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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 14:09:17 -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/t1321989076b835r62ttnm1if0.htm/, Retrieved Fri, 29 Mar 2024 08:19:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146372, Retrieved Fri, 29 Mar 2024 08:19:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
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 19:09:17] [47995d3a8fac585eeb070a274b466f8c] [Current]
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Dataseries X:
14	2	3	3	3	6
8	6	0	7	7	7
12	6	0	6	8	8
7	6	6	6	9	8
10	8	5	5	5	9
9	1	0	7	7	8
16	9	8	8	8	8
7	4	0	2	3	7
14	7	0	4	8	7
6	4	9	9	4	4
16	6	6	6	6	6
11	5	6	6	4	7
17	7	5	5	8	5
12	5	4	4	8	8
7	6	0	2	2	5
13	5	0	4	9	4
9	2	2	2	2	9
15	9	6	6	8	8
7	4	0	4	8	4
9	4	4	4	4	6
7	5	5	5	5	6
14	7	7	7	7	7
15	5	5	5	3	3
7	9	4	4	4	4
13	6	6	6	6	6
17	6	6	6	6	6
15	3	0	7	9	7
14	3	1	2	2	5
14	5	0	6	6	8
8	5	4	4	4	6
8	4	4	4	8	4
12	7	7	7	3	9
14	6	7	7	7	7
8	7	0	4	4	4
11	4	4	4	4	6
16	5	5	5	8	8
11	6	0	6	6	6
8	5	5	5	5	5
14	0	1	6	6	6
16	6	2	2	9	6
14	5	0	6	4	4
5	3	9	9	7	7
8	3	3	3	3	9
10	3	0	4	4	8
8	7	6	6	6	6
13	7	1	5	8	6
15	1	5	5	5	5
6	5	0	4	4	7
12	5	0	2	2	5
14	6	0	6	9	8
5	2	6	6	6	6
15	6	7	7	8	8
11	5	0	5	5	5
8	2	4	4	4	4
13	7	5	5	5	5
14	5	1	5	9	6
12	3	4	4	4	4
16	6	9	9	8	6
10	2	2	2	2	9
15	8	8	8	8	7
8	5	3	3	3	3
16	2	1	6	3	6
19	6	0	6	6	6
14	2	6	6	6	6
7	1	0	5	5	5
13	5	0	5	5	5
15	6	6	6	4	5
7	2	2	2	9	9
13	6	1	6	6	8
4	2	5	5	5	5
14	6	5	5	5	6
13	5	5	5	3	7
11	0	5	5	8	5
14	2	6	6	9	6
12	4	6	6	6	6
15	1	0	9	6	6
14	5	0	5	5	6
13	5	1	5	3	9
7	2	7	7	4	7
5	2	2	2	9	9
7	7	4	4	4	4
13	5	0	6	8	8
13	2	5	5	5	5
11	5	5	5	5	8
6	3	3	3	8	9
12	6	0	6	6	6
8	1	4	4	9	4
11	5	9	9	5	7
12	7	0	8	8	8
9	2	4	4	3	9
12	6	2	2	2	9
13	8	7	7	7	7
16	7	7	7	7	8
16	6	6	6	4	4
11	7	0	5	5	6
8	4	5	5	9	7
4	5	6	6	6	6
7	2	0	3	3	7
14	5	5	5	5	5
11	2	9	9	2	9
17	5	0	7	7	7
15	7	7	7	7	7
14	5	1	6	6	6
5	8	3	3	8	6
4	2	7	7	9	9
19	8	8	8	8	9
11	3	0	3	3	8
15	2	5	5	5	8
10	3	3	3	3	3
9	5	0	4	4	6
12	2	5	5	5	5
15	2	7	7	9	7
7	6	0	6	6	6
13	2	0	7	7	7
14	7	0	9	7	7
14	6	6	6	6	6
14	2	0	6	3	8
8	2	6	6	9	9
15	5	6	6	6	6
15	6	2	2	2	9
9	4	5	5	5	5
16	5	0	5	5	6
9	7	4	4	9	4
15	6	0	7	7	7
15	6	6	6	6	6
6	5	5	5	8	8
8	2	8	8	8	8
15	6	6	6	6	9
10	3	5	5	3	8
9	2	0	4	4	4
14	8	8	8	9	6
12	6	0	6	6	6
8	4	9	9	4	7
11	6	5	5	5	9
13	5	0	6	6	8
9	4	0	4	4	4
15	2	0	6	6	6
13	3	3	3	3	9
15	6	6	6	6	6
14	5	0	5	5	5
16	4	4	4	9	8
12	6	6	6	6	6
14	1	0	5	9	6
10	5	4	4	3	6
10	2	7	7	7	7
4	6	0	6	6	7
8	5	5	5	5	9
17	2	6	6	6	6
16	6	6	6	9	6
12	8	8	8	8	6
12	7	2	2	4	4
15	7	7	7	7	7
9	9	0	4	4	8
13	2	0	6	8	7
14	6	5	5	5	9
11	5	0	2	9	6




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=146372&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=146372&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&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
Schoolprestaties[t] = + 7.09477021123286 + 0.31890776231257Goingout[t] -0.124222782962212Relation[t] + 0.524420393526331Family[t] + 0.0624178602190103Friends[t] -0.0133761281617639Job[t] + 0.0047590998898964t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Schoolprestaties[t] =  +  7.09477021123286 +  0.31890776231257Goingout[t] -0.124222782962212Relation[t] +  0.524420393526331Family[t] +  0.0624178602190103Friends[t] -0.0133761281617639Job[t] +  0.0047590998898964t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146372&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Schoolprestaties[t] =  +  7.09477021123286 +  0.31890776231257Goingout[t] -0.124222782962212Relation[t] +  0.524420393526331Family[t] +  0.0624178602190103Friends[t] -0.0133761281617639Job[t] +  0.0047590998898964t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146372&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&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
Schoolprestaties[t] = + 7.09477021123286 + 0.31890776231257Goingout[t] -0.124222782962212Relation[t] + 0.524420393526331Family[t] + 0.0624178602190103Friends[t] -0.0133761281617639Job[t] + 0.0047590998898964t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.094770211232861.5242174.65477e-064e-06
Goingout0.318907762312570.1314882.42540.0164880.008244
Relation-0.1242227829622120.103833-1.19640.2334520.116726
Family0.5244203935263310.1841812.84730.0050320.002516
Friends0.06241786021901030.138570.45040.6530460.326523
Job-0.01337612816176390.174313-0.07670.9389360.469468
t0.00475909988989640.0060370.78840.431730.215865

\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) & 7.09477021123286 & 1.524217 & 4.6547 & 7e-06 & 4e-06 \tabularnewline
Goingout & 0.31890776231257 & 0.131488 & 2.4254 & 0.016488 & 0.008244 \tabularnewline
Relation & -0.124222782962212 & 0.103833 & -1.1964 & 0.233452 & 0.116726 \tabularnewline
Family & 0.524420393526331 & 0.184181 & 2.8473 & 0.005032 & 0.002516 \tabularnewline
Friends & 0.0624178602190103 & 0.13857 & 0.4504 & 0.653046 & 0.326523 \tabularnewline
Job & -0.0133761281617639 & 0.174313 & -0.0767 & 0.938936 & 0.469468 \tabularnewline
t & 0.0047590998898964 & 0.006037 & 0.7884 & 0.43173 & 0.215865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146372&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]7.09477021123286[/C][C]1.524217[/C][C]4.6547[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Goingout[/C][C]0.31890776231257[/C][C]0.131488[/C][C]2.4254[/C][C]0.016488[/C][C]0.008244[/C][/ROW]
[ROW][C]Relation[/C][C]-0.124222782962212[/C][C]0.103833[/C][C]-1.1964[/C][C]0.233452[/C][C]0.116726[/C][/ROW]
[ROW][C]Family[/C][C]0.524420393526331[/C][C]0.184181[/C][C]2.8473[/C][C]0.005032[/C][C]0.002516[/C][/ROW]
[ROW][C]Friends[/C][C]0.0624178602190103[/C][C]0.13857[/C][C]0.4504[/C][C]0.653046[/C][C]0.326523[/C][/ROW]
[ROW][C]Job[/C][C]-0.0133761281617639[/C][C]0.174313[/C][C]-0.0767[/C][C]0.938936[/C][C]0.469468[/C][/ROW]
[ROW][C]t[/C][C]0.0047590998898964[/C][C]0.006037[/C][C]0.7884[/C][C]0.43173[/C][C]0.215865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146372&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&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)7.094770211232861.5242174.65477e-064e-06
Goingout0.318907762312570.1314882.42540.0164880.008244
Relation-0.1242227829622120.103833-1.19640.2334520.116726
Family0.5244203935263310.1841812.84730.0050320.002516
Friends0.06241786021901030.138570.45040.6530460.326523
Job-0.01337612816176390.174313-0.07670.9389360.469468
t0.00475909988989640.0060370.78840.431730.215865







Multiple Linear Regression - Regression Statistics
Multiple R0.336065294677035
R-squared0.112939882286363
Adjusted R-squared0.0772193406334645
F-TEST (value)3.16176286977438
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0.00600449399077663
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.35594263195998
Sum Squared Residuals1678.09029140197

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.336065294677035 \tabularnewline
R-squared & 0.112939882286363 \tabularnewline
Adjusted R-squared & 0.0772193406334645 \tabularnewline
F-TEST (value) & 3.16176286977438 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.00600449399077663 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.35594263195998 \tabularnewline
Sum Squared Residuals & 1678.09029140197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146372&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.336065294677035[/C][/ROW]
[ROW][C]R-squared[/C][C]0.112939882286363[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0772193406334645[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.16176286977438[/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]0.00600449399077663[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.35594263195998[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1678.09029140197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146372&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&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 R0.336065294677035
R-squared0.112939882286363
Adjusted R-squared0.0772193406334645
F-TEST (value)3.16176286977438
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0.00600449399077663
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.35594263195998
Sum Squared Residuals1678.09029140197







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1149.044934479126724.95506552087328
2813.0319698639731-5.03196986397311
31212.5613503023939-0.561350302393922
4711.8831905647296-4.88319056472956
51011.8625200096427-1.86252000964267
6911.4430913238081-2.44309132380808
71613.59216851224622.40783148775382
879.55093553017966-2.55093553017966
91411.8733480051552.12665199484503
10612.2159376826882-6.21593768268816
111611.75600293962534.24399706037467
121111.3036424286029-0.30364242860287
131711.82244313975345.17755686024664
141210.74906071996871.2509392800313
15710.1863991501386-3.18639915013859
161311.37139242446341.62860757553659
1798.618336222096620.38166377790338
181512.84412338990682.1558766100932
19711.0043441016015-4.00434410160152
20910.235788372443-1.235788372443
21711.0220707054286-4.02207070542859
221412.57650014334811.42349985665188
231510.94688156925574.05311843074434
24711.876115839889-4.87611583988896
251311.82263033808391.17736966191612
261711.82738943797385.17261056202623
271512.31905979472082.68094020527917
28149.167321378807324.83267862119268
291412.24134341678061.75865658321936
30810.6022871336545-2.60228713365453
31810.5645621684314-2.56456216843143
321212.3476674450475-0.347667445047518
331412.30994247982441.69005752017559
34811.7827824460116-3.78278244601163
351110.30717487079140.692825129208556
361611.25395852811054.74604147188946
371112.6250762345359-1.62507623453591
38811.1163515317186-3.1163515317186
391410.59692507747813.40307492252193
401610.48047997483295.51952002516712
411412.22712140766841.77287859233157
42512.196446313024-7.19644631302401
4389.52359605232962-1.52359605232963
441010.5012378830133-0.501237883013262
45812.2367200981944-4.23672009819438
461312.4630084398070.536991560192978
47159.883552381477385.11644761852262
48611.1714659353598-5.17146593535975
491210.02930078408251.97069921591751
501412.84744585743811.15255414256194
51510.6707358859709-5.6707358859709
521512.44940710978972.55059289021031
531111.8088519448781-0.808851944878103
5489.78653450039786-1.78653450039786
551311.8350717544721.16492824552803
561411.93520177429992.06479822570014
571210.11971956238011.88028043761988
581613.30510918658082.69489081341916
59108.818218417472271.18178158252773
601513.53886917225991.46113082774012
61810.3273321439435-2.32733214394348
621611.15694631891384.84305368108621
631912.74881283167326.25118716832679
641410.73260418453963.26739581546044
65710.5903300943066-3.59033009430658
661311.87072024344681.12927975655324
671511.91105294118333.08894705881673
6879.29797533801441-2.29797533801441
691312.62639239172690.373607608273148
70410.3119194412576-6.31191944125757
711411.5789334622362.42106653776402
721311.12657295121351.87342704878647
73119.875634796959151.12436520304085
741410.96744876409563.03255123590445
751211.42276980795360.577230192046442
761512.7894034992582.21059650074199
771411.90969421407392.09030578592615
781311.62526642607821.37473357392178
79711.0659764448523-4.06597644485234
8059.35508453669317-4.35508453669317
81711.5095690089879-4.50956900898792
821312.61841143138320.381588568616832
831310.37378773982622.62621226017378
841111.2951417421685-0.295141742168539
85610.0355675488003-4.03556754880033
861212.8582721291408-0.858272129140831
8789.93676633554693-1.93676633554693
881112.9283447121464-1.92834471214636
891214.3383814422902-2.33838144229024
9099.8285635954063-0.828563595406304
911210.24614066319921.75385933680077
921313.2285448979534-0.228544897953441
931612.9010201073693.098979892631
941612.05292476637223.94707523362776
951112.6331735367171-1.63317353671713
96811.2963907475725-3.29639074757253
97411.8463777678438-7.84637776784385
9879.86585938917152-2.86585938917152
991411.40665662500232.59334337499772
1001111.8147247869069-0.814724786906851
1011713.18421299076033.81578700923972
1021512.95722813453982.04277186546016
1031412.49604628199431.50395371800571
104511.7606576427565-6.7606576427565
105411.4750500867612-7.47505008676117
1061913.73103551087165.26896448912841
1071110.21422292233140.785777077668605
1081510.45263685258834.54736314741166
109109.917953414033370.0820465859666293
110911.4799062566951-2.47990625669509
1111210.50704253674331.49295746325668
1121511.5351160423143.46488395768603
113712.986767826168-5.98676782616803
1141312.28935800239120.710641997608773
1151414.9374967008966-0.937496700896634
1161412.25570842806441.74429157193555
1171411.51616733949682.48383266050322
118811.1367207747657-3.1367207747657
1191511.95107796542163.04892203457843
1201510.38415456000624.61584543999377
121911.1924490602674-2.19244906026743
1221612.12385370911923.87614629088081
123912.0215405054586-3.02154050545861
1241513.61258005054051.38741994945953
1251512.29854032707352.70145967292648
126611.6822775182012-5.68227751820122
127811.9309061628458-3.93090616284576
1281512.27268924225792.72731075774209
1291010.7466499921507-0.746649992150716
130910.6451172238788-1.64511722387884
1311413.95255925282330.0474407471766966
1321213.0771907240761-1.07719072407606
133812.7611785846601-4.76117858466012
1341111.8386283708142-0.838628370814164
1351312.74580800510970.254191994890344
136911.3114873478434-2.31148734784336
1371511.82535517427533.17464482572473
138139.975710541869783.02428945813022
1391512.36516772553212.63483227446793
1401412.22289363529911.77710636470091
1411611.0969765038924.90302349610801
1421212.3794450252018-0.379445025201756
1431411.19783519843282.80216480156722
1441011.0824066608837-1.08240666088371
1451011.5673306182425-1.56733061824253
146413.1304419943729-9.13044199437285
147811.5815889070702-3.58158890707025
1481711.13236857529095.86763142470914
1491612.60001230508813.39998769491194
1501213.9805642905123-1.98056429051232
1511211.04231078015240.957689219847574
1521513.19518312903471.80481687096535
153912.9334263448874-3.93342634488739
1541312.01771946467980.982280535320237
1551411.9385694685022.06143053149801
1561110.96207336567270.0379266343272837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 9.04493447912672 & 4.95506552087328 \tabularnewline
2 & 8 & 13.0319698639731 & -5.03196986397311 \tabularnewline
3 & 12 & 12.5613503023939 & -0.561350302393922 \tabularnewline
4 & 7 & 11.8831905647296 & -4.88319056472956 \tabularnewline
5 & 10 & 11.8625200096427 & -1.86252000964267 \tabularnewline
6 & 9 & 11.4430913238081 & -2.44309132380808 \tabularnewline
7 & 16 & 13.5921685122462 & 2.40783148775382 \tabularnewline
8 & 7 & 9.55093553017966 & -2.55093553017966 \tabularnewline
9 & 14 & 11.873348005155 & 2.12665199484503 \tabularnewline
10 & 6 & 12.2159376826882 & -6.21593768268816 \tabularnewline
11 & 16 & 11.7560029396253 & 4.24399706037467 \tabularnewline
12 & 11 & 11.3036424286029 & -0.30364242860287 \tabularnewline
13 & 17 & 11.8224431397534 & 5.17755686024664 \tabularnewline
14 & 12 & 10.7490607199687 & 1.2509392800313 \tabularnewline
15 & 7 & 10.1863991501386 & -3.18639915013859 \tabularnewline
16 & 13 & 11.3713924244634 & 1.62860757553659 \tabularnewline
17 & 9 & 8.61833622209662 & 0.38166377790338 \tabularnewline
18 & 15 & 12.8441233899068 & 2.1558766100932 \tabularnewline
19 & 7 & 11.0043441016015 & -4.00434410160152 \tabularnewline
20 & 9 & 10.235788372443 & -1.235788372443 \tabularnewline
21 & 7 & 11.0220707054286 & -4.02207070542859 \tabularnewline
22 & 14 & 12.5765001433481 & 1.42349985665188 \tabularnewline
23 & 15 & 10.9468815692557 & 4.05311843074434 \tabularnewline
24 & 7 & 11.876115839889 & -4.87611583988896 \tabularnewline
25 & 13 & 11.8226303380839 & 1.17736966191612 \tabularnewline
26 & 17 & 11.8273894379738 & 5.17261056202623 \tabularnewline
27 & 15 & 12.3190597947208 & 2.68094020527917 \tabularnewline
28 & 14 & 9.16732137880732 & 4.83267862119268 \tabularnewline
29 & 14 & 12.2413434167806 & 1.75865658321936 \tabularnewline
30 & 8 & 10.6022871336545 & -2.60228713365453 \tabularnewline
31 & 8 & 10.5645621684314 & -2.56456216843143 \tabularnewline
32 & 12 & 12.3476674450475 & -0.347667445047518 \tabularnewline
33 & 14 & 12.3099424798244 & 1.69005752017559 \tabularnewline
34 & 8 & 11.7827824460116 & -3.78278244601163 \tabularnewline
35 & 11 & 10.3071748707914 & 0.692825129208556 \tabularnewline
36 & 16 & 11.2539585281105 & 4.74604147188946 \tabularnewline
37 & 11 & 12.6250762345359 & -1.62507623453591 \tabularnewline
38 & 8 & 11.1163515317186 & -3.1163515317186 \tabularnewline
39 & 14 & 10.5969250774781 & 3.40307492252193 \tabularnewline
40 & 16 & 10.4804799748329 & 5.51952002516712 \tabularnewline
41 & 14 & 12.2271214076684 & 1.77287859233157 \tabularnewline
42 & 5 & 12.196446313024 & -7.19644631302401 \tabularnewline
43 & 8 & 9.52359605232962 & -1.52359605232963 \tabularnewline
44 & 10 & 10.5012378830133 & -0.501237883013262 \tabularnewline
45 & 8 & 12.2367200981944 & -4.23672009819438 \tabularnewline
46 & 13 & 12.463008439807 & 0.536991560192978 \tabularnewline
47 & 15 & 9.88355238147738 & 5.11644761852262 \tabularnewline
48 & 6 & 11.1714659353598 & -5.17146593535975 \tabularnewline
49 & 12 & 10.0293007840825 & 1.97069921591751 \tabularnewline
50 & 14 & 12.8474458574381 & 1.15255414256194 \tabularnewline
51 & 5 & 10.6707358859709 & -5.6707358859709 \tabularnewline
52 & 15 & 12.4494071097897 & 2.55059289021031 \tabularnewline
53 & 11 & 11.8088519448781 & -0.808851944878103 \tabularnewline
54 & 8 & 9.78653450039786 & -1.78653450039786 \tabularnewline
55 & 13 & 11.835071754472 & 1.16492824552803 \tabularnewline
56 & 14 & 11.9352017742999 & 2.06479822570014 \tabularnewline
57 & 12 & 10.1197195623801 & 1.88028043761988 \tabularnewline
58 & 16 & 13.3051091865808 & 2.69489081341916 \tabularnewline
59 & 10 & 8.81821841747227 & 1.18178158252773 \tabularnewline
60 & 15 & 13.5388691722599 & 1.46113082774012 \tabularnewline
61 & 8 & 10.3273321439435 & -2.32733214394348 \tabularnewline
62 & 16 & 11.1569463189138 & 4.84305368108621 \tabularnewline
63 & 19 & 12.7488128316732 & 6.25118716832679 \tabularnewline
64 & 14 & 10.7326041845396 & 3.26739581546044 \tabularnewline
65 & 7 & 10.5903300943066 & -3.59033009430658 \tabularnewline
66 & 13 & 11.8707202434468 & 1.12927975655324 \tabularnewline
67 & 15 & 11.9110529411833 & 3.08894705881673 \tabularnewline
68 & 7 & 9.29797533801441 & -2.29797533801441 \tabularnewline
69 & 13 & 12.6263923917269 & 0.373607608273148 \tabularnewline
70 & 4 & 10.3119194412576 & -6.31191944125757 \tabularnewline
71 & 14 & 11.578933462236 & 2.42106653776402 \tabularnewline
72 & 13 & 11.1265729512135 & 1.87342704878647 \tabularnewline
73 & 11 & 9.87563479695915 & 1.12436520304085 \tabularnewline
74 & 14 & 10.9674487640956 & 3.03255123590445 \tabularnewline
75 & 12 & 11.4227698079536 & 0.577230192046442 \tabularnewline
76 & 15 & 12.789403499258 & 2.21059650074199 \tabularnewline
77 & 14 & 11.9096942140739 & 2.09030578592615 \tabularnewline
78 & 13 & 11.6252664260782 & 1.37473357392178 \tabularnewline
79 & 7 & 11.0659764448523 & -4.06597644485234 \tabularnewline
80 & 5 & 9.35508453669317 & -4.35508453669317 \tabularnewline
81 & 7 & 11.5095690089879 & -4.50956900898792 \tabularnewline
82 & 13 & 12.6184114313832 & 0.381588568616832 \tabularnewline
83 & 13 & 10.3737877398262 & 2.62621226017378 \tabularnewline
84 & 11 & 11.2951417421685 & -0.295141742168539 \tabularnewline
85 & 6 & 10.0355675488003 & -4.03556754880033 \tabularnewline
86 & 12 & 12.8582721291408 & -0.858272129140831 \tabularnewline
87 & 8 & 9.93676633554693 & -1.93676633554693 \tabularnewline
88 & 11 & 12.9283447121464 & -1.92834471214636 \tabularnewline
89 & 12 & 14.3383814422902 & -2.33838144229024 \tabularnewline
90 & 9 & 9.8285635954063 & -0.828563595406304 \tabularnewline
91 & 12 & 10.2461406631992 & 1.75385933680077 \tabularnewline
92 & 13 & 13.2285448979534 & -0.228544897953441 \tabularnewline
93 & 16 & 12.901020107369 & 3.098979892631 \tabularnewline
94 & 16 & 12.0529247663722 & 3.94707523362776 \tabularnewline
95 & 11 & 12.6331735367171 & -1.63317353671713 \tabularnewline
96 & 8 & 11.2963907475725 & -3.29639074757253 \tabularnewline
97 & 4 & 11.8463777678438 & -7.84637776784385 \tabularnewline
98 & 7 & 9.86585938917152 & -2.86585938917152 \tabularnewline
99 & 14 & 11.4066566250023 & 2.59334337499772 \tabularnewline
100 & 11 & 11.8147247869069 & -0.814724786906851 \tabularnewline
101 & 17 & 13.1842129907603 & 3.81578700923972 \tabularnewline
102 & 15 & 12.9572281345398 & 2.04277186546016 \tabularnewline
103 & 14 & 12.4960462819943 & 1.50395371800571 \tabularnewline
104 & 5 & 11.7606576427565 & -6.7606576427565 \tabularnewline
105 & 4 & 11.4750500867612 & -7.47505008676117 \tabularnewline
106 & 19 & 13.7310355108716 & 5.26896448912841 \tabularnewline
107 & 11 & 10.2142229223314 & 0.785777077668605 \tabularnewline
108 & 15 & 10.4526368525883 & 4.54736314741166 \tabularnewline
109 & 10 & 9.91795341403337 & 0.0820465859666293 \tabularnewline
110 & 9 & 11.4799062566951 & -2.47990625669509 \tabularnewline
111 & 12 & 10.5070425367433 & 1.49295746325668 \tabularnewline
112 & 15 & 11.535116042314 & 3.46488395768603 \tabularnewline
113 & 7 & 12.986767826168 & -5.98676782616803 \tabularnewline
114 & 13 & 12.2893580023912 & 0.710641997608773 \tabularnewline
115 & 14 & 14.9374967008966 & -0.937496700896634 \tabularnewline
116 & 14 & 12.2557084280644 & 1.74429157193555 \tabularnewline
117 & 14 & 11.5161673394968 & 2.48383266050322 \tabularnewline
118 & 8 & 11.1367207747657 & -3.1367207747657 \tabularnewline
119 & 15 & 11.9510779654216 & 3.04892203457843 \tabularnewline
120 & 15 & 10.3841545600062 & 4.61584543999377 \tabularnewline
121 & 9 & 11.1924490602674 & -2.19244906026743 \tabularnewline
122 & 16 & 12.1238537091192 & 3.87614629088081 \tabularnewline
123 & 9 & 12.0215405054586 & -3.02154050545861 \tabularnewline
124 & 15 & 13.6125800505405 & 1.38741994945953 \tabularnewline
125 & 15 & 12.2985403270735 & 2.70145967292648 \tabularnewline
126 & 6 & 11.6822775182012 & -5.68227751820122 \tabularnewline
127 & 8 & 11.9309061628458 & -3.93090616284576 \tabularnewline
128 & 15 & 12.2726892422579 & 2.72731075774209 \tabularnewline
129 & 10 & 10.7466499921507 & -0.746649992150716 \tabularnewline
130 & 9 & 10.6451172238788 & -1.64511722387884 \tabularnewline
131 & 14 & 13.9525592528233 & 0.0474407471766966 \tabularnewline
132 & 12 & 13.0771907240761 & -1.07719072407606 \tabularnewline
133 & 8 & 12.7611785846601 & -4.76117858466012 \tabularnewline
134 & 11 & 11.8386283708142 & -0.838628370814164 \tabularnewline
135 & 13 & 12.7458080051097 & 0.254191994890344 \tabularnewline
136 & 9 & 11.3114873478434 & -2.31148734784336 \tabularnewline
137 & 15 & 11.8253551742753 & 3.17464482572473 \tabularnewline
138 & 13 & 9.97571054186978 & 3.02428945813022 \tabularnewline
139 & 15 & 12.3651677255321 & 2.63483227446793 \tabularnewline
140 & 14 & 12.2228936352991 & 1.77710636470091 \tabularnewline
141 & 16 & 11.096976503892 & 4.90302349610801 \tabularnewline
142 & 12 & 12.3794450252018 & -0.379445025201756 \tabularnewline
143 & 14 & 11.1978351984328 & 2.80216480156722 \tabularnewline
144 & 10 & 11.0824066608837 & -1.08240666088371 \tabularnewline
145 & 10 & 11.5673306182425 & -1.56733061824253 \tabularnewline
146 & 4 & 13.1304419943729 & -9.13044199437285 \tabularnewline
147 & 8 & 11.5815889070702 & -3.58158890707025 \tabularnewline
148 & 17 & 11.1323685752909 & 5.86763142470914 \tabularnewline
149 & 16 & 12.6000123050881 & 3.39998769491194 \tabularnewline
150 & 12 & 13.9805642905123 & -1.98056429051232 \tabularnewline
151 & 12 & 11.0423107801524 & 0.957689219847574 \tabularnewline
152 & 15 & 13.1951831290347 & 1.80481687096535 \tabularnewline
153 & 9 & 12.9334263448874 & -3.93342634488739 \tabularnewline
154 & 13 & 12.0177194646798 & 0.982280535320237 \tabularnewline
155 & 14 & 11.938569468502 & 2.06143053149801 \tabularnewline
156 & 11 & 10.9620733656727 & 0.0379266343272837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146372&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]14[/C][C]9.04493447912672[/C][C]4.95506552087328[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]13.0319698639731[/C][C]-5.03196986397311[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]12.5613503023939[/C][C]-0.561350302393922[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]11.8831905647296[/C][C]-4.88319056472956[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]11.8625200096427[/C][C]-1.86252000964267[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]11.4430913238081[/C][C]-2.44309132380808[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]13.5921685122462[/C][C]2.40783148775382[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]9.55093553017966[/C][C]-2.55093553017966[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]11.873348005155[/C][C]2.12665199484503[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]12.2159376826882[/C][C]-6.21593768268816[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]11.7560029396253[/C][C]4.24399706037467[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.3036424286029[/C][C]-0.30364242860287[/C][/ROW]
[ROW][C]13[/C][C]17[/C][C]11.8224431397534[/C][C]5.17755686024664[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.7490607199687[/C][C]1.2509392800313[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]10.1863991501386[/C][C]-3.18639915013859[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]11.3713924244634[/C][C]1.62860757553659[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]8.61833622209662[/C][C]0.38166377790338[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]12.8441233899068[/C][C]2.1558766100932[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]11.0043441016015[/C][C]-4.00434410160152[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]10.235788372443[/C][C]-1.235788372443[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]11.0220707054286[/C][C]-4.02207070542859[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.5765001433481[/C][C]1.42349985665188[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.9468815692557[/C][C]4.05311843074434[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]11.876115839889[/C][C]-4.87611583988896[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]11.8226303380839[/C][C]1.17736966191612[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]11.8273894379738[/C][C]5.17261056202623[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]12.3190597947208[/C][C]2.68094020527917[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]9.16732137880732[/C][C]4.83267862119268[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.2413434167806[/C][C]1.75865658321936[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]10.6022871336545[/C][C]-2.60228713365453[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]10.5645621684314[/C][C]-2.56456216843143[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.3476674450475[/C][C]-0.347667445047518[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]12.3099424798244[/C][C]1.69005752017559[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]11.7827824460116[/C][C]-3.78278244601163[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]10.3071748707914[/C][C]0.692825129208556[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]11.2539585281105[/C][C]4.74604147188946[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.6250762345359[/C][C]-1.62507623453591[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]11.1163515317186[/C][C]-3.1163515317186[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]10.5969250774781[/C][C]3.40307492252193[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]10.4804799748329[/C][C]5.51952002516712[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.2271214076684[/C][C]1.77287859233157[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]12.196446313024[/C][C]-7.19644631302401[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]9.52359605232962[/C][C]-1.52359605232963[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.5012378830133[/C][C]-0.501237883013262[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]12.2367200981944[/C][C]-4.23672009819438[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.463008439807[/C][C]0.536991560192978[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]9.88355238147738[/C][C]5.11644761852262[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]11.1714659353598[/C][C]-5.17146593535975[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.0293007840825[/C][C]1.97069921591751[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]12.8474458574381[/C][C]1.15255414256194[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]10.6707358859709[/C][C]-5.6707358859709[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]12.4494071097897[/C][C]2.55059289021031[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.8088519448781[/C][C]-0.808851944878103[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]9.78653450039786[/C][C]-1.78653450039786[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]11.835071754472[/C][C]1.16492824552803[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]11.9352017742999[/C][C]2.06479822570014[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]10.1197195623801[/C][C]1.88028043761988[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]13.3051091865808[/C][C]2.69489081341916[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]8.81821841747227[/C][C]1.18178158252773[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.5388691722599[/C][C]1.46113082774012[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.3273321439435[/C][C]-2.32733214394348[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]11.1569463189138[/C][C]4.84305368108621[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]12.7488128316732[/C][C]6.25118716832679[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]10.7326041845396[/C][C]3.26739581546044[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]10.5903300943066[/C][C]-3.59033009430658[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]11.8707202434468[/C][C]1.12927975655324[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]11.9110529411833[/C][C]3.08894705881673[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]9.29797533801441[/C][C]-2.29797533801441[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.6263923917269[/C][C]0.373607608273148[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]10.3119194412576[/C][C]-6.31191944125757[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]11.578933462236[/C][C]2.42106653776402[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.1265729512135[/C][C]1.87342704878647[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]9.87563479695915[/C][C]1.12436520304085[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]10.9674487640956[/C][C]3.03255123590445[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4227698079536[/C][C]0.577230192046442[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]12.789403499258[/C][C]2.21059650074199[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]11.9096942140739[/C][C]2.09030578592615[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.6252664260782[/C][C]1.37473357392178[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]11.0659764448523[/C][C]-4.06597644485234[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]9.35508453669317[/C][C]-4.35508453669317[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]11.5095690089879[/C][C]-4.50956900898792[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]12.6184114313832[/C][C]0.381588568616832[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]10.3737877398262[/C][C]2.62621226017378[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]11.2951417421685[/C][C]-0.295141742168539[/C][/ROW]
[ROW][C]85[/C][C]6[/C][C]10.0355675488003[/C][C]-4.03556754880033[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.8582721291408[/C][C]-0.858272129140831[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]9.93676633554693[/C][C]-1.93676633554693[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]12.9283447121464[/C][C]-1.92834471214636[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.3383814422902[/C][C]-2.33838144229024[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]9.8285635954063[/C][C]-0.828563595406304[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.2461406631992[/C][C]1.75385933680077[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.2285448979534[/C][C]-0.228544897953441[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]12.901020107369[/C][C]3.098979892631[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]12.0529247663722[/C][C]3.94707523362776[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]12.6331735367171[/C][C]-1.63317353671713[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]11.2963907475725[/C][C]-3.29639074757253[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]11.8463777678438[/C][C]-7.84637776784385[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]9.86585938917152[/C][C]-2.86585938917152[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.4066566250023[/C][C]2.59334337499772[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]11.8147247869069[/C][C]-0.814724786906851[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]13.1842129907603[/C][C]3.81578700923972[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]12.9572281345398[/C][C]2.04277186546016[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]12.4960462819943[/C][C]1.50395371800571[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]11.7606576427565[/C][C]-6.7606576427565[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]11.4750500867612[/C][C]-7.47505008676117[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]13.7310355108716[/C][C]5.26896448912841[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]10.2142229223314[/C][C]0.785777077668605[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]10.4526368525883[/C][C]4.54736314741166[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]9.91795341403337[/C][C]0.0820465859666293[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]11.4799062566951[/C][C]-2.47990625669509[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]10.5070425367433[/C][C]1.49295746325668[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]11.535116042314[/C][C]3.46488395768603[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]12.986767826168[/C][C]-5.98676782616803[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]12.2893580023912[/C][C]0.710641997608773[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]14.9374967008966[/C][C]-0.937496700896634[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.2557084280644[/C][C]1.74429157193555[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]11.5161673394968[/C][C]2.48383266050322[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]11.1367207747657[/C][C]-3.1367207747657[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]11.9510779654216[/C][C]3.04892203457843[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]10.3841545600062[/C][C]4.61584543999377[/C][/ROW]
[ROW][C]121[/C][C]9[/C][C]11.1924490602674[/C][C]-2.19244906026743[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]12.1238537091192[/C][C]3.87614629088081[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]12.0215405054586[/C][C]-3.02154050545861[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]13.6125800505405[/C][C]1.38741994945953[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]12.2985403270735[/C][C]2.70145967292648[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]11.6822775182012[/C][C]-5.68227751820122[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]11.9309061628458[/C][C]-3.93090616284576[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]12.2726892422579[/C][C]2.72731075774209[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.7466499921507[/C][C]-0.746649992150716[/C][/ROW]
[ROW][C]130[/C][C]9[/C][C]10.6451172238788[/C][C]-1.64511722387884[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]13.9525592528233[/C][C]0.0474407471766966[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]13.0771907240761[/C][C]-1.07719072407606[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]12.7611785846601[/C][C]-4.76117858466012[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]11.8386283708142[/C][C]-0.838628370814164[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]12.7458080051097[/C][C]0.254191994890344[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]11.3114873478434[/C][C]-2.31148734784336[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]11.8253551742753[/C][C]3.17464482572473[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]9.97571054186978[/C][C]3.02428945813022[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]12.3651677255321[/C][C]2.63483227446793[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]12.2228936352991[/C][C]1.77710636470091[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]11.096976503892[/C][C]4.90302349610801[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]12.3794450252018[/C][C]-0.379445025201756[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.1978351984328[/C][C]2.80216480156722[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]11.0824066608837[/C][C]-1.08240666088371[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]11.5673306182425[/C][C]-1.56733061824253[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]13.1304419943729[/C][C]-9.13044199437285[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.5815889070702[/C][C]-3.58158890707025[/C][/ROW]
[ROW][C]148[/C][C]17[/C][C]11.1323685752909[/C][C]5.86763142470914[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]12.6000123050881[/C][C]3.39998769491194[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]13.9805642905123[/C][C]-1.98056429051232[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.0423107801524[/C][C]0.957689219847574[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]13.1951831290347[/C][C]1.80481687096535[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.9334263448874[/C][C]-3.93342634488739[/C][/ROW]
[ROW][C]154[/C][C]13[/C][C]12.0177194646798[/C][C]0.982280535320237[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.938569468502[/C][C]2.06143053149801[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]10.9620733656727[/C][C]0.0379266343272837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146372&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&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
1149.044934479126724.95506552087328
2813.0319698639731-5.03196986397311
31212.5613503023939-0.561350302393922
4711.8831905647296-4.88319056472956
51011.8625200096427-1.86252000964267
6911.4430913238081-2.44309132380808
71613.59216851224622.40783148775382
879.55093553017966-2.55093553017966
91411.8733480051552.12665199484503
10612.2159376826882-6.21593768268816
111611.75600293962534.24399706037467
121111.3036424286029-0.30364242860287
131711.82244313975345.17755686024664
141210.74906071996871.2509392800313
15710.1863991501386-3.18639915013859
161311.37139242446341.62860757553659
1798.618336222096620.38166377790338
181512.84412338990682.1558766100932
19711.0043441016015-4.00434410160152
20910.235788372443-1.235788372443
21711.0220707054286-4.02207070542859
221412.57650014334811.42349985665188
231510.94688156925574.05311843074434
24711.876115839889-4.87611583988896
251311.82263033808391.17736966191612
261711.82738943797385.17261056202623
271512.31905979472082.68094020527917
28149.167321378807324.83267862119268
291412.24134341678061.75865658321936
30810.6022871336545-2.60228713365453
31810.5645621684314-2.56456216843143
321212.3476674450475-0.347667445047518
331412.30994247982441.69005752017559
34811.7827824460116-3.78278244601163
351110.30717487079140.692825129208556
361611.25395852811054.74604147188946
371112.6250762345359-1.62507623453591
38811.1163515317186-3.1163515317186
391410.59692507747813.40307492252193
401610.48047997483295.51952002516712
411412.22712140766841.77287859233157
42512.196446313024-7.19644631302401
4389.52359605232962-1.52359605232963
441010.5012378830133-0.501237883013262
45812.2367200981944-4.23672009819438
461312.4630084398070.536991560192978
47159.883552381477385.11644761852262
48611.1714659353598-5.17146593535975
491210.02930078408251.97069921591751
501412.84744585743811.15255414256194
51510.6707358859709-5.6707358859709
521512.44940710978972.55059289021031
531111.8088519448781-0.808851944878103
5489.78653450039786-1.78653450039786
551311.8350717544721.16492824552803
561411.93520177429992.06479822570014
571210.11971956238011.88028043761988
581613.30510918658082.69489081341916
59108.818218417472271.18178158252773
601513.53886917225991.46113082774012
61810.3273321439435-2.32733214394348
621611.15694631891384.84305368108621
631912.74881283167326.25118716832679
641410.73260418453963.26739581546044
65710.5903300943066-3.59033009430658
661311.87072024344681.12927975655324
671511.91105294118333.08894705881673
6879.29797533801441-2.29797533801441
691312.62639239172690.373607608273148
70410.3119194412576-6.31191944125757
711411.5789334622362.42106653776402
721311.12657295121351.87342704878647
73119.875634796959151.12436520304085
741410.96744876409563.03255123590445
751211.42276980795360.577230192046442
761512.7894034992582.21059650074199
771411.90969421407392.09030578592615
781311.62526642607821.37473357392178
79711.0659764448523-4.06597644485234
8059.35508453669317-4.35508453669317
81711.5095690089879-4.50956900898792
821312.61841143138320.381588568616832
831310.37378773982622.62621226017378
841111.2951417421685-0.295141742168539
85610.0355675488003-4.03556754880033
861212.8582721291408-0.858272129140831
8789.93676633554693-1.93676633554693
881112.9283447121464-1.92834471214636
891214.3383814422902-2.33838144229024
9099.8285635954063-0.828563595406304
911210.24614066319921.75385933680077
921313.2285448979534-0.228544897953441
931612.9010201073693.098979892631
941612.05292476637223.94707523362776
951112.6331735367171-1.63317353671713
96811.2963907475725-3.29639074757253
97411.8463777678438-7.84637776784385
9879.86585938917152-2.86585938917152
991411.40665662500232.59334337499772
1001111.8147247869069-0.814724786906851
1011713.18421299076033.81578700923972
1021512.95722813453982.04277186546016
1031412.49604628199431.50395371800571
104511.7606576427565-6.7606576427565
105411.4750500867612-7.47505008676117
1061913.73103551087165.26896448912841
1071110.21422292233140.785777077668605
1081510.45263685258834.54736314741166
109109.917953414033370.0820465859666293
110911.4799062566951-2.47990625669509
1111210.50704253674331.49295746325668
1121511.5351160423143.46488395768603
113712.986767826168-5.98676782616803
1141312.28935800239120.710641997608773
1151414.9374967008966-0.937496700896634
1161412.25570842806441.74429157193555
1171411.51616733949682.48383266050322
118811.1367207747657-3.1367207747657
1191511.95107796542163.04892203457843
1201510.38415456000624.61584543999377
121911.1924490602674-2.19244906026743
1221612.12385370911923.87614629088081
123912.0215405054586-3.02154050545861
1241513.61258005054051.38741994945953
1251512.29854032707352.70145967292648
126611.6822775182012-5.68227751820122
127811.9309061628458-3.93090616284576
1281512.27268924225792.72731075774209
1291010.7466499921507-0.746649992150716
130910.6451172238788-1.64511722387884
1311413.95255925282330.0474407471766966
1321213.0771907240761-1.07719072407606
133812.7611785846601-4.76117858466012
1341111.8386283708142-0.838628370814164
1351312.74580800510970.254191994890344
136911.3114873478434-2.31148734784336
1371511.82535517427533.17464482572473
138139.975710541869783.02428945813022
1391512.36516772553212.63483227446793
1401412.22289363529911.77710636470091
1411611.0969765038924.90302349610801
1421212.3794450252018-0.379445025201756
1431411.19783519843282.80216480156722
1441011.0824066608837-1.08240666088371
1451011.5673306182425-1.56733061824253
146413.1304419943729-9.13044199437285
147811.5815889070702-3.58158890707025
1481711.13236857529095.86763142470914
1491612.60001230508813.39998769491194
1501213.9805642905123-1.98056429051232
1511211.04231078015240.957689219847574
1521513.19518312903471.80481687096535
153912.9334263448874-3.93342634488739
1541312.01771946467980.982280535320237
1551411.9385694685022.06143053149801
1561110.96207336567270.0379266343272837







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9471933264771990.1056133470456010.0528066735228007
110.9569044646653240.08619107066935120.0430955353346756
120.9206978886670660.1586042226658670.0793021113329336
130.8824925319936260.2350149360127480.117507468006374
140.8317873625596430.3364252748807150.168212637440357
150.8336376653472590.3327246693054830.166362334652741
160.7723371665418290.4553256669163410.227662833458171
170.7024496549580270.5951006900839460.297550345041973
180.6325402962008120.7349194075983760.367459703799188
190.6829098768855090.6341802462289830.317090123114491
200.6082752751542140.7834494496915720.391724724845786
210.5917435211670030.8165129576659950.408256478832997
220.5567149981069440.8865700037861110.443285001893056
230.6467210214526390.7065579570947230.353278978547361
240.7056134233688590.5887731532622820.294386576631141
250.6551070691042880.6897858617914240.344892930895712
260.7217081490045920.5565837019908160.278291850995408
270.7086694968276310.5826610063447390.291330503172369
280.73340359664620.5331928067076010.2665964033538
290.6886346693375940.6227306613248120.311365330662406
300.7125176109189860.5749647781620270.287482389081014
310.7562363427989990.4875273144020020.243763657201001
320.7083192326363520.5833615347272960.291680767363648
330.6560954595106990.6878090809786020.343904540489301
340.6420518430772620.7158963138454750.357948156922738
350.5854020137025210.8291959725949590.414597986297479
360.5639563260291570.8720873479416850.436043673970843
370.5112568402235650.9774863195528690.488743159776435
380.5248226555145060.9503546889709880.475177344485494
390.5033378797847780.9933242404304450.496662120215222
400.5029329642220030.9941340715559940.497067035777997
410.4950231600337150.9900463200674310.504976839966285
420.7352438705477540.5295122589044910.264756129452246
430.7198731233279770.5602537533440470.280126876672023
440.6752801143931770.6494397712136460.324719885606823
450.704033862342070.5919322753158610.29596613765793
460.6568998425865460.6862003148269070.343100157413454
470.6931008565425940.6137982869148120.306899143457406
480.7412660199544850.5174679600910310.258733980045515
490.7145014702058340.5709970595883320.285498529794166
500.672729152645720.654541694708560.32727084735428
510.76749430661960.46501138676080.2325056933804
520.7459303113517370.5081393772965260.254069688648263
530.7056164398530540.5887671202938920.294383560146946
540.6767978560105640.6464042879788720.323202143989436
550.6367180018798890.7265639962402220.363281998120111
560.5993524020402760.8012951959194470.400647597959724
570.562168621715320.875662756569360.43783137828468
580.54633363365210.9073327326957990.4536663663479
590.4993606759563510.9987213519127010.50063932404365
600.4565070893702930.9130141787405850.543492910629707
610.4350660528614840.8701321057229690.564933947138516
620.5092439514543150.981512097091370.490756048545685
630.6172189644325640.7655620711348720.382781035567436
640.6002773781719860.7994452436560270.399722621828014
650.6197037625617430.7605924748765140.380296237438257
660.5771880552008480.8456238895983040.422811944799152
670.5650771797932680.8698456404134630.434922820206732
680.5900208895666760.8199582208666480.409979110433324
690.5456256665791540.9087486668416930.454374333420846
700.6700345282699290.6599309434601410.329965471730071
710.6451899968746790.7096200062506430.354810003125321
720.6112896581813530.7774206836372940.388710341818647
730.5694279756381340.8611440487237330.430572024361866
740.5604696651351260.8790606697297490.439530334864874
750.5169670271862570.9660659456274860.483032972813743
760.4910588040232340.9821176080464670.508941195976766
770.4644253616461780.9288507232923570.535574638353822
780.4265666629137530.8531333258275060.573433337086247
790.44949749574610.8989949914922010.5505025042539
800.4907604370768650.981520874153730.509239562923135
810.5303851832782340.9392296334435320.469614816721766
820.4908054730839410.9816109461678820.509194526916059
830.4721876154187730.9443752308375460.527812384581227
840.4262270216286530.8524540432573050.573772978371347
850.4373958778238040.8747917556476070.562604122176196
860.395411083541720.790822167083440.60458891645828
870.361906240514440.7238124810288790.63809375948556
880.3310527222569620.6621054445139230.668947277743038
890.3051892324699190.6103784649398370.694810767530081
900.267310914176890.5346218283537790.73268908582311
910.2395415451667220.4790830903334450.760458454833278
920.2040820321503480.4081640643006960.795917967849652
930.2036235988602650.407247197720530.796376401139735
940.219089260135090.4381785202701790.78091073986491
950.1908728826421970.3817457652843950.809127117357803
960.1827099039140240.3654198078280490.817290096085976
970.3339195490656210.6678390981312420.666080450934379
980.3259330843713780.6518661687427560.674066915628622
990.3061534058504020.6123068117008040.693846594149598
1000.2712629080207910.5425258160415820.728737091979209
1010.2911226654031360.5822453308062730.708877334596864
1020.2709263434927430.5418526869854860.729073656507257
1030.2452075548142810.4904151096285630.754792445185719
1040.351761172803710.703522345607420.64823882719629
1050.5913300215626910.8173399568746190.408669978437309
1060.6734630295444080.6530739409111840.326536970455592
1070.6272683809856990.7454632380286010.372731619014301
1080.6425549840939590.7148900318120820.357445015906041
1090.5948254757177060.8103490485645880.405174524282294
1100.572003497547130.8559930049057390.42799650245287
1110.5231724549946470.9536550900107050.476827545005353
1120.5124169656537080.9751660686925840.487583034346292
1130.6062962245504460.7874075508991080.393703775449554
1140.5535825152186950.8928349695626090.446417484781305
1150.502068116748860.9958637665022810.49793188325114
1160.4630007733719620.9260015467439250.536999226628038
1170.4387115430151970.8774230860303950.561288456984803
1180.4592309826950650.918461965390130.540769017304935
1190.4481186443331650.8962372886663290.551881355666835
1200.48951746037820.9790349207564010.5104825396218
1210.4541141371489080.9082282742978160.545885862851092
1220.5124042307272130.9751915385455740.487595769272787
1230.5275719136770750.944856172645850.472428086322925
1240.5381268279015010.9237463441969970.461873172098498
1250.5405388116369470.9189223767261060.459461188363053
1260.7177432946373460.5645134107253080.282256705362654
1270.8286593033109280.3426813933781440.171340696689072
1280.8088729610367050.3822540779265890.191127038963295
1290.7620835399011450.4758329201977110.237916460098855
1300.745319135453750.5093617290925010.25468086454625
1310.6830555347976160.6338889304047680.316944465202384
1320.6214286887541120.7571426224917760.378571311245888
1330.6676436319069290.6647127361861420.332356368093071
1340.6069331332495480.7861337335009040.393066866750452
1350.5633897207200280.8732205585599440.436610279279972
1360.5487238039123520.9025523921752950.451276196087648
1370.5404335629090470.9191328741819060.459566437090953
1380.4829439442340270.9658878884680550.517056055765973
1390.4321147388374960.8642294776749910.567885261162504
1400.5559359546656960.8881280906686090.444064045334304
1410.6158258789171140.7683482421657710.384174121082886
1420.5371300825589640.9257398348820730.462869917441036
1430.7853922373169990.4292155253660020.214607762683001
1440.6852532064571790.6294935870856420.314746793542821
1450.6628560744691170.6742878510617650.337143925530883
1460.5674291595775520.8651416808448950.432570840422448

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.947193326477199 & 0.105613347045601 & 0.0528066735228007 \tabularnewline
11 & 0.956904464665324 & 0.0861910706693512 & 0.0430955353346756 \tabularnewline
12 & 0.920697888667066 & 0.158604222665867 & 0.0793021113329336 \tabularnewline
13 & 0.882492531993626 & 0.235014936012748 & 0.117507468006374 \tabularnewline
14 & 0.831787362559643 & 0.336425274880715 & 0.168212637440357 \tabularnewline
15 & 0.833637665347259 & 0.332724669305483 & 0.166362334652741 \tabularnewline
16 & 0.772337166541829 & 0.455325666916341 & 0.227662833458171 \tabularnewline
17 & 0.702449654958027 & 0.595100690083946 & 0.297550345041973 \tabularnewline
18 & 0.632540296200812 & 0.734919407598376 & 0.367459703799188 \tabularnewline
19 & 0.682909876885509 & 0.634180246228983 & 0.317090123114491 \tabularnewline
20 & 0.608275275154214 & 0.783449449691572 & 0.391724724845786 \tabularnewline
21 & 0.591743521167003 & 0.816512957665995 & 0.408256478832997 \tabularnewline
22 & 0.556714998106944 & 0.886570003786111 & 0.443285001893056 \tabularnewline
23 & 0.646721021452639 & 0.706557957094723 & 0.353278978547361 \tabularnewline
24 & 0.705613423368859 & 0.588773153262282 & 0.294386576631141 \tabularnewline
25 & 0.655107069104288 & 0.689785861791424 & 0.344892930895712 \tabularnewline
26 & 0.721708149004592 & 0.556583701990816 & 0.278291850995408 \tabularnewline
27 & 0.708669496827631 & 0.582661006344739 & 0.291330503172369 \tabularnewline
28 & 0.7334035966462 & 0.533192806707601 & 0.2665964033538 \tabularnewline
29 & 0.688634669337594 & 0.622730661324812 & 0.311365330662406 \tabularnewline
30 & 0.712517610918986 & 0.574964778162027 & 0.287482389081014 \tabularnewline
31 & 0.756236342798999 & 0.487527314402002 & 0.243763657201001 \tabularnewline
32 & 0.708319232636352 & 0.583361534727296 & 0.291680767363648 \tabularnewline
33 & 0.656095459510699 & 0.687809080978602 & 0.343904540489301 \tabularnewline
34 & 0.642051843077262 & 0.715896313845475 & 0.357948156922738 \tabularnewline
35 & 0.585402013702521 & 0.829195972594959 & 0.414597986297479 \tabularnewline
36 & 0.563956326029157 & 0.872087347941685 & 0.436043673970843 \tabularnewline
37 & 0.511256840223565 & 0.977486319552869 & 0.488743159776435 \tabularnewline
38 & 0.524822655514506 & 0.950354688970988 & 0.475177344485494 \tabularnewline
39 & 0.503337879784778 & 0.993324240430445 & 0.496662120215222 \tabularnewline
40 & 0.502932964222003 & 0.994134071555994 & 0.497067035777997 \tabularnewline
41 & 0.495023160033715 & 0.990046320067431 & 0.504976839966285 \tabularnewline
42 & 0.735243870547754 & 0.529512258904491 & 0.264756129452246 \tabularnewline
43 & 0.719873123327977 & 0.560253753344047 & 0.280126876672023 \tabularnewline
44 & 0.675280114393177 & 0.649439771213646 & 0.324719885606823 \tabularnewline
45 & 0.70403386234207 & 0.591932275315861 & 0.29596613765793 \tabularnewline
46 & 0.656899842586546 & 0.686200314826907 & 0.343100157413454 \tabularnewline
47 & 0.693100856542594 & 0.613798286914812 & 0.306899143457406 \tabularnewline
48 & 0.741266019954485 & 0.517467960091031 & 0.258733980045515 \tabularnewline
49 & 0.714501470205834 & 0.570997059588332 & 0.285498529794166 \tabularnewline
50 & 0.67272915264572 & 0.65454169470856 & 0.32727084735428 \tabularnewline
51 & 0.7674943066196 & 0.4650113867608 & 0.2325056933804 \tabularnewline
52 & 0.745930311351737 & 0.508139377296526 & 0.254069688648263 \tabularnewline
53 & 0.705616439853054 & 0.588767120293892 & 0.294383560146946 \tabularnewline
54 & 0.676797856010564 & 0.646404287978872 & 0.323202143989436 \tabularnewline
55 & 0.636718001879889 & 0.726563996240222 & 0.363281998120111 \tabularnewline
56 & 0.599352402040276 & 0.801295195919447 & 0.400647597959724 \tabularnewline
57 & 0.56216862171532 & 0.87566275656936 & 0.43783137828468 \tabularnewline
58 & 0.5463336336521 & 0.907332732695799 & 0.4536663663479 \tabularnewline
59 & 0.499360675956351 & 0.998721351912701 & 0.50063932404365 \tabularnewline
60 & 0.456507089370293 & 0.913014178740585 & 0.543492910629707 \tabularnewline
61 & 0.435066052861484 & 0.870132105722969 & 0.564933947138516 \tabularnewline
62 & 0.509243951454315 & 0.98151209709137 & 0.490756048545685 \tabularnewline
63 & 0.617218964432564 & 0.765562071134872 & 0.382781035567436 \tabularnewline
64 & 0.600277378171986 & 0.799445243656027 & 0.399722621828014 \tabularnewline
65 & 0.619703762561743 & 0.760592474876514 & 0.380296237438257 \tabularnewline
66 & 0.577188055200848 & 0.845623889598304 & 0.422811944799152 \tabularnewline
67 & 0.565077179793268 & 0.869845640413463 & 0.434922820206732 \tabularnewline
68 & 0.590020889566676 & 0.819958220866648 & 0.409979110433324 \tabularnewline
69 & 0.545625666579154 & 0.908748666841693 & 0.454374333420846 \tabularnewline
70 & 0.670034528269929 & 0.659930943460141 & 0.329965471730071 \tabularnewline
71 & 0.645189996874679 & 0.709620006250643 & 0.354810003125321 \tabularnewline
72 & 0.611289658181353 & 0.777420683637294 & 0.388710341818647 \tabularnewline
73 & 0.569427975638134 & 0.861144048723733 & 0.430572024361866 \tabularnewline
74 & 0.560469665135126 & 0.879060669729749 & 0.439530334864874 \tabularnewline
75 & 0.516967027186257 & 0.966065945627486 & 0.483032972813743 \tabularnewline
76 & 0.491058804023234 & 0.982117608046467 & 0.508941195976766 \tabularnewline
77 & 0.464425361646178 & 0.928850723292357 & 0.535574638353822 \tabularnewline
78 & 0.426566662913753 & 0.853133325827506 & 0.573433337086247 \tabularnewline
79 & 0.4494974957461 & 0.898994991492201 & 0.5505025042539 \tabularnewline
80 & 0.490760437076865 & 0.98152087415373 & 0.509239562923135 \tabularnewline
81 & 0.530385183278234 & 0.939229633443532 & 0.469614816721766 \tabularnewline
82 & 0.490805473083941 & 0.981610946167882 & 0.509194526916059 \tabularnewline
83 & 0.472187615418773 & 0.944375230837546 & 0.527812384581227 \tabularnewline
84 & 0.426227021628653 & 0.852454043257305 & 0.573772978371347 \tabularnewline
85 & 0.437395877823804 & 0.874791755647607 & 0.562604122176196 \tabularnewline
86 & 0.39541108354172 & 0.79082216708344 & 0.60458891645828 \tabularnewline
87 & 0.36190624051444 & 0.723812481028879 & 0.63809375948556 \tabularnewline
88 & 0.331052722256962 & 0.662105444513923 & 0.668947277743038 \tabularnewline
89 & 0.305189232469919 & 0.610378464939837 & 0.694810767530081 \tabularnewline
90 & 0.26731091417689 & 0.534621828353779 & 0.73268908582311 \tabularnewline
91 & 0.239541545166722 & 0.479083090333445 & 0.760458454833278 \tabularnewline
92 & 0.204082032150348 & 0.408164064300696 & 0.795917967849652 \tabularnewline
93 & 0.203623598860265 & 0.40724719772053 & 0.796376401139735 \tabularnewline
94 & 0.21908926013509 & 0.438178520270179 & 0.78091073986491 \tabularnewline
95 & 0.190872882642197 & 0.381745765284395 & 0.809127117357803 \tabularnewline
96 & 0.182709903914024 & 0.365419807828049 & 0.817290096085976 \tabularnewline
97 & 0.333919549065621 & 0.667839098131242 & 0.666080450934379 \tabularnewline
98 & 0.325933084371378 & 0.651866168742756 & 0.674066915628622 \tabularnewline
99 & 0.306153405850402 & 0.612306811700804 & 0.693846594149598 \tabularnewline
100 & 0.271262908020791 & 0.542525816041582 & 0.728737091979209 \tabularnewline
101 & 0.291122665403136 & 0.582245330806273 & 0.708877334596864 \tabularnewline
102 & 0.270926343492743 & 0.541852686985486 & 0.729073656507257 \tabularnewline
103 & 0.245207554814281 & 0.490415109628563 & 0.754792445185719 \tabularnewline
104 & 0.35176117280371 & 0.70352234560742 & 0.64823882719629 \tabularnewline
105 & 0.591330021562691 & 0.817339956874619 & 0.408669978437309 \tabularnewline
106 & 0.673463029544408 & 0.653073940911184 & 0.326536970455592 \tabularnewline
107 & 0.627268380985699 & 0.745463238028601 & 0.372731619014301 \tabularnewline
108 & 0.642554984093959 & 0.714890031812082 & 0.357445015906041 \tabularnewline
109 & 0.594825475717706 & 0.810349048564588 & 0.405174524282294 \tabularnewline
110 & 0.57200349754713 & 0.855993004905739 & 0.42799650245287 \tabularnewline
111 & 0.523172454994647 & 0.953655090010705 & 0.476827545005353 \tabularnewline
112 & 0.512416965653708 & 0.975166068692584 & 0.487583034346292 \tabularnewline
113 & 0.606296224550446 & 0.787407550899108 & 0.393703775449554 \tabularnewline
114 & 0.553582515218695 & 0.892834969562609 & 0.446417484781305 \tabularnewline
115 & 0.50206811674886 & 0.995863766502281 & 0.49793188325114 \tabularnewline
116 & 0.463000773371962 & 0.926001546743925 & 0.536999226628038 \tabularnewline
117 & 0.438711543015197 & 0.877423086030395 & 0.561288456984803 \tabularnewline
118 & 0.459230982695065 & 0.91846196539013 & 0.540769017304935 \tabularnewline
119 & 0.448118644333165 & 0.896237288666329 & 0.551881355666835 \tabularnewline
120 & 0.4895174603782 & 0.979034920756401 & 0.5104825396218 \tabularnewline
121 & 0.454114137148908 & 0.908228274297816 & 0.545885862851092 \tabularnewline
122 & 0.512404230727213 & 0.975191538545574 & 0.487595769272787 \tabularnewline
123 & 0.527571913677075 & 0.94485617264585 & 0.472428086322925 \tabularnewline
124 & 0.538126827901501 & 0.923746344196997 & 0.461873172098498 \tabularnewline
125 & 0.540538811636947 & 0.918922376726106 & 0.459461188363053 \tabularnewline
126 & 0.717743294637346 & 0.564513410725308 & 0.282256705362654 \tabularnewline
127 & 0.828659303310928 & 0.342681393378144 & 0.171340696689072 \tabularnewline
128 & 0.808872961036705 & 0.382254077926589 & 0.191127038963295 \tabularnewline
129 & 0.762083539901145 & 0.475832920197711 & 0.237916460098855 \tabularnewline
130 & 0.74531913545375 & 0.509361729092501 & 0.25468086454625 \tabularnewline
131 & 0.683055534797616 & 0.633888930404768 & 0.316944465202384 \tabularnewline
132 & 0.621428688754112 & 0.757142622491776 & 0.378571311245888 \tabularnewline
133 & 0.667643631906929 & 0.664712736186142 & 0.332356368093071 \tabularnewline
134 & 0.606933133249548 & 0.786133733500904 & 0.393066866750452 \tabularnewline
135 & 0.563389720720028 & 0.873220558559944 & 0.436610279279972 \tabularnewline
136 & 0.548723803912352 & 0.902552392175295 & 0.451276196087648 \tabularnewline
137 & 0.540433562909047 & 0.919132874181906 & 0.459566437090953 \tabularnewline
138 & 0.482943944234027 & 0.965887888468055 & 0.517056055765973 \tabularnewline
139 & 0.432114738837496 & 0.864229477674991 & 0.567885261162504 \tabularnewline
140 & 0.555935954665696 & 0.888128090668609 & 0.444064045334304 \tabularnewline
141 & 0.615825878917114 & 0.768348242165771 & 0.384174121082886 \tabularnewline
142 & 0.537130082558964 & 0.925739834882073 & 0.462869917441036 \tabularnewline
143 & 0.785392237316999 & 0.429215525366002 & 0.214607762683001 \tabularnewline
144 & 0.685253206457179 & 0.629493587085642 & 0.314746793542821 \tabularnewline
145 & 0.662856074469117 & 0.674287851061765 & 0.337143925530883 \tabularnewline
146 & 0.567429159577552 & 0.865141680844895 & 0.432570840422448 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146372&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]0.947193326477199[/C][C]0.105613347045601[/C][C]0.0528066735228007[/C][/ROW]
[ROW][C]11[/C][C]0.956904464665324[/C][C]0.0861910706693512[/C][C]0.0430955353346756[/C][/ROW]
[ROW][C]12[/C][C]0.920697888667066[/C][C]0.158604222665867[/C][C]0.0793021113329336[/C][/ROW]
[ROW][C]13[/C][C]0.882492531993626[/C][C]0.235014936012748[/C][C]0.117507468006374[/C][/ROW]
[ROW][C]14[/C][C]0.831787362559643[/C][C]0.336425274880715[/C][C]0.168212637440357[/C][/ROW]
[ROW][C]15[/C][C]0.833637665347259[/C][C]0.332724669305483[/C][C]0.166362334652741[/C][/ROW]
[ROW][C]16[/C][C]0.772337166541829[/C][C]0.455325666916341[/C][C]0.227662833458171[/C][/ROW]
[ROW][C]17[/C][C]0.702449654958027[/C][C]0.595100690083946[/C][C]0.297550345041973[/C][/ROW]
[ROW][C]18[/C][C]0.632540296200812[/C][C]0.734919407598376[/C][C]0.367459703799188[/C][/ROW]
[ROW][C]19[/C][C]0.682909876885509[/C][C]0.634180246228983[/C][C]0.317090123114491[/C][/ROW]
[ROW][C]20[/C][C]0.608275275154214[/C][C]0.783449449691572[/C][C]0.391724724845786[/C][/ROW]
[ROW][C]21[/C][C]0.591743521167003[/C][C]0.816512957665995[/C][C]0.408256478832997[/C][/ROW]
[ROW][C]22[/C][C]0.556714998106944[/C][C]0.886570003786111[/C][C]0.443285001893056[/C][/ROW]
[ROW][C]23[/C][C]0.646721021452639[/C][C]0.706557957094723[/C][C]0.353278978547361[/C][/ROW]
[ROW][C]24[/C][C]0.705613423368859[/C][C]0.588773153262282[/C][C]0.294386576631141[/C][/ROW]
[ROW][C]25[/C][C]0.655107069104288[/C][C]0.689785861791424[/C][C]0.344892930895712[/C][/ROW]
[ROW][C]26[/C][C]0.721708149004592[/C][C]0.556583701990816[/C][C]0.278291850995408[/C][/ROW]
[ROW][C]27[/C][C]0.708669496827631[/C][C]0.582661006344739[/C][C]0.291330503172369[/C][/ROW]
[ROW][C]28[/C][C]0.7334035966462[/C][C]0.533192806707601[/C][C]0.2665964033538[/C][/ROW]
[ROW][C]29[/C][C]0.688634669337594[/C][C]0.622730661324812[/C][C]0.311365330662406[/C][/ROW]
[ROW][C]30[/C][C]0.712517610918986[/C][C]0.574964778162027[/C][C]0.287482389081014[/C][/ROW]
[ROW][C]31[/C][C]0.756236342798999[/C][C]0.487527314402002[/C][C]0.243763657201001[/C][/ROW]
[ROW][C]32[/C][C]0.708319232636352[/C][C]0.583361534727296[/C][C]0.291680767363648[/C][/ROW]
[ROW][C]33[/C][C]0.656095459510699[/C][C]0.687809080978602[/C][C]0.343904540489301[/C][/ROW]
[ROW][C]34[/C][C]0.642051843077262[/C][C]0.715896313845475[/C][C]0.357948156922738[/C][/ROW]
[ROW][C]35[/C][C]0.585402013702521[/C][C]0.829195972594959[/C][C]0.414597986297479[/C][/ROW]
[ROW][C]36[/C][C]0.563956326029157[/C][C]0.872087347941685[/C][C]0.436043673970843[/C][/ROW]
[ROW][C]37[/C][C]0.511256840223565[/C][C]0.977486319552869[/C][C]0.488743159776435[/C][/ROW]
[ROW][C]38[/C][C]0.524822655514506[/C][C]0.950354688970988[/C][C]0.475177344485494[/C][/ROW]
[ROW][C]39[/C][C]0.503337879784778[/C][C]0.993324240430445[/C][C]0.496662120215222[/C][/ROW]
[ROW][C]40[/C][C]0.502932964222003[/C][C]0.994134071555994[/C][C]0.497067035777997[/C][/ROW]
[ROW][C]41[/C][C]0.495023160033715[/C][C]0.990046320067431[/C][C]0.504976839966285[/C][/ROW]
[ROW][C]42[/C][C]0.735243870547754[/C][C]0.529512258904491[/C][C]0.264756129452246[/C][/ROW]
[ROW][C]43[/C][C]0.719873123327977[/C][C]0.560253753344047[/C][C]0.280126876672023[/C][/ROW]
[ROW][C]44[/C][C]0.675280114393177[/C][C]0.649439771213646[/C][C]0.324719885606823[/C][/ROW]
[ROW][C]45[/C][C]0.70403386234207[/C][C]0.591932275315861[/C][C]0.29596613765793[/C][/ROW]
[ROW][C]46[/C][C]0.656899842586546[/C][C]0.686200314826907[/C][C]0.343100157413454[/C][/ROW]
[ROW][C]47[/C][C]0.693100856542594[/C][C]0.613798286914812[/C][C]0.306899143457406[/C][/ROW]
[ROW][C]48[/C][C]0.741266019954485[/C][C]0.517467960091031[/C][C]0.258733980045515[/C][/ROW]
[ROW][C]49[/C][C]0.714501470205834[/C][C]0.570997059588332[/C][C]0.285498529794166[/C][/ROW]
[ROW][C]50[/C][C]0.67272915264572[/C][C]0.65454169470856[/C][C]0.32727084735428[/C][/ROW]
[ROW][C]51[/C][C]0.7674943066196[/C][C]0.4650113867608[/C][C]0.2325056933804[/C][/ROW]
[ROW][C]52[/C][C]0.745930311351737[/C][C]0.508139377296526[/C][C]0.254069688648263[/C][/ROW]
[ROW][C]53[/C][C]0.705616439853054[/C][C]0.588767120293892[/C][C]0.294383560146946[/C][/ROW]
[ROW][C]54[/C][C]0.676797856010564[/C][C]0.646404287978872[/C][C]0.323202143989436[/C][/ROW]
[ROW][C]55[/C][C]0.636718001879889[/C][C]0.726563996240222[/C][C]0.363281998120111[/C][/ROW]
[ROW][C]56[/C][C]0.599352402040276[/C][C]0.801295195919447[/C][C]0.400647597959724[/C][/ROW]
[ROW][C]57[/C][C]0.56216862171532[/C][C]0.87566275656936[/C][C]0.43783137828468[/C][/ROW]
[ROW][C]58[/C][C]0.5463336336521[/C][C]0.907332732695799[/C][C]0.4536663663479[/C][/ROW]
[ROW][C]59[/C][C]0.499360675956351[/C][C]0.998721351912701[/C][C]0.50063932404365[/C][/ROW]
[ROW][C]60[/C][C]0.456507089370293[/C][C]0.913014178740585[/C][C]0.543492910629707[/C][/ROW]
[ROW][C]61[/C][C]0.435066052861484[/C][C]0.870132105722969[/C][C]0.564933947138516[/C][/ROW]
[ROW][C]62[/C][C]0.509243951454315[/C][C]0.98151209709137[/C][C]0.490756048545685[/C][/ROW]
[ROW][C]63[/C][C]0.617218964432564[/C][C]0.765562071134872[/C][C]0.382781035567436[/C][/ROW]
[ROW][C]64[/C][C]0.600277378171986[/C][C]0.799445243656027[/C][C]0.399722621828014[/C][/ROW]
[ROW][C]65[/C][C]0.619703762561743[/C][C]0.760592474876514[/C][C]0.380296237438257[/C][/ROW]
[ROW][C]66[/C][C]0.577188055200848[/C][C]0.845623889598304[/C][C]0.422811944799152[/C][/ROW]
[ROW][C]67[/C][C]0.565077179793268[/C][C]0.869845640413463[/C][C]0.434922820206732[/C][/ROW]
[ROW][C]68[/C][C]0.590020889566676[/C][C]0.819958220866648[/C][C]0.409979110433324[/C][/ROW]
[ROW][C]69[/C][C]0.545625666579154[/C][C]0.908748666841693[/C][C]0.454374333420846[/C][/ROW]
[ROW][C]70[/C][C]0.670034528269929[/C][C]0.659930943460141[/C][C]0.329965471730071[/C][/ROW]
[ROW][C]71[/C][C]0.645189996874679[/C][C]0.709620006250643[/C][C]0.354810003125321[/C][/ROW]
[ROW][C]72[/C][C]0.611289658181353[/C][C]0.777420683637294[/C][C]0.388710341818647[/C][/ROW]
[ROW][C]73[/C][C]0.569427975638134[/C][C]0.861144048723733[/C][C]0.430572024361866[/C][/ROW]
[ROW][C]74[/C][C]0.560469665135126[/C][C]0.879060669729749[/C][C]0.439530334864874[/C][/ROW]
[ROW][C]75[/C][C]0.516967027186257[/C][C]0.966065945627486[/C][C]0.483032972813743[/C][/ROW]
[ROW][C]76[/C][C]0.491058804023234[/C][C]0.982117608046467[/C][C]0.508941195976766[/C][/ROW]
[ROW][C]77[/C][C]0.464425361646178[/C][C]0.928850723292357[/C][C]0.535574638353822[/C][/ROW]
[ROW][C]78[/C][C]0.426566662913753[/C][C]0.853133325827506[/C][C]0.573433337086247[/C][/ROW]
[ROW][C]79[/C][C]0.4494974957461[/C][C]0.898994991492201[/C][C]0.5505025042539[/C][/ROW]
[ROW][C]80[/C][C]0.490760437076865[/C][C]0.98152087415373[/C][C]0.509239562923135[/C][/ROW]
[ROW][C]81[/C][C]0.530385183278234[/C][C]0.939229633443532[/C][C]0.469614816721766[/C][/ROW]
[ROW][C]82[/C][C]0.490805473083941[/C][C]0.981610946167882[/C][C]0.509194526916059[/C][/ROW]
[ROW][C]83[/C][C]0.472187615418773[/C][C]0.944375230837546[/C][C]0.527812384581227[/C][/ROW]
[ROW][C]84[/C][C]0.426227021628653[/C][C]0.852454043257305[/C][C]0.573772978371347[/C][/ROW]
[ROW][C]85[/C][C]0.437395877823804[/C][C]0.874791755647607[/C][C]0.562604122176196[/C][/ROW]
[ROW][C]86[/C][C]0.39541108354172[/C][C]0.79082216708344[/C][C]0.60458891645828[/C][/ROW]
[ROW][C]87[/C][C]0.36190624051444[/C][C]0.723812481028879[/C][C]0.63809375948556[/C][/ROW]
[ROW][C]88[/C][C]0.331052722256962[/C][C]0.662105444513923[/C][C]0.668947277743038[/C][/ROW]
[ROW][C]89[/C][C]0.305189232469919[/C][C]0.610378464939837[/C][C]0.694810767530081[/C][/ROW]
[ROW][C]90[/C][C]0.26731091417689[/C][C]0.534621828353779[/C][C]0.73268908582311[/C][/ROW]
[ROW][C]91[/C][C]0.239541545166722[/C][C]0.479083090333445[/C][C]0.760458454833278[/C][/ROW]
[ROW][C]92[/C][C]0.204082032150348[/C][C]0.408164064300696[/C][C]0.795917967849652[/C][/ROW]
[ROW][C]93[/C][C]0.203623598860265[/C][C]0.40724719772053[/C][C]0.796376401139735[/C][/ROW]
[ROW][C]94[/C][C]0.21908926013509[/C][C]0.438178520270179[/C][C]0.78091073986491[/C][/ROW]
[ROW][C]95[/C][C]0.190872882642197[/C][C]0.381745765284395[/C][C]0.809127117357803[/C][/ROW]
[ROW][C]96[/C][C]0.182709903914024[/C][C]0.365419807828049[/C][C]0.817290096085976[/C][/ROW]
[ROW][C]97[/C][C]0.333919549065621[/C][C]0.667839098131242[/C][C]0.666080450934379[/C][/ROW]
[ROW][C]98[/C][C]0.325933084371378[/C][C]0.651866168742756[/C][C]0.674066915628622[/C][/ROW]
[ROW][C]99[/C][C]0.306153405850402[/C][C]0.612306811700804[/C][C]0.693846594149598[/C][/ROW]
[ROW][C]100[/C][C]0.271262908020791[/C][C]0.542525816041582[/C][C]0.728737091979209[/C][/ROW]
[ROW][C]101[/C][C]0.291122665403136[/C][C]0.582245330806273[/C][C]0.708877334596864[/C][/ROW]
[ROW][C]102[/C][C]0.270926343492743[/C][C]0.541852686985486[/C][C]0.729073656507257[/C][/ROW]
[ROW][C]103[/C][C]0.245207554814281[/C][C]0.490415109628563[/C][C]0.754792445185719[/C][/ROW]
[ROW][C]104[/C][C]0.35176117280371[/C][C]0.70352234560742[/C][C]0.64823882719629[/C][/ROW]
[ROW][C]105[/C][C]0.591330021562691[/C][C]0.817339956874619[/C][C]0.408669978437309[/C][/ROW]
[ROW][C]106[/C][C]0.673463029544408[/C][C]0.653073940911184[/C][C]0.326536970455592[/C][/ROW]
[ROW][C]107[/C][C]0.627268380985699[/C][C]0.745463238028601[/C][C]0.372731619014301[/C][/ROW]
[ROW][C]108[/C][C]0.642554984093959[/C][C]0.714890031812082[/C][C]0.357445015906041[/C][/ROW]
[ROW][C]109[/C][C]0.594825475717706[/C][C]0.810349048564588[/C][C]0.405174524282294[/C][/ROW]
[ROW][C]110[/C][C]0.57200349754713[/C][C]0.855993004905739[/C][C]0.42799650245287[/C][/ROW]
[ROW][C]111[/C][C]0.523172454994647[/C][C]0.953655090010705[/C][C]0.476827545005353[/C][/ROW]
[ROW][C]112[/C][C]0.512416965653708[/C][C]0.975166068692584[/C][C]0.487583034346292[/C][/ROW]
[ROW][C]113[/C][C]0.606296224550446[/C][C]0.787407550899108[/C][C]0.393703775449554[/C][/ROW]
[ROW][C]114[/C][C]0.553582515218695[/C][C]0.892834969562609[/C][C]0.446417484781305[/C][/ROW]
[ROW][C]115[/C][C]0.50206811674886[/C][C]0.995863766502281[/C][C]0.49793188325114[/C][/ROW]
[ROW][C]116[/C][C]0.463000773371962[/C][C]0.926001546743925[/C][C]0.536999226628038[/C][/ROW]
[ROW][C]117[/C][C]0.438711543015197[/C][C]0.877423086030395[/C][C]0.561288456984803[/C][/ROW]
[ROW][C]118[/C][C]0.459230982695065[/C][C]0.91846196539013[/C][C]0.540769017304935[/C][/ROW]
[ROW][C]119[/C][C]0.448118644333165[/C][C]0.896237288666329[/C][C]0.551881355666835[/C][/ROW]
[ROW][C]120[/C][C]0.4895174603782[/C][C]0.979034920756401[/C][C]0.5104825396218[/C][/ROW]
[ROW][C]121[/C][C]0.454114137148908[/C][C]0.908228274297816[/C][C]0.545885862851092[/C][/ROW]
[ROW][C]122[/C][C]0.512404230727213[/C][C]0.975191538545574[/C][C]0.487595769272787[/C][/ROW]
[ROW][C]123[/C][C]0.527571913677075[/C][C]0.94485617264585[/C][C]0.472428086322925[/C][/ROW]
[ROW][C]124[/C][C]0.538126827901501[/C][C]0.923746344196997[/C][C]0.461873172098498[/C][/ROW]
[ROW][C]125[/C][C]0.540538811636947[/C][C]0.918922376726106[/C][C]0.459461188363053[/C][/ROW]
[ROW][C]126[/C][C]0.717743294637346[/C][C]0.564513410725308[/C][C]0.282256705362654[/C][/ROW]
[ROW][C]127[/C][C]0.828659303310928[/C][C]0.342681393378144[/C][C]0.171340696689072[/C][/ROW]
[ROW][C]128[/C][C]0.808872961036705[/C][C]0.382254077926589[/C][C]0.191127038963295[/C][/ROW]
[ROW][C]129[/C][C]0.762083539901145[/C][C]0.475832920197711[/C][C]0.237916460098855[/C][/ROW]
[ROW][C]130[/C][C]0.74531913545375[/C][C]0.509361729092501[/C][C]0.25468086454625[/C][/ROW]
[ROW][C]131[/C][C]0.683055534797616[/C][C]0.633888930404768[/C][C]0.316944465202384[/C][/ROW]
[ROW][C]132[/C][C]0.621428688754112[/C][C]0.757142622491776[/C][C]0.378571311245888[/C][/ROW]
[ROW][C]133[/C][C]0.667643631906929[/C][C]0.664712736186142[/C][C]0.332356368093071[/C][/ROW]
[ROW][C]134[/C][C]0.606933133249548[/C][C]0.786133733500904[/C][C]0.393066866750452[/C][/ROW]
[ROW][C]135[/C][C]0.563389720720028[/C][C]0.873220558559944[/C][C]0.436610279279972[/C][/ROW]
[ROW][C]136[/C][C]0.548723803912352[/C][C]0.902552392175295[/C][C]0.451276196087648[/C][/ROW]
[ROW][C]137[/C][C]0.540433562909047[/C][C]0.919132874181906[/C][C]0.459566437090953[/C][/ROW]
[ROW][C]138[/C][C]0.482943944234027[/C][C]0.965887888468055[/C][C]0.517056055765973[/C][/ROW]
[ROW][C]139[/C][C]0.432114738837496[/C][C]0.864229477674991[/C][C]0.567885261162504[/C][/ROW]
[ROW][C]140[/C][C]0.555935954665696[/C][C]0.888128090668609[/C][C]0.444064045334304[/C][/ROW]
[ROW][C]141[/C][C]0.615825878917114[/C][C]0.768348242165771[/C][C]0.384174121082886[/C][/ROW]
[ROW][C]142[/C][C]0.537130082558964[/C][C]0.925739834882073[/C][C]0.462869917441036[/C][/ROW]
[ROW][C]143[/C][C]0.785392237316999[/C][C]0.429215525366002[/C][C]0.214607762683001[/C][/ROW]
[ROW][C]144[/C][C]0.685253206457179[/C][C]0.629493587085642[/C][C]0.314746793542821[/C][/ROW]
[ROW][C]145[/C][C]0.662856074469117[/C][C]0.674287851061765[/C][C]0.337143925530883[/C][/ROW]
[ROW][C]146[/C][C]0.567429159577552[/C][C]0.865141680844895[/C][C]0.432570840422448[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146372&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&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
100.9471933264771990.1056133470456010.0528066735228007
110.9569044646653240.08619107066935120.0430955353346756
120.9206978886670660.1586042226658670.0793021113329336
130.8824925319936260.2350149360127480.117507468006374
140.8317873625596430.3364252748807150.168212637440357
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1460.5674291595775520.8651416808448950.432570840422448







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0072992700729927OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0072992700729927 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146372&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0072992700729927[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146372&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146372&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0072992700729927OK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}