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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 21:27:31 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290547537itrsvv0lgq81d1u.htm/, Retrieved Fri, 26 Apr 2024 11:13:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99667, Retrieved Fri, 26 Apr 2024 11:13:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
F   PD  [Multiple Regression] [] [2010-11-23 21:18:19] [2db311435ed525bc1ed0ddec922afb8f]
-   P       [Multiple Regression] [] [2010-11-23 21:27:31] [6fde1c772c7be11768d9b6a0344566b2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	14	11	12	12
11	11	7	8	11
11	6	17	8	14
11	12	10	8	12
11	8	12	9	21
11	10	12	7	12
11	10	11	4	22
11	11	11	11	11
11	16	12	7	10
11	11	13	7	13
11	13	14	12	10
11	12	16	10	8
11	8	11	10	15
11	12	10	8	14
11	11	11	8	10
11	4	15	4	14
11	9	9	9	14
11	8	11	8	11
11	8	17	7	10
11	14	17	11	13
11	15	11	9	7
11	16	18	11	14
11	9	14	13	12
11	14	10	8	14
11	11	11	8	11
11	8	15	9	9
11	9	15	6	11
11	9	13	9	15
11	9	16	9	14
11	9	13	6	13
11	10	9	6	9
11	16	18	16	15
11	11	18	5	10
11	8	12	7	11
11	9	17	9	13
11	16	9	6	8
11	11	9	6	20
11	16	12	5	12
11	12	18	12	10
11	12	12	7	10
11	14	18	10	9
11	9	14	9	14
11	10	15	8	8
11	9	16	5	14
11	10	10	8	11
11	12	11	8	13
11	14	14	10	9
11	14	9	6	11
11	10	12	8	15
11	14	17	7	11
11	16	5	4	10
11	9	12	8	14
11	10	12	8	18
11	6	6	4	14
11	8	24	20	11
11	13	12	8	12
11	10	12	8	13
11	8	14	6	9
11	7	7	4	10
11	15	13	8	15
11	9	12	9	20
11	10	13	6	12
11	12	14	7	12
11	13	8	9	14
11	10	11	5	13
11	11	9	5	11
11	8	11	8	17
11	9	13	8	12
11	13	10	6	13
11	11	11	8	14
11	8	12	7	13
11	9	9	7	15
11	9	15	9	13
11	15	18	11	10
11	9	15	6	11
11	10	12	8	19
11	14	13	6	13
11	12	14	9	17
11	12	10	8	13
11	11	13	6	9
11	14	13	10	11
11	6	11	8	10
11	12	13	8	9
11	8	16	10	12
11	14	8	5	12
11	11	16	7	13
11	10	11	5	13
11	14	9	8	12
11	12	16	14	15
11	10	12	7	22
11	14	14	8	13
11	5	8	6	15
11	11	9	5	13
11	10	15	6	15
11	9	11	10	10
11	10	21	12	11
11	16	14	9	16
11	13	18	12	11
11	9	12	7	11
11	10	13	8	10
11	10	15	10	10
11	7	12	6	16
11	9	19	10	12
11	8	15	10	11
11	14	11	10	16
11	14	11	5	19
11	8	10	7	11
11	9	13	10	16
11	14	15	11	15
11	14	12	6	24
11	8	12	7	14
11	8	16	12	15
11	8	9	11	11
11	7	18	11	15
11	6	8	11	12
11	8	13	5	10
11	6	17	8	14
11	11	9	6	13
11	14	15	9	9
11	11	8	4	15
11	11	7	4	15
11	11	12	7	14
11	14	14	11	11
11	8	6	6	8
11	20	8	7	11
11	11	17	8	11
11	8	10	4	8
11	11	11	8	10
11	10	14	9	11
11	14	11	8	13
11	11	13	11	11
11	9	12	8	20
11	9	11	5	10
11	8	9	4	15
11	10	12	8	12
11	13	20	10	14
11	13	12	6	23
11	12	13	9	14
11	8	12	9	16
11	13	12	13	11
11	14	9	9	12
11	12	15	10	10
11	14	24	20	14
11	15	7	5	12
11	13	17	11	12
11	16	11	6	11
11	9	17	9	12
11	9	11	7	13
11	9	12	9	11
11	8	14	10	19
11	7	11	9	12
11	16	16	8	17
11	11	21	7	9
11	9	14	6	12
11	11	20	13	19
11	9	13	6	18
11	14	11	8	15
11	13	15	10	14
11	16	19	16	11
11				9
11				18
11				16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=0

As an alternative you can also use a QR Code:  
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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 18.7294640774005 -0.457083428977557Month[t] -0.0780147047931104Doubts[t] -0.0134474047661470Expectations[t] -0.0478920443475955Criticism[t] + 0.00711727823524966t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  18.7294640774005 -0.457083428977557Month[t] -0.0780147047931104Doubts[t] -0.0134474047661470Expectations[t] -0.0478920443475955Criticism[t] +  0.00711727823524966t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  18.7294640774005 -0.457083428977557Month[t] -0.0780147047931104Doubts[t] -0.0134474047661470Expectations[t] -0.0478920443475955Criticism[t] +  0.00711727823524966t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 18.7294640774005 -0.457083428977557Month[t] -0.0780147047931104Doubts[t] -0.0134474047661470Expectations[t] -0.0478920443475955Criticism[t] + 0.00711727823524966t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.72946407740057.0333142.6630.0085580.004279
Month-0.4570834289775570.631823-0.72340.4704950.235248
Doubts-0.07801470479311040.090654-0.86060.3907930.195397
Expectations-0.01344740476614700.08802-0.15280.8787720.439386
Criticism-0.04789204434759550.108905-0.43980.660720.33036
t0.007117278235249660.0054041.31710.1897410.094871

\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) & 18.7294640774005 & 7.033314 & 2.663 & 0.008558 & 0.004279 \tabularnewline
Month & -0.457083428977557 & 0.631823 & -0.7234 & 0.470495 & 0.235248 \tabularnewline
Doubts & -0.0780147047931104 & 0.090654 & -0.8606 & 0.390793 & 0.195397 \tabularnewline
Expectations & -0.0134474047661470 & 0.08802 & -0.1528 & 0.878772 & 0.439386 \tabularnewline
Criticism & -0.0478920443475955 & 0.108905 & -0.4398 & 0.66072 & 0.33036 \tabularnewline
t & 0.00711727823524966 & 0.005404 & 1.3171 & 0.189741 & 0.094871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&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]18.7294640774005[/C][C]7.033314[/C][C]2.663[/C][C]0.008558[/C][C]0.004279[/C][/ROW]
[ROW][C]Month[/C][C]-0.457083428977557[/C][C]0.631823[/C][C]-0.7234[/C][C]0.470495[/C][C]0.235248[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.0780147047931104[/C][C]0.090654[/C][C]-0.8606[/C][C]0.390793[/C][C]0.195397[/C][/ROW]
[ROW][C]Expectations[/C][C]-0.0134474047661470[/C][C]0.08802[/C][C]-0.1528[/C][C]0.878772[/C][C]0.439386[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.0478920443475955[/C][C]0.108905[/C][C]-0.4398[/C][C]0.66072[/C][C]0.33036[/C][/ROW]
[ROW][C]t[/C][C]0.00711727823524966[/C][C]0.005404[/C][C]1.3171[/C][C]0.189741[/C][C]0.094871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=2

As an alternative you can also use a QR Code:  
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)18.72946407740057.0333142.6630.0085580.004279
Month-0.4570834289775570.631823-0.72340.4704950.235248
Doubts-0.07801470479311040.090654-0.86060.3907930.195397
Expectations-0.01344740476614700.08802-0.15280.8787720.439386
Criticism-0.04789204434759550.108905-0.43980.660720.33036
t0.007117278235249660.0054041.31710.1897410.094871






Multiple Linear Regression - Regression Statistics
Multiple R0.140873739807703
R-squared0.0198454105674083
Adjusted R-squared-0.0115698006323544
F-TEST (value)0.631713421922126
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.675807663433844
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16183684598457
Sum Squared Residuals1559.56510953760

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.140873739807703 \tabularnewline
R-squared & 0.0198454105674083 \tabularnewline
Adjusted R-squared & -0.0115698006323544 \tabularnewline
F-TEST (value) & 0.631713421922126 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0.675807663433844 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.16183684598457 \tabularnewline
Sum Squared Residuals & 1559.56510953760 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.140873739807703[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0198454105674083[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0115698006323544[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.631713421922126[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0.675807663433844[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.16183684598457[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1559.56510953760[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.140873739807703
R-squared0.0198454105674083
Adjusted R-squared-0.0115698006323544
F-TEST (value)0.631713421922126
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.675807663433844
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16183684598457
Sum Squared Residuals1559.56510953760






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11211.89383178518060.106168214819405
21112.3803509742498-1.38035097424985
31412.64306772878921.35693227121082
41212.2762286116288-0.276228611628794
52112.52061785515668.4793821448434
61212.4674898125008-0.467489812500814
72212.6317306285459.368269371455
81112.2255888915540-1.22558889155397
91012.0207534184479-2.02075341844790
101312.40449681588260.595503184117442
111012.0026770580275-2.00267705802746
12812.1566983202187-4.15669832021872
131512.54311144145712.45688855854285
141412.34740139398131.65259860601871
151012.4190859722435-2.41908597224350
161413.11008474235630.88991525764368
171412.56835270348491.43164729651508
181112.6744819213286-1.67448192132858
191012.6488068153145-2.64880681531455
201311.99626768740081.00373231259925
21712.1018387781350-5.10183877813497
221411.84102542951892.15897457048111
231212.3522511716753-0.352251171675305
241412.26254476674761.73745523325243
251112.490258754596-1.490258754596
26912.6297384837984-3.6297384837984
271112.7025171902833-1.70251719028332
281512.59285314500812.40714685499192
291412.55962820894491.44037179105511
301312.75076383452140.249236165478633
31912.7336560270281-3.73365602702810
321511.67273799013343.32726200986659
331012.5967412801578-2.59674128015776
341112.8228030126740-1.82280301267403
351312.58888447359020.411115526409759
36812.3011541894457-4.30115418944568
372012.69834499164657.30165500835352
381212.3229385759653-0.322938575965334
391012.2261859343430-2.22618593434298
401012.5534478629131-2.55344786291308
41912.1801751699224-3.18017516992245
421412.67904763553541.32095236446457
43812.6425948485590-4.64259484855902
441412.85795555986401.14204444013598
451112.7240664288603-1.72406642886025
461312.56170689274310.438293107256866
47912.2766684583985-3.27666845839853
481112.5425909378549-1.54259093785490
491512.72564073226902.27435926773104
501112.4013542118486-1.40135421184862
511012.5574870707342-2.55748707073420
521412.82500727176781.17499272823218
531812.75410984521005.24589015479004
541413.34553854860490.65446145139509
551112.1883004219018-1.18830042190177
561212.5414175655364-0.541417565536373
571312.78257895815100.217421041849046
58913.0146149251353-4.01461492513532
591013.2896628302219-3.2896628302219
601512.4004098641252.59959013587500
612012.84117073153757.15882926846253
621212.9005020332562-0.900502033256246
631212.6902504527915-0.690250452791532
641412.60425336613541.39574663386464
651312.99664072184190.00335927815811590
661112.9526381048163-1.95263810481632
671713.02322855485583.97677144514418
681212.9254363187657-0.925436318765663
691312.75662108082210.243378919177897
701412.81053627518221.18946372481776
711313.0861423073783-0.086142307378265
721513.05558709511881.94441290488115
731312.88623585606200.113764143937978
741012.2891386025450-2.28913860254498
751113.0441465455753-2.04414654557531
761912.91780724462076.0821927553793
771312.69520238761250.304797612387451
781712.70122553762514.29877446237491
791312.81002447927250.189975520727480
80912.9505983366976-3.95059833669763
811112.5321033231632-1.53210332316317
821013.2860171379708-3.28601713797078
83912.7981513779151-3.79815137791508
841212.9812011723291-0.981201172329137
851212.8672696816729-0.867269681672877
861312.90506774746310.0949322525369093
871313.1532208430174-0.153220843017377
881212.7314979785697-0.731497978569693
891512.51316056694262.48683943305744
902213.06534118426188.93465881573822
911312.68561278944470.314387210555293
921513.57133092811001.42866907188998
931313.1448046171681-0.144804617168058
941513.10136012725191.89863987274806
951013.0487135519545-3.04871355195451
961112.74755798904-1.74755798903999
971612.52439500492243.47560499507761
981112.5680906454296-1.56809064542960
991113.2074113931721-2.20741139317215
1001013.0751745175005-3.07517451750054
1011012.9596128975083-2.95961289750831
1021613.43268468181172.56731531818829
1031212.9980725397073-0.998072539707328
1041113.1369941418003-2.13699414180028
1051612.72981281034153.27018718965855
1061912.97639031031476.02360968968532
1071113.3692591333795-2.36925913337955
1081613.11434335948052.88565664051954
1091512.65660025987032.34339974012973
1102412.943519974141911.0564800258581
1111413.37083343678830.629166563211748
1121513.08470087422091.91529912577906
1131113.2338420301668-2.23384203016681
1141513.19794737029981.80205262970015
1151213.4175534009897-1.41755340098968
1161013.4887565118935-3.48875651189354
1171413.45443744760760.54556255239236
1181313.2748445287017-0.274844528701705
119912.8235571309180-3.82355713091795
1201513.39831057863351.60168942136646
1211513.41887526163491.58112473836506
1221413.21507938299670.784920617003333
1231112.7696895599299-1.76968955992991
124813.5919345267910-5.59193452679098
1251112.5880884936290-1.58808849362901
1261113.1284194277593-2.12841942775934
127813.6552808311273-5.65528083112733
1281013.2233384128267-3.22333841282672
1291113.2202361372090-2.22023613720904
1301313.0035288549179-0.00352885491788466
1311113.0741193049574-2.07411930495739
1322013.39438953058786.60561046941221
1331013.5586303466320-3.55863034663197
1341513.71854918354021.28145081645978
1351213.3377266605004-1.33772666050043
1361412.90743649753201.09256350246802
1372313.21370119128689.78629880871321
1381413.14170963650620.858290363493787
1391613.47433313868012.52566686131995
1401112.8998087155594-1.89980871555937
1411213.0608216806903-1.06082168069033
1421013.0953918955673-3.09539189556732
1431412.34653267784511.65346732215493
1441213.2226217975256-1.22262179752564
1451212.9639421716001-0.963942171600072
1461113.0571599857908-2.05715998579085
1471213.3860196359382-1.38601963593820
1481313.5696054314655-0.569605431465526
1491113.4674912162394-2.46749121623944
1501913.47783634538795.52216365461209
1511213.6512025870623-1.65120258706230
1521712.93684254267644.06315745732358
153913.3146883653941-4.31468836539408
1541213.6198589309262-1.61985893092618
1551913.05501806054525.94498193945484
1561813.64754089216284.35245910783718
1571513.19569536726961.80430463273037
1581413.13125364253820.868746357461793
1591112.5631849212440-1.56318492124396
1601113.1282002824650-2.12820028246497
1611210.98877310236091.01122689763906
162813.0561344881952-5.05613448819524

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 11.8938317851806 & 0.106168214819405 \tabularnewline
2 & 11 & 12.3803509742498 & -1.38035097424985 \tabularnewline
3 & 14 & 12.6430677287892 & 1.35693227121082 \tabularnewline
4 & 12 & 12.2762286116288 & -0.276228611628794 \tabularnewline
5 & 21 & 12.5206178551566 & 8.4793821448434 \tabularnewline
6 & 12 & 12.4674898125008 & -0.467489812500814 \tabularnewline
7 & 22 & 12.631730628545 & 9.368269371455 \tabularnewline
8 & 11 & 12.2255888915540 & -1.22558889155397 \tabularnewline
9 & 10 & 12.0207534184479 & -2.02075341844790 \tabularnewline
10 & 13 & 12.4044968158826 & 0.595503184117442 \tabularnewline
11 & 10 & 12.0026770580275 & -2.00267705802746 \tabularnewline
12 & 8 & 12.1566983202187 & -4.15669832021872 \tabularnewline
13 & 15 & 12.5431114414571 & 2.45688855854285 \tabularnewline
14 & 14 & 12.3474013939813 & 1.65259860601871 \tabularnewline
15 & 10 & 12.4190859722435 & -2.41908597224350 \tabularnewline
16 & 14 & 13.1100847423563 & 0.88991525764368 \tabularnewline
17 & 14 & 12.5683527034849 & 1.43164729651508 \tabularnewline
18 & 11 & 12.6744819213286 & -1.67448192132858 \tabularnewline
19 & 10 & 12.6488068153145 & -2.64880681531455 \tabularnewline
20 & 13 & 11.9962676874008 & 1.00373231259925 \tabularnewline
21 & 7 & 12.1018387781350 & -5.10183877813497 \tabularnewline
22 & 14 & 11.8410254295189 & 2.15897457048111 \tabularnewline
23 & 12 & 12.3522511716753 & -0.352251171675305 \tabularnewline
24 & 14 & 12.2625447667476 & 1.73745523325243 \tabularnewline
25 & 11 & 12.490258754596 & -1.490258754596 \tabularnewline
26 & 9 & 12.6297384837984 & -3.6297384837984 \tabularnewline
27 & 11 & 12.7025171902833 & -1.70251719028332 \tabularnewline
28 & 15 & 12.5928531450081 & 2.40714685499192 \tabularnewline
29 & 14 & 12.5596282089449 & 1.44037179105511 \tabularnewline
30 & 13 & 12.7507638345214 & 0.249236165478633 \tabularnewline
31 & 9 & 12.7336560270281 & -3.73365602702810 \tabularnewline
32 & 15 & 11.6727379901334 & 3.32726200986659 \tabularnewline
33 & 10 & 12.5967412801578 & -2.59674128015776 \tabularnewline
34 & 11 & 12.8228030126740 & -1.82280301267403 \tabularnewline
35 & 13 & 12.5888844735902 & 0.411115526409759 \tabularnewline
36 & 8 & 12.3011541894457 & -4.30115418944568 \tabularnewline
37 & 20 & 12.6983449916465 & 7.30165500835352 \tabularnewline
38 & 12 & 12.3229385759653 & -0.322938575965334 \tabularnewline
39 & 10 & 12.2261859343430 & -2.22618593434298 \tabularnewline
40 & 10 & 12.5534478629131 & -2.55344786291308 \tabularnewline
41 & 9 & 12.1801751699224 & -3.18017516992245 \tabularnewline
42 & 14 & 12.6790476355354 & 1.32095236446457 \tabularnewline
43 & 8 & 12.6425948485590 & -4.64259484855902 \tabularnewline
44 & 14 & 12.8579555598640 & 1.14204444013598 \tabularnewline
45 & 11 & 12.7240664288603 & -1.72406642886025 \tabularnewline
46 & 13 & 12.5617068927431 & 0.438293107256866 \tabularnewline
47 & 9 & 12.2766684583985 & -3.27666845839853 \tabularnewline
48 & 11 & 12.5425909378549 & -1.54259093785490 \tabularnewline
49 & 15 & 12.7256407322690 & 2.27435926773104 \tabularnewline
50 & 11 & 12.4013542118486 & -1.40135421184862 \tabularnewline
51 & 10 & 12.5574870707342 & -2.55748707073420 \tabularnewline
52 & 14 & 12.8250072717678 & 1.17499272823218 \tabularnewline
53 & 18 & 12.7541098452100 & 5.24589015479004 \tabularnewline
54 & 14 & 13.3455385486049 & 0.65446145139509 \tabularnewline
55 & 11 & 12.1883004219018 & -1.18830042190177 \tabularnewline
56 & 12 & 12.5414175655364 & -0.541417565536373 \tabularnewline
57 & 13 & 12.7825789581510 & 0.217421041849046 \tabularnewline
58 & 9 & 13.0146149251353 & -4.01461492513532 \tabularnewline
59 & 10 & 13.2896628302219 & -3.2896628302219 \tabularnewline
60 & 15 & 12.400409864125 & 2.59959013587500 \tabularnewline
61 & 20 & 12.8411707315375 & 7.15882926846253 \tabularnewline
62 & 12 & 12.9005020332562 & -0.900502033256246 \tabularnewline
63 & 12 & 12.6902504527915 & -0.690250452791532 \tabularnewline
64 & 14 & 12.6042533661354 & 1.39574663386464 \tabularnewline
65 & 13 & 12.9966407218419 & 0.00335927815811590 \tabularnewline
66 & 11 & 12.9526381048163 & -1.95263810481632 \tabularnewline
67 & 17 & 13.0232285548558 & 3.97677144514418 \tabularnewline
68 & 12 & 12.9254363187657 & -0.925436318765663 \tabularnewline
69 & 13 & 12.7566210808221 & 0.243378919177897 \tabularnewline
70 & 14 & 12.8105362751822 & 1.18946372481776 \tabularnewline
71 & 13 & 13.0861423073783 & -0.086142307378265 \tabularnewline
72 & 15 & 13.0555870951188 & 1.94441290488115 \tabularnewline
73 & 13 & 12.8862358560620 & 0.113764143937978 \tabularnewline
74 & 10 & 12.2891386025450 & -2.28913860254498 \tabularnewline
75 & 11 & 13.0441465455753 & -2.04414654557531 \tabularnewline
76 & 19 & 12.9178072446207 & 6.0821927553793 \tabularnewline
77 & 13 & 12.6952023876125 & 0.304797612387451 \tabularnewline
78 & 17 & 12.7012255376251 & 4.29877446237491 \tabularnewline
79 & 13 & 12.8100244792725 & 0.189975520727480 \tabularnewline
80 & 9 & 12.9505983366976 & -3.95059833669763 \tabularnewline
81 & 11 & 12.5321033231632 & -1.53210332316317 \tabularnewline
82 & 10 & 13.2860171379708 & -3.28601713797078 \tabularnewline
83 & 9 & 12.7981513779151 & -3.79815137791508 \tabularnewline
84 & 12 & 12.9812011723291 & -0.981201172329137 \tabularnewline
85 & 12 & 12.8672696816729 & -0.867269681672877 \tabularnewline
86 & 13 & 12.9050677474631 & 0.0949322525369093 \tabularnewline
87 & 13 & 13.1532208430174 & -0.153220843017377 \tabularnewline
88 & 12 & 12.7314979785697 & -0.731497978569693 \tabularnewline
89 & 15 & 12.5131605669426 & 2.48683943305744 \tabularnewline
90 & 22 & 13.0653411842618 & 8.93465881573822 \tabularnewline
91 & 13 & 12.6856127894447 & 0.314387210555293 \tabularnewline
92 & 15 & 13.5713309281100 & 1.42866907188998 \tabularnewline
93 & 13 & 13.1448046171681 & -0.144804617168058 \tabularnewline
94 & 15 & 13.1013601272519 & 1.89863987274806 \tabularnewline
95 & 10 & 13.0487135519545 & -3.04871355195451 \tabularnewline
96 & 11 & 12.74755798904 & -1.74755798903999 \tabularnewline
97 & 16 & 12.5243950049224 & 3.47560499507761 \tabularnewline
98 & 11 & 12.5680906454296 & -1.56809064542960 \tabularnewline
99 & 11 & 13.2074113931721 & -2.20741139317215 \tabularnewline
100 & 10 & 13.0751745175005 & -3.07517451750054 \tabularnewline
101 & 10 & 12.9596128975083 & -2.95961289750831 \tabularnewline
102 & 16 & 13.4326846818117 & 2.56731531818829 \tabularnewline
103 & 12 & 12.9980725397073 & -0.998072539707328 \tabularnewline
104 & 11 & 13.1369941418003 & -2.13699414180028 \tabularnewline
105 & 16 & 12.7298128103415 & 3.27018718965855 \tabularnewline
106 & 19 & 12.9763903103147 & 6.02360968968532 \tabularnewline
107 & 11 & 13.3692591333795 & -2.36925913337955 \tabularnewline
108 & 16 & 13.1143433594805 & 2.88565664051954 \tabularnewline
109 & 15 & 12.6566002598703 & 2.34339974012973 \tabularnewline
110 & 24 & 12.9435199741419 & 11.0564800258581 \tabularnewline
111 & 14 & 13.3708334367883 & 0.629166563211748 \tabularnewline
112 & 15 & 13.0847008742209 & 1.91529912577906 \tabularnewline
113 & 11 & 13.2338420301668 & -2.23384203016681 \tabularnewline
114 & 15 & 13.1979473702998 & 1.80205262970015 \tabularnewline
115 & 12 & 13.4175534009897 & -1.41755340098968 \tabularnewline
116 & 10 & 13.4887565118935 & -3.48875651189354 \tabularnewline
117 & 14 & 13.4544374476076 & 0.54556255239236 \tabularnewline
118 & 13 & 13.2748445287017 & -0.274844528701705 \tabularnewline
119 & 9 & 12.8235571309180 & -3.82355713091795 \tabularnewline
120 & 15 & 13.3983105786335 & 1.60168942136646 \tabularnewline
121 & 15 & 13.4188752616349 & 1.58112473836506 \tabularnewline
122 & 14 & 13.2150793829967 & 0.784920617003333 \tabularnewline
123 & 11 & 12.7696895599299 & -1.76968955992991 \tabularnewline
124 & 8 & 13.5919345267910 & -5.59193452679098 \tabularnewline
125 & 11 & 12.5880884936290 & -1.58808849362901 \tabularnewline
126 & 11 & 13.1284194277593 & -2.12841942775934 \tabularnewline
127 & 8 & 13.6552808311273 & -5.65528083112733 \tabularnewline
128 & 10 & 13.2233384128267 & -3.22333841282672 \tabularnewline
129 & 11 & 13.2202361372090 & -2.22023613720904 \tabularnewline
130 & 13 & 13.0035288549179 & -0.00352885491788466 \tabularnewline
131 & 11 & 13.0741193049574 & -2.07411930495739 \tabularnewline
132 & 20 & 13.3943895305878 & 6.60561046941221 \tabularnewline
133 & 10 & 13.5586303466320 & -3.55863034663197 \tabularnewline
134 & 15 & 13.7185491835402 & 1.28145081645978 \tabularnewline
135 & 12 & 13.3377266605004 & -1.33772666050043 \tabularnewline
136 & 14 & 12.9074364975320 & 1.09256350246802 \tabularnewline
137 & 23 & 13.2137011912868 & 9.78629880871321 \tabularnewline
138 & 14 & 13.1417096365062 & 0.858290363493787 \tabularnewline
139 & 16 & 13.4743331386801 & 2.52566686131995 \tabularnewline
140 & 11 & 12.8998087155594 & -1.89980871555937 \tabularnewline
141 & 12 & 13.0608216806903 & -1.06082168069033 \tabularnewline
142 & 10 & 13.0953918955673 & -3.09539189556732 \tabularnewline
143 & 14 & 12.3465326778451 & 1.65346732215493 \tabularnewline
144 & 12 & 13.2226217975256 & -1.22262179752564 \tabularnewline
145 & 12 & 12.9639421716001 & -0.963942171600072 \tabularnewline
146 & 11 & 13.0571599857908 & -2.05715998579085 \tabularnewline
147 & 12 & 13.3860196359382 & -1.38601963593820 \tabularnewline
148 & 13 & 13.5696054314655 & -0.569605431465526 \tabularnewline
149 & 11 & 13.4674912162394 & -2.46749121623944 \tabularnewline
150 & 19 & 13.4778363453879 & 5.52216365461209 \tabularnewline
151 & 12 & 13.6512025870623 & -1.65120258706230 \tabularnewline
152 & 17 & 12.9368425426764 & 4.06315745732358 \tabularnewline
153 & 9 & 13.3146883653941 & -4.31468836539408 \tabularnewline
154 & 12 & 13.6198589309262 & -1.61985893092618 \tabularnewline
155 & 19 & 13.0550180605452 & 5.94498193945484 \tabularnewline
156 & 18 & 13.6475408921628 & 4.35245910783718 \tabularnewline
157 & 15 & 13.1956953672696 & 1.80430463273037 \tabularnewline
158 & 14 & 13.1312536425382 & 0.868746357461793 \tabularnewline
159 & 11 & 12.5631849212440 & -1.56318492124396 \tabularnewline
160 & 11 & 13.1282002824650 & -2.12820028246497 \tabularnewline
161 & 12 & 10.9887731023609 & 1.01122689763906 \tabularnewline
162 & 8 & 13.0561344881952 & -5.05613448819524 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&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]12[/C][C]11.8938317851806[/C][C]0.106168214819405[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.3803509742498[/C][C]-1.38035097424985[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]12.6430677287892[/C][C]1.35693227121082[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.2762286116288[/C][C]-0.276228611628794[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.5206178551566[/C][C]8.4793821448434[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.4674898125008[/C][C]-0.467489812500814[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.631730628545[/C][C]9.368269371455[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.2255888915540[/C][C]-1.22558889155397[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.0207534184479[/C][C]-2.02075341844790[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.4044968158826[/C][C]0.595503184117442[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.0026770580275[/C][C]-2.00267705802746[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]12.1566983202187[/C][C]-4.15669832021872[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.5431114414571[/C][C]2.45688855854285[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.3474013939813[/C][C]1.65259860601871[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]12.4190859722435[/C][C]-2.41908597224350[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.1100847423563[/C][C]0.88991525764368[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.5683527034849[/C][C]1.43164729651508[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.6744819213286[/C][C]-1.67448192132858[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.6488068153145[/C][C]-2.64880681531455[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.9962676874008[/C][C]1.00373231259925[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.1018387781350[/C][C]-5.10183877813497[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]11.8410254295189[/C][C]2.15897457048111[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.3522511716753[/C][C]-0.352251171675305[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]12.2625447667476[/C][C]1.73745523325243[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.490258754596[/C][C]-1.490258754596[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]12.6297384837984[/C][C]-3.6297384837984[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]12.7025171902833[/C][C]-1.70251719028332[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.5928531450081[/C][C]2.40714685499192[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.5596282089449[/C][C]1.44037179105511[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.7507638345214[/C][C]0.249236165478633[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.7336560270281[/C][C]-3.73365602702810[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]11.6727379901334[/C][C]3.32726200986659[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.5967412801578[/C][C]-2.59674128015776[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.8228030126740[/C][C]-1.82280301267403[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.5888844735902[/C][C]0.411115526409759[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.3011541894457[/C][C]-4.30115418944568[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]12.6983449916465[/C][C]7.30165500835352[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3229385759653[/C][C]-0.322938575965334[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.2261859343430[/C][C]-2.22618593434298[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.5534478629131[/C][C]-2.55344786291308[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.1801751699224[/C][C]-3.18017516992245[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.6790476355354[/C][C]1.32095236446457[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.6425948485590[/C][C]-4.64259484855902[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]12.8579555598640[/C][C]1.14204444013598[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.7240664288603[/C][C]-1.72406642886025[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.5617068927431[/C][C]0.438293107256866[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.2766684583985[/C][C]-3.27666845839853[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.5425909378549[/C][C]-1.54259093785490[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.7256407322690[/C][C]2.27435926773104[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.4013542118486[/C][C]-1.40135421184862[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.5574870707342[/C][C]-2.55748707073420[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.8250072717678[/C][C]1.17499272823218[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]12.7541098452100[/C][C]5.24589015479004[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.3455385486049[/C][C]0.65446145139509[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.1883004219018[/C][C]-1.18830042190177[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.5414175655364[/C][C]-0.541417565536373[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.7825789581510[/C][C]0.217421041849046[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.0146149251353[/C][C]-4.01461492513532[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.2896628302219[/C][C]-3.2896628302219[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.400409864125[/C][C]2.59959013587500[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]12.8411707315375[/C][C]7.15882926846253[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9005020332562[/C][C]-0.900502033256246[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.6902504527915[/C][C]-0.690250452791532[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.6042533661354[/C][C]1.39574663386464[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.9966407218419[/C][C]0.00335927815811590[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.9526381048163[/C][C]-1.95263810481632[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]13.0232285548558[/C][C]3.97677144514418[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.9254363187657[/C][C]-0.925436318765663[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.7566210808221[/C][C]0.243378919177897[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.8105362751822[/C][C]1.18946372481776[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.0861423073783[/C][C]-0.086142307378265[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.0555870951188[/C][C]1.94441290488115[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.8862358560620[/C][C]0.113764143937978[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.2891386025450[/C][C]-2.28913860254498[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.0441465455753[/C][C]-2.04414654557531[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.9178072446207[/C][C]6.0821927553793[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.6952023876125[/C][C]0.304797612387451[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.7012255376251[/C][C]4.29877446237491[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.8100244792725[/C][C]0.189975520727480[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.9505983366976[/C][C]-3.95059833669763[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.5321033231632[/C][C]-1.53210332316317[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]13.2860171379708[/C][C]-3.28601713797078[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.7981513779151[/C][C]-3.79815137791508[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.9812011723291[/C][C]-0.981201172329137[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.8672696816729[/C][C]-0.867269681672877[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.9050677474631[/C][C]0.0949322525369093[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.1532208430174[/C][C]-0.153220843017377[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.7314979785697[/C][C]-0.731497978569693[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.5131605669426[/C][C]2.48683943305744[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.0653411842618[/C][C]8.93465881573822[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.6856127894447[/C][C]0.314387210555293[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5713309281100[/C][C]1.42866907188998[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.1448046171681[/C][C]-0.144804617168058[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.1013601272519[/C][C]1.89863987274806[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.0487135519545[/C][C]-3.04871355195451[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.74755798904[/C][C]-1.74755798903999[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.5243950049224[/C][C]3.47560499507761[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.5680906454296[/C][C]-1.56809064542960[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.2074113931721[/C][C]-2.20741139317215[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]13.0751745175005[/C][C]-3.07517451750054[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.9596128975083[/C][C]-2.95961289750831[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.4326846818117[/C][C]2.56731531818829[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.9980725397073[/C][C]-0.998072539707328[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.1369941418003[/C][C]-2.13699414180028[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.7298128103415[/C][C]3.27018718965855[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.9763903103147[/C][C]6.02360968968532[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]13.3692591333795[/C][C]-2.36925913337955[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]13.1143433594805[/C][C]2.88565664051954[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]12.6566002598703[/C][C]2.34339974012973[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]12.9435199741419[/C][C]11.0564800258581[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.3708334367883[/C][C]0.629166563211748[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]13.0847008742209[/C][C]1.91529912577906[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.2338420301668[/C][C]-2.23384203016681[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.1979473702998[/C][C]1.80205262970015[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.4175534009897[/C][C]-1.41755340098968[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.4887565118935[/C][C]-3.48875651189354[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.4544374476076[/C][C]0.54556255239236[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.2748445287017[/C][C]-0.274844528701705[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]12.8235571309180[/C][C]-3.82355713091795[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.3983105786335[/C][C]1.60168942136646[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]13.4188752616349[/C][C]1.58112473836506[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.2150793829967[/C][C]0.784920617003333[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.7696895599299[/C][C]-1.76968955992991[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]13.5919345267910[/C][C]-5.59193452679098[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.5880884936290[/C][C]-1.58808849362901[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.1284194277593[/C][C]-2.12841942775934[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.6552808311273[/C][C]-5.65528083112733[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]13.2233384128267[/C][C]-3.22333841282672[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]13.2202361372090[/C][C]-2.22023613720904[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]13.0035288549179[/C][C]-0.00352885491788466[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.0741193049574[/C][C]-2.07411930495739[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.3943895305878[/C][C]6.60561046941221[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.5586303466320[/C][C]-3.55863034663197[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.7185491835402[/C][C]1.28145081645978[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]13.3377266605004[/C][C]-1.33772666050043[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.9074364975320[/C][C]1.09256350246802[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]13.2137011912868[/C][C]9.78629880871321[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.1417096365062[/C][C]0.858290363493787[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.4743331386801[/C][C]2.52566686131995[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.8998087155594[/C][C]-1.89980871555937[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.0608216806903[/C][C]-1.06082168069033[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]13.0953918955673[/C][C]-3.09539189556732[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.3465326778451[/C][C]1.65346732215493[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]13.2226217975256[/C][C]-1.22262179752564[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.9639421716001[/C][C]-0.963942171600072[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]13.0571599857908[/C][C]-2.05715998579085[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]13.3860196359382[/C][C]-1.38601963593820[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.5696054314655[/C][C]-0.569605431465526[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.4674912162394[/C][C]-2.46749121623944[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.4778363453879[/C][C]5.52216365461209[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.6512025870623[/C][C]-1.65120258706230[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]12.9368425426764[/C][C]4.06315745732358[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]13.3146883653941[/C][C]-4.31468836539408[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.6198589309262[/C][C]-1.61985893092618[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]13.0550180605452[/C][C]5.94498193945484[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.6475408921628[/C][C]4.35245910783718[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.1956953672696[/C][C]1.80430463273037[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.1312536425382[/C][C]0.868746357461793[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.5631849212440[/C][C]-1.56318492124396[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]13.1282002824650[/C][C]-2.12820028246497[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]10.9887731023609[/C][C]1.01122689763906[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]13.0561344881952[/C][C]-5.05613448819524[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11211.89383178518060.106168214819405
21112.3803509742498-1.38035097424985
31412.64306772878921.35693227121082
41212.2762286116288-0.276228611628794
52112.52061785515668.4793821448434
61212.4674898125008-0.467489812500814
72212.6317306285459.368269371455
81112.2255888915540-1.22558889155397
91012.0207534184479-2.02075341844790
101312.40449681588260.595503184117442
111012.0026770580275-2.00267705802746
12812.1566983202187-4.15669832021872
131512.54311144145712.45688855854285
141412.34740139398131.65259860601871
151012.4190859722435-2.41908597224350
161413.11008474235630.88991525764368
171412.56835270348491.43164729651508
181112.6744819213286-1.67448192132858
191012.6488068153145-2.64880681531455
201311.99626768740081.00373231259925
21712.1018387781350-5.10183877813497
221411.84102542951892.15897457048111
231212.3522511716753-0.352251171675305
241412.26254476674761.73745523325243
251112.490258754596-1.490258754596
26912.6297384837984-3.6297384837984
271112.7025171902833-1.70251719028332
281512.59285314500812.40714685499192
291412.55962820894491.44037179105511
301312.75076383452140.249236165478633
31912.7336560270281-3.73365602702810
321511.67273799013343.32726200986659
331012.5967412801578-2.59674128015776
341112.8228030126740-1.82280301267403
351312.58888447359020.411115526409759
36812.3011541894457-4.30115418944568
372012.69834499164657.30165500835352
381212.3229385759653-0.322938575965334
391012.2261859343430-2.22618593434298
401012.5534478629131-2.55344786291308
41912.1801751699224-3.18017516992245
421412.67904763553541.32095236446457
43812.6425948485590-4.64259484855902
441412.85795555986401.14204444013598
451112.7240664288603-1.72406642886025
461312.56170689274310.438293107256866
47912.2766684583985-3.27666845839853
481112.5425909378549-1.54259093785490
491512.72564073226902.27435926773104
501112.4013542118486-1.40135421184862
511012.5574870707342-2.55748707073420
521412.82500727176781.17499272823218
531812.75410984521005.24589015479004
541413.34553854860490.65446145139509
551112.1883004219018-1.18830042190177
561212.5414175655364-0.541417565536373
571312.78257895815100.217421041849046
58913.0146149251353-4.01461492513532
591013.2896628302219-3.2896628302219
601512.4004098641252.59959013587500
612012.84117073153757.15882926846253
621212.9005020332562-0.900502033256246
631212.6902504527915-0.690250452791532
641412.60425336613541.39574663386464
651312.99664072184190.00335927815811590
661112.9526381048163-1.95263810481632
671713.02322855485583.97677144514418
681212.9254363187657-0.925436318765663
691312.75662108082210.243378919177897
701412.81053627518221.18946372481776
711313.0861423073783-0.086142307378265
721513.05558709511881.94441290488115
731312.88623585606200.113764143937978
741012.2891386025450-2.28913860254498
751113.0441465455753-2.04414654557531
761912.91780724462076.0821927553793
771312.69520238761250.304797612387451
781712.70122553762514.29877446237491
791312.81002447927250.189975520727480
80912.9505983366976-3.95059833669763
811112.5321033231632-1.53210332316317
821013.2860171379708-3.28601713797078
83912.7981513779151-3.79815137791508
841212.9812011723291-0.981201172329137
851212.8672696816729-0.867269681672877
861312.90506774746310.0949322525369093
871313.1532208430174-0.153220843017377
881212.7314979785697-0.731497978569693
891512.51316056694262.48683943305744
902213.06534118426188.93465881573822
911312.68561278944470.314387210555293
921513.57133092811001.42866907188998
931313.1448046171681-0.144804617168058
941513.10136012725191.89863987274806
951013.0487135519545-3.04871355195451
961112.74755798904-1.74755798903999
971612.52439500492243.47560499507761
981112.5680906454296-1.56809064542960
991113.2074113931721-2.20741139317215
1001013.0751745175005-3.07517451750054
1011012.9596128975083-2.95961289750831
1021613.43268468181172.56731531818829
1031212.9980725397073-0.998072539707328
1041113.1369941418003-2.13699414180028
1051612.72981281034153.27018718965855
1061912.97639031031476.02360968968532
1071113.3692591333795-2.36925913337955
1081613.11434335948052.88565664051954
1091512.65660025987032.34339974012973
1102412.943519974141911.0564800258581
1111413.37083343678830.629166563211748
1121513.08470087422091.91529912577906
1131113.2338420301668-2.23384203016681
1141513.19794737029981.80205262970015
1151213.4175534009897-1.41755340098968
1161013.4887565118935-3.48875651189354
1171413.45443744760760.54556255239236
1181313.2748445287017-0.274844528701705
119912.8235571309180-3.82355713091795
1201513.39831057863351.60168942136646
1211513.41887526163491.58112473836506
1221413.21507938299670.784920617003333
1231112.7696895599299-1.76968955992991
124813.5919345267910-5.59193452679098
1251112.5880884936290-1.58808849362901
1261113.1284194277593-2.12841942775934
127813.6552808311273-5.65528083112733
1281013.2233384128267-3.22333841282672
1291113.2202361372090-2.22023613720904
1301313.0035288549179-0.00352885491788466
1311113.0741193049574-2.07411930495739
1322013.39438953058786.60561046941221
1331013.5586303466320-3.55863034663197
1341513.71854918354021.28145081645978
1351213.3377266605004-1.33772666050043
1361412.90743649753201.09256350246802
1372313.21370119128689.78629880871321
1381413.14170963650620.858290363493787
1391613.47433313868012.52566686131995
1401112.8998087155594-1.89980871555937
1411213.0608216806903-1.06082168069033
1421013.0953918955673-3.09539189556732
1431412.34653267784511.65346732215493
1441213.2226217975256-1.22262179752564
1451212.9639421716001-0.963942171600072
1461113.0571599857908-2.05715998579085
1471213.3860196359382-1.38601963593820
1481313.5696054314655-0.569605431465526
1491113.4674912162394-2.46749121623944
1501913.47783634538795.52216365461209
1511213.6512025870623-1.65120258706230
1521712.93684254267644.06315745732358
153913.3146883653941-4.31468836539408
1541213.6198589309262-1.61985893092618
1551913.05501806054525.94498193945484
1561813.64754089216284.35245910783718
1571513.19569536726961.80430463273037
1581413.13125364253820.868746357461793
1591112.5631849212440-1.56318492124396
1601113.1282002824650-2.12820028246497
1611210.98877310236091.01122689763906
162813.0561344881952-5.05613448819524






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9796180517685860.04076389646282770.0203819482314139
100.9597360075932380.0805279848135240.040263992406762
110.9251342795906120.1497314408187760.0748657204093882
120.9047867744896680.1904264510206640.0952132255103322
130.852811923770760.2943761524584810.147188076229240
140.7890738649190710.4218522701618570.210926135080929
150.7848701033529790.4302597932940420.215129896647021
160.7959586236357950.4080827527284090.204041376364205
170.7319142422465950.536171515506810.268085757753405
180.690406119188470.6191877616230610.309593880811531
190.6239146209998890.7521707580002220.376085379000111
200.7498173974954270.5003652050091460.250182602504573
210.7302502715381450.539499456923710.269749728461855
220.820573071305430.3588538573891390.179426928694569
230.7750123023463770.4499753953072460.224987697653623
240.7635381003244180.4729237993511630.236461899675582
250.7121711918244910.5756576163510190.287828808175509
260.7017084793335350.596583041332930.298291520666465
270.6483928491561160.7032143016877680.351607150843884
280.655032778034860.689934443930280.34496722196514
290.6233988103097570.7532023793804860.376601189690243
300.562506768902130.874986462195740.43749323109787
310.5589228437457850.882154312508430.441077156254215
320.6411656612554480.7176686774891050.358834338744552
330.600595872418880.7988082551622390.399404127581120
340.5472396178997250.905520764200550.452760382100275
350.4943393213279670.9886786426559350.505660678672033
360.4651961024446590.9303922048893170.534803897555341
370.7721115057128960.4557769885742080.227888494287104
380.7334340955192530.5331318089614930.266565904480747
390.6976595959799570.6046808080400860.302340404020043
400.6645744063515080.6708511872969850.335425593648492
410.636839015678860.726321968642280.36316098432114
420.6010826810040670.7978346379918660.398917318995933
430.626646467461160.7467070650776810.373353532538840
440.5955969712649380.8088060574701240.404403028735062
450.5503585477610680.8992829044778630.449641452238932
460.5092280455585060.9815439088829870.490771954441494
470.4856695457587080.9713390915174160.514330454241292
480.4404645553682760.8809291107365510.559535444631724
490.4332048119895410.8664096239790820.566795188010459
500.3923027008246370.7846054016492740.607697299175363
510.3627957725563030.7255915451126060.637204227443697
520.3276488837457660.6552977674915330.672351116254234
530.4251238376497540.8502476752995080.574876162350246
540.379114037193020.758228074386040.62088596280698
550.3382963099418790.6765926198837590.661703690058121
560.2970116380819850.594023276163970.702988361918015
570.2564510382162120.5129020764324230.743548961783788
580.2772976569548370.5545953139096750.722702343045163
590.282074310426730.564148620853460.71792568957327
600.2984629641912410.5969259283824820.701537035808759
610.4891278169979310.9782556339958620.510872183002069
620.4443937054786660.8887874109573310.555606294521334
630.4006629724196560.8013259448393120.599337027580344
640.3635222704784710.7270445409569420.636477729521529
650.3201863387117090.6403726774234180.679813661288291
660.2935463484130340.5870926968260680.706453651586966
670.3093473787110260.6186947574220530.690652621288974
680.2729874529662280.5459749059324560.727012547033772
690.2370657586805560.4741315173611110.762934241319444
700.206323514466950.41264702893390.79367648553305
710.1747314070369780.3494628140739550.825268592963022
720.1541643945492930.3083287890985860.845835605450707
730.1279224962315610.2558449924631210.87207750376844
740.1158333498781970.2316666997563940.884166650121803
750.1027541437204270.2055082874408540.897245856279573
760.1675008039672430.3350016079344860.832499196032757
770.1431829627450360.2863659254900720.856817037254964
780.1650303584724740.3300607169449490.834969641527526
790.1381337101488410.2762674202976830.861866289851159
800.1543435133869040.3086870267738090.845656486613096
810.1353725745850850.270745149170170.864627425414915
820.1474734944251640.2949469888503270.852526505574836
830.1620049261731850.324009852346370.837995073826815
840.1385430408113290.2770860816226580.861456959188671
850.1182333701237410.2364667402474820.881766629876259
860.09961350368275630.1992270073655130.900386496317244
870.08161615427738820.1632323085547760.918383845722612
880.06763421110551450.1352684222110290.932365788894485
890.06000984695179510.1200196939035900.939990153048205
900.2126655922162780.4253311844325550.787334407783722
910.1825001264510250.3650002529020500.817499873548975
920.1633104873596450.3266209747192910.836689512640355
930.1364079664633370.2728159329266740.863592033536663
940.1196264327106370.2392528654212740.880373567289363
950.1220410895430750.2440821790861500.877958910456925
960.1086795623889280.2173591247778550.891320437611072
970.1092805987212790.2185611974425580.890719401278721
980.09706496351910590.1941299270382120.902935036480894
990.08882426008020650.1776485201604130.911175739919794
1000.09138830413828130.1827766082765630.908611695861719
1010.0944777864810050.188955572962010.905522213518995
1020.08511858617005310.1702371723401060.914881413829947
1030.07340355030198810.1468071006039760.926596449698012
1040.06835269155097080.1367053831019420.93164730844903
1050.06389609915854230.1277921983170850.936103900841458
1060.0979952020729460.1959904041458920.902004797927054
1070.09033555458465310.1806711091693060.909664445415347
1080.08304520845998320.1660904169199660.916954791540017
1090.07111102999853580.1422220599970720.928888970001464
1100.4310864628047720.8621729256095440.568913537195228
1110.3886234284742170.7772468569484350.611376571525783
1120.3636278601117200.7272557202234410.63637213988828
1130.3348293040594730.6696586081189460.665170695940527
1140.3095575119872990.6191150239745980.690442488012701
1150.2748522704796180.5497045409592360.725147729520382
1160.2711566241913950.5423132483827910.728843375808605
1170.2327199770431400.4654399540862810.76728002295686
1180.1979547689745820.3959095379491640.802045231025418
1190.2047375003525220.4094750007050430.795262499647478
1200.1869788397397890.3739576794795770.813021160260211
1210.1764152022617780.3528304045235570.823584797738222
1220.1520868600020840.3041737200041680.847913139997916
1230.1263478909533890.2526957819067780.873652109046611
1240.1509945459800060.3019890919600120.849005454019994
1250.1237786390337330.2475572780674660.876221360966267
1260.107641229559210.215282459118420.89235877044079
1270.1574478911218890.3148957822437780.842552108878111
1280.157248607787310.314497215574620.84275139221269
1290.1491171145089390.2982342290178770.850882885491061
1300.1183635921380610.2367271842761210.88163640786194
1310.1108972705408740.2217945410817470.889102729459126
1320.1846056365090680.3692112730181360.815394363490932
1330.2082599610844480.4165199221688950.791740038915552
1340.1674185848724250.334837169744850.832581415127575
1350.1457794926255840.2915589852511680.854220507374416
1360.1161289494537630.2322578989075270.883871050546237
1370.4889962634580120.9779925269160250.511003736541988
1380.4269651880819260.8539303761638520.573034811918074
1390.4198725730699950.839745146139990.580127426930005
1400.3566118826380640.7132237652761290.643388117361936
1410.2903149460272150.580629892054430.709685053972785
1420.2742004403538560.5484008807077130.725799559646144
1430.2142616269964050.428523253992810.785738373003595
1440.1603918578646040.3207837157292070.839608142135396
1450.1239564360279970.2479128720559950.876043563972003
1460.1090217338489220.2180434676978440.890978266151078
1470.09351071715337670.1870214343067530.906489282846623
1480.06770458151664470.1354091630332890.932295418483355
1490.1031453161318720.2062906322637440.896854683868128
1500.09407979963065360.1881595992613070.905920200369346
1510.1053930721135140.2107861442270280.894606927886486
1520.06170719927984390.1234143985596880.938292800720156
1530.2079946076506060.4159892153012120.792005392349394

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.979618051768586 & 0.0407638964628277 & 0.0203819482314139 \tabularnewline
10 & 0.959736007593238 & 0.080527984813524 & 0.040263992406762 \tabularnewline
11 & 0.925134279590612 & 0.149731440818776 & 0.0748657204093882 \tabularnewline
12 & 0.904786774489668 & 0.190426451020664 & 0.0952132255103322 \tabularnewline
13 & 0.85281192377076 & 0.294376152458481 & 0.147188076229240 \tabularnewline
14 & 0.789073864919071 & 0.421852270161857 & 0.210926135080929 \tabularnewline
15 & 0.784870103352979 & 0.430259793294042 & 0.215129896647021 \tabularnewline
16 & 0.795958623635795 & 0.408082752728409 & 0.204041376364205 \tabularnewline
17 & 0.731914242246595 & 0.53617151550681 & 0.268085757753405 \tabularnewline
18 & 0.69040611918847 & 0.619187761623061 & 0.309593880811531 \tabularnewline
19 & 0.623914620999889 & 0.752170758000222 & 0.376085379000111 \tabularnewline
20 & 0.749817397495427 & 0.500365205009146 & 0.250182602504573 \tabularnewline
21 & 0.730250271538145 & 0.53949945692371 & 0.269749728461855 \tabularnewline
22 & 0.82057307130543 & 0.358853857389139 & 0.179426928694569 \tabularnewline
23 & 0.775012302346377 & 0.449975395307246 & 0.224987697653623 \tabularnewline
24 & 0.763538100324418 & 0.472923799351163 & 0.236461899675582 \tabularnewline
25 & 0.712171191824491 & 0.575657616351019 & 0.287828808175509 \tabularnewline
26 & 0.701708479333535 & 0.59658304133293 & 0.298291520666465 \tabularnewline
27 & 0.648392849156116 & 0.703214301687768 & 0.351607150843884 \tabularnewline
28 & 0.65503277803486 & 0.68993444393028 & 0.34496722196514 \tabularnewline
29 & 0.623398810309757 & 0.753202379380486 & 0.376601189690243 \tabularnewline
30 & 0.56250676890213 & 0.87498646219574 & 0.43749323109787 \tabularnewline
31 & 0.558922843745785 & 0.88215431250843 & 0.441077156254215 \tabularnewline
32 & 0.641165661255448 & 0.717668677489105 & 0.358834338744552 \tabularnewline
33 & 0.60059587241888 & 0.798808255162239 & 0.399404127581120 \tabularnewline
34 & 0.547239617899725 & 0.90552076420055 & 0.452760382100275 \tabularnewline
35 & 0.494339321327967 & 0.988678642655935 & 0.505660678672033 \tabularnewline
36 & 0.465196102444659 & 0.930392204889317 & 0.534803897555341 \tabularnewline
37 & 0.772111505712896 & 0.455776988574208 & 0.227888494287104 \tabularnewline
38 & 0.733434095519253 & 0.533131808961493 & 0.266565904480747 \tabularnewline
39 & 0.697659595979957 & 0.604680808040086 & 0.302340404020043 \tabularnewline
40 & 0.664574406351508 & 0.670851187296985 & 0.335425593648492 \tabularnewline
41 & 0.63683901567886 & 0.72632196864228 & 0.36316098432114 \tabularnewline
42 & 0.601082681004067 & 0.797834637991866 & 0.398917318995933 \tabularnewline
43 & 0.62664646746116 & 0.746707065077681 & 0.373353532538840 \tabularnewline
44 & 0.595596971264938 & 0.808806057470124 & 0.404403028735062 \tabularnewline
45 & 0.550358547761068 & 0.899282904477863 & 0.449641452238932 \tabularnewline
46 & 0.509228045558506 & 0.981543908882987 & 0.490771954441494 \tabularnewline
47 & 0.485669545758708 & 0.971339091517416 & 0.514330454241292 \tabularnewline
48 & 0.440464555368276 & 0.880929110736551 & 0.559535444631724 \tabularnewline
49 & 0.433204811989541 & 0.866409623979082 & 0.566795188010459 \tabularnewline
50 & 0.392302700824637 & 0.784605401649274 & 0.607697299175363 \tabularnewline
51 & 0.362795772556303 & 0.725591545112606 & 0.637204227443697 \tabularnewline
52 & 0.327648883745766 & 0.655297767491533 & 0.672351116254234 \tabularnewline
53 & 0.425123837649754 & 0.850247675299508 & 0.574876162350246 \tabularnewline
54 & 0.37911403719302 & 0.75822807438604 & 0.62088596280698 \tabularnewline
55 & 0.338296309941879 & 0.676592619883759 & 0.661703690058121 \tabularnewline
56 & 0.297011638081985 & 0.59402327616397 & 0.702988361918015 \tabularnewline
57 & 0.256451038216212 & 0.512902076432423 & 0.743548961783788 \tabularnewline
58 & 0.277297656954837 & 0.554595313909675 & 0.722702343045163 \tabularnewline
59 & 0.28207431042673 & 0.56414862085346 & 0.71792568957327 \tabularnewline
60 & 0.298462964191241 & 0.596925928382482 & 0.701537035808759 \tabularnewline
61 & 0.489127816997931 & 0.978255633995862 & 0.510872183002069 \tabularnewline
62 & 0.444393705478666 & 0.888787410957331 & 0.555606294521334 \tabularnewline
63 & 0.400662972419656 & 0.801325944839312 & 0.599337027580344 \tabularnewline
64 & 0.363522270478471 & 0.727044540956942 & 0.636477729521529 \tabularnewline
65 & 0.320186338711709 & 0.640372677423418 & 0.679813661288291 \tabularnewline
66 & 0.293546348413034 & 0.587092696826068 & 0.706453651586966 \tabularnewline
67 & 0.309347378711026 & 0.618694757422053 & 0.690652621288974 \tabularnewline
68 & 0.272987452966228 & 0.545974905932456 & 0.727012547033772 \tabularnewline
69 & 0.237065758680556 & 0.474131517361111 & 0.762934241319444 \tabularnewline
70 & 0.20632351446695 & 0.4126470289339 & 0.79367648553305 \tabularnewline
71 & 0.174731407036978 & 0.349462814073955 & 0.825268592963022 \tabularnewline
72 & 0.154164394549293 & 0.308328789098586 & 0.845835605450707 \tabularnewline
73 & 0.127922496231561 & 0.255844992463121 & 0.87207750376844 \tabularnewline
74 & 0.115833349878197 & 0.231666699756394 & 0.884166650121803 \tabularnewline
75 & 0.102754143720427 & 0.205508287440854 & 0.897245856279573 \tabularnewline
76 & 0.167500803967243 & 0.335001607934486 & 0.832499196032757 \tabularnewline
77 & 0.143182962745036 & 0.286365925490072 & 0.856817037254964 \tabularnewline
78 & 0.165030358472474 & 0.330060716944949 & 0.834969641527526 \tabularnewline
79 & 0.138133710148841 & 0.276267420297683 & 0.861866289851159 \tabularnewline
80 & 0.154343513386904 & 0.308687026773809 & 0.845656486613096 \tabularnewline
81 & 0.135372574585085 & 0.27074514917017 & 0.864627425414915 \tabularnewline
82 & 0.147473494425164 & 0.294946988850327 & 0.852526505574836 \tabularnewline
83 & 0.162004926173185 & 0.32400985234637 & 0.837995073826815 \tabularnewline
84 & 0.138543040811329 & 0.277086081622658 & 0.861456959188671 \tabularnewline
85 & 0.118233370123741 & 0.236466740247482 & 0.881766629876259 \tabularnewline
86 & 0.0996135036827563 & 0.199227007365513 & 0.900386496317244 \tabularnewline
87 & 0.0816161542773882 & 0.163232308554776 & 0.918383845722612 \tabularnewline
88 & 0.0676342111055145 & 0.135268422211029 & 0.932365788894485 \tabularnewline
89 & 0.0600098469517951 & 0.120019693903590 & 0.939990153048205 \tabularnewline
90 & 0.212665592216278 & 0.425331184432555 & 0.787334407783722 \tabularnewline
91 & 0.182500126451025 & 0.365000252902050 & 0.817499873548975 \tabularnewline
92 & 0.163310487359645 & 0.326620974719291 & 0.836689512640355 \tabularnewline
93 & 0.136407966463337 & 0.272815932926674 & 0.863592033536663 \tabularnewline
94 & 0.119626432710637 & 0.239252865421274 & 0.880373567289363 \tabularnewline
95 & 0.122041089543075 & 0.244082179086150 & 0.877958910456925 \tabularnewline
96 & 0.108679562388928 & 0.217359124777855 & 0.891320437611072 \tabularnewline
97 & 0.109280598721279 & 0.218561197442558 & 0.890719401278721 \tabularnewline
98 & 0.0970649635191059 & 0.194129927038212 & 0.902935036480894 \tabularnewline
99 & 0.0888242600802065 & 0.177648520160413 & 0.911175739919794 \tabularnewline
100 & 0.0913883041382813 & 0.182776608276563 & 0.908611695861719 \tabularnewline
101 & 0.094477786481005 & 0.18895557296201 & 0.905522213518995 \tabularnewline
102 & 0.0851185861700531 & 0.170237172340106 & 0.914881413829947 \tabularnewline
103 & 0.0734035503019881 & 0.146807100603976 & 0.926596449698012 \tabularnewline
104 & 0.0683526915509708 & 0.136705383101942 & 0.93164730844903 \tabularnewline
105 & 0.0638960991585423 & 0.127792198317085 & 0.936103900841458 \tabularnewline
106 & 0.097995202072946 & 0.195990404145892 & 0.902004797927054 \tabularnewline
107 & 0.0903355545846531 & 0.180671109169306 & 0.909664445415347 \tabularnewline
108 & 0.0830452084599832 & 0.166090416919966 & 0.916954791540017 \tabularnewline
109 & 0.0711110299985358 & 0.142222059997072 & 0.928888970001464 \tabularnewline
110 & 0.431086462804772 & 0.862172925609544 & 0.568913537195228 \tabularnewline
111 & 0.388623428474217 & 0.777246856948435 & 0.611376571525783 \tabularnewline
112 & 0.363627860111720 & 0.727255720223441 & 0.63637213988828 \tabularnewline
113 & 0.334829304059473 & 0.669658608118946 & 0.665170695940527 \tabularnewline
114 & 0.309557511987299 & 0.619115023974598 & 0.690442488012701 \tabularnewline
115 & 0.274852270479618 & 0.549704540959236 & 0.725147729520382 \tabularnewline
116 & 0.271156624191395 & 0.542313248382791 & 0.728843375808605 \tabularnewline
117 & 0.232719977043140 & 0.465439954086281 & 0.76728002295686 \tabularnewline
118 & 0.197954768974582 & 0.395909537949164 & 0.802045231025418 \tabularnewline
119 & 0.204737500352522 & 0.409475000705043 & 0.795262499647478 \tabularnewline
120 & 0.186978839739789 & 0.373957679479577 & 0.813021160260211 \tabularnewline
121 & 0.176415202261778 & 0.352830404523557 & 0.823584797738222 \tabularnewline
122 & 0.152086860002084 & 0.304173720004168 & 0.847913139997916 \tabularnewline
123 & 0.126347890953389 & 0.252695781906778 & 0.873652109046611 \tabularnewline
124 & 0.150994545980006 & 0.301989091960012 & 0.849005454019994 \tabularnewline
125 & 0.123778639033733 & 0.247557278067466 & 0.876221360966267 \tabularnewline
126 & 0.10764122955921 & 0.21528245911842 & 0.89235877044079 \tabularnewline
127 & 0.157447891121889 & 0.314895782243778 & 0.842552108878111 \tabularnewline
128 & 0.15724860778731 & 0.31449721557462 & 0.84275139221269 \tabularnewline
129 & 0.149117114508939 & 0.298234229017877 & 0.850882885491061 \tabularnewline
130 & 0.118363592138061 & 0.236727184276121 & 0.88163640786194 \tabularnewline
131 & 0.110897270540874 & 0.221794541081747 & 0.889102729459126 \tabularnewline
132 & 0.184605636509068 & 0.369211273018136 & 0.815394363490932 \tabularnewline
133 & 0.208259961084448 & 0.416519922168895 & 0.791740038915552 \tabularnewline
134 & 0.167418584872425 & 0.33483716974485 & 0.832581415127575 \tabularnewline
135 & 0.145779492625584 & 0.291558985251168 & 0.854220507374416 \tabularnewline
136 & 0.116128949453763 & 0.232257898907527 & 0.883871050546237 \tabularnewline
137 & 0.488996263458012 & 0.977992526916025 & 0.511003736541988 \tabularnewline
138 & 0.426965188081926 & 0.853930376163852 & 0.573034811918074 \tabularnewline
139 & 0.419872573069995 & 0.83974514613999 & 0.580127426930005 \tabularnewline
140 & 0.356611882638064 & 0.713223765276129 & 0.643388117361936 \tabularnewline
141 & 0.290314946027215 & 0.58062989205443 & 0.709685053972785 \tabularnewline
142 & 0.274200440353856 & 0.548400880707713 & 0.725799559646144 \tabularnewline
143 & 0.214261626996405 & 0.42852325399281 & 0.785738373003595 \tabularnewline
144 & 0.160391857864604 & 0.320783715729207 & 0.839608142135396 \tabularnewline
145 & 0.123956436027997 & 0.247912872055995 & 0.876043563972003 \tabularnewline
146 & 0.109021733848922 & 0.218043467697844 & 0.890978266151078 \tabularnewline
147 & 0.0935107171533767 & 0.187021434306753 & 0.906489282846623 \tabularnewline
148 & 0.0677045815166447 & 0.135409163033289 & 0.932295418483355 \tabularnewline
149 & 0.103145316131872 & 0.206290632263744 & 0.896854683868128 \tabularnewline
150 & 0.0940797996306536 & 0.188159599261307 & 0.905920200369346 \tabularnewline
151 & 0.105393072113514 & 0.210786144227028 & 0.894606927886486 \tabularnewline
152 & 0.0617071992798439 & 0.123414398559688 & 0.938292800720156 \tabularnewline
153 & 0.207994607650606 & 0.415989215301212 & 0.792005392349394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&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]9[/C][C]0.979618051768586[/C][C]0.0407638964628277[/C][C]0.0203819482314139[/C][/ROW]
[ROW][C]10[/C][C]0.959736007593238[/C][C]0.080527984813524[/C][C]0.040263992406762[/C][/ROW]
[ROW][C]11[/C][C]0.925134279590612[/C][C]0.149731440818776[/C][C]0.0748657204093882[/C][/ROW]
[ROW][C]12[/C][C]0.904786774489668[/C][C]0.190426451020664[/C][C]0.0952132255103322[/C][/ROW]
[ROW][C]13[/C][C]0.85281192377076[/C][C]0.294376152458481[/C][C]0.147188076229240[/C][/ROW]
[ROW][C]14[/C][C]0.789073864919071[/C][C]0.421852270161857[/C][C]0.210926135080929[/C][/ROW]
[ROW][C]15[/C][C]0.784870103352979[/C][C]0.430259793294042[/C][C]0.215129896647021[/C][/ROW]
[ROW][C]16[/C][C]0.795958623635795[/C][C]0.408082752728409[/C][C]0.204041376364205[/C][/ROW]
[ROW][C]17[/C][C]0.731914242246595[/C][C]0.53617151550681[/C][C]0.268085757753405[/C][/ROW]
[ROW][C]18[/C][C]0.69040611918847[/C][C]0.619187761623061[/C][C]0.309593880811531[/C][/ROW]
[ROW][C]19[/C][C]0.623914620999889[/C][C]0.752170758000222[/C][C]0.376085379000111[/C][/ROW]
[ROW][C]20[/C][C]0.749817397495427[/C][C]0.500365205009146[/C][C]0.250182602504573[/C][/ROW]
[ROW][C]21[/C][C]0.730250271538145[/C][C]0.53949945692371[/C][C]0.269749728461855[/C][/ROW]
[ROW][C]22[/C][C]0.82057307130543[/C][C]0.358853857389139[/C][C]0.179426928694569[/C][/ROW]
[ROW][C]23[/C][C]0.775012302346377[/C][C]0.449975395307246[/C][C]0.224987697653623[/C][/ROW]
[ROW][C]24[/C][C]0.763538100324418[/C][C]0.472923799351163[/C][C]0.236461899675582[/C][/ROW]
[ROW][C]25[/C][C]0.712171191824491[/C][C]0.575657616351019[/C][C]0.287828808175509[/C][/ROW]
[ROW][C]26[/C][C]0.701708479333535[/C][C]0.59658304133293[/C][C]0.298291520666465[/C][/ROW]
[ROW][C]27[/C][C]0.648392849156116[/C][C]0.703214301687768[/C][C]0.351607150843884[/C][/ROW]
[ROW][C]28[/C][C]0.65503277803486[/C][C]0.68993444393028[/C][C]0.34496722196514[/C][/ROW]
[ROW][C]29[/C][C]0.623398810309757[/C][C]0.753202379380486[/C][C]0.376601189690243[/C][/ROW]
[ROW][C]30[/C][C]0.56250676890213[/C][C]0.87498646219574[/C][C]0.43749323109787[/C][/ROW]
[ROW][C]31[/C][C]0.558922843745785[/C][C]0.88215431250843[/C][C]0.441077156254215[/C][/ROW]
[ROW][C]32[/C][C]0.641165661255448[/C][C]0.717668677489105[/C][C]0.358834338744552[/C][/ROW]
[ROW][C]33[/C][C]0.60059587241888[/C][C]0.798808255162239[/C][C]0.399404127581120[/C][/ROW]
[ROW][C]34[/C][C]0.547239617899725[/C][C]0.90552076420055[/C][C]0.452760382100275[/C][/ROW]
[ROW][C]35[/C][C]0.494339321327967[/C][C]0.988678642655935[/C][C]0.505660678672033[/C][/ROW]
[ROW][C]36[/C][C]0.465196102444659[/C][C]0.930392204889317[/C][C]0.534803897555341[/C][/ROW]
[ROW][C]37[/C][C]0.772111505712896[/C][C]0.455776988574208[/C][C]0.227888494287104[/C][/ROW]
[ROW][C]38[/C][C]0.733434095519253[/C][C]0.533131808961493[/C][C]0.266565904480747[/C][/ROW]
[ROW][C]39[/C][C]0.697659595979957[/C][C]0.604680808040086[/C][C]0.302340404020043[/C][/ROW]
[ROW][C]40[/C][C]0.664574406351508[/C][C]0.670851187296985[/C][C]0.335425593648492[/C][/ROW]
[ROW][C]41[/C][C]0.63683901567886[/C][C]0.72632196864228[/C][C]0.36316098432114[/C][/ROW]
[ROW][C]42[/C][C]0.601082681004067[/C][C]0.797834637991866[/C][C]0.398917318995933[/C][/ROW]
[ROW][C]43[/C][C]0.62664646746116[/C][C]0.746707065077681[/C][C]0.373353532538840[/C][/ROW]
[ROW][C]44[/C][C]0.595596971264938[/C][C]0.808806057470124[/C][C]0.404403028735062[/C][/ROW]
[ROW][C]45[/C][C]0.550358547761068[/C][C]0.899282904477863[/C][C]0.449641452238932[/C][/ROW]
[ROW][C]46[/C][C]0.509228045558506[/C][C]0.981543908882987[/C][C]0.490771954441494[/C][/ROW]
[ROW][C]47[/C][C]0.485669545758708[/C][C]0.971339091517416[/C][C]0.514330454241292[/C][/ROW]
[ROW][C]48[/C][C]0.440464555368276[/C][C]0.880929110736551[/C][C]0.559535444631724[/C][/ROW]
[ROW][C]49[/C][C]0.433204811989541[/C][C]0.866409623979082[/C][C]0.566795188010459[/C][/ROW]
[ROW][C]50[/C][C]0.392302700824637[/C][C]0.784605401649274[/C][C]0.607697299175363[/C][/ROW]
[ROW][C]51[/C][C]0.362795772556303[/C][C]0.725591545112606[/C][C]0.637204227443697[/C][/ROW]
[ROW][C]52[/C][C]0.327648883745766[/C][C]0.655297767491533[/C][C]0.672351116254234[/C][/ROW]
[ROW][C]53[/C][C]0.425123837649754[/C][C]0.850247675299508[/C][C]0.574876162350246[/C][/ROW]
[ROW][C]54[/C][C]0.37911403719302[/C][C]0.75822807438604[/C][C]0.62088596280698[/C][/ROW]
[ROW][C]55[/C][C]0.338296309941879[/C][C]0.676592619883759[/C][C]0.661703690058121[/C][/ROW]
[ROW][C]56[/C][C]0.297011638081985[/C][C]0.59402327616397[/C][C]0.702988361918015[/C][/ROW]
[ROW][C]57[/C][C]0.256451038216212[/C][C]0.512902076432423[/C][C]0.743548961783788[/C][/ROW]
[ROW][C]58[/C][C]0.277297656954837[/C][C]0.554595313909675[/C][C]0.722702343045163[/C][/ROW]
[ROW][C]59[/C][C]0.28207431042673[/C][C]0.56414862085346[/C][C]0.71792568957327[/C][/ROW]
[ROW][C]60[/C][C]0.298462964191241[/C][C]0.596925928382482[/C][C]0.701537035808759[/C][/ROW]
[ROW][C]61[/C][C]0.489127816997931[/C][C]0.978255633995862[/C][C]0.510872183002069[/C][/ROW]
[ROW][C]62[/C][C]0.444393705478666[/C][C]0.888787410957331[/C][C]0.555606294521334[/C][/ROW]
[ROW][C]63[/C][C]0.400662972419656[/C][C]0.801325944839312[/C][C]0.599337027580344[/C][/ROW]
[ROW][C]64[/C][C]0.363522270478471[/C][C]0.727044540956942[/C][C]0.636477729521529[/C][/ROW]
[ROW][C]65[/C][C]0.320186338711709[/C][C]0.640372677423418[/C][C]0.679813661288291[/C][/ROW]
[ROW][C]66[/C][C]0.293546348413034[/C][C]0.587092696826068[/C][C]0.706453651586966[/C][/ROW]
[ROW][C]67[/C][C]0.309347378711026[/C][C]0.618694757422053[/C][C]0.690652621288974[/C][/ROW]
[ROW][C]68[/C][C]0.272987452966228[/C][C]0.545974905932456[/C][C]0.727012547033772[/C][/ROW]
[ROW][C]69[/C][C]0.237065758680556[/C][C]0.474131517361111[/C][C]0.762934241319444[/C][/ROW]
[ROW][C]70[/C][C]0.20632351446695[/C][C]0.4126470289339[/C][C]0.79367648553305[/C][/ROW]
[ROW][C]71[/C][C]0.174731407036978[/C][C]0.349462814073955[/C][C]0.825268592963022[/C][/ROW]
[ROW][C]72[/C][C]0.154164394549293[/C][C]0.308328789098586[/C][C]0.845835605450707[/C][/ROW]
[ROW][C]73[/C][C]0.127922496231561[/C][C]0.255844992463121[/C][C]0.87207750376844[/C][/ROW]
[ROW][C]74[/C][C]0.115833349878197[/C][C]0.231666699756394[/C][C]0.884166650121803[/C][/ROW]
[ROW][C]75[/C][C]0.102754143720427[/C][C]0.205508287440854[/C][C]0.897245856279573[/C][/ROW]
[ROW][C]76[/C][C]0.167500803967243[/C][C]0.335001607934486[/C][C]0.832499196032757[/C][/ROW]
[ROW][C]77[/C][C]0.143182962745036[/C][C]0.286365925490072[/C][C]0.856817037254964[/C][/ROW]
[ROW][C]78[/C][C]0.165030358472474[/C][C]0.330060716944949[/C][C]0.834969641527526[/C][/ROW]
[ROW][C]79[/C][C]0.138133710148841[/C][C]0.276267420297683[/C][C]0.861866289851159[/C][/ROW]
[ROW][C]80[/C][C]0.154343513386904[/C][C]0.308687026773809[/C][C]0.845656486613096[/C][/ROW]
[ROW][C]81[/C][C]0.135372574585085[/C][C]0.27074514917017[/C][C]0.864627425414915[/C][/ROW]
[ROW][C]82[/C][C]0.147473494425164[/C][C]0.294946988850327[/C][C]0.852526505574836[/C][/ROW]
[ROW][C]83[/C][C]0.162004926173185[/C][C]0.32400985234637[/C][C]0.837995073826815[/C][/ROW]
[ROW][C]84[/C][C]0.138543040811329[/C][C]0.277086081622658[/C][C]0.861456959188671[/C][/ROW]
[ROW][C]85[/C][C]0.118233370123741[/C][C]0.236466740247482[/C][C]0.881766629876259[/C][/ROW]
[ROW][C]86[/C][C]0.0996135036827563[/C][C]0.199227007365513[/C][C]0.900386496317244[/C][/ROW]
[ROW][C]87[/C][C]0.0816161542773882[/C][C]0.163232308554776[/C][C]0.918383845722612[/C][/ROW]
[ROW][C]88[/C][C]0.0676342111055145[/C][C]0.135268422211029[/C][C]0.932365788894485[/C][/ROW]
[ROW][C]89[/C][C]0.0600098469517951[/C][C]0.120019693903590[/C][C]0.939990153048205[/C][/ROW]
[ROW][C]90[/C][C]0.212665592216278[/C][C]0.425331184432555[/C][C]0.787334407783722[/C][/ROW]
[ROW][C]91[/C][C]0.182500126451025[/C][C]0.365000252902050[/C][C]0.817499873548975[/C][/ROW]
[ROW][C]92[/C][C]0.163310487359645[/C][C]0.326620974719291[/C][C]0.836689512640355[/C][/ROW]
[ROW][C]93[/C][C]0.136407966463337[/C][C]0.272815932926674[/C][C]0.863592033536663[/C][/ROW]
[ROW][C]94[/C][C]0.119626432710637[/C][C]0.239252865421274[/C][C]0.880373567289363[/C][/ROW]
[ROW][C]95[/C][C]0.122041089543075[/C][C]0.244082179086150[/C][C]0.877958910456925[/C][/ROW]
[ROW][C]96[/C][C]0.108679562388928[/C][C]0.217359124777855[/C][C]0.891320437611072[/C][/ROW]
[ROW][C]97[/C][C]0.109280598721279[/C][C]0.218561197442558[/C][C]0.890719401278721[/C][/ROW]
[ROW][C]98[/C][C]0.0970649635191059[/C][C]0.194129927038212[/C][C]0.902935036480894[/C][/ROW]
[ROW][C]99[/C][C]0.0888242600802065[/C][C]0.177648520160413[/C][C]0.911175739919794[/C][/ROW]
[ROW][C]100[/C][C]0.0913883041382813[/C][C]0.182776608276563[/C][C]0.908611695861719[/C][/ROW]
[ROW][C]101[/C][C]0.094477786481005[/C][C]0.18895557296201[/C][C]0.905522213518995[/C][/ROW]
[ROW][C]102[/C][C]0.0851185861700531[/C][C]0.170237172340106[/C][C]0.914881413829947[/C][/ROW]
[ROW][C]103[/C][C]0.0734035503019881[/C][C]0.146807100603976[/C][C]0.926596449698012[/C][/ROW]
[ROW][C]104[/C][C]0.0683526915509708[/C][C]0.136705383101942[/C][C]0.93164730844903[/C][/ROW]
[ROW][C]105[/C][C]0.0638960991585423[/C][C]0.127792198317085[/C][C]0.936103900841458[/C][/ROW]
[ROW][C]106[/C][C]0.097995202072946[/C][C]0.195990404145892[/C][C]0.902004797927054[/C][/ROW]
[ROW][C]107[/C][C]0.0903355545846531[/C][C]0.180671109169306[/C][C]0.909664445415347[/C][/ROW]
[ROW][C]108[/C][C]0.0830452084599832[/C][C]0.166090416919966[/C][C]0.916954791540017[/C][/ROW]
[ROW][C]109[/C][C]0.0711110299985358[/C][C]0.142222059997072[/C][C]0.928888970001464[/C][/ROW]
[ROW][C]110[/C][C]0.431086462804772[/C][C]0.862172925609544[/C][C]0.568913537195228[/C][/ROW]
[ROW][C]111[/C][C]0.388623428474217[/C][C]0.777246856948435[/C][C]0.611376571525783[/C][/ROW]
[ROW][C]112[/C][C]0.363627860111720[/C][C]0.727255720223441[/C][C]0.63637213988828[/C][/ROW]
[ROW][C]113[/C][C]0.334829304059473[/C][C]0.669658608118946[/C][C]0.665170695940527[/C][/ROW]
[ROW][C]114[/C][C]0.309557511987299[/C][C]0.619115023974598[/C][C]0.690442488012701[/C][/ROW]
[ROW][C]115[/C][C]0.274852270479618[/C][C]0.549704540959236[/C][C]0.725147729520382[/C][/ROW]
[ROW][C]116[/C][C]0.271156624191395[/C][C]0.542313248382791[/C][C]0.728843375808605[/C][/ROW]
[ROW][C]117[/C][C]0.232719977043140[/C][C]0.465439954086281[/C][C]0.76728002295686[/C][/ROW]
[ROW][C]118[/C][C]0.197954768974582[/C][C]0.395909537949164[/C][C]0.802045231025418[/C][/ROW]
[ROW][C]119[/C][C]0.204737500352522[/C][C]0.409475000705043[/C][C]0.795262499647478[/C][/ROW]
[ROW][C]120[/C][C]0.186978839739789[/C][C]0.373957679479577[/C][C]0.813021160260211[/C][/ROW]
[ROW][C]121[/C][C]0.176415202261778[/C][C]0.352830404523557[/C][C]0.823584797738222[/C][/ROW]
[ROW][C]122[/C][C]0.152086860002084[/C][C]0.304173720004168[/C][C]0.847913139997916[/C][/ROW]
[ROW][C]123[/C][C]0.126347890953389[/C][C]0.252695781906778[/C][C]0.873652109046611[/C][/ROW]
[ROW][C]124[/C][C]0.150994545980006[/C][C]0.301989091960012[/C][C]0.849005454019994[/C][/ROW]
[ROW][C]125[/C][C]0.123778639033733[/C][C]0.247557278067466[/C][C]0.876221360966267[/C][/ROW]
[ROW][C]126[/C][C]0.10764122955921[/C][C]0.21528245911842[/C][C]0.89235877044079[/C][/ROW]
[ROW][C]127[/C][C]0.157447891121889[/C][C]0.314895782243778[/C][C]0.842552108878111[/C][/ROW]
[ROW][C]128[/C][C]0.15724860778731[/C][C]0.31449721557462[/C][C]0.84275139221269[/C][/ROW]
[ROW][C]129[/C][C]0.149117114508939[/C][C]0.298234229017877[/C][C]0.850882885491061[/C][/ROW]
[ROW][C]130[/C][C]0.118363592138061[/C][C]0.236727184276121[/C][C]0.88163640786194[/C][/ROW]
[ROW][C]131[/C][C]0.110897270540874[/C][C]0.221794541081747[/C][C]0.889102729459126[/C][/ROW]
[ROW][C]132[/C][C]0.184605636509068[/C][C]0.369211273018136[/C][C]0.815394363490932[/C][/ROW]
[ROW][C]133[/C][C]0.208259961084448[/C][C]0.416519922168895[/C][C]0.791740038915552[/C][/ROW]
[ROW][C]134[/C][C]0.167418584872425[/C][C]0.33483716974485[/C][C]0.832581415127575[/C][/ROW]
[ROW][C]135[/C][C]0.145779492625584[/C][C]0.291558985251168[/C][C]0.854220507374416[/C][/ROW]
[ROW][C]136[/C][C]0.116128949453763[/C][C]0.232257898907527[/C][C]0.883871050546237[/C][/ROW]
[ROW][C]137[/C][C]0.488996263458012[/C][C]0.977992526916025[/C][C]0.511003736541988[/C][/ROW]
[ROW][C]138[/C][C]0.426965188081926[/C][C]0.853930376163852[/C][C]0.573034811918074[/C][/ROW]
[ROW][C]139[/C][C]0.419872573069995[/C][C]0.83974514613999[/C][C]0.580127426930005[/C][/ROW]
[ROW][C]140[/C][C]0.356611882638064[/C][C]0.713223765276129[/C][C]0.643388117361936[/C][/ROW]
[ROW][C]141[/C][C]0.290314946027215[/C][C]0.58062989205443[/C][C]0.709685053972785[/C][/ROW]
[ROW][C]142[/C][C]0.274200440353856[/C][C]0.548400880707713[/C][C]0.725799559646144[/C][/ROW]
[ROW][C]143[/C][C]0.214261626996405[/C][C]0.42852325399281[/C][C]0.785738373003595[/C][/ROW]
[ROW][C]144[/C][C]0.160391857864604[/C][C]0.320783715729207[/C][C]0.839608142135396[/C][/ROW]
[ROW][C]145[/C][C]0.123956436027997[/C][C]0.247912872055995[/C][C]0.876043563972003[/C][/ROW]
[ROW][C]146[/C][C]0.109021733848922[/C][C]0.218043467697844[/C][C]0.890978266151078[/C][/ROW]
[ROW][C]147[/C][C]0.0935107171533767[/C][C]0.187021434306753[/C][C]0.906489282846623[/C][/ROW]
[ROW][C]148[/C][C]0.0677045815166447[/C][C]0.135409163033289[/C][C]0.932295418483355[/C][/ROW]
[ROW][C]149[/C][C]0.103145316131872[/C][C]0.206290632263744[/C][C]0.896854683868128[/C][/ROW]
[ROW][C]150[/C][C]0.0940797996306536[/C][C]0.188159599261307[/C][C]0.905920200369346[/C][/ROW]
[ROW][C]151[/C][C]0.105393072113514[/C][C]0.210786144227028[/C][C]0.894606927886486[/C][/ROW]
[ROW][C]152[/C][C]0.0617071992798439[/C][C]0.123414398559688[/C][C]0.938292800720156[/C][/ROW]
[ROW][C]153[/C][C]0.207994607650606[/C][C]0.415989215301212[/C][C]0.792005392349394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9796180517685860.04076389646282770.0203819482314139
100.9597360075932380.0805279848135240.040263992406762
110.9251342795906120.1497314408187760.0748657204093882
120.9047867744896680.1904264510206640.0952132255103322
130.852811923770760.2943761524584810.147188076229240
140.7890738649190710.4218522701618570.210926135080929
150.7848701033529790.4302597932940420.215129896647021
160.7959586236357950.4080827527284090.204041376364205
170.7319142422465950.536171515506810.268085757753405
180.690406119188470.6191877616230610.309593880811531
190.6239146209998890.7521707580002220.376085379000111
200.7498173974954270.5003652050091460.250182602504573
210.7302502715381450.539499456923710.269749728461855
220.820573071305430.3588538573891390.179426928694569
230.7750123023463770.4499753953072460.224987697653623
240.7635381003244180.4729237993511630.236461899675582
250.7121711918244910.5756576163510190.287828808175509
260.7017084793335350.596583041332930.298291520666465
270.6483928491561160.7032143016877680.351607150843884
280.655032778034860.689934443930280.34496722196514
290.6233988103097570.7532023793804860.376601189690243
300.562506768902130.874986462195740.43749323109787
310.5589228437457850.882154312508430.441077156254215
320.6411656612554480.7176686774891050.358834338744552
330.600595872418880.7988082551622390.399404127581120
340.5472396178997250.905520764200550.452760382100275
350.4943393213279670.9886786426559350.505660678672033
360.4651961024446590.9303922048893170.534803897555341
370.7721115057128960.4557769885742080.227888494287104
380.7334340955192530.5331318089614930.266565904480747
390.6976595959799570.6046808080400860.302340404020043
400.6645744063515080.6708511872969850.335425593648492
410.636839015678860.726321968642280.36316098432114
420.6010826810040670.7978346379918660.398917318995933
430.626646467461160.7467070650776810.373353532538840
440.5955969712649380.8088060574701240.404403028735062
450.5503585477610680.8992829044778630.449641452238932
460.5092280455585060.9815439088829870.490771954441494
470.4856695457587080.9713390915174160.514330454241292
480.4404645553682760.8809291107365510.559535444631724
490.4332048119895410.8664096239790820.566795188010459
500.3923027008246370.7846054016492740.607697299175363
510.3627957725563030.7255915451126060.637204227443697
520.3276488837457660.6552977674915330.672351116254234
530.4251238376497540.8502476752995080.574876162350246
540.379114037193020.758228074386040.62088596280698
550.3382963099418790.6765926198837590.661703690058121
560.2970116380819850.594023276163970.702988361918015
570.2564510382162120.5129020764324230.743548961783788
580.2772976569548370.5545953139096750.722702343045163
590.282074310426730.564148620853460.71792568957327
600.2984629641912410.5969259283824820.701537035808759
610.4891278169979310.9782556339958620.510872183002069
620.4443937054786660.8887874109573310.555606294521334
630.4006629724196560.8013259448393120.599337027580344
640.3635222704784710.7270445409569420.636477729521529
650.3201863387117090.6403726774234180.679813661288291
660.2935463484130340.5870926968260680.706453651586966
670.3093473787110260.6186947574220530.690652621288974
680.2729874529662280.5459749059324560.727012547033772
690.2370657586805560.4741315173611110.762934241319444
700.206323514466950.41264702893390.79367648553305
710.1747314070369780.3494628140739550.825268592963022
720.1541643945492930.3083287890985860.845835605450707
730.1279224962315610.2558449924631210.87207750376844
740.1158333498781970.2316666997563940.884166650121803
750.1027541437204270.2055082874408540.897245856279573
760.1675008039672430.3350016079344860.832499196032757
770.1431829627450360.2863659254900720.856817037254964
780.1650303584724740.3300607169449490.834969641527526
790.1381337101488410.2762674202976830.861866289851159
800.1543435133869040.3086870267738090.845656486613096
810.1353725745850850.270745149170170.864627425414915
820.1474734944251640.2949469888503270.852526505574836
830.1620049261731850.324009852346370.837995073826815
840.1385430408113290.2770860816226580.861456959188671
850.1182333701237410.2364667402474820.881766629876259
860.09961350368275630.1992270073655130.900386496317244
870.08161615427738820.1632323085547760.918383845722612
880.06763421110551450.1352684222110290.932365788894485
890.06000984695179510.1200196939035900.939990153048205
900.2126655922162780.4253311844325550.787334407783722
910.1825001264510250.3650002529020500.817499873548975
920.1633104873596450.3266209747192910.836689512640355
930.1364079664633370.2728159329266740.863592033536663
940.1196264327106370.2392528654212740.880373567289363
950.1220410895430750.2440821790861500.877958910456925
960.1086795623889280.2173591247778550.891320437611072
970.1092805987212790.2185611974425580.890719401278721
980.09706496351910590.1941299270382120.902935036480894
990.08882426008020650.1776485201604130.911175739919794
1000.09138830413828130.1827766082765630.908611695861719
1010.0944777864810050.188955572962010.905522213518995
1020.08511858617005310.1702371723401060.914881413829947
1030.07340355030198810.1468071006039760.926596449698012
1040.06835269155097080.1367053831019420.93164730844903
1050.06389609915854230.1277921983170850.936103900841458
1060.0979952020729460.1959904041458920.902004797927054
1070.09033555458465310.1806711091693060.909664445415347
1080.08304520845998320.1660904169199660.916954791540017
1090.07111102999853580.1422220599970720.928888970001464
1100.4310864628047720.8621729256095440.568913537195228
1110.3886234284742170.7772468569484350.611376571525783
1120.3636278601117200.7272557202234410.63637213988828
1130.3348293040594730.6696586081189460.665170695940527
1140.3095575119872990.6191150239745980.690442488012701
1150.2748522704796180.5497045409592360.725147729520382
1160.2711566241913950.5423132483827910.728843375808605
1170.2327199770431400.4654399540862810.76728002295686
1180.1979547689745820.3959095379491640.802045231025418
1190.2047375003525220.4094750007050430.795262499647478
1200.1869788397397890.3739576794795770.813021160260211
1210.1764152022617780.3528304045235570.823584797738222
1220.1520868600020840.3041737200041680.847913139997916
1230.1263478909533890.2526957819067780.873652109046611
1240.1509945459800060.3019890919600120.849005454019994
1250.1237786390337330.2475572780674660.876221360966267
1260.107641229559210.215282459118420.89235877044079
1270.1574478911218890.3148957822437780.842552108878111
1280.157248607787310.314497215574620.84275139221269
1290.1491171145089390.2982342290178770.850882885491061
1300.1183635921380610.2367271842761210.88163640786194
1310.1108972705408740.2217945410817470.889102729459126
1320.1846056365090680.3692112730181360.815394363490932
1330.2082599610844480.4165199221688950.791740038915552
1340.1674185848724250.334837169744850.832581415127575
1350.1457794926255840.2915589852511680.854220507374416
1360.1161289494537630.2322578989075270.883871050546237
1370.4889962634580120.9779925269160250.511003736541988
1380.4269651880819260.8539303761638520.573034811918074
1390.4198725730699950.839745146139990.580127426930005
1400.3566118826380640.7132237652761290.643388117361936
1410.2903149460272150.580629892054430.709685053972785
1420.2742004403538560.5484008807077130.725799559646144
1430.2142616269964050.428523253992810.785738373003595
1440.1603918578646040.3207837157292070.839608142135396
1450.1239564360279970.2479128720559950.876043563972003
1460.1090217338489220.2180434676978440.890978266151078
1470.09351071715337670.1870214343067530.906489282846623
1480.06770458151664470.1354091630332890.932295418483355
1490.1031453161318720.2062906322637440.896854683868128
1500.09407979963065360.1881595992613070.905920200369346
1510.1053930721135140.2107861442270280.894606927886486
1520.06170719927984390.1234143985596880.938292800720156
1530.2079946076506060.4159892153012120.792005392349394






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00689655172413793OK
10% type I error level20.0137931034482759OK

\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 & 1 & 0.00689655172413793 & OK \tabularnewline
10% type I error level & 2 & 0.0137931034482759 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99667&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]1[/C][C]0.00689655172413793[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0137931034482759[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99667&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99667&T=6

As an alternative you can also use a QR Code:  
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 level10.00689655172413793OK
10% type I error level20.0137931034482759OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}