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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 computationMon, 05 Nov 2012 13:16:29 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t135214827699q152760k06c79.htm/, Retrieved Mon, 06 Feb 2023 00:09:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186289, Retrieved Mon, 06 Feb 2023 00:09:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [W71] [2012-11-05 18:16:29] [23d3ba14def8a953a1634f373cd0859d] [Current]
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Dataseries X:
10070	21725
10137	27192
9984	21790
9732	13253
9103	37702
9155	30364
9308	32609
9394	30212
9948	29965
10177	28352
10002	25814
9728	22414
10002	20506
10063	28806
10018	22228
9960	13971
10236	36845
10893	35338
10756	35022
10940	34777
10997	26887
10827	23970
10166	22780
10186	17351
10457	21382
10368	24561
10244	17409
10511	11514
10812	31514
10738	27071
10171	29462
9721	26105
9897	22397
9828	23843
9924	21705
10371	18089
10846	20764
10413	25316
10709	17704
10662	15548
10570	28029
10297	29383
10635	36438
10872	32034
10296	22679
10383	24319
10431	18004
10574	17537
10653	20366
10805	22782
10872	19169
10625	13807
10407	29743
10463	25591
10556	29096
10646	26482
10702	22405
11353	27044
11346	17970
11451	18730
11964	19684
12574	19785
13031	18479
13812	10698
14544	31956
14931	29506
14886	34506
16005	27165
17064	26736
15168	23691
16050	18157
15839	17328
15137	18205
14954	20995
15648	17382
15305	9367
15579	31124
16348	26551
15928	30651
16171	25859
15937	25100
15713	25778
15594	20418
15683	18688
16438	20424
17032	24776
17696	19814
17745	12738
19394	31566
20148	30111
20108	30019
18584	31934
18441	25826
18391	26835
19178	20205
18079	17789
18483	20520
19644	22518
19195	15572
19650	11509
20830	25447
23595	24090
22937	27786
21814	26195
21928	20516
21777	22759
21383	19028
21467	16971
22052	20036
22680	22485
24320	18730
24977	14538
25204	27561
25739	25985
26434	34670
27525	32066
30695	27186
32436	29586
30160	21359
30236	21553
31293	19573
31077	24256
32226	22380
33865	16167
32810	27297
32242	28287
32700	33474
32819	28229
33947	28785
34148	25597
35261	18130
39506	20198
41591	22849
39148	23118
41216	21925
40225	20801
41126	18785
42362	20659
40740	29367
40256	23992
39804	20645
41002	22356
41702	17902
42254	15879
43605	16963




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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 time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
nieuwewagens[t] = + 24749.4095927135 -0.0576306827337014Goudkoersbxl[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
nieuwewagens[t] =  +  24749.4095927135 -0.0576306827337014Goudkoersbxl[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]nieuwewagens[t] =  +  24749.4095927135 -0.0576306827337014Goudkoersbxl[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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
nieuwewagens[t] = + 24749.4095927135 -0.0576306827337014Goudkoersbxl[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24749.40959271351033.83172923.939500
Goudkoersbxl-0.05763068273370140.048538-1.18730.2370630.118531

\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) & 24749.4095927135 & 1033.831729 & 23.9395 & 0 & 0 \tabularnewline
Goudkoersbxl & -0.0576306827337014 & 0.048538 & -1.1873 & 0.237063 & 0.118531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&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]24749.4095927135[/C][C]1033.831729[/C][C]23.9395[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudkoersbxl[/C][C]-0.0576306827337014[/C][C]0.048538[/C][C]-1.1873[/C][C]0.237063[/C][C]0.118531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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)24749.40959271351033.83172923.939500
Goudkoersbxl-0.05763068273370140.048538-1.18730.2370630.118531







Multiple Linear Regression - Regression Statistics
Multiple R0.0988043460861024
R-squared0.00976229880550231
Adjusted R-squared0.00283755963630994
F-TEST (value)1.40977133823814
F-TEST (DF numerator)1
F-TEST (DF denominator)143
p-value0.237062913479331
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5907.30914638812
Sum Squared Residuals4990171093.19311

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0988043460861024 \tabularnewline
R-squared & 0.00976229880550231 \tabularnewline
Adjusted R-squared & 0.00283755963630994 \tabularnewline
F-TEST (value) & 1.40977133823814 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 0.237062913479331 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5907.30914638812 \tabularnewline
Sum Squared Residuals & 4990171093.19311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0988043460861024[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00976229880550231[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00283755963630994[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.40977133823814[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]0.237062913479331[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5907.30914638812[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4990171093.19311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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 R0.0988043460861024
R-squared0.00976229880550231
Adjusted R-squared0.00283755963630994
F-TEST (value)1.40977133823814
F-TEST (DF numerator)1
F-TEST (DF denominator)143
p-value0.237062913479331
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5907.30914638812
Sum Squared Residuals4990171093.19311







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12172524169.0686175851-2444.06861758507
22719224165.2073618423026.79263815805
32179024174.0248563002-2384.02485630021
41325324188.5477883491-10935.5477883491
53770224224.797487788613477.2025122114
63036424221.80069228646142.19930771355
73260924212.98319782828396.01680217181
83021224208.02695911316003.97304088691
92996524176.09956087865788.90043912138
102835224162.90213453264189.0978654674
112581424172.9875040111641.012495989
122241424188.77831108-1774.77831108004
132050624172.987504011-3666.987504011
142880624169.47203236424636.52796763575
152222824172.0654130873-1944.06541308726
161397124175.4079926858-10204.4079926858
173684524159.501924251312685.4980757487
183533824121.638565695311216.3614343047
193502224129.533969229810892.4660307702
203477724118.929923606810658.0700763932
212688724115.6449746912771.35502530903
222397024125.4421907557-155.442190755698
232278024163.5360720427-1383.53607204267
241735124162.383458388-6811.383458388
252138224146.7655433672-2764.76554336717
262456124151.8946741305409.105325869533
271740924159.0408787894-6750.04087878945
281151424143.6534864995-12629.6534864995
293151424126.30665099677387.6933490033
302707124130.5713215192940.428678481
312946224163.2479186295298.75208137099
322610524189.18172585921915.81827414083
332239724179.038725698-1782.03872569804
342384324183.0152428067-340.015242806665
352170524177.4826972642-2472.48269726423
361808924151.7217820823-6062.72178208227
372076424124.3472077838-3360.34720778376
382531624149.30129340751166.69870659255
391770424132.2426113183-6428.24261131827
401554824134.9512534068-8586.95125340676
412802924140.25327621833888.74672378174
422938324155.98645260465227.01354739544
433643824136.507281840612301.4927181594
443203424122.84881003277911.15118996732
452267924156.0440832873-1477.04408328729
462431924151.0302138895167.969786110539
471800424148.2639411182-6144.26394111824
481753724140.0227534873-6603.02275348733
492036624135.4699295514-3769.46992955136
502278224126.7100657758-1344.71006577584
511916924122.8488100327-4953.84881003268
521380724137.0835886679-10330.0835886679
532974324149.64707750395593.35292249615
542559124146.41975927081444.58024072924
552909624141.06010577654954.93989422347
562648224135.87334433052346.1266556695
572240524132.6460260974-1727.64602609741
582704424095.12845163782948.87154836223
591797024095.5318664169-6125.53186641691
601873024089.4806447299-5359.48064472987
611968424059.9161044875-4375.91610448748
621978524024.7613880199-4239.76138801992
631847923998.4241660106-5519.42416601062
641069823953.4146027956-13255.4146027956
653195623911.22894303458044.77105696547
662950623888.92586881665617.07413118341
673450623891.519249539610614.4807504604
682716523827.03051556063337.96948443941
692673623765.99962254562970.0003774544
702369123875.2673970087-184.2673970087
711815723824.4371348376-5667.43713483758
721732823836.5972088944-6508.59720889439
731820523877.0539481734-5672.05394817345
742099523887.6003631137-2892.60036311371
751738223847.6046692965-6465.60466929652
76936723867.3719934742-14500.3719934742
773112423851.58118640527272.41881359485
782655123807.26319138292743.73680861707
793065123831.46807813116819.53192186891
802585923817.46382222682041.5361777732
812510023830.94940198651269.05059801352
822577823843.85867491881934.14132508117
832041823850.7167261641-3432.71672616414
841868823845.5875954008-5157.58759540084
852042423802.0764299369-3378.0764299369
862477623767.84380439311008.15619560692
871981423729.5770310579-3915.5770310579
881273823726.753127604-10988.753127604
893156623631.72013177617934.27986822392
903011123588.26659699496522.73340300513
913001923590.57182430426428.42817569578
923193423678.40098479048255.59901520962
932582623686.64217242132139.3578275787
942683523689.5237065583145.47629344202
952020523644.1683592466-3439.16835924656
961778923707.5044795709-5918.5044795709
972052023684.2216837465-3164.22168374648
982251823617.3124610927-1099.31246109265
991557223643.1886376401-8071.18863764009
1001150923616.9666769962-12107.9666769962
1012544723548.96247137051898.03752862952
1022409023389.6136336118700.386366388202
1032778623427.53462285064358.46537714942
1042619523492.25387956052702.74612043948
1052051623485.6839817289-2969.68398172888
1062275923494.3862148217-735.386214821668
1071902823517.0927038187-4489.09270381875
1081697123512.2517264691-6541.25172646912
1092003623478.5377770699-3442.5377770699
1102248523442.3457083131-957.345708313135
1111873023347.8313886299-4617.83138862987
1121453823309.9680300738-8771.96803007382
1132756123296.88586509334264.11413490673
1142598523266.05344983072718.94655016926
1153467023226.000125330811443.9998746692
1163206623163.12505046848902.87494953165
1172718622980.43578620254205.56421379748
1182958622880.10076756316705.89923243685
1192135923011.268201465-1652.26820146505
1202155323006.8882695773-1453.88826957729
1211957322945.9726379278-3372.97263792776
1222425622958.42086539821297.57913460175
1232238022892.2032109372-512.203210937222
1241616722797.7465219367-6630.74652193669
1252729722858.54689222074438.45310777926
1262828722891.28112001355395.71887998652
1273347422864.886267321410609.1137326786
1282822922858.02821607615370.97178392386
1292878522793.02080595255991.97919404748
1302559722781.4370387232815.56296127695
1311813022717.2940888404-4587.29408884044
1322019822472.6518406359-2274.65184063588
1332284922352.4918671361496.508132863891
1342311822493.2836250545624.716374945459
1352192522374.1033731612-449.103373161247
1362080122431.2153797503-1630.21537975034
1371878522379.2901346073-3594.29013460728
1382065922308.0586107484-1649.05861074843
1392936722401.53557814256965.46442185751
1402399222429.42882858561562.5711714144
1412064522455.4778971812-1810.47789718123
1422235622386.4363392663-30.4363392662589
1431790222346.0948613527-4444.09486135267
1441587922314.2827244837-6435.28272448367
1451696322236.4236721104-5273.42367211044

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21725 & 24169.0686175851 & -2444.06861758507 \tabularnewline
2 & 27192 & 24165.207361842 & 3026.79263815805 \tabularnewline
3 & 21790 & 24174.0248563002 & -2384.02485630021 \tabularnewline
4 & 13253 & 24188.5477883491 & -10935.5477883491 \tabularnewline
5 & 37702 & 24224.7974877886 & 13477.2025122114 \tabularnewline
6 & 30364 & 24221.8006922864 & 6142.19930771355 \tabularnewline
7 & 32609 & 24212.9831978282 & 8396.01680217181 \tabularnewline
8 & 30212 & 24208.0269591131 & 6003.97304088691 \tabularnewline
9 & 29965 & 24176.0995608786 & 5788.90043912138 \tabularnewline
10 & 28352 & 24162.9021345326 & 4189.0978654674 \tabularnewline
11 & 25814 & 24172.987504011 & 1641.012495989 \tabularnewline
12 & 22414 & 24188.77831108 & -1774.77831108004 \tabularnewline
13 & 20506 & 24172.987504011 & -3666.987504011 \tabularnewline
14 & 28806 & 24169.4720323642 & 4636.52796763575 \tabularnewline
15 & 22228 & 24172.0654130873 & -1944.06541308726 \tabularnewline
16 & 13971 & 24175.4079926858 & -10204.4079926858 \tabularnewline
17 & 36845 & 24159.5019242513 & 12685.4980757487 \tabularnewline
18 & 35338 & 24121.6385656953 & 11216.3614343047 \tabularnewline
19 & 35022 & 24129.5339692298 & 10892.4660307702 \tabularnewline
20 & 34777 & 24118.9299236068 & 10658.0700763932 \tabularnewline
21 & 26887 & 24115.644974691 & 2771.35502530903 \tabularnewline
22 & 23970 & 24125.4421907557 & -155.442190755698 \tabularnewline
23 & 22780 & 24163.5360720427 & -1383.53607204267 \tabularnewline
24 & 17351 & 24162.383458388 & -6811.383458388 \tabularnewline
25 & 21382 & 24146.7655433672 & -2764.76554336717 \tabularnewline
26 & 24561 & 24151.8946741305 & 409.105325869533 \tabularnewline
27 & 17409 & 24159.0408787894 & -6750.04087878945 \tabularnewline
28 & 11514 & 24143.6534864995 & -12629.6534864995 \tabularnewline
29 & 31514 & 24126.3066509967 & 7387.6933490033 \tabularnewline
30 & 27071 & 24130.571321519 & 2940.428678481 \tabularnewline
31 & 29462 & 24163.247918629 & 5298.75208137099 \tabularnewline
32 & 26105 & 24189.1817258592 & 1915.81827414083 \tabularnewline
33 & 22397 & 24179.038725698 & -1782.03872569804 \tabularnewline
34 & 23843 & 24183.0152428067 & -340.015242806665 \tabularnewline
35 & 21705 & 24177.4826972642 & -2472.48269726423 \tabularnewline
36 & 18089 & 24151.7217820823 & -6062.72178208227 \tabularnewline
37 & 20764 & 24124.3472077838 & -3360.34720778376 \tabularnewline
38 & 25316 & 24149.3012934075 & 1166.69870659255 \tabularnewline
39 & 17704 & 24132.2426113183 & -6428.24261131827 \tabularnewline
40 & 15548 & 24134.9512534068 & -8586.95125340676 \tabularnewline
41 & 28029 & 24140.2532762183 & 3888.74672378174 \tabularnewline
42 & 29383 & 24155.9864526046 & 5227.01354739544 \tabularnewline
43 & 36438 & 24136.5072818406 & 12301.4927181594 \tabularnewline
44 & 32034 & 24122.8488100327 & 7911.15118996732 \tabularnewline
45 & 22679 & 24156.0440832873 & -1477.04408328729 \tabularnewline
46 & 24319 & 24151.0302138895 & 167.969786110539 \tabularnewline
47 & 18004 & 24148.2639411182 & -6144.26394111824 \tabularnewline
48 & 17537 & 24140.0227534873 & -6603.02275348733 \tabularnewline
49 & 20366 & 24135.4699295514 & -3769.46992955136 \tabularnewline
50 & 22782 & 24126.7100657758 & -1344.71006577584 \tabularnewline
51 & 19169 & 24122.8488100327 & -4953.84881003268 \tabularnewline
52 & 13807 & 24137.0835886679 & -10330.0835886679 \tabularnewline
53 & 29743 & 24149.6470775039 & 5593.35292249615 \tabularnewline
54 & 25591 & 24146.4197592708 & 1444.58024072924 \tabularnewline
55 & 29096 & 24141.0601057765 & 4954.93989422347 \tabularnewline
56 & 26482 & 24135.8733443305 & 2346.1266556695 \tabularnewline
57 & 22405 & 24132.6460260974 & -1727.64602609741 \tabularnewline
58 & 27044 & 24095.1284516378 & 2948.87154836223 \tabularnewline
59 & 17970 & 24095.5318664169 & -6125.53186641691 \tabularnewline
60 & 18730 & 24089.4806447299 & -5359.48064472987 \tabularnewline
61 & 19684 & 24059.9161044875 & -4375.91610448748 \tabularnewline
62 & 19785 & 24024.7613880199 & -4239.76138801992 \tabularnewline
63 & 18479 & 23998.4241660106 & -5519.42416601062 \tabularnewline
64 & 10698 & 23953.4146027956 & -13255.4146027956 \tabularnewline
65 & 31956 & 23911.2289430345 & 8044.77105696547 \tabularnewline
66 & 29506 & 23888.9258688166 & 5617.07413118341 \tabularnewline
67 & 34506 & 23891.5192495396 & 10614.4807504604 \tabularnewline
68 & 27165 & 23827.0305155606 & 3337.96948443941 \tabularnewline
69 & 26736 & 23765.9996225456 & 2970.0003774544 \tabularnewline
70 & 23691 & 23875.2673970087 & -184.2673970087 \tabularnewline
71 & 18157 & 23824.4371348376 & -5667.43713483758 \tabularnewline
72 & 17328 & 23836.5972088944 & -6508.59720889439 \tabularnewline
73 & 18205 & 23877.0539481734 & -5672.05394817345 \tabularnewline
74 & 20995 & 23887.6003631137 & -2892.60036311371 \tabularnewline
75 & 17382 & 23847.6046692965 & -6465.60466929652 \tabularnewline
76 & 9367 & 23867.3719934742 & -14500.3719934742 \tabularnewline
77 & 31124 & 23851.5811864052 & 7272.41881359485 \tabularnewline
78 & 26551 & 23807.2631913829 & 2743.73680861707 \tabularnewline
79 & 30651 & 23831.4680781311 & 6819.53192186891 \tabularnewline
80 & 25859 & 23817.4638222268 & 2041.5361777732 \tabularnewline
81 & 25100 & 23830.9494019865 & 1269.05059801352 \tabularnewline
82 & 25778 & 23843.8586749188 & 1934.14132508117 \tabularnewline
83 & 20418 & 23850.7167261641 & -3432.71672616414 \tabularnewline
84 & 18688 & 23845.5875954008 & -5157.58759540084 \tabularnewline
85 & 20424 & 23802.0764299369 & -3378.0764299369 \tabularnewline
86 & 24776 & 23767.8438043931 & 1008.15619560692 \tabularnewline
87 & 19814 & 23729.5770310579 & -3915.5770310579 \tabularnewline
88 & 12738 & 23726.753127604 & -10988.753127604 \tabularnewline
89 & 31566 & 23631.7201317761 & 7934.27986822392 \tabularnewline
90 & 30111 & 23588.2665969949 & 6522.73340300513 \tabularnewline
91 & 30019 & 23590.5718243042 & 6428.42817569578 \tabularnewline
92 & 31934 & 23678.4009847904 & 8255.59901520962 \tabularnewline
93 & 25826 & 23686.6421724213 & 2139.3578275787 \tabularnewline
94 & 26835 & 23689.523706558 & 3145.47629344202 \tabularnewline
95 & 20205 & 23644.1683592466 & -3439.16835924656 \tabularnewline
96 & 17789 & 23707.5044795709 & -5918.5044795709 \tabularnewline
97 & 20520 & 23684.2216837465 & -3164.22168374648 \tabularnewline
98 & 22518 & 23617.3124610927 & -1099.31246109265 \tabularnewline
99 & 15572 & 23643.1886376401 & -8071.18863764009 \tabularnewline
100 & 11509 & 23616.9666769962 & -12107.9666769962 \tabularnewline
101 & 25447 & 23548.9624713705 & 1898.03752862952 \tabularnewline
102 & 24090 & 23389.6136336118 & 700.386366388202 \tabularnewline
103 & 27786 & 23427.5346228506 & 4358.46537714942 \tabularnewline
104 & 26195 & 23492.2538795605 & 2702.74612043948 \tabularnewline
105 & 20516 & 23485.6839817289 & -2969.68398172888 \tabularnewline
106 & 22759 & 23494.3862148217 & -735.386214821668 \tabularnewline
107 & 19028 & 23517.0927038187 & -4489.09270381875 \tabularnewline
108 & 16971 & 23512.2517264691 & -6541.25172646912 \tabularnewline
109 & 20036 & 23478.5377770699 & -3442.5377770699 \tabularnewline
110 & 22485 & 23442.3457083131 & -957.345708313135 \tabularnewline
111 & 18730 & 23347.8313886299 & -4617.83138862987 \tabularnewline
112 & 14538 & 23309.9680300738 & -8771.96803007382 \tabularnewline
113 & 27561 & 23296.8858650933 & 4264.11413490673 \tabularnewline
114 & 25985 & 23266.0534498307 & 2718.94655016926 \tabularnewline
115 & 34670 & 23226.0001253308 & 11443.9998746692 \tabularnewline
116 & 32066 & 23163.1250504684 & 8902.87494953165 \tabularnewline
117 & 27186 & 22980.4357862025 & 4205.56421379748 \tabularnewline
118 & 29586 & 22880.1007675631 & 6705.89923243685 \tabularnewline
119 & 21359 & 23011.268201465 & -1652.26820146505 \tabularnewline
120 & 21553 & 23006.8882695773 & -1453.88826957729 \tabularnewline
121 & 19573 & 22945.9726379278 & -3372.97263792776 \tabularnewline
122 & 24256 & 22958.4208653982 & 1297.57913460175 \tabularnewline
123 & 22380 & 22892.2032109372 & -512.203210937222 \tabularnewline
124 & 16167 & 22797.7465219367 & -6630.74652193669 \tabularnewline
125 & 27297 & 22858.5468922207 & 4438.45310777926 \tabularnewline
126 & 28287 & 22891.2811200135 & 5395.71887998652 \tabularnewline
127 & 33474 & 22864.8862673214 & 10609.1137326786 \tabularnewline
128 & 28229 & 22858.0282160761 & 5370.97178392386 \tabularnewline
129 & 28785 & 22793.0208059525 & 5991.97919404748 \tabularnewline
130 & 25597 & 22781.437038723 & 2815.56296127695 \tabularnewline
131 & 18130 & 22717.2940888404 & -4587.29408884044 \tabularnewline
132 & 20198 & 22472.6518406359 & -2274.65184063588 \tabularnewline
133 & 22849 & 22352.4918671361 & 496.508132863891 \tabularnewline
134 & 23118 & 22493.2836250545 & 624.716374945459 \tabularnewline
135 & 21925 & 22374.1033731612 & -449.103373161247 \tabularnewline
136 & 20801 & 22431.2153797503 & -1630.21537975034 \tabularnewline
137 & 18785 & 22379.2901346073 & -3594.29013460728 \tabularnewline
138 & 20659 & 22308.0586107484 & -1649.05861074843 \tabularnewline
139 & 29367 & 22401.5355781425 & 6965.46442185751 \tabularnewline
140 & 23992 & 22429.4288285856 & 1562.5711714144 \tabularnewline
141 & 20645 & 22455.4778971812 & -1810.47789718123 \tabularnewline
142 & 22356 & 22386.4363392663 & -30.4363392662589 \tabularnewline
143 & 17902 & 22346.0948613527 & -4444.09486135267 \tabularnewline
144 & 15879 & 22314.2827244837 & -6435.28272448367 \tabularnewline
145 & 16963 & 22236.4236721104 & -5273.42367211044 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&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]21725[/C][C]24169.0686175851[/C][C]-2444.06861758507[/C][/ROW]
[ROW][C]2[/C][C]27192[/C][C]24165.207361842[/C][C]3026.79263815805[/C][/ROW]
[ROW][C]3[/C][C]21790[/C][C]24174.0248563002[/C][C]-2384.02485630021[/C][/ROW]
[ROW][C]4[/C][C]13253[/C][C]24188.5477883491[/C][C]-10935.5477883491[/C][/ROW]
[ROW][C]5[/C][C]37702[/C][C]24224.7974877886[/C][C]13477.2025122114[/C][/ROW]
[ROW][C]6[/C][C]30364[/C][C]24221.8006922864[/C][C]6142.19930771355[/C][/ROW]
[ROW][C]7[/C][C]32609[/C][C]24212.9831978282[/C][C]8396.01680217181[/C][/ROW]
[ROW][C]8[/C][C]30212[/C][C]24208.0269591131[/C][C]6003.97304088691[/C][/ROW]
[ROW][C]9[/C][C]29965[/C][C]24176.0995608786[/C][C]5788.90043912138[/C][/ROW]
[ROW][C]10[/C][C]28352[/C][C]24162.9021345326[/C][C]4189.0978654674[/C][/ROW]
[ROW][C]11[/C][C]25814[/C][C]24172.987504011[/C][C]1641.012495989[/C][/ROW]
[ROW][C]12[/C][C]22414[/C][C]24188.77831108[/C][C]-1774.77831108004[/C][/ROW]
[ROW][C]13[/C][C]20506[/C][C]24172.987504011[/C][C]-3666.987504011[/C][/ROW]
[ROW][C]14[/C][C]28806[/C][C]24169.4720323642[/C][C]4636.52796763575[/C][/ROW]
[ROW][C]15[/C][C]22228[/C][C]24172.0654130873[/C][C]-1944.06541308726[/C][/ROW]
[ROW][C]16[/C][C]13971[/C][C]24175.4079926858[/C][C]-10204.4079926858[/C][/ROW]
[ROW][C]17[/C][C]36845[/C][C]24159.5019242513[/C][C]12685.4980757487[/C][/ROW]
[ROW][C]18[/C][C]35338[/C][C]24121.6385656953[/C][C]11216.3614343047[/C][/ROW]
[ROW][C]19[/C][C]35022[/C][C]24129.5339692298[/C][C]10892.4660307702[/C][/ROW]
[ROW][C]20[/C][C]34777[/C][C]24118.9299236068[/C][C]10658.0700763932[/C][/ROW]
[ROW][C]21[/C][C]26887[/C][C]24115.644974691[/C][C]2771.35502530903[/C][/ROW]
[ROW][C]22[/C][C]23970[/C][C]24125.4421907557[/C][C]-155.442190755698[/C][/ROW]
[ROW][C]23[/C][C]22780[/C][C]24163.5360720427[/C][C]-1383.53607204267[/C][/ROW]
[ROW][C]24[/C][C]17351[/C][C]24162.383458388[/C][C]-6811.383458388[/C][/ROW]
[ROW][C]25[/C][C]21382[/C][C]24146.7655433672[/C][C]-2764.76554336717[/C][/ROW]
[ROW][C]26[/C][C]24561[/C][C]24151.8946741305[/C][C]409.105325869533[/C][/ROW]
[ROW][C]27[/C][C]17409[/C][C]24159.0408787894[/C][C]-6750.04087878945[/C][/ROW]
[ROW][C]28[/C][C]11514[/C][C]24143.6534864995[/C][C]-12629.6534864995[/C][/ROW]
[ROW][C]29[/C][C]31514[/C][C]24126.3066509967[/C][C]7387.6933490033[/C][/ROW]
[ROW][C]30[/C][C]27071[/C][C]24130.571321519[/C][C]2940.428678481[/C][/ROW]
[ROW][C]31[/C][C]29462[/C][C]24163.247918629[/C][C]5298.75208137099[/C][/ROW]
[ROW][C]32[/C][C]26105[/C][C]24189.1817258592[/C][C]1915.81827414083[/C][/ROW]
[ROW][C]33[/C][C]22397[/C][C]24179.038725698[/C][C]-1782.03872569804[/C][/ROW]
[ROW][C]34[/C][C]23843[/C][C]24183.0152428067[/C][C]-340.015242806665[/C][/ROW]
[ROW][C]35[/C][C]21705[/C][C]24177.4826972642[/C][C]-2472.48269726423[/C][/ROW]
[ROW][C]36[/C][C]18089[/C][C]24151.7217820823[/C][C]-6062.72178208227[/C][/ROW]
[ROW][C]37[/C][C]20764[/C][C]24124.3472077838[/C][C]-3360.34720778376[/C][/ROW]
[ROW][C]38[/C][C]25316[/C][C]24149.3012934075[/C][C]1166.69870659255[/C][/ROW]
[ROW][C]39[/C][C]17704[/C][C]24132.2426113183[/C][C]-6428.24261131827[/C][/ROW]
[ROW][C]40[/C][C]15548[/C][C]24134.9512534068[/C][C]-8586.95125340676[/C][/ROW]
[ROW][C]41[/C][C]28029[/C][C]24140.2532762183[/C][C]3888.74672378174[/C][/ROW]
[ROW][C]42[/C][C]29383[/C][C]24155.9864526046[/C][C]5227.01354739544[/C][/ROW]
[ROW][C]43[/C][C]36438[/C][C]24136.5072818406[/C][C]12301.4927181594[/C][/ROW]
[ROW][C]44[/C][C]32034[/C][C]24122.8488100327[/C][C]7911.15118996732[/C][/ROW]
[ROW][C]45[/C][C]22679[/C][C]24156.0440832873[/C][C]-1477.04408328729[/C][/ROW]
[ROW][C]46[/C][C]24319[/C][C]24151.0302138895[/C][C]167.969786110539[/C][/ROW]
[ROW][C]47[/C][C]18004[/C][C]24148.2639411182[/C][C]-6144.26394111824[/C][/ROW]
[ROW][C]48[/C][C]17537[/C][C]24140.0227534873[/C][C]-6603.02275348733[/C][/ROW]
[ROW][C]49[/C][C]20366[/C][C]24135.4699295514[/C][C]-3769.46992955136[/C][/ROW]
[ROW][C]50[/C][C]22782[/C][C]24126.7100657758[/C][C]-1344.71006577584[/C][/ROW]
[ROW][C]51[/C][C]19169[/C][C]24122.8488100327[/C][C]-4953.84881003268[/C][/ROW]
[ROW][C]52[/C][C]13807[/C][C]24137.0835886679[/C][C]-10330.0835886679[/C][/ROW]
[ROW][C]53[/C][C]29743[/C][C]24149.6470775039[/C][C]5593.35292249615[/C][/ROW]
[ROW][C]54[/C][C]25591[/C][C]24146.4197592708[/C][C]1444.58024072924[/C][/ROW]
[ROW][C]55[/C][C]29096[/C][C]24141.0601057765[/C][C]4954.93989422347[/C][/ROW]
[ROW][C]56[/C][C]26482[/C][C]24135.8733443305[/C][C]2346.1266556695[/C][/ROW]
[ROW][C]57[/C][C]22405[/C][C]24132.6460260974[/C][C]-1727.64602609741[/C][/ROW]
[ROW][C]58[/C][C]27044[/C][C]24095.1284516378[/C][C]2948.87154836223[/C][/ROW]
[ROW][C]59[/C][C]17970[/C][C]24095.5318664169[/C][C]-6125.53186641691[/C][/ROW]
[ROW][C]60[/C][C]18730[/C][C]24089.4806447299[/C][C]-5359.48064472987[/C][/ROW]
[ROW][C]61[/C][C]19684[/C][C]24059.9161044875[/C][C]-4375.91610448748[/C][/ROW]
[ROW][C]62[/C][C]19785[/C][C]24024.7613880199[/C][C]-4239.76138801992[/C][/ROW]
[ROW][C]63[/C][C]18479[/C][C]23998.4241660106[/C][C]-5519.42416601062[/C][/ROW]
[ROW][C]64[/C][C]10698[/C][C]23953.4146027956[/C][C]-13255.4146027956[/C][/ROW]
[ROW][C]65[/C][C]31956[/C][C]23911.2289430345[/C][C]8044.77105696547[/C][/ROW]
[ROW][C]66[/C][C]29506[/C][C]23888.9258688166[/C][C]5617.07413118341[/C][/ROW]
[ROW][C]67[/C][C]34506[/C][C]23891.5192495396[/C][C]10614.4807504604[/C][/ROW]
[ROW][C]68[/C][C]27165[/C][C]23827.0305155606[/C][C]3337.96948443941[/C][/ROW]
[ROW][C]69[/C][C]26736[/C][C]23765.9996225456[/C][C]2970.0003774544[/C][/ROW]
[ROW][C]70[/C][C]23691[/C][C]23875.2673970087[/C][C]-184.2673970087[/C][/ROW]
[ROW][C]71[/C][C]18157[/C][C]23824.4371348376[/C][C]-5667.43713483758[/C][/ROW]
[ROW][C]72[/C][C]17328[/C][C]23836.5972088944[/C][C]-6508.59720889439[/C][/ROW]
[ROW][C]73[/C][C]18205[/C][C]23877.0539481734[/C][C]-5672.05394817345[/C][/ROW]
[ROW][C]74[/C][C]20995[/C][C]23887.6003631137[/C][C]-2892.60036311371[/C][/ROW]
[ROW][C]75[/C][C]17382[/C][C]23847.6046692965[/C][C]-6465.60466929652[/C][/ROW]
[ROW][C]76[/C][C]9367[/C][C]23867.3719934742[/C][C]-14500.3719934742[/C][/ROW]
[ROW][C]77[/C][C]31124[/C][C]23851.5811864052[/C][C]7272.41881359485[/C][/ROW]
[ROW][C]78[/C][C]26551[/C][C]23807.2631913829[/C][C]2743.73680861707[/C][/ROW]
[ROW][C]79[/C][C]30651[/C][C]23831.4680781311[/C][C]6819.53192186891[/C][/ROW]
[ROW][C]80[/C][C]25859[/C][C]23817.4638222268[/C][C]2041.5361777732[/C][/ROW]
[ROW][C]81[/C][C]25100[/C][C]23830.9494019865[/C][C]1269.05059801352[/C][/ROW]
[ROW][C]82[/C][C]25778[/C][C]23843.8586749188[/C][C]1934.14132508117[/C][/ROW]
[ROW][C]83[/C][C]20418[/C][C]23850.7167261641[/C][C]-3432.71672616414[/C][/ROW]
[ROW][C]84[/C][C]18688[/C][C]23845.5875954008[/C][C]-5157.58759540084[/C][/ROW]
[ROW][C]85[/C][C]20424[/C][C]23802.0764299369[/C][C]-3378.0764299369[/C][/ROW]
[ROW][C]86[/C][C]24776[/C][C]23767.8438043931[/C][C]1008.15619560692[/C][/ROW]
[ROW][C]87[/C][C]19814[/C][C]23729.5770310579[/C][C]-3915.5770310579[/C][/ROW]
[ROW][C]88[/C][C]12738[/C][C]23726.753127604[/C][C]-10988.753127604[/C][/ROW]
[ROW][C]89[/C][C]31566[/C][C]23631.7201317761[/C][C]7934.27986822392[/C][/ROW]
[ROW][C]90[/C][C]30111[/C][C]23588.2665969949[/C][C]6522.73340300513[/C][/ROW]
[ROW][C]91[/C][C]30019[/C][C]23590.5718243042[/C][C]6428.42817569578[/C][/ROW]
[ROW][C]92[/C][C]31934[/C][C]23678.4009847904[/C][C]8255.59901520962[/C][/ROW]
[ROW][C]93[/C][C]25826[/C][C]23686.6421724213[/C][C]2139.3578275787[/C][/ROW]
[ROW][C]94[/C][C]26835[/C][C]23689.523706558[/C][C]3145.47629344202[/C][/ROW]
[ROW][C]95[/C][C]20205[/C][C]23644.1683592466[/C][C]-3439.16835924656[/C][/ROW]
[ROW][C]96[/C][C]17789[/C][C]23707.5044795709[/C][C]-5918.5044795709[/C][/ROW]
[ROW][C]97[/C][C]20520[/C][C]23684.2216837465[/C][C]-3164.22168374648[/C][/ROW]
[ROW][C]98[/C][C]22518[/C][C]23617.3124610927[/C][C]-1099.31246109265[/C][/ROW]
[ROW][C]99[/C][C]15572[/C][C]23643.1886376401[/C][C]-8071.18863764009[/C][/ROW]
[ROW][C]100[/C][C]11509[/C][C]23616.9666769962[/C][C]-12107.9666769962[/C][/ROW]
[ROW][C]101[/C][C]25447[/C][C]23548.9624713705[/C][C]1898.03752862952[/C][/ROW]
[ROW][C]102[/C][C]24090[/C][C]23389.6136336118[/C][C]700.386366388202[/C][/ROW]
[ROW][C]103[/C][C]27786[/C][C]23427.5346228506[/C][C]4358.46537714942[/C][/ROW]
[ROW][C]104[/C][C]26195[/C][C]23492.2538795605[/C][C]2702.74612043948[/C][/ROW]
[ROW][C]105[/C][C]20516[/C][C]23485.6839817289[/C][C]-2969.68398172888[/C][/ROW]
[ROW][C]106[/C][C]22759[/C][C]23494.3862148217[/C][C]-735.386214821668[/C][/ROW]
[ROW][C]107[/C][C]19028[/C][C]23517.0927038187[/C][C]-4489.09270381875[/C][/ROW]
[ROW][C]108[/C][C]16971[/C][C]23512.2517264691[/C][C]-6541.25172646912[/C][/ROW]
[ROW][C]109[/C][C]20036[/C][C]23478.5377770699[/C][C]-3442.5377770699[/C][/ROW]
[ROW][C]110[/C][C]22485[/C][C]23442.3457083131[/C][C]-957.345708313135[/C][/ROW]
[ROW][C]111[/C][C]18730[/C][C]23347.8313886299[/C][C]-4617.83138862987[/C][/ROW]
[ROW][C]112[/C][C]14538[/C][C]23309.9680300738[/C][C]-8771.96803007382[/C][/ROW]
[ROW][C]113[/C][C]27561[/C][C]23296.8858650933[/C][C]4264.11413490673[/C][/ROW]
[ROW][C]114[/C][C]25985[/C][C]23266.0534498307[/C][C]2718.94655016926[/C][/ROW]
[ROW][C]115[/C][C]34670[/C][C]23226.0001253308[/C][C]11443.9998746692[/C][/ROW]
[ROW][C]116[/C][C]32066[/C][C]23163.1250504684[/C][C]8902.87494953165[/C][/ROW]
[ROW][C]117[/C][C]27186[/C][C]22980.4357862025[/C][C]4205.56421379748[/C][/ROW]
[ROW][C]118[/C][C]29586[/C][C]22880.1007675631[/C][C]6705.89923243685[/C][/ROW]
[ROW][C]119[/C][C]21359[/C][C]23011.268201465[/C][C]-1652.26820146505[/C][/ROW]
[ROW][C]120[/C][C]21553[/C][C]23006.8882695773[/C][C]-1453.88826957729[/C][/ROW]
[ROW][C]121[/C][C]19573[/C][C]22945.9726379278[/C][C]-3372.97263792776[/C][/ROW]
[ROW][C]122[/C][C]24256[/C][C]22958.4208653982[/C][C]1297.57913460175[/C][/ROW]
[ROW][C]123[/C][C]22380[/C][C]22892.2032109372[/C][C]-512.203210937222[/C][/ROW]
[ROW][C]124[/C][C]16167[/C][C]22797.7465219367[/C][C]-6630.74652193669[/C][/ROW]
[ROW][C]125[/C][C]27297[/C][C]22858.5468922207[/C][C]4438.45310777926[/C][/ROW]
[ROW][C]126[/C][C]28287[/C][C]22891.2811200135[/C][C]5395.71887998652[/C][/ROW]
[ROW][C]127[/C][C]33474[/C][C]22864.8862673214[/C][C]10609.1137326786[/C][/ROW]
[ROW][C]128[/C][C]28229[/C][C]22858.0282160761[/C][C]5370.97178392386[/C][/ROW]
[ROW][C]129[/C][C]28785[/C][C]22793.0208059525[/C][C]5991.97919404748[/C][/ROW]
[ROW][C]130[/C][C]25597[/C][C]22781.437038723[/C][C]2815.56296127695[/C][/ROW]
[ROW][C]131[/C][C]18130[/C][C]22717.2940888404[/C][C]-4587.29408884044[/C][/ROW]
[ROW][C]132[/C][C]20198[/C][C]22472.6518406359[/C][C]-2274.65184063588[/C][/ROW]
[ROW][C]133[/C][C]22849[/C][C]22352.4918671361[/C][C]496.508132863891[/C][/ROW]
[ROW][C]134[/C][C]23118[/C][C]22493.2836250545[/C][C]624.716374945459[/C][/ROW]
[ROW][C]135[/C][C]21925[/C][C]22374.1033731612[/C][C]-449.103373161247[/C][/ROW]
[ROW][C]136[/C][C]20801[/C][C]22431.2153797503[/C][C]-1630.21537975034[/C][/ROW]
[ROW][C]137[/C][C]18785[/C][C]22379.2901346073[/C][C]-3594.29013460728[/C][/ROW]
[ROW][C]138[/C][C]20659[/C][C]22308.0586107484[/C][C]-1649.05861074843[/C][/ROW]
[ROW][C]139[/C][C]29367[/C][C]22401.5355781425[/C][C]6965.46442185751[/C][/ROW]
[ROW][C]140[/C][C]23992[/C][C]22429.4288285856[/C][C]1562.5711714144[/C][/ROW]
[ROW][C]141[/C][C]20645[/C][C]22455.4778971812[/C][C]-1810.47789718123[/C][/ROW]
[ROW][C]142[/C][C]22356[/C][C]22386.4363392663[/C][C]-30.4363392662589[/C][/ROW]
[ROW][C]143[/C][C]17902[/C][C]22346.0948613527[/C][C]-4444.09486135267[/C][/ROW]
[ROW][C]144[/C][C]15879[/C][C]22314.2827244837[/C][C]-6435.28272448367[/C][/ROW]
[ROW][C]145[/C][C]16963[/C][C]22236.4236721104[/C][C]-5273.42367211044[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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
12172524169.0686175851-2444.06861758507
22719224165.2073618423026.79263815805
32179024174.0248563002-2384.02485630021
41325324188.5477883491-10935.5477883491
53770224224.797487788613477.2025122114
63036424221.80069228646142.19930771355
73260924212.98319782828396.01680217181
83021224208.02695911316003.97304088691
92996524176.09956087865788.90043912138
102835224162.90213453264189.0978654674
112581424172.9875040111641.012495989
122241424188.77831108-1774.77831108004
132050624172.987504011-3666.987504011
142880624169.47203236424636.52796763575
152222824172.0654130873-1944.06541308726
161397124175.4079926858-10204.4079926858
173684524159.501924251312685.4980757487
183533824121.638565695311216.3614343047
193502224129.533969229810892.4660307702
203477724118.929923606810658.0700763932
212688724115.6449746912771.35502530903
222397024125.4421907557-155.442190755698
232278024163.5360720427-1383.53607204267
241735124162.383458388-6811.383458388
252138224146.7655433672-2764.76554336717
262456124151.8946741305409.105325869533
271740924159.0408787894-6750.04087878945
281151424143.6534864995-12629.6534864995
293151424126.30665099677387.6933490033
302707124130.5713215192940.428678481
312946224163.2479186295298.75208137099
322610524189.18172585921915.81827414083
332239724179.038725698-1782.03872569804
342384324183.0152428067-340.015242806665
352170524177.4826972642-2472.48269726423
361808924151.7217820823-6062.72178208227
372076424124.3472077838-3360.34720778376
382531624149.30129340751166.69870659255
391770424132.2426113183-6428.24261131827
401554824134.9512534068-8586.95125340676
412802924140.25327621833888.74672378174
422938324155.98645260465227.01354739544
433643824136.507281840612301.4927181594
443203424122.84881003277911.15118996732
452267924156.0440832873-1477.04408328729
462431924151.0302138895167.969786110539
471800424148.2639411182-6144.26394111824
481753724140.0227534873-6603.02275348733
492036624135.4699295514-3769.46992955136
502278224126.7100657758-1344.71006577584
511916924122.8488100327-4953.84881003268
521380724137.0835886679-10330.0835886679
532974324149.64707750395593.35292249615
542559124146.41975927081444.58024072924
552909624141.06010577654954.93989422347
562648224135.87334433052346.1266556695
572240524132.6460260974-1727.64602609741
582704424095.12845163782948.87154836223
591797024095.5318664169-6125.53186641691
601873024089.4806447299-5359.48064472987
611968424059.9161044875-4375.91610448748
621978524024.7613880199-4239.76138801992
631847923998.4241660106-5519.42416601062
641069823953.4146027956-13255.4146027956
653195623911.22894303458044.77105696547
662950623888.92586881665617.07413118341
673450623891.519249539610614.4807504604
682716523827.03051556063337.96948443941
692673623765.99962254562970.0003774544
702369123875.2673970087-184.2673970087
711815723824.4371348376-5667.43713483758
721732823836.5972088944-6508.59720889439
731820523877.0539481734-5672.05394817345
742099523887.6003631137-2892.60036311371
751738223847.6046692965-6465.60466929652
76936723867.3719934742-14500.3719934742
773112423851.58118640527272.41881359485
782655123807.26319138292743.73680861707
793065123831.46807813116819.53192186891
802585923817.46382222682041.5361777732
812510023830.94940198651269.05059801352
822577823843.85867491881934.14132508117
832041823850.7167261641-3432.71672616414
841868823845.5875954008-5157.58759540084
852042423802.0764299369-3378.0764299369
862477623767.84380439311008.15619560692
871981423729.5770310579-3915.5770310579
881273823726.753127604-10988.753127604
893156623631.72013177617934.27986822392
903011123588.26659699496522.73340300513
913001923590.57182430426428.42817569578
923193423678.40098479048255.59901520962
932582623686.64217242132139.3578275787
942683523689.5237065583145.47629344202
952020523644.1683592466-3439.16835924656
961778923707.5044795709-5918.5044795709
972052023684.2216837465-3164.22168374648
982251823617.3124610927-1099.31246109265
991557223643.1886376401-8071.18863764009
1001150923616.9666769962-12107.9666769962
1012544723548.96247137051898.03752862952
1022409023389.6136336118700.386366388202
1032778623427.53462285064358.46537714942
1042619523492.25387956052702.74612043948
1052051623485.6839817289-2969.68398172888
1062275923494.3862148217-735.386214821668
1071902823517.0927038187-4489.09270381875
1081697123512.2517264691-6541.25172646912
1092003623478.5377770699-3442.5377770699
1102248523442.3457083131-957.345708313135
1111873023347.8313886299-4617.83138862987
1121453823309.9680300738-8771.96803007382
1132756123296.88586509334264.11413490673
1142598523266.05344983072718.94655016926
1153467023226.000125330811443.9998746692
1163206623163.12505046848902.87494953165
1172718622980.43578620254205.56421379748
1182958622880.10076756316705.89923243685
1192135923011.268201465-1652.26820146505
1202155323006.8882695773-1453.88826957729
1211957322945.9726379278-3372.97263792776
1222425622958.42086539821297.57913460175
1232238022892.2032109372-512.203210937222
1241616722797.7465219367-6630.74652193669
1252729722858.54689222074438.45310777926
1262828722891.28112001355395.71887998652
1273347422864.886267321410609.1137326786
1282822922858.02821607615370.97178392386
1292878522793.02080595255991.97919404748
1302559722781.4370387232815.56296127695
1311813022717.2940888404-4587.29408884044
1322019822472.6518406359-2274.65184063588
1332284922352.4918671361496.508132863891
1342311822493.2836250545624.716374945459
1352192522374.1033731612-449.103373161247
1362080122431.2153797503-1630.21537975034
1371878522379.2901346073-3594.29013460728
1382065922308.0586107484-1649.05861074843
1392936722401.53557814256965.46442185751
1402399222429.42882858561562.5711714144
1412064522455.4778971812-1810.47789718123
1422235622386.4363392663-30.4363392662589
1431790222346.0948613527-4444.09486135267
1441587922314.2827244837-6435.28272448367
1451696322236.4236721104-5273.42367211044







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.8988625797891350.2022748404217310.101137420210866
60.8232726361745940.3534547276508130.176727363825406
70.745610144204520.508779711590960.25438985579548
80.6427073545098210.7145852909803580.357292645490179
90.6585089706082730.6829820587834550.341491029391727
100.6499849375407360.7000301249185280.350015062459264
110.5547640369709790.8904719260580410.445235963029021
120.5121962264260990.9756075471478020.487803773573901
130.4561763841310520.9123527682621030.543823615868948
140.4344259223909970.8688518447819950.565574077609003
150.361276049147160.7225520982943190.63872395085284
160.5397020262434990.9205959475130030.460297973756501
170.8334206326824090.3331587346351810.166579367317591
180.9323034650977820.1353930698044360.0676965349022178
190.9519946605014370.09601067899712560.0480053394985628
200.9579794075168470.08404118496630710.0420205924831535
210.9442430468414780.1115139063170430.0557569531585217
220.932261483991990.135477032016020.0677385160080098
230.9187636743732180.1624726512535640.0812363256267819
240.9408332184219750.1183335631560490.0591667815780247
250.9330365990341110.1339268019317790.0669634009658893
260.9127741048722320.1744517902555360.0872258951277679
270.9292756160914650.1414487678170710.0707243839085353
280.9779735874341640.04405282513167260.0220264125658363
290.978847027926990.0423059441460210.0211529720730105
300.9716525660212350.0566948679575310.0283474339787655
310.9670077486754270.06598450264914540.0329922513245727
320.9560828993492780.08783420130144290.0439171006507215
330.9448427589221240.1103144821557520.055157241077876
340.9287831414760160.1424337170479670.0712168585239837
350.9145782942355320.1708434115289350.0854217057644675
360.9195448483844690.1609103032310620.0804551516155309
370.9073528502894590.1852942994210830.0926471497105414
380.8838814947426740.2322370105146520.116118505257326
390.889529280685540.2209414386289190.11047071931446
400.9122066947578090.1755866104843810.0877933052421906
410.8991192674039190.2017614651921620.100880732596081
420.8918004684112710.2163990631774580.108199531588729
430.9481512775037070.1036974449925860.051848722496293
440.956287989911330.08742402017733970.0437120100886698
450.9449240129894290.1101519740211420.0550759870105712
460.9300651267130320.1398697465739350.0699348732869677
470.9321086081667010.1357827836665990.0678913918332993
480.9358943049083690.1282113901832620.0641056950916312
490.9258513042941690.1482973914116610.0741486957058307
500.9078291833227980.1843416333544050.0921708166772024
510.8995689452358620.2008621095282770.100431054764138
520.9331594732899880.1336810534200250.0668405267100124
530.9318457857059410.1363084285881180.0681542142940588
540.9158464176949630.1683071646100740.0841535823050371
550.9116085298732930.1767829402534130.0883914701267066
560.8954063580324930.2091872839350130.104593641967507
570.8731406176512610.2537187646974780.126859382348739
580.8569546306287240.2860907387425510.143045369371276
590.8514635286240650.2970729427518690.148536471375935
600.8375952166651150.324809566669770.162404783334885
610.8135341164550560.3729317670898870.186465883544944
620.7852816518049870.4294366963900260.214718348195013
630.761028321584790.4779433568304210.23897167841521
640.8247056353069530.3505887293860930.175294364693047
650.9078395609932090.1843208780135810.0921604390067907
660.9208917922845210.1582164154309590.0791082077154794
670.9586512337457490.08269753250850250.0413487662542512
680.9515361229897390.09692775402052250.0484638770102612
690.9421400399079650.115719920184070.0578599600920348
700.9275087383893720.1449825232212560.0724912616106282
710.9252315070526880.1495369858946240.0747684929473118
720.9256684097010940.1486631805978120.0743315902989059
730.9208358254438540.1583283491122910.0791641745561457
740.9044548669421740.1910902661156520.0955451330578261
750.9030993513802370.1938012972395260.096900648619763
760.9677927490774490.06441450184510110.0322072509225505
770.9750846951849520.04983060963009590.0249153048150479
780.9703505541138360.05929889177232780.0296494458861639
790.9753064501107360.04938709977852830.0246935498892641
800.9692025086751560.06159498264968880.0307974913248444
810.9607917573467510.07841648530649780.0392082426532489
820.9516881514836150.09662369703276960.0483118485163848
830.941515502037710.116968995924580.05848449796229
840.9359690118658890.1280619762682220.0640309881341112
850.9234709171767530.1530581656464940.0765290828232468
860.9056977148493560.1886045703012870.0943022851506436
870.8913803181697610.2172393636604790.108619681830239
880.9368969992536080.1262060014927830.0631030007463916
890.9529591781259840.09408164374803270.0470408218740164
900.9583863463741240.0832273072517520.041613653625876
910.9627444638648930.0745110722702140.037255536135107
920.974903615656640.05019276868672010.0250963843433601
930.9687593356528090.06248132869438240.0312406643471912
940.9640022039673950.07199559206520930.0359977960326046
950.9553954863193510.08920902736129750.0446045136806488
960.9532763312862320.09344733742753570.0467236687137678
970.9421600234447820.1156799531104370.0578399765552185
980.9254850043656710.1490299912686580.0745149956343289
990.9398667381321510.1202665237356970.0601332618678487
1000.9806218374849140.03875632503017230.0193781625150861
1010.9740067578619710.0519864842760580.025993242138029
1020.9648641642216760.07027167155664870.0351358357783243
1030.9597481396490750.08050372070184930.0402518603509246
1040.9491878516649940.1016242966700120.0508121483350058
1050.9387877662030330.1224244675939340.0612122337969672
1060.9209140087544720.1581719824910550.0790859912455277
1070.9184454431015340.1631091137969320.081554556898466
1080.9392781517218280.1214436965563440.0607218482781722
1090.9407585381545940.1184829236908120.0592414618454062
1100.9319203355949810.1361593288100380.0680796644050191
1110.9503996766393390.09920064672132190.049600323360661
1120.9931385116695190.01372297666096260.00686148833048129
1130.9906377022770620.01872459544587630.00936229772293815
1140.9883437804345480.02331243913090440.0116562195654522
1150.9920884690839290.01582306183214230.00791153091607115
1160.9925500976315460.0148998047369080.00744990236845401
1170.9891170931195160.02176581376096770.0108829068804838
1180.9893541334463830.02129173310723410.010645866553617
1190.9872919589465180.02541608210696340.0127080410534817
1200.9855749998488150.02885000030236960.0144250001511848
1210.9895969655550410.02080606888991790.010403034444959
1220.9854307146591310.0291385706817370.0145692853408685
1230.9837467139967120.03250657200657640.0162532860032882
1240.9980484860412840.003903027917432240.00195151395871612
1250.9964973114296970.007005377140606230.00350268857030312
1260.9938065609090940.01238687818181170.00619343909090587
1270.9960735358440290.007852928311942780.00392646415597139
1280.9932545873308510.01349082533829810.00674541266914906
1290.9923996360290150.01520072794196970.00760036397098487
1300.9881663885672160.0236672228655680.011833611432784
1310.9942106020915540.01157879581689140.00578939790844572
1320.9920943289816770.01581134203664550.00790567101832274
1330.9870372875353910.0259254249292170.0129627124646085
1340.9753875370162170.04922492596756580.0246124629837829
1350.9533167718086940.09336645638261230.0466832281913062
1360.9234184208165420.1531631583669160.0765815791834578
1370.8859259623748460.2281480752503070.114074037625154
1380.8125010421034210.3749979157931570.187498957896579
1390.9645938199489290.07081236010214280.0354061800510714
1400.9446252401125980.1107495197748040.0553747598874018

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.898862579789135 & 0.202274840421731 & 0.101137420210866 \tabularnewline
6 & 0.823272636174594 & 0.353454727650813 & 0.176727363825406 \tabularnewline
7 & 0.74561014420452 & 0.50877971159096 & 0.25438985579548 \tabularnewline
8 & 0.642707354509821 & 0.714585290980358 & 0.357292645490179 \tabularnewline
9 & 0.658508970608273 & 0.682982058783455 & 0.341491029391727 \tabularnewline
10 & 0.649984937540736 & 0.700030124918528 & 0.350015062459264 \tabularnewline
11 & 0.554764036970979 & 0.890471926058041 & 0.445235963029021 \tabularnewline
12 & 0.512196226426099 & 0.975607547147802 & 0.487803773573901 \tabularnewline
13 & 0.456176384131052 & 0.912352768262103 & 0.543823615868948 \tabularnewline
14 & 0.434425922390997 & 0.868851844781995 & 0.565574077609003 \tabularnewline
15 & 0.36127604914716 & 0.722552098294319 & 0.63872395085284 \tabularnewline
16 & 0.539702026243499 & 0.920595947513003 & 0.460297973756501 \tabularnewline
17 & 0.833420632682409 & 0.333158734635181 & 0.166579367317591 \tabularnewline
18 & 0.932303465097782 & 0.135393069804436 & 0.0676965349022178 \tabularnewline
19 & 0.951994660501437 & 0.0960106789971256 & 0.0480053394985628 \tabularnewline
20 & 0.957979407516847 & 0.0840411849663071 & 0.0420205924831535 \tabularnewline
21 & 0.944243046841478 & 0.111513906317043 & 0.0557569531585217 \tabularnewline
22 & 0.93226148399199 & 0.13547703201602 & 0.0677385160080098 \tabularnewline
23 & 0.918763674373218 & 0.162472651253564 & 0.0812363256267819 \tabularnewline
24 & 0.940833218421975 & 0.118333563156049 & 0.0591667815780247 \tabularnewline
25 & 0.933036599034111 & 0.133926801931779 & 0.0669634009658893 \tabularnewline
26 & 0.912774104872232 & 0.174451790255536 & 0.0872258951277679 \tabularnewline
27 & 0.929275616091465 & 0.141448767817071 & 0.0707243839085353 \tabularnewline
28 & 0.977973587434164 & 0.0440528251316726 & 0.0220264125658363 \tabularnewline
29 & 0.97884702792699 & 0.042305944146021 & 0.0211529720730105 \tabularnewline
30 & 0.971652566021235 & 0.056694867957531 & 0.0283474339787655 \tabularnewline
31 & 0.967007748675427 & 0.0659845026491454 & 0.0329922513245727 \tabularnewline
32 & 0.956082899349278 & 0.0878342013014429 & 0.0439171006507215 \tabularnewline
33 & 0.944842758922124 & 0.110314482155752 & 0.055157241077876 \tabularnewline
34 & 0.928783141476016 & 0.142433717047967 & 0.0712168585239837 \tabularnewline
35 & 0.914578294235532 & 0.170843411528935 & 0.0854217057644675 \tabularnewline
36 & 0.919544848384469 & 0.160910303231062 & 0.0804551516155309 \tabularnewline
37 & 0.907352850289459 & 0.185294299421083 & 0.0926471497105414 \tabularnewline
38 & 0.883881494742674 & 0.232237010514652 & 0.116118505257326 \tabularnewline
39 & 0.88952928068554 & 0.220941438628919 & 0.11047071931446 \tabularnewline
40 & 0.912206694757809 & 0.175586610484381 & 0.0877933052421906 \tabularnewline
41 & 0.899119267403919 & 0.201761465192162 & 0.100880732596081 \tabularnewline
42 & 0.891800468411271 & 0.216399063177458 & 0.108199531588729 \tabularnewline
43 & 0.948151277503707 & 0.103697444992586 & 0.051848722496293 \tabularnewline
44 & 0.95628798991133 & 0.0874240201773397 & 0.0437120100886698 \tabularnewline
45 & 0.944924012989429 & 0.110151974021142 & 0.0550759870105712 \tabularnewline
46 & 0.930065126713032 & 0.139869746573935 & 0.0699348732869677 \tabularnewline
47 & 0.932108608166701 & 0.135782783666599 & 0.0678913918332993 \tabularnewline
48 & 0.935894304908369 & 0.128211390183262 & 0.0641056950916312 \tabularnewline
49 & 0.925851304294169 & 0.148297391411661 & 0.0741486957058307 \tabularnewline
50 & 0.907829183322798 & 0.184341633354405 & 0.0921708166772024 \tabularnewline
51 & 0.899568945235862 & 0.200862109528277 & 0.100431054764138 \tabularnewline
52 & 0.933159473289988 & 0.133681053420025 & 0.0668405267100124 \tabularnewline
53 & 0.931845785705941 & 0.136308428588118 & 0.0681542142940588 \tabularnewline
54 & 0.915846417694963 & 0.168307164610074 & 0.0841535823050371 \tabularnewline
55 & 0.911608529873293 & 0.176782940253413 & 0.0883914701267066 \tabularnewline
56 & 0.895406358032493 & 0.209187283935013 & 0.104593641967507 \tabularnewline
57 & 0.873140617651261 & 0.253718764697478 & 0.126859382348739 \tabularnewline
58 & 0.856954630628724 & 0.286090738742551 & 0.143045369371276 \tabularnewline
59 & 0.851463528624065 & 0.297072942751869 & 0.148536471375935 \tabularnewline
60 & 0.837595216665115 & 0.32480956666977 & 0.162404783334885 \tabularnewline
61 & 0.813534116455056 & 0.372931767089887 & 0.186465883544944 \tabularnewline
62 & 0.785281651804987 & 0.429436696390026 & 0.214718348195013 \tabularnewline
63 & 0.76102832158479 & 0.477943356830421 & 0.23897167841521 \tabularnewline
64 & 0.824705635306953 & 0.350588729386093 & 0.175294364693047 \tabularnewline
65 & 0.907839560993209 & 0.184320878013581 & 0.0921604390067907 \tabularnewline
66 & 0.920891792284521 & 0.158216415430959 & 0.0791082077154794 \tabularnewline
67 & 0.958651233745749 & 0.0826975325085025 & 0.0413487662542512 \tabularnewline
68 & 0.951536122989739 & 0.0969277540205225 & 0.0484638770102612 \tabularnewline
69 & 0.942140039907965 & 0.11571992018407 & 0.0578599600920348 \tabularnewline
70 & 0.927508738389372 & 0.144982523221256 & 0.0724912616106282 \tabularnewline
71 & 0.925231507052688 & 0.149536985894624 & 0.0747684929473118 \tabularnewline
72 & 0.925668409701094 & 0.148663180597812 & 0.0743315902989059 \tabularnewline
73 & 0.920835825443854 & 0.158328349112291 & 0.0791641745561457 \tabularnewline
74 & 0.904454866942174 & 0.191090266115652 & 0.0955451330578261 \tabularnewline
75 & 0.903099351380237 & 0.193801297239526 & 0.096900648619763 \tabularnewline
76 & 0.967792749077449 & 0.0644145018451011 & 0.0322072509225505 \tabularnewline
77 & 0.975084695184952 & 0.0498306096300959 & 0.0249153048150479 \tabularnewline
78 & 0.970350554113836 & 0.0592988917723278 & 0.0296494458861639 \tabularnewline
79 & 0.975306450110736 & 0.0493870997785283 & 0.0246935498892641 \tabularnewline
80 & 0.969202508675156 & 0.0615949826496888 & 0.0307974913248444 \tabularnewline
81 & 0.960791757346751 & 0.0784164853064978 & 0.0392082426532489 \tabularnewline
82 & 0.951688151483615 & 0.0966236970327696 & 0.0483118485163848 \tabularnewline
83 & 0.94151550203771 & 0.11696899592458 & 0.05848449796229 \tabularnewline
84 & 0.935969011865889 & 0.128061976268222 & 0.0640309881341112 \tabularnewline
85 & 0.923470917176753 & 0.153058165646494 & 0.0765290828232468 \tabularnewline
86 & 0.905697714849356 & 0.188604570301287 & 0.0943022851506436 \tabularnewline
87 & 0.891380318169761 & 0.217239363660479 & 0.108619681830239 \tabularnewline
88 & 0.936896999253608 & 0.126206001492783 & 0.0631030007463916 \tabularnewline
89 & 0.952959178125984 & 0.0940816437480327 & 0.0470408218740164 \tabularnewline
90 & 0.958386346374124 & 0.083227307251752 & 0.041613653625876 \tabularnewline
91 & 0.962744463864893 & 0.074511072270214 & 0.037255536135107 \tabularnewline
92 & 0.97490361565664 & 0.0501927686867201 & 0.0250963843433601 \tabularnewline
93 & 0.968759335652809 & 0.0624813286943824 & 0.0312406643471912 \tabularnewline
94 & 0.964002203967395 & 0.0719955920652093 & 0.0359977960326046 \tabularnewline
95 & 0.955395486319351 & 0.0892090273612975 & 0.0446045136806488 \tabularnewline
96 & 0.953276331286232 & 0.0934473374275357 & 0.0467236687137678 \tabularnewline
97 & 0.942160023444782 & 0.115679953110437 & 0.0578399765552185 \tabularnewline
98 & 0.925485004365671 & 0.149029991268658 & 0.0745149956343289 \tabularnewline
99 & 0.939866738132151 & 0.120266523735697 & 0.0601332618678487 \tabularnewline
100 & 0.980621837484914 & 0.0387563250301723 & 0.0193781625150861 \tabularnewline
101 & 0.974006757861971 & 0.051986484276058 & 0.025993242138029 \tabularnewline
102 & 0.964864164221676 & 0.0702716715566487 & 0.0351358357783243 \tabularnewline
103 & 0.959748139649075 & 0.0805037207018493 & 0.0402518603509246 \tabularnewline
104 & 0.949187851664994 & 0.101624296670012 & 0.0508121483350058 \tabularnewline
105 & 0.938787766203033 & 0.122424467593934 & 0.0612122337969672 \tabularnewline
106 & 0.920914008754472 & 0.158171982491055 & 0.0790859912455277 \tabularnewline
107 & 0.918445443101534 & 0.163109113796932 & 0.081554556898466 \tabularnewline
108 & 0.939278151721828 & 0.121443696556344 & 0.0607218482781722 \tabularnewline
109 & 0.940758538154594 & 0.118482923690812 & 0.0592414618454062 \tabularnewline
110 & 0.931920335594981 & 0.136159328810038 & 0.0680796644050191 \tabularnewline
111 & 0.950399676639339 & 0.0992006467213219 & 0.049600323360661 \tabularnewline
112 & 0.993138511669519 & 0.0137229766609626 & 0.00686148833048129 \tabularnewline
113 & 0.990637702277062 & 0.0187245954458763 & 0.00936229772293815 \tabularnewline
114 & 0.988343780434548 & 0.0233124391309044 & 0.0116562195654522 \tabularnewline
115 & 0.992088469083929 & 0.0158230618321423 & 0.00791153091607115 \tabularnewline
116 & 0.992550097631546 & 0.014899804736908 & 0.00744990236845401 \tabularnewline
117 & 0.989117093119516 & 0.0217658137609677 & 0.0108829068804838 \tabularnewline
118 & 0.989354133446383 & 0.0212917331072341 & 0.010645866553617 \tabularnewline
119 & 0.987291958946518 & 0.0254160821069634 & 0.0127080410534817 \tabularnewline
120 & 0.985574999848815 & 0.0288500003023696 & 0.0144250001511848 \tabularnewline
121 & 0.989596965555041 & 0.0208060688899179 & 0.010403034444959 \tabularnewline
122 & 0.985430714659131 & 0.029138570681737 & 0.0145692853408685 \tabularnewline
123 & 0.983746713996712 & 0.0325065720065764 & 0.0162532860032882 \tabularnewline
124 & 0.998048486041284 & 0.00390302791743224 & 0.00195151395871612 \tabularnewline
125 & 0.996497311429697 & 0.00700537714060623 & 0.00350268857030312 \tabularnewline
126 & 0.993806560909094 & 0.0123868781818117 & 0.00619343909090587 \tabularnewline
127 & 0.996073535844029 & 0.00785292831194278 & 0.00392646415597139 \tabularnewline
128 & 0.993254587330851 & 0.0134908253382981 & 0.00674541266914906 \tabularnewline
129 & 0.992399636029015 & 0.0152007279419697 & 0.00760036397098487 \tabularnewline
130 & 0.988166388567216 & 0.023667222865568 & 0.011833611432784 \tabularnewline
131 & 0.994210602091554 & 0.0115787958168914 & 0.00578939790844572 \tabularnewline
132 & 0.992094328981677 & 0.0158113420366455 & 0.00790567101832274 \tabularnewline
133 & 0.987037287535391 & 0.025925424929217 & 0.0129627124646085 \tabularnewline
134 & 0.975387537016217 & 0.0492249259675658 & 0.0246124629837829 \tabularnewline
135 & 0.953316771808694 & 0.0933664563826123 & 0.0466832281913062 \tabularnewline
136 & 0.923418420816542 & 0.153163158366916 & 0.0765815791834578 \tabularnewline
137 & 0.885925962374846 & 0.228148075250307 & 0.114074037625154 \tabularnewline
138 & 0.812501042103421 & 0.374997915793157 & 0.187498957896579 \tabularnewline
139 & 0.964593819948929 & 0.0708123601021428 & 0.0354061800510714 \tabularnewline
140 & 0.944625240112598 & 0.110749519774804 & 0.0553747598874018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&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]5[/C][C]0.898862579789135[/C][C]0.202274840421731[/C][C]0.101137420210866[/C][/ROW]
[ROW][C]6[/C][C]0.823272636174594[/C][C]0.353454727650813[/C][C]0.176727363825406[/C][/ROW]
[ROW][C]7[/C][C]0.74561014420452[/C][C]0.50877971159096[/C][C]0.25438985579548[/C][/ROW]
[ROW][C]8[/C][C]0.642707354509821[/C][C]0.714585290980358[/C][C]0.357292645490179[/C][/ROW]
[ROW][C]9[/C][C]0.658508970608273[/C][C]0.682982058783455[/C][C]0.341491029391727[/C][/ROW]
[ROW][C]10[/C][C]0.649984937540736[/C][C]0.700030124918528[/C][C]0.350015062459264[/C][/ROW]
[ROW][C]11[/C][C]0.554764036970979[/C][C]0.890471926058041[/C][C]0.445235963029021[/C][/ROW]
[ROW][C]12[/C][C]0.512196226426099[/C][C]0.975607547147802[/C][C]0.487803773573901[/C][/ROW]
[ROW][C]13[/C][C]0.456176384131052[/C][C]0.912352768262103[/C][C]0.543823615868948[/C][/ROW]
[ROW][C]14[/C][C]0.434425922390997[/C][C]0.868851844781995[/C][C]0.565574077609003[/C][/ROW]
[ROW][C]15[/C][C]0.36127604914716[/C][C]0.722552098294319[/C][C]0.63872395085284[/C][/ROW]
[ROW][C]16[/C][C]0.539702026243499[/C][C]0.920595947513003[/C][C]0.460297973756501[/C][/ROW]
[ROW][C]17[/C][C]0.833420632682409[/C][C]0.333158734635181[/C][C]0.166579367317591[/C][/ROW]
[ROW][C]18[/C][C]0.932303465097782[/C][C]0.135393069804436[/C][C]0.0676965349022178[/C][/ROW]
[ROW][C]19[/C][C]0.951994660501437[/C][C]0.0960106789971256[/C][C]0.0480053394985628[/C][/ROW]
[ROW][C]20[/C][C]0.957979407516847[/C][C]0.0840411849663071[/C][C]0.0420205924831535[/C][/ROW]
[ROW][C]21[/C][C]0.944243046841478[/C][C]0.111513906317043[/C][C]0.0557569531585217[/C][/ROW]
[ROW][C]22[/C][C]0.93226148399199[/C][C]0.13547703201602[/C][C]0.0677385160080098[/C][/ROW]
[ROW][C]23[/C][C]0.918763674373218[/C][C]0.162472651253564[/C][C]0.0812363256267819[/C][/ROW]
[ROW][C]24[/C][C]0.940833218421975[/C][C]0.118333563156049[/C][C]0.0591667815780247[/C][/ROW]
[ROW][C]25[/C][C]0.933036599034111[/C][C]0.133926801931779[/C][C]0.0669634009658893[/C][/ROW]
[ROW][C]26[/C][C]0.912774104872232[/C][C]0.174451790255536[/C][C]0.0872258951277679[/C][/ROW]
[ROW][C]27[/C][C]0.929275616091465[/C][C]0.141448767817071[/C][C]0.0707243839085353[/C][/ROW]
[ROW][C]28[/C][C]0.977973587434164[/C][C]0.0440528251316726[/C][C]0.0220264125658363[/C][/ROW]
[ROW][C]29[/C][C]0.97884702792699[/C][C]0.042305944146021[/C][C]0.0211529720730105[/C][/ROW]
[ROW][C]30[/C][C]0.971652566021235[/C][C]0.056694867957531[/C][C]0.0283474339787655[/C][/ROW]
[ROW][C]31[/C][C]0.967007748675427[/C][C]0.0659845026491454[/C][C]0.0329922513245727[/C][/ROW]
[ROW][C]32[/C][C]0.956082899349278[/C][C]0.0878342013014429[/C][C]0.0439171006507215[/C][/ROW]
[ROW][C]33[/C][C]0.944842758922124[/C][C]0.110314482155752[/C][C]0.055157241077876[/C][/ROW]
[ROW][C]34[/C][C]0.928783141476016[/C][C]0.142433717047967[/C][C]0.0712168585239837[/C][/ROW]
[ROW][C]35[/C][C]0.914578294235532[/C][C]0.170843411528935[/C][C]0.0854217057644675[/C][/ROW]
[ROW][C]36[/C][C]0.919544848384469[/C][C]0.160910303231062[/C][C]0.0804551516155309[/C][/ROW]
[ROW][C]37[/C][C]0.907352850289459[/C][C]0.185294299421083[/C][C]0.0926471497105414[/C][/ROW]
[ROW][C]38[/C][C]0.883881494742674[/C][C]0.232237010514652[/C][C]0.116118505257326[/C][/ROW]
[ROW][C]39[/C][C]0.88952928068554[/C][C]0.220941438628919[/C][C]0.11047071931446[/C][/ROW]
[ROW][C]40[/C][C]0.912206694757809[/C][C]0.175586610484381[/C][C]0.0877933052421906[/C][/ROW]
[ROW][C]41[/C][C]0.899119267403919[/C][C]0.201761465192162[/C][C]0.100880732596081[/C][/ROW]
[ROW][C]42[/C][C]0.891800468411271[/C][C]0.216399063177458[/C][C]0.108199531588729[/C][/ROW]
[ROW][C]43[/C][C]0.948151277503707[/C][C]0.103697444992586[/C][C]0.051848722496293[/C][/ROW]
[ROW][C]44[/C][C]0.95628798991133[/C][C]0.0874240201773397[/C][C]0.0437120100886698[/C][/ROW]
[ROW][C]45[/C][C]0.944924012989429[/C][C]0.110151974021142[/C][C]0.0550759870105712[/C][/ROW]
[ROW][C]46[/C][C]0.930065126713032[/C][C]0.139869746573935[/C][C]0.0699348732869677[/C][/ROW]
[ROW][C]47[/C][C]0.932108608166701[/C][C]0.135782783666599[/C][C]0.0678913918332993[/C][/ROW]
[ROW][C]48[/C][C]0.935894304908369[/C][C]0.128211390183262[/C][C]0.0641056950916312[/C][/ROW]
[ROW][C]49[/C][C]0.925851304294169[/C][C]0.148297391411661[/C][C]0.0741486957058307[/C][/ROW]
[ROW][C]50[/C][C]0.907829183322798[/C][C]0.184341633354405[/C][C]0.0921708166772024[/C][/ROW]
[ROW][C]51[/C][C]0.899568945235862[/C][C]0.200862109528277[/C][C]0.100431054764138[/C][/ROW]
[ROW][C]52[/C][C]0.933159473289988[/C][C]0.133681053420025[/C][C]0.0668405267100124[/C][/ROW]
[ROW][C]53[/C][C]0.931845785705941[/C][C]0.136308428588118[/C][C]0.0681542142940588[/C][/ROW]
[ROW][C]54[/C][C]0.915846417694963[/C][C]0.168307164610074[/C][C]0.0841535823050371[/C][/ROW]
[ROW][C]55[/C][C]0.911608529873293[/C][C]0.176782940253413[/C][C]0.0883914701267066[/C][/ROW]
[ROW][C]56[/C][C]0.895406358032493[/C][C]0.209187283935013[/C][C]0.104593641967507[/C][/ROW]
[ROW][C]57[/C][C]0.873140617651261[/C][C]0.253718764697478[/C][C]0.126859382348739[/C][/ROW]
[ROW][C]58[/C][C]0.856954630628724[/C][C]0.286090738742551[/C][C]0.143045369371276[/C][/ROW]
[ROW][C]59[/C][C]0.851463528624065[/C][C]0.297072942751869[/C][C]0.148536471375935[/C][/ROW]
[ROW][C]60[/C][C]0.837595216665115[/C][C]0.32480956666977[/C][C]0.162404783334885[/C][/ROW]
[ROW][C]61[/C][C]0.813534116455056[/C][C]0.372931767089887[/C][C]0.186465883544944[/C][/ROW]
[ROW][C]62[/C][C]0.785281651804987[/C][C]0.429436696390026[/C][C]0.214718348195013[/C][/ROW]
[ROW][C]63[/C][C]0.76102832158479[/C][C]0.477943356830421[/C][C]0.23897167841521[/C][/ROW]
[ROW][C]64[/C][C]0.824705635306953[/C][C]0.350588729386093[/C][C]0.175294364693047[/C][/ROW]
[ROW][C]65[/C][C]0.907839560993209[/C][C]0.184320878013581[/C][C]0.0921604390067907[/C][/ROW]
[ROW][C]66[/C][C]0.920891792284521[/C][C]0.158216415430959[/C][C]0.0791082077154794[/C][/ROW]
[ROW][C]67[/C][C]0.958651233745749[/C][C]0.0826975325085025[/C][C]0.0413487662542512[/C][/ROW]
[ROW][C]68[/C][C]0.951536122989739[/C][C]0.0969277540205225[/C][C]0.0484638770102612[/C][/ROW]
[ROW][C]69[/C][C]0.942140039907965[/C][C]0.11571992018407[/C][C]0.0578599600920348[/C][/ROW]
[ROW][C]70[/C][C]0.927508738389372[/C][C]0.144982523221256[/C][C]0.0724912616106282[/C][/ROW]
[ROW][C]71[/C][C]0.925231507052688[/C][C]0.149536985894624[/C][C]0.0747684929473118[/C][/ROW]
[ROW][C]72[/C][C]0.925668409701094[/C][C]0.148663180597812[/C][C]0.0743315902989059[/C][/ROW]
[ROW][C]73[/C][C]0.920835825443854[/C][C]0.158328349112291[/C][C]0.0791641745561457[/C][/ROW]
[ROW][C]74[/C][C]0.904454866942174[/C][C]0.191090266115652[/C][C]0.0955451330578261[/C][/ROW]
[ROW][C]75[/C][C]0.903099351380237[/C][C]0.193801297239526[/C][C]0.096900648619763[/C][/ROW]
[ROW][C]76[/C][C]0.967792749077449[/C][C]0.0644145018451011[/C][C]0.0322072509225505[/C][/ROW]
[ROW][C]77[/C][C]0.975084695184952[/C][C]0.0498306096300959[/C][C]0.0249153048150479[/C][/ROW]
[ROW][C]78[/C][C]0.970350554113836[/C][C]0.0592988917723278[/C][C]0.0296494458861639[/C][/ROW]
[ROW][C]79[/C][C]0.975306450110736[/C][C]0.0493870997785283[/C][C]0.0246935498892641[/C][/ROW]
[ROW][C]80[/C][C]0.969202508675156[/C][C]0.0615949826496888[/C][C]0.0307974913248444[/C][/ROW]
[ROW][C]81[/C][C]0.960791757346751[/C][C]0.0784164853064978[/C][C]0.0392082426532489[/C][/ROW]
[ROW][C]82[/C][C]0.951688151483615[/C][C]0.0966236970327696[/C][C]0.0483118485163848[/C][/ROW]
[ROW][C]83[/C][C]0.94151550203771[/C][C]0.11696899592458[/C][C]0.05848449796229[/C][/ROW]
[ROW][C]84[/C][C]0.935969011865889[/C][C]0.128061976268222[/C][C]0.0640309881341112[/C][/ROW]
[ROW][C]85[/C][C]0.923470917176753[/C][C]0.153058165646494[/C][C]0.0765290828232468[/C][/ROW]
[ROW][C]86[/C][C]0.905697714849356[/C][C]0.188604570301287[/C][C]0.0943022851506436[/C][/ROW]
[ROW][C]87[/C][C]0.891380318169761[/C][C]0.217239363660479[/C][C]0.108619681830239[/C][/ROW]
[ROW][C]88[/C][C]0.936896999253608[/C][C]0.126206001492783[/C][C]0.0631030007463916[/C][/ROW]
[ROW][C]89[/C][C]0.952959178125984[/C][C]0.0940816437480327[/C][C]0.0470408218740164[/C][/ROW]
[ROW][C]90[/C][C]0.958386346374124[/C][C]0.083227307251752[/C][C]0.041613653625876[/C][/ROW]
[ROW][C]91[/C][C]0.962744463864893[/C][C]0.074511072270214[/C][C]0.037255536135107[/C][/ROW]
[ROW][C]92[/C][C]0.97490361565664[/C][C]0.0501927686867201[/C][C]0.0250963843433601[/C][/ROW]
[ROW][C]93[/C][C]0.968759335652809[/C][C]0.0624813286943824[/C][C]0.0312406643471912[/C][/ROW]
[ROW][C]94[/C][C]0.964002203967395[/C][C]0.0719955920652093[/C][C]0.0359977960326046[/C][/ROW]
[ROW][C]95[/C][C]0.955395486319351[/C][C]0.0892090273612975[/C][C]0.0446045136806488[/C][/ROW]
[ROW][C]96[/C][C]0.953276331286232[/C][C]0.0934473374275357[/C][C]0.0467236687137678[/C][/ROW]
[ROW][C]97[/C][C]0.942160023444782[/C][C]0.115679953110437[/C][C]0.0578399765552185[/C][/ROW]
[ROW][C]98[/C][C]0.925485004365671[/C][C]0.149029991268658[/C][C]0.0745149956343289[/C][/ROW]
[ROW][C]99[/C][C]0.939866738132151[/C][C]0.120266523735697[/C][C]0.0601332618678487[/C][/ROW]
[ROW][C]100[/C][C]0.980621837484914[/C][C]0.0387563250301723[/C][C]0.0193781625150861[/C][/ROW]
[ROW][C]101[/C][C]0.974006757861971[/C][C]0.051986484276058[/C][C]0.025993242138029[/C][/ROW]
[ROW][C]102[/C][C]0.964864164221676[/C][C]0.0702716715566487[/C][C]0.0351358357783243[/C][/ROW]
[ROW][C]103[/C][C]0.959748139649075[/C][C]0.0805037207018493[/C][C]0.0402518603509246[/C][/ROW]
[ROW][C]104[/C][C]0.949187851664994[/C][C]0.101624296670012[/C][C]0.0508121483350058[/C][/ROW]
[ROW][C]105[/C][C]0.938787766203033[/C][C]0.122424467593934[/C][C]0.0612122337969672[/C][/ROW]
[ROW][C]106[/C][C]0.920914008754472[/C][C]0.158171982491055[/C][C]0.0790859912455277[/C][/ROW]
[ROW][C]107[/C][C]0.918445443101534[/C][C]0.163109113796932[/C][C]0.081554556898466[/C][/ROW]
[ROW][C]108[/C][C]0.939278151721828[/C][C]0.121443696556344[/C][C]0.0607218482781722[/C][/ROW]
[ROW][C]109[/C][C]0.940758538154594[/C][C]0.118482923690812[/C][C]0.0592414618454062[/C][/ROW]
[ROW][C]110[/C][C]0.931920335594981[/C][C]0.136159328810038[/C][C]0.0680796644050191[/C][/ROW]
[ROW][C]111[/C][C]0.950399676639339[/C][C]0.0992006467213219[/C][C]0.049600323360661[/C][/ROW]
[ROW][C]112[/C][C]0.993138511669519[/C][C]0.0137229766609626[/C][C]0.00686148833048129[/C][/ROW]
[ROW][C]113[/C][C]0.990637702277062[/C][C]0.0187245954458763[/C][C]0.00936229772293815[/C][/ROW]
[ROW][C]114[/C][C]0.988343780434548[/C][C]0.0233124391309044[/C][C]0.0116562195654522[/C][/ROW]
[ROW][C]115[/C][C]0.992088469083929[/C][C]0.0158230618321423[/C][C]0.00791153091607115[/C][/ROW]
[ROW][C]116[/C][C]0.992550097631546[/C][C]0.014899804736908[/C][C]0.00744990236845401[/C][/ROW]
[ROW][C]117[/C][C]0.989117093119516[/C][C]0.0217658137609677[/C][C]0.0108829068804838[/C][/ROW]
[ROW][C]118[/C][C]0.989354133446383[/C][C]0.0212917331072341[/C][C]0.010645866553617[/C][/ROW]
[ROW][C]119[/C][C]0.987291958946518[/C][C]0.0254160821069634[/C][C]0.0127080410534817[/C][/ROW]
[ROW][C]120[/C][C]0.985574999848815[/C][C]0.0288500003023696[/C][C]0.0144250001511848[/C][/ROW]
[ROW][C]121[/C][C]0.989596965555041[/C][C]0.0208060688899179[/C][C]0.010403034444959[/C][/ROW]
[ROW][C]122[/C][C]0.985430714659131[/C][C]0.029138570681737[/C][C]0.0145692853408685[/C][/ROW]
[ROW][C]123[/C][C]0.983746713996712[/C][C]0.0325065720065764[/C][C]0.0162532860032882[/C][/ROW]
[ROW][C]124[/C][C]0.998048486041284[/C][C]0.00390302791743224[/C][C]0.00195151395871612[/C][/ROW]
[ROW][C]125[/C][C]0.996497311429697[/C][C]0.00700537714060623[/C][C]0.00350268857030312[/C][/ROW]
[ROW][C]126[/C][C]0.993806560909094[/C][C]0.0123868781818117[/C][C]0.00619343909090587[/C][/ROW]
[ROW][C]127[/C][C]0.996073535844029[/C][C]0.00785292831194278[/C][C]0.00392646415597139[/C][/ROW]
[ROW][C]128[/C][C]0.993254587330851[/C][C]0.0134908253382981[/C][C]0.00674541266914906[/C][/ROW]
[ROW][C]129[/C][C]0.992399636029015[/C][C]0.0152007279419697[/C][C]0.00760036397098487[/C][/ROW]
[ROW][C]130[/C][C]0.988166388567216[/C][C]0.023667222865568[/C][C]0.011833611432784[/C][/ROW]
[ROW][C]131[/C][C]0.994210602091554[/C][C]0.0115787958168914[/C][C]0.00578939790844572[/C][/ROW]
[ROW][C]132[/C][C]0.992094328981677[/C][C]0.0158113420366455[/C][C]0.00790567101832274[/C][/ROW]
[ROW][C]133[/C][C]0.987037287535391[/C][C]0.025925424929217[/C][C]0.0129627124646085[/C][/ROW]
[ROW][C]134[/C][C]0.975387537016217[/C][C]0.0492249259675658[/C][C]0.0246124629837829[/C][/ROW]
[ROW][C]135[/C][C]0.953316771808694[/C][C]0.0933664563826123[/C][C]0.0466832281913062[/C][/ROW]
[ROW][C]136[/C][C]0.923418420816542[/C][C]0.153163158366916[/C][C]0.0765815791834578[/C][/ROW]
[ROW][C]137[/C][C]0.885925962374846[/C][C]0.228148075250307[/C][C]0.114074037625154[/C][/ROW]
[ROW][C]138[/C][C]0.812501042103421[/C][C]0.374997915793157[/C][C]0.187498957896579[/C][/ROW]
[ROW][C]139[/C][C]0.964593819948929[/C][C]0.0708123601021428[/C][C]0.0354061800510714[/C][/ROW]
[ROW][C]140[/C][C]0.944625240112598[/C][C]0.110749519774804[/C][C]0.0553747598874018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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
50.8988625797891350.2022748404217310.101137420210866
60.8232726361745940.3534547276508130.176727363825406
70.745610144204520.508779711590960.25438985579548
80.6427073545098210.7145852909803580.357292645490179
90.6585089706082730.6829820587834550.341491029391727
100.6499849375407360.7000301249185280.350015062459264
110.5547640369709790.8904719260580410.445235963029021
120.5121962264260990.9756075471478020.487803773573901
130.4561763841310520.9123527682621030.543823615868948
140.4344259223909970.8688518447819950.565574077609003
150.361276049147160.7225520982943190.63872395085284
160.5397020262434990.9205959475130030.460297973756501
170.8334206326824090.3331587346351810.166579367317591
180.9323034650977820.1353930698044360.0676965349022178
190.9519946605014370.09601067899712560.0480053394985628
200.9579794075168470.08404118496630710.0420205924831535
210.9442430468414780.1115139063170430.0557569531585217
220.932261483991990.135477032016020.0677385160080098
230.9187636743732180.1624726512535640.0812363256267819
240.9408332184219750.1183335631560490.0591667815780247
250.9330365990341110.1339268019317790.0669634009658893
260.9127741048722320.1744517902555360.0872258951277679
270.9292756160914650.1414487678170710.0707243839085353
280.9779735874341640.04405282513167260.0220264125658363
290.978847027926990.0423059441460210.0211529720730105
300.9716525660212350.0566948679575310.0283474339787655
310.9670077486754270.06598450264914540.0329922513245727
320.9560828993492780.08783420130144290.0439171006507215
330.9448427589221240.1103144821557520.055157241077876
340.9287831414760160.1424337170479670.0712168585239837
350.9145782942355320.1708434115289350.0854217057644675
360.9195448483844690.1609103032310620.0804551516155309
370.9073528502894590.1852942994210830.0926471497105414
380.8838814947426740.2322370105146520.116118505257326
390.889529280685540.2209414386289190.11047071931446
400.9122066947578090.1755866104843810.0877933052421906
410.8991192674039190.2017614651921620.100880732596081
420.8918004684112710.2163990631774580.108199531588729
430.9481512775037070.1036974449925860.051848722496293
440.956287989911330.08742402017733970.0437120100886698
450.9449240129894290.1101519740211420.0550759870105712
460.9300651267130320.1398697465739350.0699348732869677
470.9321086081667010.1357827836665990.0678913918332993
480.9358943049083690.1282113901832620.0641056950916312
490.9258513042941690.1482973914116610.0741486957058307
500.9078291833227980.1843416333544050.0921708166772024
510.8995689452358620.2008621095282770.100431054764138
520.9331594732899880.1336810534200250.0668405267100124
530.9318457857059410.1363084285881180.0681542142940588
540.9158464176949630.1683071646100740.0841535823050371
550.9116085298732930.1767829402534130.0883914701267066
560.8954063580324930.2091872839350130.104593641967507
570.8731406176512610.2537187646974780.126859382348739
580.8569546306287240.2860907387425510.143045369371276
590.8514635286240650.2970729427518690.148536471375935
600.8375952166651150.324809566669770.162404783334885
610.8135341164550560.3729317670898870.186465883544944
620.7852816518049870.4294366963900260.214718348195013
630.761028321584790.4779433568304210.23897167841521
640.8247056353069530.3505887293860930.175294364693047
650.9078395609932090.1843208780135810.0921604390067907
660.9208917922845210.1582164154309590.0791082077154794
670.9586512337457490.08269753250850250.0413487662542512
680.9515361229897390.09692775402052250.0484638770102612
690.9421400399079650.115719920184070.0578599600920348
700.9275087383893720.1449825232212560.0724912616106282
710.9252315070526880.1495369858946240.0747684929473118
720.9256684097010940.1486631805978120.0743315902989059
730.9208358254438540.1583283491122910.0791641745561457
740.9044548669421740.1910902661156520.0955451330578261
750.9030993513802370.1938012972395260.096900648619763
760.9677927490774490.06441450184510110.0322072509225505
770.9750846951849520.04983060963009590.0249153048150479
780.9703505541138360.05929889177232780.0296494458861639
790.9753064501107360.04938709977852830.0246935498892641
800.9692025086751560.06159498264968880.0307974913248444
810.9607917573467510.07841648530649780.0392082426532489
820.9516881514836150.09662369703276960.0483118485163848
830.941515502037710.116968995924580.05848449796229
840.9359690118658890.1280619762682220.0640309881341112
850.9234709171767530.1530581656464940.0765290828232468
860.9056977148493560.1886045703012870.0943022851506436
870.8913803181697610.2172393636604790.108619681830239
880.9368969992536080.1262060014927830.0631030007463916
890.9529591781259840.09408164374803270.0470408218740164
900.9583863463741240.0832273072517520.041613653625876
910.9627444638648930.0745110722702140.037255536135107
920.974903615656640.05019276868672010.0250963843433601
930.9687593356528090.06248132869438240.0312406643471912
940.9640022039673950.07199559206520930.0359977960326046
950.9553954863193510.08920902736129750.0446045136806488
960.9532763312862320.09344733742753570.0467236687137678
970.9421600234447820.1156799531104370.0578399765552185
980.9254850043656710.1490299912686580.0745149956343289
990.9398667381321510.1202665237356970.0601332618678487
1000.9806218374849140.03875632503017230.0193781625150861
1010.9740067578619710.0519864842760580.025993242138029
1020.9648641642216760.07027167155664870.0351358357783243
1030.9597481396490750.08050372070184930.0402518603509246
1040.9491878516649940.1016242966700120.0508121483350058
1050.9387877662030330.1224244675939340.0612122337969672
1060.9209140087544720.1581719824910550.0790859912455277
1070.9184454431015340.1631091137969320.081554556898466
1080.9392781517218280.1214436965563440.0607218482781722
1090.9407585381545940.1184829236908120.0592414618454062
1100.9319203355949810.1361593288100380.0680796644050191
1110.9503996766393390.09920064672132190.049600323360661
1120.9931385116695190.01372297666096260.00686148833048129
1130.9906377022770620.01872459544587630.00936229772293815
1140.9883437804345480.02331243913090440.0116562195654522
1150.9920884690839290.01582306183214230.00791153091607115
1160.9925500976315460.0148998047369080.00744990236845401
1170.9891170931195160.02176581376096770.0108829068804838
1180.9893541334463830.02129173310723410.010645866553617
1190.9872919589465180.02541608210696340.0127080410534817
1200.9855749998488150.02885000030236960.0144250001511848
1210.9895969655550410.02080606888991790.010403034444959
1220.9854307146591310.0291385706817370.0145692853408685
1230.9837467139967120.03250657200657640.0162532860032882
1240.9980484860412840.003903027917432240.00195151395871612
1250.9964973114296970.007005377140606230.00350268857030312
1260.9938065609090940.01238687818181170.00619343909090587
1270.9960735358440290.007852928311942780.00392646415597139
1280.9932545873308510.01349082533829810.00674541266914906
1290.9923996360290150.01520072794196970.00760036397098487
1300.9881663885672160.0236672228655680.011833611432784
1310.9942106020915540.01157879581689140.00578939790844572
1320.9920943289816770.01581134203664550.00790567101832274
1330.9870372875353910.0259254249292170.0129627124646085
1340.9753875370162170.04922492596756580.0246124629837829
1350.9533167718086940.09336645638261230.0466832281913062
1360.9234184208165420.1531631583669160.0765815791834578
1370.8859259623748460.2281480752503070.114074037625154
1380.8125010421034210.3749979157931570.187498957896579
1390.9645938199489290.07081236010214280.0354061800510714
1400.9446252401125980.1107495197748040.0553747598874018







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0220588235294118NOK
5% type I error level280.205882352941176NOK
10% type I error level550.404411764705882NOK

\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 & 3 & 0.0220588235294118 & NOK \tabularnewline
5% type I error level & 28 & 0.205882352941176 & NOK \tabularnewline
10% type I error level & 55 & 0.404411764705882 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186289&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]3[/C][C]0.0220588235294118[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.205882352941176[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.404411764705882[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186289&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186289&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 level30.0220588235294118NOK
5% type I error level280.205882352941176NOK
10% type I error level550.404411764705882NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}