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
76 & 4 & 4.04372842113531 & -0.0437284211353072 \tabularnewline
77 & 4 & 4.04372842113531 & -0.0437284211353072 \tabularnewline
78 & 6 & 4.89362306455733 & 1.10637693544267 \tabularnewline
79 & 7 & 6.18209891370539 & 0.817901086294606 \tabularnewline
80 & 4 & 4.07099618916535 & -0.0709961891653513 \tabularnewline
81 & 5 & 6.31804262641733 & -1.31804262641733 \tabularnewline
82 & 6 & 4.06751388960516 & 1.93248611039484 \tabularnewline
83 & 6 & 5.4681479829953 & 0.531852017004697 \tabularnewline
84 & 5 & 4.91740853302719 & 0.0825914669728096 \tabularnewline
85 & 3 & 4.61825333957328 & -1.61825333957328 \tabularnewline
86 & 7 & 5.74351770797936 & 1.25648229202064 \tabularnewline
87 & 6 & 5.90672918872134 & 0.0932708112786621 \tabularnewline
88 & 4 & 4.48230962686134 & -0.482309626861342 \tabularnewline
89 & 4 & 4.59098557154323 & -0.590985571543233 \tabularnewline
90 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
91 & 3 & 4.04372842113531 & -1.04372842113531 \tabularnewline
92 & 7 & 6.15483114567535 & 0.84516885432465 \tabularnewline
93 & 6 & 5.19277825801125 & 0.807221741988753 \tabularnewline
94 & 6 & 5.30493650225332 & 0.695063497746676 \tabularnewline
95 & 4 & 4.04372842113531 & -0.0437284211353072 \tabularnewline
96 & 5 & 4.61825333957328 & 0.381746660426723 \tabularnewline
97 & 6 & 6.18209891370539 & -0.182098913705394 \tabularnewline
98 & 5 & 4.78146482031526 & 0.218535179684744 \tabularnewline
99 & 6 & 4.48230962686134 & 1.51769037313866 \tabularnewline
100 & 6 & 6.15483114567535 & -0.15483114567535 \tabularnewline
101 & 4 & 5.4681479829953 & -1.4681479829953 \tabularnewline
102 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
103 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
104 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
105 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
106 & 4 & 4.61825333957328 & -0.618253339573277 \tabularnewline
107 & 4 & 4.4550418588313 & -0.455041858831298 \tabularnewline
108 & 6 & 6.04615520099346 & -0.0461552009934594 \tabularnewline
109 & 5 & 6.04267290143327 & -1.04267290143327 \tabularnewline
110 & 6 & 5.02956677726927 & 0.970433222730732 \tabularnewline
111 & 5 & 5.76730317644922 & -0.767303176449217 \tabularnewline
112 & 6 & 5.19277825801125 & 0.807221741988753 \tabularnewline
113 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
114 & 4 & 4.7576793518454 & -0.757679351845399 \tabularnewline
115 & 6 & 6.18209891370539 & -0.182098913705394 \tabularnewline
116 & 4 & 4.7576793518454 & -0.757679351845399 \tabularnewline
117 & 5 & 4.07099618916535 & 0.929003810834649 \tabularnewline
118 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
119 & 6 & 4.34288361458922 & 1.65711638541078 \tabularnewline
120 & 3 & 3.74109092812121 & -0.741090928121207 \tabularnewline
121 & 5 & 4.61825333957328 & 0.381746660426723 \tabularnewline
122 & 4 & 4.89362306455733 & -0.893623064557333 \tabularnewline
123 & 5 & 3.62893268387913 & 1.37106731612087 \tabularnewline
124 & 5 & 4.18315443340743 & 0.816845566592571 \tabularnewline
125 & 7 & 6.59341235140139 & 0.406587648598615 \tabularnewline
126 & 5 & 5.74351770797936 & -0.743517707979359 \tabularnewline
127 & 7 & 5.74351770797936 & 1.25648229202064 \tabularnewline
128 & 5 & 5.19626055757143 & -0.196260557571433 \tabularnewline
129 & 4 & 4.48230962686134 & -0.482309626861342 \tabularnewline
130 & 6 & 5.30493650225332 & 0.695063497746676 \tabularnewline
131 & 4 & 4.20693990187729 & -0.206939901877286 \tabularnewline
132 & 4 & 6.01888743296341 & -2.01888743296341 \tabularnewline
133 & 4 & 2.61930885927531 & 1.38069114072469 \tabularnewline
134 & 4 & 4.61825333957328 & -0.618253339573277 \tabularnewline
135 & 6 & 6.04615520099346 & -0.0461552009934594 \tabularnewline
136 & 6 & 5.4681479829953 & 0.531852017004697 \tabularnewline
137 & 5 & 5.16899278954139 & -0.168992789541389 \tabularnewline
138 & 3 & 4.48230962686134 & -1.48230962686134 \tabularnewline
139 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
140 & 5 & 5.74351770797936 & -0.743517707979359 \tabularnewline
141 & 4 & 4.61825333957328 & -0.618253339573277 \tabularnewline
142 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
143 & 2 & 2.61930885927531 & -0.619308859275312 \tabularnewline
144 & 5 & 6.18209891370539 & -1.18209891370539 \tabularnewline
145 & 7 & 5.90672918872134 & 1.09327081127866 \tabularnewline
146 & 4 & 5.16899278954139 & -1.16899278954139 \tabularnewline
147 & 4 & 5.4681479829953 & -1.4681479829953 \tabularnewline
148 & 7 & 5.44088021496526 & 1.55911978503474 \tabularnewline
149 & 6 & 5.33220427028337 & 0.667795729716632 \tabularnewline
150 & 5 & 5.16899278954139 & -0.168992789541389 \tabularnewline
151 & 5 & 5.33220427028337 & -0.332204270283368 \tabularnewline
152 & 5 & 5.4681479829953 & -0.468147982995303 \tabularnewline
153 & 7 & 6.31804262641733 & 0.681957373582671 \tabularnewline
154 & 6 & 5.60409169570724 & 0.395908304292762 \tabularnewline
155 & 6 & 5.74351770797936 & 0.256482292020641 \tabularnewline
156 & 5 & 5.33220427028337 & -0.332204270283368 \tabularnewline
157 & 2 & 5.19277825801125 & -3.19277825801125 \tabularnewline
158 & 4 & 4.04372842113531 & -0.0437284211353072 \tabularnewline
159 & 6 & 5.19277825801125 & 0.807221741988753 \tabularnewline
160 & 5 & 4.59098557154323 & 0.409014428456767 \tabularnewline
161 & 5 & 4.48230962686134 & 0.517690373138658 \tabularnewline
162 & 5 & 4.89362306455733 & 0.106376935442667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&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]7[/C][C]5.71624993994932[/C][C]1.28375006005068[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442666[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]4.31909814611936[/C][C]1.68090185388064[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]4.89362306455733[/C][C]-0.893623064557333[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]6.18209891370539[/C][C]-0.182098913705394[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]5.76730317644922[/C][C]1.23269682355078[/C][/ROW]
[ROW][C]8[/C][C]6[/C][C]5.33220427028337[/C][C]0.667795729716632[/C][/ROW]
[ROW][C]9[/C][C]6[/C][C]6.15483114567535[/C][C]-0.15483114567535[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]5.33220427028337[/C][C]0.667795729716632[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.37015138261927[/C][C]0.629848617380735[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]5.19626055757143[/C][C]-1.19626055757143[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]6.31804262641733[/C][C]-0.318042626417329[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]5.02956677726927[/C][C]-0.029566777269268[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.32977749042522[/C][C]-0.329777490425216[/C][/ROW]
[ROW][C]18[/C][C]7[/C][C]6.18209891370539[/C][C]0.817901086294606[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]5.4681479829953[/C][C]-2.4681479829953[/C][/ROW]
[ROW][C]20[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]3.76835869615125[/C][C]-0.768358696151251[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]5.33220427028337[/C][C]-0.332204270283368[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.0685694093072[/C][C]-0.0685694093071992[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]5.4681479829953[/C][C]0.531852017004697[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]4.61825333957328[/C][C]-1.61825333957328[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]6.18209891370539[/C][C]-0.182098913705394[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]6.18209891370539[/C][C]-0.182098913705394[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]4.61825333957328[/C][C]0.381746660426723[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]5.05683454529931[/C][C]-0.056834545299312[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]5.33220427028337[/C][C]1.66779572971663[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]5.19277825801125[/C][C]0.807221741988753[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]5.16899278954139[/C][C]-0.168992789541389[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.76835869615125[/C][C]0.231641303848749[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]5.02956677726927[/C][C]-1.02956677726927[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]5.33220427028337[/C][C]0.667795729716632[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]4.34288361458922[/C][C]0.657116385410779[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]6.59341235140139[/C][C]0.406587648598615[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]5.60757399526742[/C][C]-0.607573995267424[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.17967213384724[/C][C]0.820327866152758[/C][/ROW]
[ROW][C]42[/C][C]6[/C][C]5.1655104899812[/C][C]0.834489510018797[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]44[/C][C]6[/C][C]5.87946142069129[/C][C]0.120538579308706[/C][/ROW]
[ROW][C]45[/C][C]7[/C][C]6.01888743296342[/C][C]0.981112567036585[/C][/ROW]
[ROW][C]46[/C][C]5[/C][C]3.63241498343932[/C][C]1.36758501656068[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]4.04372842113531[/C][C]0.956271578864692[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]5.74351770797936[/C][C]-0.743517707979359[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]4.89362306455733[/C][C]1.10637693544267[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]3.93157017689323[/C][C]-1.93157017689323[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.92089083258738[/C][C]-0.920890832587377[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]4.75419705228521[/C][C]-0.754197052285212[/C][/ROW]
[ROW][C]53[/C][C]6[/C][C]5.33220427028337[/C][C]0.667795729716632[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]4.48230962686134[/C][C]-1.48230962686134[/C][/ROW]
[ROW][C]55[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]4.06751388960516[/C][C]1.93248611039484[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]5.05683454529931[/C][C]-0.056834545299312[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]3.87703464083314[/C][C]-2.87703464083314[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]5.60757399526742[/C][C]1.39242600473258[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]4.61825333957328[/C][C]-0.618253339573277[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]5.16899278954139[/C][C]-0.168992789541389[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]5.60757399526742[/C][C]0.392426004732576[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]4.31909814611936[/C][C]-0.319098146119363[/C][/ROW]
[ROW][C]66[/C][C]6[/C][C]5.16899278954139[/C][C]0.83100721045861[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]5.19277825801125[/C][C]-0.192778258011247[/C][/ROW]
[ROW][C]69[/C][C]5[/C][C]5.60757399526742[/C][C]-0.607573995267424[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]4.34288361458922[/C][C]-1.34288361458922[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.33220427028337[/C][C]-0.332204270283368[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]5.4681479829953[/C][C]0.531852017004697[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]4.04372842113531[/C][C]-0.0437284211353072[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.04372842113531[/C][C]-0.0437284211353072[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]4.89362306455733[/C][C]1.10637693544267[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]6.18209891370539[/C][C]0.817901086294606[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]4.07099618916535[/C][C]-0.0709961891653513[/C][/ROW]
[ROW][C]81[/C][C]5[/C][C]6.31804262641733[/C][C]-1.31804262641733[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]4.06751388960516[/C][C]1.93248611039484[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]5.4681479829953[/C][C]0.531852017004697[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]4.91740853302719[/C][C]0.0825914669728096[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]4.61825333957328[/C][C]-1.61825333957328[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]5.74351770797936[/C][C]1.25648229202064[/C][/ROW]
[ROW][C]87[/C][C]6[/C][C]5.90672918872134[/C][C]0.0932708112786621[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.48230962686134[/C][C]-0.482309626861342[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]4.59098557154323[/C][C]-0.590985571543233[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]4.04372842113531[/C][C]-1.04372842113531[/C][/ROW]
[ROW][C]92[/C][C]7[/C][C]6.15483114567535[/C][C]0.84516885432465[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.19277825801125[/C][C]0.807221741988753[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]5.30493650225332[/C][C]0.695063497746676[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]4.04372842113531[/C][C]-0.0437284211353072[/C][/ROW]
[ROW][C]96[/C][C]5[/C][C]4.61825333957328[/C][C]0.381746660426723[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]6.18209891370539[/C][C]-0.182098913705394[/C][/ROW]
[ROW][C]98[/C][C]5[/C][C]4.78146482031526[/C][C]0.218535179684744[/C][/ROW]
[ROW][C]99[/C][C]6[/C][C]4.48230962686134[/C][C]1.51769037313866[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]6.15483114567535[/C][C]-0.15483114567535[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]5.4681479829953[/C][C]-1.4681479829953[/C][/ROW]
[ROW][C]102[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]105[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]106[/C][C]4[/C][C]4.61825333957328[/C][C]-0.618253339573277[/C][/ROW]
[ROW][C]107[/C][C]4[/C][C]4.4550418588313[/C][C]-0.455041858831298[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]6.04615520099346[/C][C]-0.0461552009934594[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]6.04267290143327[/C][C]-1.04267290143327[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]5.02956677726927[/C][C]0.970433222730732[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]5.76730317644922[/C][C]-0.767303176449217[/C][/ROW]
[ROW][C]112[/C][C]6[/C][C]5.19277825801125[/C][C]0.807221741988753[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]4.7576793518454[/C][C]-0.757679351845399[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]6.18209891370539[/C][C]-0.182098913705394[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]4.7576793518454[/C][C]-0.757679351845399[/C][/ROW]
[ROW][C]117[/C][C]5[/C][C]4.07099618916535[/C][C]0.929003810834649[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]4.34288361458922[/C][C]1.65711638541078[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]3.74109092812121[/C][C]-0.741090928121207[/C][/ROW]
[ROW][C]121[/C][C]5[/C][C]4.61825333957328[/C][C]0.381746660426723[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]4.89362306455733[/C][C]-0.893623064557333[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]3.62893268387913[/C][C]1.37106731612087[/C][/ROW]
[ROW][C]124[/C][C]5[/C][C]4.18315443340743[/C][C]0.816845566592571[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]6.59341235140139[/C][C]0.406587648598615[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]5.74351770797936[/C][C]-0.743517707979359[/C][/ROW]
[ROW][C]127[/C][C]7[/C][C]5.74351770797936[/C][C]1.25648229202064[/C][/ROW]
[ROW][C]128[/C][C]5[/C][C]5.19626055757143[/C][C]-0.196260557571433[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]4.48230962686134[/C][C]-0.482309626861342[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]5.30493650225332[/C][C]0.695063497746676[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]4.20693990187729[/C][C]-0.206939901877286[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]6.01888743296341[/C][C]-2.01888743296341[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]2.61930885927531[/C][C]1.38069114072469[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]4.61825333957328[/C][C]-0.618253339573277[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]6.04615520099346[/C][C]-0.0461552009934594[/C][/ROW]
[ROW][C]136[/C][C]6[/C][C]5.4681479829953[/C][C]0.531852017004697[/C][/ROW]
[ROW][C]137[/C][C]5[/C][C]5.16899278954139[/C][C]-0.168992789541389[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]4.48230962686134[/C][C]-1.48230962686134[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]5.74351770797936[/C][C]-0.743517707979359[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]4.61825333957328[/C][C]-0.618253339573277[/C][/ROW]
[ROW][C]142[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]2.61930885927531[/C][C]-0.619308859275312[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]6.18209891370539[/C][C]-1.18209891370539[/C][/ROW]
[ROW][C]145[/C][C]7[/C][C]5.90672918872134[/C][C]1.09327081127866[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]5.16899278954139[/C][C]-1.16899278954139[/C][/ROW]
[ROW][C]147[/C][C]4[/C][C]5.4681479829953[/C][C]-1.4681479829953[/C][/ROW]
[ROW][C]148[/C][C]7[/C][C]5.44088021496526[/C][C]1.55911978503474[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]5.33220427028337[/C][C]0.667795729716632[/C][/ROW]
[ROW][C]150[/C][C]5[/C][C]5.16899278954139[/C][C]-0.168992789541389[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]5.33220427028337[/C][C]-0.332204270283368[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]5.4681479829953[/C][C]-0.468147982995303[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]6.31804262641733[/C][C]0.681957373582671[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]5.60409169570724[/C][C]0.395908304292762[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]5.74351770797936[/C][C]0.256482292020641[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]5.33220427028337[/C][C]-0.332204270283368[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]5.19277825801125[/C][C]-3.19277825801125[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]4.04372842113531[/C][C]-0.0437284211353072[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]5.19277825801125[/C][C]0.807221741988753[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]4.59098557154323[/C][C]0.409014428456767[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]4.48230962686134[/C][C]0.517690373138658[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]4.89362306455733[/C][C]0.106376935442667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&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
175.716249939949321.28375006005068
254.893623064557330.106376935442666
364.319098146119361.68090185388064
444.89362306455733-0.893623064557333
554.893623064557330.106376935442667
666.18209891370539-0.182098913705394
775.767303176449221.23269682355078
865.332204270283370.667795729716632
966.15483114567535-0.15483114567535
1065.332204270283370.667795729716632
1154.370151382619270.629848617380735
1255.4681479829953-0.468147982995303
1345.19626055757143-1.19626055757143
1465.743517707979360.256482292020641
1566.31804262641733-0.318042626417329
1655.02956677726927-0.029566777269268
1733.32977749042522-0.329777490425216
1876.182098913705390.817901086294606
1935.4681479829953-2.4681479829953
2055.4681479829953-0.468147982995303
2133.76835869615125-0.768358696151251
2255.33220427028337-0.332204270283368
2322.0685694093072-0.0685694093071992
2465.46814798299530.531852017004697
2534.61825333957328-1.61825333957328
2666.18209891370539-0.182098913705394
2766.18209891370539-0.182098913705394
2854.618253339573280.381746660426723
2955.05683454529931-0.056834545299312
3075.332204270283371.66779572971663
3165.192778258011250.807221741988753
3255.4681479829953-0.468147982995303
3355.16899278954139-0.168992789541389
3443.768358696151250.231641303848749
3545.02956677726927-1.02956677726927
3665.332204270283370.667795729716632
3754.342883614589220.657116385410779
3854.893623064557330.106376935442667
3976.593412351401390.406587648598615
4055.60757399526742-0.607573995267424
4154.179672133847240.820327866152758
4265.16551048998120.834489510018797
4354.893623064557330.106376935442667
4465.879461420691290.120538579308706
4576.018887432963420.981112567036585
4653.632414983439321.36758501656068
4754.043728421135310.956271578864692
4855.74351770797936-0.743517707979359
4964.893623064557331.10637693544267
5023.93157017689323-1.93157017689323
5144.92089083258738-0.920890832587377
5244.75419705228521-0.754197052285212
5365.332204270283370.667795729716632
5434.48230962686134-1.48230962686134
5565.743517707979360.256482292020641
5664.067513889605161.93248611039484
5755.05683454529931-0.056834545299312
5865.743517707979360.256482292020641
5913.87703464083314-2.87703464083314
6055.4681479829953-0.468147982995303
6175.607573995267421.39242600473258
6244.61825333957328-0.618253339573277
6355.16899278954139-0.168992789541389
6465.607573995267420.392426004732576
6544.31909814611936-0.319098146119363
6665.168992789541390.83100721045861
6765.743517707979360.256482292020641
6855.19277825801125-0.192778258011247
6955.60757399526742-0.607573995267424
7034.34288361458922-1.34288361458922
7155.33220427028337-0.332204270283368
7265.46814798299530.531852017004697
7355.4681479829953-0.468147982995303
7465.743517707979360.256482292020641
7565.743517707979360.256482292020641
7644.04372842113531-0.0437284211353072
7744.04372842113531-0.0437284211353072
7864.893623064557331.10637693544267
7976.182098913705390.817901086294606
8044.07099618916535-0.0709961891653513
8156.31804262641733-1.31804262641733
8264.067513889605161.93248611039484
8365.46814798299530.531852017004697
8454.917408533027190.0825914669728096
8534.61825333957328-1.61825333957328
8675.743517707979361.25648229202064
8765.906729188721340.0932708112786621
8844.48230962686134-0.482309626861342
8944.59098557154323-0.590985571543233
9054.893623064557330.106376935442667
9134.04372842113531-1.04372842113531
9276.154831145675350.84516885432465
9365.192778258011250.807221741988753
9465.304936502253320.695063497746676
9544.04372842113531-0.0437284211353072
9654.618253339573280.381746660426723
9766.18209891370539-0.182098913705394
9854.781464820315260.218535179684744
9964.482309626861341.51769037313866
10066.15483114567535-0.15483114567535
10145.4681479829953-1.4681479829953
10254.893623064557330.106376935442667
10365.743517707979360.256482292020641
10455.4681479829953-0.468147982995303
10554.893623064557330.106376935442667
10644.61825333957328-0.618253339573277
10744.4550418588313-0.455041858831298
10866.04615520099346-0.0461552009934594
10956.04267290143327-1.04267290143327
11065.029566777269270.970433222730732
11155.76730317644922-0.767303176449217
11265.192778258011250.807221741988753
11354.893623064557330.106376935442667
11444.7576793518454-0.757679351845399
11566.18209891370539-0.182098913705394
11644.7576793518454-0.757679351845399
11754.070996189165350.929003810834649
11854.893623064557330.106376935442667
11964.342883614589221.65711638541078
12033.74109092812121-0.741090928121207
12154.618253339573280.381746660426723
12244.89362306455733-0.893623064557333
12353.628932683879131.37106731612087
12454.183154433407430.816845566592571
12576.593412351401390.406587648598615
12655.74351770797936-0.743517707979359
12775.743517707979361.25648229202064
12855.19626055757143-0.196260557571433
12944.48230962686134-0.482309626861342
13065.304936502253320.695063497746676
13144.20693990187729-0.206939901877286
13246.01888743296341-2.01888743296341
13342.619308859275311.38069114072469
13444.61825333957328-0.618253339573277
13566.04615520099346-0.0461552009934594
13665.46814798299530.531852017004697
13755.16899278954139-0.168992789541389
13834.48230962686134-1.48230962686134
13965.743517707979360.256482292020641
14055.74351770797936-0.743517707979359
14144.61825333957328-0.618253339573277
14254.893623064557330.106376935442667
14322.61930885927531-0.619308859275312
14456.18209891370539-1.18209891370539
14575.906729188721341.09327081127866
14645.16899278954139-1.16899278954139
14745.4681479829953-1.4681479829953
14875.440880214965261.55911978503474
14965.332204270283370.667795729716632
15055.16899278954139-0.168992789541389
15155.33220427028337-0.332204270283368
15255.4681479829953-0.468147982995303
15376.318042626417330.681957373582671
15465.604091695707240.395908304292762
15565.743517707979360.256482292020641
15655.33220427028337-0.332204270283368
15725.19277825801125-3.19277825801125
15844.04372842113531-0.0437284211353072
15965.192778258011250.807221741988753
16054.590985571543230.409014428456767
16154.482309626861340.517690373138658
16254.893623064557330.106376935442667






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.811323637820680.3773527243586410.18867636217932
80.746736751126740.506526497746520.25326324887326
90.6649619872502130.6700760254995750.335038012749787
100.5801183463458140.8397633073083710.419881653654186
110.4661928139687250.932385627937450.533807186031275
120.4764223193692070.9528446387384130.523577680630793
130.4619646167269990.9239292334539980.538035383273001
140.3689979239304330.7379958478608670.631002076069567
150.2941184972705280.5882369945410550.705881502729472
160.2974780293656290.5949560587312580.702521970634371
170.3631947988631950.7263895977263910.636805201136805
180.3454558241660770.6909116483321550.654544175833923
190.8352713185423790.3294573629152430.164728681457621
200.8005851601332370.3988296797335270.199414839866763
210.7814392613695380.4371214772609240.218560738630462
220.7311504827109810.5376990345780370.268849517289019
230.6714051266320910.6571897467358180.328594873367909
240.6265169622228750.746966075554250.373483037777125
250.733612391777780.532775216444440.26638760822222
260.6791903893766620.6416192212466760.320809610623338
270.62111714962020.7577657007595990.3788828503798
280.5742410967572180.8515178064855650.425758903242782
290.5123615837617390.9752768324765220.487638416238261
300.652329150127210.695341699745580.34767084987279
310.6390831019553690.7218337960892620.360916898044631
320.5994135801141820.8011728397716370.400586419885819
330.5440743380300340.9118513239399320.455925661969966
340.4910369166856410.9820738333712820.508963083314359
350.5063089365415990.9873821269168020.493691063458401
360.4767346694778750.953469338955750.523265330522125
370.4512911439825230.9025822879650450.548708856017477
380.3971313812677670.7942627625355330.602868618732234
390.3523989114568650.704797822913730.647601088543135
400.3268403696451210.6536807392902410.673159630354879
410.3179021814157020.6358043628314030.682097818584298
420.298735887524310.5974717750486190.70126411247569
430.2542479250221480.5084958500442960.745752074977852
440.2138606682154230.4277213364308470.786139331784577
450.2189277455312750.4378554910625490.781072254468725
460.2724315147493580.5448630294987150.727568485250642
470.2685196086559550.5370392173119090.731480391344045
480.2611758533314140.5223517066628290.738824146668586
490.2731641935414380.5463283870828760.726835806458562
500.4374532239944960.8749064479889930.562546776005504
510.4331986985070220.8663973970140450.566801301492977
520.4205473740474810.8410947480949630.579452625952519
530.3974272447959330.7948544895918660.602572755204067
540.4803953683969220.9607907367938440.519604631603078
550.4345080395530550.869016079106110.565491960446945
560.611697446047590.776605107904820.38830255395241
570.5649203469757280.8701593060485430.435079653024272
580.5200687859097090.9598624281805820.479931214090291
590.8696409573015410.2607180853969180.130359042698459
600.8503879375837040.2992241248325930.149612062416296
610.8790734299256940.2418531401486120.120926570074306
620.865601609584480.2687967808310410.13439839041552
630.8403769775301210.3192460449397580.159623022469879
640.8160546844954980.3678906310090050.183945315504502
650.7881046720963480.4237906558073040.211895327903652
660.7807050728152290.4385898543695420.219294927184771
670.7478470195121530.5043059609756930.252152980487847
680.7111972733933340.5776054532133310.288802726606666
690.6933645257230370.6132709485539260.306635474276963
700.7391809606513340.5216380786973330.260819039348666
710.7066809844347620.5866380311304770.293319015565238
720.6801751661318850.639649667736230.319824833868115
730.6493151383660460.7013697232679090.350684861633954
740.6098556332549440.7802887334901110.390144366745056
750.569400880544990.861198238910020.43059911945501
760.52427779319250.9514444136149990.4757222068075
770.478891372983790.957782745967580.52110862701621
780.5008832246447730.9982335507104550.499116775355227
790.4961024896078980.9922049792157970.503897510392102
800.4510839001000990.9021678002001980.548916099899901
810.4990742980606570.9981485961213140.500925701939343
820.6549451105145880.6901097789708250.345054889485412
830.6271535457051950.7456929085896110.372846454294805
840.5834712274022450.8330575451955110.416528772597755
850.6790519026654860.6418961946690280.320948097334514
860.719874247347220.5602515053055590.28012575265278
870.6816354711838970.6367290576322070.318364528816103
880.6516639950903330.6966720098193330.348336004909667
890.6318456954052070.7363086091895860.368154304594793
900.5882742491227630.8234515017544730.411725750877237
910.6070407822991540.7859184354016920.392959217700846
920.6069687895424140.7860624209151730.393031210457586
930.5955349576294240.8089300847411530.404465042370576
940.5792696264425670.8414607471148660.420730373557433
950.5342162600603130.9315674798793740.465783739939687
960.4948174005876810.9896348011753620.505182599412319
970.4527975142573560.9055950285147120.547202485742644
980.4086915543141770.8173831086283540.591308445685823
990.4950411587942560.9900823175885120.504958841205744
1000.4494781723316770.8989563446633540.550521827668323
1010.5207612857306950.958477428538610.479238714269305
1020.4743625300263710.9487250600527420.525637469973629
1030.4347316710718150.869463342143630.565268328928185
1040.3989062002181330.7978124004362660.601093799781867
1050.3544328979077170.7088657958154340.645567102092283
1060.3326670072134440.6653340144268880.667332992786556
1070.3029019694734180.6058039389468370.697098030526582
1080.270535127462590.541070254925180.72946487253741
1090.2802139311306690.5604278622613370.719786068869331
1100.2803770220948070.5607540441896130.719622977905193
1110.2771343232915110.5542686465830220.722865676708489
1120.2611835230475420.5223670460950830.738816476952458
1130.2233068502448740.4466137004897480.776693149755126
1140.2060085497938740.4120170995877470.793991450206126
1150.1737373236935050.347474647387010.826262676306495
1160.1585321762817130.3170643525634260.841467823718287
1170.162107930141230.3242158602824590.83789206985877
1180.1333247149433350.266649429886670.866675285056665
1190.1958477673095170.3916955346190350.804152232690483
1200.2074577148896310.4149154297792620.792542285110369
1210.1775255494215440.3550510988430880.822474450578456
1220.1742215720201340.3484431440402690.825778427979866
1230.2063806447381040.4127612894762070.793619355261896
1240.2039770738480960.4079541476961930.796022926151904
1250.1770872667295090.3541745334590170.822912733270492
1260.1606084016471980.3212168032943960.839391598352802
1270.1989341778162550.397868355632510.801065822183745
1280.1687806657958230.3375613315916460.831219334204177
1290.1384796123848680.2769592247697360.861520387615132
1300.1217886768946470.2435773537892940.878211323105353
1310.09550902401781830.1910180480356370.904490975982182
1320.2128221618592020.4256443237184030.787177838140798
1330.3201414959299670.6402829918599340.679858504070033
1340.2743047290330980.5486094580661970.725695270966902
1350.2321634277015340.4643268554030690.767836572298466
1360.2077501857200460.4155003714400920.792249814279954
1370.1660453829706180.3320907659412350.833954617029382
1380.172023400959020.344046801918040.82797659904098
1390.1349779800512760.2699559601025520.865022019948724
1400.1226686704123020.2453373408246050.877331329587698
1410.09521357162749560.1904271432549910.904786428372504
1420.06959878329357060.1391975665871410.93040121670643
1430.05027849496391060.1005569899278210.949721505036089
1440.05632311036133220.1126462207226640.943676889638668
1450.07684915607765580.1536983121553120.923150843922344
1460.132520129547740.2650402590954790.86747987045226
1470.1771402375460710.3542804750921410.822859762453929
1480.1733517505167240.3467035010334490.826648249483276
1490.1513542441444880.3027084882889750.848645755855512
1500.1348869440937350.269773888187470.865113055906265
1510.09062569739489730.1812513947897950.909374302605103
1520.05818096641985830.1163619328397170.941819033580142
1530.03804741913922880.07609483827845760.961952580860771
1540.03057119282248780.06114238564497570.969428807177512
1550.01367089974419820.02734179948839630.986329100255802

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.81132363782068 & 0.377352724358641 & 0.18867636217932 \tabularnewline
8 & 0.74673675112674 & 0.50652649774652 & 0.25326324887326 \tabularnewline
9 & 0.664961987250213 & 0.670076025499575 & 0.335038012749787 \tabularnewline
10 & 0.580118346345814 & 0.839763307308371 & 0.419881653654186 \tabularnewline
11 & 0.466192813968725 & 0.93238562793745 & 0.533807186031275 \tabularnewline
12 & 0.476422319369207 & 0.952844638738413 & 0.523577680630793 \tabularnewline
13 & 0.461964616726999 & 0.923929233453998 & 0.538035383273001 \tabularnewline
14 & 0.368997923930433 & 0.737995847860867 & 0.631002076069567 \tabularnewline
15 & 0.294118497270528 & 0.588236994541055 & 0.705881502729472 \tabularnewline
16 & 0.297478029365629 & 0.594956058731258 & 0.702521970634371 \tabularnewline
17 & 0.363194798863195 & 0.726389597726391 & 0.636805201136805 \tabularnewline
18 & 0.345455824166077 & 0.690911648332155 & 0.654544175833923 \tabularnewline
19 & 0.835271318542379 & 0.329457362915243 & 0.164728681457621 \tabularnewline
20 & 0.800585160133237 & 0.398829679733527 & 0.199414839866763 \tabularnewline
21 & 0.781439261369538 & 0.437121477260924 & 0.218560738630462 \tabularnewline
22 & 0.731150482710981 & 0.537699034578037 & 0.268849517289019 \tabularnewline
23 & 0.671405126632091 & 0.657189746735818 & 0.328594873367909 \tabularnewline
24 & 0.626516962222875 & 0.74696607555425 & 0.373483037777125 \tabularnewline
25 & 0.73361239177778 & 0.53277521644444 & 0.26638760822222 \tabularnewline
26 & 0.679190389376662 & 0.641619221246676 & 0.320809610623338 \tabularnewline
27 & 0.6211171496202 & 0.757765700759599 & 0.3788828503798 \tabularnewline
28 & 0.574241096757218 & 0.851517806485565 & 0.425758903242782 \tabularnewline
29 & 0.512361583761739 & 0.975276832476522 & 0.487638416238261 \tabularnewline
30 & 0.65232915012721 & 0.69534169974558 & 0.34767084987279 \tabularnewline
31 & 0.639083101955369 & 0.721833796089262 & 0.360916898044631 \tabularnewline
32 & 0.599413580114182 & 0.801172839771637 & 0.400586419885819 \tabularnewline
33 & 0.544074338030034 & 0.911851323939932 & 0.455925661969966 \tabularnewline
34 & 0.491036916685641 & 0.982073833371282 & 0.508963083314359 \tabularnewline
35 & 0.506308936541599 & 0.987382126916802 & 0.493691063458401 \tabularnewline
36 & 0.476734669477875 & 0.95346933895575 & 0.523265330522125 \tabularnewline
37 & 0.451291143982523 & 0.902582287965045 & 0.548708856017477 \tabularnewline
38 & 0.397131381267767 & 0.794262762535533 & 0.602868618732234 \tabularnewline
39 & 0.352398911456865 & 0.70479782291373 & 0.647601088543135 \tabularnewline
40 & 0.326840369645121 & 0.653680739290241 & 0.673159630354879 \tabularnewline
41 & 0.317902181415702 & 0.635804362831403 & 0.682097818584298 \tabularnewline
42 & 0.29873588752431 & 0.597471775048619 & 0.70126411247569 \tabularnewline
43 & 0.254247925022148 & 0.508495850044296 & 0.745752074977852 \tabularnewline
44 & 0.213860668215423 & 0.427721336430847 & 0.786139331784577 \tabularnewline
45 & 0.218927745531275 & 0.437855491062549 & 0.781072254468725 \tabularnewline
46 & 0.272431514749358 & 0.544863029498715 & 0.727568485250642 \tabularnewline
47 & 0.268519608655955 & 0.537039217311909 & 0.731480391344045 \tabularnewline
48 & 0.261175853331414 & 0.522351706662829 & 0.738824146668586 \tabularnewline
49 & 0.273164193541438 & 0.546328387082876 & 0.726835806458562 \tabularnewline
50 & 0.437453223994496 & 0.874906447988993 & 0.562546776005504 \tabularnewline
51 & 0.433198698507022 & 0.866397397014045 & 0.566801301492977 \tabularnewline
52 & 0.420547374047481 & 0.841094748094963 & 0.579452625952519 \tabularnewline
53 & 0.397427244795933 & 0.794854489591866 & 0.602572755204067 \tabularnewline
54 & 0.480395368396922 & 0.960790736793844 & 0.519604631603078 \tabularnewline
55 & 0.434508039553055 & 0.86901607910611 & 0.565491960446945 \tabularnewline
56 & 0.61169744604759 & 0.77660510790482 & 0.38830255395241 \tabularnewline
57 & 0.564920346975728 & 0.870159306048543 & 0.435079653024272 \tabularnewline
58 & 0.520068785909709 & 0.959862428180582 & 0.479931214090291 \tabularnewline
59 & 0.869640957301541 & 0.260718085396918 & 0.130359042698459 \tabularnewline
60 & 0.850387937583704 & 0.299224124832593 & 0.149612062416296 \tabularnewline
61 & 0.879073429925694 & 0.241853140148612 & 0.120926570074306 \tabularnewline
62 & 0.86560160958448 & 0.268796780831041 & 0.13439839041552 \tabularnewline
63 & 0.840376977530121 & 0.319246044939758 & 0.159623022469879 \tabularnewline
64 & 0.816054684495498 & 0.367890631009005 & 0.183945315504502 \tabularnewline
65 & 0.788104672096348 & 0.423790655807304 & 0.211895327903652 \tabularnewline
66 & 0.780705072815229 & 0.438589854369542 & 0.219294927184771 \tabularnewline
67 & 0.747847019512153 & 0.504305960975693 & 0.252152980487847 \tabularnewline
68 & 0.711197273393334 & 0.577605453213331 & 0.288802726606666 \tabularnewline
69 & 0.693364525723037 & 0.613270948553926 & 0.306635474276963 \tabularnewline
70 & 0.739180960651334 & 0.521638078697333 & 0.260819039348666 \tabularnewline
71 & 0.706680984434762 & 0.586638031130477 & 0.293319015565238 \tabularnewline
72 & 0.680175166131885 & 0.63964966773623 & 0.319824833868115 \tabularnewline
73 & 0.649315138366046 & 0.701369723267909 & 0.350684861633954 \tabularnewline
74 & 0.609855633254944 & 0.780288733490111 & 0.390144366745056 \tabularnewline
75 & 0.56940088054499 & 0.86119823891002 & 0.43059911945501 \tabularnewline
76 & 0.5242777931925 & 0.951444413614999 & 0.4757222068075 \tabularnewline
77 & 0.47889137298379 & 0.95778274596758 & 0.52110862701621 \tabularnewline
78 & 0.500883224644773 & 0.998233550710455 & 0.499116775355227 \tabularnewline
79 & 0.496102489607898 & 0.992204979215797 & 0.503897510392102 \tabularnewline
80 & 0.451083900100099 & 0.902167800200198 & 0.548916099899901 \tabularnewline
81 & 0.499074298060657 & 0.998148596121314 & 0.500925701939343 \tabularnewline
82 & 0.654945110514588 & 0.690109778970825 & 0.345054889485412 \tabularnewline
83 & 0.627153545705195 & 0.745692908589611 & 0.372846454294805 \tabularnewline
84 & 0.583471227402245 & 0.833057545195511 & 0.416528772597755 \tabularnewline
85 & 0.679051902665486 & 0.641896194669028 & 0.320948097334514 \tabularnewline
86 & 0.71987424734722 & 0.560251505305559 & 0.28012575265278 \tabularnewline
87 & 0.681635471183897 & 0.636729057632207 & 0.318364528816103 \tabularnewline
88 & 0.651663995090333 & 0.696672009819333 & 0.348336004909667 \tabularnewline
89 & 0.631845695405207 & 0.736308609189586 & 0.368154304594793 \tabularnewline
90 & 0.588274249122763 & 0.823451501754473 & 0.411725750877237 \tabularnewline
91 & 0.607040782299154 & 0.785918435401692 & 0.392959217700846 \tabularnewline
92 & 0.606968789542414 & 0.786062420915173 & 0.393031210457586 \tabularnewline
93 & 0.595534957629424 & 0.808930084741153 & 0.404465042370576 \tabularnewline
94 & 0.579269626442567 & 0.841460747114866 & 0.420730373557433 \tabularnewline
95 & 0.534216260060313 & 0.931567479879374 & 0.465783739939687 \tabularnewline
96 & 0.494817400587681 & 0.989634801175362 & 0.505182599412319 \tabularnewline
97 & 0.452797514257356 & 0.905595028514712 & 0.547202485742644 \tabularnewline
98 & 0.408691554314177 & 0.817383108628354 & 0.591308445685823 \tabularnewline
99 & 0.495041158794256 & 0.990082317588512 & 0.504958841205744 \tabularnewline
100 & 0.449478172331677 & 0.898956344663354 & 0.550521827668323 \tabularnewline
101 & 0.520761285730695 & 0.95847742853861 & 0.479238714269305 \tabularnewline
102 & 0.474362530026371 & 0.948725060052742 & 0.525637469973629 \tabularnewline
103 & 0.434731671071815 & 0.86946334214363 & 0.565268328928185 \tabularnewline
104 & 0.398906200218133 & 0.797812400436266 & 0.601093799781867 \tabularnewline
105 & 0.354432897907717 & 0.708865795815434 & 0.645567102092283 \tabularnewline
106 & 0.332667007213444 & 0.665334014426888 & 0.667332992786556 \tabularnewline
107 & 0.302901969473418 & 0.605803938946837 & 0.697098030526582 \tabularnewline
108 & 0.27053512746259 & 0.54107025492518 & 0.72946487253741 \tabularnewline
109 & 0.280213931130669 & 0.560427862261337 & 0.719786068869331 \tabularnewline
110 & 0.280377022094807 & 0.560754044189613 & 0.719622977905193 \tabularnewline
111 & 0.277134323291511 & 0.554268646583022 & 0.722865676708489 \tabularnewline
112 & 0.261183523047542 & 0.522367046095083 & 0.738816476952458 \tabularnewline
113 & 0.223306850244874 & 0.446613700489748 & 0.776693149755126 \tabularnewline
114 & 0.206008549793874 & 0.412017099587747 & 0.793991450206126 \tabularnewline
115 & 0.173737323693505 & 0.34747464738701 & 0.826262676306495 \tabularnewline
116 & 0.158532176281713 & 0.317064352563426 & 0.841467823718287 \tabularnewline
117 & 0.16210793014123 & 0.324215860282459 & 0.83789206985877 \tabularnewline
118 & 0.133324714943335 & 0.26664942988667 & 0.866675285056665 \tabularnewline
119 & 0.195847767309517 & 0.391695534619035 & 0.804152232690483 \tabularnewline
120 & 0.207457714889631 & 0.414915429779262 & 0.792542285110369 \tabularnewline
121 & 0.177525549421544 & 0.355051098843088 & 0.822474450578456 \tabularnewline
122 & 0.174221572020134 & 0.348443144040269 & 0.825778427979866 \tabularnewline
123 & 0.206380644738104 & 0.412761289476207 & 0.793619355261896 \tabularnewline
124 & 0.203977073848096 & 0.407954147696193 & 0.796022926151904 \tabularnewline
125 & 0.177087266729509 & 0.354174533459017 & 0.822912733270492 \tabularnewline
126 & 0.160608401647198 & 0.321216803294396 & 0.839391598352802 \tabularnewline
127 & 0.198934177816255 & 0.39786835563251 & 0.801065822183745 \tabularnewline
128 & 0.168780665795823 & 0.337561331591646 & 0.831219334204177 \tabularnewline
129 & 0.138479612384868 & 0.276959224769736 & 0.861520387615132 \tabularnewline
130 & 0.121788676894647 & 0.243577353789294 & 0.878211323105353 \tabularnewline
131 & 0.0955090240178183 & 0.191018048035637 & 0.904490975982182 \tabularnewline
132 & 0.212822161859202 & 0.425644323718403 & 0.787177838140798 \tabularnewline
133 & 0.320141495929967 & 0.640282991859934 & 0.679858504070033 \tabularnewline
134 & 0.274304729033098 & 0.548609458066197 & 0.725695270966902 \tabularnewline
135 & 0.232163427701534 & 0.464326855403069 & 0.767836572298466 \tabularnewline
136 & 0.207750185720046 & 0.415500371440092 & 0.792249814279954 \tabularnewline
137 & 0.166045382970618 & 0.332090765941235 & 0.833954617029382 \tabularnewline
138 & 0.17202340095902 & 0.34404680191804 & 0.82797659904098 \tabularnewline
139 & 0.134977980051276 & 0.269955960102552 & 0.865022019948724 \tabularnewline
140 & 0.122668670412302 & 0.245337340824605 & 0.877331329587698 \tabularnewline
141 & 0.0952135716274956 & 0.190427143254991 & 0.904786428372504 \tabularnewline
142 & 0.0695987832935706 & 0.139197566587141 & 0.93040121670643 \tabularnewline
143 & 0.0502784949639106 & 0.100556989927821 & 0.949721505036089 \tabularnewline
144 & 0.0563231103613322 & 0.112646220722664 & 0.943676889638668 \tabularnewline
145 & 0.0768491560776558 & 0.153698312155312 & 0.923150843922344 \tabularnewline
146 & 0.13252012954774 & 0.265040259095479 & 0.86747987045226 \tabularnewline
147 & 0.177140237546071 & 0.354280475092141 & 0.822859762453929 \tabularnewline
148 & 0.173351750516724 & 0.346703501033449 & 0.826648249483276 \tabularnewline
149 & 0.151354244144488 & 0.302708488288975 & 0.848645755855512 \tabularnewline
150 & 0.134886944093735 & 0.26977388818747 & 0.865113055906265 \tabularnewline
151 & 0.0906256973948973 & 0.181251394789795 & 0.909374302605103 \tabularnewline
152 & 0.0581809664198583 & 0.116361932839717 & 0.941819033580142 \tabularnewline
153 & 0.0380474191392288 & 0.0760948382784576 & 0.961952580860771 \tabularnewline
154 & 0.0305711928224878 & 0.0611423856449757 & 0.969428807177512 \tabularnewline
155 & 0.0136708997441982 & 0.0273417994883963 & 0.986329100255802 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&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]7[/C][C]0.81132363782068[/C][C]0.377352724358641[/C][C]0.18867636217932[/C][/ROW]
[ROW][C]8[/C][C]0.74673675112674[/C][C]0.50652649774652[/C][C]0.25326324887326[/C][/ROW]
[ROW][C]9[/C][C]0.664961987250213[/C][C]0.670076025499575[/C][C]0.335038012749787[/C][/ROW]
[ROW][C]10[/C][C]0.580118346345814[/C][C]0.839763307308371[/C][C]0.419881653654186[/C][/ROW]
[ROW][C]11[/C][C]0.466192813968725[/C][C]0.93238562793745[/C][C]0.533807186031275[/C][/ROW]
[ROW][C]12[/C][C]0.476422319369207[/C][C]0.952844638738413[/C][C]0.523577680630793[/C][/ROW]
[ROW][C]13[/C][C]0.461964616726999[/C][C]0.923929233453998[/C][C]0.538035383273001[/C][/ROW]
[ROW][C]14[/C][C]0.368997923930433[/C][C]0.737995847860867[/C][C]0.631002076069567[/C][/ROW]
[ROW][C]15[/C][C]0.294118497270528[/C][C]0.588236994541055[/C][C]0.705881502729472[/C][/ROW]
[ROW][C]16[/C][C]0.297478029365629[/C][C]0.594956058731258[/C][C]0.702521970634371[/C][/ROW]
[ROW][C]17[/C][C]0.363194798863195[/C][C]0.726389597726391[/C][C]0.636805201136805[/C][/ROW]
[ROW][C]18[/C][C]0.345455824166077[/C][C]0.690911648332155[/C][C]0.654544175833923[/C][/ROW]
[ROW][C]19[/C][C]0.835271318542379[/C][C]0.329457362915243[/C][C]0.164728681457621[/C][/ROW]
[ROW][C]20[/C][C]0.800585160133237[/C][C]0.398829679733527[/C][C]0.199414839866763[/C][/ROW]
[ROW][C]21[/C][C]0.781439261369538[/C][C]0.437121477260924[/C][C]0.218560738630462[/C][/ROW]
[ROW][C]22[/C][C]0.731150482710981[/C][C]0.537699034578037[/C][C]0.268849517289019[/C][/ROW]
[ROW][C]23[/C][C]0.671405126632091[/C][C]0.657189746735818[/C][C]0.328594873367909[/C][/ROW]
[ROW][C]24[/C][C]0.626516962222875[/C][C]0.74696607555425[/C][C]0.373483037777125[/C][/ROW]
[ROW][C]25[/C][C]0.73361239177778[/C][C]0.53277521644444[/C][C]0.26638760822222[/C][/ROW]
[ROW][C]26[/C][C]0.679190389376662[/C][C]0.641619221246676[/C][C]0.320809610623338[/C][/ROW]
[ROW][C]27[/C][C]0.6211171496202[/C][C]0.757765700759599[/C][C]0.3788828503798[/C][/ROW]
[ROW][C]28[/C][C]0.574241096757218[/C][C]0.851517806485565[/C][C]0.425758903242782[/C][/ROW]
[ROW][C]29[/C][C]0.512361583761739[/C][C]0.975276832476522[/C][C]0.487638416238261[/C][/ROW]
[ROW][C]30[/C][C]0.65232915012721[/C][C]0.69534169974558[/C][C]0.34767084987279[/C][/ROW]
[ROW][C]31[/C][C]0.639083101955369[/C][C]0.721833796089262[/C][C]0.360916898044631[/C][/ROW]
[ROW][C]32[/C][C]0.599413580114182[/C][C]0.801172839771637[/C][C]0.400586419885819[/C][/ROW]
[ROW][C]33[/C][C]0.544074338030034[/C][C]0.911851323939932[/C][C]0.455925661969966[/C][/ROW]
[ROW][C]34[/C][C]0.491036916685641[/C][C]0.982073833371282[/C][C]0.508963083314359[/C][/ROW]
[ROW][C]35[/C][C]0.506308936541599[/C][C]0.987382126916802[/C][C]0.493691063458401[/C][/ROW]
[ROW][C]36[/C][C]0.476734669477875[/C][C]0.95346933895575[/C][C]0.523265330522125[/C][/ROW]
[ROW][C]37[/C][C]0.451291143982523[/C][C]0.902582287965045[/C][C]0.548708856017477[/C][/ROW]
[ROW][C]38[/C][C]0.397131381267767[/C][C]0.794262762535533[/C][C]0.602868618732234[/C][/ROW]
[ROW][C]39[/C][C]0.352398911456865[/C][C]0.70479782291373[/C][C]0.647601088543135[/C][/ROW]
[ROW][C]40[/C][C]0.326840369645121[/C][C]0.653680739290241[/C][C]0.673159630354879[/C][/ROW]
[ROW][C]41[/C][C]0.317902181415702[/C][C]0.635804362831403[/C][C]0.682097818584298[/C][/ROW]
[ROW][C]42[/C][C]0.29873588752431[/C][C]0.597471775048619[/C][C]0.70126411247569[/C][/ROW]
[ROW][C]43[/C][C]0.254247925022148[/C][C]0.508495850044296[/C][C]0.745752074977852[/C][/ROW]
[ROW][C]44[/C][C]0.213860668215423[/C][C]0.427721336430847[/C][C]0.786139331784577[/C][/ROW]
[ROW][C]45[/C][C]0.218927745531275[/C][C]0.437855491062549[/C][C]0.781072254468725[/C][/ROW]
[ROW][C]46[/C][C]0.272431514749358[/C][C]0.544863029498715[/C][C]0.727568485250642[/C][/ROW]
[ROW][C]47[/C][C]0.268519608655955[/C][C]0.537039217311909[/C][C]0.731480391344045[/C][/ROW]
[ROW][C]48[/C][C]0.261175853331414[/C][C]0.522351706662829[/C][C]0.738824146668586[/C][/ROW]
[ROW][C]49[/C][C]0.273164193541438[/C][C]0.546328387082876[/C][C]0.726835806458562[/C][/ROW]
[ROW][C]50[/C][C]0.437453223994496[/C][C]0.874906447988993[/C][C]0.562546776005504[/C][/ROW]
[ROW][C]51[/C][C]0.433198698507022[/C][C]0.866397397014045[/C][C]0.566801301492977[/C][/ROW]
[ROW][C]52[/C][C]0.420547374047481[/C][C]0.841094748094963[/C][C]0.579452625952519[/C][/ROW]
[ROW][C]53[/C][C]0.397427244795933[/C][C]0.794854489591866[/C][C]0.602572755204067[/C][/ROW]
[ROW][C]54[/C][C]0.480395368396922[/C][C]0.960790736793844[/C][C]0.519604631603078[/C][/ROW]
[ROW][C]55[/C][C]0.434508039553055[/C][C]0.86901607910611[/C][C]0.565491960446945[/C][/ROW]
[ROW][C]56[/C][C]0.61169744604759[/C][C]0.77660510790482[/C][C]0.38830255395241[/C][/ROW]
[ROW][C]57[/C][C]0.564920346975728[/C][C]0.870159306048543[/C][C]0.435079653024272[/C][/ROW]
[ROW][C]58[/C][C]0.520068785909709[/C][C]0.959862428180582[/C][C]0.479931214090291[/C][/ROW]
[ROW][C]59[/C][C]0.869640957301541[/C][C]0.260718085396918[/C][C]0.130359042698459[/C][/ROW]
[ROW][C]60[/C][C]0.850387937583704[/C][C]0.299224124832593[/C][C]0.149612062416296[/C][/ROW]
[ROW][C]61[/C][C]0.879073429925694[/C][C]0.241853140148612[/C][C]0.120926570074306[/C][/ROW]
[ROW][C]62[/C][C]0.86560160958448[/C][C]0.268796780831041[/C][C]0.13439839041552[/C][/ROW]
[ROW][C]63[/C][C]0.840376977530121[/C][C]0.319246044939758[/C][C]0.159623022469879[/C][/ROW]
[ROW][C]64[/C][C]0.816054684495498[/C][C]0.367890631009005[/C][C]0.183945315504502[/C][/ROW]
[ROW][C]65[/C][C]0.788104672096348[/C][C]0.423790655807304[/C][C]0.211895327903652[/C][/ROW]
[ROW][C]66[/C][C]0.780705072815229[/C][C]0.438589854369542[/C][C]0.219294927184771[/C][/ROW]
[ROW][C]67[/C][C]0.747847019512153[/C][C]0.504305960975693[/C][C]0.252152980487847[/C][/ROW]
[ROW][C]68[/C][C]0.711197273393334[/C][C]0.577605453213331[/C][C]0.288802726606666[/C][/ROW]
[ROW][C]69[/C][C]0.693364525723037[/C][C]0.613270948553926[/C][C]0.306635474276963[/C][/ROW]
[ROW][C]70[/C][C]0.739180960651334[/C][C]0.521638078697333[/C][C]0.260819039348666[/C][/ROW]
[ROW][C]71[/C][C]0.706680984434762[/C][C]0.586638031130477[/C][C]0.293319015565238[/C][/ROW]
[ROW][C]72[/C][C]0.680175166131885[/C][C]0.63964966773623[/C][C]0.319824833868115[/C][/ROW]
[ROW][C]73[/C][C]0.649315138366046[/C][C]0.701369723267909[/C][C]0.350684861633954[/C][/ROW]
[ROW][C]74[/C][C]0.609855633254944[/C][C]0.780288733490111[/C][C]0.390144366745056[/C][/ROW]
[ROW][C]75[/C][C]0.56940088054499[/C][C]0.86119823891002[/C][C]0.43059911945501[/C][/ROW]
[ROW][C]76[/C][C]0.5242777931925[/C][C]0.951444413614999[/C][C]0.4757222068075[/C][/ROW]
[ROW][C]77[/C][C]0.47889137298379[/C][C]0.95778274596758[/C][C]0.52110862701621[/C][/ROW]
[ROW][C]78[/C][C]0.500883224644773[/C][C]0.998233550710455[/C][C]0.499116775355227[/C][/ROW]
[ROW][C]79[/C][C]0.496102489607898[/C][C]0.992204979215797[/C][C]0.503897510392102[/C][/ROW]
[ROW][C]80[/C][C]0.451083900100099[/C][C]0.902167800200198[/C][C]0.548916099899901[/C][/ROW]
[ROW][C]81[/C][C]0.499074298060657[/C][C]0.998148596121314[/C][C]0.500925701939343[/C][/ROW]
[ROW][C]82[/C][C]0.654945110514588[/C][C]0.690109778970825[/C][C]0.345054889485412[/C][/ROW]
[ROW][C]83[/C][C]0.627153545705195[/C][C]0.745692908589611[/C][C]0.372846454294805[/C][/ROW]
[ROW][C]84[/C][C]0.583471227402245[/C][C]0.833057545195511[/C][C]0.416528772597755[/C][/ROW]
[ROW][C]85[/C][C]0.679051902665486[/C][C]0.641896194669028[/C][C]0.320948097334514[/C][/ROW]
[ROW][C]86[/C][C]0.71987424734722[/C][C]0.560251505305559[/C][C]0.28012575265278[/C][/ROW]
[ROW][C]87[/C][C]0.681635471183897[/C][C]0.636729057632207[/C][C]0.318364528816103[/C][/ROW]
[ROW][C]88[/C][C]0.651663995090333[/C][C]0.696672009819333[/C][C]0.348336004909667[/C][/ROW]
[ROW][C]89[/C][C]0.631845695405207[/C][C]0.736308609189586[/C][C]0.368154304594793[/C][/ROW]
[ROW][C]90[/C][C]0.588274249122763[/C][C]0.823451501754473[/C][C]0.411725750877237[/C][/ROW]
[ROW][C]91[/C][C]0.607040782299154[/C][C]0.785918435401692[/C][C]0.392959217700846[/C][/ROW]
[ROW][C]92[/C][C]0.606968789542414[/C][C]0.786062420915173[/C][C]0.393031210457586[/C][/ROW]
[ROW][C]93[/C][C]0.595534957629424[/C][C]0.808930084741153[/C][C]0.404465042370576[/C][/ROW]
[ROW][C]94[/C][C]0.579269626442567[/C][C]0.841460747114866[/C][C]0.420730373557433[/C][/ROW]
[ROW][C]95[/C][C]0.534216260060313[/C][C]0.931567479879374[/C][C]0.465783739939687[/C][/ROW]
[ROW][C]96[/C][C]0.494817400587681[/C][C]0.989634801175362[/C][C]0.505182599412319[/C][/ROW]
[ROW][C]97[/C][C]0.452797514257356[/C][C]0.905595028514712[/C][C]0.547202485742644[/C][/ROW]
[ROW][C]98[/C][C]0.408691554314177[/C][C]0.817383108628354[/C][C]0.591308445685823[/C][/ROW]
[ROW][C]99[/C][C]0.495041158794256[/C][C]0.990082317588512[/C][C]0.504958841205744[/C][/ROW]
[ROW][C]100[/C][C]0.449478172331677[/C][C]0.898956344663354[/C][C]0.550521827668323[/C][/ROW]
[ROW][C]101[/C][C]0.520761285730695[/C][C]0.95847742853861[/C][C]0.479238714269305[/C][/ROW]
[ROW][C]102[/C][C]0.474362530026371[/C][C]0.948725060052742[/C][C]0.525637469973629[/C][/ROW]
[ROW][C]103[/C][C]0.434731671071815[/C][C]0.86946334214363[/C][C]0.565268328928185[/C][/ROW]
[ROW][C]104[/C][C]0.398906200218133[/C][C]0.797812400436266[/C][C]0.601093799781867[/C][/ROW]
[ROW][C]105[/C][C]0.354432897907717[/C][C]0.708865795815434[/C][C]0.645567102092283[/C][/ROW]
[ROW][C]106[/C][C]0.332667007213444[/C][C]0.665334014426888[/C][C]0.667332992786556[/C][/ROW]
[ROW][C]107[/C][C]0.302901969473418[/C][C]0.605803938946837[/C][C]0.697098030526582[/C][/ROW]
[ROW][C]108[/C][C]0.27053512746259[/C][C]0.54107025492518[/C][C]0.72946487253741[/C][/ROW]
[ROW][C]109[/C][C]0.280213931130669[/C][C]0.560427862261337[/C][C]0.719786068869331[/C][/ROW]
[ROW][C]110[/C][C]0.280377022094807[/C][C]0.560754044189613[/C][C]0.719622977905193[/C][/ROW]
[ROW][C]111[/C][C]0.277134323291511[/C][C]0.554268646583022[/C][C]0.722865676708489[/C][/ROW]
[ROW][C]112[/C][C]0.261183523047542[/C][C]0.522367046095083[/C][C]0.738816476952458[/C][/ROW]
[ROW][C]113[/C][C]0.223306850244874[/C][C]0.446613700489748[/C][C]0.776693149755126[/C][/ROW]
[ROW][C]114[/C][C]0.206008549793874[/C][C]0.412017099587747[/C][C]0.793991450206126[/C][/ROW]
[ROW][C]115[/C][C]0.173737323693505[/C][C]0.34747464738701[/C][C]0.826262676306495[/C][/ROW]
[ROW][C]116[/C][C]0.158532176281713[/C][C]0.317064352563426[/C][C]0.841467823718287[/C][/ROW]
[ROW][C]117[/C][C]0.16210793014123[/C][C]0.324215860282459[/C][C]0.83789206985877[/C][/ROW]
[ROW][C]118[/C][C]0.133324714943335[/C][C]0.26664942988667[/C][C]0.866675285056665[/C][/ROW]
[ROW][C]119[/C][C]0.195847767309517[/C][C]0.391695534619035[/C][C]0.804152232690483[/C][/ROW]
[ROW][C]120[/C][C]0.207457714889631[/C][C]0.414915429779262[/C][C]0.792542285110369[/C][/ROW]
[ROW][C]121[/C][C]0.177525549421544[/C][C]0.355051098843088[/C][C]0.822474450578456[/C][/ROW]
[ROW][C]122[/C][C]0.174221572020134[/C][C]0.348443144040269[/C][C]0.825778427979866[/C][/ROW]
[ROW][C]123[/C][C]0.206380644738104[/C][C]0.412761289476207[/C][C]0.793619355261896[/C][/ROW]
[ROW][C]124[/C][C]0.203977073848096[/C][C]0.407954147696193[/C][C]0.796022926151904[/C][/ROW]
[ROW][C]125[/C][C]0.177087266729509[/C][C]0.354174533459017[/C][C]0.822912733270492[/C][/ROW]
[ROW][C]126[/C][C]0.160608401647198[/C][C]0.321216803294396[/C][C]0.839391598352802[/C][/ROW]
[ROW][C]127[/C][C]0.198934177816255[/C][C]0.39786835563251[/C][C]0.801065822183745[/C][/ROW]
[ROW][C]128[/C][C]0.168780665795823[/C][C]0.337561331591646[/C][C]0.831219334204177[/C][/ROW]
[ROW][C]129[/C][C]0.138479612384868[/C][C]0.276959224769736[/C][C]0.861520387615132[/C][/ROW]
[ROW][C]130[/C][C]0.121788676894647[/C][C]0.243577353789294[/C][C]0.878211323105353[/C][/ROW]
[ROW][C]131[/C][C]0.0955090240178183[/C][C]0.191018048035637[/C][C]0.904490975982182[/C][/ROW]
[ROW][C]132[/C][C]0.212822161859202[/C][C]0.425644323718403[/C][C]0.787177838140798[/C][/ROW]
[ROW][C]133[/C][C]0.320141495929967[/C][C]0.640282991859934[/C][C]0.679858504070033[/C][/ROW]
[ROW][C]134[/C][C]0.274304729033098[/C][C]0.548609458066197[/C][C]0.725695270966902[/C][/ROW]
[ROW][C]135[/C][C]0.232163427701534[/C][C]0.464326855403069[/C][C]0.767836572298466[/C][/ROW]
[ROW][C]136[/C][C]0.207750185720046[/C][C]0.415500371440092[/C][C]0.792249814279954[/C][/ROW]
[ROW][C]137[/C][C]0.166045382970618[/C][C]0.332090765941235[/C][C]0.833954617029382[/C][/ROW]
[ROW][C]138[/C][C]0.17202340095902[/C][C]0.34404680191804[/C][C]0.82797659904098[/C][/ROW]
[ROW][C]139[/C][C]0.134977980051276[/C][C]0.269955960102552[/C][C]0.865022019948724[/C][/ROW]
[ROW][C]140[/C][C]0.122668670412302[/C][C]0.245337340824605[/C][C]0.877331329587698[/C][/ROW]
[ROW][C]141[/C][C]0.0952135716274956[/C][C]0.190427143254991[/C][C]0.904786428372504[/C][/ROW]
[ROW][C]142[/C][C]0.0695987832935706[/C][C]0.139197566587141[/C][C]0.93040121670643[/C][/ROW]
[ROW][C]143[/C][C]0.0502784949639106[/C][C]0.100556989927821[/C][C]0.949721505036089[/C][/ROW]
[ROW][C]144[/C][C]0.0563231103613322[/C][C]0.112646220722664[/C][C]0.943676889638668[/C][/ROW]
[ROW][C]145[/C][C]0.0768491560776558[/C][C]0.153698312155312[/C][C]0.923150843922344[/C][/ROW]
[ROW][C]146[/C][C]0.13252012954774[/C][C]0.265040259095479[/C][C]0.86747987045226[/C][/ROW]
[ROW][C]147[/C][C]0.177140237546071[/C][C]0.354280475092141[/C][C]0.822859762453929[/C][/ROW]
[ROW][C]148[/C][C]0.173351750516724[/C][C]0.346703501033449[/C][C]0.826648249483276[/C][/ROW]
[ROW][C]149[/C][C]0.151354244144488[/C][C]0.302708488288975[/C][C]0.848645755855512[/C][/ROW]
[ROW][C]150[/C][C]0.134886944093735[/C][C]0.26977388818747[/C][C]0.865113055906265[/C][/ROW]
[ROW][C]151[/C][C]0.0906256973948973[/C][C]0.181251394789795[/C][C]0.909374302605103[/C][/ROW]
[ROW][C]152[/C][C]0.0581809664198583[/C][C]0.116361932839717[/C][C]0.941819033580142[/C][/ROW]
[ROW][C]153[/C][C]0.0380474191392288[/C][C]0.0760948382784576[/C][C]0.961952580860771[/C][/ROW]
[ROW][C]154[/C][C]0.0305711928224878[/C][C]0.0611423856449757[/C][C]0.969428807177512[/C][/ROW]
[ROW][C]155[/C][C]0.0136708997441982[/C][C]0.0273417994883963[/C][C]0.986329100255802[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&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
70.811323637820680.3773527243586410.18867636217932
80.746736751126740.506526497746520.25326324887326
90.6649619872502130.6700760254995750.335038012749787
100.5801183463458140.8397633073083710.419881653654186
110.4661928139687250.932385627937450.533807186031275
120.4764223193692070.9528446387384130.523577680630793
130.4619646167269990.9239292334539980.538035383273001
140.3689979239304330.7379958478608670.631002076069567
150.2941184972705280.5882369945410550.705881502729472
160.2974780293656290.5949560587312580.702521970634371
170.3631947988631950.7263895977263910.636805201136805
180.3454558241660770.6909116483321550.654544175833923
190.8352713185423790.3294573629152430.164728681457621
200.8005851601332370.3988296797335270.199414839866763
210.7814392613695380.4371214772609240.218560738630462
220.7311504827109810.5376990345780370.268849517289019
230.6714051266320910.6571897467358180.328594873367909
240.6265169622228750.746966075554250.373483037777125
250.733612391777780.532775216444440.26638760822222
260.6791903893766620.6416192212466760.320809610623338
270.62111714962020.7577657007595990.3788828503798
280.5742410967572180.8515178064855650.425758903242782
290.5123615837617390.9752768324765220.487638416238261
300.652329150127210.695341699745580.34767084987279
310.6390831019553690.7218337960892620.360916898044631
320.5994135801141820.8011728397716370.400586419885819
330.5440743380300340.9118513239399320.455925661969966
340.4910369166856410.9820738333712820.508963083314359
350.5063089365415990.9873821269168020.493691063458401
360.4767346694778750.953469338955750.523265330522125
370.4512911439825230.9025822879650450.548708856017477
380.3971313812677670.7942627625355330.602868618732234
390.3523989114568650.704797822913730.647601088543135
400.3268403696451210.6536807392902410.673159630354879
410.3179021814157020.6358043628314030.682097818584298
420.298735887524310.5974717750486190.70126411247569
430.2542479250221480.5084958500442960.745752074977852
440.2138606682154230.4277213364308470.786139331784577
450.2189277455312750.4378554910625490.781072254468725
460.2724315147493580.5448630294987150.727568485250642
470.2685196086559550.5370392173119090.731480391344045
480.2611758533314140.5223517066628290.738824146668586
490.2731641935414380.5463283870828760.726835806458562
500.4374532239944960.8749064479889930.562546776005504
510.4331986985070220.8663973970140450.566801301492977
520.4205473740474810.8410947480949630.579452625952519
530.3974272447959330.7948544895918660.602572755204067
540.4803953683969220.9607907367938440.519604631603078
550.4345080395530550.869016079106110.565491960446945
560.611697446047590.776605107904820.38830255395241
570.5649203469757280.8701593060485430.435079653024272
580.5200687859097090.9598624281805820.479931214090291
590.8696409573015410.2607180853969180.130359042698459
600.8503879375837040.2992241248325930.149612062416296
610.8790734299256940.2418531401486120.120926570074306
620.865601609584480.2687967808310410.13439839041552
630.8403769775301210.3192460449397580.159623022469879
640.8160546844954980.3678906310090050.183945315504502
650.7881046720963480.4237906558073040.211895327903652
660.7807050728152290.4385898543695420.219294927184771
670.7478470195121530.5043059609756930.252152980487847
680.7111972733933340.5776054532133310.288802726606666
690.6933645257230370.6132709485539260.306635474276963
700.7391809606513340.5216380786973330.260819039348666
710.7066809844347620.5866380311304770.293319015565238
720.6801751661318850.639649667736230.319824833868115
730.6493151383660460.7013697232679090.350684861633954
740.6098556332549440.7802887334901110.390144366745056
750.569400880544990.861198238910020.43059911945501
760.52427779319250.9514444136149990.4757222068075
770.478891372983790.957782745967580.52110862701621
780.5008832246447730.9982335507104550.499116775355227
790.4961024896078980.9922049792157970.503897510392102
800.4510839001000990.9021678002001980.548916099899901
810.4990742980606570.9981485961213140.500925701939343
820.6549451105145880.6901097789708250.345054889485412
830.6271535457051950.7456929085896110.372846454294805
840.5834712274022450.8330575451955110.416528772597755
850.6790519026654860.6418961946690280.320948097334514
860.719874247347220.5602515053055590.28012575265278
870.6816354711838970.6367290576322070.318364528816103
880.6516639950903330.6966720098193330.348336004909667
890.6318456954052070.7363086091895860.368154304594793
900.5882742491227630.8234515017544730.411725750877237
910.6070407822991540.7859184354016920.392959217700846
920.6069687895424140.7860624209151730.393031210457586
930.5955349576294240.8089300847411530.404465042370576
940.5792696264425670.8414607471148660.420730373557433
950.5342162600603130.9315674798793740.465783739939687
960.4948174005876810.9896348011753620.505182599412319
970.4527975142573560.9055950285147120.547202485742644
980.4086915543141770.8173831086283540.591308445685823
990.4950411587942560.9900823175885120.504958841205744
1000.4494781723316770.8989563446633540.550521827668323
1010.5207612857306950.958477428538610.479238714269305
1020.4743625300263710.9487250600527420.525637469973629
1030.4347316710718150.869463342143630.565268328928185
1040.3989062002181330.7978124004362660.601093799781867
1050.3544328979077170.7088657958154340.645567102092283
1060.3326670072134440.6653340144268880.667332992786556
1070.3029019694734180.6058039389468370.697098030526582
1080.270535127462590.541070254925180.72946487253741
1090.2802139311306690.5604278622613370.719786068869331
1100.2803770220948070.5607540441896130.719622977905193
1110.2771343232915110.5542686465830220.722865676708489
1120.2611835230475420.5223670460950830.738816476952458
1130.2233068502448740.4466137004897480.776693149755126
1140.2060085497938740.4120170995877470.793991450206126
1150.1737373236935050.347474647387010.826262676306495
1160.1585321762817130.3170643525634260.841467823718287
1170.162107930141230.3242158602824590.83789206985877
1180.1333247149433350.266649429886670.866675285056665
1190.1958477673095170.3916955346190350.804152232690483
1200.2074577148896310.4149154297792620.792542285110369
1210.1775255494215440.3550510988430880.822474450578456
1220.1742215720201340.3484431440402690.825778427979866
1230.2063806447381040.4127612894762070.793619355261896
1240.2039770738480960.4079541476961930.796022926151904
1250.1770872667295090.3541745334590170.822912733270492
1260.1606084016471980.3212168032943960.839391598352802
1270.1989341778162550.397868355632510.801065822183745
1280.1687806657958230.3375613315916460.831219334204177
1290.1384796123848680.2769592247697360.861520387615132
1300.1217886768946470.2435773537892940.878211323105353
1310.09550902401781830.1910180480356370.904490975982182
1320.2128221618592020.4256443237184030.787177838140798
1330.3201414959299670.6402829918599340.679858504070033
1340.2743047290330980.5486094580661970.725695270966902
1350.2321634277015340.4643268554030690.767836572298466
1360.2077501857200460.4155003714400920.792249814279954
1370.1660453829706180.3320907659412350.833954617029382
1380.172023400959020.344046801918040.82797659904098
1390.1349779800512760.2699559601025520.865022019948724
1400.1226686704123020.2453373408246050.877331329587698
1410.09521357162749560.1904271432549910.904786428372504
1420.06959878329357060.1391975665871410.93040121670643
1430.05027849496391060.1005569899278210.949721505036089
1440.05632311036133220.1126462207226640.943676889638668
1450.07684915607765580.1536983121553120.923150843922344
1460.132520129547740.2650402590954790.86747987045226
1470.1771402375460710.3542804750921410.822859762453929
1480.1733517505167240.3467035010334490.826648249483276
1490.1513542441444880.3027084882889750.848645755855512
1500.1348869440937350.269773888187470.865113055906265
1510.09062569739489730.1812513947897950.909374302605103
1520.05818096641985830.1163619328397170.941819033580142
1530.03804741913922880.07609483827845760.961952580860771
1540.03057119282248780.06114238564497570.969428807177512
1550.01367089974419820.02734179948839630.986329100255802






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00671140939597315OK
10% type I error level30.0201342281879195OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00671140939597315 & OK \tabularnewline
10% type I error level & 3 & 0.0201342281879195 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190421&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00671140939597315[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0201342281879195[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190421&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190421&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00671140939597315OK
10% type I error level30.0201342281879195OK


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')
}