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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 computationSat, 03 Nov 2012 09:32:52 -0400
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/03/t13519503002h78u799ekkt45n.htm/, Retrieved Tue, 09 Aug 2022 20:08:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185722, Retrieved Tue, 09 Aug 2022 20:08:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-03 13:32:52] [cd784a0623f47f402dddaa62da6ddd9f] [Current]
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Dataseries X:
13	41	38
16	39	32
19	30	35
15	31	33
14	34	37
13	35	29
19	39	31
15	34	36
14	36	35
15	37	38
16	38	31
16	36	34
16	38	35
16	39	38
17	33	37
15	32	33
15	36	32
20	38	38
18	39	38
16	32	32
16	32	33
16	31	31
19	39	38
16	37	39
17	39	32
17	41	32
16	36	35
15	33	37
16	33	33
14	34	33
15	31	28
12	27	32
14	37	31
16	34	37
14	34	30
7	32	33
10	29	31
14	36	33
16	29	31
16	35	33
16	37	32
14	34	33
20	38	32
14	35	33
14	38	28
11	37	35
14	38	39
15	33	34
16	36	38
14	38	32
16	32	38
14	32	30
12	32	33
16	34	38
9	32	32
14	37	32
16	39	34
16	29	34
15	37	36
16	35	34
12	30	28
16	38	34
16	34	35
14	31	35
16	34	31
17	35	37
18	36	35
18	30	27
12	39	40
16	35	37
10	38	36
14	31	38
18	34	39
18	38	41
16	34	27
17	39	30
16	37	37
16	34	31
13	28	31
16	37	27
16	33	36
20	37	38
16	35	37
15	37	33
15	32	34
16	33	31
14	38	39
16	33	34
16	29	32
15	33	33
12	31	36
17	36	32
16	35	41
15	32	28
13	29	30
16	39	36
16	37	35
16	35	31
16	37	34
14	32	36
16	38	36
16	37	35
20	36	37
15	32	28
16	33	39
13	40	32
17	38	35
16	41	39
16	36	35
12	43	42
16	30	34
16	31	33
17	32	41
13	32	33
12	37	34
18	37	32
14	33	40
14	34	40
13	33	35
16	38	36
13	33	37
16	31	27
13	38	39
16	37	38
15	33	31
16	31	33
15	39	32
17	44	39
15	33	36
12	35	33
16	32	33
10	28	32
16	40	37
12	27	30
14	37	38
15	32	29
13	28	22
15	34	35
11	30	35
12	35	34
8	31	35
16	32	34
15	30	34
17	30	35
16	31	23
10	40	31
18	32	27
13	36	36
16	32	31
13	35	32
10	38	39
15	42	37
16	34	38
16	35	39
14	35	34
10	33	31
17	36	32
13	32	37
15	33	36
16	34	32
12	32	35
13	34	36




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 9.99029068354278 + 0.14257750156273Connected[t] + 0.000698117508729918Separate[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  9.99029068354278 +  0.14257750156273Connected[t] +  0.000698117508729918Separate[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  9.99029068354278 +  0.14257750156273Connected[t] +  0.000698117508729918Separate[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&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
Learning[t] = + 9.99029068354278 + 0.14257750156273Connected[t] + 0.000698117508729918Separate[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.990290683542782.1079284.73945e-062e-06
Connected0.142577501562730.0557032.55960.0114110.005706
Separate0.0006981175087299180.0529250.01320.9894920.494746

\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) & 9.99029068354278 & 2.107928 & 4.7394 & 5e-06 & 2e-06 \tabularnewline
Connected & 0.14257750156273 & 0.055703 & 2.5596 & 0.011411 & 0.005706 \tabularnewline
Separate & 0.000698117508729918 & 0.052925 & 0.0132 & 0.989492 & 0.494746 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&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]9.99029068354278[/C][C]2.107928[/C][C]4.7394[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Connected[/C][C]0.14257750156273[/C][C]0.055703[/C][C]2.5596[/C][C]0.011411[/C][C]0.005706[/C][/ROW]
[ROW][C]Separate[/C][C]0.000698117508729918[/C][C]0.052925[/C][C]0.0132[/C][C]0.989492[/C][C]0.494746[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&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)9.990290683542782.1079284.73945e-062e-06
Connected0.142577501562730.0557032.55960.0114110.005706
Separate0.0006981175087299180.0529250.01320.9894920.494746







Multiple Linear Regression - Regression Statistics
Multiple R0.213688027211599
R-squared0.0456625729735849
Adjusted R-squared0.0336583286084727
F-TEST (value)3.80386899706022
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.0243391563836944
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21796505403771
Sum Squared Residuals782.179667968267

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.213688027211599 \tabularnewline
R-squared & 0.0456625729735849 \tabularnewline
Adjusted R-squared & 0.0336583286084727 \tabularnewline
F-TEST (value) & 3.80386899706022 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0.0243391563836944 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21796505403771 \tabularnewline
Sum Squared Residuals & 782.179667968267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.213688027211599[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0456625729735849[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0336583286084727[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.80386899706022[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]0.0243391563836944[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.21796505403771[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]782.179667968267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&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 R0.213688027211599
R-squared0.0456625729735849
Adjusted R-squared0.0336583286084727
F-TEST (value)3.80386899706022
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.0243391563836944
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21796505403771
Sum Squared Residuals782.179667968267







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.8624967129465-2.86249671294649
21615.57315300476860.42684699523138
31914.29204984323024.70795015676976
41514.43323110977550.566768890224493
51414.8637560844986-0.863756084498617
61315.0007486459915-2.00074864599151
71915.57245488725993.42754511274011
81514.86305796698990.136942033010113
91415.1475148526066-1.14751485260662
101515.2921867066955-0.292186706695538
111615.42987738569720.570122614302841
121615.14681673509790.853183264902112
131615.43266985573210.567330144267921
141615.5773417098210.422658290179001
151714.72117858293592.27882141706411
161514.57580861133820.424191388661763
171515.1454205000804-0.145420500080428
182015.43476420825834.56523579174173
191815.5773417098212.422658290179
201614.57511049382951.42488950617049
211614.57580861133821.42419138866176
221614.4318348747581.56816512524195
231915.5773417098213.422658290179
241615.29288482420430.707115175795732
251715.57315300476861.42684699523138
261715.85830800789411.14169199210592
271615.14751485260660.852485147393382
281514.72117858293590.278821417064113
291614.7183861129011.28161388709903
301414.8609636144637-0.860963614463698
311514.42974052223190.570259477768143
321213.8622229860159-1.86222298601586
331415.2872998841344-1.28729988413443
341614.86375608449861.13624391550138
351414.8588692619375-0.858869261937508
36714.5758086113382-7.57580861133824
371014.1466798716326-4.14667987163259
381415.1461186175892-1.14611861758916
391614.14667987163261.85332012836741
401615.00354111602640.996458883973572
411615.28799800164320.712001998356841
421414.8609636144637-0.860963614463698
432015.43057550320594.56942449679411
441415.0035411160264-1.00354111602643
451415.427783033171-1.42778303317097
461115.2900923541693-4.29009235416935
471415.435462325767-1.435462325767
481514.71908423040970.280915769590303
491615.14960920513280.850390794867192
501415.4305755032059-1.43057550320589
511614.57929919888191.42070080111811
521414.573714258812-0.573714258812047
531214.5758086113382-2.57580861133824
541614.86445420200731.13554579799265
55914.5751104938295-5.57511049382951
561415.2879980016432-1.28799800164316
571615.57454923978610.425450760213921
581614.14877422415881.85122577584122
591515.2907904716781-0.290790471678078
601615.00423923353520.995760766464842
611214.2871630206691-2.28716302066913
621615.43197173822330.568028261776651
631614.86235984948121.13764015051884
641414.434627344793-0.434627344792966
651614.85956737944621.14043262055376
661715.00633358606131.99366641393865
671815.14751485260662.85248514739338
681814.28646490316043.7135350968396
691215.5787379448385-3.57873794483846
701615.00633358606130.993666413938652
711015.4333679732408-5.43336797324081
721414.4367216973192-0.436721697319156
731814.86515231951613.13484768048392
741815.43685856078452.56314143921554
751614.85677490941131.14322509058868
761715.57175676975121.42824323024884
771615.29148858918680.708511410813192
781614.85956737944621.14043262055376
791314.0041023700699-1.00410237006986
801615.28450741409950.715492585900491
811614.72048046542721.27951953457284
822015.29218670669554.70781329330446
831615.00633358606130.993666413938652
841515.2886961191519-0.288696119151888
851514.5765067288470.423493271153033
861614.71698987788351.28301012211649
871415.435462325767-1.435462325767
881614.71908423040971.2809157695903
891614.14737798914131.85262201085868
901514.7183861129010.281613887099033
911214.4353254623017-2.4353254623017
921715.14542050008041.85457949991957
931615.00912605609630.990873943903733
941514.57231802379460.427681976205413
951314.1459817541239-1.14598175412386
961615.57594547480350.424054525196461
971615.29009235416930.709907645830652
981615.0021448810090.997855118991032
991615.28939423666060.710605763339382
1001414.5779029638644-0.577902963864427
1011615.43336797324080.566632026759191
1021615.29009235416930.709907645830652
1032015.14891108762414.85108891237592
1041514.57231802379460.427681976205413
1051614.72257481795331.27742518204665
1061315.7157305063314-2.71573050633135
1071715.43266985573211.56733014426792
1081615.86319483045520.136805169544811
1091615.14751485260660.852485147393382
1101216.1504441861068-4.15044418610684
1111614.29135172572151.70864827427849
1121614.43323110977551.56676889022449
1131714.58139355140812.41860644859192
1141314.5758086113382-1.57580861133824
1151215.2893942366606-3.28939423666062
1161815.28799800164322.71200199835684
1171414.7232729354621-0.723272935462077
1181414.8658504370248-0.865850437024807
1191314.7197823479184-1.71978234791843
1201615.43336797324080.566632026759191
1211314.7211785829359-1.72117858293589
1221614.42904240472311.57095759527687
1231315.435462325767-2.435462325767
1241615.29218670669550.707813293304462
1251514.71698987788350.283010122116493
1261614.43323110977551.56676889022449
1271515.5731530047686-0.573153004768619
1281716.29092733514340.709072664856619
1291514.72048046542720.279519534572843
1301215.0035411160264-3.00354111602643
1311614.57580861133821.42419138866176
1321014.0048004875786-4.00480048757859
1331615.7192210938750.280778906125001
1341213.8608267509984-1.8608267509984
1351415.2921867066955-1.29218670669554
1361514.57301614130330.426983858696683
1371313.9978193124913-0.997819312491287
1381514.86235984948120.137640150518843
1391114.2920498432302-3.29204984323024
1401215.0042392335352-3.00423923353516
141814.434627344793-6.43462734479297
1421614.5765067288471.42349327115303
1431514.29135172572150.708648274278494
1441714.29204984323022.70795015676976
1451614.42624993468821.57375006531179
1461015.7150323888226-5.71503238882262
1471814.57161990628593.42838009371414
1481315.1482129701153-2.14821297011535
1491614.57441237632081.42558762367922
1501315.0028429985177-2.0028429985177
1511015.435462325767-5.435462325767
1521516.0043760970005-1.00437609700046
1531614.86445420200731.13554579799265
1541615.00772982107880.992270178921193
1551415.0042392335352-1.00423923353516
1561014.7169898778835-4.71698987788351
1571715.14542050008041.85457949991957
1581314.5786010813732-1.57860108137316
1591514.72048046542720.279519534572843
1601614.8602654969551.13973450304503
1611214.5772048463557-2.5772048463557
1621314.8630579669899-1.86305796698989

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.8624967129465 & -2.86249671294649 \tabularnewline
2 & 16 & 15.5731530047686 & 0.42684699523138 \tabularnewline
3 & 19 & 14.2920498432302 & 4.70795015676976 \tabularnewline
4 & 15 & 14.4332311097755 & 0.566768890224493 \tabularnewline
5 & 14 & 14.8637560844986 & -0.863756084498617 \tabularnewline
6 & 13 & 15.0007486459915 & -2.00074864599151 \tabularnewline
7 & 19 & 15.5724548872599 & 3.42754511274011 \tabularnewline
8 & 15 & 14.8630579669899 & 0.136942033010113 \tabularnewline
9 & 14 & 15.1475148526066 & -1.14751485260662 \tabularnewline
10 & 15 & 15.2921867066955 & -0.292186706695538 \tabularnewline
11 & 16 & 15.4298773856972 & 0.570122614302841 \tabularnewline
12 & 16 & 15.1468167350979 & 0.853183264902112 \tabularnewline
13 & 16 & 15.4326698557321 & 0.567330144267921 \tabularnewline
14 & 16 & 15.577341709821 & 0.422658290179001 \tabularnewline
15 & 17 & 14.7211785829359 & 2.27882141706411 \tabularnewline
16 & 15 & 14.5758086113382 & 0.424191388661763 \tabularnewline
17 & 15 & 15.1454205000804 & -0.145420500080428 \tabularnewline
18 & 20 & 15.4347642082583 & 4.56523579174173 \tabularnewline
19 & 18 & 15.577341709821 & 2.422658290179 \tabularnewline
20 & 16 & 14.5751104938295 & 1.42488950617049 \tabularnewline
21 & 16 & 14.5758086113382 & 1.42419138866176 \tabularnewline
22 & 16 & 14.431834874758 & 1.56816512524195 \tabularnewline
23 & 19 & 15.577341709821 & 3.422658290179 \tabularnewline
24 & 16 & 15.2928848242043 & 0.707115175795732 \tabularnewline
25 & 17 & 15.5731530047686 & 1.42684699523138 \tabularnewline
26 & 17 & 15.8583080078941 & 1.14169199210592 \tabularnewline
27 & 16 & 15.1475148526066 & 0.852485147393382 \tabularnewline
28 & 15 & 14.7211785829359 & 0.278821417064113 \tabularnewline
29 & 16 & 14.718386112901 & 1.28161388709903 \tabularnewline
30 & 14 & 14.8609636144637 & -0.860963614463698 \tabularnewline
31 & 15 & 14.4297405222319 & 0.570259477768143 \tabularnewline
32 & 12 & 13.8622229860159 & -1.86222298601586 \tabularnewline
33 & 14 & 15.2872998841344 & -1.28729988413443 \tabularnewline
34 & 16 & 14.8637560844986 & 1.13624391550138 \tabularnewline
35 & 14 & 14.8588692619375 & -0.858869261937508 \tabularnewline
36 & 7 & 14.5758086113382 & -7.57580861133824 \tabularnewline
37 & 10 & 14.1466798716326 & -4.14667987163259 \tabularnewline
38 & 14 & 15.1461186175892 & -1.14611861758916 \tabularnewline
39 & 16 & 14.1466798716326 & 1.85332012836741 \tabularnewline
40 & 16 & 15.0035411160264 & 0.996458883973572 \tabularnewline
41 & 16 & 15.2879980016432 & 0.712001998356841 \tabularnewline
42 & 14 & 14.8609636144637 & -0.860963614463698 \tabularnewline
43 & 20 & 15.4305755032059 & 4.56942449679411 \tabularnewline
44 & 14 & 15.0035411160264 & -1.00354111602643 \tabularnewline
45 & 14 & 15.427783033171 & -1.42778303317097 \tabularnewline
46 & 11 & 15.2900923541693 & -4.29009235416935 \tabularnewline
47 & 14 & 15.435462325767 & -1.435462325767 \tabularnewline
48 & 15 & 14.7190842304097 & 0.280915769590303 \tabularnewline
49 & 16 & 15.1496092051328 & 0.850390794867192 \tabularnewline
50 & 14 & 15.4305755032059 & -1.43057550320589 \tabularnewline
51 & 16 & 14.5792991988819 & 1.42070080111811 \tabularnewline
52 & 14 & 14.573714258812 & -0.573714258812047 \tabularnewline
53 & 12 & 14.5758086113382 & -2.57580861133824 \tabularnewline
54 & 16 & 14.8644542020073 & 1.13554579799265 \tabularnewline
55 & 9 & 14.5751104938295 & -5.57511049382951 \tabularnewline
56 & 14 & 15.2879980016432 & -1.28799800164316 \tabularnewline
57 & 16 & 15.5745492397861 & 0.425450760213921 \tabularnewline
58 & 16 & 14.1487742241588 & 1.85122577584122 \tabularnewline
59 & 15 & 15.2907904716781 & -0.290790471678078 \tabularnewline
60 & 16 & 15.0042392335352 & 0.995760766464842 \tabularnewline
61 & 12 & 14.2871630206691 & -2.28716302066913 \tabularnewline
62 & 16 & 15.4319717382233 & 0.568028261776651 \tabularnewline
63 & 16 & 14.8623598494812 & 1.13764015051884 \tabularnewline
64 & 14 & 14.434627344793 & -0.434627344792966 \tabularnewline
65 & 16 & 14.8595673794462 & 1.14043262055376 \tabularnewline
66 & 17 & 15.0063335860613 & 1.99366641393865 \tabularnewline
67 & 18 & 15.1475148526066 & 2.85248514739338 \tabularnewline
68 & 18 & 14.2864649031604 & 3.7135350968396 \tabularnewline
69 & 12 & 15.5787379448385 & -3.57873794483846 \tabularnewline
70 & 16 & 15.0063335860613 & 0.993666413938652 \tabularnewline
71 & 10 & 15.4333679732408 & -5.43336797324081 \tabularnewline
72 & 14 & 14.4367216973192 & -0.436721697319156 \tabularnewline
73 & 18 & 14.8651523195161 & 3.13484768048392 \tabularnewline
74 & 18 & 15.4368585607845 & 2.56314143921554 \tabularnewline
75 & 16 & 14.8567749094113 & 1.14322509058868 \tabularnewline
76 & 17 & 15.5717567697512 & 1.42824323024884 \tabularnewline
77 & 16 & 15.2914885891868 & 0.708511410813192 \tabularnewline
78 & 16 & 14.8595673794462 & 1.14043262055376 \tabularnewline
79 & 13 & 14.0041023700699 & -1.00410237006986 \tabularnewline
80 & 16 & 15.2845074140995 & 0.715492585900491 \tabularnewline
81 & 16 & 14.7204804654272 & 1.27951953457284 \tabularnewline
82 & 20 & 15.2921867066955 & 4.70781329330446 \tabularnewline
83 & 16 & 15.0063335860613 & 0.993666413938652 \tabularnewline
84 & 15 & 15.2886961191519 & -0.288696119151888 \tabularnewline
85 & 15 & 14.576506728847 & 0.423493271153033 \tabularnewline
86 & 16 & 14.7169898778835 & 1.28301012211649 \tabularnewline
87 & 14 & 15.435462325767 & -1.435462325767 \tabularnewline
88 & 16 & 14.7190842304097 & 1.2809157695903 \tabularnewline
89 & 16 & 14.1473779891413 & 1.85262201085868 \tabularnewline
90 & 15 & 14.718386112901 & 0.281613887099033 \tabularnewline
91 & 12 & 14.4353254623017 & -2.4353254623017 \tabularnewline
92 & 17 & 15.1454205000804 & 1.85457949991957 \tabularnewline
93 & 16 & 15.0091260560963 & 0.990873943903733 \tabularnewline
94 & 15 & 14.5723180237946 & 0.427681976205413 \tabularnewline
95 & 13 & 14.1459817541239 & -1.14598175412386 \tabularnewline
96 & 16 & 15.5759454748035 & 0.424054525196461 \tabularnewline
97 & 16 & 15.2900923541693 & 0.709907645830652 \tabularnewline
98 & 16 & 15.002144881009 & 0.997855118991032 \tabularnewline
99 & 16 & 15.2893942366606 & 0.710605763339382 \tabularnewline
100 & 14 & 14.5779029638644 & -0.577902963864427 \tabularnewline
101 & 16 & 15.4333679732408 & 0.566632026759191 \tabularnewline
102 & 16 & 15.2900923541693 & 0.709907645830652 \tabularnewline
103 & 20 & 15.1489110876241 & 4.85108891237592 \tabularnewline
104 & 15 & 14.5723180237946 & 0.427681976205413 \tabularnewline
105 & 16 & 14.7225748179533 & 1.27742518204665 \tabularnewline
106 & 13 & 15.7157305063314 & -2.71573050633135 \tabularnewline
107 & 17 & 15.4326698557321 & 1.56733014426792 \tabularnewline
108 & 16 & 15.8631948304552 & 0.136805169544811 \tabularnewline
109 & 16 & 15.1475148526066 & 0.852485147393382 \tabularnewline
110 & 12 & 16.1504441861068 & -4.15044418610684 \tabularnewline
111 & 16 & 14.2913517257215 & 1.70864827427849 \tabularnewline
112 & 16 & 14.4332311097755 & 1.56676889022449 \tabularnewline
113 & 17 & 14.5813935514081 & 2.41860644859192 \tabularnewline
114 & 13 & 14.5758086113382 & -1.57580861133824 \tabularnewline
115 & 12 & 15.2893942366606 & -3.28939423666062 \tabularnewline
116 & 18 & 15.2879980016432 & 2.71200199835684 \tabularnewline
117 & 14 & 14.7232729354621 & -0.723272935462077 \tabularnewline
118 & 14 & 14.8658504370248 & -0.865850437024807 \tabularnewline
119 & 13 & 14.7197823479184 & -1.71978234791843 \tabularnewline
120 & 16 & 15.4333679732408 & 0.566632026759191 \tabularnewline
121 & 13 & 14.7211785829359 & -1.72117858293589 \tabularnewline
122 & 16 & 14.4290424047231 & 1.57095759527687 \tabularnewline
123 & 13 & 15.435462325767 & -2.435462325767 \tabularnewline
124 & 16 & 15.2921867066955 & 0.707813293304462 \tabularnewline
125 & 15 & 14.7169898778835 & 0.283010122116493 \tabularnewline
126 & 16 & 14.4332311097755 & 1.56676889022449 \tabularnewline
127 & 15 & 15.5731530047686 & -0.573153004768619 \tabularnewline
128 & 17 & 16.2909273351434 & 0.709072664856619 \tabularnewline
129 & 15 & 14.7204804654272 & 0.279519534572843 \tabularnewline
130 & 12 & 15.0035411160264 & -3.00354111602643 \tabularnewline
131 & 16 & 14.5758086113382 & 1.42419138866176 \tabularnewline
132 & 10 & 14.0048004875786 & -4.00480048757859 \tabularnewline
133 & 16 & 15.719221093875 & 0.280778906125001 \tabularnewline
134 & 12 & 13.8608267509984 & -1.8608267509984 \tabularnewline
135 & 14 & 15.2921867066955 & -1.29218670669554 \tabularnewline
136 & 15 & 14.5730161413033 & 0.426983858696683 \tabularnewline
137 & 13 & 13.9978193124913 & -0.997819312491287 \tabularnewline
138 & 15 & 14.8623598494812 & 0.137640150518843 \tabularnewline
139 & 11 & 14.2920498432302 & -3.29204984323024 \tabularnewline
140 & 12 & 15.0042392335352 & -3.00423923353516 \tabularnewline
141 & 8 & 14.434627344793 & -6.43462734479297 \tabularnewline
142 & 16 & 14.576506728847 & 1.42349327115303 \tabularnewline
143 & 15 & 14.2913517257215 & 0.708648274278494 \tabularnewline
144 & 17 & 14.2920498432302 & 2.70795015676976 \tabularnewline
145 & 16 & 14.4262499346882 & 1.57375006531179 \tabularnewline
146 & 10 & 15.7150323888226 & -5.71503238882262 \tabularnewline
147 & 18 & 14.5716199062859 & 3.42838009371414 \tabularnewline
148 & 13 & 15.1482129701153 & -2.14821297011535 \tabularnewline
149 & 16 & 14.5744123763208 & 1.42558762367922 \tabularnewline
150 & 13 & 15.0028429985177 & -2.0028429985177 \tabularnewline
151 & 10 & 15.435462325767 & -5.435462325767 \tabularnewline
152 & 15 & 16.0043760970005 & -1.00437609700046 \tabularnewline
153 & 16 & 14.8644542020073 & 1.13554579799265 \tabularnewline
154 & 16 & 15.0077298210788 & 0.992270178921193 \tabularnewline
155 & 14 & 15.0042392335352 & -1.00423923353516 \tabularnewline
156 & 10 & 14.7169898778835 & -4.71698987788351 \tabularnewline
157 & 17 & 15.1454205000804 & 1.85457949991957 \tabularnewline
158 & 13 & 14.5786010813732 & -1.57860108137316 \tabularnewline
159 & 15 & 14.7204804654272 & 0.279519534572843 \tabularnewline
160 & 16 & 14.860265496955 & 1.13973450304503 \tabularnewline
161 & 12 & 14.5772048463557 & -2.5772048463557 \tabularnewline
162 & 13 & 14.8630579669899 & -1.86305796698989 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&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]13[/C][C]15.8624967129465[/C][C]-2.86249671294649[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.5731530047686[/C][C]0.42684699523138[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]14.2920498432302[/C][C]4.70795015676976[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]14.4332311097755[/C][C]0.566768890224493[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]14.8637560844986[/C][C]-0.863756084498617[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0007486459915[/C][C]-2.00074864599151[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.5724548872599[/C][C]3.42754511274011[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]14.8630579669899[/C][C]0.136942033010113[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.1475148526066[/C][C]-1.14751485260662[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.2921867066955[/C][C]-0.292186706695538[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4298773856972[/C][C]0.570122614302841[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.1468167350979[/C][C]0.853183264902112[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4326698557321[/C][C]0.567330144267921[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.577341709821[/C][C]0.422658290179001[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]14.7211785829359[/C][C]2.27882141706411[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.5758086113382[/C][C]0.424191388661763[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]15.1454205000804[/C][C]-0.145420500080428[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.4347642082583[/C][C]4.56523579174173[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.577341709821[/C][C]2.422658290179[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.5751104938295[/C][C]1.42488950617049[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]14.5758086113382[/C][C]1.42419138866176[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.431834874758[/C][C]1.56816512524195[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.577341709821[/C][C]3.422658290179[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.2928848242043[/C][C]0.707115175795732[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.5731530047686[/C][C]1.42684699523138[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.8583080078941[/C][C]1.14169199210592[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1475148526066[/C][C]0.852485147393382[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.7211785829359[/C][C]0.278821417064113[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.718386112901[/C][C]1.28161388709903[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.8609636144637[/C][C]-0.860963614463698[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]14.4297405222319[/C][C]0.570259477768143[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.8622229860159[/C][C]-1.86222298601586[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2872998841344[/C][C]-1.28729988413443[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]14.8637560844986[/C][C]1.13624391550138[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.8588692619375[/C][C]-0.858869261937508[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]14.5758086113382[/C][C]-7.57580861133824[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]14.1466798716326[/C][C]-4.14667987163259[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.1461186175892[/C][C]-1.14611861758916[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1466798716326[/C][C]1.85332012836741[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.0035411160264[/C][C]0.996458883973572[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.2879980016432[/C][C]0.712001998356841[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.8609636144637[/C][C]-0.860963614463698[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]15.4305755032059[/C][C]4.56942449679411[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.0035411160264[/C][C]-1.00354111602643[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.427783033171[/C][C]-1.42778303317097[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.2900923541693[/C][C]-4.29009235416935[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]15.435462325767[/C][C]-1.435462325767[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.7190842304097[/C][C]0.280915769590303[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1496092051328[/C][C]0.850390794867192[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.4305755032059[/C][C]-1.43057550320589[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.5792991988819[/C][C]1.42070080111811[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.573714258812[/C][C]-0.573714258812047[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5758086113382[/C][C]-2.57580861133824[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.8644542020073[/C][C]1.13554579799265[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]14.5751104938295[/C][C]-5.57511049382951[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.2879980016432[/C][C]-1.28799800164316[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.5745492397861[/C][C]0.425450760213921[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.1487742241588[/C][C]1.85122577584122[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2907904716781[/C][C]-0.290790471678078[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]15.0042392335352[/C][C]0.995760766464842[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]14.2871630206691[/C][C]-2.28716302066913[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.4319717382233[/C][C]0.568028261776651[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.8623598494812[/C][C]1.13764015051884[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.434627344793[/C][C]-0.434627344792966[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]14.8595673794462[/C][C]1.14043262055376[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.0063335860613[/C][C]1.99366641393865[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.1475148526066[/C][C]2.85248514739338[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.2864649031604[/C][C]3.7135350968396[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.5787379448385[/C][C]-3.57873794483846[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.0063335860613[/C][C]0.993666413938652[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]15.4333679732408[/C][C]-5.43336797324081[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.4367216973192[/C][C]-0.436721697319156[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]14.8651523195161[/C][C]3.13484768048392[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]15.4368585607845[/C][C]2.56314143921554[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]14.8567749094113[/C][C]1.14322509058868[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]15.5717567697512[/C][C]1.42824323024884[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.2914885891868[/C][C]0.708511410813192[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.8595673794462[/C][C]1.14043262055376[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.0041023700699[/C][C]-1.00410237006986[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.2845074140995[/C][C]0.715492585900491[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]14.7204804654272[/C][C]1.27951953457284[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.2921867066955[/C][C]4.70781329330446[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.0063335860613[/C][C]0.993666413938652[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.2886961191519[/C][C]-0.288696119151888[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.576506728847[/C][C]0.423493271153033[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.7169898778835[/C][C]1.28301012211649[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]15.435462325767[/C][C]-1.435462325767[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.7190842304097[/C][C]1.2809157695903[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.1473779891413[/C][C]1.85262201085868[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.718386112901[/C][C]0.281613887099033[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]14.4353254623017[/C][C]-2.4353254623017[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]15.1454205000804[/C][C]1.85457949991957[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0091260560963[/C][C]0.990873943903733[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.5723180237946[/C][C]0.427681976205413[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.1459817541239[/C][C]-1.14598175412386[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.5759454748035[/C][C]0.424054525196461[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.2900923541693[/C][C]0.709907645830652[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.002144881009[/C][C]0.997855118991032[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.2893942366606[/C][C]0.710605763339382[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.5779029638644[/C][C]-0.577902963864427[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4333679732408[/C][C]0.566632026759191[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.2900923541693[/C][C]0.709907645830652[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]15.1489110876241[/C][C]4.85108891237592[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5723180237946[/C][C]0.427681976205413[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.7225748179533[/C][C]1.27742518204665[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.7157305063314[/C][C]-2.71573050633135[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.4326698557321[/C][C]1.56733014426792[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8631948304552[/C][C]0.136805169544811[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]15.1475148526066[/C][C]0.852485147393382[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]16.1504441861068[/C][C]-4.15044418610684[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.2913517257215[/C][C]1.70864827427849[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]14.4332311097755[/C][C]1.56676889022449[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5813935514081[/C][C]2.41860644859192[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.5758086113382[/C][C]-1.57580861133824[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.2893942366606[/C][C]-3.28939423666062[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]15.2879980016432[/C][C]2.71200199835684[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.7232729354621[/C][C]-0.723272935462077[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.8658504370248[/C][C]-0.865850437024807[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.7197823479184[/C][C]-1.71978234791843[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.4333679732408[/C][C]0.566632026759191[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.7211785829359[/C][C]-1.72117858293589[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]14.4290424047231[/C][C]1.57095759527687[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.435462325767[/C][C]-2.435462325767[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]15.2921867066955[/C][C]0.707813293304462[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.7169898778835[/C][C]0.283010122116493[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]14.4332311097755[/C][C]1.56676889022449[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5731530047686[/C][C]-0.573153004768619[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2909273351434[/C][C]0.709072664856619[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.7204804654272[/C][C]0.279519534572843[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0035411160264[/C][C]-3.00354111602643[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.5758086113382[/C][C]1.42419138866176[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]14.0048004875786[/C][C]-4.00480048757859[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]15.719221093875[/C][C]0.280778906125001[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8608267509984[/C][C]-1.8608267509984[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.2921867066955[/C][C]-1.29218670669554[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.5730161413033[/C][C]0.426983858696683[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.9978193124913[/C][C]-0.997819312491287[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.8623598494812[/C][C]0.137640150518843[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]14.2920498432302[/C][C]-3.29204984323024[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]15.0042392335352[/C][C]-3.00423923353516[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]14.434627344793[/C][C]-6.43462734479297[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]14.576506728847[/C][C]1.42349327115303[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]14.2913517257215[/C][C]0.708648274278494[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]14.2920498432302[/C][C]2.70795015676976[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.4262499346882[/C][C]1.57375006531179[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]15.7150323888226[/C][C]-5.71503238882262[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]14.5716199062859[/C][C]3.42838009371414[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1482129701153[/C][C]-2.14821297011535[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.5744123763208[/C][C]1.42558762367922[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]15.0028429985177[/C][C]-2.0028429985177[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]15.435462325767[/C][C]-5.435462325767[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.0043760970005[/C][C]-1.00437609700046[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.8644542020073[/C][C]1.13554579799265[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]15.0077298210788[/C][C]0.992270178921193[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]15.0042392335352[/C][C]-1.00423923353516[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]14.7169898778835[/C][C]-4.71698987788351[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]15.1454205000804[/C][C]1.85457949991957[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]14.5786010813732[/C][C]-1.57860108137316[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.7204804654272[/C][C]0.279519534572843[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.860265496955[/C][C]1.13973450304503[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.5772048463557[/C][C]-2.5772048463557[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]14.8630579669899[/C][C]-1.86305796698989[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&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
11315.8624967129465-2.86249671294649
21615.57315300476860.42684699523138
31914.29204984323024.70795015676976
41514.43323110977550.566768890224493
51414.8637560844986-0.863756084498617
61315.0007486459915-2.00074864599151
71915.57245488725993.42754511274011
81514.86305796698990.136942033010113
91415.1475148526066-1.14751485260662
101515.2921867066955-0.292186706695538
111615.42987738569720.570122614302841
121615.14681673509790.853183264902112
131615.43266985573210.567330144267921
141615.5773417098210.422658290179001
151714.72117858293592.27882141706411
161514.57580861133820.424191388661763
171515.1454205000804-0.145420500080428
182015.43476420825834.56523579174173
191815.5773417098212.422658290179
201614.57511049382951.42488950617049
211614.57580861133821.42419138866176
221614.4318348747581.56816512524195
231915.5773417098213.422658290179
241615.29288482420430.707115175795732
251715.57315300476861.42684699523138
261715.85830800789411.14169199210592
271615.14751485260660.852485147393382
281514.72117858293590.278821417064113
291614.7183861129011.28161388709903
301414.8609636144637-0.860963614463698
311514.42974052223190.570259477768143
321213.8622229860159-1.86222298601586
331415.2872998841344-1.28729988413443
341614.86375608449861.13624391550138
351414.8588692619375-0.858869261937508
36714.5758086113382-7.57580861133824
371014.1466798716326-4.14667987163259
381415.1461186175892-1.14611861758916
391614.14667987163261.85332012836741
401615.00354111602640.996458883973572
411615.28799800164320.712001998356841
421414.8609636144637-0.860963614463698
432015.43057550320594.56942449679411
441415.0035411160264-1.00354111602643
451415.427783033171-1.42778303317097
461115.2900923541693-4.29009235416935
471415.435462325767-1.435462325767
481514.71908423040970.280915769590303
491615.14960920513280.850390794867192
501415.4305755032059-1.43057550320589
511614.57929919888191.42070080111811
521414.573714258812-0.573714258812047
531214.5758086113382-2.57580861133824
541614.86445420200731.13554579799265
55914.5751104938295-5.57511049382951
561415.2879980016432-1.28799800164316
571615.57454923978610.425450760213921
581614.14877422415881.85122577584122
591515.2907904716781-0.290790471678078
601615.00423923353520.995760766464842
611214.2871630206691-2.28716302066913
621615.43197173822330.568028261776651
631614.86235984948121.13764015051884
641414.434627344793-0.434627344792966
651614.85956737944621.14043262055376
661715.00633358606131.99366641393865
671815.14751485260662.85248514739338
681814.28646490316043.7135350968396
691215.5787379448385-3.57873794483846
701615.00633358606130.993666413938652
711015.4333679732408-5.43336797324081
721414.4367216973192-0.436721697319156
731814.86515231951613.13484768048392
741815.43685856078452.56314143921554
751614.85677490941131.14322509058868
761715.57175676975121.42824323024884
771615.29148858918680.708511410813192
781614.85956737944621.14043262055376
791314.0041023700699-1.00410237006986
801615.28450741409950.715492585900491
811614.72048046542721.27951953457284
822015.29218670669554.70781329330446
831615.00633358606130.993666413938652
841515.2886961191519-0.288696119151888
851514.5765067288470.423493271153033
861614.71698987788351.28301012211649
871415.435462325767-1.435462325767
881614.71908423040971.2809157695903
891614.14737798914131.85262201085868
901514.7183861129010.281613887099033
911214.4353254623017-2.4353254623017
921715.14542050008041.85457949991957
931615.00912605609630.990873943903733
941514.57231802379460.427681976205413
951314.1459817541239-1.14598175412386
961615.57594547480350.424054525196461
971615.29009235416930.709907645830652
981615.0021448810090.997855118991032
991615.28939423666060.710605763339382
1001414.5779029638644-0.577902963864427
1011615.43336797324080.566632026759191
1021615.29009235416930.709907645830652
1032015.14891108762414.85108891237592
1041514.57231802379460.427681976205413
1051614.72257481795331.27742518204665
1061315.7157305063314-2.71573050633135
1071715.43266985573211.56733014426792
1081615.86319483045520.136805169544811
1091615.14751485260660.852485147393382
1101216.1504441861068-4.15044418610684
1111614.29135172572151.70864827427849
1121614.43323110977551.56676889022449
1131714.58139355140812.41860644859192
1141314.5758086113382-1.57580861133824
1151215.2893942366606-3.28939423666062
1161815.28799800164322.71200199835684
1171414.7232729354621-0.723272935462077
1181414.8658504370248-0.865850437024807
1191314.7197823479184-1.71978234791843
1201615.43336797324080.566632026759191
1211314.7211785829359-1.72117858293589
1221614.42904240472311.57095759527687
1231315.435462325767-2.435462325767
1241615.29218670669550.707813293304462
1251514.71698987788350.283010122116493
1261614.43323110977551.56676889022449
1271515.5731530047686-0.573153004768619
1281716.29092733514340.709072664856619
1291514.72048046542720.279519534572843
1301215.0035411160264-3.00354111602643
1311614.57580861133821.42419138866176
1321014.0048004875786-4.00480048757859
1331615.7192210938750.280778906125001
1341213.8608267509984-1.8608267509984
1351415.2921867066955-1.29218670669554
1361514.57301614130330.426983858696683
1371313.9978193124913-0.997819312491287
1381514.86235984948120.137640150518843
1391114.2920498432302-3.29204984323024
1401215.0042392335352-3.00423923353516
141814.434627344793-6.43462734479297
1421614.5765067288471.42349327115303
1431514.29135172572150.708648274278494
1441714.29204984323022.70795015676976
1451614.42624993468821.57375006531179
1461015.7150323888226-5.71503238882262
1471814.57161990628593.42838009371414
1481315.1482129701153-2.14821297011535
1491614.57441237632081.42558762367922
1501315.0028429985177-2.0028429985177
1511015.435462325767-5.435462325767
1521516.0043760970005-1.00437609700046
1531614.86445420200731.13554579799265
1541615.00772982107880.992270178921193
1551415.0042392335352-1.00423923353516
1561014.7169898778835-4.71698987788351
1571715.14542050008041.85457949991957
1581314.5786010813732-1.57860108137316
1591514.72048046542720.279519534572843
1601614.8602654969551.13973450304503
1611214.5772048463557-2.5772048463557
1621314.8630579669899-1.86305796698989







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.6967504094083810.6064991811832380.303249590591619
70.8872825229297960.2254349541404080.112717477070204
80.8126787744870840.3746424510258330.187321225512916
90.7420770657522980.5158458684954030.257922934247702
100.6454915404988340.7090169190023310.354508459501166
110.5434844858085940.9130310283828110.456515514191406
120.4478550714631490.8957101429262990.552144928536851
130.3672361616945470.7344723233890950.632763838305453
140.3075599920994930.6151199841989850.692440007900507
150.2683845978399030.5367691956798060.731615402160097
160.214802729583760.429605459167520.78519727041624
170.1621715212116510.3243430424233020.837828478788349
180.3915817678913470.7831635357826950.608418232108653
190.377617653530170.7552353070603410.62238234646983
200.3122265348007410.6244530696014820.687773465199259
210.2524843323457910.5049686646915820.747515667654209
220.201823034163230.403646068326460.79817696583677
230.2387455031025130.4774910062050250.761254496897487
240.1923745084674140.3847490169348280.807625491532586
250.1619680216948360.3239360433896710.838031978305165
260.1308123011825380.2616246023650770.869187698817462
270.09933589600974110.1986717920194820.900664103990259
280.07938741515025070.1587748303005010.920612584849749
290.05915394072458510.118307881449170.940846059275415
300.05423706128671670.1084741225734330.945762938713283
310.0390196409913790.0780392819827580.960980359008621
320.05005279842309540.1001055968461910.949947201576905
330.04573402586522410.09146805173044820.954265974134776
340.03351518302492380.06703036604984750.966484816975076
350.02635510358714690.05271020717429380.973644896412853
360.4744414595020650.948882919004130.525558540497935
370.5799284886387210.8401430227225580.420071511361279
380.5455928786081860.9088142427836280.454407121391814
390.5492978227718170.9014043544563660.450702177228183
400.5035210696362080.9929578607275840.496478930363792
410.4538613007803980.9077226015607960.546138699219602
420.4119683040688910.8239366081377820.588031695931109
430.5569166450104670.8861667099790650.443083354989533
440.521363702248130.9572725955037410.47863629775187
450.4899489880587160.9798979761174320.510051011941284
460.6614533450643880.6770933098712250.338546654935612
470.6566905470167340.6866189059665320.343309452983266
480.6099706455824220.7800587088351560.390029354417578
490.5648020109963480.8703959780073040.435197989003652
500.5392983478547970.9214033042904070.460701652145203
510.503065113390750.9938697732184990.49693488660925
520.4554814672293790.9109629344587570.544518532770621
530.4695844518935810.9391689037871610.530415548106419
540.4285584218366690.8571168436733380.571441578163331
550.6582634053505210.6834731892989570.341736594649479
560.627837533758340.7443249324833210.37216246624166
570.5830822250993160.8338355498013690.416917774900684
580.5747447126383610.8505105747232790.425255287361639
590.5317807069485760.9364385861028490.468219293051424
600.4931822591827620.9863645183655230.506817740817238
610.4793305267029080.9586610534058150.520669473297092
620.4349374132445720.8698748264891430.565062586755428
630.3992661526918820.7985323053837640.600733847308118
640.3567131050971470.7134262101942940.643286894902853
650.3304075155310970.6608150310621940.669592484468903
660.3147963326195290.6295926652390590.685203667380471
670.3360110880513960.6720221761027920.663988911948604
680.4555888413141040.9111776826282090.544411158685896
690.5560059019923280.8879881960153430.443994098007671
700.5178314880894050.964337023821190.482168511910595
710.7349041620285940.5301916759428110.265095837971406
720.6985807715864330.6028384568271330.301419228413567
730.730783388196930.5384332236061410.26921661180307
740.7387646439899910.5224707120200190.261235356010009
750.7139311775415790.5721376449168420.286068822458421
760.691794965931690.6164100681366190.30820503406831
770.6554855155163750.6890289689672490.344514484483624
780.6248654921002310.7502690157995390.375134507899769
790.5895017455905930.8209965088188140.410498254409407
800.5508986980542920.8982026038914170.449101301945708
810.52036293847270.95927412305460.4796370615273
820.6728642194025650.6542715611948690.327135780597435
830.6412428834639990.7175142330720010.358757116536001
840.5992122372142120.8015755255715770.400787762785788
850.5567455849002120.8865088301995770.443254415099789
860.5283760503869220.9432478992261550.471623949613077
870.5061011612296210.9877976775407590.493898838770379
880.477264070495030.9545281409900610.52273592950497
890.4648003690429090.9296007380858180.535199630957091
900.4212111632029680.8424223264059370.578788836797032
910.430101743960210.8602034879204190.56989825603979
920.4207248285464040.8414496570928070.579275171453596
930.3898051823282420.7796103646564850.610194817671758
940.3487474256092210.6974948512184420.651252574390779
950.3169584882274180.6339169764548360.683041511772582
960.2822959826126230.5645919652252470.717704017387377
970.2516288080517390.5032576161034780.748371191948261
980.2255210792907570.4510421585815140.774478920709243
990.1987646992379110.3975293984758220.801235300762089
1000.1699890138812580.3399780277625150.830010986118742
1010.147382968209910.2947659364198190.85261703179009
1020.1276734279438660.2553468558877320.872326572056134
1030.2656512082721890.5313024165443780.734348791727811
1040.2309380871298120.4618761742596230.769061912870188
1050.2147452787552940.4294905575105880.785254721244706
1060.2219874589911370.4439749179822740.778012541008863
1070.2166251507198580.4332503014397160.783374849280142
1080.1940777569683450.3881555139366910.805922243031655
1090.1746656541691290.3493313083382580.825334345830871
1100.2295259910626480.4590519821252960.770474008937352
1110.2203451816916190.4406903633832380.779654818308381
1120.2092699367009390.4185398734018780.790730063299061
1130.2476561502753240.4953123005506480.752343849724676
1140.2229808076829120.4459616153658250.777019192317087
1150.2521168289692990.5042336579385970.747883171030701
1160.2885081886992070.5770163773984140.711491811300793
1170.2536311488498450.5072622976996890.746368851150155
1180.2207604564185480.4415209128370960.779239543581452
1190.1967911830033820.3935823660067630.803208816996618
1200.1743668888569090.3487337777138170.825633111143091
1210.1523686879259590.3047373758519180.847631312074041
1220.1389222013996860.2778444027993710.861077798600314
1230.1285081505386930.2570163010773860.871491849461307
1240.1148028945483770.2296057890967530.885197105451623
1250.09338352245074960.1867670449014990.90661647754925
1260.09040742599766080.1808148519953220.909592574002339
1270.07082247975533090.1416449595106620.929177520244669
1280.06703976901151050.1340795380230210.93296023098849
1290.05518214586966460.1103642917393290.944817854130335
1300.05613587287549440.1122717457509890.943864127124506
1310.05303671823951850.1060734364790370.946963281760482
1320.08164330546176330.1632866109235270.918356694538237
1330.07569444202280490.151388884045610.924305557977195
1340.07344898080779750.1468979616155950.926551019192203
1350.0575604938048110.1151209876096220.942439506195189
1360.04307985304467930.08615970608935850.956920146955321
1370.04200916658216010.08401833316432030.95799083341784
1380.03247018750200240.06494037500400470.967529812497998
1390.04400758526093870.08801517052187740.955992414739061
1400.04122247234440740.08244494468881480.958777527655593
1410.3029329553740370.6058659107480740.697067044625963
1420.2597650617746050.5195301235492090.740234938225395
1430.2062403833131950.4124807666263910.793759616686805
1440.1946529563509940.3893059127019880.805347043649006
1450.1497718805850250.2995437611700490.850228119414975
1460.2916943145958250.5833886291916510.708305685404175
1470.3314274873504980.6628549747009970.668572512649502
1480.2740206377758750.5480412755517490.725979362224125
1490.2590119215893050.5180238431786110.740988078410695
1500.2028184747294520.4056369494589050.797181525270548
1510.531699294853140.9366014102937190.46830070514686
1520.7858939314268530.4282121371462940.214106068573147
1530.703498067411390.593003865177220.29650193258861
1540.5879711861234810.8240576277530380.412028813876519
1550.5335559900316590.9328880199366820.466444009968341
1560.7784804045813490.4430391908373010.22151959541865

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.696750409408381 & 0.606499181183238 & 0.303249590591619 \tabularnewline
7 & 0.887282522929796 & 0.225434954140408 & 0.112717477070204 \tabularnewline
8 & 0.812678774487084 & 0.374642451025833 & 0.187321225512916 \tabularnewline
9 & 0.742077065752298 & 0.515845868495403 & 0.257922934247702 \tabularnewline
10 & 0.645491540498834 & 0.709016919002331 & 0.354508459501166 \tabularnewline
11 & 0.543484485808594 & 0.913031028382811 & 0.456515514191406 \tabularnewline
12 & 0.447855071463149 & 0.895710142926299 & 0.552144928536851 \tabularnewline
13 & 0.367236161694547 & 0.734472323389095 & 0.632763838305453 \tabularnewline
14 & 0.307559992099493 & 0.615119984198985 & 0.692440007900507 \tabularnewline
15 & 0.268384597839903 & 0.536769195679806 & 0.731615402160097 \tabularnewline
16 & 0.21480272958376 & 0.42960545916752 & 0.78519727041624 \tabularnewline
17 & 0.162171521211651 & 0.324343042423302 & 0.837828478788349 \tabularnewline
18 & 0.391581767891347 & 0.783163535782695 & 0.608418232108653 \tabularnewline
19 & 0.37761765353017 & 0.755235307060341 & 0.62238234646983 \tabularnewline
20 & 0.312226534800741 & 0.624453069601482 & 0.687773465199259 \tabularnewline
21 & 0.252484332345791 & 0.504968664691582 & 0.747515667654209 \tabularnewline
22 & 0.20182303416323 & 0.40364606832646 & 0.79817696583677 \tabularnewline
23 & 0.238745503102513 & 0.477491006205025 & 0.761254496897487 \tabularnewline
24 & 0.192374508467414 & 0.384749016934828 & 0.807625491532586 \tabularnewline
25 & 0.161968021694836 & 0.323936043389671 & 0.838031978305165 \tabularnewline
26 & 0.130812301182538 & 0.261624602365077 & 0.869187698817462 \tabularnewline
27 & 0.0993358960097411 & 0.198671792019482 & 0.900664103990259 \tabularnewline
28 & 0.0793874151502507 & 0.158774830300501 & 0.920612584849749 \tabularnewline
29 & 0.0591539407245851 & 0.11830788144917 & 0.940846059275415 \tabularnewline
30 & 0.0542370612867167 & 0.108474122573433 & 0.945762938713283 \tabularnewline
31 & 0.039019640991379 & 0.078039281982758 & 0.960980359008621 \tabularnewline
32 & 0.0500527984230954 & 0.100105596846191 & 0.949947201576905 \tabularnewline
33 & 0.0457340258652241 & 0.0914680517304482 & 0.954265974134776 \tabularnewline
34 & 0.0335151830249238 & 0.0670303660498475 & 0.966484816975076 \tabularnewline
35 & 0.0263551035871469 & 0.0527102071742938 & 0.973644896412853 \tabularnewline
36 & 0.474441459502065 & 0.94888291900413 & 0.525558540497935 \tabularnewline
37 & 0.579928488638721 & 0.840143022722558 & 0.420071511361279 \tabularnewline
38 & 0.545592878608186 & 0.908814242783628 & 0.454407121391814 \tabularnewline
39 & 0.549297822771817 & 0.901404354456366 & 0.450702177228183 \tabularnewline
40 & 0.503521069636208 & 0.992957860727584 & 0.496478930363792 \tabularnewline
41 & 0.453861300780398 & 0.907722601560796 & 0.546138699219602 \tabularnewline
42 & 0.411968304068891 & 0.823936608137782 & 0.588031695931109 \tabularnewline
43 & 0.556916645010467 & 0.886166709979065 & 0.443083354989533 \tabularnewline
44 & 0.52136370224813 & 0.957272595503741 & 0.47863629775187 \tabularnewline
45 & 0.489948988058716 & 0.979897976117432 & 0.510051011941284 \tabularnewline
46 & 0.661453345064388 & 0.677093309871225 & 0.338546654935612 \tabularnewline
47 & 0.656690547016734 & 0.686618905966532 & 0.343309452983266 \tabularnewline
48 & 0.609970645582422 & 0.780058708835156 & 0.390029354417578 \tabularnewline
49 & 0.564802010996348 & 0.870395978007304 & 0.435197989003652 \tabularnewline
50 & 0.539298347854797 & 0.921403304290407 & 0.460701652145203 \tabularnewline
51 & 0.50306511339075 & 0.993869773218499 & 0.49693488660925 \tabularnewline
52 & 0.455481467229379 & 0.910962934458757 & 0.544518532770621 \tabularnewline
53 & 0.469584451893581 & 0.939168903787161 & 0.530415548106419 \tabularnewline
54 & 0.428558421836669 & 0.857116843673338 & 0.571441578163331 \tabularnewline
55 & 0.658263405350521 & 0.683473189298957 & 0.341736594649479 \tabularnewline
56 & 0.62783753375834 & 0.744324932483321 & 0.37216246624166 \tabularnewline
57 & 0.583082225099316 & 0.833835549801369 & 0.416917774900684 \tabularnewline
58 & 0.574744712638361 & 0.850510574723279 & 0.425255287361639 \tabularnewline
59 & 0.531780706948576 & 0.936438586102849 & 0.468219293051424 \tabularnewline
60 & 0.493182259182762 & 0.986364518365523 & 0.506817740817238 \tabularnewline
61 & 0.479330526702908 & 0.958661053405815 & 0.520669473297092 \tabularnewline
62 & 0.434937413244572 & 0.869874826489143 & 0.565062586755428 \tabularnewline
63 & 0.399266152691882 & 0.798532305383764 & 0.600733847308118 \tabularnewline
64 & 0.356713105097147 & 0.713426210194294 & 0.643286894902853 \tabularnewline
65 & 0.330407515531097 & 0.660815031062194 & 0.669592484468903 \tabularnewline
66 & 0.314796332619529 & 0.629592665239059 & 0.685203667380471 \tabularnewline
67 & 0.336011088051396 & 0.672022176102792 & 0.663988911948604 \tabularnewline
68 & 0.455588841314104 & 0.911177682628209 & 0.544411158685896 \tabularnewline
69 & 0.556005901992328 & 0.887988196015343 & 0.443994098007671 \tabularnewline
70 & 0.517831488089405 & 0.96433702382119 & 0.482168511910595 \tabularnewline
71 & 0.734904162028594 & 0.530191675942811 & 0.265095837971406 \tabularnewline
72 & 0.698580771586433 & 0.602838456827133 & 0.301419228413567 \tabularnewline
73 & 0.73078338819693 & 0.538433223606141 & 0.26921661180307 \tabularnewline
74 & 0.738764643989991 & 0.522470712020019 & 0.261235356010009 \tabularnewline
75 & 0.713931177541579 & 0.572137644916842 & 0.286068822458421 \tabularnewline
76 & 0.69179496593169 & 0.616410068136619 & 0.30820503406831 \tabularnewline
77 & 0.655485515516375 & 0.689028968967249 & 0.344514484483624 \tabularnewline
78 & 0.624865492100231 & 0.750269015799539 & 0.375134507899769 \tabularnewline
79 & 0.589501745590593 & 0.820996508818814 & 0.410498254409407 \tabularnewline
80 & 0.550898698054292 & 0.898202603891417 & 0.449101301945708 \tabularnewline
81 & 0.5203629384727 & 0.9592741230546 & 0.4796370615273 \tabularnewline
82 & 0.672864219402565 & 0.654271561194869 & 0.327135780597435 \tabularnewline
83 & 0.641242883463999 & 0.717514233072001 & 0.358757116536001 \tabularnewline
84 & 0.599212237214212 & 0.801575525571577 & 0.400787762785788 \tabularnewline
85 & 0.556745584900212 & 0.886508830199577 & 0.443254415099789 \tabularnewline
86 & 0.528376050386922 & 0.943247899226155 & 0.471623949613077 \tabularnewline
87 & 0.506101161229621 & 0.987797677540759 & 0.493898838770379 \tabularnewline
88 & 0.47726407049503 & 0.954528140990061 & 0.52273592950497 \tabularnewline
89 & 0.464800369042909 & 0.929600738085818 & 0.535199630957091 \tabularnewline
90 & 0.421211163202968 & 0.842422326405937 & 0.578788836797032 \tabularnewline
91 & 0.43010174396021 & 0.860203487920419 & 0.56989825603979 \tabularnewline
92 & 0.420724828546404 & 0.841449657092807 & 0.579275171453596 \tabularnewline
93 & 0.389805182328242 & 0.779610364656485 & 0.610194817671758 \tabularnewline
94 & 0.348747425609221 & 0.697494851218442 & 0.651252574390779 \tabularnewline
95 & 0.316958488227418 & 0.633916976454836 & 0.683041511772582 \tabularnewline
96 & 0.282295982612623 & 0.564591965225247 & 0.717704017387377 \tabularnewline
97 & 0.251628808051739 & 0.503257616103478 & 0.748371191948261 \tabularnewline
98 & 0.225521079290757 & 0.451042158581514 & 0.774478920709243 \tabularnewline
99 & 0.198764699237911 & 0.397529398475822 & 0.801235300762089 \tabularnewline
100 & 0.169989013881258 & 0.339978027762515 & 0.830010986118742 \tabularnewline
101 & 0.14738296820991 & 0.294765936419819 & 0.85261703179009 \tabularnewline
102 & 0.127673427943866 & 0.255346855887732 & 0.872326572056134 \tabularnewline
103 & 0.265651208272189 & 0.531302416544378 & 0.734348791727811 \tabularnewline
104 & 0.230938087129812 & 0.461876174259623 & 0.769061912870188 \tabularnewline
105 & 0.214745278755294 & 0.429490557510588 & 0.785254721244706 \tabularnewline
106 & 0.221987458991137 & 0.443974917982274 & 0.778012541008863 \tabularnewline
107 & 0.216625150719858 & 0.433250301439716 & 0.783374849280142 \tabularnewline
108 & 0.194077756968345 & 0.388155513936691 & 0.805922243031655 \tabularnewline
109 & 0.174665654169129 & 0.349331308338258 & 0.825334345830871 \tabularnewline
110 & 0.229525991062648 & 0.459051982125296 & 0.770474008937352 \tabularnewline
111 & 0.220345181691619 & 0.440690363383238 & 0.779654818308381 \tabularnewline
112 & 0.209269936700939 & 0.418539873401878 & 0.790730063299061 \tabularnewline
113 & 0.247656150275324 & 0.495312300550648 & 0.752343849724676 \tabularnewline
114 & 0.222980807682912 & 0.445961615365825 & 0.777019192317087 \tabularnewline
115 & 0.252116828969299 & 0.504233657938597 & 0.747883171030701 \tabularnewline
116 & 0.288508188699207 & 0.577016377398414 & 0.711491811300793 \tabularnewline
117 & 0.253631148849845 & 0.507262297699689 & 0.746368851150155 \tabularnewline
118 & 0.220760456418548 & 0.441520912837096 & 0.779239543581452 \tabularnewline
119 & 0.196791183003382 & 0.393582366006763 & 0.803208816996618 \tabularnewline
120 & 0.174366888856909 & 0.348733777713817 & 0.825633111143091 \tabularnewline
121 & 0.152368687925959 & 0.304737375851918 & 0.847631312074041 \tabularnewline
122 & 0.138922201399686 & 0.277844402799371 & 0.861077798600314 \tabularnewline
123 & 0.128508150538693 & 0.257016301077386 & 0.871491849461307 \tabularnewline
124 & 0.114802894548377 & 0.229605789096753 & 0.885197105451623 \tabularnewline
125 & 0.0933835224507496 & 0.186767044901499 & 0.90661647754925 \tabularnewline
126 & 0.0904074259976608 & 0.180814851995322 & 0.909592574002339 \tabularnewline
127 & 0.0708224797553309 & 0.141644959510662 & 0.929177520244669 \tabularnewline
128 & 0.0670397690115105 & 0.134079538023021 & 0.93296023098849 \tabularnewline
129 & 0.0551821458696646 & 0.110364291739329 & 0.944817854130335 \tabularnewline
130 & 0.0561358728754944 & 0.112271745750989 & 0.943864127124506 \tabularnewline
131 & 0.0530367182395185 & 0.106073436479037 & 0.946963281760482 \tabularnewline
132 & 0.0816433054617633 & 0.163286610923527 & 0.918356694538237 \tabularnewline
133 & 0.0756944420228049 & 0.15138888404561 & 0.924305557977195 \tabularnewline
134 & 0.0734489808077975 & 0.146897961615595 & 0.926551019192203 \tabularnewline
135 & 0.057560493804811 & 0.115120987609622 & 0.942439506195189 \tabularnewline
136 & 0.0430798530446793 & 0.0861597060893585 & 0.956920146955321 \tabularnewline
137 & 0.0420091665821601 & 0.0840183331643203 & 0.95799083341784 \tabularnewline
138 & 0.0324701875020024 & 0.0649403750040047 & 0.967529812497998 \tabularnewline
139 & 0.0440075852609387 & 0.0880151705218774 & 0.955992414739061 \tabularnewline
140 & 0.0412224723444074 & 0.0824449446888148 & 0.958777527655593 \tabularnewline
141 & 0.302932955374037 & 0.605865910748074 & 0.697067044625963 \tabularnewline
142 & 0.259765061774605 & 0.519530123549209 & 0.740234938225395 \tabularnewline
143 & 0.206240383313195 & 0.412480766626391 & 0.793759616686805 \tabularnewline
144 & 0.194652956350994 & 0.389305912701988 & 0.805347043649006 \tabularnewline
145 & 0.149771880585025 & 0.299543761170049 & 0.850228119414975 \tabularnewline
146 & 0.291694314595825 & 0.583388629191651 & 0.708305685404175 \tabularnewline
147 & 0.331427487350498 & 0.662854974700997 & 0.668572512649502 \tabularnewline
148 & 0.274020637775875 & 0.548041275551749 & 0.725979362224125 \tabularnewline
149 & 0.259011921589305 & 0.518023843178611 & 0.740988078410695 \tabularnewline
150 & 0.202818474729452 & 0.405636949458905 & 0.797181525270548 \tabularnewline
151 & 0.53169929485314 & 0.936601410293719 & 0.46830070514686 \tabularnewline
152 & 0.785893931426853 & 0.428212137146294 & 0.214106068573147 \tabularnewline
153 & 0.70349806741139 & 0.59300386517722 & 0.29650193258861 \tabularnewline
154 & 0.587971186123481 & 0.824057627753038 & 0.412028813876519 \tabularnewline
155 & 0.533555990031659 & 0.932888019936682 & 0.466444009968341 \tabularnewline
156 & 0.778480404581349 & 0.443039190837301 & 0.22151959541865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&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]6[/C][C]0.696750409408381[/C][C]0.606499181183238[/C][C]0.303249590591619[/C][/ROW]
[ROW][C]7[/C][C]0.887282522929796[/C][C]0.225434954140408[/C][C]0.112717477070204[/C][/ROW]
[ROW][C]8[/C][C]0.812678774487084[/C][C]0.374642451025833[/C][C]0.187321225512916[/C][/ROW]
[ROW][C]9[/C][C]0.742077065752298[/C][C]0.515845868495403[/C][C]0.257922934247702[/C][/ROW]
[ROW][C]10[/C][C]0.645491540498834[/C][C]0.709016919002331[/C][C]0.354508459501166[/C][/ROW]
[ROW][C]11[/C][C]0.543484485808594[/C][C]0.913031028382811[/C][C]0.456515514191406[/C][/ROW]
[ROW][C]12[/C][C]0.447855071463149[/C][C]0.895710142926299[/C][C]0.552144928536851[/C][/ROW]
[ROW][C]13[/C][C]0.367236161694547[/C][C]0.734472323389095[/C][C]0.632763838305453[/C][/ROW]
[ROW][C]14[/C][C]0.307559992099493[/C][C]0.615119984198985[/C][C]0.692440007900507[/C][/ROW]
[ROW][C]15[/C][C]0.268384597839903[/C][C]0.536769195679806[/C][C]0.731615402160097[/C][/ROW]
[ROW][C]16[/C][C]0.21480272958376[/C][C]0.42960545916752[/C][C]0.78519727041624[/C][/ROW]
[ROW][C]17[/C][C]0.162171521211651[/C][C]0.324343042423302[/C][C]0.837828478788349[/C][/ROW]
[ROW][C]18[/C][C]0.391581767891347[/C][C]0.783163535782695[/C][C]0.608418232108653[/C][/ROW]
[ROW][C]19[/C][C]0.37761765353017[/C][C]0.755235307060341[/C][C]0.62238234646983[/C][/ROW]
[ROW][C]20[/C][C]0.312226534800741[/C][C]0.624453069601482[/C][C]0.687773465199259[/C][/ROW]
[ROW][C]21[/C][C]0.252484332345791[/C][C]0.504968664691582[/C][C]0.747515667654209[/C][/ROW]
[ROW][C]22[/C][C]0.20182303416323[/C][C]0.40364606832646[/C][C]0.79817696583677[/C][/ROW]
[ROW][C]23[/C][C]0.238745503102513[/C][C]0.477491006205025[/C][C]0.761254496897487[/C][/ROW]
[ROW][C]24[/C][C]0.192374508467414[/C][C]0.384749016934828[/C][C]0.807625491532586[/C][/ROW]
[ROW][C]25[/C][C]0.161968021694836[/C][C]0.323936043389671[/C][C]0.838031978305165[/C][/ROW]
[ROW][C]26[/C][C]0.130812301182538[/C][C]0.261624602365077[/C][C]0.869187698817462[/C][/ROW]
[ROW][C]27[/C][C]0.0993358960097411[/C][C]0.198671792019482[/C][C]0.900664103990259[/C][/ROW]
[ROW][C]28[/C][C]0.0793874151502507[/C][C]0.158774830300501[/C][C]0.920612584849749[/C][/ROW]
[ROW][C]29[/C][C]0.0591539407245851[/C][C]0.11830788144917[/C][C]0.940846059275415[/C][/ROW]
[ROW][C]30[/C][C]0.0542370612867167[/C][C]0.108474122573433[/C][C]0.945762938713283[/C][/ROW]
[ROW][C]31[/C][C]0.039019640991379[/C][C]0.078039281982758[/C][C]0.960980359008621[/C][/ROW]
[ROW][C]32[/C][C]0.0500527984230954[/C][C]0.100105596846191[/C][C]0.949947201576905[/C][/ROW]
[ROW][C]33[/C][C]0.0457340258652241[/C][C]0.0914680517304482[/C][C]0.954265974134776[/C][/ROW]
[ROW][C]34[/C][C]0.0335151830249238[/C][C]0.0670303660498475[/C][C]0.966484816975076[/C][/ROW]
[ROW][C]35[/C][C]0.0263551035871469[/C][C]0.0527102071742938[/C][C]0.973644896412853[/C][/ROW]
[ROW][C]36[/C][C]0.474441459502065[/C][C]0.94888291900413[/C][C]0.525558540497935[/C][/ROW]
[ROW][C]37[/C][C]0.579928488638721[/C][C]0.840143022722558[/C][C]0.420071511361279[/C][/ROW]
[ROW][C]38[/C][C]0.545592878608186[/C][C]0.908814242783628[/C][C]0.454407121391814[/C][/ROW]
[ROW][C]39[/C][C]0.549297822771817[/C][C]0.901404354456366[/C][C]0.450702177228183[/C][/ROW]
[ROW][C]40[/C][C]0.503521069636208[/C][C]0.992957860727584[/C][C]0.496478930363792[/C][/ROW]
[ROW][C]41[/C][C]0.453861300780398[/C][C]0.907722601560796[/C][C]0.546138699219602[/C][/ROW]
[ROW][C]42[/C][C]0.411968304068891[/C][C]0.823936608137782[/C][C]0.588031695931109[/C][/ROW]
[ROW][C]43[/C][C]0.556916645010467[/C][C]0.886166709979065[/C][C]0.443083354989533[/C][/ROW]
[ROW][C]44[/C][C]0.52136370224813[/C][C]0.957272595503741[/C][C]0.47863629775187[/C][/ROW]
[ROW][C]45[/C][C]0.489948988058716[/C][C]0.979897976117432[/C][C]0.510051011941284[/C][/ROW]
[ROW][C]46[/C][C]0.661453345064388[/C][C]0.677093309871225[/C][C]0.338546654935612[/C][/ROW]
[ROW][C]47[/C][C]0.656690547016734[/C][C]0.686618905966532[/C][C]0.343309452983266[/C][/ROW]
[ROW][C]48[/C][C]0.609970645582422[/C][C]0.780058708835156[/C][C]0.390029354417578[/C][/ROW]
[ROW][C]49[/C][C]0.564802010996348[/C][C]0.870395978007304[/C][C]0.435197989003652[/C][/ROW]
[ROW][C]50[/C][C]0.539298347854797[/C][C]0.921403304290407[/C][C]0.460701652145203[/C][/ROW]
[ROW][C]51[/C][C]0.50306511339075[/C][C]0.993869773218499[/C][C]0.49693488660925[/C][/ROW]
[ROW][C]52[/C][C]0.455481467229379[/C][C]0.910962934458757[/C][C]0.544518532770621[/C][/ROW]
[ROW][C]53[/C][C]0.469584451893581[/C][C]0.939168903787161[/C][C]0.530415548106419[/C][/ROW]
[ROW][C]54[/C][C]0.428558421836669[/C][C]0.857116843673338[/C][C]0.571441578163331[/C][/ROW]
[ROW][C]55[/C][C]0.658263405350521[/C][C]0.683473189298957[/C][C]0.341736594649479[/C][/ROW]
[ROW][C]56[/C][C]0.62783753375834[/C][C]0.744324932483321[/C][C]0.37216246624166[/C][/ROW]
[ROW][C]57[/C][C]0.583082225099316[/C][C]0.833835549801369[/C][C]0.416917774900684[/C][/ROW]
[ROW][C]58[/C][C]0.574744712638361[/C][C]0.850510574723279[/C][C]0.425255287361639[/C][/ROW]
[ROW][C]59[/C][C]0.531780706948576[/C][C]0.936438586102849[/C][C]0.468219293051424[/C][/ROW]
[ROW][C]60[/C][C]0.493182259182762[/C][C]0.986364518365523[/C][C]0.506817740817238[/C][/ROW]
[ROW][C]61[/C][C]0.479330526702908[/C][C]0.958661053405815[/C][C]0.520669473297092[/C][/ROW]
[ROW][C]62[/C][C]0.434937413244572[/C][C]0.869874826489143[/C][C]0.565062586755428[/C][/ROW]
[ROW][C]63[/C][C]0.399266152691882[/C][C]0.798532305383764[/C][C]0.600733847308118[/C][/ROW]
[ROW][C]64[/C][C]0.356713105097147[/C][C]0.713426210194294[/C][C]0.643286894902853[/C][/ROW]
[ROW][C]65[/C][C]0.330407515531097[/C][C]0.660815031062194[/C][C]0.669592484468903[/C][/ROW]
[ROW][C]66[/C][C]0.314796332619529[/C][C]0.629592665239059[/C][C]0.685203667380471[/C][/ROW]
[ROW][C]67[/C][C]0.336011088051396[/C][C]0.672022176102792[/C][C]0.663988911948604[/C][/ROW]
[ROW][C]68[/C][C]0.455588841314104[/C][C]0.911177682628209[/C][C]0.544411158685896[/C][/ROW]
[ROW][C]69[/C][C]0.556005901992328[/C][C]0.887988196015343[/C][C]0.443994098007671[/C][/ROW]
[ROW][C]70[/C][C]0.517831488089405[/C][C]0.96433702382119[/C][C]0.482168511910595[/C][/ROW]
[ROW][C]71[/C][C]0.734904162028594[/C][C]0.530191675942811[/C][C]0.265095837971406[/C][/ROW]
[ROW][C]72[/C][C]0.698580771586433[/C][C]0.602838456827133[/C][C]0.301419228413567[/C][/ROW]
[ROW][C]73[/C][C]0.73078338819693[/C][C]0.538433223606141[/C][C]0.26921661180307[/C][/ROW]
[ROW][C]74[/C][C]0.738764643989991[/C][C]0.522470712020019[/C][C]0.261235356010009[/C][/ROW]
[ROW][C]75[/C][C]0.713931177541579[/C][C]0.572137644916842[/C][C]0.286068822458421[/C][/ROW]
[ROW][C]76[/C][C]0.69179496593169[/C][C]0.616410068136619[/C][C]0.30820503406831[/C][/ROW]
[ROW][C]77[/C][C]0.655485515516375[/C][C]0.689028968967249[/C][C]0.344514484483624[/C][/ROW]
[ROW][C]78[/C][C]0.624865492100231[/C][C]0.750269015799539[/C][C]0.375134507899769[/C][/ROW]
[ROW][C]79[/C][C]0.589501745590593[/C][C]0.820996508818814[/C][C]0.410498254409407[/C][/ROW]
[ROW][C]80[/C][C]0.550898698054292[/C][C]0.898202603891417[/C][C]0.449101301945708[/C][/ROW]
[ROW][C]81[/C][C]0.5203629384727[/C][C]0.9592741230546[/C][C]0.4796370615273[/C][/ROW]
[ROW][C]82[/C][C]0.672864219402565[/C][C]0.654271561194869[/C][C]0.327135780597435[/C][/ROW]
[ROW][C]83[/C][C]0.641242883463999[/C][C]0.717514233072001[/C][C]0.358757116536001[/C][/ROW]
[ROW][C]84[/C][C]0.599212237214212[/C][C]0.801575525571577[/C][C]0.400787762785788[/C][/ROW]
[ROW][C]85[/C][C]0.556745584900212[/C][C]0.886508830199577[/C][C]0.443254415099789[/C][/ROW]
[ROW][C]86[/C][C]0.528376050386922[/C][C]0.943247899226155[/C][C]0.471623949613077[/C][/ROW]
[ROW][C]87[/C][C]0.506101161229621[/C][C]0.987797677540759[/C][C]0.493898838770379[/C][/ROW]
[ROW][C]88[/C][C]0.47726407049503[/C][C]0.954528140990061[/C][C]0.52273592950497[/C][/ROW]
[ROW][C]89[/C][C]0.464800369042909[/C][C]0.929600738085818[/C][C]0.535199630957091[/C][/ROW]
[ROW][C]90[/C][C]0.421211163202968[/C][C]0.842422326405937[/C][C]0.578788836797032[/C][/ROW]
[ROW][C]91[/C][C]0.43010174396021[/C][C]0.860203487920419[/C][C]0.56989825603979[/C][/ROW]
[ROW][C]92[/C][C]0.420724828546404[/C][C]0.841449657092807[/C][C]0.579275171453596[/C][/ROW]
[ROW][C]93[/C][C]0.389805182328242[/C][C]0.779610364656485[/C][C]0.610194817671758[/C][/ROW]
[ROW][C]94[/C][C]0.348747425609221[/C][C]0.697494851218442[/C][C]0.651252574390779[/C][/ROW]
[ROW][C]95[/C][C]0.316958488227418[/C][C]0.633916976454836[/C][C]0.683041511772582[/C][/ROW]
[ROW][C]96[/C][C]0.282295982612623[/C][C]0.564591965225247[/C][C]0.717704017387377[/C][/ROW]
[ROW][C]97[/C][C]0.251628808051739[/C][C]0.503257616103478[/C][C]0.748371191948261[/C][/ROW]
[ROW][C]98[/C][C]0.225521079290757[/C][C]0.451042158581514[/C][C]0.774478920709243[/C][/ROW]
[ROW][C]99[/C][C]0.198764699237911[/C][C]0.397529398475822[/C][C]0.801235300762089[/C][/ROW]
[ROW][C]100[/C][C]0.169989013881258[/C][C]0.339978027762515[/C][C]0.830010986118742[/C][/ROW]
[ROW][C]101[/C][C]0.14738296820991[/C][C]0.294765936419819[/C][C]0.85261703179009[/C][/ROW]
[ROW][C]102[/C][C]0.127673427943866[/C][C]0.255346855887732[/C][C]0.872326572056134[/C][/ROW]
[ROW][C]103[/C][C]0.265651208272189[/C][C]0.531302416544378[/C][C]0.734348791727811[/C][/ROW]
[ROW][C]104[/C][C]0.230938087129812[/C][C]0.461876174259623[/C][C]0.769061912870188[/C][/ROW]
[ROW][C]105[/C][C]0.214745278755294[/C][C]0.429490557510588[/C][C]0.785254721244706[/C][/ROW]
[ROW][C]106[/C][C]0.221987458991137[/C][C]0.443974917982274[/C][C]0.778012541008863[/C][/ROW]
[ROW][C]107[/C][C]0.216625150719858[/C][C]0.433250301439716[/C][C]0.783374849280142[/C][/ROW]
[ROW][C]108[/C][C]0.194077756968345[/C][C]0.388155513936691[/C][C]0.805922243031655[/C][/ROW]
[ROW][C]109[/C][C]0.174665654169129[/C][C]0.349331308338258[/C][C]0.825334345830871[/C][/ROW]
[ROW][C]110[/C][C]0.229525991062648[/C][C]0.459051982125296[/C][C]0.770474008937352[/C][/ROW]
[ROW][C]111[/C][C]0.220345181691619[/C][C]0.440690363383238[/C][C]0.779654818308381[/C][/ROW]
[ROW][C]112[/C][C]0.209269936700939[/C][C]0.418539873401878[/C][C]0.790730063299061[/C][/ROW]
[ROW][C]113[/C][C]0.247656150275324[/C][C]0.495312300550648[/C][C]0.752343849724676[/C][/ROW]
[ROW][C]114[/C][C]0.222980807682912[/C][C]0.445961615365825[/C][C]0.777019192317087[/C][/ROW]
[ROW][C]115[/C][C]0.252116828969299[/C][C]0.504233657938597[/C][C]0.747883171030701[/C][/ROW]
[ROW][C]116[/C][C]0.288508188699207[/C][C]0.577016377398414[/C][C]0.711491811300793[/C][/ROW]
[ROW][C]117[/C][C]0.253631148849845[/C][C]0.507262297699689[/C][C]0.746368851150155[/C][/ROW]
[ROW][C]118[/C][C]0.220760456418548[/C][C]0.441520912837096[/C][C]0.779239543581452[/C][/ROW]
[ROW][C]119[/C][C]0.196791183003382[/C][C]0.393582366006763[/C][C]0.803208816996618[/C][/ROW]
[ROW][C]120[/C][C]0.174366888856909[/C][C]0.348733777713817[/C][C]0.825633111143091[/C][/ROW]
[ROW][C]121[/C][C]0.152368687925959[/C][C]0.304737375851918[/C][C]0.847631312074041[/C][/ROW]
[ROW][C]122[/C][C]0.138922201399686[/C][C]0.277844402799371[/C][C]0.861077798600314[/C][/ROW]
[ROW][C]123[/C][C]0.128508150538693[/C][C]0.257016301077386[/C][C]0.871491849461307[/C][/ROW]
[ROW][C]124[/C][C]0.114802894548377[/C][C]0.229605789096753[/C][C]0.885197105451623[/C][/ROW]
[ROW][C]125[/C][C]0.0933835224507496[/C][C]0.186767044901499[/C][C]0.90661647754925[/C][/ROW]
[ROW][C]126[/C][C]0.0904074259976608[/C][C]0.180814851995322[/C][C]0.909592574002339[/C][/ROW]
[ROW][C]127[/C][C]0.0708224797553309[/C][C]0.141644959510662[/C][C]0.929177520244669[/C][/ROW]
[ROW][C]128[/C][C]0.0670397690115105[/C][C]0.134079538023021[/C][C]0.93296023098849[/C][/ROW]
[ROW][C]129[/C][C]0.0551821458696646[/C][C]0.110364291739329[/C][C]0.944817854130335[/C][/ROW]
[ROW][C]130[/C][C]0.0561358728754944[/C][C]0.112271745750989[/C][C]0.943864127124506[/C][/ROW]
[ROW][C]131[/C][C]0.0530367182395185[/C][C]0.106073436479037[/C][C]0.946963281760482[/C][/ROW]
[ROW][C]132[/C][C]0.0816433054617633[/C][C]0.163286610923527[/C][C]0.918356694538237[/C][/ROW]
[ROW][C]133[/C][C]0.0756944420228049[/C][C]0.15138888404561[/C][C]0.924305557977195[/C][/ROW]
[ROW][C]134[/C][C]0.0734489808077975[/C][C]0.146897961615595[/C][C]0.926551019192203[/C][/ROW]
[ROW][C]135[/C][C]0.057560493804811[/C][C]0.115120987609622[/C][C]0.942439506195189[/C][/ROW]
[ROW][C]136[/C][C]0.0430798530446793[/C][C]0.0861597060893585[/C][C]0.956920146955321[/C][/ROW]
[ROW][C]137[/C][C]0.0420091665821601[/C][C]0.0840183331643203[/C][C]0.95799083341784[/C][/ROW]
[ROW][C]138[/C][C]0.0324701875020024[/C][C]0.0649403750040047[/C][C]0.967529812497998[/C][/ROW]
[ROW][C]139[/C][C]0.0440075852609387[/C][C]0.0880151705218774[/C][C]0.955992414739061[/C][/ROW]
[ROW][C]140[/C][C]0.0412224723444074[/C][C]0.0824449446888148[/C][C]0.958777527655593[/C][/ROW]
[ROW][C]141[/C][C]0.302932955374037[/C][C]0.605865910748074[/C][C]0.697067044625963[/C][/ROW]
[ROW][C]142[/C][C]0.259765061774605[/C][C]0.519530123549209[/C][C]0.740234938225395[/C][/ROW]
[ROW][C]143[/C][C]0.206240383313195[/C][C]0.412480766626391[/C][C]0.793759616686805[/C][/ROW]
[ROW][C]144[/C][C]0.194652956350994[/C][C]0.389305912701988[/C][C]0.805347043649006[/C][/ROW]
[ROW][C]145[/C][C]0.149771880585025[/C][C]0.299543761170049[/C][C]0.850228119414975[/C][/ROW]
[ROW][C]146[/C][C]0.291694314595825[/C][C]0.583388629191651[/C][C]0.708305685404175[/C][/ROW]
[ROW][C]147[/C][C]0.331427487350498[/C][C]0.662854974700997[/C][C]0.668572512649502[/C][/ROW]
[ROW][C]148[/C][C]0.274020637775875[/C][C]0.548041275551749[/C][C]0.725979362224125[/C][/ROW]
[ROW][C]149[/C][C]0.259011921589305[/C][C]0.518023843178611[/C][C]0.740988078410695[/C][/ROW]
[ROW][C]150[/C][C]0.202818474729452[/C][C]0.405636949458905[/C][C]0.797181525270548[/C][/ROW]
[ROW][C]151[/C][C]0.53169929485314[/C][C]0.936601410293719[/C][C]0.46830070514686[/C][/ROW]
[ROW][C]152[/C][C]0.785893931426853[/C][C]0.428212137146294[/C][C]0.214106068573147[/C][/ROW]
[ROW][C]153[/C][C]0.70349806741139[/C][C]0.59300386517722[/C][C]0.29650193258861[/C][/ROW]
[ROW][C]154[/C][C]0.587971186123481[/C][C]0.824057627753038[/C][C]0.412028813876519[/C][/ROW]
[ROW][C]155[/C][C]0.533555990031659[/C][C]0.932888019936682[/C][C]0.466444009968341[/C][/ROW]
[ROW][C]156[/C][C]0.778480404581349[/C][C]0.443039190837301[/C][C]0.22151959541865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&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
60.6967504094083810.6064991811832380.303249590591619
70.8872825229297960.2254349541404080.112717477070204
80.8126787744870840.3746424510258330.187321225512916
90.7420770657522980.5158458684954030.257922934247702
100.6454915404988340.7090169190023310.354508459501166
110.5434844858085940.9130310283828110.456515514191406
120.4478550714631490.8957101429262990.552144928536851
130.3672361616945470.7344723233890950.632763838305453
140.3075599920994930.6151199841989850.692440007900507
150.2683845978399030.5367691956798060.731615402160097
160.214802729583760.429605459167520.78519727041624
170.1621715212116510.3243430424233020.837828478788349
180.3915817678913470.7831635357826950.608418232108653
190.377617653530170.7552353070603410.62238234646983
200.3122265348007410.6244530696014820.687773465199259
210.2524843323457910.5049686646915820.747515667654209
220.201823034163230.403646068326460.79817696583677
230.2387455031025130.4774910062050250.761254496897487
240.1923745084674140.3847490169348280.807625491532586
250.1619680216948360.3239360433896710.838031978305165
260.1308123011825380.2616246023650770.869187698817462
270.09933589600974110.1986717920194820.900664103990259
280.07938741515025070.1587748303005010.920612584849749
290.05915394072458510.118307881449170.940846059275415
300.05423706128671670.1084741225734330.945762938713283
310.0390196409913790.0780392819827580.960980359008621
320.05005279842309540.1001055968461910.949947201576905
330.04573402586522410.09146805173044820.954265974134776
340.03351518302492380.06703036604984750.966484816975076
350.02635510358714690.05271020717429380.973644896412853
360.4744414595020650.948882919004130.525558540497935
370.5799284886387210.8401430227225580.420071511361279
380.5455928786081860.9088142427836280.454407121391814
390.5492978227718170.9014043544563660.450702177228183
400.5035210696362080.9929578607275840.496478930363792
410.4538613007803980.9077226015607960.546138699219602
420.4119683040688910.8239366081377820.588031695931109
430.5569166450104670.8861667099790650.443083354989533
440.521363702248130.9572725955037410.47863629775187
450.4899489880587160.9798979761174320.510051011941284
460.6614533450643880.6770933098712250.338546654935612
470.6566905470167340.6866189059665320.343309452983266
480.6099706455824220.7800587088351560.390029354417578
490.5648020109963480.8703959780073040.435197989003652
500.5392983478547970.9214033042904070.460701652145203
510.503065113390750.9938697732184990.49693488660925
520.4554814672293790.9109629344587570.544518532770621
530.4695844518935810.9391689037871610.530415548106419
540.4285584218366690.8571168436733380.571441578163331
550.6582634053505210.6834731892989570.341736594649479
560.627837533758340.7443249324833210.37216246624166
570.5830822250993160.8338355498013690.416917774900684
580.5747447126383610.8505105747232790.425255287361639
590.5317807069485760.9364385861028490.468219293051424
600.4931822591827620.9863645183655230.506817740817238
610.4793305267029080.9586610534058150.520669473297092
620.4349374132445720.8698748264891430.565062586755428
630.3992661526918820.7985323053837640.600733847308118
640.3567131050971470.7134262101942940.643286894902853
650.3304075155310970.6608150310621940.669592484468903
660.3147963326195290.6295926652390590.685203667380471
670.3360110880513960.6720221761027920.663988911948604
680.4555888413141040.9111776826282090.544411158685896
690.5560059019923280.8879881960153430.443994098007671
700.5178314880894050.964337023821190.482168511910595
710.7349041620285940.5301916759428110.265095837971406
720.6985807715864330.6028384568271330.301419228413567
730.730783388196930.5384332236061410.26921661180307
740.7387646439899910.5224707120200190.261235356010009
750.7139311775415790.5721376449168420.286068822458421
760.691794965931690.6164100681366190.30820503406831
770.6554855155163750.6890289689672490.344514484483624
780.6248654921002310.7502690157995390.375134507899769
790.5895017455905930.8209965088188140.410498254409407
800.5508986980542920.8982026038914170.449101301945708
810.52036293847270.95927412305460.4796370615273
820.6728642194025650.6542715611948690.327135780597435
830.6412428834639990.7175142330720010.358757116536001
840.5992122372142120.8015755255715770.400787762785788
850.5567455849002120.8865088301995770.443254415099789
860.5283760503869220.9432478992261550.471623949613077
870.5061011612296210.9877976775407590.493898838770379
880.477264070495030.9545281409900610.52273592950497
890.4648003690429090.9296007380858180.535199630957091
900.4212111632029680.8424223264059370.578788836797032
910.430101743960210.8602034879204190.56989825603979
920.4207248285464040.8414496570928070.579275171453596
930.3898051823282420.7796103646564850.610194817671758
940.3487474256092210.6974948512184420.651252574390779
950.3169584882274180.6339169764548360.683041511772582
960.2822959826126230.5645919652252470.717704017387377
970.2516288080517390.5032576161034780.748371191948261
980.2255210792907570.4510421585815140.774478920709243
990.1987646992379110.3975293984758220.801235300762089
1000.1699890138812580.3399780277625150.830010986118742
1010.147382968209910.2947659364198190.85261703179009
1020.1276734279438660.2553468558877320.872326572056134
1030.2656512082721890.5313024165443780.734348791727811
1040.2309380871298120.4618761742596230.769061912870188
1050.2147452787552940.4294905575105880.785254721244706
1060.2219874589911370.4439749179822740.778012541008863
1070.2166251507198580.4332503014397160.783374849280142
1080.1940777569683450.3881555139366910.805922243031655
1090.1746656541691290.3493313083382580.825334345830871
1100.2295259910626480.4590519821252960.770474008937352
1110.2203451816916190.4406903633832380.779654818308381
1120.2092699367009390.4185398734018780.790730063299061
1130.2476561502753240.4953123005506480.752343849724676
1140.2229808076829120.4459616153658250.777019192317087
1150.2521168289692990.5042336579385970.747883171030701
1160.2885081886992070.5770163773984140.711491811300793
1170.2536311488498450.5072622976996890.746368851150155
1180.2207604564185480.4415209128370960.779239543581452
1190.1967911830033820.3935823660067630.803208816996618
1200.1743668888569090.3487337777138170.825633111143091
1210.1523686879259590.3047373758519180.847631312074041
1220.1389222013996860.2778444027993710.861077798600314
1230.1285081505386930.2570163010773860.871491849461307
1240.1148028945483770.2296057890967530.885197105451623
1250.09338352245074960.1867670449014990.90661647754925
1260.09040742599766080.1808148519953220.909592574002339
1270.07082247975533090.1416449595106620.929177520244669
1280.06703976901151050.1340795380230210.93296023098849
1290.05518214586966460.1103642917393290.944817854130335
1300.05613587287549440.1122717457509890.943864127124506
1310.05303671823951850.1060734364790370.946963281760482
1320.08164330546176330.1632866109235270.918356694538237
1330.07569444202280490.151388884045610.924305557977195
1340.07344898080779750.1468979616155950.926551019192203
1350.0575604938048110.1151209876096220.942439506195189
1360.04307985304467930.08615970608935850.956920146955321
1370.04200916658216010.08401833316432030.95799083341784
1380.03247018750200240.06494037500400470.967529812497998
1390.04400758526093870.08801517052187740.955992414739061
1400.04122247234440740.08244494468881480.958777527655593
1410.3029329553740370.6058659107480740.697067044625963
1420.2597650617746050.5195301235492090.740234938225395
1430.2062403833131950.4124807666263910.793759616686805
1440.1946529563509940.3893059127019880.805347043649006
1450.1497718805850250.2995437611700490.850228119414975
1460.2916943145958250.5833886291916510.708305685404175
1470.3314274873504980.6628549747009970.668572512649502
1480.2740206377758750.5480412755517490.725979362224125
1490.2590119215893050.5180238431786110.740988078410695
1500.2028184747294520.4056369494589050.797181525270548
1510.531699294853140.9366014102937190.46830070514686
1520.7858939314268530.4282121371462940.214106068573147
1530.703498067411390.593003865177220.29650193258861
1540.5879711861234810.8240576277530380.412028813876519
1550.5335559900316590.9328880199366820.466444009968341
1560.7784804045813490.4430391908373010.22151959541865







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level90.0596026490066225OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 9 & 0.0596026490066225 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185722&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0596026490066225[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185722&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185722&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level90.0596026490066225OK



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