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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 14:16:13 -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/2011/Nov/22/t1321989391lxrmzx16gk03c9m.htm/, Retrieved Fri, 29 Mar 2024 07:52:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146376, Retrieved Fri, 29 Mar 2024 07:52:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [ws7 Tutorial Popu...] [2010-11-22 11:00:33] [afe9379cca749d06b3d6872e02cc47ed]
- R  D    [Multiple Regression] [ws7-4] [2011-11-22 18:15:17] [f7a862281046b7153543b12c78921b36]
-    D        [Multiple Regression] [ws7-5] [2011-11-22 19:16:13] [47995d3a8fac585eeb070a274b466f8c] [Current]
-               [Multiple Regression] [ws7-5] [2011-11-22 19:42:43] [f7a862281046b7153543b12c78921b36]
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Dataseries X:
14	3	2	3	3	7
8	5	6	0	7	2
12	6	6	0	6	3
7	6	6	6	6	8
10	7	8	5	5	7
9	3	1	0	7	7
16	8	9	8	8	9
7	4	4	0	2	2
14	7	7	0	4	4
6	4	4	9	9	4
16	6	6	6	6	6
11	6	5	6	6	4
17	7	7	5	5	9
12	4	5	4	4	8
7	6	6	0	2	7
13	5	5	0	4	4
9	0	2	2	2	2
15	9	9	6	6	8
7	4	4	0	4	4
9	4	4	4	4	4
7	2	5	5	5	2
14	7	7	7	7	9
15	5	5	5	5	3
7	9	9	4	4	4
13	6	6	6	6	6
17	6	6	6	6	6
15	7	3	0	7	7
14	3	3	1	2	2
14	6	5	0	6	6
8	6	5	4	4	4
8	4	4	4	4	2
12	7	7	7	7	9
14	7	6	7	7	7
8	7	7	0	4	4
11	4	4	4	4	4
16	5	5	5	5	7
11	6	6	0	6	6
8	5	5	5	5	5
14	6	0	1	6	6
16	6	6	2	2	2
14	6	5	0	6	2
5	3	3	9	9	7
8	3	3	3	3	3
10	3	3	0	4	4
8	6	7	6	6	6
13	7	7	1	5	5
15	5	1	5	5	7
6	5	5	0	4	4
12	5	5	0	2	2
14	6	6	0	6	6
5	6	2	6	6	9
15	6	6	7	7	8
11	5	5	0	5	5
8	4	2	4	4	4
13	7	7	5	5	2
14	5	5	1	5	9
12	3	3	4	4	4
16	6	6	9	9	6
10	2	2	2	2	2
15	8	8	8	8	8
8	3	5	3	3	3
16	0	2	1	6	3
19	6	6	0	6	7
14	8	2	6	6	2
7	4	1	0	5	9
13	5	5	0	5	5
15	6	6	6	6	4
7	5	2	2	2	2
13	6	6	1	6	6
4	2	2	5	5	5
14	6	6	5	5	5
13	5	5	5	5	9
11	5	0	5	5	2
14	6	2	6	6	6
12	4	4	6	6	6
15	6	1	0	9	6
14	5	5	0	5	5
13	5	5	1	5	3
7	4	2	7	7	2
5	2	2	2	2	2
7	7	7	4	4	4
13	5	5	0	6	8
13	6	2	5	5	5
11	5	5	5	5	9
6	3	3	3	3	2
12	6	6	0	6	6
8	4	1	4	4	4
11	5	5	9	9	5
12	7	7	0	8	8
9	4	2	4	4	3
12	6	6	2	2	2
13	8	8	7	7	7
16	7	7	7	7	7
16	6	6	6	6	9
11	7	7	0	5	5
8	4	4	5	5	5
4	0	5	6	6	2
7	3	2	0	3	3
14	5	5	5	5	5
11	6	2	9	9	2
17	5	5	0	7	7
15	7	7	7	7	7
14	6	5	1	6	6
5	8	8	3	3	3
4	7	2	7	7	3
19	8	8	8	8	2
11	3	3	0	3	3
15	8	2	5	5	5
10	3	3	3	3	3
9	4	5	0	4	4
12	2	2	5	5	5
15	7	2	7	7	7
7	6	6	0	6	6
13	2	2	0	7	7
14	7	7	0	9	2
14	6	6	6	6	6
14	6	2	0	6	9
8	6	2	6	6	4
15	6	5	6	6	6
15	6	6	2	2	2
9	4	4	5	5	2
16	5	5	0	5	5
9	7	7	4	4	4
15	6	6	0	7	7
15	6	6	6	6	6
6	5	5	5	5	7
8	8	2	8	8	8
15	6	6	6	6	6
10	0	3	5	5	3
9	4	2	0	4	4
14	8	8	8	8	8
12	6	6	0	6	9
8	4	4	9	9	2
11	6	6	5	5	5
13	2	5	0	6	6
9	4	4	0	4	4
15	6	2	0	6	6
13	3	3	3	3	3
15	6	6	6	6	6
14	5	5	0	5	5
16	4	4	4	4	8
12	6	6	6	6	6
14	1	1	0	5	5
10	4	5	4	4	3
10	4	2	7	7	2
4	6	6	0	6	6
8	5	5	5	5	5
17	9	2	6	6	6
16	6	6	6	6	6
12	8	8	8	8	9
12	7	7	2	2	4
15	7	7	7	7	7
9	0	9	0	4	4
13	6	2	0	6	7
14	6	6	5	5	5
11	5	5	0	2	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&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]6 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=146376&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Sport[t] = + 0.860807153968655 + 0.0940010147694601Schoolprestaties[t] + 0.351194714202645Goingout[t] + 0.0700472772259891Relation[t] + 0.155068476245874Family[t] + 0.120399608960879Coach[t] -0.000713750470618953t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Sport[t] =  +  0.860807153968655 +  0.0940010147694601Schoolprestaties[t] +  0.351194714202645Goingout[t] +  0.0700472772259891Relation[t] +  0.155068476245874Family[t] +  0.120399608960879Coach[t] -0.000713750470618953t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Sport[t] =  +  0.860807153968655 +  0.0940010147694601Schoolprestaties[t] +  0.351194714202645Goingout[t] +  0.0700472772259891Relation[t] +  0.155068476245874Family[t] +  0.120399608960879Coach[t] -0.000713750470618953t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146376&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Sport[t] = + 0.860807153968655 + 0.0940010147694601Schoolprestaties[t] + 0.351194714202645Goingout[t] + 0.0700472772259891Relation[t] + 0.155068476245874Family[t] + 0.120399608960879Coach[t] -0.000713750470618953t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8608071539686550.556891.54570.1242890.062144
Schoolprestaties0.09400101476946010.0374442.51040.0131260.006563
Goingout0.3511947142026450.0601975.834100
Relation0.07004727722598910.046511.50610.1341640.067082
Family0.1550684762458740.0860921.80120.0736930.036847
Coach0.1203996089608790.0641041.87820.0623110.031155
t-0.0007137504706189530.002688-0.26560.7909320.395466

\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) & 0.860807153968655 & 0.55689 & 1.5457 & 0.124289 & 0.062144 \tabularnewline
Schoolprestaties & 0.0940010147694601 & 0.037444 & 2.5104 & 0.013126 & 0.006563 \tabularnewline
Goingout & 0.351194714202645 & 0.060197 & 5.8341 & 0 & 0 \tabularnewline
Relation & 0.0700472772259891 & 0.04651 & 1.5061 & 0.134164 & 0.067082 \tabularnewline
Family & 0.155068476245874 & 0.086092 & 1.8012 & 0.073693 & 0.036847 \tabularnewline
Coach & 0.120399608960879 & 0.064104 & 1.8782 & 0.062311 & 0.031155 \tabularnewline
t & -0.000713750470618953 & 0.002688 & -0.2656 & 0.790932 & 0.395466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&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]0.860807153968655[/C][C]0.55689[/C][C]1.5457[/C][C]0.124289[/C][C]0.062144[/C][/ROW]
[ROW][C]Schoolprestaties[/C][C]0.0940010147694601[/C][C]0.037444[/C][C]2.5104[/C][C]0.013126[/C][C]0.006563[/C][/ROW]
[ROW][C]Goingout[/C][C]0.351194714202645[/C][C]0.060197[/C][C]5.8341[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Relation[/C][C]0.0700472772259891[/C][C]0.04651[/C][C]1.5061[/C][C]0.134164[/C][C]0.067082[/C][/ROW]
[ROW][C]Family[/C][C]0.155068476245874[/C][C]0.086092[/C][C]1.8012[/C][C]0.073693[/C][C]0.036847[/C][/ROW]
[ROW][C]Coach[/C][C]0.120399608960879[/C][C]0.064104[/C][C]1.8782[/C][C]0.062311[/C][C]0.031155[/C][/ROW]
[ROW][C]t[/C][C]-0.000713750470618953[/C][C]0.002688[/C][C]-0.2656[/C][C]0.790932[/C][C]0.395466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146376&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8608071539686550.556891.54570.1242890.062144
Schoolprestaties0.09400101476946010.0374442.51040.0131260.006563
Goingout0.3511947142026450.0601975.834100
Relation0.07004727722598910.046511.50610.1341640.067082
Family0.1550684762458740.0860921.80120.0736930.036847
Coach0.1203996089608790.0641041.87820.0623110.031155
t-0.0007137504706189530.002688-0.26560.7909320.395466







Multiple Linear Regression - Regression Statistics
Multiple R0.618545960031752
R-squared0.382599104671602
Adjusted R-squared0.357737323651667
F-TEST (value)15.3890465194273
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value1.10134124042816e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.49666428136881
Sum Squared Residuals333.760591697657

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.618545960031752 \tabularnewline
R-squared & 0.382599104671602 \tabularnewline
Adjusted R-squared & 0.357737323651667 \tabularnewline
F-TEST (value) & 15.3890465194273 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 1.10134124042816e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.49666428136881 \tabularnewline
Sum Squared Residuals & 333.760591697657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.618545960031752[/C][/ROW]
[ROW][C]R-squared[/C][C]0.382599104671602[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.357737323651667[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.3890465194273[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]1.10134124042816e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.49666428136881[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]333.760591697657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146376&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.618545960031752
R-squared0.382599104671602
Adjusted R-squared0.357737323651667
F-TEST (value)15.3890465194273
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value1.10134124042816e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.49666428136881
Sum Squared Residuals333.760591697657







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134.39664156181751-1.39664156181751
255.04483460804184-0.0448346080418442
365.385456049364070.614543950635927
465.937018933206480.062981066793519
576.575182293016790.424817706983209
633.98200509472-0.982005094719995
788.4051020732323-0.405102073232306
843.468819280814010.531180719185988
975.730632946751051.26936705324895
1045.33009481178009-1.33009481178009
1166.53723259491553-0.537232594915531
1265.474519838473210.525480161526791
1377.11708389635717-0.117083896357173
1445.59846028120122-1.59846028120122
1564.768210500729361.23178949927064
1654.929246250281970.0707537497180289
1703.08810268216405-3.08810268216405
1897.732618687381431.26738131261857
1944.01190419605071-0.0119041960507081
2044.47938158402297-0.479381584022966
2124.62617705376618-2.62617705376618
2277.27888860475695-0.278888604756947
2355.4971572799415-0.497157279941499
2496.04449812361482.9555018763852
2566.24523704401849-0.245237044018486
2666.62052735262571-0.620527352625707
2775.233411851859051.76658814814095
2833.8314039378112-0.831403937811197
2965.564904679346890.43509532065311
3064.729437778749961.27056222125004
3144.13673009615494-0.136730096154939
3277.08374907051184-0.0837490705118377
3376.679043417455740.320956582544264
3475.148783096368821.85121690363118
3544.6566773565026-0.656677356502602
3656.06347797443643-1.06347797443643
3765.62838634547620.371613654523797
3855.06924313741775-0.0692431374177516
3963.871840880853462.12815911914654
4065.134472381536620.865527618463385
4165.074741237855950.925258762144053
4235.22325789463081-2.22325789463081
4333.67225423179388-0.672254231793883
4433.92486876439097-0.924868764390969
4566.11215167496145-0.112151674961451
4675.955738527001431.04426147299857
4754.556846847679580.443153152320422
4854.248399131835940.751600868164056
4954.260755299568580.739244700431419
5065.901110633666540.0988893663334626
5164.431091383698771.56890861630123
5266.87988278224431-0.879882782244314
5354.99030353853690.00969646146309786
5443.658723624847170.34127637515283
5575.868305054787181.13169494521282
5655.82181104450293-0.82181104450293
5734.3837811467158-1.3837811467158
5867.17903358321203-1.17903358321203
5923.15212617716751-1.15212617716751
6087.801677960356520.198322039643483
6134.36179615172803-1.36179615172803
6204.3846172510908-4.3846172510908
6366.48223656035667-0.48223656035667
6484.425024497779713.57497550222029
6543.682554052844570.317445947155432
6655.16902681195778-0.169026811957776
6766.16246233586965-0.162462335869652
6852.863699378623562.13630062137644
6965.863595637181310.136404362818694
7023.61681492067217-1.61681492067217
7165.960890174706730.0391098252932677
7255.99657913110752-0.996579131107523
7353.20909251735861.7909074826414
7464.899485428917041.10051457108296
7545.41315907731279-1.41315907731279
7664.68578599392431.3142140060757
7755.25517657155043-0.255176571550428
7854.989709865614580.0102901343854201
7943.981426890806070.0185731091939333
8022.66713234343721-0.667132343437214
8175.301424918384231.69857508161577
8255.67387410755638-0.673874107556383
8364.453545297479261.54645470252074
8455.80001209592118-0.800012095921176
8533.33387507352809-0.333875073528088
8665.687413587185330.312586412814665
8743.28397514511410.716024854885901
8856.21602167208264-1.21602167208264
8976.587403220389630.412596779610372
9043.606630013713470.393369986286531
9164.722067048457211.27793295154279
9287.245320553325050.754679446674952
9377.17541513296016-0.175415132960164
9466.8391901327368-0.839190132736795
9575.66271544717621.3372845528238
9644.67665089591921-0.676650895919207
9704.51504472716262-4.51504472716262
9832.977660395259770.0223396047402326
9955.58971044732676-0.589710447326756
10064.792673696944631.20732630305537
10156.07098577497746-1.07098577497746
10277.07499036395513-0.0749903639551337
10365.582134421747080.417865578252923
10485.102685979790972.89731402020903
10573.801265943222473.19873405677753
10687.422451844020610.577548155979388
10733.69843541430468-0.698435414304683
10884.623703565252713.37629643474729
10933.81314873027195-0.813148730271952
11044.48614964696595-0.486149646965949
11124.33955926953247-2.33955926953247
11275.311879288235721.68812071176428
11365.198137250631320.801862749368677
11424.63211881717363-2.63211881717363
11576.189518560173060.810481439826944
11666.27428676596162-0.274286765961621
11764.809709322207121.19029067779288
11864.063275101671281.93672489832872
11966.01495181511658-0.0149518151165788
12064.983371329117641.01662867088236
12144.39160932204056-0.391609322040556
12255.41105982991149-0.411059829911495
12375.459449428157151.54055057184285
12466.21776219881695-0.217762198816947
12566.36186402649551-0.36186402649551
12655.05923028438612-0.0592302843861213
12784.988681290222953.01131870977705
12866.35972277508365-0.359722775083654
12904.2491052278033-4.2491052278033
13043.418290494945630.581709505054366
13187.657000662173110.342999337826889
13266.0157798924195-0.0157798924194996
13345.18950631551112-1.18950631551112
13465.633920850749360.366079149250642
13525.39524611469182-3.39524611469182
13644.11639742052721-0.116397420527211
13764.528236500681571.47176349931843
13834.07445301093238-1.07445301093238
13966.35187151990685-0.351871519906845
14055.21021029190143-0.210210291901434
14145.53262331630781-1.53262331630781
14266.06772722418661-0.067727224186608
14313.803290183679-2.803290183679
14444.71567264567744-0.715672645677442
14544.2163224040536-0.216322404053596
14664.892580440792521.10741955920748
14754.991444336120290.00855566387971428
14895.128670938399613.87132906160039
14966.43873502997012-0.438735029970116
15087.575836982653310.424163017346691
15175.271235952344471.72876404765553
15277.03930284042419-0.0393028404241861
15305.86023723353992-5.86023723353992
15464.4485003221031.551499677897
15565.900935135174740.0990648648252597
15654.090382984442890.909617015557108

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 4.39664156181751 & -1.39664156181751 \tabularnewline
2 & 5 & 5.04483460804184 & -0.0448346080418442 \tabularnewline
3 & 6 & 5.38545604936407 & 0.614543950635927 \tabularnewline
4 & 6 & 5.93701893320648 & 0.062981066793519 \tabularnewline
5 & 7 & 6.57518229301679 & 0.424817706983209 \tabularnewline
6 & 3 & 3.98200509472 & -0.982005094719995 \tabularnewline
7 & 8 & 8.4051020732323 & -0.405102073232306 \tabularnewline
8 & 4 & 3.46881928081401 & 0.531180719185988 \tabularnewline
9 & 7 & 5.73063294675105 & 1.26936705324895 \tabularnewline
10 & 4 & 5.33009481178009 & -1.33009481178009 \tabularnewline
11 & 6 & 6.53723259491553 & -0.537232594915531 \tabularnewline
12 & 6 & 5.47451983847321 & 0.525480161526791 \tabularnewline
13 & 7 & 7.11708389635717 & -0.117083896357173 \tabularnewline
14 & 4 & 5.59846028120122 & -1.59846028120122 \tabularnewline
15 & 6 & 4.76821050072936 & 1.23178949927064 \tabularnewline
16 & 5 & 4.92924625028197 & 0.0707537497180289 \tabularnewline
17 & 0 & 3.08810268216405 & -3.08810268216405 \tabularnewline
18 & 9 & 7.73261868738143 & 1.26738131261857 \tabularnewline
19 & 4 & 4.01190419605071 & -0.0119041960507081 \tabularnewline
20 & 4 & 4.47938158402297 & -0.479381584022966 \tabularnewline
21 & 2 & 4.62617705376618 & -2.62617705376618 \tabularnewline
22 & 7 & 7.27888860475695 & -0.278888604756947 \tabularnewline
23 & 5 & 5.4971572799415 & -0.497157279941499 \tabularnewline
24 & 9 & 6.0444981236148 & 2.9555018763852 \tabularnewline
25 & 6 & 6.24523704401849 & -0.245237044018486 \tabularnewline
26 & 6 & 6.62052735262571 & -0.620527352625707 \tabularnewline
27 & 7 & 5.23341185185905 & 1.76658814814095 \tabularnewline
28 & 3 & 3.8314039378112 & -0.831403937811197 \tabularnewline
29 & 6 & 5.56490467934689 & 0.43509532065311 \tabularnewline
30 & 6 & 4.72943777874996 & 1.27056222125004 \tabularnewline
31 & 4 & 4.13673009615494 & -0.136730096154939 \tabularnewline
32 & 7 & 7.08374907051184 & -0.0837490705118377 \tabularnewline
33 & 7 & 6.67904341745574 & 0.320956582544264 \tabularnewline
34 & 7 & 5.14878309636882 & 1.85121690363118 \tabularnewline
35 & 4 & 4.6566773565026 & -0.656677356502602 \tabularnewline
36 & 5 & 6.06347797443643 & -1.06347797443643 \tabularnewline
37 & 6 & 5.6283863454762 & 0.371613654523797 \tabularnewline
38 & 5 & 5.06924313741775 & -0.0692431374177516 \tabularnewline
39 & 6 & 3.87184088085346 & 2.12815911914654 \tabularnewline
40 & 6 & 5.13447238153662 & 0.865527618463385 \tabularnewline
41 & 6 & 5.07474123785595 & 0.925258762144053 \tabularnewline
42 & 3 & 5.22325789463081 & -2.22325789463081 \tabularnewline
43 & 3 & 3.67225423179388 & -0.672254231793883 \tabularnewline
44 & 3 & 3.92486876439097 & -0.924868764390969 \tabularnewline
45 & 6 & 6.11215167496145 & -0.112151674961451 \tabularnewline
46 & 7 & 5.95573852700143 & 1.04426147299857 \tabularnewline
47 & 5 & 4.55684684767958 & 0.443153152320422 \tabularnewline
48 & 5 & 4.24839913183594 & 0.751600868164056 \tabularnewline
49 & 5 & 4.26075529956858 & 0.739244700431419 \tabularnewline
50 & 6 & 5.90111063366654 & 0.0988893663334626 \tabularnewline
51 & 6 & 4.43109138369877 & 1.56890861630123 \tabularnewline
52 & 6 & 6.87988278224431 & -0.879882782244314 \tabularnewline
53 & 5 & 4.9903035385369 & 0.00969646146309786 \tabularnewline
54 & 4 & 3.65872362484717 & 0.34127637515283 \tabularnewline
55 & 7 & 5.86830505478718 & 1.13169494521282 \tabularnewline
56 & 5 & 5.82181104450293 & -0.82181104450293 \tabularnewline
57 & 3 & 4.3837811467158 & -1.3837811467158 \tabularnewline
58 & 6 & 7.17903358321203 & -1.17903358321203 \tabularnewline
59 & 2 & 3.15212617716751 & -1.15212617716751 \tabularnewline
60 & 8 & 7.80167796035652 & 0.198322039643483 \tabularnewline
61 & 3 & 4.36179615172803 & -1.36179615172803 \tabularnewline
62 & 0 & 4.3846172510908 & -4.3846172510908 \tabularnewline
63 & 6 & 6.48223656035667 & -0.48223656035667 \tabularnewline
64 & 8 & 4.42502449777971 & 3.57497550222029 \tabularnewline
65 & 4 & 3.68255405284457 & 0.317445947155432 \tabularnewline
66 & 5 & 5.16902681195778 & -0.169026811957776 \tabularnewline
67 & 6 & 6.16246233586965 & -0.162462335869652 \tabularnewline
68 & 5 & 2.86369937862356 & 2.13630062137644 \tabularnewline
69 & 6 & 5.86359563718131 & 0.136404362818694 \tabularnewline
70 & 2 & 3.61681492067217 & -1.61681492067217 \tabularnewline
71 & 6 & 5.96089017470673 & 0.0391098252932677 \tabularnewline
72 & 5 & 5.99657913110752 & -0.996579131107523 \tabularnewline
73 & 5 & 3.2090925173586 & 1.7909074826414 \tabularnewline
74 & 6 & 4.89948542891704 & 1.10051457108296 \tabularnewline
75 & 4 & 5.41315907731279 & -1.41315907731279 \tabularnewline
76 & 6 & 4.6857859939243 & 1.3142140060757 \tabularnewline
77 & 5 & 5.25517657155043 & -0.255176571550428 \tabularnewline
78 & 5 & 4.98970986561458 & 0.0102901343854201 \tabularnewline
79 & 4 & 3.98142689080607 & 0.0185731091939333 \tabularnewline
80 & 2 & 2.66713234343721 & -0.667132343437214 \tabularnewline
81 & 7 & 5.30142491838423 & 1.69857508161577 \tabularnewline
82 & 5 & 5.67387410755638 & -0.673874107556383 \tabularnewline
83 & 6 & 4.45354529747926 & 1.54645470252074 \tabularnewline
84 & 5 & 5.80001209592118 & -0.800012095921176 \tabularnewline
85 & 3 & 3.33387507352809 & -0.333875073528088 \tabularnewline
86 & 6 & 5.68741358718533 & 0.312586412814665 \tabularnewline
87 & 4 & 3.2839751451141 & 0.716024854885901 \tabularnewline
88 & 5 & 6.21602167208264 & -1.21602167208264 \tabularnewline
89 & 7 & 6.58740322038963 & 0.412596779610372 \tabularnewline
90 & 4 & 3.60663001371347 & 0.393369986286531 \tabularnewline
91 & 6 & 4.72206704845721 & 1.27793295154279 \tabularnewline
92 & 8 & 7.24532055332505 & 0.754679446674952 \tabularnewline
93 & 7 & 7.17541513296016 & -0.175415132960164 \tabularnewline
94 & 6 & 6.8391901327368 & -0.839190132736795 \tabularnewline
95 & 7 & 5.6627154471762 & 1.3372845528238 \tabularnewline
96 & 4 & 4.67665089591921 & -0.676650895919207 \tabularnewline
97 & 0 & 4.51504472716262 & -4.51504472716262 \tabularnewline
98 & 3 & 2.97766039525977 & 0.0223396047402326 \tabularnewline
99 & 5 & 5.58971044732676 & -0.589710447326756 \tabularnewline
100 & 6 & 4.79267369694463 & 1.20732630305537 \tabularnewline
101 & 5 & 6.07098577497746 & -1.07098577497746 \tabularnewline
102 & 7 & 7.07499036395513 & -0.0749903639551337 \tabularnewline
103 & 6 & 5.58213442174708 & 0.417865578252923 \tabularnewline
104 & 8 & 5.10268597979097 & 2.89731402020903 \tabularnewline
105 & 7 & 3.80126594322247 & 3.19873405677753 \tabularnewline
106 & 8 & 7.42245184402061 & 0.577548155979388 \tabularnewline
107 & 3 & 3.69843541430468 & -0.698435414304683 \tabularnewline
108 & 8 & 4.62370356525271 & 3.37629643474729 \tabularnewline
109 & 3 & 3.81314873027195 & -0.813148730271952 \tabularnewline
110 & 4 & 4.48614964696595 & -0.486149646965949 \tabularnewline
111 & 2 & 4.33955926953247 & -2.33955926953247 \tabularnewline
112 & 7 & 5.31187928823572 & 1.68812071176428 \tabularnewline
113 & 6 & 5.19813725063132 & 0.801862749368677 \tabularnewline
114 & 2 & 4.63211881717363 & -2.63211881717363 \tabularnewline
115 & 7 & 6.18951856017306 & 0.810481439826944 \tabularnewline
116 & 6 & 6.27428676596162 & -0.274286765961621 \tabularnewline
117 & 6 & 4.80970932220712 & 1.19029067779288 \tabularnewline
118 & 6 & 4.06327510167128 & 1.93672489832872 \tabularnewline
119 & 6 & 6.01495181511658 & -0.0149518151165788 \tabularnewline
120 & 6 & 4.98337132911764 & 1.01662867088236 \tabularnewline
121 & 4 & 4.39160932204056 & -0.391609322040556 \tabularnewline
122 & 5 & 5.41105982991149 & -0.411059829911495 \tabularnewline
123 & 7 & 5.45944942815715 & 1.54055057184285 \tabularnewline
124 & 6 & 6.21776219881695 & -0.217762198816947 \tabularnewline
125 & 6 & 6.36186402649551 & -0.36186402649551 \tabularnewline
126 & 5 & 5.05923028438612 & -0.0592302843861213 \tabularnewline
127 & 8 & 4.98868129022295 & 3.01131870977705 \tabularnewline
128 & 6 & 6.35972277508365 & -0.359722775083654 \tabularnewline
129 & 0 & 4.2491052278033 & -4.2491052278033 \tabularnewline
130 & 4 & 3.41829049494563 & 0.581709505054366 \tabularnewline
131 & 8 & 7.65700066217311 & 0.342999337826889 \tabularnewline
132 & 6 & 6.0157798924195 & -0.0157798924194996 \tabularnewline
133 & 4 & 5.18950631551112 & -1.18950631551112 \tabularnewline
134 & 6 & 5.63392085074936 & 0.366079149250642 \tabularnewline
135 & 2 & 5.39524611469182 & -3.39524611469182 \tabularnewline
136 & 4 & 4.11639742052721 & -0.116397420527211 \tabularnewline
137 & 6 & 4.52823650068157 & 1.47176349931843 \tabularnewline
138 & 3 & 4.07445301093238 & -1.07445301093238 \tabularnewline
139 & 6 & 6.35187151990685 & -0.351871519906845 \tabularnewline
140 & 5 & 5.21021029190143 & -0.210210291901434 \tabularnewline
141 & 4 & 5.53262331630781 & -1.53262331630781 \tabularnewline
142 & 6 & 6.06772722418661 & -0.067727224186608 \tabularnewline
143 & 1 & 3.803290183679 & -2.803290183679 \tabularnewline
144 & 4 & 4.71567264567744 & -0.715672645677442 \tabularnewline
145 & 4 & 4.2163224040536 & -0.216322404053596 \tabularnewline
146 & 6 & 4.89258044079252 & 1.10741955920748 \tabularnewline
147 & 5 & 4.99144433612029 & 0.00855566387971428 \tabularnewline
148 & 9 & 5.12867093839961 & 3.87132906160039 \tabularnewline
149 & 6 & 6.43873502997012 & -0.438735029970116 \tabularnewline
150 & 8 & 7.57583698265331 & 0.424163017346691 \tabularnewline
151 & 7 & 5.27123595234447 & 1.72876404765553 \tabularnewline
152 & 7 & 7.03930284042419 & -0.0393028404241861 \tabularnewline
153 & 0 & 5.86023723353992 & -5.86023723353992 \tabularnewline
154 & 6 & 4.448500322103 & 1.551499677897 \tabularnewline
155 & 6 & 5.90093513517474 & 0.0990648648252597 \tabularnewline
156 & 5 & 4.09038298444289 & 0.909617015557108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&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]3[/C][C]4.39664156181751[/C][C]-1.39664156181751[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]5.04483460804184[/C][C]-0.0448346080418442[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.38545604936407[/C][C]0.614543950635927[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]5.93701893320648[/C][C]0.062981066793519[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]6.57518229301679[/C][C]0.424817706983209[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]3.98200509472[/C][C]-0.982005094719995[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]8.4051020732323[/C][C]-0.405102073232306[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]3.46881928081401[/C][C]0.531180719185988[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]5.73063294675105[/C][C]1.26936705324895[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.33009481178009[/C][C]-1.33009481178009[/C][/ROW]
[ROW][C]11[/C][C]6[/C][C]6.53723259491553[/C][C]-0.537232594915531[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]5.47451983847321[/C][C]0.525480161526791[/C][/ROW]
[ROW][C]13[/C][C]7[/C][C]7.11708389635717[/C][C]-0.117083896357173[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]5.59846028120122[/C][C]-1.59846028120122[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]4.76821050072936[/C][C]1.23178949927064[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]4.92924625028197[/C][C]0.0707537497180289[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]3.08810268216405[/C][C]-3.08810268216405[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]7.73261868738143[/C][C]1.26738131261857[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.01190419605071[/C][C]-0.0119041960507081[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.47938158402297[/C][C]-0.479381584022966[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]4.62617705376618[/C][C]-2.62617705376618[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.27888860475695[/C][C]-0.278888604756947[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]5.4971572799415[/C][C]-0.497157279941499[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]6.0444981236148[/C][C]2.9555018763852[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]6.24523704401849[/C][C]-0.245237044018486[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]6.62052735262571[/C][C]-0.620527352625707[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]5.23341185185905[/C][C]1.76658814814095[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]3.8314039378112[/C][C]-0.831403937811197[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.56490467934689[/C][C]0.43509532065311[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]4.72943777874996[/C][C]1.27056222125004[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.13673009615494[/C][C]-0.136730096154939[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]7.08374907051184[/C][C]-0.0837490705118377[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]6.67904341745574[/C][C]0.320956582544264[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]5.14878309636882[/C][C]1.85121690363118[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.6566773565026[/C][C]-0.656677356502602[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]6.06347797443643[/C][C]-1.06347797443643[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]5.6283863454762[/C][C]0.371613654523797[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]5.06924313741775[/C][C]-0.0692431374177516[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]3.87184088085346[/C][C]2.12815911914654[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]5.13447238153662[/C][C]0.865527618463385[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]5.07474123785595[/C][C]0.925258762144053[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]5.22325789463081[/C][C]-2.22325789463081[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.67225423179388[/C][C]-0.672254231793883[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]3.92486876439097[/C][C]-0.924868764390969[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]6.11215167496145[/C][C]-0.112151674961451[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]5.95573852700143[/C][C]1.04426147299857[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]4.55684684767958[/C][C]0.443153152320422[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]4.24839913183594[/C][C]0.751600868164056[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]4.26075529956858[/C][C]0.739244700431419[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]5.90111063366654[/C][C]0.0988893663334626[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]4.43109138369877[/C][C]1.56890861630123[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]6.87988278224431[/C][C]-0.879882782244314[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.9903035385369[/C][C]0.00969646146309786[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.65872362484717[/C][C]0.34127637515283[/C][/ROW]
[ROW][C]55[/C][C]7[/C][C]5.86830505478718[/C][C]1.13169494521282[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.82181104450293[/C][C]-0.82181104450293[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]4.3837811467158[/C][C]-1.3837811467158[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]7.17903358321203[/C][C]-1.17903358321203[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]3.15212617716751[/C][C]-1.15212617716751[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.80167796035652[/C][C]0.198322039643483[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]4.36179615172803[/C][C]-1.36179615172803[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]4.3846172510908[/C][C]-4.3846172510908[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]6.48223656035667[/C][C]-0.48223656035667[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]4.42502449777971[/C][C]3.57497550222029[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.68255405284457[/C][C]0.317445947155432[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.16902681195778[/C][C]-0.169026811957776[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]6.16246233586965[/C][C]-0.162462335869652[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]2.86369937862356[/C][C]2.13630062137644[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]5.86359563718131[/C][C]0.136404362818694[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]3.61681492067217[/C][C]-1.61681492067217[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]5.96089017470673[/C][C]0.0391098252932677[/C][/ROW]
[ROW][C]72[/C][C]5[/C][C]5.99657913110752[/C][C]-0.996579131107523[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]3.2090925173586[/C][C]1.7909074826414[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]4.89948542891704[/C][C]1.10051457108296[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]5.41315907731279[/C][C]-1.41315907731279[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]4.6857859939243[/C][C]1.3142140060757[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]5.25517657155043[/C][C]-0.255176571550428[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]4.98970986561458[/C][C]0.0102901343854201[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.98142689080607[/C][C]0.0185731091939333[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]2.66713234343721[/C][C]-0.667132343437214[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]5.30142491838423[/C][C]1.69857508161577[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]5.67387410755638[/C][C]-0.673874107556383[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]4.45354529747926[/C][C]1.54645470252074[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]5.80001209592118[/C][C]-0.800012095921176[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]3.33387507352809[/C][C]-0.333875073528088[/C][/ROW]
[ROW][C]86[/C][C]6[/C][C]5.68741358718533[/C][C]0.312586412814665[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.2839751451141[/C][C]0.716024854885901[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]6.21602167208264[/C][C]-1.21602167208264[/C][/ROW]
[ROW][C]89[/C][C]7[/C][C]6.58740322038963[/C][C]0.412596779610372[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]3.60663001371347[/C][C]0.393369986286531[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]4.72206704845721[/C][C]1.27793295154279[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]7.24532055332505[/C][C]0.754679446674952[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]7.17541513296016[/C][C]-0.175415132960164[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]6.8391901327368[/C][C]-0.839190132736795[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]5.6627154471762[/C][C]1.3372845528238[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]4.67665089591921[/C][C]-0.676650895919207[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]4.51504472716262[/C][C]-4.51504472716262[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]2.97766039525977[/C][C]0.0223396047402326[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]5.58971044732676[/C][C]-0.589710447326756[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]4.79267369694463[/C][C]1.20732630305537[/C][/ROW]
[ROW][C]101[/C][C]5[/C][C]6.07098577497746[/C][C]-1.07098577497746[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]7.07499036395513[/C][C]-0.0749903639551337[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]5.58213442174708[/C][C]0.417865578252923[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]5.10268597979097[/C][C]2.89731402020903[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]3.80126594322247[/C][C]3.19873405677753[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]7.42245184402061[/C][C]0.577548155979388[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]3.69843541430468[/C][C]-0.698435414304683[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]4.62370356525271[/C][C]3.37629643474729[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]3.81314873027195[/C][C]-0.813148730271952[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.48614964696595[/C][C]-0.486149646965949[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]4.33955926953247[/C][C]-2.33955926953247[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]5.31187928823572[/C][C]1.68812071176428[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]5.19813725063132[/C][C]0.801862749368677[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]4.63211881717363[/C][C]-2.63211881717363[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]6.18951856017306[/C][C]0.810481439826944[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]6.27428676596162[/C][C]-0.274286765961621[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]4.80970932220712[/C][C]1.19029067779288[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]4.06327510167128[/C][C]1.93672489832872[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]6.01495181511658[/C][C]-0.0149518151165788[/C][/ROW]
[ROW][C]120[/C][C]6[/C][C]4.98337132911764[/C][C]1.01662867088236[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]4.39160932204056[/C][C]-0.391609322040556[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]5.41105982991149[/C][C]-0.411059829911495[/C][/ROW]
[ROW][C]123[/C][C]7[/C][C]5.45944942815715[/C][C]1.54055057184285[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]6.21776219881695[/C][C]-0.217762198816947[/C][/ROW]
[ROW][C]125[/C][C]6[/C][C]6.36186402649551[/C][C]-0.36186402649551[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]5.05923028438612[/C][C]-0.0592302843861213[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]4.98868129022295[/C][C]3.01131870977705[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]6.35972277508365[/C][C]-0.359722775083654[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]4.2491052278033[/C][C]-4.2491052278033[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]3.41829049494563[/C][C]0.581709505054366[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]7.65700066217311[/C][C]0.342999337826889[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]6.0157798924195[/C][C]-0.0157798924194996[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]5.18950631551112[/C][C]-1.18950631551112[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.63392085074936[/C][C]0.366079149250642[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]5.39524611469182[/C][C]-3.39524611469182[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.11639742052721[/C][C]-0.116397420527211[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]4.52823650068157[/C][C]1.47176349931843[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]4.07445301093238[/C][C]-1.07445301093238[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]6.35187151990685[/C][C]-0.351871519906845[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]5.21021029190143[/C][C]-0.210210291901434[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]5.53262331630781[/C][C]-1.53262331630781[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]6.06772722418661[/C][C]-0.067727224186608[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]3.803290183679[/C][C]-2.803290183679[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]4.71567264567744[/C][C]-0.715672645677442[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]4.2163224040536[/C][C]-0.216322404053596[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]4.89258044079252[/C][C]1.10741955920748[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.99144433612029[/C][C]0.00855566387971428[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]5.12867093839961[/C][C]3.87132906160039[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]6.43873502997012[/C][C]-0.438735029970116[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]7.57583698265331[/C][C]0.424163017346691[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]5.27123595234447[/C][C]1.72876404765553[/C][/ROW]
[ROW][C]152[/C][C]7[/C][C]7.03930284042419[/C][C]-0.0393028404241861[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]5.86023723353992[/C][C]-5.86023723353992[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]4.448500322103[/C][C]1.551499677897[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]5.90093513517474[/C][C]0.0990648648252597[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]4.09038298444289[/C][C]0.909617015557108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146376&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134.39664156181751-1.39664156181751
255.04483460804184-0.0448346080418442
365.385456049364070.614543950635927
465.937018933206480.062981066793519
576.575182293016790.424817706983209
633.98200509472-0.982005094719995
788.4051020732323-0.405102073232306
843.468819280814010.531180719185988
975.730632946751051.26936705324895
1045.33009481178009-1.33009481178009
1166.53723259491553-0.537232594915531
1265.474519838473210.525480161526791
1377.11708389635717-0.117083896357173
1445.59846028120122-1.59846028120122
1564.768210500729361.23178949927064
1654.929246250281970.0707537497180289
1703.08810268216405-3.08810268216405
1897.732618687381431.26738131261857
1944.01190419605071-0.0119041960507081
2044.47938158402297-0.479381584022966
2124.62617705376618-2.62617705376618
2277.27888860475695-0.278888604756947
2355.4971572799415-0.497157279941499
2496.04449812361482.9555018763852
2566.24523704401849-0.245237044018486
2666.62052735262571-0.620527352625707
2775.233411851859051.76658814814095
2833.8314039378112-0.831403937811197
2965.564904679346890.43509532065311
3064.729437778749961.27056222125004
3144.13673009615494-0.136730096154939
3277.08374907051184-0.0837490705118377
3376.679043417455740.320956582544264
3475.148783096368821.85121690363118
3544.6566773565026-0.656677356502602
3656.06347797443643-1.06347797443643
3765.62838634547620.371613654523797
3855.06924313741775-0.0692431374177516
3963.871840880853462.12815911914654
4065.134472381536620.865527618463385
4165.074741237855950.925258762144053
4235.22325789463081-2.22325789463081
4333.67225423179388-0.672254231793883
4433.92486876439097-0.924868764390969
4566.11215167496145-0.112151674961451
4675.955738527001431.04426147299857
4754.556846847679580.443153152320422
4854.248399131835940.751600868164056
4954.260755299568580.739244700431419
5065.901110633666540.0988893663334626
5164.431091383698771.56890861630123
5266.87988278224431-0.879882782244314
5354.99030353853690.00969646146309786
5443.658723624847170.34127637515283
5575.868305054787181.13169494521282
5655.82181104450293-0.82181104450293
5734.3837811467158-1.3837811467158
5867.17903358321203-1.17903358321203
5923.15212617716751-1.15212617716751
6087.801677960356520.198322039643483
6134.36179615172803-1.36179615172803
6204.3846172510908-4.3846172510908
6366.48223656035667-0.48223656035667
6484.425024497779713.57497550222029
6543.682554052844570.317445947155432
6655.16902681195778-0.169026811957776
6766.16246233586965-0.162462335869652
6852.863699378623562.13630062137644
6965.863595637181310.136404362818694
7023.61681492067217-1.61681492067217
7165.960890174706730.0391098252932677
7255.99657913110752-0.996579131107523
7353.20909251735861.7909074826414
7464.899485428917041.10051457108296
7545.41315907731279-1.41315907731279
7664.68578599392431.3142140060757
7755.25517657155043-0.255176571550428
7854.989709865614580.0102901343854201
7943.981426890806070.0185731091939333
8022.66713234343721-0.667132343437214
8175.301424918384231.69857508161577
8255.67387410755638-0.673874107556383
8364.453545297479261.54645470252074
8455.80001209592118-0.800012095921176
8533.33387507352809-0.333875073528088
8665.687413587185330.312586412814665
8743.28397514511410.716024854885901
8856.21602167208264-1.21602167208264
8976.587403220389630.412596779610372
9043.606630013713470.393369986286531
9164.722067048457211.27793295154279
9287.245320553325050.754679446674952
9377.17541513296016-0.175415132960164
9466.8391901327368-0.839190132736795
9575.66271544717621.3372845528238
9644.67665089591921-0.676650895919207
9704.51504472716262-4.51504472716262
9832.977660395259770.0223396047402326
9955.58971044732676-0.589710447326756
10064.792673696944631.20732630305537
10156.07098577497746-1.07098577497746
10277.07499036395513-0.0749903639551337
10365.582134421747080.417865578252923
10485.102685979790972.89731402020903
10573.801265943222473.19873405677753
10687.422451844020610.577548155979388
10733.69843541430468-0.698435414304683
10884.623703565252713.37629643474729
10933.81314873027195-0.813148730271952
11044.48614964696595-0.486149646965949
11124.33955926953247-2.33955926953247
11275.311879288235721.68812071176428
11365.198137250631320.801862749368677
11424.63211881717363-2.63211881717363
11576.189518560173060.810481439826944
11666.27428676596162-0.274286765961621
11764.809709322207121.19029067779288
11864.063275101671281.93672489832872
11966.01495181511658-0.0149518151165788
12064.983371329117641.01662867088236
12144.39160932204056-0.391609322040556
12255.41105982991149-0.411059829911495
12375.459449428157151.54055057184285
12466.21776219881695-0.217762198816947
12566.36186402649551-0.36186402649551
12655.05923028438612-0.0592302843861213
12784.988681290222953.01131870977705
12866.35972277508365-0.359722775083654
12904.2491052278033-4.2491052278033
13043.418290494945630.581709505054366
13187.657000662173110.342999337826889
13266.0157798924195-0.0157798924194996
13345.18950631551112-1.18950631551112
13465.633920850749360.366079149250642
13525.39524611469182-3.39524611469182
13644.11639742052721-0.116397420527211
13764.528236500681571.47176349931843
13834.07445301093238-1.07445301093238
13966.35187151990685-0.351871519906845
14055.21021029190143-0.210210291901434
14145.53262331630781-1.53262331630781
14266.06772722418661-0.067727224186608
14313.803290183679-2.803290183679
14444.71567264567744-0.715672645677442
14544.2163224040536-0.216322404053596
14664.892580440792521.10741955920748
14754.991444336120290.00855566387971428
14895.128670938399613.87132906160039
14966.43873502997012-0.438735029970116
15087.575836982653310.424163017346691
15175.271235952344471.72876404765553
15277.03930284042419-0.0393028404241861
15305.86023723353992-5.86023723353992
15464.4485003221031.551499677897
15565.900935135174740.0990648648252597
15654.090382984442890.909617015557108







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.02138174504856410.04276349009712830.978618254951436
110.004541181994242060.009082363988484120.995458818005758
120.006592910662958760.01318582132591750.993407089337041
130.002806286630737450.005612573261474910.997193713369263
140.01482900482877130.02965800965754250.985170995171229
150.007325858809754560.01465171761950910.992674141190245
160.003160718243466770.006321436486933540.996839281756533
170.03086380856069370.06172761712138730.969136191439306
180.02126111566157770.04252223132315540.978738884338422
190.0110919199011030.02218383980220590.988908080098897
200.006145269488600050.01229053897720010.9938547305114
210.01678616973500210.03357233947000420.983213830264998
220.009349297728916750.01869859545783350.990650702271083
230.006629670439290030.01325934087858010.99337032956071
240.01324367434000220.02648734868000440.986756325659998
250.007789473712463010.0155789474249260.992210526287537
260.004430940369052750.008861880738105490.995569059630947
270.008247461824740740.01649492364948150.991752538175259
280.004936430732966710.009872861465933420.995063569267033
290.003506626780566850.007013253561133710.996493373219433
300.003788835295372870.007577670590745750.996211164704627
310.002255176780232970.004510353560465940.997744823219767
320.001622124530737980.003244249061475970.998377875469262
330.000952881367468540.001905762734937080.999047118632531
340.0006179724240262990.00123594484805260.999382027575974
350.0003533707223471470.0007067414446942940.999646629277653
360.0002707132128965950.000541426425793190.999729286787103
370.0002982536834529590.0005965073669059190.999701746316547
380.000160878518440340.0003217570368806790.99983912148156
390.00209186545094460.00418373090188920.997908134549055
400.00137248684620130.00274497369240260.998627513153799
410.0008835602543176090.001767120508635220.999116439745682
420.001129340368553950.002258680737107910.998870659631446
430.0007169962947534860.001433992589506970.999283003705246
440.0008072668091868090.001614533618373620.999192733190813
450.0005364492344049380.001072898468809880.999463550765595
460.000369195079835210.0007383901596704210.999630804920165
470.0004393496520979060.0008786993041958110.999560650347902
480.0002765873498565430.0005531746997130850.999723412650143
490.0001700682340628920.0003401364681257850.999829931765937
500.0001904181678664320.0003808363357328650.999809581832134
510.0003692752941080940.0007385505882161870.999630724705892
520.0003271607479350430.0006543214958700860.999672839252065
530.0002701056724544880.0005402113449089750.999729894327546
540.0001898192500639060.0003796385001278120.999810180749936
550.000152368271735410.000304736543470820.999847631728265
560.0002376243797905140.0004752487595810290.99976237562021
570.0002178514520849230.0004357029041698470.999782148547915
580.0001681601338986970.0003363202677973940.999831839866101
590.0001283960300305710.0002567920600611410.999871603969969
607.65765684158924e-050.0001531531368317850.999923423431584
619.16492814734758e-050.0001832985629469520.999908350718527
620.003395163483277170.006790326966554340.996604836516723
630.002734650017710380.005469300035420770.99726534998229
640.05055111445278460.1011022289055690.949448885547215
650.03932974827884090.07865949655768180.960670251721159
660.03104034443753320.06208068887506650.968959655562467
670.02353623390529160.04707246781058320.976463766094708
680.03311036189490560.06622072378981130.966889638105094
690.02560812646908920.05121625293817850.974391873530911
700.02729021288643130.05458042577286270.972709787113569
710.02050712737081510.04101425474163020.979492872629185
720.01843143859407030.03686287718814060.98156856140593
730.0268906858399210.0537813716798420.973109314160079
740.02528443307335240.05056886614670470.974715566926648
750.02596145553690220.05192291107380440.974038544463098
760.02381134488221220.04762268976442450.976188655117788
770.01882224941794190.03764449883588390.981177750582058
780.01420034547649520.02840069095299050.985799654523505
790.0105362708215430.02107254164308590.989463729178457
800.008472429165766590.01694485833153320.991527570834233
810.008173188446943250.01634637689388650.991826811553057
820.006858684914780220.01371736982956040.99314131508522
830.007037514200751530.01407502840150310.992962485799249
840.00606036210470650.0121207242094130.993939637895293
850.004495812671592520.008991625343185040.995504187328408
860.00320895646306640.006417912926132790.996791043536934
870.002401203143218850.00480240628643770.997598796856781
880.002281014728734640.004562029457469280.997718985271265
890.0015990147747310.003198029549462010.998400985225269
900.001098532291342020.002197064582684030.998901467708658
910.0009214431810244810.001842886362048960.999078556818976
920.0006406083512631780.001281216702526360.999359391648737
930.0004301126678782290.0008602253357564580.999569887332122
940.0003638633521521250.0007277267043042490.999636136647848
950.0003196723097946310.0006393446195892620.999680327690205
960.0002441062379225040.0004882124758450080.999755893762078
970.008420789087542160.01684157817508430.991579210912458
980.006082992455953270.01216598491190650.993917007544047
990.004886557640394720.009773115280789450.995113442359605
1000.004458161141311360.008916322282622720.995541838858689
1010.003923185940642870.007846371881285730.996076814059357
1020.00291194361448740.005823887228974810.997088056385513
1030.001994818171516540.003989636343033080.998005181828483
1040.004316648105929090.008633296211858170.995683351894071
1050.01068764327855590.02137528655711180.989312356721444
1060.008062010953517690.01612402190703540.991937989046482
1070.006208943026220960.01241788605244190.993791056973779
1080.01775992895903910.03551985791807820.982240071040961
1090.01420557207150320.02841114414300630.985794427928497
1100.0107915441486510.02158308829730210.989208455851349
1110.0201978648169170.04039572963383390.979802135183083
1120.01812464012408670.03624928024817340.981875359875913
1130.01485214221920280.02970428443840560.985147857780797
1140.0323072837630260.06461456752605210.967692716236974
1150.04978368671558540.09956737343117080.950216313284415
1160.03808425778227490.07616851556454980.961915742217725
1170.03000117068955040.06000234137910080.96999882931045
1180.0293116617835790.05862332356715810.970688338216421
1190.02138820530132660.04277641060265310.978611794698673
1200.02405190080796880.04810380161593770.975948099192031
1210.01804773159945210.03609546319890420.981952268400548
1220.01435941104932120.02871882209864240.985640588950679
1230.02685817454368370.05371634908736750.973141825456316
1240.02621112443735230.05242224887470470.973788875562648
1250.02030797159943990.04061594319887980.97969202840056
1260.01491109217752770.02982218435505550.985088907822472
1270.01594763526975190.03189527053950370.984052364730248
1280.01196088916941080.02392177833882170.988039110830589
1290.07083094890707720.1416618978141540.929169051092923
1300.05361727970169410.1072345594033880.946382720298306
1310.04570740265934450.09141480531868910.954292597340655
1320.03664030554562460.07328061109124910.963359694454375
1330.02611009652346960.05222019304693930.97388990347653
1340.02258034268722910.04516068537445810.977419657312771
1350.02816226248585670.05632452497171340.971837737514143
1360.02052659646327450.04105319292654910.979473403536726
1370.02772477084317690.05544954168635390.972275229156823
1380.01845153976627990.03690307953255990.98154846023372
1390.01254351624602360.02508703249204730.987456483753976
1400.06997880263516790.1399576052703360.930021197364832
1410.1522277963636250.3044555927272490.847772203636375
1420.1104333481543240.2208666963086490.889566651845676
1430.2754845508439260.5509691016878530.724515449156074
1440.221904762112880.4438095242257590.77809523788712
1450.140109520137640.280219040275280.85989047986236
1460.8056127769608970.3887744460782070.194387223039103

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0213817450485641 & 0.0427634900971283 & 0.978618254951436 \tabularnewline
11 & 0.00454118199424206 & 0.00908236398848412 & 0.995458818005758 \tabularnewline
12 & 0.00659291066295876 & 0.0131858213259175 & 0.993407089337041 \tabularnewline
13 & 0.00280628663073745 & 0.00561257326147491 & 0.997193713369263 \tabularnewline
14 & 0.0148290048287713 & 0.0296580096575425 & 0.985170995171229 \tabularnewline
15 & 0.00732585880975456 & 0.0146517176195091 & 0.992674141190245 \tabularnewline
16 & 0.00316071824346677 & 0.00632143648693354 & 0.996839281756533 \tabularnewline
17 & 0.0308638085606937 & 0.0617276171213873 & 0.969136191439306 \tabularnewline
18 & 0.0212611156615777 & 0.0425222313231554 & 0.978738884338422 \tabularnewline
19 & 0.011091919901103 & 0.0221838398022059 & 0.988908080098897 \tabularnewline
20 & 0.00614526948860005 & 0.0122905389772001 & 0.9938547305114 \tabularnewline
21 & 0.0167861697350021 & 0.0335723394700042 & 0.983213830264998 \tabularnewline
22 & 0.00934929772891675 & 0.0186985954578335 & 0.990650702271083 \tabularnewline
23 & 0.00662967043929003 & 0.0132593408785801 & 0.99337032956071 \tabularnewline
24 & 0.0132436743400022 & 0.0264873486800044 & 0.986756325659998 \tabularnewline
25 & 0.00778947371246301 & 0.015578947424926 & 0.992210526287537 \tabularnewline
26 & 0.00443094036905275 & 0.00886188073810549 & 0.995569059630947 \tabularnewline
27 & 0.00824746182474074 & 0.0164949236494815 & 0.991752538175259 \tabularnewline
28 & 0.00493643073296671 & 0.00987286146593342 & 0.995063569267033 \tabularnewline
29 & 0.00350662678056685 & 0.00701325356113371 & 0.996493373219433 \tabularnewline
30 & 0.00378883529537287 & 0.00757767059074575 & 0.996211164704627 \tabularnewline
31 & 0.00225517678023297 & 0.00451035356046594 & 0.997744823219767 \tabularnewline
32 & 0.00162212453073798 & 0.00324424906147597 & 0.998377875469262 \tabularnewline
33 & 0.00095288136746854 & 0.00190576273493708 & 0.999047118632531 \tabularnewline
34 & 0.000617972424026299 & 0.0012359448480526 & 0.999382027575974 \tabularnewline
35 & 0.000353370722347147 & 0.000706741444694294 & 0.999646629277653 \tabularnewline
36 & 0.000270713212896595 & 0.00054142642579319 & 0.999729286787103 \tabularnewline
37 & 0.000298253683452959 & 0.000596507366905919 & 0.999701746316547 \tabularnewline
38 & 0.00016087851844034 & 0.000321757036880679 & 0.99983912148156 \tabularnewline
39 & 0.0020918654509446 & 0.0041837309018892 & 0.997908134549055 \tabularnewline
40 & 0.0013724868462013 & 0.0027449736924026 & 0.998627513153799 \tabularnewline
41 & 0.000883560254317609 & 0.00176712050863522 & 0.999116439745682 \tabularnewline
42 & 0.00112934036855395 & 0.00225868073710791 & 0.998870659631446 \tabularnewline
43 & 0.000716996294753486 & 0.00143399258950697 & 0.999283003705246 \tabularnewline
44 & 0.000807266809186809 & 0.00161453361837362 & 0.999192733190813 \tabularnewline
45 & 0.000536449234404938 & 0.00107289846880988 & 0.999463550765595 \tabularnewline
46 & 0.00036919507983521 & 0.000738390159670421 & 0.999630804920165 \tabularnewline
47 & 0.000439349652097906 & 0.000878699304195811 & 0.999560650347902 \tabularnewline
48 & 0.000276587349856543 & 0.000553174699713085 & 0.999723412650143 \tabularnewline
49 & 0.000170068234062892 & 0.000340136468125785 & 0.999829931765937 \tabularnewline
50 & 0.000190418167866432 & 0.000380836335732865 & 0.999809581832134 \tabularnewline
51 & 0.000369275294108094 & 0.000738550588216187 & 0.999630724705892 \tabularnewline
52 & 0.000327160747935043 & 0.000654321495870086 & 0.999672839252065 \tabularnewline
53 & 0.000270105672454488 & 0.000540211344908975 & 0.999729894327546 \tabularnewline
54 & 0.000189819250063906 & 0.000379638500127812 & 0.999810180749936 \tabularnewline
55 & 0.00015236827173541 & 0.00030473654347082 & 0.999847631728265 \tabularnewline
56 & 0.000237624379790514 & 0.000475248759581029 & 0.99976237562021 \tabularnewline
57 & 0.000217851452084923 & 0.000435702904169847 & 0.999782148547915 \tabularnewline
58 & 0.000168160133898697 & 0.000336320267797394 & 0.999831839866101 \tabularnewline
59 & 0.000128396030030571 & 0.000256792060061141 & 0.999871603969969 \tabularnewline
60 & 7.65765684158924e-05 & 0.000153153136831785 & 0.999923423431584 \tabularnewline
61 & 9.16492814734758e-05 & 0.000183298562946952 & 0.999908350718527 \tabularnewline
62 & 0.00339516348327717 & 0.00679032696655434 & 0.996604836516723 \tabularnewline
63 & 0.00273465001771038 & 0.00546930003542077 & 0.99726534998229 \tabularnewline
64 & 0.0505511144527846 & 0.101102228905569 & 0.949448885547215 \tabularnewline
65 & 0.0393297482788409 & 0.0786594965576818 & 0.960670251721159 \tabularnewline
66 & 0.0310403444375332 & 0.0620806888750665 & 0.968959655562467 \tabularnewline
67 & 0.0235362339052916 & 0.0470724678105832 & 0.976463766094708 \tabularnewline
68 & 0.0331103618949056 & 0.0662207237898113 & 0.966889638105094 \tabularnewline
69 & 0.0256081264690892 & 0.0512162529381785 & 0.974391873530911 \tabularnewline
70 & 0.0272902128864313 & 0.0545804257728627 & 0.972709787113569 \tabularnewline
71 & 0.0205071273708151 & 0.0410142547416302 & 0.979492872629185 \tabularnewline
72 & 0.0184314385940703 & 0.0368628771881406 & 0.98156856140593 \tabularnewline
73 & 0.026890685839921 & 0.053781371679842 & 0.973109314160079 \tabularnewline
74 & 0.0252844330733524 & 0.0505688661467047 & 0.974715566926648 \tabularnewline
75 & 0.0259614555369022 & 0.0519229110738044 & 0.974038544463098 \tabularnewline
76 & 0.0238113448822122 & 0.0476226897644245 & 0.976188655117788 \tabularnewline
77 & 0.0188222494179419 & 0.0376444988358839 & 0.981177750582058 \tabularnewline
78 & 0.0142003454764952 & 0.0284006909529905 & 0.985799654523505 \tabularnewline
79 & 0.010536270821543 & 0.0210725416430859 & 0.989463729178457 \tabularnewline
80 & 0.00847242916576659 & 0.0169448583315332 & 0.991527570834233 \tabularnewline
81 & 0.00817318844694325 & 0.0163463768938865 & 0.991826811553057 \tabularnewline
82 & 0.00685868491478022 & 0.0137173698295604 & 0.99314131508522 \tabularnewline
83 & 0.00703751420075153 & 0.0140750284015031 & 0.992962485799249 \tabularnewline
84 & 0.0060603621047065 & 0.012120724209413 & 0.993939637895293 \tabularnewline
85 & 0.00449581267159252 & 0.00899162534318504 & 0.995504187328408 \tabularnewline
86 & 0.0032089564630664 & 0.00641791292613279 & 0.996791043536934 \tabularnewline
87 & 0.00240120314321885 & 0.0048024062864377 & 0.997598796856781 \tabularnewline
88 & 0.00228101472873464 & 0.00456202945746928 & 0.997718985271265 \tabularnewline
89 & 0.001599014774731 & 0.00319802954946201 & 0.998400985225269 \tabularnewline
90 & 0.00109853229134202 & 0.00219706458268403 & 0.998901467708658 \tabularnewline
91 & 0.000921443181024481 & 0.00184288636204896 & 0.999078556818976 \tabularnewline
92 & 0.000640608351263178 & 0.00128121670252636 & 0.999359391648737 \tabularnewline
93 & 0.000430112667878229 & 0.000860225335756458 & 0.999569887332122 \tabularnewline
94 & 0.000363863352152125 & 0.000727726704304249 & 0.999636136647848 \tabularnewline
95 & 0.000319672309794631 & 0.000639344619589262 & 0.999680327690205 \tabularnewline
96 & 0.000244106237922504 & 0.000488212475845008 & 0.999755893762078 \tabularnewline
97 & 0.00842078908754216 & 0.0168415781750843 & 0.991579210912458 \tabularnewline
98 & 0.00608299245595327 & 0.0121659849119065 & 0.993917007544047 \tabularnewline
99 & 0.00488655764039472 & 0.00977311528078945 & 0.995113442359605 \tabularnewline
100 & 0.00445816114131136 & 0.00891632228262272 & 0.995541838858689 \tabularnewline
101 & 0.00392318594064287 & 0.00784637188128573 & 0.996076814059357 \tabularnewline
102 & 0.0029119436144874 & 0.00582388722897481 & 0.997088056385513 \tabularnewline
103 & 0.00199481817151654 & 0.00398963634303308 & 0.998005181828483 \tabularnewline
104 & 0.00431664810592909 & 0.00863329621185817 & 0.995683351894071 \tabularnewline
105 & 0.0106876432785559 & 0.0213752865571118 & 0.989312356721444 \tabularnewline
106 & 0.00806201095351769 & 0.0161240219070354 & 0.991937989046482 \tabularnewline
107 & 0.00620894302622096 & 0.0124178860524419 & 0.993791056973779 \tabularnewline
108 & 0.0177599289590391 & 0.0355198579180782 & 0.982240071040961 \tabularnewline
109 & 0.0142055720715032 & 0.0284111441430063 & 0.985794427928497 \tabularnewline
110 & 0.010791544148651 & 0.0215830882973021 & 0.989208455851349 \tabularnewline
111 & 0.020197864816917 & 0.0403957296338339 & 0.979802135183083 \tabularnewline
112 & 0.0181246401240867 & 0.0362492802481734 & 0.981875359875913 \tabularnewline
113 & 0.0148521422192028 & 0.0297042844384056 & 0.985147857780797 \tabularnewline
114 & 0.032307283763026 & 0.0646145675260521 & 0.967692716236974 \tabularnewline
115 & 0.0497836867155854 & 0.0995673734311708 & 0.950216313284415 \tabularnewline
116 & 0.0380842577822749 & 0.0761685155645498 & 0.961915742217725 \tabularnewline
117 & 0.0300011706895504 & 0.0600023413791008 & 0.96999882931045 \tabularnewline
118 & 0.029311661783579 & 0.0586233235671581 & 0.970688338216421 \tabularnewline
119 & 0.0213882053013266 & 0.0427764106026531 & 0.978611794698673 \tabularnewline
120 & 0.0240519008079688 & 0.0481038016159377 & 0.975948099192031 \tabularnewline
121 & 0.0180477315994521 & 0.0360954631989042 & 0.981952268400548 \tabularnewline
122 & 0.0143594110493212 & 0.0287188220986424 & 0.985640588950679 \tabularnewline
123 & 0.0268581745436837 & 0.0537163490873675 & 0.973141825456316 \tabularnewline
124 & 0.0262111244373523 & 0.0524222488747047 & 0.973788875562648 \tabularnewline
125 & 0.0203079715994399 & 0.0406159431988798 & 0.97969202840056 \tabularnewline
126 & 0.0149110921775277 & 0.0298221843550555 & 0.985088907822472 \tabularnewline
127 & 0.0159476352697519 & 0.0318952705395037 & 0.984052364730248 \tabularnewline
128 & 0.0119608891694108 & 0.0239217783388217 & 0.988039110830589 \tabularnewline
129 & 0.0708309489070772 & 0.141661897814154 & 0.929169051092923 \tabularnewline
130 & 0.0536172797016941 & 0.107234559403388 & 0.946382720298306 \tabularnewline
131 & 0.0457074026593445 & 0.0914148053186891 & 0.954292597340655 \tabularnewline
132 & 0.0366403055456246 & 0.0732806110912491 & 0.963359694454375 \tabularnewline
133 & 0.0261100965234696 & 0.0522201930469393 & 0.97388990347653 \tabularnewline
134 & 0.0225803426872291 & 0.0451606853744581 & 0.977419657312771 \tabularnewline
135 & 0.0281622624858567 & 0.0563245249717134 & 0.971837737514143 \tabularnewline
136 & 0.0205265964632745 & 0.0410531929265491 & 0.979473403536726 \tabularnewline
137 & 0.0277247708431769 & 0.0554495416863539 & 0.972275229156823 \tabularnewline
138 & 0.0184515397662799 & 0.0369030795325599 & 0.98154846023372 \tabularnewline
139 & 0.0125435162460236 & 0.0250870324920473 & 0.987456483753976 \tabularnewline
140 & 0.0699788026351679 & 0.139957605270336 & 0.930021197364832 \tabularnewline
141 & 0.152227796363625 & 0.304455592727249 & 0.847772203636375 \tabularnewline
142 & 0.110433348154324 & 0.220866696308649 & 0.889566651845676 \tabularnewline
143 & 0.275484550843926 & 0.550969101687853 & 0.724515449156074 \tabularnewline
144 & 0.22190476211288 & 0.443809524225759 & 0.77809523788712 \tabularnewline
145 & 0.14010952013764 & 0.28021904027528 & 0.85989047986236 \tabularnewline
146 & 0.805612776960897 & 0.388774446078207 & 0.194387223039103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0213817450485641[/C][C]0.0427634900971283[/C][C]0.978618254951436[/C][/ROW]
[ROW][C]11[/C][C]0.00454118199424206[/C][C]0.00908236398848412[/C][C]0.995458818005758[/C][/ROW]
[ROW][C]12[/C][C]0.00659291066295876[/C][C]0.0131858213259175[/C][C]0.993407089337041[/C][/ROW]
[ROW][C]13[/C][C]0.00280628663073745[/C][C]0.00561257326147491[/C][C]0.997193713369263[/C][/ROW]
[ROW][C]14[/C][C]0.0148290048287713[/C][C]0.0296580096575425[/C][C]0.985170995171229[/C][/ROW]
[ROW][C]15[/C][C]0.00732585880975456[/C][C]0.0146517176195091[/C][C]0.992674141190245[/C][/ROW]
[ROW][C]16[/C][C]0.00316071824346677[/C][C]0.00632143648693354[/C][C]0.996839281756533[/C][/ROW]
[ROW][C]17[/C][C]0.0308638085606937[/C][C]0.0617276171213873[/C][C]0.969136191439306[/C][/ROW]
[ROW][C]18[/C][C]0.0212611156615777[/C][C]0.0425222313231554[/C][C]0.978738884338422[/C][/ROW]
[ROW][C]19[/C][C]0.011091919901103[/C][C]0.0221838398022059[/C][C]0.988908080098897[/C][/ROW]
[ROW][C]20[/C][C]0.00614526948860005[/C][C]0.0122905389772001[/C][C]0.9938547305114[/C][/ROW]
[ROW][C]21[/C][C]0.0167861697350021[/C][C]0.0335723394700042[/C][C]0.983213830264998[/C][/ROW]
[ROW][C]22[/C][C]0.00934929772891675[/C][C]0.0186985954578335[/C][C]0.990650702271083[/C][/ROW]
[ROW][C]23[/C][C]0.00662967043929003[/C][C]0.0132593408785801[/C][C]0.99337032956071[/C][/ROW]
[ROW][C]24[/C][C]0.0132436743400022[/C][C]0.0264873486800044[/C][C]0.986756325659998[/C][/ROW]
[ROW][C]25[/C][C]0.00778947371246301[/C][C]0.015578947424926[/C][C]0.992210526287537[/C][/ROW]
[ROW][C]26[/C][C]0.00443094036905275[/C][C]0.00886188073810549[/C][C]0.995569059630947[/C][/ROW]
[ROW][C]27[/C][C]0.00824746182474074[/C][C]0.0164949236494815[/C][C]0.991752538175259[/C][/ROW]
[ROW][C]28[/C][C]0.00493643073296671[/C][C]0.00987286146593342[/C][C]0.995063569267033[/C][/ROW]
[ROW][C]29[/C][C]0.00350662678056685[/C][C]0.00701325356113371[/C][C]0.996493373219433[/C][/ROW]
[ROW][C]30[/C][C]0.00378883529537287[/C][C]0.00757767059074575[/C][C]0.996211164704627[/C][/ROW]
[ROW][C]31[/C][C]0.00225517678023297[/C][C]0.00451035356046594[/C][C]0.997744823219767[/C][/ROW]
[ROW][C]32[/C][C]0.00162212453073798[/C][C]0.00324424906147597[/C][C]0.998377875469262[/C][/ROW]
[ROW][C]33[/C][C]0.00095288136746854[/C][C]0.00190576273493708[/C][C]0.999047118632531[/C][/ROW]
[ROW][C]34[/C][C]0.000617972424026299[/C][C]0.0012359448480526[/C][C]0.999382027575974[/C][/ROW]
[ROW][C]35[/C][C]0.000353370722347147[/C][C]0.000706741444694294[/C][C]0.999646629277653[/C][/ROW]
[ROW][C]36[/C][C]0.000270713212896595[/C][C]0.00054142642579319[/C][C]0.999729286787103[/C][/ROW]
[ROW][C]37[/C][C]0.000298253683452959[/C][C]0.000596507366905919[/C][C]0.999701746316547[/C][/ROW]
[ROW][C]38[/C][C]0.00016087851844034[/C][C]0.000321757036880679[/C][C]0.99983912148156[/C][/ROW]
[ROW][C]39[/C][C]0.0020918654509446[/C][C]0.0041837309018892[/C][C]0.997908134549055[/C][/ROW]
[ROW][C]40[/C][C]0.0013724868462013[/C][C]0.0027449736924026[/C][C]0.998627513153799[/C][/ROW]
[ROW][C]41[/C][C]0.000883560254317609[/C][C]0.00176712050863522[/C][C]0.999116439745682[/C][/ROW]
[ROW][C]42[/C][C]0.00112934036855395[/C][C]0.00225868073710791[/C][C]0.998870659631446[/C][/ROW]
[ROW][C]43[/C][C]0.000716996294753486[/C][C]0.00143399258950697[/C][C]0.999283003705246[/C][/ROW]
[ROW][C]44[/C][C]0.000807266809186809[/C][C]0.00161453361837362[/C][C]0.999192733190813[/C][/ROW]
[ROW][C]45[/C][C]0.000536449234404938[/C][C]0.00107289846880988[/C][C]0.999463550765595[/C][/ROW]
[ROW][C]46[/C][C]0.00036919507983521[/C][C]0.000738390159670421[/C][C]0.999630804920165[/C][/ROW]
[ROW][C]47[/C][C]0.000439349652097906[/C][C]0.000878699304195811[/C][C]0.999560650347902[/C][/ROW]
[ROW][C]48[/C][C]0.000276587349856543[/C][C]0.000553174699713085[/C][C]0.999723412650143[/C][/ROW]
[ROW][C]49[/C][C]0.000170068234062892[/C][C]0.000340136468125785[/C][C]0.999829931765937[/C][/ROW]
[ROW][C]50[/C][C]0.000190418167866432[/C][C]0.000380836335732865[/C][C]0.999809581832134[/C][/ROW]
[ROW][C]51[/C][C]0.000369275294108094[/C][C]0.000738550588216187[/C][C]0.999630724705892[/C][/ROW]
[ROW][C]52[/C][C]0.000327160747935043[/C][C]0.000654321495870086[/C][C]0.999672839252065[/C][/ROW]
[ROW][C]53[/C][C]0.000270105672454488[/C][C]0.000540211344908975[/C][C]0.999729894327546[/C][/ROW]
[ROW][C]54[/C][C]0.000189819250063906[/C][C]0.000379638500127812[/C][C]0.999810180749936[/C][/ROW]
[ROW][C]55[/C][C]0.00015236827173541[/C][C]0.00030473654347082[/C][C]0.999847631728265[/C][/ROW]
[ROW][C]56[/C][C]0.000237624379790514[/C][C]0.000475248759581029[/C][C]0.99976237562021[/C][/ROW]
[ROW][C]57[/C][C]0.000217851452084923[/C][C]0.000435702904169847[/C][C]0.999782148547915[/C][/ROW]
[ROW][C]58[/C][C]0.000168160133898697[/C][C]0.000336320267797394[/C][C]0.999831839866101[/C][/ROW]
[ROW][C]59[/C][C]0.000128396030030571[/C][C]0.000256792060061141[/C][C]0.999871603969969[/C][/ROW]
[ROW][C]60[/C][C]7.65765684158924e-05[/C][C]0.000153153136831785[/C][C]0.999923423431584[/C][/ROW]
[ROW][C]61[/C][C]9.16492814734758e-05[/C][C]0.000183298562946952[/C][C]0.999908350718527[/C][/ROW]
[ROW][C]62[/C][C]0.00339516348327717[/C][C]0.00679032696655434[/C][C]0.996604836516723[/C][/ROW]
[ROW][C]63[/C][C]0.00273465001771038[/C][C]0.00546930003542077[/C][C]0.99726534998229[/C][/ROW]
[ROW][C]64[/C][C]0.0505511144527846[/C][C]0.101102228905569[/C][C]0.949448885547215[/C][/ROW]
[ROW][C]65[/C][C]0.0393297482788409[/C][C]0.0786594965576818[/C][C]0.960670251721159[/C][/ROW]
[ROW][C]66[/C][C]0.0310403444375332[/C][C]0.0620806888750665[/C][C]0.968959655562467[/C][/ROW]
[ROW][C]67[/C][C]0.0235362339052916[/C][C]0.0470724678105832[/C][C]0.976463766094708[/C][/ROW]
[ROW][C]68[/C][C]0.0331103618949056[/C][C]0.0662207237898113[/C][C]0.966889638105094[/C][/ROW]
[ROW][C]69[/C][C]0.0256081264690892[/C][C]0.0512162529381785[/C][C]0.974391873530911[/C][/ROW]
[ROW][C]70[/C][C]0.0272902128864313[/C][C]0.0545804257728627[/C][C]0.972709787113569[/C][/ROW]
[ROW][C]71[/C][C]0.0205071273708151[/C][C]0.0410142547416302[/C][C]0.979492872629185[/C][/ROW]
[ROW][C]72[/C][C]0.0184314385940703[/C][C]0.0368628771881406[/C][C]0.98156856140593[/C][/ROW]
[ROW][C]73[/C][C]0.026890685839921[/C][C]0.053781371679842[/C][C]0.973109314160079[/C][/ROW]
[ROW][C]74[/C][C]0.0252844330733524[/C][C]0.0505688661467047[/C][C]0.974715566926648[/C][/ROW]
[ROW][C]75[/C][C]0.0259614555369022[/C][C]0.0519229110738044[/C][C]0.974038544463098[/C][/ROW]
[ROW][C]76[/C][C]0.0238113448822122[/C][C]0.0476226897644245[/C][C]0.976188655117788[/C][/ROW]
[ROW][C]77[/C][C]0.0188222494179419[/C][C]0.0376444988358839[/C][C]0.981177750582058[/C][/ROW]
[ROW][C]78[/C][C]0.0142003454764952[/C][C]0.0284006909529905[/C][C]0.985799654523505[/C][/ROW]
[ROW][C]79[/C][C]0.010536270821543[/C][C]0.0210725416430859[/C][C]0.989463729178457[/C][/ROW]
[ROW][C]80[/C][C]0.00847242916576659[/C][C]0.0169448583315332[/C][C]0.991527570834233[/C][/ROW]
[ROW][C]81[/C][C]0.00817318844694325[/C][C]0.0163463768938865[/C][C]0.991826811553057[/C][/ROW]
[ROW][C]82[/C][C]0.00685868491478022[/C][C]0.0137173698295604[/C][C]0.99314131508522[/C][/ROW]
[ROW][C]83[/C][C]0.00703751420075153[/C][C]0.0140750284015031[/C][C]0.992962485799249[/C][/ROW]
[ROW][C]84[/C][C]0.0060603621047065[/C][C]0.012120724209413[/C][C]0.993939637895293[/C][/ROW]
[ROW][C]85[/C][C]0.00449581267159252[/C][C]0.00899162534318504[/C][C]0.995504187328408[/C][/ROW]
[ROW][C]86[/C][C]0.0032089564630664[/C][C]0.00641791292613279[/C][C]0.996791043536934[/C][/ROW]
[ROW][C]87[/C][C]0.00240120314321885[/C][C]0.0048024062864377[/C][C]0.997598796856781[/C][/ROW]
[ROW][C]88[/C][C]0.00228101472873464[/C][C]0.00456202945746928[/C][C]0.997718985271265[/C][/ROW]
[ROW][C]89[/C][C]0.001599014774731[/C][C]0.00319802954946201[/C][C]0.998400985225269[/C][/ROW]
[ROW][C]90[/C][C]0.00109853229134202[/C][C]0.00219706458268403[/C][C]0.998901467708658[/C][/ROW]
[ROW][C]91[/C][C]0.000921443181024481[/C][C]0.00184288636204896[/C][C]0.999078556818976[/C][/ROW]
[ROW][C]92[/C][C]0.000640608351263178[/C][C]0.00128121670252636[/C][C]0.999359391648737[/C][/ROW]
[ROW][C]93[/C][C]0.000430112667878229[/C][C]0.000860225335756458[/C][C]0.999569887332122[/C][/ROW]
[ROW][C]94[/C][C]0.000363863352152125[/C][C]0.000727726704304249[/C][C]0.999636136647848[/C][/ROW]
[ROW][C]95[/C][C]0.000319672309794631[/C][C]0.000639344619589262[/C][C]0.999680327690205[/C][/ROW]
[ROW][C]96[/C][C]0.000244106237922504[/C][C]0.000488212475845008[/C][C]0.999755893762078[/C][/ROW]
[ROW][C]97[/C][C]0.00842078908754216[/C][C]0.0168415781750843[/C][C]0.991579210912458[/C][/ROW]
[ROW][C]98[/C][C]0.00608299245595327[/C][C]0.0121659849119065[/C][C]0.993917007544047[/C][/ROW]
[ROW][C]99[/C][C]0.00488655764039472[/C][C]0.00977311528078945[/C][C]0.995113442359605[/C][/ROW]
[ROW][C]100[/C][C]0.00445816114131136[/C][C]0.00891632228262272[/C][C]0.995541838858689[/C][/ROW]
[ROW][C]101[/C][C]0.00392318594064287[/C][C]0.00784637188128573[/C][C]0.996076814059357[/C][/ROW]
[ROW][C]102[/C][C]0.0029119436144874[/C][C]0.00582388722897481[/C][C]0.997088056385513[/C][/ROW]
[ROW][C]103[/C][C]0.00199481817151654[/C][C]0.00398963634303308[/C][C]0.998005181828483[/C][/ROW]
[ROW][C]104[/C][C]0.00431664810592909[/C][C]0.00863329621185817[/C][C]0.995683351894071[/C][/ROW]
[ROW][C]105[/C][C]0.0106876432785559[/C][C]0.0213752865571118[/C][C]0.989312356721444[/C][/ROW]
[ROW][C]106[/C][C]0.00806201095351769[/C][C]0.0161240219070354[/C][C]0.991937989046482[/C][/ROW]
[ROW][C]107[/C][C]0.00620894302622096[/C][C]0.0124178860524419[/C][C]0.993791056973779[/C][/ROW]
[ROW][C]108[/C][C]0.0177599289590391[/C][C]0.0355198579180782[/C][C]0.982240071040961[/C][/ROW]
[ROW][C]109[/C][C]0.0142055720715032[/C][C]0.0284111441430063[/C][C]0.985794427928497[/C][/ROW]
[ROW][C]110[/C][C]0.010791544148651[/C][C]0.0215830882973021[/C][C]0.989208455851349[/C][/ROW]
[ROW][C]111[/C][C]0.020197864816917[/C][C]0.0403957296338339[/C][C]0.979802135183083[/C][/ROW]
[ROW][C]112[/C][C]0.0181246401240867[/C][C]0.0362492802481734[/C][C]0.981875359875913[/C][/ROW]
[ROW][C]113[/C][C]0.0148521422192028[/C][C]0.0297042844384056[/C][C]0.985147857780797[/C][/ROW]
[ROW][C]114[/C][C]0.032307283763026[/C][C]0.0646145675260521[/C][C]0.967692716236974[/C][/ROW]
[ROW][C]115[/C][C]0.0497836867155854[/C][C]0.0995673734311708[/C][C]0.950216313284415[/C][/ROW]
[ROW][C]116[/C][C]0.0380842577822749[/C][C]0.0761685155645498[/C][C]0.961915742217725[/C][/ROW]
[ROW][C]117[/C][C]0.0300011706895504[/C][C]0.0600023413791008[/C][C]0.96999882931045[/C][/ROW]
[ROW][C]118[/C][C]0.029311661783579[/C][C]0.0586233235671581[/C][C]0.970688338216421[/C][/ROW]
[ROW][C]119[/C][C]0.0213882053013266[/C][C]0.0427764106026531[/C][C]0.978611794698673[/C][/ROW]
[ROW][C]120[/C][C]0.0240519008079688[/C][C]0.0481038016159377[/C][C]0.975948099192031[/C][/ROW]
[ROW][C]121[/C][C]0.0180477315994521[/C][C]0.0360954631989042[/C][C]0.981952268400548[/C][/ROW]
[ROW][C]122[/C][C]0.0143594110493212[/C][C]0.0287188220986424[/C][C]0.985640588950679[/C][/ROW]
[ROW][C]123[/C][C]0.0268581745436837[/C][C]0.0537163490873675[/C][C]0.973141825456316[/C][/ROW]
[ROW][C]124[/C][C]0.0262111244373523[/C][C]0.0524222488747047[/C][C]0.973788875562648[/C][/ROW]
[ROW][C]125[/C][C]0.0203079715994399[/C][C]0.0406159431988798[/C][C]0.97969202840056[/C][/ROW]
[ROW][C]126[/C][C]0.0149110921775277[/C][C]0.0298221843550555[/C][C]0.985088907822472[/C][/ROW]
[ROW][C]127[/C][C]0.0159476352697519[/C][C]0.0318952705395037[/C][C]0.984052364730248[/C][/ROW]
[ROW][C]128[/C][C]0.0119608891694108[/C][C]0.0239217783388217[/C][C]0.988039110830589[/C][/ROW]
[ROW][C]129[/C][C]0.0708309489070772[/C][C]0.141661897814154[/C][C]0.929169051092923[/C][/ROW]
[ROW][C]130[/C][C]0.0536172797016941[/C][C]0.107234559403388[/C][C]0.946382720298306[/C][/ROW]
[ROW][C]131[/C][C]0.0457074026593445[/C][C]0.0914148053186891[/C][C]0.954292597340655[/C][/ROW]
[ROW][C]132[/C][C]0.0366403055456246[/C][C]0.0732806110912491[/C][C]0.963359694454375[/C][/ROW]
[ROW][C]133[/C][C]0.0261100965234696[/C][C]0.0522201930469393[/C][C]0.97388990347653[/C][/ROW]
[ROW][C]134[/C][C]0.0225803426872291[/C][C]0.0451606853744581[/C][C]0.977419657312771[/C][/ROW]
[ROW][C]135[/C][C]0.0281622624858567[/C][C]0.0563245249717134[/C][C]0.971837737514143[/C][/ROW]
[ROW][C]136[/C][C]0.0205265964632745[/C][C]0.0410531929265491[/C][C]0.979473403536726[/C][/ROW]
[ROW][C]137[/C][C]0.0277247708431769[/C][C]0.0554495416863539[/C][C]0.972275229156823[/C][/ROW]
[ROW][C]138[/C][C]0.0184515397662799[/C][C]0.0369030795325599[/C][C]0.98154846023372[/C][/ROW]
[ROW][C]139[/C][C]0.0125435162460236[/C][C]0.0250870324920473[/C][C]0.987456483753976[/C][/ROW]
[ROW][C]140[/C][C]0.0699788026351679[/C][C]0.139957605270336[/C][C]0.930021197364832[/C][/ROW]
[ROW][C]141[/C][C]0.152227796363625[/C][C]0.304455592727249[/C][C]0.847772203636375[/C][/ROW]
[ROW][C]142[/C][C]0.110433348154324[/C][C]0.220866696308649[/C][C]0.889566651845676[/C][/ROW]
[ROW][C]143[/C][C]0.275484550843926[/C][C]0.550969101687853[/C][C]0.724515449156074[/C][/ROW]
[ROW][C]144[/C][C]0.22190476211288[/C][C]0.443809524225759[/C][C]0.77809523788712[/C][/ROW]
[ROW][C]145[/C][C]0.14010952013764[/C][C]0.28021904027528[/C][C]0.85989047986236[/C][/ROW]
[ROW][C]146[/C][C]0.805612776960897[/C][C]0.388774446078207[/C][C]0.194387223039103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146376&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.02138174504856410.04276349009712830.978618254951436
110.004541181994242060.009082363988484120.995458818005758
120.006592910662958760.01318582132591750.993407089337041
130.002806286630737450.005612573261474910.997193713369263
140.01482900482877130.02965800965754250.985170995171229
150.007325858809754560.01465171761950910.992674141190245
160.003160718243466770.006321436486933540.996839281756533
170.03086380856069370.06172761712138730.969136191439306
180.02126111566157770.04252223132315540.978738884338422
190.0110919199011030.02218383980220590.988908080098897
200.006145269488600050.01229053897720010.9938547305114
210.01678616973500210.03357233947000420.983213830264998
220.009349297728916750.01869859545783350.990650702271083
230.006629670439290030.01325934087858010.99337032956071
240.01324367434000220.02648734868000440.986756325659998
250.007789473712463010.0155789474249260.992210526287537
260.004430940369052750.008861880738105490.995569059630947
270.008247461824740740.01649492364948150.991752538175259
280.004936430732966710.009872861465933420.995063569267033
290.003506626780566850.007013253561133710.996493373219433
300.003788835295372870.007577670590745750.996211164704627
310.002255176780232970.004510353560465940.997744823219767
320.001622124530737980.003244249061475970.998377875469262
330.000952881367468540.001905762734937080.999047118632531
340.0006179724240262990.00123594484805260.999382027575974
350.0003533707223471470.0007067414446942940.999646629277653
360.0002707132128965950.000541426425793190.999729286787103
370.0002982536834529590.0005965073669059190.999701746316547
380.000160878518440340.0003217570368806790.99983912148156
390.00209186545094460.00418373090188920.997908134549055
400.00137248684620130.00274497369240260.998627513153799
410.0008835602543176090.001767120508635220.999116439745682
420.001129340368553950.002258680737107910.998870659631446
430.0007169962947534860.001433992589506970.999283003705246
440.0008072668091868090.001614533618373620.999192733190813
450.0005364492344049380.001072898468809880.999463550765595
460.000369195079835210.0007383901596704210.999630804920165
470.0004393496520979060.0008786993041958110.999560650347902
480.0002765873498565430.0005531746997130850.999723412650143
490.0001700682340628920.0003401364681257850.999829931765937
500.0001904181678664320.0003808363357328650.999809581832134
510.0003692752941080940.0007385505882161870.999630724705892
520.0003271607479350430.0006543214958700860.999672839252065
530.0002701056724544880.0005402113449089750.999729894327546
540.0001898192500639060.0003796385001278120.999810180749936
550.000152368271735410.000304736543470820.999847631728265
560.0002376243797905140.0004752487595810290.99976237562021
570.0002178514520849230.0004357029041698470.999782148547915
580.0001681601338986970.0003363202677973940.999831839866101
590.0001283960300305710.0002567920600611410.999871603969969
607.65765684158924e-050.0001531531368317850.999923423431584
619.16492814734758e-050.0001832985629469520.999908350718527
620.003395163483277170.006790326966554340.996604836516723
630.002734650017710380.005469300035420770.99726534998229
640.05055111445278460.1011022289055690.949448885547215
650.03932974827884090.07865949655768180.960670251721159
660.03104034443753320.06208068887506650.968959655562467
670.02353623390529160.04707246781058320.976463766094708
680.03311036189490560.06622072378981130.966889638105094
690.02560812646908920.05121625293817850.974391873530911
700.02729021288643130.05458042577286270.972709787113569
710.02050712737081510.04101425474163020.979492872629185
720.01843143859407030.03686287718814060.98156856140593
730.0268906858399210.0537813716798420.973109314160079
740.02528443307335240.05056886614670470.974715566926648
750.02596145553690220.05192291107380440.974038544463098
760.02381134488221220.04762268976442450.976188655117788
770.01882224941794190.03764449883588390.981177750582058
780.01420034547649520.02840069095299050.985799654523505
790.0105362708215430.02107254164308590.989463729178457
800.008472429165766590.01694485833153320.991527570834233
810.008173188446943250.01634637689388650.991826811553057
820.006858684914780220.01371736982956040.99314131508522
830.007037514200751530.01407502840150310.992962485799249
840.00606036210470650.0121207242094130.993939637895293
850.004495812671592520.008991625343185040.995504187328408
860.00320895646306640.006417912926132790.996791043536934
870.002401203143218850.00480240628643770.997598796856781
880.002281014728734640.004562029457469280.997718985271265
890.0015990147747310.003198029549462010.998400985225269
900.001098532291342020.002197064582684030.998901467708658
910.0009214431810244810.001842886362048960.999078556818976
920.0006406083512631780.001281216702526360.999359391648737
930.0004301126678782290.0008602253357564580.999569887332122
940.0003638633521521250.0007277267043042490.999636136647848
950.0003196723097946310.0006393446195892620.999680327690205
960.0002441062379225040.0004882124758450080.999755893762078
970.008420789087542160.01684157817508430.991579210912458
980.006082992455953270.01216598491190650.993917007544047
990.004886557640394720.009773115280789450.995113442359605
1000.004458161141311360.008916322282622720.995541838858689
1010.003923185940642870.007846371881285730.996076814059357
1020.00291194361448740.005823887228974810.997088056385513
1030.001994818171516540.003989636343033080.998005181828483
1040.004316648105929090.008633296211858170.995683351894071
1050.01068764327855590.02137528655711180.989312356721444
1060.008062010953517690.01612402190703540.991937989046482
1070.006208943026220960.01241788605244190.993791056973779
1080.01775992895903910.03551985791807820.982240071040961
1090.01420557207150320.02841114414300630.985794427928497
1100.0107915441486510.02158308829730210.989208455851349
1110.0201978648169170.04039572963383390.979802135183083
1120.01812464012408670.03624928024817340.981875359875913
1130.01485214221920280.02970428443840560.985147857780797
1140.0323072837630260.06461456752605210.967692716236974
1150.04978368671558540.09956737343117080.950216313284415
1160.03808425778227490.07616851556454980.961915742217725
1170.03000117068955040.06000234137910080.96999882931045
1180.0293116617835790.05862332356715810.970688338216421
1190.02138820530132660.04277641060265310.978611794698673
1200.02405190080796880.04810380161593770.975948099192031
1210.01804773159945210.03609546319890420.981952268400548
1220.01435941104932120.02871882209864240.985640588950679
1230.02685817454368370.05371634908736750.973141825456316
1240.02621112443735230.05242224887470470.973788875562648
1250.02030797159943990.04061594319887980.97969202840056
1260.01491109217752770.02982218435505550.985088907822472
1270.01594763526975190.03189527053950370.984052364730248
1280.01196088916941080.02392177833882170.988039110830589
1290.07083094890707720.1416618978141540.929169051092923
1300.05361727970169410.1072345594033880.946382720298306
1310.04570740265934450.09141480531868910.954292597340655
1320.03664030554562460.07328061109124910.963359694454375
1330.02611009652346960.05222019304693930.97388990347653
1340.02258034268722910.04516068537445810.977419657312771
1350.02816226248585670.05632452497171340.971837737514143
1360.02052659646327450.04105319292654910.979473403536726
1370.02772477084317690.05544954168635390.972275229156823
1380.01845153976627990.03690307953255990.98154846023372
1390.01254351624602360.02508703249204730.987456483753976
1400.06997880263516790.1399576052703360.930021197364832
1410.1522277963636250.3044555927272490.847772203636375
1420.1104333481543240.2208666963086490.889566651845676
1430.2754845508439260.5509691016878530.724515449156074
1440.221904762112880.4438095242257590.77809523788712
1450.140109520137640.280219040275280.85989047986236
1460.8056127769608970.3887744460782070.194387223039103







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level580.423357664233577NOK
5% type I error level1060.773722627737226NOK
10% type I error level1270.927007299270073NOK

\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 & 58 & 0.423357664233577 & NOK \tabularnewline
5% type I error level & 106 & 0.773722627737226 & NOK \tabularnewline
10% type I error level & 127 & 0.927007299270073 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146376&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]58[/C][C]0.423357664233577[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]106[/C][C]0.773722627737226[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]127[/C][C]0.927007299270073[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146376&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146376&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level580.423357664233577NOK
5% type I error level1060.773722627737226NOK
10% type I error level1270.927007299270073NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}