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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, 23 Nov 2010 22:04:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t12905497787bm4v110ez6xlsj.htm/, Retrieved Thu, 28 Mar 2024 09:50:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99679, Retrieved Thu, 28 Mar 2024 09:50:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
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]
-   PD  [Multiple Regression] [deterministische ...] [2010-11-19 12:14:25] [74deae64b71f9d77c839af86f7c687b5]
-   PD      [Multiple Regression] [] [2010-11-23 22:04:55] [e26438ba7029caa0090c95690001dbf5] [Current]
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Dataseries X:
41	12	14	12
39	11	18	11
30	15	11	14
31	6	12	12
34	13	16	21
35	10	18	12
39	12	14	22
34	14	14	11
36	12	15	10
37	6	15	13
38	10	17	10
36	12	19	8
38	12	10	15
39	11	16	14
33	15	18	10
32	12	14	14
36	10	14	14
38	12	17	11
39	11	14	10
32	12	16	13
32	11	18	7
31	12	11	14
39	13	14	12
37	11	12	14
39	9	17	11
41	13	9	9
36	10	16	11
33	14	14	15
33	12	15	14
34	10	11	13
31	12	16	9
27	8	13	15
37	10	17	10
34	12	15	11
34	12	14	13
32	7	16	8
29	6	9	20
36	12	15	12
29	10	17	10
35	10	13	10
37	10	15	9
34	12	16	14
38	15	16	8
35	10	12	14
38	10	12	11
37	12	11	13
38	13	15	9
33	11	15	11
36	11	17	15
38	12	13	11
32	14	16	10
32	10	14	14
32	12	11	18
34	13	12	14
32	5	12	11
37	6	15	12
39	12	16	13
29	12	15	9
37	11	12	10
35	10	12	15
30	7	8	20
38	12	13	12
34	14	11	12
31	11	14	14
34	12	15	13
35	13	10	11
36	14	11	17
30	11	12	12
39	12	15	13
35	12	15	14
38	8	14	13
31	11	16	15
34	14	15	13
38	14	15	10
34	12	13	11
39	9	12	19
37	13	17	13
34	11	13	17
28	12	15	13
37	12	13	9
33	12	15	11
37	12	16	10
35	12	15	9
37	12	16	12
32	11	15	12
33	10	14	13
38	9	15	13
33	12	14	12
29	12	13	15
33	12	7	22
31	9	17	13
36	15	13	15
35	12	15	13
32	12	14	15
29	12	13	10
39	10	16	11
37	13	12	16
35	9	14	11
37	12	17	11
32	10	15	10
38	14	17	10
37	11	12	16
36	15	16	12
32	11	11	11
33	11	15	16
40	12	9	19
38	12	16	11
41	12	15	16
36	11	10	15
43	7	10	24
30	12	15	14
31	14	11	15
32	11	13	11
32	11	14	15
37	10	18	12
37	13	16	10
33	13	14	14
34	8	14	13
33	11	14	9
38	12	14	15
33	11	12	15
31	13	14	14
38	12	15	11
37	14	15	8
33	13	15	11
31	15	13	11
39	10	17	8
44	11	17	10
33	9	19	11
35	11	15	13
32	10	13	11
28	11	9	20
40	8	15	10
27	11	15	15
37	12	15	12
32	12	16	14
28	9	11	23
34	11	14	14
30	10	11	16
35	8	15	11
31	9	13	12
32	8	15	10
30	9	16	14
30	15	14	12
31	11	15	12
40	8	16	11
32	13	16	12
36	12	11	13
32	12	12	11
35	9	9	19
38	7	16	12
42	13	13	17
34	9	16	9
35	6	12	12
35	8	9	19
33	8	13	18
36	15	13	15
32	6	14	14
33	9	19	11
34	11	13	9
32	8	12	18
34	8	13	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 33.0875718780359 + 0.0537248567570098Software[t] + 0.152393156666027Happiness[t] -0.0487180136506633Depression[t] -0.0069668331728648t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  33.0875718780359 +  0.0537248567570098Software[t] +  0.152393156666027Happiness[t] -0.0487180136506633Depression[t] -0.0069668331728648t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  33.0875718780359 +  0.0537248567570098Software[t] +  0.152393156666027Happiness[t] -0.0487180136506633Depression[t] -0.0069668331728648t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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
Connected[t] = + 33.0875718780359 + 0.0537248567570098Software[t] + 0.152393156666027Happiness[t] -0.0487180136506633Depression[t] -0.0069668331728648t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)33.08757187803593.2688710.12200
Software0.05372485675700980.1258270.4270.6699840.334992
Happiness0.1523931566660270.1350811.12820.2609710.130486
Depression-0.04871801365066330.100588-0.48430.6288240.314412
t-0.00696683317286480.005744-1.21280.2270140.113507

\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) & 33.0875718780359 & 3.26887 & 10.122 & 0 & 0 \tabularnewline
Software & 0.0537248567570098 & 0.125827 & 0.427 & 0.669984 & 0.334992 \tabularnewline
Happiness & 0.152393156666027 & 0.135081 & 1.1282 & 0.260971 & 0.130486 \tabularnewline
Depression & -0.0487180136506633 & 0.100588 & -0.4843 & 0.628824 & 0.314412 \tabularnewline
t & -0.0069668331728648 & 0.005744 & -1.2128 & 0.227014 & 0.113507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&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]33.0875718780359[/C][C]3.26887[/C][C]10.122[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software[/C][C]0.0537248567570098[/C][C]0.125827[/C][C]0.427[/C][C]0.669984[/C][C]0.334992[/C][/ROW]
[ROW][C]Happiness[/C][C]0.152393156666027[/C][C]0.135081[/C][C]1.1282[/C][C]0.260971[/C][C]0.130486[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0487180136506633[/C][C]0.100588[/C][C]-0.4843[/C][C]0.628824[/C][C]0.314412[/C][/ROW]
[ROW][C]t[/C][C]-0.0069668331728648[/C][C]0.005744[/C][C]-1.2128[/C][C]0.227014[/C][C]0.113507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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)33.08757187803593.2688710.12200
Software0.05372485675700980.1258270.4270.6699840.334992
Happiness0.1523931566660270.1350811.12820.2609710.130486
Depression-0.04871801365066330.100588-0.48430.6288240.314412
t-0.00696683317286480.005744-1.21280.2270140.113507







Multiple Linear Regression - Regression Statistics
Multiple R0.184759663662724
R-squared0.034136133316763
Adjusted R-squared0.0095281367133685
F-TEST (value)1.38719676643868
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.240829939787867
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.35901121152838
Sum Squared Residuals1771.42414211022

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.184759663662724 \tabularnewline
R-squared & 0.034136133316763 \tabularnewline
Adjusted R-squared & 0.0095281367133685 \tabularnewline
F-TEST (value) & 1.38719676643868 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.240829939787867 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.35901121152838 \tabularnewline
Sum Squared Residuals & 1771.42414211022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.184759663662724[/C][/ROW]
[ROW][C]R-squared[/C][C]0.034136133316763[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0095281367133685[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.38719676643868[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0.240829939787867[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.35901121152838[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1771.42414211022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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 R0.184759663662724
R-squared0.034136133316763
Adjusted R-squared0.0095281367133685
F-TEST (value)1.38719676643868
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.240829939787867
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.35901121152838
Sum Squared Residuals1771.42414211022







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.27419135546405.72580864453596
23935.87179030584853.1282096941515
33034.8668167620895-4.8668167620895
43134.6261554020709-3.6261554020709
53435.1663730700052-1.16637307000524
63535.7414801027494-0.741480102749372
73934.74521021991984.25478978008021
83435.3815912504182-1.38159125041824
93635.46828587404800.531714125951957
103734.99281585938112.00718414061887
113835.65168880752032.34831119247965
123636.1543940284949-0.154394028494883
133834.43486268977313.56513731022687
143935.33724795349013.66275204650992
153336.04483891528-3.04483891527997
163235.0722528305693-3.07225283056931
173634.95783628388241.04216371611757
183835.66165267517362.33834732482635
193935.19249952889643.80750047110364
203235.3978898248606-3.39788982486057
213235.9342925301667-3.93429253016673
223134.573272361534-3.57327236153404
233935.17464588241763.82535411758241
243734.65800699509732.34199300490267
253935.45171027269263.54828972730743
264134.53793364052096.46206635947915
273635.33910830643780.66089169356218
283335.0473825323583-2.04738253235829
293335.1340771559881-2.13407715598809
303434.4588059962878-0.458805996287765
313135.5161267145617-4.51612671456171
322734.5447729024587-7.54477290245874
333735.49841847771731.50158152228268
343435.2453970310758-1.24539703107576
353434.9886010139355-0.988601013935542
363235.261386278563-3.261386278563
372933.549326328163-4.54932632816298
383635.16881168473360.831188315266362
392935.4566174786801-6.45661747868013
403534.84007801884320.159921981156839
413735.1866155126531.81338448734699
423435.1959014814069-1.19590148140688
433835.6424173004092.35758269959098
443534.4649454748830.535054525116978
453834.60413268266213.39586731733785
463734.45478637903602.54521362096405
473835.30598908388692.69401091611315
483335.0941365098986-2.09413650989864
493635.19708393545520.80291606454482
503834.82914138697793.17085861302213
513235.4355217509678-3.43552175096777
523234.7139971228322-2.71399712283216
533234.1624284785726-2.16242847857258
543434.5564517134254-0.556451713425404
553234.2658400671484-2.26584006714845
563734.721059547082.27894045291999
573935.14011699746463.85988300253543
582935.1756290622283-6.17562906222833
593734.60903988864972.39096011135029
603534.30475813046650.695241869533478
613033.2834540321052-3.2834540321052
623834.69682137525283.30317862474717
633434.4925179422619-0.49251794226193
643134.6841199815148-3.68411998151479
653434.9319891754156-0.931989175415625
663534.31421744297100.685782557029038
673634.22106054131721.77893945868285
683034.4489023627926-4.4489023627926
693934.90412184272424.09587815727583
703534.84843699590060.151563004099362
713834.52289559268443.47710440731563
723134.8844536158133-3.88445361581326
733434.9837042235467-0.983704223546727
743835.12289143132592.87710856867415
753434.6549705576562-0.65497055765625
763933.9446918883415.05530811165898
773735.20689834743031.79310165256969
783434.2880371194767-0.288037119476666
792834.8344535109955-6.83445351099552
803734.71757241909332.28242758090675
813334.9179558719511-1.91795587195111
823735.11210020909491.88789979090506
833535.0014582329067-0.0014582329067116
843735.00073051544791.99926948455212
853234.787645668852-2.78764566885198
863334.5258428086054-1.52584280860542
873834.61754427534163.38245572465843
883334.6680768694244-1.66807686942437
892934.3625628386335-5.36256283863349
903333.1002109699098-0.100210969909821
913134.8944632559822-3.89446325598216
923634.50283690938591.49716309061407
933534.73691784657540.263082153424589
943234.4801218294352-2.48012182943519
952934.5643519078496-5.56435190784962
963934.85839681751014.14160318248985
973734.15944185969092.84055814030911
983534.48595198107540.514048018924644
993735.09733918817161.90266081182840
1003234.7268543418033-2.72685434180333
1013835.23957324899062.76042675100944
1023734.01715798031252.98284201968745
1033635.02953525543450.970464744565517
1043234.0944212255541-2.09442122555411
1053334.453436950792-1.45343695079203
1064033.4396819934286.56031800657197
1073834.88921136612273.11078863387734
1084134.48626130803046.51373869196955
1093633.71232184842112.28767815157890
1104333.05199346536429.94800653463577
1113034.5627968358132-4.56279683581318
1123134.0049890758396-3.00498907583957
1133234.3365060403304-2.33650604033038
1143234.2870603092209-2.28706030922089
1153734.98209528690712.01790471309289
1163734.92895273797452.07104726202545
1173334.422327536867-1.42232753686698
1183434.1954544335597-0.195454433559724
1193334.5445342252605-1.54453422526054
1203834.29898416694073.70101583305929
1213333.9335061636788-0.93350616367878
1223134.3874933710026-3.38749337100265
1233834.62534887869083.37465112130921
1243734.87198579998392.12801420001606
1253334.6651400691021-1.66514006910207
1263134.4608366361112-3.46083663611117
1273934.94097218676944.05902781323064
1284434.89029418305229.10970581694782
1293335.0319459360467-2.03194593604668
1303534.42542016242240.574579837577597
1313234.1573781864618-2.1573781864618
1322833.1561014605259-5.15610146052587
1334034.38949913358485.61050086641523
1342734.3001168024296-7.30011680242962
1353734.49302886696582.50697113303425
1363234.5410191631576-2.54101916315759
1372833.1724498535276-5.17244985352759
1383434.1685743267228-0.168574326722795
1393033.5532671394935-3.55326713949351
1403534.29201328772410.707986712275948
1413133.9852669843255-2.98526698432548
1423234.326797635029-2.32679763502899
1433034.3310767606765-4.33107676067650
1443034.4391087820150-4.43910878201497
1453134.3696356784801-3.36963567848009
1464034.40260544535295.59739455464711
1473234.6155448823144-2.61554488231441
1483633.74416939540372.25583060459626
1493233.9870317461982-1.98703174619823
1503532.97196676355092.02803323644905
1513834.26532840908093.73467159091911
1524233.87994117819878.1200588218013
1533434.5049984972012-0.504998497201173
1543533.58113042614121.41886957385882
1553532.88340774092962.11659225907039
1563333.5347315480715-0.534731548071519
1573634.04999275314971.95000724685029
1583233.7606133794804-1.76061337948045
1593334.8229409408607-1.82294094086074
1603434.1065009085071-0.106500908507059
1613233.3475042255412-1.34750422554117
1623433.59036657633570.409633423664343

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.2741913554640 & 5.72580864453596 \tabularnewline
2 & 39 & 35.8717903058485 & 3.1282096941515 \tabularnewline
3 & 30 & 34.8668167620895 & -4.8668167620895 \tabularnewline
4 & 31 & 34.6261554020709 & -3.6261554020709 \tabularnewline
5 & 34 & 35.1663730700052 & -1.16637307000524 \tabularnewline
6 & 35 & 35.7414801027494 & -0.741480102749372 \tabularnewline
7 & 39 & 34.7452102199198 & 4.25478978008021 \tabularnewline
8 & 34 & 35.3815912504182 & -1.38159125041824 \tabularnewline
9 & 36 & 35.4682858740480 & 0.531714125951957 \tabularnewline
10 & 37 & 34.9928158593811 & 2.00718414061887 \tabularnewline
11 & 38 & 35.6516888075203 & 2.34831119247965 \tabularnewline
12 & 36 & 36.1543940284949 & -0.154394028494883 \tabularnewline
13 & 38 & 34.4348626897731 & 3.56513731022687 \tabularnewline
14 & 39 & 35.3372479534901 & 3.66275204650992 \tabularnewline
15 & 33 & 36.04483891528 & -3.04483891527997 \tabularnewline
16 & 32 & 35.0722528305693 & -3.07225283056931 \tabularnewline
17 & 36 & 34.9578362838824 & 1.04216371611757 \tabularnewline
18 & 38 & 35.6616526751736 & 2.33834732482635 \tabularnewline
19 & 39 & 35.1924995288964 & 3.80750047110364 \tabularnewline
20 & 32 & 35.3978898248606 & -3.39788982486057 \tabularnewline
21 & 32 & 35.9342925301667 & -3.93429253016673 \tabularnewline
22 & 31 & 34.573272361534 & -3.57327236153404 \tabularnewline
23 & 39 & 35.1746458824176 & 3.82535411758241 \tabularnewline
24 & 37 & 34.6580069950973 & 2.34199300490267 \tabularnewline
25 & 39 & 35.4517102726926 & 3.54828972730743 \tabularnewline
26 & 41 & 34.5379336405209 & 6.46206635947915 \tabularnewline
27 & 36 & 35.3391083064378 & 0.66089169356218 \tabularnewline
28 & 33 & 35.0473825323583 & -2.04738253235829 \tabularnewline
29 & 33 & 35.1340771559881 & -2.13407715598809 \tabularnewline
30 & 34 & 34.4588059962878 & -0.458805996287765 \tabularnewline
31 & 31 & 35.5161267145617 & -4.51612671456171 \tabularnewline
32 & 27 & 34.5447729024587 & -7.54477290245874 \tabularnewline
33 & 37 & 35.4984184777173 & 1.50158152228268 \tabularnewline
34 & 34 & 35.2453970310758 & -1.24539703107576 \tabularnewline
35 & 34 & 34.9886010139355 & -0.988601013935542 \tabularnewline
36 & 32 & 35.261386278563 & -3.261386278563 \tabularnewline
37 & 29 & 33.549326328163 & -4.54932632816298 \tabularnewline
38 & 36 & 35.1688116847336 & 0.831188315266362 \tabularnewline
39 & 29 & 35.4566174786801 & -6.45661747868013 \tabularnewline
40 & 35 & 34.8400780188432 & 0.159921981156839 \tabularnewline
41 & 37 & 35.186615512653 & 1.81338448734699 \tabularnewline
42 & 34 & 35.1959014814069 & -1.19590148140688 \tabularnewline
43 & 38 & 35.642417300409 & 2.35758269959098 \tabularnewline
44 & 35 & 34.464945474883 & 0.535054525116978 \tabularnewline
45 & 38 & 34.6041326826621 & 3.39586731733785 \tabularnewline
46 & 37 & 34.4547863790360 & 2.54521362096405 \tabularnewline
47 & 38 & 35.3059890838869 & 2.69401091611315 \tabularnewline
48 & 33 & 35.0941365098986 & -2.09413650989864 \tabularnewline
49 & 36 & 35.1970839354552 & 0.80291606454482 \tabularnewline
50 & 38 & 34.8291413869779 & 3.17085861302213 \tabularnewline
51 & 32 & 35.4355217509678 & -3.43552175096777 \tabularnewline
52 & 32 & 34.7139971228322 & -2.71399712283216 \tabularnewline
53 & 32 & 34.1624284785726 & -2.16242847857258 \tabularnewline
54 & 34 & 34.5564517134254 & -0.556451713425404 \tabularnewline
55 & 32 & 34.2658400671484 & -2.26584006714845 \tabularnewline
56 & 37 & 34.72105954708 & 2.27894045291999 \tabularnewline
57 & 39 & 35.1401169974646 & 3.85988300253543 \tabularnewline
58 & 29 & 35.1756290622283 & -6.17562906222833 \tabularnewline
59 & 37 & 34.6090398886497 & 2.39096011135029 \tabularnewline
60 & 35 & 34.3047581304665 & 0.695241869533478 \tabularnewline
61 & 30 & 33.2834540321052 & -3.2834540321052 \tabularnewline
62 & 38 & 34.6968213752528 & 3.30317862474717 \tabularnewline
63 & 34 & 34.4925179422619 & -0.49251794226193 \tabularnewline
64 & 31 & 34.6841199815148 & -3.68411998151479 \tabularnewline
65 & 34 & 34.9319891754156 & -0.931989175415625 \tabularnewline
66 & 35 & 34.3142174429710 & 0.685782557029038 \tabularnewline
67 & 36 & 34.2210605413172 & 1.77893945868285 \tabularnewline
68 & 30 & 34.4489023627926 & -4.4489023627926 \tabularnewline
69 & 39 & 34.9041218427242 & 4.09587815727583 \tabularnewline
70 & 35 & 34.8484369959006 & 0.151563004099362 \tabularnewline
71 & 38 & 34.5228955926844 & 3.47710440731563 \tabularnewline
72 & 31 & 34.8844536158133 & -3.88445361581326 \tabularnewline
73 & 34 & 34.9837042235467 & -0.983704223546727 \tabularnewline
74 & 38 & 35.1228914313259 & 2.87710856867415 \tabularnewline
75 & 34 & 34.6549705576562 & -0.65497055765625 \tabularnewline
76 & 39 & 33.944691888341 & 5.05530811165898 \tabularnewline
77 & 37 & 35.2068983474303 & 1.79310165256969 \tabularnewline
78 & 34 & 34.2880371194767 & -0.288037119476666 \tabularnewline
79 & 28 & 34.8344535109955 & -6.83445351099552 \tabularnewline
80 & 37 & 34.7175724190933 & 2.28242758090675 \tabularnewline
81 & 33 & 34.9179558719511 & -1.91795587195111 \tabularnewline
82 & 37 & 35.1121002090949 & 1.88789979090506 \tabularnewline
83 & 35 & 35.0014582329067 & -0.0014582329067116 \tabularnewline
84 & 37 & 35.0007305154479 & 1.99926948455212 \tabularnewline
85 & 32 & 34.787645668852 & -2.78764566885198 \tabularnewline
86 & 33 & 34.5258428086054 & -1.52584280860542 \tabularnewline
87 & 38 & 34.6175442753416 & 3.38245572465843 \tabularnewline
88 & 33 & 34.6680768694244 & -1.66807686942437 \tabularnewline
89 & 29 & 34.3625628386335 & -5.36256283863349 \tabularnewline
90 & 33 & 33.1002109699098 & -0.100210969909821 \tabularnewline
91 & 31 & 34.8944632559822 & -3.89446325598216 \tabularnewline
92 & 36 & 34.5028369093859 & 1.49716309061407 \tabularnewline
93 & 35 & 34.7369178465754 & 0.263082153424589 \tabularnewline
94 & 32 & 34.4801218294352 & -2.48012182943519 \tabularnewline
95 & 29 & 34.5643519078496 & -5.56435190784962 \tabularnewline
96 & 39 & 34.8583968175101 & 4.14160318248985 \tabularnewline
97 & 37 & 34.1594418596909 & 2.84055814030911 \tabularnewline
98 & 35 & 34.4859519810754 & 0.514048018924644 \tabularnewline
99 & 37 & 35.0973391881716 & 1.90266081182840 \tabularnewline
100 & 32 & 34.7268543418033 & -2.72685434180333 \tabularnewline
101 & 38 & 35.2395732489906 & 2.76042675100944 \tabularnewline
102 & 37 & 34.0171579803125 & 2.98284201968745 \tabularnewline
103 & 36 & 35.0295352554345 & 0.970464744565517 \tabularnewline
104 & 32 & 34.0944212255541 & -2.09442122555411 \tabularnewline
105 & 33 & 34.453436950792 & -1.45343695079203 \tabularnewline
106 & 40 & 33.439681993428 & 6.56031800657197 \tabularnewline
107 & 38 & 34.8892113661227 & 3.11078863387734 \tabularnewline
108 & 41 & 34.4862613080304 & 6.51373869196955 \tabularnewline
109 & 36 & 33.7123218484211 & 2.28767815157890 \tabularnewline
110 & 43 & 33.0519934653642 & 9.94800653463577 \tabularnewline
111 & 30 & 34.5627968358132 & -4.56279683581318 \tabularnewline
112 & 31 & 34.0049890758396 & -3.00498907583957 \tabularnewline
113 & 32 & 34.3365060403304 & -2.33650604033038 \tabularnewline
114 & 32 & 34.2870603092209 & -2.28706030922089 \tabularnewline
115 & 37 & 34.9820952869071 & 2.01790471309289 \tabularnewline
116 & 37 & 34.9289527379745 & 2.07104726202545 \tabularnewline
117 & 33 & 34.422327536867 & -1.42232753686698 \tabularnewline
118 & 34 & 34.1954544335597 & -0.195454433559724 \tabularnewline
119 & 33 & 34.5445342252605 & -1.54453422526054 \tabularnewline
120 & 38 & 34.2989841669407 & 3.70101583305929 \tabularnewline
121 & 33 & 33.9335061636788 & -0.93350616367878 \tabularnewline
122 & 31 & 34.3874933710026 & -3.38749337100265 \tabularnewline
123 & 38 & 34.6253488786908 & 3.37465112130921 \tabularnewline
124 & 37 & 34.8719857999839 & 2.12801420001606 \tabularnewline
125 & 33 & 34.6651400691021 & -1.66514006910207 \tabularnewline
126 & 31 & 34.4608366361112 & -3.46083663611117 \tabularnewline
127 & 39 & 34.9409721867694 & 4.05902781323064 \tabularnewline
128 & 44 & 34.8902941830522 & 9.10970581694782 \tabularnewline
129 & 33 & 35.0319459360467 & -2.03194593604668 \tabularnewline
130 & 35 & 34.4254201624224 & 0.574579837577597 \tabularnewline
131 & 32 & 34.1573781864618 & -2.1573781864618 \tabularnewline
132 & 28 & 33.1561014605259 & -5.15610146052587 \tabularnewline
133 & 40 & 34.3894991335848 & 5.61050086641523 \tabularnewline
134 & 27 & 34.3001168024296 & -7.30011680242962 \tabularnewline
135 & 37 & 34.4930288669658 & 2.50697113303425 \tabularnewline
136 & 32 & 34.5410191631576 & -2.54101916315759 \tabularnewline
137 & 28 & 33.1724498535276 & -5.17244985352759 \tabularnewline
138 & 34 & 34.1685743267228 & -0.168574326722795 \tabularnewline
139 & 30 & 33.5532671394935 & -3.55326713949351 \tabularnewline
140 & 35 & 34.2920132877241 & 0.707986712275948 \tabularnewline
141 & 31 & 33.9852669843255 & -2.98526698432548 \tabularnewline
142 & 32 & 34.326797635029 & -2.32679763502899 \tabularnewline
143 & 30 & 34.3310767606765 & -4.33107676067650 \tabularnewline
144 & 30 & 34.4391087820150 & -4.43910878201497 \tabularnewline
145 & 31 & 34.3696356784801 & -3.36963567848009 \tabularnewline
146 & 40 & 34.4026054453529 & 5.59739455464711 \tabularnewline
147 & 32 & 34.6155448823144 & -2.61554488231441 \tabularnewline
148 & 36 & 33.7441693954037 & 2.25583060459626 \tabularnewline
149 & 32 & 33.9870317461982 & -1.98703174619823 \tabularnewline
150 & 35 & 32.9719667635509 & 2.02803323644905 \tabularnewline
151 & 38 & 34.2653284090809 & 3.73467159091911 \tabularnewline
152 & 42 & 33.8799411781987 & 8.1200588218013 \tabularnewline
153 & 34 & 34.5049984972012 & -0.504998497201173 \tabularnewline
154 & 35 & 33.5811304261412 & 1.41886957385882 \tabularnewline
155 & 35 & 32.8834077409296 & 2.11659225907039 \tabularnewline
156 & 33 & 33.5347315480715 & -0.534731548071519 \tabularnewline
157 & 36 & 34.0499927531497 & 1.95000724685029 \tabularnewline
158 & 32 & 33.7606133794804 & -1.76061337948045 \tabularnewline
159 & 33 & 34.8229409408607 & -1.82294094086074 \tabularnewline
160 & 34 & 34.1065009085071 & -0.106500908507059 \tabularnewline
161 & 32 & 33.3475042255412 & -1.34750422554117 \tabularnewline
162 & 34 & 33.5903665763357 & 0.409633423664343 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&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]41[/C][C]35.2741913554640[/C][C]5.72580864453596[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]35.8717903058485[/C][C]3.1282096941515[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]34.8668167620895[/C][C]-4.8668167620895[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.6261554020709[/C][C]-3.6261554020709[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.1663730700052[/C][C]-1.16637307000524[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]35.7414801027494[/C][C]-0.741480102749372[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.7452102199198[/C][C]4.25478978008021[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.3815912504182[/C][C]-1.38159125041824[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]35.4682858740480[/C][C]0.531714125951957[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]34.9928158593811[/C][C]2.00718414061887[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]35.6516888075203[/C][C]2.34831119247965[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]36.1543940284949[/C][C]-0.154394028494883[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.4348626897731[/C][C]3.56513731022687[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.3372479534901[/C][C]3.66275204650992[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.04483891528[/C][C]-3.04483891527997[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]35.0722528305693[/C][C]-3.07225283056931[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.9578362838824[/C][C]1.04216371611757[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]35.6616526751736[/C][C]2.33834732482635[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]35.1924995288964[/C][C]3.80750047110364[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]35.3978898248606[/C][C]-3.39788982486057[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.9342925301667[/C][C]-3.93429253016673[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]34.573272361534[/C][C]-3.57327236153404[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]35.1746458824176[/C][C]3.82535411758241[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]34.6580069950973[/C][C]2.34199300490267[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.4517102726926[/C][C]3.54828972730743[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.5379336405209[/C][C]6.46206635947915[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.3391083064378[/C][C]0.66089169356218[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.0473825323583[/C][C]-2.04738253235829[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]35.1340771559881[/C][C]-2.13407715598809[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.4588059962878[/C][C]-0.458805996287765[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]35.5161267145617[/C][C]-4.51612671456171[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]34.5447729024587[/C][C]-7.54477290245874[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]35.4984184777173[/C][C]1.50158152228268[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.2453970310758[/C][C]-1.24539703107576[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]34.9886010139355[/C][C]-0.988601013935542[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]35.261386278563[/C][C]-3.261386278563[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]33.549326328163[/C][C]-4.54932632816298[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]35.1688116847336[/C][C]0.831188315266362[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]35.4566174786801[/C][C]-6.45661747868013[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.8400780188432[/C][C]0.159921981156839[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]35.186615512653[/C][C]1.81338448734699[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]35.1959014814069[/C][C]-1.19590148140688[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.642417300409[/C][C]2.35758269959098[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.464945474883[/C][C]0.535054525116978[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]34.6041326826621[/C][C]3.39586731733785[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]34.4547863790360[/C][C]2.54521362096405[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.3059890838869[/C][C]2.69401091611315[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]35.0941365098986[/C][C]-2.09413650989864[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]35.1970839354552[/C][C]0.80291606454482[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.8291413869779[/C][C]3.17085861302213[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]35.4355217509678[/C][C]-3.43552175096777[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]34.7139971228322[/C][C]-2.71399712283216[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]34.1624284785726[/C][C]-2.16242847857258[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]34.5564517134254[/C][C]-0.556451713425404[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]34.2658400671484[/C][C]-2.26584006714845[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.72105954708[/C][C]2.27894045291999[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]35.1401169974646[/C][C]3.85988300253543[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]35.1756290622283[/C][C]-6.17562906222833[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]34.6090398886497[/C][C]2.39096011135029[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.3047581304665[/C][C]0.695241869533478[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]33.2834540321052[/C][C]-3.2834540321052[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.6968213752528[/C][C]3.30317862474717[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.4925179422619[/C][C]-0.49251794226193[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.6841199815148[/C][C]-3.68411998151479[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]34.9319891754156[/C][C]-0.931989175415625[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]34.3142174429710[/C][C]0.685782557029038[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.2210605413172[/C][C]1.77893945868285[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]34.4489023627926[/C][C]-4.4489023627926[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]34.9041218427242[/C][C]4.09587815727583[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]34.8484369959006[/C][C]0.151563004099362[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.5228955926844[/C][C]3.47710440731563[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]34.8844536158133[/C][C]-3.88445361581326[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]34.9837042235467[/C][C]-0.983704223546727[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]35.1228914313259[/C][C]2.87710856867415[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]34.6549705576562[/C][C]-0.65497055765625[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]33.944691888341[/C][C]5.05530811165898[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.2068983474303[/C][C]1.79310165256969[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]34.2880371194767[/C][C]-0.288037119476666[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]34.8344535109955[/C][C]-6.83445351099552[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]34.7175724190933[/C][C]2.28242758090675[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]34.9179558719511[/C][C]-1.91795587195111[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]35.1121002090949[/C][C]1.88789979090506[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.0014582329067[/C][C]-0.0014582329067116[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]35.0007305154479[/C][C]1.99926948455212[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.787645668852[/C][C]-2.78764566885198[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.5258428086054[/C][C]-1.52584280860542[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]34.6175442753416[/C][C]3.38245572465843[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.6680768694244[/C][C]-1.66807686942437[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]34.3625628386335[/C][C]-5.36256283863349[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.1002109699098[/C][C]-0.100210969909821[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.8944632559822[/C][C]-3.89446325598216[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.5028369093859[/C][C]1.49716309061407[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]34.7369178465754[/C][C]0.263082153424589[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]34.4801218294352[/C][C]-2.48012182943519[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]34.5643519078496[/C][C]-5.56435190784962[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]34.8583968175101[/C][C]4.14160318248985[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.1594418596909[/C][C]2.84055814030911[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.4859519810754[/C][C]0.514048018924644[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.0973391881716[/C][C]1.90266081182840[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]34.7268543418033[/C][C]-2.72685434180333[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.2395732489906[/C][C]2.76042675100944[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.0171579803125[/C][C]2.98284201968745[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]35.0295352554345[/C][C]0.970464744565517[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]34.0944212255541[/C][C]-2.09442122555411[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]34.453436950792[/C][C]-1.45343695079203[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.439681993428[/C][C]6.56031800657197[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]34.8892113661227[/C][C]3.11078863387734[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]34.4862613080304[/C][C]6.51373869196955[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]33.7123218484211[/C][C]2.28767815157890[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]33.0519934653642[/C][C]9.94800653463577[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.5627968358132[/C][C]-4.56279683581318[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.0049890758396[/C][C]-3.00498907583957[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]34.3365060403304[/C][C]-2.33650604033038[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]34.2870603092209[/C][C]-2.28706030922089[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.9820952869071[/C][C]2.01790471309289[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.9289527379745[/C][C]2.07104726202545[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]34.422327536867[/C][C]-1.42232753686698[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]34.1954544335597[/C][C]-0.195454433559724[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.5445342252605[/C][C]-1.54453422526054[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]34.2989841669407[/C][C]3.70101583305929[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]33.9335061636788[/C][C]-0.93350616367878[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]34.3874933710026[/C][C]-3.38749337100265[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]34.6253488786908[/C][C]3.37465112130921[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]34.8719857999839[/C][C]2.12801420001606[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]34.6651400691021[/C][C]-1.66514006910207[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.4608366361112[/C][C]-3.46083663611117[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.9409721867694[/C][C]4.05902781323064[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]34.8902941830522[/C][C]9.10970581694782[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.0319459360467[/C][C]-2.03194593604668[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]34.4254201624224[/C][C]0.574579837577597[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.1573781864618[/C][C]-2.1573781864618[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]33.1561014605259[/C][C]-5.15610146052587[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]34.3894991335848[/C][C]5.61050086641523[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]34.3001168024296[/C][C]-7.30011680242962[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]34.4930288669658[/C][C]2.50697113303425[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]34.5410191631576[/C][C]-2.54101916315759[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]33.1724498535276[/C][C]-5.17244985352759[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.1685743267228[/C][C]-0.168574326722795[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.5532671394935[/C][C]-3.55326713949351[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.2920132877241[/C][C]0.707986712275948[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]33.9852669843255[/C][C]-2.98526698432548[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.326797635029[/C][C]-2.32679763502899[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.3310767606765[/C][C]-4.33107676067650[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.4391087820150[/C][C]-4.43910878201497[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]34.3696356784801[/C][C]-3.36963567848009[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]34.4026054453529[/C][C]5.59739455464711[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]34.6155448823144[/C][C]-2.61554488231441[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]33.7441693954037[/C][C]2.25583060459626[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.9870317461982[/C][C]-1.98703174619823[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]32.9719667635509[/C][C]2.02803323644905[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]34.2653284090809[/C][C]3.73467159091911[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]33.8799411781987[/C][C]8.1200588218013[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]34.5049984972012[/C][C]-0.504998497201173[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]33.5811304261412[/C][C]1.41886957385882[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]32.8834077409296[/C][C]2.11659225907039[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]33.5347315480715[/C][C]-0.534731548071519[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]34.0499927531497[/C][C]1.95000724685029[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]33.7606133794804[/C][C]-1.76061337948045[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]34.8229409408607[/C][C]-1.82294094086074[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.1065009085071[/C][C]-0.106500908507059[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.3475042255412[/C][C]-1.34750422554117[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]33.5903665763357[/C][C]0.409633423664343[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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
14135.27419135546405.72580864453596
23935.87179030584853.1282096941515
33034.8668167620895-4.8668167620895
43134.6261554020709-3.6261554020709
53435.1663730700052-1.16637307000524
63535.7414801027494-0.741480102749372
73934.74521021991984.25478978008021
83435.3815912504182-1.38159125041824
93635.46828587404800.531714125951957
103734.99281585938112.00718414061887
113835.65168880752032.34831119247965
123636.1543940284949-0.154394028494883
133834.43486268977313.56513731022687
143935.33724795349013.66275204650992
153336.04483891528-3.04483891527997
163235.0722528305693-3.07225283056931
173634.95783628388241.04216371611757
183835.66165267517362.33834732482635
193935.19249952889643.80750047110364
203235.3978898248606-3.39788982486057
213235.9342925301667-3.93429253016673
223134.573272361534-3.57327236153404
233935.17464588241763.82535411758241
243734.65800699509732.34199300490267
253935.45171027269263.54828972730743
264134.53793364052096.46206635947915
273635.33910830643780.66089169356218
283335.0473825323583-2.04738253235829
293335.1340771559881-2.13407715598809
303434.4588059962878-0.458805996287765
313135.5161267145617-4.51612671456171
322734.5447729024587-7.54477290245874
333735.49841847771731.50158152228268
343435.2453970310758-1.24539703107576
353434.9886010139355-0.988601013935542
363235.261386278563-3.261386278563
372933.549326328163-4.54932632816298
383635.16881168473360.831188315266362
392935.4566174786801-6.45661747868013
403534.84007801884320.159921981156839
413735.1866155126531.81338448734699
423435.1959014814069-1.19590148140688
433835.6424173004092.35758269959098
443534.4649454748830.535054525116978
453834.60413268266213.39586731733785
463734.45478637903602.54521362096405
473835.30598908388692.69401091611315
483335.0941365098986-2.09413650989864
493635.19708393545520.80291606454482
503834.82914138697793.17085861302213
513235.4355217509678-3.43552175096777
523234.7139971228322-2.71399712283216
533234.1624284785726-2.16242847857258
543434.5564517134254-0.556451713425404
553234.2658400671484-2.26584006714845
563734.721059547082.27894045291999
573935.14011699746463.85988300253543
582935.1756290622283-6.17562906222833
593734.60903988864972.39096011135029
603534.30475813046650.695241869533478
613033.2834540321052-3.2834540321052
623834.69682137525283.30317862474717
633434.4925179422619-0.49251794226193
643134.6841199815148-3.68411998151479
653434.9319891754156-0.931989175415625
663534.31421744297100.685782557029038
673634.22106054131721.77893945868285
683034.4489023627926-4.4489023627926
693934.90412184272424.09587815727583
703534.84843699590060.151563004099362
713834.52289559268443.47710440731563
723134.8844536158133-3.88445361581326
733434.9837042235467-0.983704223546727
743835.12289143132592.87710856867415
753434.6549705576562-0.65497055765625
763933.9446918883415.05530811165898
773735.20689834743031.79310165256969
783434.2880371194767-0.288037119476666
792834.8344535109955-6.83445351099552
803734.71757241909332.28242758090675
813334.9179558719511-1.91795587195111
823735.11210020909491.88789979090506
833535.0014582329067-0.0014582329067116
843735.00073051544791.99926948455212
853234.787645668852-2.78764566885198
863334.5258428086054-1.52584280860542
873834.61754427534163.38245572465843
883334.6680768694244-1.66807686942437
892934.3625628386335-5.36256283863349
903333.1002109699098-0.100210969909821
913134.8944632559822-3.89446325598216
923634.50283690938591.49716309061407
933534.73691784657540.263082153424589
943234.4801218294352-2.48012182943519
952934.5643519078496-5.56435190784962
963934.85839681751014.14160318248985
973734.15944185969092.84055814030911
983534.48595198107540.514048018924644
993735.09733918817161.90266081182840
1003234.7268543418033-2.72685434180333
1013835.23957324899062.76042675100944
1023734.01715798031252.98284201968745
1033635.02953525543450.970464744565517
1043234.0944212255541-2.09442122555411
1053334.453436950792-1.45343695079203
1064033.4396819934286.56031800657197
1073834.88921136612273.11078863387734
1084134.48626130803046.51373869196955
1093633.71232184842112.28767815157890
1104333.05199346536429.94800653463577
1113034.5627968358132-4.56279683581318
1123134.0049890758396-3.00498907583957
1133234.3365060403304-2.33650604033038
1143234.2870603092209-2.28706030922089
1153734.98209528690712.01790471309289
1163734.92895273797452.07104726202545
1173334.422327536867-1.42232753686698
1183434.1954544335597-0.195454433559724
1193334.5445342252605-1.54453422526054
1203834.29898416694073.70101583305929
1213333.9335061636788-0.93350616367878
1223134.3874933710026-3.38749337100265
1233834.62534887869083.37465112130921
1243734.87198579998392.12801420001606
1253334.6651400691021-1.66514006910207
1263134.4608366361112-3.46083663611117
1273934.94097218676944.05902781323064
1284434.89029418305229.10970581694782
1293335.0319459360467-2.03194593604668
1303534.42542016242240.574579837577597
1313234.1573781864618-2.1573781864618
1322833.1561014605259-5.15610146052587
1334034.38949913358485.61050086641523
1342734.3001168024296-7.30011680242962
1353734.49302886696582.50697113303425
1363234.5410191631576-2.54101916315759
1372833.1724498535276-5.17244985352759
1383434.1685743267228-0.168574326722795
1393033.5532671394935-3.55326713949351
1403534.29201328772410.707986712275948
1413133.9852669843255-2.98526698432548
1423234.326797635029-2.32679763502899
1433034.3310767606765-4.33107676067650
1443034.4391087820150-4.43910878201497
1453134.3696356784801-3.36963567848009
1464034.40260544535295.59739455464711
1473234.6155448823144-2.61554488231441
1483633.74416939540372.25583060459626
1493233.9870317461982-1.98703174619823
1503532.97196676355092.02803323644905
1513834.26532840908093.73467159091911
1524233.87994117819878.1200588218013
1533434.5049984972012-0.504998497201173
1543533.58113042614121.41886957385882
1553532.88340774092962.11659225907039
1563333.5347315480715-0.534731548071519
1573634.04999275314971.95000724685029
1583233.7606133794804-1.76061337948045
1593334.8229409408607-1.82294094086074
1603434.1065009085071-0.106500908507059
1613233.3475042255412-1.34750422554117
1623433.59036657633570.409633423664343







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8962439755022550.2075120489954910.103756024497745
90.845289012554690.309421974890620.15471098744531
100.7917146191512720.4165707616974560.208285380848728
110.7067050446331780.5865899107336430.293294955366822
120.6265973252144640.7468053495710720.373402674785536
130.6849942762827290.6300114474345430.315005723717271
140.6138315434043580.7723369131912840.386168456595642
150.634556362044550.7308872759109010.365443637955451
160.6345367394375910.7309265211248190.365463260562409
170.5517111439457340.8965777121085320.448288856054266
180.4966554163223940.9933108326447870.503344583677606
190.4966975313113490.9933950626226980.503302468688651
200.5404535354297930.9190929291404140.459546464570207
210.5542759049815950.8914481900368110.445724095018405
220.5269170727798860.9461658544402280.473082927220114
230.5917675861241150.816464827751770.408232413875885
240.550826876519510.898346246960980.44917312348049
250.5224853863342290.9550292273315430.477514613665771
260.6654342587136580.6691314825726850.334565741286342
270.6084497706343690.7831004587312620.391550229365631
280.5832468407506860.8335063184986290.416753159249314
290.5556845171655940.8886309656688130.444315482834406
300.5081221347876620.9837557304246770.491877865212338
310.5415465167991550.916906966401690.458453483200845
320.7364556176090430.5270887647819140.263544382390957
330.7131972224008730.5736055551982550.286802777599127
340.6624613644854790.6750772710290430.337538635514521
350.6087189091442630.7825621817114730.391281090855737
360.5818412804844850.8363174390310310.418158719515515
370.5727876133413930.8544247733172150.427212386658607
380.5410260685433310.9179478629133390.458973931456669
390.6069166907839540.7861666184320920.393083309216046
400.5659348268656560.8681303462686880.434065173134344
410.5579551834916930.8840896330166140.442044816508307
420.5108870678766240.9782258642467510.489112932123376
430.4973984434186370.9947968868372740.502601556581363
440.4604722809955170.9209445619910330.539527719004483
450.4822233683999480.9644467367998970.517776631600052
460.4664350339873390.9328700679746770.533564966012661
470.450263785403020.900527570806040.54973621459698
480.4104405111154770.8208810222309530.589559488884523
490.3818802912254590.7637605824509180.618119708774541
500.3774571488202710.7549142976405420.622542851179729
510.3774798743969020.7549597487938040.622520125603098
520.3471403623075540.6942807246151090.652859637692446
530.3114638169461870.6229276338923730.688536183053813
540.2689317707903200.5378635415806410.73106822920968
550.2379753261638180.4759506523276360.762024673836182
560.2443765881440220.4887531762880450.755623411855978
570.2753545029439710.5507090058879420.724645497056029
580.3686651320828610.7373302641657210.63133486791714
590.3499222577712170.6998445155424340.650077742228783
600.3124181539653610.6248363079307220.687581846034639
610.2998150395293960.5996300790587920.700184960470604
620.303916327379860.607832654759720.69608367262014
630.2648744960881850.5297489921763690.735125503911815
640.2626454548679740.5252909097359480.737354545132026
650.2273916398522450.4547832797044890.772608360147755
660.1945923060556350.3891846121112700.805407693944365
670.1757734945237140.3515469890474270.824226505476286
680.1946998346600680.3893996693201350.805300165339932
690.2232654748686130.4465309497372260.776734525131387
700.1913564410599610.3827128821199220.808643558940039
710.2033118998818240.4066237997636470.796688100118176
720.2089950235459140.4179900470918280.791004976454086
730.1789937394081890.3579874788163770.821006260591811
740.1717186799727870.3434373599455750.828281320027213
750.1445469116429800.2890938232859610.85545308835702
760.1902382420383430.3804764840766860.809761757961657
770.1689626601395370.3379253202790750.831037339860463
780.1416826356654570.2833652713309140.858317364334543
790.2341195559743520.4682391119487040.765880444025648
800.2161210842180650.4322421684361290.783878915781935
810.1930868150114310.3861736300228620.806913184988569
820.1729869419794950.3459738839589890.827013058020505
830.1450791573737750.2901583147475500.854920842626225
840.1292599587798810.2585199175597630.870740041220119
850.1211538798315510.2423077596631020.878846120168449
860.1040079317061480.2080158634122960.895992068293852
870.1048766043889800.2097532087779590.89512339561102
880.08983719436414220.1796743887282840.910162805635858
890.1216126244980800.2432252489961610.87838737550192
900.1007171852114000.2014343704227990.8992828147886
910.1142775168957010.2285550337914030.885722483104299
920.09719118793685420.1943823758737080.902808812063146
930.07967334273612460.1593466854722490.920326657263875
940.07492992882829820.1498598576565960.925070071171702
950.1122476360501020.2244952721002040.887752363949898
960.1201204579556840.2402409159113680.879879542044316
970.1111745724868490.2223491449736990.88882542751315
980.0926200146137360.1852400292274720.907379985386264
990.07883297081963580.1576659416392720.921167029180364
1000.07933396392756540.1586679278551310.920666036072435
1010.07140963419741770.1428192683948350.928590365802582
1020.06499628173520870.1299925634704170.935003718264791
1030.05203553873462550.1040710774692510.947964461265374
1040.04763762960003350.0952752592000670.952362370399966
1050.0410046114439890.0820092228879780.95899538855601
1060.07051307387248810.1410261477449760.929486926127512
1070.06466871139855820.1293374227971160.935331288601442
1080.1095724366250360.2191448732500720.890427563374964
1090.09598802044862270.1919760408972450.904011979551377
1100.4028298613079320.8056597226158630.597170138692068
1110.4230463065156660.8460926130313320.576953693484334
1120.3984346909668720.7968693819337440.601565309033128
1130.3722514247787860.7445028495575720.627748575221214
1140.3391682280510080.6783364561020160.660831771948992
1150.3096640180293310.6193280360586620.690335981970669
1160.2818255773337250.5636511546674490.718174422666275
1170.2437229583330350.4874459166660700.756277041666965
1180.2049481422728110.4098962845456220.79505185772719
1190.1813000952002030.3626001904004070.818699904799797
1200.2098264889355000.4196529778710010.7901735110645
1210.1755680247528300.3511360495056600.82443197524717
1220.1613289142972780.3226578285945560.838671085702722
1230.1640792724098490.3281585448196980.835920727590151
1240.1437915415717970.2875830831435940.856208458428203
1250.1180810021594310.2361620043188610.88191899784057
1260.1118267209737370.2236534419474740.888173279026263
1270.1146211122035910.2292422244071820.88537888779641
1280.4331488406934030.8662976813868060.566851159306597
1290.3831460968622030.7662921937244060.616853903137797
1300.3596994278982340.7193988557964680.640300572101766
1310.3133063141803620.6266126283607230.686693685819638
1320.3188370609051640.6376741218103280.681162939094836
1330.5023636705129490.9952726589741020.497636329487051
1340.5977133983696770.8045732032606460.402286601630323
1350.6422053753709890.7155892492580220.357794624629011
1360.5838440029131210.8323119941737580.416155997086879
1370.6170191147011720.7659617705976560.382980885298828
1380.5560927706738330.8878144586523350.443907229326167
1390.5510645042740960.8978709914518070.448935495725903
1400.5029764455442640.9940471089114720.497023554455736
1410.4620721036737010.9241442073474020.537927896326299
1420.4010429207411430.8020858414822870.598957079258857
1430.4563880968065960.9127761936131930.543611903193404
1440.5549560814164620.8900878371670760.445043918583538
1450.6824302443484130.6351395113031750.317569755651587
1460.7492148693658230.5015702612683540.250785130634177
1470.8602561445195590.2794877109608810.139743855480441
1480.7967444707231620.4065110585536750.203255529276838
1490.927452205917820.1450955881643620.0725477940821809
1500.937697086851010.1246058262979780.062302913148989
1510.9183949465511030.1632101068977940.0816050534488968
1520.994091159944360.01181768011128040.00590884005564021
1530.9814974060170360.03700518796592890.0185025939829644
1540.956818861669190.08636227666162170.0431811383308109

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.896243975502255 & 0.207512048995491 & 0.103756024497745 \tabularnewline
9 & 0.84528901255469 & 0.30942197489062 & 0.15471098744531 \tabularnewline
10 & 0.791714619151272 & 0.416570761697456 & 0.208285380848728 \tabularnewline
11 & 0.706705044633178 & 0.586589910733643 & 0.293294955366822 \tabularnewline
12 & 0.626597325214464 & 0.746805349571072 & 0.373402674785536 \tabularnewline
13 & 0.684994276282729 & 0.630011447434543 & 0.315005723717271 \tabularnewline
14 & 0.613831543404358 & 0.772336913191284 & 0.386168456595642 \tabularnewline
15 & 0.63455636204455 & 0.730887275910901 & 0.365443637955451 \tabularnewline
16 & 0.634536739437591 & 0.730926521124819 & 0.365463260562409 \tabularnewline
17 & 0.551711143945734 & 0.896577712108532 & 0.448288856054266 \tabularnewline
18 & 0.496655416322394 & 0.993310832644787 & 0.503344583677606 \tabularnewline
19 & 0.496697531311349 & 0.993395062622698 & 0.503302468688651 \tabularnewline
20 & 0.540453535429793 & 0.919092929140414 & 0.459546464570207 \tabularnewline
21 & 0.554275904981595 & 0.891448190036811 & 0.445724095018405 \tabularnewline
22 & 0.526917072779886 & 0.946165854440228 & 0.473082927220114 \tabularnewline
23 & 0.591767586124115 & 0.81646482775177 & 0.408232413875885 \tabularnewline
24 & 0.55082687651951 & 0.89834624696098 & 0.44917312348049 \tabularnewline
25 & 0.522485386334229 & 0.955029227331543 & 0.477514613665771 \tabularnewline
26 & 0.665434258713658 & 0.669131482572685 & 0.334565741286342 \tabularnewline
27 & 0.608449770634369 & 0.783100458731262 & 0.391550229365631 \tabularnewline
28 & 0.583246840750686 & 0.833506318498629 & 0.416753159249314 \tabularnewline
29 & 0.555684517165594 & 0.888630965668813 & 0.444315482834406 \tabularnewline
30 & 0.508122134787662 & 0.983755730424677 & 0.491877865212338 \tabularnewline
31 & 0.541546516799155 & 0.91690696640169 & 0.458453483200845 \tabularnewline
32 & 0.736455617609043 & 0.527088764781914 & 0.263544382390957 \tabularnewline
33 & 0.713197222400873 & 0.573605555198255 & 0.286802777599127 \tabularnewline
34 & 0.662461364485479 & 0.675077271029043 & 0.337538635514521 \tabularnewline
35 & 0.608718909144263 & 0.782562181711473 & 0.391281090855737 \tabularnewline
36 & 0.581841280484485 & 0.836317439031031 & 0.418158719515515 \tabularnewline
37 & 0.572787613341393 & 0.854424773317215 & 0.427212386658607 \tabularnewline
38 & 0.541026068543331 & 0.917947862913339 & 0.458973931456669 \tabularnewline
39 & 0.606916690783954 & 0.786166618432092 & 0.393083309216046 \tabularnewline
40 & 0.565934826865656 & 0.868130346268688 & 0.434065173134344 \tabularnewline
41 & 0.557955183491693 & 0.884089633016614 & 0.442044816508307 \tabularnewline
42 & 0.510887067876624 & 0.978225864246751 & 0.489112932123376 \tabularnewline
43 & 0.497398443418637 & 0.994796886837274 & 0.502601556581363 \tabularnewline
44 & 0.460472280995517 & 0.920944561991033 & 0.539527719004483 \tabularnewline
45 & 0.482223368399948 & 0.964446736799897 & 0.517776631600052 \tabularnewline
46 & 0.466435033987339 & 0.932870067974677 & 0.533564966012661 \tabularnewline
47 & 0.45026378540302 & 0.90052757080604 & 0.54973621459698 \tabularnewline
48 & 0.410440511115477 & 0.820881022230953 & 0.589559488884523 \tabularnewline
49 & 0.381880291225459 & 0.763760582450918 & 0.618119708774541 \tabularnewline
50 & 0.377457148820271 & 0.754914297640542 & 0.622542851179729 \tabularnewline
51 & 0.377479874396902 & 0.754959748793804 & 0.622520125603098 \tabularnewline
52 & 0.347140362307554 & 0.694280724615109 & 0.652859637692446 \tabularnewline
53 & 0.311463816946187 & 0.622927633892373 & 0.688536183053813 \tabularnewline
54 & 0.268931770790320 & 0.537863541580641 & 0.73106822920968 \tabularnewline
55 & 0.237975326163818 & 0.475950652327636 & 0.762024673836182 \tabularnewline
56 & 0.244376588144022 & 0.488753176288045 & 0.755623411855978 \tabularnewline
57 & 0.275354502943971 & 0.550709005887942 & 0.724645497056029 \tabularnewline
58 & 0.368665132082861 & 0.737330264165721 & 0.63133486791714 \tabularnewline
59 & 0.349922257771217 & 0.699844515542434 & 0.650077742228783 \tabularnewline
60 & 0.312418153965361 & 0.624836307930722 & 0.687581846034639 \tabularnewline
61 & 0.299815039529396 & 0.599630079058792 & 0.700184960470604 \tabularnewline
62 & 0.30391632737986 & 0.60783265475972 & 0.69608367262014 \tabularnewline
63 & 0.264874496088185 & 0.529748992176369 & 0.735125503911815 \tabularnewline
64 & 0.262645454867974 & 0.525290909735948 & 0.737354545132026 \tabularnewline
65 & 0.227391639852245 & 0.454783279704489 & 0.772608360147755 \tabularnewline
66 & 0.194592306055635 & 0.389184612111270 & 0.805407693944365 \tabularnewline
67 & 0.175773494523714 & 0.351546989047427 & 0.824226505476286 \tabularnewline
68 & 0.194699834660068 & 0.389399669320135 & 0.805300165339932 \tabularnewline
69 & 0.223265474868613 & 0.446530949737226 & 0.776734525131387 \tabularnewline
70 & 0.191356441059961 & 0.382712882119922 & 0.808643558940039 \tabularnewline
71 & 0.203311899881824 & 0.406623799763647 & 0.796688100118176 \tabularnewline
72 & 0.208995023545914 & 0.417990047091828 & 0.791004976454086 \tabularnewline
73 & 0.178993739408189 & 0.357987478816377 & 0.821006260591811 \tabularnewline
74 & 0.171718679972787 & 0.343437359945575 & 0.828281320027213 \tabularnewline
75 & 0.144546911642980 & 0.289093823285961 & 0.85545308835702 \tabularnewline
76 & 0.190238242038343 & 0.380476484076686 & 0.809761757961657 \tabularnewline
77 & 0.168962660139537 & 0.337925320279075 & 0.831037339860463 \tabularnewline
78 & 0.141682635665457 & 0.283365271330914 & 0.858317364334543 \tabularnewline
79 & 0.234119555974352 & 0.468239111948704 & 0.765880444025648 \tabularnewline
80 & 0.216121084218065 & 0.432242168436129 & 0.783878915781935 \tabularnewline
81 & 0.193086815011431 & 0.386173630022862 & 0.806913184988569 \tabularnewline
82 & 0.172986941979495 & 0.345973883958989 & 0.827013058020505 \tabularnewline
83 & 0.145079157373775 & 0.290158314747550 & 0.854920842626225 \tabularnewline
84 & 0.129259958779881 & 0.258519917559763 & 0.870740041220119 \tabularnewline
85 & 0.121153879831551 & 0.242307759663102 & 0.878846120168449 \tabularnewline
86 & 0.104007931706148 & 0.208015863412296 & 0.895992068293852 \tabularnewline
87 & 0.104876604388980 & 0.209753208777959 & 0.89512339561102 \tabularnewline
88 & 0.0898371943641422 & 0.179674388728284 & 0.910162805635858 \tabularnewline
89 & 0.121612624498080 & 0.243225248996161 & 0.87838737550192 \tabularnewline
90 & 0.100717185211400 & 0.201434370422799 & 0.8992828147886 \tabularnewline
91 & 0.114277516895701 & 0.228555033791403 & 0.885722483104299 \tabularnewline
92 & 0.0971911879368542 & 0.194382375873708 & 0.902808812063146 \tabularnewline
93 & 0.0796733427361246 & 0.159346685472249 & 0.920326657263875 \tabularnewline
94 & 0.0749299288282982 & 0.149859857656596 & 0.925070071171702 \tabularnewline
95 & 0.112247636050102 & 0.224495272100204 & 0.887752363949898 \tabularnewline
96 & 0.120120457955684 & 0.240240915911368 & 0.879879542044316 \tabularnewline
97 & 0.111174572486849 & 0.222349144973699 & 0.88882542751315 \tabularnewline
98 & 0.092620014613736 & 0.185240029227472 & 0.907379985386264 \tabularnewline
99 & 0.0788329708196358 & 0.157665941639272 & 0.921167029180364 \tabularnewline
100 & 0.0793339639275654 & 0.158667927855131 & 0.920666036072435 \tabularnewline
101 & 0.0714096341974177 & 0.142819268394835 & 0.928590365802582 \tabularnewline
102 & 0.0649962817352087 & 0.129992563470417 & 0.935003718264791 \tabularnewline
103 & 0.0520355387346255 & 0.104071077469251 & 0.947964461265374 \tabularnewline
104 & 0.0476376296000335 & 0.095275259200067 & 0.952362370399966 \tabularnewline
105 & 0.041004611443989 & 0.082009222887978 & 0.95899538855601 \tabularnewline
106 & 0.0705130738724881 & 0.141026147744976 & 0.929486926127512 \tabularnewline
107 & 0.0646687113985582 & 0.129337422797116 & 0.935331288601442 \tabularnewline
108 & 0.109572436625036 & 0.219144873250072 & 0.890427563374964 \tabularnewline
109 & 0.0959880204486227 & 0.191976040897245 & 0.904011979551377 \tabularnewline
110 & 0.402829861307932 & 0.805659722615863 & 0.597170138692068 \tabularnewline
111 & 0.423046306515666 & 0.846092613031332 & 0.576953693484334 \tabularnewline
112 & 0.398434690966872 & 0.796869381933744 & 0.601565309033128 \tabularnewline
113 & 0.372251424778786 & 0.744502849557572 & 0.627748575221214 \tabularnewline
114 & 0.339168228051008 & 0.678336456102016 & 0.660831771948992 \tabularnewline
115 & 0.309664018029331 & 0.619328036058662 & 0.690335981970669 \tabularnewline
116 & 0.281825577333725 & 0.563651154667449 & 0.718174422666275 \tabularnewline
117 & 0.243722958333035 & 0.487445916666070 & 0.756277041666965 \tabularnewline
118 & 0.204948142272811 & 0.409896284545622 & 0.79505185772719 \tabularnewline
119 & 0.181300095200203 & 0.362600190400407 & 0.818699904799797 \tabularnewline
120 & 0.209826488935500 & 0.419652977871001 & 0.7901735110645 \tabularnewline
121 & 0.175568024752830 & 0.351136049505660 & 0.82443197524717 \tabularnewline
122 & 0.161328914297278 & 0.322657828594556 & 0.838671085702722 \tabularnewline
123 & 0.164079272409849 & 0.328158544819698 & 0.835920727590151 \tabularnewline
124 & 0.143791541571797 & 0.287583083143594 & 0.856208458428203 \tabularnewline
125 & 0.118081002159431 & 0.236162004318861 & 0.88191899784057 \tabularnewline
126 & 0.111826720973737 & 0.223653441947474 & 0.888173279026263 \tabularnewline
127 & 0.114621112203591 & 0.229242224407182 & 0.88537888779641 \tabularnewline
128 & 0.433148840693403 & 0.866297681386806 & 0.566851159306597 \tabularnewline
129 & 0.383146096862203 & 0.766292193724406 & 0.616853903137797 \tabularnewline
130 & 0.359699427898234 & 0.719398855796468 & 0.640300572101766 \tabularnewline
131 & 0.313306314180362 & 0.626612628360723 & 0.686693685819638 \tabularnewline
132 & 0.318837060905164 & 0.637674121810328 & 0.681162939094836 \tabularnewline
133 & 0.502363670512949 & 0.995272658974102 & 0.497636329487051 \tabularnewline
134 & 0.597713398369677 & 0.804573203260646 & 0.402286601630323 \tabularnewline
135 & 0.642205375370989 & 0.715589249258022 & 0.357794624629011 \tabularnewline
136 & 0.583844002913121 & 0.832311994173758 & 0.416155997086879 \tabularnewline
137 & 0.617019114701172 & 0.765961770597656 & 0.382980885298828 \tabularnewline
138 & 0.556092770673833 & 0.887814458652335 & 0.443907229326167 \tabularnewline
139 & 0.551064504274096 & 0.897870991451807 & 0.448935495725903 \tabularnewline
140 & 0.502976445544264 & 0.994047108911472 & 0.497023554455736 \tabularnewline
141 & 0.462072103673701 & 0.924144207347402 & 0.537927896326299 \tabularnewline
142 & 0.401042920741143 & 0.802085841482287 & 0.598957079258857 \tabularnewline
143 & 0.456388096806596 & 0.912776193613193 & 0.543611903193404 \tabularnewline
144 & 0.554956081416462 & 0.890087837167076 & 0.445043918583538 \tabularnewline
145 & 0.682430244348413 & 0.635139511303175 & 0.317569755651587 \tabularnewline
146 & 0.749214869365823 & 0.501570261268354 & 0.250785130634177 \tabularnewline
147 & 0.860256144519559 & 0.279487710960881 & 0.139743855480441 \tabularnewline
148 & 0.796744470723162 & 0.406511058553675 & 0.203255529276838 \tabularnewline
149 & 0.92745220591782 & 0.145095588164362 & 0.0725477940821809 \tabularnewline
150 & 0.93769708685101 & 0.124605826297978 & 0.062302913148989 \tabularnewline
151 & 0.918394946551103 & 0.163210106897794 & 0.0816050534488968 \tabularnewline
152 & 0.99409115994436 & 0.0118176801112804 & 0.00590884005564021 \tabularnewline
153 & 0.981497406017036 & 0.0370051879659289 & 0.0185025939829644 \tabularnewline
154 & 0.95681886166919 & 0.0863622766616217 & 0.0431811383308109 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&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]8[/C][C]0.896243975502255[/C][C]0.207512048995491[/C][C]0.103756024497745[/C][/ROW]
[ROW][C]9[/C][C]0.84528901255469[/C][C]0.30942197489062[/C][C]0.15471098744531[/C][/ROW]
[ROW][C]10[/C][C]0.791714619151272[/C][C]0.416570761697456[/C][C]0.208285380848728[/C][/ROW]
[ROW][C]11[/C][C]0.706705044633178[/C][C]0.586589910733643[/C][C]0.293294955366822[/C][/ROW]
[ROW][C]12[/C][C]0.626597325214464[/C][C]0.746805349571072[/C][C]0.373402674785536[/C][/ROW]
[ROW][C]13[/C][C]0.684994276282729[/C][C]0.630011447434543[/C][C]0.315005723717271[/C][/ROW]
[ROW][C]14[/C][C]0.613831543404358[/C][C]0.772336913191284[/C][C]0.386168456595642[/C][/ROW]
[ROW][C]15[/C][C]0.63455636204455[/C][C]0.730887275910901[/C][C]0.365443637955451[/C][/ROW]
[ROW][C]16[/C][C]0.634536739437591[/C][C]0.730926521124819[/C][C]0.365463260562409[/C][/ROW]
[ROW][C]17[/C][C]0.551711143945734[/C][C]0.896577712108532[/C][C]0.448288856054266[/C][/ROW]
[ROW][C]18[/C][C]0.496655416322394[/C][C]0.993310832644787[/C][C]0.503344583677606[/C][/ROW]
[ROW][C]19[/C][C]0.496697531311349[/C][C]0.993395062622698[/C][C]0.503302468688651[/C][/ROW]
[ROW][C]20[/C][C]0.540453535429793[/C][C]0.919092929140414[/C][C]0.459546464570207[/C][/ROW]
[ROW][C]21[/C][C]0.554275904981595[/C][C]0.891448190036811[/C][C]0.445724095018405[/C][/ROW]
[ROW][C]22[/C][C]0.526917072779886[/C][C]0.946165854440228[/C][C]0.473082927220114[/C][/ROW]
[ROW][C]23[/C][C]0.591767586124115[/C][C]0.81646482775177[/C][C]0.408232413875885[/C][/ROW]
[ROW][C]24[/C][C]0.55082687651951[/C][C]0.89834624696098[/C][C]0.44917312348049[/C][/ROW]
[ROW][C]25[/C][C]0.522485386334229[/C][C]0.955029227331543[/C][C]0.477514613665771[/C][/ROW]
[ROW][C]26[/C][C]0.665434258713658[/C][C]0.669131482572685[/C][C]0.334565741286342[/C][/ROW]
[ROW][C]27[/C][C]0.608449770634369[/C][C]0.783100458731262[/C][C]0.391550229365631[/C][/ROW]
[ROW][C]28[/C][C]0.583246840750686[/C][C]0.833506318498629[/C][C]0.416753159249314[/C][/ROW]
[ROW][C]29[/C][C]0.555684517165594[/C][C]0.888630965668813[/C][C]0.444315482834406[/C][/ROW]
[ROW][C]30[/C][C]0.508122134787662[/C][C]0.983755730424677[/C][C]0.491877865212338[/C][/ROW]
[ROW][C]31[/C][C]0.541546516799155[/C][C]0.91690696640169[/C][C]0.458453483200845[/C][/ROW]
[ROW][C]32[/C][C]0.736455617609043[/C][C]0.527088764781914[/C][C]0.263544382390957[/C][/ROW]
[ROW][C]33[/C][C]0.713197222400873[/C][C]0.573605555198255[/C][C]0.286802777599127[/C][/ROW]
[ROW][C]34[/C][C]0.662461364485479[/C][C]0.675077271029043[/C][C]0.337538635514521[/C][/ROW]
[ROW][C]35[/C][C]0.608718909144263[/C][C]0.782562181711473[/C][C]0.391281090855737[/C][/ROW]
[ROW][C]36[/C][C]0.581841280484485[/C][C]0.836317439031031[/C][C]0.418158719515515[/C][/ROW]
[ROW][C]37[/C][C]0.572787613341393[/C][C]0.854424773317215[/C][C]0.427212386658607[/C][/ROW]
[ROW][C]38[/C][C]0.541026068543331[/C][C]0.917947862913339[/C][C]0.458973931456669[/C][/ROW]
[ROW][C]39[/C][C]0.606916690783954[/C][C]0.786166618432092[/C][C]0.393083309216046[/C][/ROW]
[ROW][C]40[/C][C]0.565934826865656[/C][C]0.868130346268688[/C][C]0.434065173134344[/C][/ROW]
[ROW][C]41[/C][C]0.557955183491693[/C][C]0.884089633016614[/C][C]0.442044816508307[/C][/ROW]
[ROW][C]42[/C][C]0.510887067876624[/C][C]0.978225864246751[/C][C]0.489112932123376[/C][/ROW]
[ROW][C]43[/C][C]0.497398443418637[/C][C]0.994796886837274[/C][C]0.502601556581363[/C][/ROW]
[ROW][C]44[/C][C]0.460472280995517[/C][C]0.920944561991033[/C][C]0.539527719004483[/C][/ROW]
[ROW][C]45[/C][C]0.482223368399948[/C][C]0.964446736799897[/C][C]0.517776631600052[/C][/ROW]
[ROW][C]46[/C][C]0.466435033987339[/C][C]0.932870067974677[/C][C]0.533564966012661[/C][/ROW]
[ROW][C]47[/C][C]0.45026378540302[/C][C]0.90052757080604[/C][C]0.54973621459698[/C][/ROW]
[ROW][C]48[/C][C]0.410440511115477[/C][C]0.820881022230953[/C][C]0.589559488884523[/C][/ROW]
[ROW][C]49[/C][C]0.381880291225459[/C][C]0.763760582450918[/C][C]0.618119708774541[/C][/ROW]
[ROW][C]50[/C][C]0.377457148820271[/C][C]0.754914297640542[/C][C]0.622542851179729[/C][/ROW]
[ROW][C]51[/C][C]0.377479874396902[/C][C]0.754959748793804[/C][C]0.622520125603098[/C][/ROW]
[ROW][C]52[/C][C]0.347140362307554[/C][C]0.694280724615109[/C][C]0.652859637692446[/C][/ROW]
[ROW][C]53[/C][C]0.311463816946187[/C][C]0.622927633892373[/C][C]0.688536183053813[/C][/ROW]
[ROW][C]54[/C][C]0.268931770790320[/C][C]0.537863541580641[/C][C]0.73106822920968[/C][/ROW]
[ROW][C]55[/C][C]0.237975326163818[/C][C]0.475950652327636[/C][C]0.762024673836182[/C][/ROW]
[ROW][C]56[/C][C]0.244376588144022[/C][C]0.488753176288045[/C][C]0.755623411855978[/C][/ROW]
[ROW][C]57[/C][C]0.275354502943971[/C][C]0.550709005887942[/C][C]0.724645497056029[/C][/ROW]
[ROW][C]58[/C][C]0.368665132082861[/C][C]0.737330264165721[/C][C]0.63133486791714[/C][/ROW]
[ROW][C]59[/C][C]0.349922257771217[/C][C]0.699844515542434[/C][C]0.650077742228783[/C][/ROW]
[ROW][C]60[/C][C]0.312418153965361[/C][C]0.624836307930722[/C][C]0.687581846034639[/C][/ROW]
[ROW][C]61[/C][C]0.299815039529396[/C][C]0.599630079058792[/C][C]0.700184960470604[/C][/ROW]
[ROW][C]62[/C][C]0.30391632737986[/C][C]0.60783265475972[/C][C]0.69608367262014[/C][/ROW]
[ROW][C]63[/C][C]0.264874496088185[/C][C]0.529748992176369[/C][C]0.735125503911815[/C][/ROW]
[ROW][C]64[/C][C]0.262645454867974[/C][C]0.525290909735948[/C][C]0.737354545132026[/C][/ROW]
[ROW][C]65[/C][C]0.227391639852245[/C][C]0.454783279704489[/C][C]0.772608360147755[/C][/ROW]
[ROW][C]66[/C][C]0.194592306055635[/C][C]0.389184612111270[/C][C]0.805407693944365[/C][/ROW]
[ROW][C]67[/C][C]0.175773494523714[/C][C]0.351546989047427[/C][C]0.824226505476286[/C][/ROW]
[ROW][C]68[/C][C]0.194699834660068[/C][C]0.389399669320135[/C][C]0.805300165339932[/C][/ROW]
[ROW][C]69[/C][C]0.223265474868613[/C][C]0.446530949737226[/C][C]0.776734525131387[/C][/ROW]
[ROW][C]70[/C][C]0.191356441059961[/C][C]0.382712882119922[/C][C]0.808643558940039[/C][/ROW]
[ROW][C]71[/C][C]0.203311899881824[/C][C]0.406623799763647[/C][C]0.796688100118176[/C][/ROW]
[ROW][C]72[/C][C]0.208995023545914[/C][C]0.417990047091828[/C][C]0.791004976454086[/C][/ROW]
[ROW][C]73[/C][C]0.178993739408189[/C][C]0.357987478816377[/C][C]0.821006260591811[/C][/ROW]
[ROW][C]74[/C][C]0.171718679972787[/C][C]0.343437359945575[/C][C]0.828281320027213[/C][/ROW]
[ROW][C]75[/C][C]0.144546911642980[/C][C]0.289093823285961[/C][C]0.85545308835702[/C][/ROW]
[ROW][C]76[/C][C]0.190238242038343[/C][C]0.380476484076686[/C][C]0.809761757961657[/C][/ROW]
[ROW][C]77[/C][C]0.168962660139537[/C][C]0.337925320279075[/C][C]0.831037339860463[/C][/ROW]
[ROW][C]78[/C][C]0.141682635665457[/C][C]0.283365271330914[/C][C]0.858317364334543[/C][/ROW]
[ROW][C]79[/C][C]0.234119555974352[/C][C]0.468239111948704[/C][C]0.765880444025648[/C][/ROW]
[ROW][C]80[/C][C]0.216121084218065[/C][C]0.432242168436129[/C][C]0.783878915781935[/C][/ROW]
[ROW][C]81[/C][C]0.193086815011431[/C][C]0.386173630022862[/C][C]0.806913184988569[/C][/ROW]
[ROW][C]82[/C][C]0.172986941979495[/C][C]0.345973883958989[/C][C]0.827013058020505[/C][/ROW]
[ROW][C]83[/C][C]0.145079157373775[/C][C]0.290158314747550[/C][C]0.854920842626225[/C][/ROW]
[ROW][C]84[/C][C]0.129259958779881[/C][C]0.258519917559763[/C][C]0.870740041220119[/C][/ROW]
[ROW][C]85[/C][C]0.121153879831551[/C][C]0.242307759663102[/C][C]0.878846120168449[/C][/ROW]
[ROW][C]86[/C][C]0.104007931706148[/C][C]0.208015863412296[/C][C]0.895992068293852[/C][/ROW]
[ROW][C]87[/C][C]0.104876604388980[/C][C]0.209753208777959[/C][C]0.89512339561102[/C][/ROW]
[ROW][C]88[/C][C]0.0898371943641422[/C][C]0.179674388728284[/C][C]0.910162805635858[/C][/ROW]
[ROW][C]89[/C][C]0.121612624498080[/C][C]0.243225248996161[/C][C]0.87838737550192[/C][/ROW]
[ROW][C]90[/C][C]0.100717185211400[/C][C]0.201434370422799[/C][C]0.8992828147886[/C][/ROW]
[ROW][C]91[/C][C]0.114277516895701[/C][C]0.228555033791403[/C][C]0.885722483104299[/C][/ROW]
[ROW][C]92[/C][C]0.0971911879368542[/C][C]0.194382375873708[/C][C]0.902808812063146[/C][/ROW]
[ROW][C]93[/C][C]0.0796733427361246[/C][C]0.159346685472249[/C][C]0.920326657263875[/C][/ROW]
[ROW][C]94[/C][C]0.0749299288282982[/C][C]0.149859857656596[/C][C]0.925070071171702[/C][/ROW]
[ROW][C]95[/C][C]0.112247636050102[/C][C]0.224495272100204[/C][C]0.887752363949898[/C][/ROW]
[ROW][C]96[/C][C]0.120120457955684[/C][C]0.240240915911368[/C][C]0.879879542044316[/C][/ROW]
[ROW][C]97[/C][C]0.111174572486849[/C][C]0.222349144973699[/C][C]0.88882542751315[/C][/ROW]
[ROW][C]98[/C][C]0.092620014613736[/C][C]0.185240029227472[/C][C]0.907379985386264[/C][/ROW]
[ROW][C]99[/C][C]0.0788329708196358[/C][C]0.157665941639272[/C][C]0.921167029180364[/C][/ROW]
[ROW][C]100[/C][C]0.0793339639275654[/C][C]0.158667927855131[/C][C]0.920666036072435[/C][/ROW]
[ROW][C]101[/C][C]0.0714096341974177[/C][C]0.142819268394835[/C][C]0.928590365802582[/C][/ROW]
[ROW][C]102[/C][C]0.0649962817352087[/C][C]0.129992563470417[/C][C]0.935003718264791[/C][/ROW]
[ROW][C]103[/C][C]0.0520355387346255[/C][C]0.104071077469251[/C][C]0.947964461265374[/C][/ROW]
[ROW][C]104[/C][C]0.0476376296000335[/C][C]0.095275259200067[/C][C]0.952362370399966[/C][/ROW]
[ROW][C]105[/C][C]0.041004611443989[/C][C]0.082009222887978[/C][C]0.95899538855601[/C][/ROW]
[ROW][C]106[/C][C]0.0705130738724881[/C][C]0.141026147744976[/C][C]0.929486926127512[/C][/ROW]
[ROW][C]107[/C][C]0.0646687113985582[/C][C]0.129337422797116[/C][C]0.935331288601442[/C][/ROW]
[ROW][C]108[/C][C]0.109572436625036[/C][C]0.219144873250072[/C][C]0.890427563374964[/C][/ROW]
[ROW][C]109[/C][C]0.0959880204486227[/C][C]0.191976040897245[/C][C]0.904011979551377[/C][/ROW]
[ROW][C]110[/C][C]0.402829861307932[/C][C]0.805659722615863[/C][C]0.597170138692068[/C][/ROW]
[ROW][C]111[/C][C]0.423046306515666[/C][C]0.846092613031332[/C][C]0.576953693484334[/C][/ROW]
[ROW][C]112[/C][C]0.398434690966872[/C][C]0.796869381933744[/C][C]0.601565309033128[/C][/ROW]
[ROW][C]113[/C][C]0.372251424778786[/C][C]0.744502849557572[/C][C]0.627748575221214[/C][/ROW]
[ROW][C]114[/C][C]0.339168228051008[/C][C]0.678336456102016[/C][C]0.660831771948992[/C][/ROW]
[ROW][C]115[/C][C]0.309664018029331[/C][C]0.619328036058662[/C][C]0.690335981970669[/C][/ROW]
[ROW][C]116[/C][C]0.281825577333725[/C][C]0.563651154667449[/C][C]0.718174422666275[/C][/ROW]
[ROW][C]117[/C][C]0.243722958333035[/C][C]0.487445916666070[/C][C]0.756277041666965[/C][/ROW]
[ROW][C]118[/C][C]0.204948142272811[/C][C]0.409896284545622[/C][C]0.79505185772719[/C][/ROW]
[ROW][C]119[/C][C]0.181300095200203[/C][C]0.362600190400407[/C][C]0.818699904799797[/C][/ROW]
[ROW][C]120[/C][C]0.209826488935500[/C][C]0.419652977871001[/C][C]0.7901735110645[/C][/ROW]
[ROW][C]121[/C][C]0.175568024752830[/C][C]0.351136049505660[/C][C]0.82443197524717[/C][/ROW]
[ROW][C]122[/C][C]0.161328914297278[/C][C]0.322657828594556[/C][C]0.838671085702722[/C][/ROW]
[ROW][C]123[/C][C]0.164079272409849[/C][C]0.328158544819698[/C][C]0.835920727590151[/C][/ROW]
[ROW][C]124[/C][C]0.143791541571797[/C][C]0.287583083143594[/C][C]0.856208458428203[/C][/ROW]
[ROW][C]125[/C][C]0.118081002159431[/C][C]0.236162004318861[/C][C]0.88191899784057[/C][/ROW]
[ROW][C]126[/C][C]0.111826720973737[/C][C]0.223653441947474[/C][C]0.888173279026263[/C][/ROW]
[ROW][C]127[/C][C]0.114621112203591[/C][C]0.229242224407182[/C][C]0.88537888779641[/C][/ROW]
[ROW][C]128[/C][C]0.433148840693403[/C][C]0.866297681386806[/C][C]0.566851159306597[/C][/ROW]
[ROW][C]129[/C][C]0.383146096862203[/C][C]0.766292193724406[/C][C]0.616853903137797[/C][/ROW]
[ROW][C]130[/C][C]0.359699427898234[/C][C]0.719398855796468[/C][C]0.640300572101766[/C][/ROW]
[ROW][C]131[/C][C]0.313306314180362[/C][C]0.626612628360723[/C][C]0.686693685819638[/C][/ROW]
[ROW][C]132[/C][C]0.318837060905164[/C][C]0.637674121810328[/C][C]0.681162939094836[/C][/ROW]
[ROW][C]133[/C][C]0.502363670512949[/C][C]0.995272658974102[/C][C]0.497636329487051[/C][/ROW]
[ROW][C]134[/C][C]0.597713398369677[/C][C]0.804573203260646[/C][C]0.402286601630323[/C][/ROW]
[ROW][C]135[/C][C]0.642205375370989[/C][C]0.715589249258022[/C][C]0.357794624629011[/C][/ROW]
[ROW][C]136[/C][C]0.583844002913121[/C][C]0.832311994173758[/C][C]0.416155997086879[/C][/ROW]
[ROW][C]137[/C][C]0.617019114701172[/C][C]0.765961770597656[/C][C]0.382980885298828[/C][/ROW]
[ROW][C]138[/C][C]0.556092770673833[/C][C]0.887814458652335[/C][C]0.443907229326167[/C][/ROW]
[ROW][C]139[/C][C]0.551064504274096[/C][C]0.897870991451807[/C][C]0.448935495725903[/C][/ROW]
[ROW][C]140[/C][C]0.502976445544264[/C][C]0.994047108911472[/C][C]0.497023554455736[/C][/ROW]
[ROW][C]141[/C][C]0.462072103673701[/C][C]0.924144207347402[/C][C]0.537927896326299[/C][/ROW]
[ROW][C]142[/C][C]0.401042920741143[/C][C]0.802085841482287[/C][C]0.598957079258857[/C][/ROW]
[ROW][C]143[/C][C]0.456388096806596[/C][C]0.912776193613193[/C][C]0.543611903193404[/C][/ROW]
[ROW][C]144[/C][C]0.554956081416462[/C][C]0.890087837167076[/C][C]0.445043918583538[/C][/ROW]
[ROW][C]145[/C][C]0.682430244348413[/C][C]0.635139511303175[/C][C]0.317569755651587[/C][/ROW]
[ROW][C]146[/C][C]0.749214869365823[/C][C]0.501570261268354[/C][C]0.250785130634177[/C][/ROW]
[ROW][C]147[/C][C]0.860256144519559[/C][C]0.279487710960881[/C][C]0.139743855480441[/C][/ROW]
[ROW][C]148[/C][C]0.796744470723162[/C][C]0.406511058553675[/C][C]0.203255529276838[/C][/ROW]
[ROW][C]149[/C][C]0.92745220591782[/C][C]0.145095588164362[/C][C]0.0725477940821809[/C][/ROW]
[ROW][C]150[/C][C]0.93769708685101[/C][C]0.124605826297978[/C][C]0.062302913148989[/C][/ROW]
[ROW][C]151[/C][C]0.918394946551103[/C][C]0.163210106897794[/C][C]0.0816050534488968[/C][/ROW]
[ROW][C]152[/C][C]0.99409115994436[/C][C]0.0118176801112804[/C][C]0.00590884005564021[/C][/ROW]
[ROW][C]153[/C][C]0.981497406017036[/C][C]0.0370051879659289[/C][C]0.0185025939829644[/C][/ROW]
[ROW][C]154[/C][C]0.95681886166919[/C][C]0.0863622766616217[/C][C]0.0431811383308109[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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
80.8962439755022550.2075120489954910.103756024497745
90.845289012554690.309421974890620.15471098744531
100.7917146191512720.4165707616974560.208285380848728
110.7067050446331780.5865899107336430.293294955366822
120.6265973252144640.7468053495710720.373402674785536
130.6849942762827290.6300114474345430.315005723717271
140.6138315434043580.7723369131912840.386168456595642
150.634556362044550.7308872759109010.365443637955451
160.6345367394375910.7309265211248190.365463260562409
170.5517111439457340.8965777121085320.448288856054266
180.4966554163223940.9933108326447870.503344583677606
190.4966975313113490.9933950626226980.503302468688651
200.5404535354297930.9190929291404140.459546464570207
210.5542759049815950.8914481900368110.445724095018405
220.5269170727798860.9461658544402280.473082927220114
230.5917675861241150.816464827751770.408232413875885
240.550826876519510.898346246960980.44917312348049
250.5224853863342290.9550292273315430.477514613665771
260.6654342587136580.6691314825726850.334565741286342
270.6084497706343690.7831004587312620.391550229365631
280.5832468407506860.8335063184986290.416753159249314
290.5556845171655940.8886309656688130.444315482834406
300.5081221347876620.9837557304246770.491877865212338
310.5415465167991550.916906966401690.458453483200845
320.7364556176090430.5270887647819140.263544382390957
330.7131972224008730.5736055551982550.286802777599127
340.6624613644854790.6750772710290430.337538635514521
350.6087189091442630.7825621817114730.391281090855737
360.5818412804844850.8363174390310310.418158719515515
370.5727876133413930.8544247733172150.427212386658607
380.5410260685433310.9179478629133390.458973931456669
390.6069166907839540.7861666184320920.393083309216046
400.5659348268656560.8681303462686880.434065173134344
410.5579551834916930.8840896330166140.442044816508307
420.5108870678766240.9782258642467510.489112932123376
430.4973984434186370.9947968868372740.502601556581363
440.4604722809955170.9209445619910330.539527719004483
450.4822233683999480.9644467367998970.517776631600052
460.4664350339873390.9328700679746770.533564966012661
470.450263785403020.900527570806040.54973621459698
480.4104405111154770.8208810222309530.589559488884523
490.3818802912254590.7637605824509180.618119708774541
500.3774571488202710.7549142976405420.622542851179729
510.3774798743969020.7549597487938040.622520125603098
520.3471403623075540.6942807246151090.652859637692446
530.3114638169461870.6229276338923730.688536183053813
540.2689317707903200.5378635415806410.73106822920968
550.2379753261638180.4759506523276360.762024673836182
560.2443765881440220.4887531762880450.755623411855978
570.2753545029439710.5507090058879420.724645497056029
580.3686651320828610.7373302641657210.63133486791714
590.3499222577712170.6998445155424340.650077742228783
600.3124181539653610.6248363079307220.687581846034639
610.2998150395293960.5996300790587920.700184960470604
620.303916327379860.607832654759720.69608367262014
630.2648744960881850.5297489921763690.735125503911815
640.2626454548679740.5252909097359480.737354545132026
650.2273916398522450.4547832797044890.772608360147755
660.1945923060556350.3891846121112700.805407693944365
670.1757734945237140.3515469890474270.824226505476286
680.1946998346600680.3893996693201350.805300165339932
690.2232654748686130.4465309497372260.776734525131387
700.1913564410599610.3827128821199220.808643558940039
710.2033118998818240.4066237997636470.796688100118176
720.2089950235459140.4179900470918280.791004976454086
730.1789937394081890.3579874788163770.821006260591811
740.1717186799727870.3434373599455750.828281320027213
750.1445469116429800.2890938232859610.85545308835702
760.1902382420383430.3804764840766860.809761757961657
770.1689626601395370.3379253202790750.831037339860463
780.1416826356654570.2833652713309140.858317364334543
790.2341195559743520.4682391119487040.765880444025648
800.2161210842180650.4322421684361290.783878915781935
810.1930868150114310.3861736300228620.806913184988569
820.1729869419794950.3459738839589890.827013058020505
830.1450791573737750.2901583147475500.854920842626225
840.1292599587798810.2585199175597630.870740041220119
850.1211538798315510.2423077596631020.878846120168449
860.1040079317061480.2080158634122960.895992068293852
870.1048766043889800.2097532087779590.89512339561102
880.08983719436414220.1796743887282840.910162805635858
890.1216126244980800.2432252489961610.87838737550192
900.1007171852114000.2014343704227990.8992828147886
910.1142775168957010.2285550337914030.885722483104299
920.09719118793685420.1943823758737080.902808812063146
930.07967334273612460.1593466854722490.920326657263875
940.07492992882829820.1498598576565960.925070071171702
950.1122476360501020.2244952721002040.887752363949898
960.1201204579556840.2402409159113680.879879542044316
970.1111745724868490.2223491449736990.88882542751315
980.0926200146137360.1852400292274720.907379985386264
990.07883297081963580.1576659416392720.921167029180364
1000.07933396392756540.1586679278551310.920666036072435
1010.07140963419741770.1428192683948350.928590365802582
1020.06499628173520870.1299925634704170.935003718264791
1030.05203553873462550.1040710774692510.947964461265374
1040.04763762960003350.0952752592000670.952362370399966
1050.0410046114439890.0820092228879780.95899538855601
1060.07051307387248810.1410261477449760.929486926127512
1070.06466871139855820.1293374227971160.935331288601442
1080.1095724366250360.2191448732500720.890427563374964
1090.09598802044862270.1919760408972450.904011979551377
1100.4028298613079320.8056597226158630.597170138692068
1110.4230463065156660.8460926130313320.576953693484334
1120.3984346909668720.7968693819337440.601565309033128
1130.3722514247787860.7445028495575720.627748575221214
1140.3391682280510080.6783364561020160.660831771948992
1150.3096640180293310.6193280360586620.690335981970669
1160.2818255773337250.5636511546674490.718174422666275
1170.2437229583330350.4874459166660700.756277041666965
1180.2049481422728110.4098962845456220.79505185772719
1190.1813000952002030.3626001904004070.818699904799797
1200.2098264889355000.4196529778710010.7901735110645
1210.1755680247528300.3511360495056600.82443197524717
1220.1613289142972780.3226578285945560.838671085702722
1230.1640792724098490.3281585448196980.835920727590151
1240.1437915415717970.2875830831435940.856208458428203
1250.1180810021594310.2361620043188610.88191899784057
1260.1118267209737370.2236534419474740.888173279026263
1270.1146211122035910.2292422244071820.88537888779641
1280.4331488406934030.8662976813868060.566851159306597
1290.3831460968622030.7662921937244060.616853903137797
1300.3596994278982340.7193988557964680.640300572101766
1310.3133063141803620.6266126283607230.686693685819638
1320.3188370609051640.6376741218103280.681162939094836
1330.5023636705129490.9952726589741020.497636329487051
1340.5977133983696770.8045732032606460.402286601630323
1350.6422053753709890.7155892492580220.357794624629011
1360.5838440029131210.8323119941737580.416155997086879
1370.6170191147011720.7659617705976560.382980885298828
1380.5560927706738330.8878144586523350.443907229326167
1390.5510645042740960.8978709914518070.448935495725903
1400.5029764455442640.9940471089114720.497023554455736
1410.4620721036737010.9241442073474020.537927896326299
1420.4010429207411430.8020858414822870.598957079258857
1430.4563880968065960.9127761936131930.543611903193404
1440.5549560814164620.8900878371670760.445043918583538
1450.6824302443484130.6351395113031750.317569755651587
1460.7492148693658230.5015702612683540.250785130634177
1470.8602561445195590.2794877109608810.139743855480441
1480.7967444707231620.4065110585536750.203255529276838
1490.927452205917820.1450955881643620.0725477940821809
1500.937697086851010.1246058262979780.062302913148989
1510.9183949465511030.1632101068977940.0816050534488968
1520.994091159944360.01181768011128040.00590884005564021
1530.9814974060170360.03700518796592890.0185025939829644
1540.956818861669190.08636227666162170.0431811383308109







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0136054421768707OK
10% type I error level50.0340136054421769OK

\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 & 2 & 0.0136054421768707 & OK \tabularnewline
10% type I error level & 5 & 0.0340136054421769 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99679&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]2[/C][C]0.0136054421768707[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0340136054421769[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99679&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99679&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 level20.0136054421768707OK
10% type I error level50.0340136054421769OK



Parameters (Session):
par1 = 1 ; 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')
}