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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 computationThu, 15 Dec 2011 10:10:44 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/15/t13239618577radz9w44764siz.htm/, Retrieved Wed, 08 May 2024 15:05:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155474, Retrieved Wed, 08 May 2024 15:05:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:07:41] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:24:09] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-           [Recursive Partitioning (Regression Trees)] [] [2010-12-25 21:51:47] [2e1e44f0ae3cb9513dc28781dfdb387b]
- RMPD          [Multiple Regression] [workshop 10] [2011-12-15 15:10:44] [1c5701688738a71acfb394dd0fcb74f6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14	1
2	39	32	16	11	18	1
2	30	35	19	15	11	1
1	31	33	15	6	12	0
2	34	37	14	13	16	0
2	35	29	13	10	18	1
2	39	31	19	12	14	1
2	34	36	15	14	14	1
2	36	35	14	12	15	0
2	37	38	15	6	15	0
1	38	31	16	10	17	1
2	36	34	16	12	19	0
1	38	35	16	12	10	0
2	39	38	16	11	16	0
2	33	37	17	15	18	0
1	32	33	15	12	14	0
1	36	32	15	10	14	1
2	38	38	20	12	17	1
1	39	38	18	11	14	1
2	32	32	16	12	16	0
1	32	33	16	11	18	0
2	31	31	16	12	11	1
2	39	38	19	13	14	1
2	37	39	16	11	12	0
1	39	32	17	9	17	1
2	41	32	17	13	9	1
1	36	35	16	10	16	1
2	33	37	15	14	14	1
2	33	33	16	12	15	0
1	34	33	14	10	11	0
2	31	28	15	12	16	1
1	27	32	12	8	13	1
2	37	31	14	10	17	1
2	34	37	16	12	15	0
1	34	30	14	12	14	0
1	32	33	7	7	16	0
1	29	31	10	6	9	1
1	36	33	14	12	15	1
2	29	31	16	10	17	0
1	35	33	16	10	13	0
1	37	32	16	10	15	1
2	34	33	14	12	16	1
1	38	32	20	15	16	1
1	35	33	14	10	12	1
2	38	28	14	10	12	1
2	37	35	11	12	11	0
2	38	39	14	13	15	0
2	33	34	15	11	15	0
2	36	38	16	11	17	1
1	38	32	14	12	13	1
2	32	38	16	14	16	0
1	32	30	14	10	14	0
1	32	33	12	12	11	1
2	34	38	16	13	12	0
1	32	32	9	5	12	0
2	37	32	14	6	15	0
2	39	34	16	12	16	0
2	29	34	16	12	15	0
1	37	36	15	11	12	0
2	35	34	16	10	12	0
1	30	28	12	7	8	0
1	38	34	16	12	13	0
2	34	35	16	14	11	1
2	31	35	14	11	14	1
2	34	31	16	12	15	1
1	35	37	17	13	10	0
2	36	35	18	14	11	0
1	30	27	18	11	12	0
2	39	40	12	12	15	0
1	35	37	16	12	15	1
1	38	36	10	8	14	1
2	31	38	14	11	16	0
2	34	39	18	14	15	0
1	38	41	18	14	15	0
1	34	27	16	12	13	0
2	39	30	17	9	12	1
2	37	37	16	13	17	1
2	34	31	16	11	13	0
1	28	31	13	12	15	1
1	37	27	16	12	13	1
1	33	36	16	12	15	1
1	37	38	20	12	16	0
2	35	37	16	12	15	0
1	37	33	15	12	16	0
2	32	34	15	11	15	1
2	33	31	16	10	14	0
1	38	39	14	9	15	0
2	33	34	16	12	14	1
2	29	32	16	12	13	1
2	33	33	15	12	7	0
2	31	36	12	9	17	0
2	36	32	17	15	13	1
2	35	41	16	12	15	1
2	32	28	15	12	14	1
2	29	30	13	12	13	0
2	39	36	16	10	16	1
2	37	35	16	13	12	1
2	35	31	16	9	14	1
1	37	34	16	12	17	1
1	32	36	14	10	15	0
2	38	36	16	14	17	0
1	37	35	16	11	12	1
2	36	37	20	15	16	0
1	32	28	15	11	11	0
2	33	39	16	11	15	0
1	40	32	13	12	9	0
2	38	35	17	12	16	1
1	41	39	16	12	15	1
1	36	35	16	11	10	0
2	43	42	12	7	10	0
2	30	34	16	12	15	1
2	31	33	16	14	11	1
2	32	41	17	11	13	1
1	32	33	13	11	14	0
2	37	34	12	10	18	0
1	37	32	18	13	16	1
2	33	40	14	13	14	0
2	34	40	14	8	14	0
2	33	35	13	11	14	0
2	38	36	16	12	14	0
2	33	37	13	11	12	0
2	31	27	16	13	14	1
2	38	39	13	12	15	1
2	37	38	16	14	15	1
2	33	31	15	13	15	0
2	31	33	16	15	13	0
1	39	32	15	10	17	1
2	44	39	17	11	17	1
2	33	36	15	9	19	0
2	35	33	12	11	15	1
1	32	33	16	10	13	1
1	28	32	10	11	9	1
2	40	37	16	8	15	1
1	27	30	12	11	15	0
1	37	38	14	12	15	0
2	32	29	15	12	16	1
1	28	22	13	9	11	1
1	34	35	15	11	14	1
2	30	35	11	10	11	0
2	35	34	12	8	15	0
1	31	35	8	9	13	0
2	32	34	16	8	15	1
1	30	34	15	9	16	1
2	30	35	17	15	14	0
1	31	23	16	11	15	0
2	40	31	10	8	16	1
2	32	27	18	13	16	1
1	36	36	13	12	11	1
1	32	31	16	12	12	1
1	35	32	13	9	9	1
2	38	39	10	7	16	0
2	42	37	15	13	13	0
1	34	38	16	9	16	0
2	35	39	16	6	12	1
2	35	34	14	8	9	1
2	33	31	10	8	13	0
2	36	32	17	15	13	0
2	32	37	13	6	14	1
2	33	36	15	9	19	0
2	34	32	16	11	13	0
2	32	35	12	8	12	0
2	34	36	13	8	13	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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 time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
connected[t] = + 17.9543218399761 -0.30076193384719gender[t] + 0.362476346350203separate[t] + 0.296395330644805learning[t] -0.117562231046897software[t] + 0.08611900150039hapiness[t] + 0.953759553908994pop[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
connected[t] =  +  17.9543218399761 -0.30076193384719gender[t] +  0.362476346350203separate[t] +  0.296395330644805learning[t] -0.117562231046897software[t] +  0.08611900150039hapiness[t] +  0.953759553908994pop[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]connected[t] =  +  17.9543218399761 -0.30076193384719gender[t] +  0.362476346350203separate[t] +  0.296395330644805learning[t] -0.117562231046897software[t] +  0.08611900150039hapiness[t] +  0.953759553908994pop[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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
connected[t] = + 17.9543218399761 -0.30076193384719gender[t] + 0.362476346350203separate[t] + 0.296395330644805learning[t] -0.117562231046897software[t] + 0.08611900150039hapiness[t] + 0.953759553908994pop[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.95432183997612.9371126.112900
gender-0.300761933847190.525698-0.57210.568070.284035
separate0.3624763463502030.0718635.0441e-061e-06
learning0.2963953306448050.1317782.24920.0259090.012954
software-0.1175622310468970.136416-0.86180.3901350.195067
hapiness0.086119001500390.1077980.79890.4255740.212787
pop0.9537595539089940.4971831.91830.0569080.028454

\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) & 17.9543218399761 & 2.937112 & 6.1129 & 0 & 0 \tabularnewline
gender & -0.30076193384719 & 0.525698 & -0.5721 & 0.56807 & 0.284035 \tabularnewline
separate & 0.362476346350203 & 0.071863 & 5.044 & 1e-06 & 1e-06 \tabularnewline
learning & 0.296395330644805 & 0.131778 & 2.2492 & 0.025909 & 0.012954 \tabularnewline
software & -0.117562231046897 & 0.136416 & -0.8618 & 0.390135 & 0.195067 \tabularnewline
hapiness & 0.08611900150039 & 0.107798 & 0.7989 & 0.425574 & 0.212787 \tabularnewline
pop & 0.953759553908994 & 0.497183 & 1.9183 & 0.056908 & 0.028454 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&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]17.9543218399761[/C][C]2.937112[/C][C]6.1129[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]gender[/C][C]-0.30076193384719[/C][C]0.525698[/C][C]-0.5721[/C][C]0.56807[/C][C]0.284035[/C][/ROW]
[ROW][C]separate[/C][C]0.362476346350203[/C][C]0.071863[/C][C]5.044[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]learning[/C][C]0.296395330644805[/C][C]0.131778[/C][C]2.2492[/C][C]0.025909[/C][C]0.012954[/C][/ROW]
[ROW][C]software[/C][C]-0.117562231046897[/C][C]0.136416[/C][C]-0.8618[/C][C]0.390135[/C][C]0.195067[/C][/ROW]
[ROW][C]hapiness[/C][C]0.08611900150039[/C][C]0.107798[/C][C]0.7989[/C][C]0.425574[/C][C]0.212787[/C][/ROW]
[ROW][C]pop[/C][C]0.953759553908994[/C][C]0.497183[/C][C]1.9183[/C][C]0.056908[/C][C]0.028454[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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)17.95432183997612.9371126.112900
gender-0.300761933847190.525698-0.57210.568070.284035
separate0.3624763463502030.0718635.0441e-061e-06
learning0.2963953306448050.1317782.24920.0259090.012954
software-0.1175622310468970.136416-0.86180.3901350.195067
hapiness0.086119001500390.1077980.79890.4255740.212787
pop0.9537595539089940.4971831.91830.0569080.028454






Multiple Linear Regression - Regression Statistics
Multiple R0.446254629320547
R-squared0.199143194190019
Adjusted R-squared0.168142285578019
F-TEST (value)6.42378572455577
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value4.59662991469934e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.07832809634748
Sum Squared Residuals1468.79609965815

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.446254629320547 \tabularnewline
R-squared & 0.199143194190019 \tabularnewline
Adjusted R-squared & 0.168142285578019 \tabularnewline
F-TEST (value) & 6.42378572455577 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 4.59662991469934e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.07832809634748 \tabularnewline
Sum Squared Residuals & 1468.79609965815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.446254629320547[/C][/ROW]
[ROW][C]R-squared[/C][C]0.199143194190019[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.168142285578019[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.42378572455577[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]4.59662991469934e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.07832809634748[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1468.79609965815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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 R0.446254629320547
R-squared0.199143194190019
Adjusted R-squared0.168142285578019
F-TEST (value)6.42378572455577
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value4.59662991469934e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.07832809634748
Sum Squared Residuals1468.79609965815






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.72871723432365.27128276567635
23934.90508338520524.09491661479477
33035.8086164814999-5.80861648149993
43134.389263927081-3.38926392708097
53434.7635524366631-0.763552436663068
63533.04603058526711.9539694147329
73934.9697547937414.03024520625902
83435.361430740819-1.36143074081899
93634.07004297350921.92995702649083
103736.1592407294860.840759270514035
113834.87481220224873.12518779775128
123634.64483329445011.35516670554986
133834.5330005611443.46699943885598
143935.95394390639673.04605609360333
153335.5898519695045-2.58985196950447
163233.8561285438004-1.85612854380037
173634.68253621345291.31746378654705
183838.0618415533384-0.0618415533383817
193937.62901805244171.37098194755831
203233.6615235972486-1.66152359724856
213234.6145621114936-2.61456211149363
223133.8222117973054-2.8222117973054
233937.38952698714551.61047301285449
243735.97194424674531.02805575325468
253935.65124611029063.34875388970937
264134.19128324025276.80871675974727
273636.2385985861491-0.238598586149146
283335.7239070871692-2.72390708716919
293333.9378809420984-0.937880942098372
303433.53650067074820.463499329251814
313132.8689824351119-1.86898243511194
322733.9423556821119-6.94235568211194
333733.98125960711193.01874039288808
343435.3877863274992-1.38778632749919
353432.4723041741051.52769582589505
363232.2450150568772-0.245015056877194
372932.8777371305644-3.87773713056436
383634.59961176856491.40038823143505
392933.6202907144925-4.62029071449254
403534.30152933503860.698470664961425
413735.06505054559811.93494945440185
423434.3849688362181-0.384968836218147
433835.74893971444332.25106028555673
443534.57637922615760.42362077384243
453832.46323556055945.53676443944064
463732.83638097557324.16361902442681
473835.40238612786312.59761387213691
483334.1215241888507-1.12152418885067
493636.9938224618061-0.99382246180606
503834.0648974192143.93510258078604
513235.601257213256-3.60125721325599
523232.7074286361987-0.707428636198746
533233.6623451012738-1.66234510127378
543435.3743434383013-1.37434343830132
553232.3659778279088-0.365977827908833
563733.68798732073993.31201267926006
573934.3864762899494.61352371005103
582934.3003572884486-5.30035728844858
593734.88888181089712.11111818910291
603534.27712474604120.722875253958801
613031.2256579663471-1.22565796634708
623834.4288812192953.57111878070502
633435.0369927206124-1.03699272061242
643135.0552457569647-4.05524575696467
653434.166687803307-0.16668780330696
663535.4367863534423-0.436786353442333
673634.6760238279931.32397617200697
683032.5157806856797-2.51578068567968
693935.28963404397063.71036595602942
703536.6423078152554-1.64230781525537
713834.88558940772353.11441059227646
723135.3611532451071-4.36115324510707
733436.4704052193954-2.47040521939541
743837.4961198459430.503880154056995
753431.89154679484362.10845320515644
763934.19493647624114.80506352375892
773736.39622165336210.603778346637937
783433.15825247744410.841747522555918
792833.5782637452197-5.57826374521974
803732.84530634875264.15469365124744
813336.2798314689052-3.27983146890517
823737.3227249317762-0.322724931776187
833535.3877863274992-0.387786327499185
843734.02836654680112.97163345319885
853235.0752837427597-3.07528374275966
863333.3619337099914-0.361933709991369
873836.17339698589791.82660301410214
883335.1679978408572-2.16799784085718
892934.3569261466564-5.35692614665638
903332.95253359945040.0474664005495531
913134.3646533547112-3.36465335471123
923634.30063478416051.6993652158395
933537.791451266809-2.79145126680899
943232.6967444321112-0.696744432111156
952931.7890279081126-2.78902790811257
963936.30031299865222.69968700134784
973735.24067395315971.75932604684029
983534.43325549494730.56674450505274
993735.72711677920551.27288322079446
1003234.9684057158004-2.96840571580036
1013834.9624235220563.03757647794403
1023735.77656034910071.22343965089931
1033636.3067999584381-0.306799958438104
1043231.90295203859510.0970479614049223
1053336.2303012512465-3.23030125124649
1064032.47026652865867.5297334713414
1073835.9991075208532.00089247914703
1084137.36726050795583.63273949204422
1093634.65056279219091.34943720780908
1104336.17180288440356.82819711559648
1113035.2541168423576-5.25411684235757
1123134.312040027912-3.31204002791201
1133238.0331708254999-6.03317082549991
1143233.3809001135577-1.38090011355765
1153733.60825743246433.39174256753568
1163735.39127351524751.60872648475254
1173335.6787434727129-2.6787434727129
1183436.2665546279474-2.26655462794738
1193333.8050908724109-0.80509087241087
1203834.93919097964863.06080902035141
1213334.3578055621105-1.3578055621105
1223132.5131011853589-1.51310118535886
1233836.17731258217421.82268741782583
1243736.46889776566460.53110223433541
1253332.79897068770630.201029312293736
1263133.4129562459569-2.4129562459569
1273934.94089321795414.05910678204588
1284437.65269413880116.34730586119893
1293335.4260773496464-2.42607734964643
1303533.8236214044751.17637859552496
1313235.2552888889476-3.25528888894757
1322832.6524023216801-4.65240232168008
1334036.81179480559583.18820519440423
1342732.0831947453626-5.08319474536263
1353735.4582339464071.54176605359303
1363233.2314587814621-1.23145878146214
1372830.324187315207-2.32418731520704
1383435.6524030214567-1.65240302145666
1393033.071505437667-3.07150543766699
1403533.58502489005691.41497510994306
1413132.7728816136274-1.77288161362744
1423235.7243657665452-3.72436576654516
1433035.697289140201-5.69728914020104
1443034.5204232708025-4.5204232708025
1453130.73144164349040.268558356509575
1464032.94468374512617.05531625487389
1473233.2781298496492-1.27812984964925
1483635.04616947096920.953830529030808
1493234.209092732653-2.20909273265298
1503533.77671277570831.22328722429171
1513835.00829719306562.99170280693436
1524234.80159076280677.1984092371933
1533436.4898303023377-2.48983030233766
1543537.5135149558888-2.5135149558888
1553534.61486109625320.385138903746793
1563331.73256718671591.26743281328406
1573633.34687523025152.6531247697485
1583236.0716142742548-4.07161427425476
1593335.4260773496464-2.42607734964643
1603433.52072882379430.479271176205714
1613233.689144231906-1.68914423190598
1623434.4341349104014-0.434134910401375

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.7287172343236 & 5.27128276567635 \tabularnewline
2 & 39 & 34.9050833852052 & 4.09491661479477 \tabularnewline
3 & 30 & 35.8086164814999 & -5.80861648149993 \tabularnewline
4 & 31 & 34.389263927081 & -3.38926392708097 \tabularnewline
5 & 34 & 34.7635524366631 & -0.763552436663068 \tabularnewline
6 & 35 & 33.0460305852671 & 1.9539694147329 \tabularnewline
7 & 39 & 34.969754793741 & 4.03024520625902 \tabularnewline
8 & 34 & 35.361430740819 & -1.36143074081899 \tabularnewline
9 & 36 & 34.0700429735092 & 1.92995702649083 \tabularnewline
10 & 37 & 36.159240729486 & 0.840759270514035 \tabularnewline
11 & 38 & 34.8748122022487 & 3.12518779775128 \tabularnewline
12 & 36 & 34.6448332944501 & 1.35516670554986 \tabularnewline
13 & 38 & 34.533000561144 & 3.46699943885598 \tabularnewline
14 & 39 & 35.9539439063967 & 3.04605609360333 \tabularnewline
15 & 33 & 35.5898519695045 & -2.58985196950447 \tabularnewline
16 & 32 & 33.8561285438004 & -1.85612854380037 \tabularnewline
17 & 36 & 34.6825362134529 & 1.31746378654705 \tabularnewline
18 & 38 & 38.0618415533384 & -0.0618415533383817 \tabularnewline
19 & 39 & 37.6290180524417 & 1.37098194755831 \tabularnewline
20 & 32 & 33.6615235972486 & -1.66152359724856 \tabularnewline
21 & 32 & 34.6145621114936 & -2.61456211149363 \tabularnewline
22 & 31 & 33.8222117973054 & -2.8222117973054 \tabularnewline
23 & 39 & 37.3895269871455 & 1.61047301285449 \tabularnewline
24 & 37 & 35.9719442467453 & 1.02805575325468 \tabularnewline
25 & 39 & 35.6512461102906 & 3.34875388970937 \tabularnewline
26 & 41 & 34.1912832402527 & 6.80871675974727 \tabularnewline
27 & 36 & 36.2385985861491 & -0.238598586149146 \tabularnewline
28 & 33 & 35.7239070871692 & -2.72390708716919 \tabularnewline
29 & 33 & 33.9378809420984 & -0.937880942098372 \tabularnewline
30 & 34 & 33.5365006707482 & 0.463499329251814 \tabularnewline
31 & 31 & 32.8689824351119 & -1.86898243511194 \tabularnewline
32 & 27 & 33.9423556821119 & -6.94235568211194 \tabularnewline
33 & 37 & 33.9812596071119 & 3.01874039288808 \tabularnewline
34 & 34 & 35.3877863274992 & -1.38778632749919 \tabularnewline
35 & 34 & 32.472304174105 & 1.52769582589505 \tabularnewline
36 & 32 & 32.2450150568772 & -0.245015056877194 \tabularnewline
37 & 29 & 32.8777371305644 & -3.87773713056436 \tabularnewline
38 & 36 & 34.5996117685649 & 1.40038823143505 \tabularnewline
39 & 29 & 33.6202907144925 & -4.62029071449254 \tabularnewline
40 & 35 & 34.3015293350386 & 0.698470664961425 \tabularnewline
41 & 37 & 35.0650505455981 & 1.93494945440185 \tabularnewline
42 & 34 & 34.3849688362181 & -0.384968836218147 \tabularnewline
43 & 38 & 35.7489397144433 & 2.25106028555673 \tabularnewline
44 & 35 & 34.5763792261576 & 0.42362077384243 \tabularnewline
45 & 38 & 32.4632355605594 & 5.53676443944064 \tabularnewline
46 & 37 & 32.8363809755732 & 4.16361902442681 \tabularnewline
47 & 38 & 35.4023861278631 & 2.59761387213691 \tabularnewline
48 & 33 & 34.1215241888507 & -1.12152418885067 \tabularnewline
49 & 36 & 36.9938224618061 & -0.99382246180606 \tabularnewline
50 & 38 & 34.064897419214 & 3.93510258078604 \tabularnewline
51 & 32 & 35.601257213256 & -3.60125721325599 \tabularnewline
52 & 32 & 32.7074286361987 & -0.707428636198746 \tabularnewline
53 & 32 & 33.6623451012738 & -1.66234510127378 \tabularnewline
54 & 34 & 35.3743434383013 & -1.37434343830132 \tabularnewline
55 & 32 & 32.3659778279088 & -0.365977827908833 \tabularnewline
56 & 37 & 33.6879873207399 & 3.31201267926006 \tabularnewline
57 & 39 & 34.386476289949 & 4.61352371005103 \tabularnewline
58 & 29 & 34.3003572884486 & -5.30035728844858 \tabularnewline
59 & 37 & 34.8888818108971 & 2.11111818910291 \tabularnewline
60 & 35 & 34.2771247460412 & 0.722875253958801 \tabularnewline
61 & 30 & 31.2256579663471 & -1.22565796634708 \tabularnewline
62 & 38 & 34.428881219295 & 3.57111878070502 \tabularnewline
63 & 34 & 35.0369927206124 & -1.03699272061242 \tabularnewline
64 & 31 & 35.0552457569647 & -4.05524575696467 \tabularnewline
65 & 34 & 34.166687803307 & -0.16668780330696 \tabularnewline
66 & 35 & 35.4367863534423 & -0.436786353442333 \tabularnewline
67 & 36 & 34.676023827993 & 1.32397617200697 \tabularnewline
68 & 30 & 32.5157806856797 & -2.51578068567968 \tabularnewline
69 & 39 & 35.2896340439706 & 3.71036595602942 \tabularnewline
70 & 35 & 36.6423078152554 & -1.64230781525537 \tabularnewline
71 & 38 & 34.8855894077235 & 3.11441059227646 \tabularnewline
72 & 31 & 35.3611532451071 & -4.36115324510707 \tabularnewline
73 & 34 & 36.4704052193954 & -2.47040521939541 \tabularnewline
74 & 38 & 37.496119845943 & 0.503880154056995 \tabularnewline
75 & 34 & 31.8915467948436 & 2.10845320515644 \tabularnewline
76 & 39 & 34.1949364762411 & 4.80506352375892 \tabularnewline
77 & 37 & 36.3962216533621 & 0.603778346637937 \tabularnewline
78 & 34 & 33.1582524774441 & 0.841747522555918 \tabularnewline
79 & 28 & 33.5782637452197 & -5.57826374521974 \tabularnewline
80 & 37 & 32.8453063487526 & 4.15469365124744 \tabularnewline
81 & 33 & 36.2798314689052 & -3.27983146890517 \tabularnewline
82 & 37 & 37.3227249317762 & -0.322724931776187 \tabularnewline
83 & 35 & 35.3877863274992 & -0.387786327499185 \tabularnewline
84 & 37 & 34.0283665468011 & 2.97163345319885 \tabularnewline
85 & 32 & 35.0752837427597 & -3.07528374275966 \tabularnewline
86 & 33 & 33.3619337099914 & -0.361933709991369 \tabularnewline
87 & 38 & 36.1733969858979 & 1.82660301410214 \tabularnewline
88 & 33 & 35.1679978408572 & -2.16799784085718 \tabularnewline
89 & 29 & 34.3569261466564 & -5.35692614665638 \tabularnewline
90 & 33 & 32.9525335994504 & 0.0474664005495531 \tabularnewline
91 & 31 & 34.3646533547112 & -3.36465335471123 \tabularnewline
92 & 36 & 34.3006347841605 & 1.6993652158395 \tabularnewline
93 & 35 & 37.791451266809 & -2.79145126680899 \tabularnewline
94 & 32 & 32.6967444321112 & -0.696744432111156 \tabularnewline
95 & 29 & 31.7890279081126 & -2.78902790811257 \tabularnewline
96 & 39 & 36.3003129986522 & 2.69968700134784 \tabularnewline
97 & 37 & 35.2406739531597 & 1.75932604684029 \tabularnewline
98 & 35 & 34.4332554949473 & 0.56674450505274 \tabularnewline
99 & 37 & 35.7271167792055 & 1.27288322079446 \tabularnewline
100 & 32 & 34.9684057158004 & -2.96840571580036 \tabularnewline
101 & 38 & 34.962423522056 & 3.03757647794403 \tabularnewline
102 & 37 & 35.7765603491007 & 1.22343965089931 \tabularnewline
103 & 36 & 36.3067999584381 & -0.306799958438104 \tabularnewline
104 & 32 & 31.9029520385951 & 0.0970479614049223 \tabularnewline
105 & 33 & 36.2303012512465 & -3.23030125124649 \tabularnewline
106 & 40 & 32.4702665286586 & 7.5297334713414 \tabularnewline
107 & 38 & 35.999107520853 & 2.00089247914703 \tabularnewline
108 & 41 & 37.3672605079558 & 3.63273949204422 \tabularnewline
109 & 36 & 34.6505627921909 & 1.34943720780908 \tabularnewline
110 & 43 & 36.1718028844035 & 6.82819711559648 \tabularnewline
111 & 30 & 35.2541168423576 & -5.25411684235757 \tabularnewline
112 & 31 & 34.312040027912 & -3.31204002791201 \tabularnewline
113 & 32 & 38.0331708254999 & -6.03317082549991 \tabularnewline
114 & 32 & 33.3809001135577 & -1.38090011355765 \tabularnewline
115 & 37 & 33.6082574324643 & 3.39174256753568 \tabularnewline
116 & 37 & 35.3912735152475 & 1.60872648475254 \tabularnewline
117 & 33 & 35.6787434727129 & -2.6787434727129 \tabularnewline
118 & 34 & 36.2665546279474 & -2.26655462794738 \tabularnewline
119 & 33 & 33.8050908724109 & -0.80509087241087 \tabularnewline
120 & 38 & 34.9391909796486 & 3.06080902035141 \tabularnewline
121 & 33 & 34.3578055621105 & -1.3578055621105 \tabularnewline
122 & 31 & 32.5131011853589 & -1.51310118535886 \tabularnewline
123 & 38 & 36.1773125821742 & 1.82268741782583 \tabularnewline
124 & 37 & 36.4688977656646 & 0.53110223433541 \tabularnewline
125 & 33 & 32.7989706877063 & 0.201029312293736 \tabularnewline
126 & 31 & 33.4129562459569 & -2.4129562459569 \tabularnewline
127 & 39 & 34.9408932179541 & 4.05910678204588 \tabularnewline
128 & 44 & 37.6526941388011 & 6.34730586119893 \tabularnewline
129 & 33 & 35.4260773496464 & -2.42607734964643 \tabularnewline
130 & 35 & 33.823621404475 & 1.17637859552496 \tabularnewline
131 & 32 & 35.2552888889476 & -3.25528888894757 \tabularnewline
132 & 28 & 32.6524023216801 & -4.65240232168008 \tabularnewline
133 & 40 & 36.8117948055958 & 3.18820519440423 \tabularnewline
134 & 27 & 32.0831947453626 & -5.08319474536263 \tabularnewline
135 & 37 & 35.458233946407 & 1.54176605359303 \tabularnewline
136 & 32 & 33.2314587814621 & -1.23145878146214 \tabularnewline
137 & 28 & 30.324187315207 & -2.32418731520704 \tabularnewline
138 & 34 & 35.6524030214567 & -1.65240302145666 \tabularnewline
139 & 30 & 33.071505437667 & -3.07150543766699 \tabularnewline
140 & 35 & 33.5850248900569 & 1.41497510994306 \tabularnewline
141 & 31 & 32.7728816136274 & -1.77288161362744 \tabularnewline
142 & 32 & 35.7243657665452 & -3.72436576654516 \tabularnewline
143 & 30 & 35.697289140201 & -5.69728914020104 \tabularnewline
144 & 30 & 34.5204232708025 & -4.5204232708025 \tabularnewline
145 & 31 & 30.7314416434904 & 0.268558356509575 \tabularnewline
146 & 40 & 32.9446837451261 & 7.05531625487389 \tabularnewline
147 & 32 & 33.2781298496492 & -1.27812984964925 \tabularnewline
148 & 36 & 35.0461694709692 & 0.953830529030808 \tabularnewline
149 & 32 & 34.209092732653 & -2.20909273265298 \tabularnewline
150 & 35 & 33.7767127757083 & 1.22328722429171 \tabularnewline
151 & 38 & 35.0082971930656 & 2.99170280693436 \tabularnewline
152 & 42 & 34.8015907628067 & 7.1984092371933 \tabularnewline
153 & 34 & 36.4898303023377 & -2.48983030233766 \tabularnewline
154 & 35 & 37.5135149558888 & -2.5135149558888 \tabularnewline
155 & 35 & 34.6148610962532 & 0.385138903746793 \tabularnewline
156 & 33 & 31.7325671867159 & 1.26743281328406 \tabularnewline
157 & 36 & 33.3468752302515 & 2.6531247697485 \tabularnewline
158 & 32 & 36.0716142742548 & -4.07161427425476 \tabularnewline
159 & 33 & 35.4260773496464 & -2.42607734964643 \tabularnewline
160 & 34 & 33.5207288237943 & 0.479271176205714 \tabularnewline
161 & 32 & 33.689144231906 & -1.68914423190598 \tabularnewline
162 & 34 & 34.4341349104014 & -0.434134910401375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]41[/C][C]35.7287172343236[/C][C]5.27128276567635[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.9050833852052[/C][C]4.09491661479477[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.8086164814999[/C][C]-5.80861648149993[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.389263927081[/C][C]-3.38926392708097[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.7635524366631[/C][C]-0.763552436663068[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]33.0460305852671[/C][C]1.9539694147329[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.969754793741[/C][C]4.03024520625902[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.361430740819[/C][C]-1.36143074081899[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.0700429735092[/C][C]1.92995702649083[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.159240729486[/C][C]0.840759270514035[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.8748122022487[/C][C]3.12518779775128[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.6448332944501[/C][C]1.35516670554986[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.533000561144[/C][C]3.46699943885598[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.9539439063967[/C][C]3.04605609360333[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]35.5898519695045[/C][C]-2.58985196950447[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.8561285438004[/C][C]-1.85612854380037[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.6825362134529[/C][C]1.31746378654705[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]38.0618415533384[/C][C]-0.0618415533383817[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]37.6290180524417[/C][C]1.37098194755831[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.6615235972486[/C][C]-1.66152359724856[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.6145621114936[/C][C]-2.61456211149363[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.8222117973054[/C][C]-2.8222117973054[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]37.3895269871455[/C][C]1.61047301285449[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]35.9719442467453[/C][C]1.02805575325468[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.6512461102906[/C][C]3.34875388970937[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.1912832402527[/C][C]6.80871675974727[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]36.2385985861491[/C][C]-0.238598586149146[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.7239070871692[/C][C]-2.72390708716919[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.9378809420984[/C][C]-0.937880942098372[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.5365006707482[/C][C]0.463499329251814[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.8689824351119[/C][C]-1.86898243511194[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.9423556821119[/C][C]-6.94235568211194[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.9812596071119[/C][C]3.01874039288808[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.3877863274992[/C][C]-1.38778632749919[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.472304174105[/C][C]1.52769582589505[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.2450150568772[/C][C]-0.245015056877194[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.8777371305644[/C][C]-3.87773713056436[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.5996117685649[/C][C]1.40038823143505[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.6202907144925[/C][C]-4.62029071449254[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.3015293350386[/C][C]0.698470664961425[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]35.0650505455981[/C][C]1.93494945440185[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.3849688362181[/C][C]-0.384968836218147[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.7489397144433[/C][C]2.25106028555673[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.5763792261576[/C][C]0.42362077384243[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.4632355605594[/C][C]5.53676443944064[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]32.8363809755732[/C][C]4.16361902442681[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.4023861278631[/C][C]2.59761387213691[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.1215241888507[/C][C]-1.12152418885067[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.9938224618061[/C][C]-0.99382246180606[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.064897419214[/C][C]3.93510258078604[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]35.601257213256[/C][C]-3.60125721325599[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]32.7074286361987[/C][C]-0.707428636198746[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.6623451012738[/C][C]-1.66234510127378[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.3743434383013[/C][C]-1.37434343830132[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.3659778279088[/C][C]-0.365977827908833[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]33.6879873207399[/C][C]3.31201267926006[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.386476289949[/C][C]4.61352371005103[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.3003572884486[/C][C]-5.30035728844858[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]34.8888818108971[/C][C]2.11111818910291[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.2771247460412[/C][C]0.722875253958801[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.2256579663471[/C][C]-1.22565796634708[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.428881219295[/C][C]3.57111878070502[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]35.0369927206124[/C][C]-1.03699272061242[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]35.0552457569647[/C][C]-4.05524575696467[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]34.166687803307[/C][C]-0.16668780330696[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.4367863534423[/C][C]-0.436786353442333[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.676023827993[/C][C]1.32397617200697[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]32.5157806856797[/C][C]-2.51578068567968[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.2896340439706[/C][C]3.71036595602942[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]36.6423078152554[/C][C]-1.64230781525537[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.8855894077235[/C][C]3.11441059227646[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3611532451071[/C][C]-4.36115324510707[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.4704052193954[/C][C]-2.47040521939541[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.496119845943[/C][C]0.503880154056995[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]31.8915467948436[/C][C]2.10845320515644[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.1949364762411[/C][C]4.80506352375892[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]36.3962216533621[/C][C]0.603778346637937[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.1582524774441[/C][C]0.841747522555918[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.5782637452197[/C][C]-5.57826374521974[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.8453063487526[/C][C]4.15469365124744[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]36.2798314689052[/C][C]-3.27983146890517[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.3227249317762[/C][C]-0.322724931776187[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.3877863274992[/C][C]-0.387786327499185[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.0283665468011[/C][C]2.97163345319885[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]35.0752837427597[/C][C]-3.07528374275966[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]33.3619337099914[/C][C]-0.361933709991369[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.1733969858979[/C][C]1.82660301410214[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]35.1679978408572[/C][C]-2.16799784085718[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]34.3569261466564[/C][C]-5.35692614665638[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]32.9525335994504[/C][C]0.0474664005495531[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.3646533547112[/C][C]-3.36465335471123[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.3006347841605[/C][C]1.6993652158395[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.791451266809[/C][C]-2.79145126680899[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.6967444321112[/C][C]-0.696744432111156[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]31.7890279081126[/C][C]-2.78902790811257[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.3003129986522[/C][C]2.69968700134784[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]35.2406739531597[/C][C]1.75932604684029[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.4332554949473[/C][C]0.56674450505274[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.7271167792055[/C][C]1.27288322079446[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]34.9684057158004[/C][C]-2.96840571580036[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]34.962423522056[/C][C]3.03757647794403[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.7765603491007[/C][C]1.22343965089931[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.3067999584381[/C][C]-0.306799958438104[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]31.9029520385951[/C][C]0.0970479614049223[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.2303012512465[/C][C]-3.23030125124649[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.4702665286586[/C][C]7.5297334713414[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.999107520853[/C][C]2.00089247914703[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]37.3672605079558[/C][C]3.63273949204422[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.6505627921909[/C][C]1.34943720780908[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.1718028844035[/C][C]6.82819711559648[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]35.2541168423576[/C][C]-5.25411684235757[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.312040027912[/C][C]-3.31204002791201[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]38.0331708254999[/C][C]-6.03317082549991[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.3809001135577[/C][C]-1.38090011355765[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]33.6082574324643[/C][C]3.39174256753568[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.3912735152475[/C][C]1.60872648475254[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.6787434727129[/C][C]-2.6787434727129[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.2665546279474[/C][C]-2.26655462794738[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]33.8050908724109[/C][C]-0.80509087241087[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]34.9391909796486[/C][C]3.06080902035141[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.3578055621105[/C][C]-1.3578055621105[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.5131011853589[/C][C]-1.51310118535886[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]36.1773125821742[/C][C]1.82268741782583[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.4688977656646[/C][C]0.53110223433541[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]32.7989706877063[/C][C]0.201029312293736[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]33.4129562459569[/C][C]-2.4129562459569[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.9408932179541[/C][C]4.05910678204588[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.6526941388011[/C][C]6.34730586119893[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.4260773496464[/C][C]-2.42607734964643[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.823621404475[/C][C]1.17637859552496[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]35.2552888889476[/C][C]-3.25528888894757[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]32.6524023216801[/C][C]-4.65240232168008[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.8117948055958[/C][C]3.18820519440423[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.0831947453626[/C][C]-5.08319474536263[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.458233946407[/C][C]1.54176605359303[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]33.2314587814621[/C][C]-1.23145878146214[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]30.324187315207[/C][C]-2.32418731520704[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]35.6524030214567[/C][C]-1.65240302145666[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.071505437667[/C][C]-3.07150543766699[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.5850248900569[/C][C]1.41497510994306[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.7728816136274[/C][C]-1.77288161362744[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.7243657665452[/C][C]-3.72436576654516[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.697289140201[/C][C]-5.69728914020104[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.5204232708025[/C][C]-4.5204232708025[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]30.7314416434904[/C][C]0.268558356509575[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.9446837451261[/C][C]7.05531625487389[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.2781298496492[/C][C]-1.27812984964925[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]35.0461694709692[/C][C]0.953830529030808[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]34.209092732653[/C][C]-2.20909273265298[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.7767127757083[/C][C]1.22328722429171[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.0082971930656[/C][C]2.99170280693436[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]34.8015907628067[/C][C]7.1984092371933[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.4898303023377[/C][C]-2.48983030233766[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.5135149558888[/C][C]-2.5135149558888[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.6148610962532[/C][C]0.385138903746793[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]31.7325671867159[/C][C]1.26743281328406[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.3468752302515[/C][C]2.6531247697485[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]36.0716142742548[/C][C]-4.07161427425476[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.4260773496464[/C][C]-2.42607734964643[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]33.5207288237943[/C][C]0.479271176205714[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.689144231906[/C][C]-1.68914423190598[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.4341349104014[/C][C]-0.434134910401375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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
14135.72871723432365.27128276567635
23934.90508338520524.09491661479477
33035.8086164814999-5.80861648149993
43134.389263927081-3.38926392708097
53434.7635524366631-0.763552436663068
63533.04603058526711.9539694147329
73934.9697547937414.03024520625902
83435.361430740819-1.36143074081899
93634.07004297350921.92995702649083
103736.1592407294860.840759270514035
113834.87481220224873.12518779775128
123634.64483329445011.35516670554986
133834.5330005611443.46699943885598
143935.95394390639673.04605609360333
153335.5898519695045-2.58985196950447
163233.8561285438004-1.85612854380037
173634.68253621345291.31746378654705
183838.0618415533384-0.0618415533383817
193937.62901805244171.37098194755831
203233.6615235972486-1.66152359724856
213234.6145621114936-2.61456211149363
223133.8222117973054-2.8222117973054
233937.38952698714551.61047301285449
243735.97194424674531.02805575325468
253935.65124611029063.34875388970937
264134.19128324025276.80871675974727
273636.2385985861491-0.238598586149146
283335.7239070871692-2.72390708716919
293333.9378809420984-0.937880942098372
303433.53650067074820.463499329251814
313132.8689824351119-1.86898243511194
322733.9423556821119-6.94235568211194
333733.98125960711193.01874039288808
343435.3877863274992-1.38778632749919
353432.4723041741051.52769582589505
363232.2450150568772-0.245015056877194
372932.8777371305644-3.87773713056436
383634.59961176856491.40038823143505
392933.6202907144925-4.62029071449254
403534.30152933503860.698470664961425
413735.06505054559811.93494945440185
423434.3849688362181-0.384968836218147
433835.74893971444332.25106028555673
443534.57637922615760.42362077384243
453832.46323556055945.53676443944064
463732.83638097557324.16361902442681
473835.40238612786312.59761387213691
483334.1215241888507-1.12152418885067
493636.9938224618061-0.99382246180606
503834.0648974192143.93510258078604
513235.601257213256-3.60125721325599
523232.7074286361987-0.707428636198746
533233.6623451012738-1.66234510127378
543435.3743434383013-1.37434343830132
553232.3659778279088-0.365977827908833
563733.68798732073993.31201267926006
573934.3864762899494.61352371005103
582934.3003572884486-5.30035728844858
593734.88888181089712.11111818910291
603534.27712474604120.722875253958801
613031.2256579663471-1.22565796634708
623834.4288812192953.57111878070502
633435.0369927206124-1.03699272061242
643135.0552457569647-4.05524575696467
653434.166687803307-0.16668780330696
663535.4367863534423-0.436786353442333
673634.6760238279931.32397617200697
683032.5157806856797-2.51578068567968
693935.28963404397063.71036595602942
703536.6423078152554-1.64230781525537
713834.88558940772353.11441059227646
723135.3611532451071-4.36115324510707
733436.4704052193954-2.47040521939541
743837.4961198459430.503880154056995
753431.89154679484362.10845320515644
763934.19493647624114.80506352375892
773736.39622165336210.603778346637937
783433.15825247744410.841747522555918
792833.5782637452197-5.57826374521974
803732.84530634875264.15469365124744
813336.2798314689052-3.27983146890517
823737.3227249317762-0.322724931776187
833535.3877863274992-0.387786327499185
843734.02836654680112.97163345319885
853235.0752837427597-3.07528374275966
863333.3619337099914-0.361933709991369
873836.17339698589791.82660301410214
883335.1679978408572-2.16799784085718
892934.3569261466564-5.35692614665638
903332.95253359945040.0474664005495531
913134.3646533547112-3.36465335471123
923634.30063478416051.6993652158395
933537.791451266809-2.79145126680899
943232.6967444321112-0.696744432111156
952931.7890279081126-2.78902790811257
963936.30031299865222.69968700134784
973735.24067395315971.75932604684029
983534.43325549494730.56674450505274
993735.72711677920551.27288322079446
1003234.9684057158004-2.96840571580036
1013834.9624235220563.03757647794403
1023735.77656034910071.22343965089931
1033636.3067999584381-0.306799958438104
1043231.90295203859510.0970479614049223
1053336.2303012512465-3.23030125124649
1064032.47026652865867.5297334713414
1073835.9991075208532.00089247914703
1084137.36726050795583.63273949204422
1093634.65056279219091.34943720780908
1104336.17180288440356.82819711559648
1113035.2541168423576-5.25411684235757
1123134.312040027912-3.31204002791201
1133238.0331708254999-6.03317082549991
1143233.3809001135577-1.38090011355765
1153733.60825743246433.39174256753568
1163735.39127351524751.60872648475254
1173335.6787434727129-2.6787434727129
1183436.2665546279474-2.26655462794738
1193333.8050908724109-0.80509087241087
1203834.93919097964863.06080902035141
1213334.3578055621105-1.3578055621105
1223132.5131011853589-1.51310118535886
1233836.17731258217421.82268741782583
1243736.46889776566460.53110223433541
1253332.79897068770630.201029312293736
1263133.4129562459569-2.4129562459569
1273934.94089321795414.05910678204588
1284437.65269413880116.34730586119893
1293335.4260773496464-2.42607734964643
1303533.8236214044751.17637859552496
1313235.2552888889476-3.25528888894757
1322832.6524023216801-4.65240232168008
1334036.81179480559583.18820519440423
1342732.0831947453626-5.08319474536263
1353735.4582339464071.54176605359303
1363233.2314587814621-1.23145878146214
1372830.324187315207-2.32418731520704
1383435.6524030214567-1.65240302145666
1393033.071505437667-3.07150543766699
1403533.58502489005691.41497510994306
1413132.7728816136274-1.77288161362744
1423235.7243657665452-3.72436576654516
1433035.697289140201-5.69728914020104
1443034.5204232708025-4.5204232708025
1453130.73144164349040.268558356509575
1464032.94468374512617.05531625487389
1473233.2781298496492-1.27812984964925
1483635.04616947096920.953830529030808
1493234.209092732653-2.20909273265298
1503533.77671277570831.22328722429171
1513835.00829719306562.99170280693436
1524234.80159076280677.1984092371933
1533436.4898303023377-2.48983030233766
1543537.5135149558888-2.5135149558888
1553534.61486109625320.385138903746793
1563331.73256718671591.26743281328406
1573633.34687523025152.6531247697485
1583236.0716142742548-4.07161427425476
1593335.4260773496464-2.42607734964643
1603433.52072882379430.479271176205714
1613233.689144231906-1.68914423190598
1623434.4341349104014-0.434134910401375






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8747946083003950.2504107833992110.125205391699605
110.7929888444023680.4140223111952630.207011155597632
120.6901930999682730.6196138000634540.309806900031727
130.8851590866203640.2296818267592710.114840913379636
140.8467211554932950.306557689013410.153278844506705
150.8454896303631760.3090207392736490.154510369636824
160.8027271698745980.3945456602508040.197272830125402
170.7357149601836390.5285700796327210.264285039816361
180.6626297565767390.6747404868465230.337370243423261
190.5829119694817430.8341760610365140.417088030518257
200.5125926527634850.9748146944730290.487407347236515
210.4882607178025790.9765214356051590.511739282197421
220.4743377406728670.9486754813457350.525662259327133
230.4115243887775860.8230487775551710.588475611222414
240.3518624527272510.7037249054545020.648137547272749
250.3084203741777580.6168407483555160.691579625822242
260.5864310461881160.8271379076237680.413568953811884
270.5418455662414540.9163088675170930.458154433758546
280.5762011802361940.8475976395276120.423798819763806
290.5139550427642570.9720899144714850.486044957235743
300.4501754136313520.9003508272627040.549824586368648
310.4429131966295410.8858263932590830.557086803370459
320.7411932772793740.5176134454412530.258806722720626
330.7167644523646250.566471095270750.283235547635375
340.6756154240215990.6487691519568020.324384575978401
350.6579450573733460.6841098852533090.342054942626654
360.6039752512596870.7920494974806260.396024748740313
370.6201135622005690.7597728755988630.379886437799431
380.5784454556958160.8431090886083680.421554544304184
390.6490523085062530.7018953829874930.350947691493747
400.6028521947319720.7942956105360560.397147805268028
410.5616640066105540.8766719867788920.438335993389446
420.5099125255724550.980174948855090.490087474427545
430.4697985922757850.9395971845515690.530201407724215
440.4166592168889870.8333184337779740.583340783111013
450.5225602221126370.9548795557747260.477439777887363
460.5782971564614270.8434056870771450.421702843538573
470.5589042597553240.8821914804893520.441095740244676
480.5151158907617260.9697682184765490.484884109238274
490.477223998218590.954447996437180.52277600178141
500.4920679958127760.9841359916255510.507932004187224
510.5109249245583430.9781501508833130.489075075441657
520.4620368110603680.9240736221207360.537963188939632
530.4347749265644440.8695498531288870.565225073435556
540.3943774531802550.7887549063605110.605622546819744
550.347956333636420.695912667272840.65204366636358
560.3492187676039210.6984375352078420.650781232396079
570.4013494746668870.8026989493337740.598650525333113
580.5001284306437980.9997431387124040.499871569356202
590.4792246323945140.9584492647890280.520775367605486
600.4323969208092720.8647938416185440.567603079190728
610.3933967574679960.7867935149359930.606603242532004
620.4113802822801550.8227605645603090.588619717719845
630.3748884003888150.7497768007776290.625111599611185
640.4144219795804830.8288439591609650.585578020419517
650.37087776943550.7417555388710.6291222305645
660.3274053688182450.654810737636490.672594631181755
670.2918050487375230.5836100974750460.708194951262477
680.2826295145253410.5652590290506810.717370485474659
690.2992003963323710.5984007926647420.700799603667629
700.272913897767970.5458277955359390.72708610223203
710.2684877381554330.5369754763108650.731512261844567
720.3095320164384220.6190640328768440.690467983561578
730.2937041275454790.5874082550909570.706295872454521
740.2559189719833590.5118379439667190.744081028016641
750.2358460153202350.471692030640470.764153984679765
760.2825255634439510.5650511268879020.717474436556049
770.2457728214965640.4915456429931280.754227178503436
780.2128823354124910.4257646708249820.787117664587509
790.2968259184133150.593651836826630.703174081586685
800.3318250443610480.6636500887220960.668174955638952
810.3358838012067710.6717676024135430.664116198793228
820.2944527207556480.5889054415112960.705547279244352
830.2560722588939720.5121445177879430.743927741106028
840.2554066360202530.5108132720405070.744593363979747
850.2565550154130450.5131100308260890.743444984586955
860.2222629212588960.4445258425177930.777737078741104
870.2017103657584510.4034207315169030.798289634241549
880.1858908813249290.3717817626498580.814109118675071
890.2518662936739620.5037325873479240.748133706326038
900.2161177762402050.432235552480410.783882223759795
910.2217885681468850.4435771362937690.778211431853115
920.1976123781158420.3952247562316850.802387621884158
930.192534502419660.385069004839320.80746549758034
940.1638248141694220.3276496283388430.836175185830578
950.1572613988979810.3145227977959610.842738601102019
960.1501956603406840.3003913206813680.849804339659316
970.1319548209457020.2639096418914030.868045179054298
980.1111529511032970.2223059022065940.888847048896703
990.09392569735388890.1878513947077780.906074302646111
1000.09094538286396670.1818907657279330.909054617136033
1010.0886921416234890.1773842832469780.911307858376511
1020.07491960585262280.1498392117052460.925080394147377
1030.05947045585687630.1189409117137530.940529544143124
1040.04719837028756540.09439674057513080.952801629712435
1050.047223290511490.09444658102298010.95277670948851
1060.1498247506021480.2996495012042950.850175249397852
1070.1341266971157790.2682533942315590.865873302884221
1080.1500630212063150.300126042412630.849936978793685
1090.1435580707764160.2871161415528320.856441929223584
1100.313319507170470.626639014340940.68668049282953
1110.4084595117878160.8169190235756320.591540488212184
1120.4062420411773920.8124840823547850.593757958822608
1130.5369212826680230.9261574346639530.463078717331977
1140.4892654483162810.9785308966325630.510734551683719
1150.4784920357853610.9569840715707220.521507964214639
1160.4528621900760110.9057243801520230.547137809923989
1170.454016848872120.908033697744240.54598315112788
1180.4229131493126170.8458262986252330.577086850687383
1190.3765934953973070.7531869907946130.623406504602693
1200.3802802594738760.7605605189477520.619719740526124
1210.338245079726840.676490159453680.66175492027316
1220.3039142061207190.6078284122414380.696085793879281
1230.2627660137357420.5255320274714830.737233986264258
1240.2254864047002390.4509728094004780.774513595299761
1250.1852303014915350.370460602983070.814769698508465
1260.178414758164290.356829516328580.82158524183571
1270.2450775888917280.4901551777834550.754922411108272
1280.3880327315325650.7760654630651290.611967268467435
1290.357387033997370.714774067994740.64261296600263
1300.3032633873021550.6065267746043110.696736612697844
1310.2672565695555310.5345131391110620.732743430444469
1320.3312724838679330.6625449677358660.668727516132067
1330.3893415138382330.7786830276764650.610658486161767
1340.4889305833753790.9778611667507590.511069416624621
1350.4579361048130220.9158722096260430.542063895186978
1360.4185675493895640.8371350987791280.581432450610436
1370.3820968599049910.7641937198099830.617903140095009
1380.3190400852189610.6380801704379230.680959914781039
1390.4490487793669520.8980975587339050.550951220633048
1400.3798318539509950.759663707901990.620168146049005
1410.4825279370941370.9650558741882740.517472062905863
1420.4196705300809150.8393410601618310.580329469919085
1430.4479390310703890.8958780621407770.552060968929611
1440.7344855903314350.5310288193371310.265514409668565
1450.7457640018479540.5084719963040930.254235998152046
1460.9411734098594070.1176531802811860.0588265901405932
1470.919948972428050.16010205514390.0800510275719498
1480.9386638563601820.1226722872796360.0613361436398181
1490.9229742647026130.1540514705947740.0770257352973869
1500.8622177884825090.2755644230349820.137782211517491
1510.7850475407943370.4299049184113260.214952459205663
1520.9281605091106590.1436789817786810.0718394908893406

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.874794608300395 & 0.250410783399211 & 0.125205391699605 \tabularnewline
11 & 0.792988844402368 & 0.414022311195263 & 0.207011155597632 \tabularnewline
12 & 0.690193099968273 & 0.619613800063454 & 0.309806900031727 \tabularnewline
13 & 0.885159086620364 & 0.229681826759271 & 0.114840913379636 \tabularnewline
14 & 0.846721155493295 & 0.30655768901341 & 0.153278844506705 \tabularnewline
15 & 0.845489630363176 & 0.309020739273649 & 0.154510369636824 \tabularnewline
16 & 0.802727169874598 & 0.394545660250804 & 0.197272830125402 \tabularnewline
17 & 0.735714960183639 & 0.528570079632721 & 0.264285039816361 \tabularnewline
18 & 0.662629756576739 & 0.674740486846523 & 0.337370243423261 \tabularnewline
19 & 0.582911969481743 & 0.834176061036514 & 0.417088030518257 \tabularnewline
20 & 0.512592652763485 & 0.974814694473029 & 0.487407347236515 \tabularnewline
21 & 0.488260717802579 & 0.976521435605159 & 0.511739282197421 \tabularnewline
22 & 0.474337740672867 & 0.948675481345735 & 0.525662259327133 \tabularnewline
23 & 0.411524388777586 & 0.823048777555171 & 0.588475611222414 \tabularnewline
24 & 0.351862452727251 & 0.703724905454502 & 0.648137547272749 \tabularnewline
25 & 0.308420374177758 & 0.616840748355516 & 0.691579625822242 \tabularnewline
26 & 0.586431046188116 & 0.827137907623768 & 0.413568953811884 \tabularnewline
27 & 0.541845566241454 & 0.916308867517093 & 0.458154433758546 \tabularnewline
28 & 0.576201180236194 & 0.847597639527612 & 0.423798819763806 \tabularnewline
29 & 0.513955042764257 & 0.972089914471485 & 0.486044957235743 \tabularnewline
30 & 0.450175413631352 & 0.900350827262704 & 0.549824586368648 \tabularnewline
31 & 0.442913196629541 & 0.885826393259083 & 0.557086803370459 \tabularnewline
32 & 0.741193277279374 & 0.517613445441253 & 0.258806722720626 \tabularnewline
33 & 0.716764452364625 & 0.56647109527075 & 0.283235547635375 \tabularnewline
34 & 0.675615424021599 & 0.648769151956802 & 0.324384575978401 \tabularnewline
35 & 0.657945057373346 & 0.684109885253309 & 0.342054942626654 \tabularnewline
36 & 0.603975251259687 & 0.792049497480626 & 0.396024748740313 \tabularnewline
37 & 0.620113562200569 & 0.759772875598863 & 0.379886437799431 \tabularnewline
38 & 0.578445455695816 & 0.843109088608368 & 0.421554544304184 \tabularnewline
39 & 0.649052308506253 & 0.701895382987493 & 0.350947691493747 \tabularnewline
40 & 0.602852194731972 & 0.794295610536056 & 0.397147805268028 \tabularnewline
41 & 0.561664006610554 & 0.876671986778892 & 0.438335993389446 \tabularnewline
42 & 0.509912525572455 & 0.98017494885509 & 0.490087474427545 \tabularnewline
43 & 0.469798592275785 & 0.939597184551569 & 0.530201407724215 \tabularnewline
44 & 0.416659216888987 & 0.833318433777974 & 0.583340783111013 \tabularnewline
45 & 0.522560222112637 & 0.954879555774726 & 0.477439777887363 \tabularnewline
46 & 0.578297156461427 & 0.843405687077145 & 0.421702843538573 \tabularnewline
47 & 0.558904259755324 & 0.882191480489352 & 0.441095740244676 \tabularnewline
48 & 0.515115890761726 & 0.969768218476549 & 0.484884109238274 \tabularnewline
49 & 0.47722399821859 & 0.95444799643718 & 0.52277600178141 \tabularnewline
50 & 0.492067995812776 & 0.984135991625551 & 0.507932004187224 \tabularnewline
51 & 0.510924924558343 & 0.978150150883313 & 0.489075075441657 \tabularnewline
52 & 0.462036811060368 & 0.924073622120736 & 0.537963188939632 \tabularnewline
53 & 0.434774926564444 & 0.869549853128887 & 0.565225073435556 \tabularnewline
54 & 0.394377453180255 & 0.788754906360511 & 0.605622546819744 \tabularnewline
55 & 0.34795633363642 & 0.69591266727284 & 0.65204366636358 \tabularnewline
56 & 0.349218767603921 & 0.698437535207842 & 0.650781232396079 \tabularnewline
57 & 0.401349474666887 & 0.802698949333774 & 0.598650525333113 \tabularnewline
58 & 0.500128430643798 & 0.999743138712404 & 0.499871569356202 \tabularnewline
59 & 0.479224632394514 & 0.958449264789028 & 0.520775367605486 \tabularnewline
60 & 0.432396920809272 & 0.864793841618544 & 0.567603079190728 \tabularnewline
61 & 0.393396757467996 & 0.786793514935993 & 0.606603242532004 \tabularnewline
62 & 0.411380282280155 & 0.822760564560309 & 0.588619717719845 \tabularnewline
63 & 0.374888400388815 & 0.749776800777629 & 0.625111599611185 \tabularnewline
64 & 0.414421979580483 & 0.828843959160965 & 0.585578020419517 \tabularnewline
65 & 0.3708777694355 & 0.741755538871 & 0.6291222305645 \tabularnewline
66 & 0.327405368818245 & 0.65481073763649 & 0.672594631181755 \tabularnewline
67 & 0.291805048737523 & 0.583610097475046 & 0.708194951262477 \tabularnewline
68 & 0.282629514525341 & 0.565259029050681 & 0.717370485474659 \tabularnewline
69 & 0.299200396332371 & 0.598400792664742 & 0.700799603667629 \tabularnewline
70 & 0.27291389776797 & 0.545827795535939 & 0.72708610223203 \tabularnewline
71 & 0.268487738155433 & 0.536975476310865 & 0.731512261844567 \tabularnewline
72 & 0.309532016438422 & 0.619064032876844 & 0.690467983561578 \tabularnewline
73 & 0.293704127545479 & 0.587408255090957 & 0.706295872454521 \tabularnewline
74 & 0.255918971983359 & 0.511837943966719 & 0.744081028016641 \tabularnewline
75 & 0.235846015320235 & 0.47169203064047 & 0.764153984679765 \tabularnewline
76 & 0.282525563443951 & 0.565051126887902 & 0.717474436556049 \tabularnewline
77 & 0.245772821496564 & 0.491545642993128 & 0.754227178503436 \tabularnewline
78 & 0.212882335412491 & 0.425764670824982 & 0.787117664587509 \tabularnewline
79 & 0.296825918413315 & 0.59365183682663 & 0.703174081586685 \tabularnewline
80 & 0.331825044361048 & 0.663650088722096 & 0.668174955638952 \tabularnewline
81 & 0.335883801206771 & 0.671767602413543 & 0.664116198793228 \tabularnewline
82 & 0.294452720755648 & 0.588905441511296 & 0.705547279244352 \tabularnewline
83 & 0.256072258893972 & 0.512144517787943 & 0.743927741106028 \tabularnewline
84 & 0.255406636020253 & 0.510813272040507 & 0.744593363979747 \tabularnewline
85 & 0.256555015413045 & 0.513110030826089 & 0.743444984586955 \tabularnewline
86 & 0.222262921258896 & 0.444525842517793 & 0.777737078741104 \tabularnewline
87 & 0.201710365758451 & 0.403420731516903 & 0.798289634241549 \tabularnewline
88 & 0.185890881324929 & 0.371781762649858 & 0.814109118675071 \tabularnewline
89 & 0.251866293673962 & 0.503732587347924 & 0.748133706326038 \tabularnewline
90 & 0.216117776240205 & 0.43223555248041 & 0.783882223759795 \tabularnewline
91 & 0.221788568146885 & 0.443577136293769 & 0.778211431853115 \tabularnewline
92 & 0.197612378115842 & 0.395224756231685 & 0.802387621884158 \tabularnewline
93 & 0.19253450241966 & 0.38506900483932 & 0.80746549758034 \tabularnewline
94 & 0.163824814169422 & 0.327649628338843 & 0.836175185830578 \tabularnewline
95 & 0.157261398897981 & 0.314522797795961 & 0.842738601102019 \tabularnewline
96 & 0.150195660340684 & 0.300391320681368 & 0.849804339659316 \tabularnewline
97 & 0.131954820945702 & 0.263909641891403 & 0.868045179054298 \tabularnewline
98 & 0.111152951103297 & 0.222305902206594 & 0.888847048896703 \tabularnewline
99 & 0.0939256973538889 & 0.187851394707778 & 0.906074302646111 \tabularnewline
100 & 0.0909453828639667 & 0.181890765727933 & 0.909054617136033 \tabularnewline
101 & 0.088692141623489 & 0.177384283246978 & 0.911307858376511 \tabularnewline
102 & 0.0749196058526228 & 0.149839211705246 & 0.925080394147377 \tabularnewline
103 & 0.0594704558568763 & 0.118940911713753 & 0.940529544143124 \tabularnewline
104 & 0.0471983702875654 & 0.0943967405751308 & 0.952801629712435 \tabularnewline
105 & 0.04722329051149 & 0.0944465810229801 & 0.95277670948851 \tabularnewline
106 & 0.149824750602148 & 0.299649501204295 & 0.850175249397852 \tabularnewline
107 & 0.134126697115779 & 0.268253394231559 & 0.865873302884221 \tabularnewline
108 & 0.150063021206315 & 0.30012604241263 & 0.849936978793685 \tabularnewline
109 & 0.143558070776416 & 0.287116141552832 & 0.856441929223584 \tabularnewline
110 & 0.31331950717047 & 0.62663901434094 & 0.68668049282953 \tabularnewline
111 & 0.408459511787816 & 0.816919023575632 & 0.591540488212184 \tabularnewline
112 & 0.406242041177392 & 0.812484082354785 & 0.593757958822608 \tabularnewline
113 & 0.536921282668023 & 0.926157434663953 & 0.463078717331977 \tabularnewline
114 & 0.489265448316281 & 0.978530896632563 & 0.510734551683719 \tabularnewline
115 & 0.478492035785361 & 0.956984071570722 & 0.521507964214639 \tabularnewline
116 & 0.452862190076011 & 0.905724380152023 & 0.547137809923989 \tabularnewline
117 & 0.45401684887212 & 0.90803369774424 & 0.54598315112788 \tabularnewline
118 & 0.422913149312617 & 0.845826298625233 & 0.577086850687383 \tabularnewline
119 & 0.376593495397307 & 0.753186990794613 & 0.623406504602693 \tabularnewline
120 & 0.380280259473876 & 0.760560518947752 & 0.619719740526124 \tabularnewline
121 & 0.33824507972684 & 0.67649015945368 & 0.66175492027316 \tabularnewline
122 & 0.303914206120719 & 0.607828412241438 & 0.696085793879281 \tabularnewline
123 & 0.262766013735742 & 0.525532027471483 & 0.737233986264258 \tabularnewline
124 & 0.225486404700239 & 0.450972809400478 & 0.774513595299761 \tabularnewline
125 & 0.185230301491535 & 0.37046060298307 & 0.814769698508465 \tabularnewline
126 & 0.17841475816429 & 0.35682951632858 & 0.82158524183571 \tabularnewline
127 & 0.245077588891728 & 0.490155177783455 & 0.754922411108272 \tabularnewline
128 & 0.388032731532565 & 0.776065463065129 & 0.611967268467435 \tabularnewline
129 & 0.35738703399737 & 0.71477406799474 & 0.64261296600263 \tabularnewline
130 & 0.303263387302155 & 0.606526774604311 & 0.696736612697844 \tabularnewline
131 & 0.267256569555531 & 0.534513139111062 & 0.732743430444469 \tabularnewline
132 & 0.331272483867933 & 0.662544967735866 & 0.668727516132067 \tabularnewline
133 & 0.389341513838233 & 0.778683027676465 & 0.610658486161767 \tabularnewline
134 & 0.488930583375379 & 0.977861166750759 & 0.511069416624621 \tabularnewline
135 & 0.457936104813022 & 0.915872209626043 & 0.542063895186978 \tabularnewline
136 & 0.418567549389564 & 0.837135098779128 & 0.581432450610436 \tabularnewline
137 & 0.382096859904991 & 0.764193719809983 & 0.617903140095009 \tabularnewline
138 & 0.319040085218961 & 0.638080170437923 & 0.680959914781039 \tabularnewline
139 & 0.449048779366952 & 0.898097558733905 & 0.550951220633048 \tabularnewline
140 & 0.379831853950995 & 0.75966370790199 & 0.620168146049005 \tabularnewline
141 & 0.482527937094137 & 0.965055874188274 & 0.517472062905863 \tabularnewline
142 & 0.419670530080915 & 0.839341060161831 & 0.580329469919085 \tabularnewline
143 & 0.447939031070389 & 0.895878062140777 & 0.552060968929611 \tabularnewline
144 & 0.734485590331435 & 0.531028819337131 & 0.265514409668565 \tabularnewline
145 & 0.745764001847954 & 0.508471996304093 & 0.254235998152046 \tabularnewline
146 & 0.941173409859407 & 0.117653180281186 & 0.0588265901405932 \tabularnewline
147 & 0.91994897242805 & 0.1601020551439 & 0.0800510275719498 \tabularnewline
148 & 0.938663856360182 & 0.122672287279636 & 0.0613361436398181 \tabularnewline
149 & 0.922974264702613 & 0.154051470594774 & 0.0770257352973869 \tabularnewline
150 & 0.862217788482509 & 0.275564423034982 & 0.137782211517491 \tabularnewline
151 & 0.785047540794337 & 0.429904918411326 & 0.214952459205663 \tabularnewline
152 & 0.928160509110659 & 0.143678981778681 & 0.0718394908893406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.874794608300395[/C][C]0.250410783399211[/C][C]0.125205391699605[/C][/ROW]
[ROW][C]11[/C][C]0.792988844402368[/C][C]0.414022311195263[/C][C]0.207011155597632[/C][/ROW]
[ROW][C]12[/C][C]0.690193099968273[/C][C]0.619613800063454[/C][C]0.309806900031727[/C][/ROW]
[ROW][C]13[/C][C]0.885159086620364[/C][C]0.229681826759271[/C][C]0.114840913379636[/C][/ROW]
[ROW][C]14[/C][C]0.846721155493295[/C][C]0.30655768901341[/C][C]0.153278844506705[/C][/ROW]
[ROW][C]15[/C][C]0.845489630363176[/C][C]0.309020739273649[/C][C]0.154510369636824[/C][/ROW]
[ROW][C]16[/C][C]0.802727169874598[/C][C]0.394545660250804[/C][C]0.197272830125402[/C][/ROW]
[ROW][C]17[/C][C]0.735714960183639[/C][C]0.528570079632721[/C][C]0.264285039816361[/C][/ROW]
[ROW][C]18[/C][C]0.662629756576739[/C][C]0.674740486846523[/C][C]0.337370243423261[/C][/ROW]
[ROW][C]19[/C][C]0.582911969481743[/C][C]0.834176061036514[/C][C]0.417088030518257[/C][/ROW]
[ROW][C]20[/C][C]0.512592652763485[/C][C]0.974814694473029[/C][C]0.487407347236515[/C][/ROW]
[ROW][C]21[/C][C]0.488260717802579[/C][C]0.976521435605159[/C][C]0.511739282197421[/C][/ROW]
[ROW][C]22[/C][C]0.474337740672867[/C][C]0.948675481345735[/C][C]0.525662259327133[/C][/ROW]
[ROW][C]23[/C][C]0.411524388777586[/C][C]0.823048777555171[/C][C]0.588475611222414[/C][/ROW]
[ROW][C]24[/C][C]0.351862452727251[/C][C]0.703724905454502[/C][C]0.648137547272749[/C][/ROW]
[ROW][C]25[/C][C]0.308420374177758[/C][C]0.616840748355516[/C][C]0.691579625822242[/C][/ROW]
[ROW][C]26[/C][C]0.586431046188116[/C][C]0.827137907623768[/C][C]0.413568953811884[/C][/ROW]
[ROW][C]27[/C][C]0.541845566241454[/C][C]0.916308867517093[/C][C]0.458154433758546[/C][/ROW]
[ROW][C]28[/C][C]0.576201180236194[/C][C]0.847597639527612[/C][C]0.423798819763806[/C][/ROW]
[ROW][C]29[/C][C]0.513955042764257[/C][C]0.972089914471485[/C][C]0.486044957235743[/C][/ROW]
[ROW][C]30[/C][C]0.450175413631352[/C][C]0.900350827262704[/C][C]0.549824586368648[/C][/ROW]
[ROW][C]31[/C][C]0.442913196629541[/C][C]0.885826393259083[/C][C]0.557086803370459[/C][/ROW]
[ROW][C]32[/C][C]0.741193277279374[/C][C]0.517613445441253[/C][C]0.258806722720626[/C][/ROW]
[ROW][C]33[/C][C]0.716764452364625[/C][C]0.56647109527075[/C][C]0.283235547635375[/C][/ROW]
[ROW][C]34[/C][C]0.675615424021599[/C][C]0.648769151956802[/C][C]0.324384575978401[/C][/ROW]
[ROW][C]35[/C][C]0.657945057373346[/C][C]0.684109885253309[/C][C]0.342054942626654[/C][/ROW]
[ROW][C]36[/C][C]0.603975251259687[/C][C]0.792049497480626[/C][C]0.396024748740313[/C][/ROW]
[ROW][C]37[/C][C]0.620113562200569[/C][C]0.759772875598863[/C][C]0.379886437799431[/C][/ROW]
[ROW][C]38[/C][C]0.578445455695816[/C][C]0.843109088608368[/C][C]0.421554544304184[/C][/ROW]
[ROW][C]39[/C][C]0.649052308506253[/C][C]0.701895382987493[/C][C]0.350947691493747[/C][/ROW]
[ROW][C]40[/C][C]0.602852194731972[/C][C]0.794295610536056[/C][C]0.397147805268028[/C][/ROW]
[ROW][C]41[/C][C]0.561664006610554[/C][C]0.876671986778892[/C][C]0.438335993389446[/C][/ROW]
[ROW][C]42[/C][C]0.509912525572455[/C][C]0.98017494885509[/C][C]0.490087474427545[/C][/ROW]
[ROW][C]43[/C][C]0.469798592275785[/C][C]0.939597184551569[/C][C]0.530201407724215[/C][/ROW]
[ROW][C]44[/C][C]0.416659216888987[/C][C]0.833318433777974[/C][C]0.583340783111013[/C][/ROW]
[ROW][C]45[/C][C]0.522560222112637[/C][C]0.954879555774726[/C][C]0.477439777887363[/C][/ROW]
[ROW][C]46[/C][C]0.578297156461427[/C][C]0.843405687077145[/C][C]0.421702843538573[/C][/ROW]
[ROW][C]47[/C][C]0.558904259755324[/C][C]0.882191480489352[/C][C]0.441095740244676[/C][/ROW]
[ROW][C]48[/C][C]0.515115890761726[/C][C]0.969768218476549[/C][C]0.484884109238274[/C][/ROW]
[ROW][C]49[/C][C]0.47722399821859[/C][C]0.95444799643718[/C][C]0.52277600178141[/C][/ROW]
[ROW][C]50[/C][C]0.492067995812776[/C][C]0.984135991625551[/C][C]0.507932004187224[/C][/ROW]
[ROW][C]51[/C][C]0.510924924558343[/C][C]0.978150150883313[/C][C]0.489075075441657[/C][/ROW]
[ROW][C]52[/C][C]0.462036811060368[/C][C]0.924073622120736[/C][C]0.537963188939632[/C][/ROW]
[ROW][C]53[/C][C]0.434774926564444[/C][C]0.869549853128887[/C][C]0.565225073435556[/C][/ROW]
[ROW][C]54[/C][C]0.394377453180255[/C][C]0.788754906360511[/C][C]0.605622546819744[/C][/ROW]
[ROW][C]55[/C][C]0.34795633363642[/C][C]0.69591266727284[/C][C]0.65204366636358[/C][/ROW]
[ROW][C]56[/C][C]0.349218767603921[/C][C]0.698437535207842[/C][C]0.650781232396079[/C][/ROW]
[ROW][C]57[/C][C]0.401349474666887[/C][C]0.802698949333774[/C][C]0.598650525333113[/C][/ROW]
[ROW][C]58[/C][C]0.500128430643798[/C][C]0.999743138712404[/C][C]0.499871569356202[/C][/ROW]
[ROW][C]59[/C][C]0.479224632394514[/C][C]0.958449264789028[/C][C]0.520775367605486[/C][/ROW]
[ROW][C]60[/C][C]0.432396920809272[/C][C]0.864793841618544[/C][C]0.567603079190728[/C][/ROW]
[ROW][C]61[/C][C]0.393396757467996[/C][C]0.786793514935993[/C][C]0.606603242532004[/C][/ROW]
[ROW][C]62[/C][C]0.411380282280155[/C][C]0.822760564560309[/C][C]0.588619717719845[/C][/ROW]
[ROW][C]63[/C][C]0.374888400388815[/C][C]0.749776800777629[/C][C]0.625111599611185[/C][/ROW]
[ROW][C]64[/C][C]0.414421979580483[/C][C]0.828843959160965[/C][C]0.585578020419517[/C][/ROW]
[ROW][C]65[/C][C]0.3708777694355[/C][C]0.741755538871[/C][C]0.6291222305645[/C][/ROW]
[ROW][C]66[/C][C]0.327405368818245[/C][C]0.65481073763649[/C][C]0.672594631181755[/C][/ROW]
[ROW][C]67[/C][C]0.291805048737523[/C][C]0.583610097475046[/C][C]0.708194951262477[/C][/ROW]
[ROW][C]68[/C][C]0.282629514525341[/C][C]0.565259029050681[/C][C]0.717370485474659[/C][/ROW]
[ROW][C]69[/C][C]0.299200396332371[/C][C]0.598400792664742[/C][C]0.700799603667629[/C][/ROW]
[ROW][C]70[/C][C]0.27291389776797[/C][C]0.545827795535939[/C][C]0.72708610223203[/C][/ROW]
[ROW][C]71[/C][C]0.268487738155433[/C][C]0.536975476310865[/C][C]0.731512261844567[/C][/ROW]
[ROW][C]72[/C][C]0.309532016438422[/C][C]0.619064032876844[/C][C]0.690467983561578[/C][/ROW]
[ROW][C]73[/C][C]0.293704127545479[/C][C]0.587408255090957[/C][C]0.706295872454521[/C][/ROW]
[ROW][C]74[/C][C]0.255918971983359[/C][C]0.511837943966719[/C][C]0.744081028016641[/C][/ROW]
[ROW][C]75[/C][C]0.235846015320235[/C][C]0.47169203064047[/C][C]0.764153984679765[/C][/ROW]
[ROW][C]76[/C][C]0.282525563443951[/C][C]0.565051126887902[/C][C]0.717474436556049[/C][/ROW]
[ROW][C]77[/C][C]0.245772821496564[/C][C]0.491545642993128[/C][C]0.754227178503436[/C][/ROW]
[ROW][C]78[/C][C]0.212882335412491[/C][C]0.425764670824982[/C][C]0.787117664587509[/C][/ROW]
[ROW][C]79[/C][C]0.296825918413315[/C][C]0.59365183682663[/C][C]0.703174081586685[/C][/ROW]
[ROW][C]80[/C][C]0.331825044361048[/C][C]0.663650088722096[/C][C]0.668174955638952[/C][/ROW]
[ROW][C]81[/C][C]0.335883801206771[/C][C]0.671767602413543[/C][C]0.664116198793228[/C][/ROW]
[ROW][C]82[/C][C]0.294452720755648[/C][C]0.588905441511296[/C][C]0.705547279244352[/C][/ROW]
[ROW][C]83[/C][C]0.256072258893972[/C][C]0.512144517787943[/C][C]0.743927741106028[/C][/ROW]
[ROW][C]84[/C][C]0.255406636020253[/C][C]0.510813272040507[/C][C]0.744593363979747[/C][/ROW]
[ROW][C]85[/C][C]0.256555015413045[/C][C]0.513110030826089[/C][C]0.743444984586955[/C][/ROW]
[ROW][C]86[/C][C]0.222262921258896[/C][C]0.444525842517793[/C][C]0.777737078741104[/C][/ROW]
[ROW][C]87[/C][C]0.201710365758451[/C][C]0.403420731516903[/C][C]0.798289634241549[/C][/ROW]
[ROW][C]88[/C][C]0.185890881324929[/C][C]0.371781762649858[/C][C]0.814109118675071[/C][/ROW]
[ROW][C]89[/C][C]0.251866293673962[/C][C]0.503732587347924[/C][C]0.748133706326038[/C][/ROW]
[ROW][C]90[/C][C]0.216117776240205[/C][C]0.43223555248041[/C][C]0.783882223759795[/C][/ROW]
[ROW][C]91[/C][C]0.221788568146885[/C][C]0.443577136293769[/C][C]0.778211431853115[/C][/ROW]
[ROW][C]92[/C][C]0.197612378115842[/C][C]0.395224756231685[/C][C]0.802387621884158[/C][/ROW]
[ROW][C]93[/C][C]0.19253450241966[/C][C]0.38506900483932[/C][C]0.80746549758034[/C][/ROW]
[ROW][C]94[/C][C]0.163824814169422[/C][C]0.327649628338843[/C][C]0.836175185830578[/C][/ROW]
[ROW][C]95[/C][C]0.157261398897981[/C][C]0.314522797795961[/C][C]0.842738601102019[/C][/ROW]
[ROW][C]96[/C][C]0.150195660340684[/C][C]0.300391320681368[/C][C]0.849804339659316[/C][/ROW]
[ROW][C]97[/C][C]0.131954820945702[/C][C]0.263909641891403[/C][C]0.868045179054298[/C][/ROW]
[ROW][C]98[/C][C]0.111152951103297[/C][C]0.222305902206594[/C][C]0.888847048896703[/C][/ROW]
[ROW][C]99[/C][C]0.0939256973538889[/C][C]0.187851394707778[/C][C]0.906074302646111[/C][/ROW]
[ROW][C]100[/C][C]0.0909453828639667[/C][C]0.181890765727933[/C][C]0.909054617136033[/C][/ROW]
[ROW][C]101[/C][C]0.088692141623489[/C][C]0.177384283246978[/C][C]0.911307858376511[/C][/ROW]
[ROW][C]102[/C][C]0.0749196058526228[/C][C]0.149839211705246[/C][C]0.925080394147377[/C][/ROW]
[ROW][C]103[/C][C]0.0594704558568763[/C][C]0.118940911713753[/C][C]0.940529544143124[/C][/ROW]
[ROW][C]104[/C][C]0.0471983702875654[/C][C]0.0943967405751308[/C][C]0.952801629712435[/C][/ROW]
[ROW][C]105[/C][C]0.04722329051149[/C][C]0.0944465810229801[/C][C]0.95277670948851[/C][/ROW]
[ROW][C]106[/C][C]0.149824750602148[/C][C]0.299649501204295[/C][C]0.850175249397852[/C][/ROW]
[ROW][C]107[/C][C]0.134126697115779[/C][C]0.268253394231559[/C][C]0.865873302884221[/C][/ROW]
[ROW][C]108[/C][C]0.150063021206315[/C][C]0.30012604241263[/C][C]0.849936978793685[/C][/ROW]
[ROW][C]109[/C][C]0.143558070776416[/C][C]0.287116141552832[/C][C]0.856441929223584[/C][/ROW]
[ROW][C]110[/C][C]0.31331950717047[/C][C]0.62663901434094[/C][C]0.68668049282953[/C][/ROW]
[ROW][C]111[/C][C]0.408459511787816[/C][C]0.816919023575632[/C][C]0.591540488212184[/C][/ROW]
[ROW][C]112[/C][C]0.406242041177392[/C][C]0.812484082354785[/C][C]0.593757958822608[/C][/ROW]
[ROW][C]113[/C][C]0.536921282668023[/C][C]0.926157434663953[/C][C]0.463078717331977[/C][/ROW]
[ROW][C]114[/C][C]0.489265448316281[/C][C]0.978530896632563[/C][C]0.510734551683719[/C][/ROW]
[ROW][C]115[/C][C]0.478492035785361[/C][C]0.956984071570722[/C][C]0.521507964214639[/C][/ROW]
[ROW][C]116[/C][C]0.452862190076011[/C][C]0.905724380152023[/C][C]0.547137809923989[/C][/ROW]
[ROW][C]117[/C][C]0.45401684887212[/C][C]0.90803369774424[/C][C]0.54598315112788[/C][/ROW]
[ROW][C]118[/C][C]0.422913149312617[/C][C]0.845826298625233[/C][C]0.577086850687383[/C][/ROW]
[ROW][C]119[/C][C]0.376593495397307[/C][C]0.753186990794613[/C][C]0.623406504602693[/C][/ROW]
[ROW][C]120[/C][C]0.380280259473876[/C][C]0.760560518947752[/C][C]0.619719740526124[/C][/ROW]
[ROW][C]121[/C][C]0.33824507972684[/C][C]0.67649015945368[/C][C]0.66175492027316[/C][/ROW]
[ROW][C]122[/C][C]0.303914206120719[/C][C]0.607828412241438[/C][C]0.696085793879281[/C][/ROW]
[ROW][C]123[/C][C]0.262766013735742[/C][C]0.525532027471483[/C][C]0.737233986264258[/C][/ROW]
[ROW][C]124[/C][C]0.225486404700239[/C][C]0.450972809400478[/C][C]0.774513595299761[/C][/ROW]
[ROW][C]125[/C][C]0.185230301491535[/C][C]0.37046060298307[/C][C]0.814769698508465[/C][/ROW]
[ROW][C]126[/C][C]0.17841475816429[/C][C]0.35682951632858[/C][C]0.82158524183571[/C][/ROW]
[ROW][C]127[/C][C]0.245077588891728[/C][C]0.490155177783455[/C][C]0.754922411108272[/C][/ROW]
[ROW][C]128[/C][C]0.388032731532565[/C][C]0.776065463065129[/C][C]0.611967268467435[/C][/ROW]
[ROW][C]129[/C][C]0.35738703399737[/C][C]0.71477406799474[/C][C]0.64261296600263[/C][/ROW]
[ROW][C]130[/C][C]0.303263387302155[/C][C]0.606526774604311[/C][C]0.696736612697844[/C][/ROW]
[ROW][C]131[/C][C]0.267256569555531[/C][C]0.534513139111062[/C][C]0.732743430444469[/C][/ROW]
[ROW][C]132[/C][C]0.331272483867933[/C][C]0.662544967735866[/C][C]0.668727516132067[/C][/ROW]
[ROW][C]133[/C][C]0.389341513838233[/C][C]0.778683027676465[/C][C]0.610658486161767[/C][/ROW]
[ROW][C]134[/C][C]0.488930583375379[/C][C]0.977861166750759[/C][C]0.511069416624621[/C][/ROW]
[ROW][C]135[/C][C]0.457936104813022[/C][C]0.915872209626043[/C][C]0.542063895186978[/C][/ROW]
[ROW][C]136[/C][C]0.418567549389564[/C][C]0.837135098779128[/C][C]0.581432450610436[/C][/ROW]
[ROW][C]137[/C][C]0.382096859904991[/C][C]0.764193719809983[/C][C]0.617903140095009[/C][/ROW]
[ROW][C]138[/C][C]0.319040085218961[/C][C]0.638080170437923[/C][C]0.680959914781039[/C][/ROW]
[ROW][C]139[/C][C]0.449048779366952[/C][C]0.898097558733905[/C][C]0.550951220633048[/C][/ROW]
[ROW][C]140[/C][C]0.379831853950995[/C][C]0.75966370790199[/C][C]0.620168146049005[/C][/ROW]
[ROW][C]141[/C][C]0.482527937094137[/C][C]0.965055874188274[/C][C]0.517472062905863[/C][/ROW]
[ROW][C]142[/C][C]0.419670530080915[/C][C]0.839341060161831[/C][C]0.580329469919085[/C][/ROW]
[ROW][C]143[/C][C]0.447939031070389[/C][C]0.895878062140777[/C][C]0.552060968929611[/C][/ROW]
[ROW][C]144[/C][C]0.734485590331435[/C][C]0.531028819337131[/C][C]0.265514409668565[/C][/ROW]
[ROW][C]145[/C][C]0.745764001847954[/C][C]0.508471996304093[/C][C]0.254235998152046[/C][/ROW]
[ROW][C]146[/C][C]0.941173409859407[/C][C]0.117653180281186[/C][C]0.0588265901405932[/C][/ROW]
[ROW][C]147[/C][C]0.91994897242805[/C][C]0.1601020551439[/C][C]0.0800510275719498[/C][/ROW]
[ROW][C]148[/C][C]0.938663856360182[/C][C]0.122672287279636[/C][C]0.0613361436398181[/C][/ROW]
[ROW][C]149[/C][C]0.922974264702613[/C][C]0.154051470594774[/C][C]0.0770257352973869[/C][/ROW]
[ROW][C]150[/C][C]0.862217788482509[/C][C]0.275564423034982[/C][C]0.137782211517491[/C][/ROW]
[ROW][C]151[/C][C]0.785047540794337[/C][C]0.429904918411326[/C][C]0.214952459205663[/C][/ROW]
[ROW][C]152[/C][C]0.928160509110659[/C][C]0.143678981778681[/C][C]0.0718394908893406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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
100.8747946083003950.2504107833992110.125205391699605
110.7929888444023680.4140223111952630.207011155597632
120.6901930999682730.6196138000634540.309806900031727
130.8851590866203640.2296818267592710.114840913379636
140.8467211554932950.306557689013410.153278844506705
150.8454896303631760.3090207392736490.154510369636824
160.8027271698745980.3945456602508040.197272830125402
170.7357149601836390.5285700796327210.264285039816361
180.6626297565767390.6747404868465230.337370243423261
190.5829119694817430.8341760610365140.417088030518257
200.5125926527634850.9748146944730290.487407347236515
210.4882607178025790.9765214356051590.511739282197421
220.4743377406728670.9486754813457350.525662259327133
230.4115243887775860.8230487775551710.588475611222414
240.3518624527272510.7037249054545020.648137547272749
250.3084203741777580.6168407483555160.691579625822242
260.5864310461881160.8271379076237680.413568953811884
270.5418455662414540.9163088675170930.458154433758546
280.5762011802361940.8475976395276120.423798819763806
290.5139550427642570.9720899144714850.486044957235743
300.4501754136313520.9003508272627040.549824586368648
310.4429131966295410.8858263932590830.557086803370459
320.7411932772793740.5176134454412530.258806722720626
330.7167644523646250.566471095270750.283235547635375
340.6756154240215990.6487691519568020.324384575978401
350.6579450573733460.6841098852533090.342054942626654
360.6039752512596870.7920494974806260.396024748740313
370.6201135622005690.7597728755988630.379886437799431
380.5784454556958160.8431090886083680.421554544304184
390.6490523085062530.7018953829874930.350947691493747
400.6028521947319720.7942956105360560.397147805268028
410.5616640066105540.8766719867788920.438335993389446
420.5099125255724550.980174948855090.490087474427545
430.4697985922757850.9395971845515690.530201407724215
440.4166592168889870.8333184337779740.583340783111013
450.5225602221126370.9548795557747260.477439777887363
460.5782971564614270.8434056870771450.421702843538573
470.5589042597553240.8821914804893520.441095740244676
480.5151158907617260.9697682184765490.484884109238274
490.477223998218590.954447996437180.52277600178141
500.4920679958127760.9841359916255510.507932004187224
510.5109249245583430.9781501508833130.489075075441657
520.4620368110603680.9240736221207360.537963188939632
530.4347749265644440.8695498531288870.565225073435556
540.3943774531802550.7887549063605110.605622546819744
550.347956333636420.695912667272840.65204366636358
560.3492187676039210.6984375352078420.650781232396079
570.4013494746668870.8026989493337740.598650525333113
580.5001284306437980.9997431387124040.499871569356202
590.4792246323945140.9584492647890280.520775367605486
600.4323969208092720.8647938416185440.567603079190728
610.3933967574679960.7867935149359930.606603242532004
620.4113802822801550.8227605645603090.588619717719845
630.3748884003888150.7497768007776290.625111599611185
640.4144219795804830.8288439591609650.585578020419517
650.37087776943550.7417555388710.6291222305645
660.3274053688182450.654810737636490.672594631181755
670.2918050487375230.5836100974750460.708194951262477
680.2826295145253410.5652590290506810.717370485474659
690.2992003963323710.5984007926647420.700799603667629
700.272913897767970.5458277955359390.72708610223203
710.2684877381554330.5369754763108650.731512261844567
720.3095320164384220.6190640328768440.690467983561578
730.2937041275454790.5874082550909570.706295872454521
740.2559189719833590.5118379439667190.744081028016641
750.2358460153202350.471692030640470.764153984679765
760.2825255634439510.5650511268879020.717474436556049
770.2457728214965640.4915456429931280.754227178503436
780.2128823354124910.4257646708249820.787117664587509
790.2968259184133150.593651836826630.703174081586685
800.3318250443610480.6636500887220960.668174955638952
810.3358838012067710.6717676024135430.664116198793228
820.2944527207556480.5889054415112960.705547279244352
830.2560722588939720.5121445177879430.743927741106028
840.2554066360202530.5108132720405070.744593363979747
850.2565550154130450.5131100308260890.743444984586955
860.2222629212588960.4445258425177930.777737078741104
870.2017103657584510.4034207315169030.798289634241549
880.1858908813249290.3717817626498580.814109118675071
890.2518662936739620.5037325873479240.748133706326038
900.2161177762402050.432235552480410.783882223759795
910.2217885681468850.4435771362937690.778211431853115
920.1976123781158420.3952247562316850.802387621884158
930.192534502419660.385069004839320.80746549758034
940.1638248141694220.3276496283388430.836175185830578
950.1572613988979810.3145227977959610.842738601102019
960.1501956603406840.3003913206813680.849804339659316
970.1319548209457020.2639096418914030.868045179054298
980.1111529511032970.2223059022065940.888847048896703
990.09392569735388890.1878513947077780.906074302646111
1000.09094538286396670.1818907657279330.909054617136033
1010.0886921416234890.1773842832469780.911307858376511
1020.07491960585262280.1498392117052460.925080394147377
1030.05947045585687630.1189409117137530.940529544143124
1040.04719837028756540.09439674057513080.952801629712435
1050.047223290511490.09444658102298010.95277670948851
1060.1498247506021480.2996495012042950.850175249397852
1070.1341266971157790.2682533942315590.865873302884221
1080.1500630212063150.300126042412630.849936978793685
1090.1435580707764160.2871161415528320.856441929223584
1100.313319507170470.626639014340940.68668049282953
1110.4084595117878160.8169190235756320.591540488212184
1120.4062420411773920.8124840823547850.593757958822608
1130.5369212826680230.9261574346639530.463078717331977
1140.4892654483162810.9785308966325630.510734551683719
1150.4784920357853610.9569840715707220.521507964214639
1160.4528621900760110.9057243801520230.547137809923989
1170.454016848872120.908033697744240.54598315112788
1180.4229131493126170.8458262986252330.577086850687383
1190.3765934953973070.7531869907946130.623406504602693
1200.3802802594738760.7605605189477520.619719740526124
1210.338245079726840.676490159453680.66175492027316
1220.3039142061207190.6078284122414380.696085793879281
1230.2627660137357420.5255320274714830.737233986264258
1240.2254864047002390.4509728094004780.774513595299761
1250.1852303014915350.370460602983070.814769698508465
1260.178414758164290.356829516328580.82158524183571
1270.2450775888917280.4901551777834550.754922411108272
1280.3880327315325650.7760654630651290.611967268467435
1290.357387033997370.714774067994740.64261296600263
1300.3032633873021550.6065267746043110.696736612697844
1310.2672565695555310.5345131391110620.732743430444469
1320.3312724838679330.6625449677358660.668727516132067
1330.3893415138382330.7786830276764650.610658486161767
1340.4889305833753790.9778611667507590.511069416624621
1350.4579361048130220.9158722096260430.542063895186978
1360.4185675493895640.8371350987791280.581432450610436
1370.3820968599049910.7641937198099830.617903140095009
1380.3190400852189610.6380801704379230.680959914781039
1390.4490487793669520.8980975587339050.550951220633048
1400.3798318539509950.759663707901990.620168146049005
1410.4825279370941370.9650558741882740.517472062905863
1420.4196705300809150.8393410601618310.580329469919085
1430.4479390310703890.8958780621407770.552060968929611
1440.7344855903314350.5310288193371310.265514409668565
1450.7457640018479540.5084719963040930.254235998152046
1460.9411734098594070.1176531802811860.0588265901405932
1470.919948972428050.16010205514390.0800510275719498
1480.9386638563601820.1226722872796360.0613361436398181
1490.9229742647026130.1540514705947740.0770257352973869
1500.8622177884825090.2755644230349820.137782211517491
1510.7850475407943370.4299049184113260.214952459205663
1520.9281605091106590.1436789817786810.0718394908893406






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.013986013986014OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.013986013986014 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155474&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.013986013986014[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155474&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155474&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 level00OK
10% type I error level20.013986013986014OK


Figures (Output of Computation)

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