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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 computationFri, 24 Dec 2010 13:32:30 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/24/t1293197449u4ehrs4m9jxe9hr.htm/, Retrieved Tue, 30 Apr 2024 06:22:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114929, Retrieved Tue, 30 Apr 2024 06:22:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

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]
- RMPD    [Multiple Regression] [MR] [2010-12-24 13:32:30] [4cd8ab5c565aadc5ce193fb2faa1c53a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	1	27	5	26	49	35
4	1	36	4	25	45	34
5	1	25	4	17	54	13
2	1	27	3	37	36	35
3	2	25	3	35	36	28
5	2	44	3	15	53	32
4	1	50	4	27	46	35
4	1	41	4	36	42	36
4	1	48	5	25	41	27
4	2	43	4	30	45	29
5	2	47	2	27	47	27
4	2	41	3	33	42	28
3	1	44	2	29	45	29
4	2	47	5	30	40	28
3	2	40	3	25	45	30
3	2	46	3	23	40	25
4	1	28	3	26	42	15
3	1	56	3	24	45	33
4	2	49	4	35	47	31
2	2	25	4	39	31	37
4	2	41	4	23	46	37
3	2	26	3	32	34	34
4	1	50	5	29	43	32
4	1	47	4	26	45	21
3	1	52	2	21	42	25
3	2	37	5	35	51	32
2	2	41	3	23	44	28
4	1	45	4	21	47	22
5	2	26	4	28	47	25
4	1	0	3	30	41	26
2	1	52	4	21	44	34
5	1	46	2	29	51	34
4	1	58	3	28	46	36
3	1	54	5	19	47	36
4	1	29	3	26	46	26
2	2	50	3	33	38	26
3	1	43	2	34	50	34
3	2	30	3	33	48	33
3	2	47	2	40	36	31
5	1	45	3	24	51	33
0	2	48	1	35	35	22
4	2	48	3	35	49	29
4	2	26	4	32	38	24
4	1	46	5	20	47	37
2	2	0	3	35	36	32
4	2	50	3	35	47	23
3	1	25	4	21	46	29
4	1	47	2	33	43	35
1	2	47	2	40	53	20
2	1	41	3	22	55	28
2	2	45	2	35	39	26
4	2	41	4	20	55	36
3	2	45	5	28	41	26
4	2	40	3	46	33	33
3	1	29	4	18	52	25
3	2	34	5	22	42	29
5	1	45	5	20	56	32
3	2	52	3	25	46	35
2	2	41	4	31	33	24
1	2	48	3	21	51	31
2	2	45	3	23	46	29
5	1	54	2	26	46	27
4	2	25	3	34	50	29
4	2	26	4	31	46	29
3	1	28	4	23	51	27
4	2	50	4	31	48	34
4	2	48	4	26	44	32
2	2	51	3	36	38	31
3	2	53	3	28	42	31
4	1	37	3	34	39	31
3	1	56	2	25	45	16
2	1	43	3	33	31	25
4	1	34	3	46	29	27
4	1	42	3	24	48	32
3	2	32	3	32	38	28
5	2	31	5	33	55	25
1	1	46	3	42	32	25
3	2	30	5	17	51	36
3	2	47	4	36	53	36
5	2	33	4	40	47	36
2	1	25	4	30	45	27
3	1	25	5	19	33	29
3	2	21	4	33	49	32
4	2	36	5	35	46	29
2	2	50	3	23	42	31
4	2	48	3	15	56	34
3	2	48	2	38	35	27
3	1	25	3	37	40	28
3	1	48	4	23	44	32
2	2	49	5	41	46	33
3	1	27	5	34	46	29
2	1	28	3	38	39	32
4	2	43	2	45	35	35
4	2	48	3	27	48	33
2	2	48	4	46	42	27
1	1	25	1	26	39	16
5	2	49	4	44	39	32
4	1	26	3	36	41	26
4	1	51	3	20	52	32
4	2	25	4	44	45	38
3	1	29	3	27	42	24
3	1	29	4	27	44	26
1	1	43	2	41	33	19
5	2	46	3	30	42	37
3	1	44	3	33	46	25
3	1	25	3	37	45	24
2	1	51	2	30	40	23
4	1	42	5	20	48	28
4	2	53	5	44	32	38
3	1	25	4	20	53	28
4	2	49	2	33	39	28
4	1	51	3	31	45	26
2	2	20	3	23	36	21
3	2	44	3	33	38	35
3	2	38	4	33	49	31
3	1	46	5	32	46	34
4	2	42	4	25	43	30
5	1	29	0	22	37	30
3	2	46	4	16	48	24
3	2	49	2	36	45	27
2	2	51	3	35	32	26
3	1	38	3	25	46	30
1	1	41	1	27	20	15
4	2	47	3	32	42	28
4	2	44	3	36	45	34
4	2	47	3	51	29	29
3	2	46	3	30	51	26
5	1	44	4	20	55	31
2	2	28	3	29	50	28
2	2	47	4	26	44	33
3	2	28	4	20	41	32
3	1	41	5	40	40	33
2	2	45	4	29	47	31
1	2	46	4	32	42	37
3	1	46	4	33	40	27
5	2	22	3	32	51	19
4	2	33	3	34	43	27
4	1	41	4	24	45	31
4	2	47	5	25	41	38
3	1	25	3	41	41	22
5	2	42	3	39	37	35
3	2	47	3	21	46	35
3	2	50	3	38	38	30
3	1	55	5	28	39	41
3	1	21	3	37	45	25
4	1	0	3	26	46	28
2	1	52	3	30	39	45
2	2	49	4	25	21	21
4	2	46	4	38	31	33
3	1	0	4	31	35	25
3	2	45	3	31	49	29
2	2	52	3	27	40	31
3	1	0	3	21	45	29
3	2	40	4	26	46	31
4	2	49	4	37	45	31
1	1	38	5	28	34	25
1	1	32	5	29	41	27
5	2	46	4	33	43	26
4	2	32	3	41	45	26
3	2	41	3	19	48	23
3	2	43	3	37	43	27
4	1	44	4	36	45	24
3	1	47	5	27	45	35
2	2	28	3	33	34	24
1	1	52	1	29	40	32
1	1	27	2	42	40	24
5	2	45	5	27	55	24
4	1	27	4	47	44	38
3	1	25	4	17	44	36
4	1	28	4	34	48	24
5	1	25	3	32	51	18
4	1	52	4	25	49	34
4	1	44	3	27	33	23
2	2	43	3	37	43	35
3	2	47	4	34	44	22
4	2	52	4	27	44	34
3	2	40	2	37	41	28
4	1	42	3	32	45	34
3	1	45	5	26	44	32
4	1	45	2	29	44	24
1	1	50	5	28	40	34
2	1	49	3	19	48	33
3	1	52	2	46	49	33
3	2	48	3	31	46	29
5	2	51	3	42	49	38
4	2	49	4	33	55	24
3	2	31	4	39	51	25
3	2	43	3	27	46	37
3	2	31	3	35	37	33
3	2	28	4	23	43	30
4	2	43	4	32	41	22
3	2	31	3	22	45	28
2	2	51	3	17	39	24
4	2	58	4	35	38	33
2	2	25	5	34	41	37

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 14 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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 time14 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Teamwork33[t] = -0.627811222048486 + 0.000559943079122198geslacht[t] -0.00134553113394833leeftijd[t] + 0.096198517648116opleiding[t] + 0.0166751551093951Neuroticisme[t] + 0.0635975737699658Extraversie[t] + 0.0110399029138406Openheid[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Teamwork33[t] =  -0.627811222048486 +  0.000559943079122198geslacht[t] -0.00134553113394833leeftijd[t] +  0.096198517648116opleiding[t] +  0.0166751551093951Neuroticisme[t] +  0.0635975737699658Extraversie[t] +  0.0110399029138406Openheid[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Teamwork33[t] =  -0.627811222048486 +  0.000559943079122198geslacht[t] -0.00134553113394833leeftijd[t] +  0.096198517648116opleiding[t] +  0.0166751551093951Neuroticisme[t] +  0.0635975737699658Extraversie[t] +  0.0110399029138406Openheid[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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
Teamwork33[t] = -0.627811222048486 + 0.000559943079122198geslacht[t] -0.00134553113394833leeftijd[t] + 0.096198517648116opleiding[t] + 0.0166751551093951Neuroticisme[t] + 0.0635975737699658Extraversie[t] + 0.0110399029138406Openheid[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6278112220484860.816352-0.7690.4428320.221416
geslacht0.0005599430791221980.1491910.00380.9970090.498505
leeftijd-0.001345531133948330.006477-0.20770.8356650.417832
opleiding0.0961985176481160.0787211.2220.2232320.111616
Neuroticisme0.01667515510939510.0108541.53630.1261430.063072
Extraversie0.06359757376996580.0124895.09231e-060
Openheid0.01103990291384060.0146230.7550.4512170.225608

\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) & -0.627811222048486 & 0.816352 & -0.769 & 0.442832 & 0.221416 \tabularnewline
geslacht & 0.000559943079122198 & 0.149191 & 0.0038 & 0.997009 & 0.498505 \tabularnewline
leeftijd & -0.00134553113394833 & 0.006477 & -0.2077 & 0.835665 & 0.417832 \tabularnewline
opleiding & 0.096198517648116 & 0.078721 & 1.222 & 0.223232 & 0.111616 \tabularnewline
Neuroticisme & 0.0166751551093951 & 0.010854 & 1.5363 & 0.126143 & 0.063072 \tabularnewline
Extraversie & 0.0635975737699658 & 0.012489 & 5.0923 & 1e-06 & 0 \tabularnewline
Openheid & 0.0110399029138406 & 0.014623 & 0.755 & 0.451217 & 0.225608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&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]-0.627811222048486[/C][C]0.816352[/C][C]-0.769[/C][C]0.442832[/C][C]0.221416[/C][/ROW]
[ROW][C]geslacht[/C][C]0.000559943079122198[/C][C]0.149191[/C][C]0.0038[/C][C]0.997009[/C][C]0.498505[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.00134553113394833[/C][C]0.006477[/C][C]-0.2077[/C][C]0.835665[/C][C]0.417832[/C][/ROW]
[ROW][C]opleiding[/C][C]0.096198517648116[/C][C]0.078721[/C][C]1.222[/C][C]0.223232[/C][C]0.111616[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.0166751551093951[/C][C]0.010854[/C][C]1.5363[/C][C]0.126143[/C][C]0.063072[/C][/ROW]
[ROW][C]Extraversie[/C][C]0.0635975737699658[/C][C]0.012489[/C][C]5.0923[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Openheid[/C][C]0.0110399029138406[/C][C]0.014623[/C][C]0.755[/C][C]0.451217[/C][C]0.225608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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)-0.6278112220484860.816352-0.7690.4428320.221416
geslacht0.0005599430791221980.1491910.00380.9970090.498505
leeftijd-0.001345531133948330.006477-0.20770.8356650.417832
opleiding0.0961985176481160.0787211.2220.2232320.111616
Neuroticisme0.01667515510939510.0108541.53630.1261430.063072
Extraversie0.06359757376996580.0124895.09231e-060
Openheid0.01103990291384060.0146230.7550.4512170.225608






Multiple Linear Regression - Regression Statistics
Multiple R0.386354784279872
R-squared0.149270019335946
Adjusted R-squared0.122119062506242
F-TEST (value)5.49778117479238
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value2.87937910941061e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.00036654065481
Sum Squared Residuals188.137844544393

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.386354784279872 \tabularnewline
R-squared & 0.149270019335946 \tabularnewline
Adjusted R-squared & 0.122119062506242 \tabularnewline
F-TEST (value) & 5.49778117479238 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 188 \tabularnewline
p-value & 2.87937910941061e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.00036654065481 \tabularnewline
Sum Squared Residuals & 188.137844544393 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.386354784279872[/C][/ROW]
[ROW][C]R-squared[/C][C]0.149270019335946[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.122119062506242[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.49778117479238[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]188[/C][/ROW]
[ROW][C]p-value[/C][C]2.87937910941061e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.00036654065481[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]188.137844544393[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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 R0.386354784279872
R-squared0.149270019335946
Adjusted R-squared0.122119062506242
F-TEST (value)5.49778117479238
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value2.87937910941061e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.00036654065481
Sum Squared Residuals188.137844544393






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
143.753643718211550.246356281788452
243.363230067254870.636769932745128
353.585169871592181.41483012840782
422.91790493010918-0.917904930109179
532.810526304840530.189473695159474
653.576776476852391.42322352314761
743.452380418282190.547619581717807
843.371216202306260.628783797693738
943.111612595818860.888387404181138
1043.382547553374130.61745244662587
1153.239858269926171.76014173007383
1243.137232939098360.862767060901642
1333.17156988875543-0.171569888755432
1443.144336174722780.855663825277217
1533.21804975649472-0.218049756494724
1632.803438876053210.196561123946788
1742.893920077114871.10607992288513
1833.21240586890456-0.212405868904555
1943.607125095485030.392874904514971
2022.75479670030096-0.754796700300959
2143.420429326956950.579570673043048
2232.698199578321500.301800421678495
2343.358016816097680.64198318390232
2443.221585642010910.77841435798909
2532.792452065843420.207547934156575
2633.98490018473423-0.984900184734229
2723.09767653554434-1.09767653554434
2843.27913597918560.720864020814397
2953.455106808317031.54489319168297
3043.056136927585280.943863072414716
3123.21140337490415-1.21140337490415
3253.605663783676531.39433621632347
3343.373132709585730.626867290414273
3433.48443304720316-0.484433047203162
3543.268403773113030.731596226886968
3622.84865305798528-0.848653057985278
3733.62947857885539-0.629478578855389
3833.58881873876079-0.588818738760788
3932.801221586534040.198778413465955
4053.608792153997781.39120784600222
4102.45734506221047-2.45734506221047
4243.617387450683110.382612549316889
4342.938389361911081.06161063808892
4443.522912354297980.477087645702016
4522.8883241948446-0.888324194844597
4643.421261823392240.57873817660776
4733.31972834849149-0.319728348491487
4843.173278185734280.82672181426572
4913.76094140857122-2.76094140857122
5023.78001474882545-1.78001474882545
5122.85613007999566-0.85613007999566
5243.931742122644620.0682578773553817
5333.15519469471417-0.155194694714174
5442.838176837293951.16182316270605
5533.60174658959194-0.601746589591941
5633.16666188904272-0.166661889042723
5754.041436534792420.958563465207578
5833.32070047122651-0.320700471226513
5922.58354337094263-0.583543370942629
6013.53321023251919-2.53321023251919
6123.23052946146232-1.23052946146232
6253.149606880030051.85039311996995
6343.695257085424490.304742914575507
6443.485694311530610.514305688469387
6533.64495012833058-0.644950128330581
6643.635796226424990.364203773575012
6743.278641412238360.721358587761636
6822.95253250674872-0.952532506748719
6933.07083049868552-0.0708304986855244
7043.001057263096050.998942736903952
7132.945204156830540.0547958431694561
7222.40128891354019-0.401288913540193
7342.525060368455611.47493963154439
7443.410996123175890.589003876824111
7532.878277269114630.121722730885366
7654.136734036002110.863265963997893
7712.61092628989287-1.61092628989287
7833.73832572235812-0.738325722358117
7934.06327627005132-1.06327627005132
8053.767228883744381.23277111625562
8123.38412736487840-1.38412736487840
8232.555808096911260.444191903088743
8333.74968470747056-0.749684707470564
8443.635138138276830.364861861723174
8522.99149131654039-0.991491316540394
8643.784266879454170.215733120545827
8732.658768559755980.341231440244022
8833.09770696706006-0.0977069670600576
8933.22805600383106-0.228056003831056
9023.76185677584723-1.76185677584723
9133.63001282029384-0.630012820293843
9223.09090756665300-1.09090756665300
9342.870541524502211.12945847549779
9443.464548247693350.535451752306653
9523.42974985231713-1.42974985231713
9612.52580681682443-1.52580681682443
9753.25946080422371.7405391957763
9843.1212040487590.878795951241001
9943.586576017612640.413423982387363
10043.73957841154130.260421588458703
10133.00860882731488-0.00860882731488219
10233.25408229833061-0.254082298330611
10312.49944736682413-1.49944736682413
10453.179838944325001.82016105567500
10533.35390698895573-0.353906988955732
10633.37153522425452-0.371535224254524
10722.79459903959432-0.794599039594317
10843.492532926379180.507467073620822
10942.971333598429301.02866640157070
11033.73719630685801-0.737196306858013
11142.839477451068761.16052254893124
11243.258580289943180.741419710056822
11322.53987277880064-0.539872778800642
11432.956085371013530.0439146289864661
11533.7157707752796-0.715770775279602
11633.62629693309924-0.626296933099238
11743.184362064335010.815637935664988
11852.384889047456772.61511095254323
11933.28065199518145-0.280651995181448
12033.26004845610290-0.260048456102898
12122.49907239445033-0.499072394450325
12233.28377844945346-0.283778449453464
12311.30155966924746-0.301559669247457
12443.112484597185270.887515402814727
12543.440253949817640.55974605018236
12642.613583988168061.38641601183194
12733.63077817620244-0.630778176202442
12853.871946071594451.12805392840555
12923.59680481356183-1.59680481356183
13023.29102684628615-1.29102684628615
13133.01470838295106-0.0147083829510622
13233.37380048411051-0.373800484110505
13323.51245628936445-1.51245628936445
13413.3093877721919-2.30938777219190
13533.08790880754384-0.0879088075438377
13653.619141913239111.38085808676089
13743.217230014135470.782769985864535
13843.306707547734220.693292452265785
13943.234957002084180.76504299791582
14033.16176574378456-0.161765743784560
14152.995229790167842.00477020983216
14233.26072750645867-0.260727506458674
14332.976188445187620.0238115548123835
14433.17958283646325-0.179582836463246
14533.38795725170416-0.387957251704158
14643.329503981825210.670496018174786
14723.06873231644301-1.06873231644301
14821.676437597233560.32356240276644
14942.665705779723291.33429422027671
15032.776385254809160.223614745190840
15133.55472342364738-0.554723423647376
15222.92830572717015-0.928305727170146
15333.19357053542211-0.193570535422113
15433.40556090593604-0.405560905936042
15543.513280258163890.486719741836113
15612.70783055026909-1.70783055026909
15713.19984171439962-2.19984171439962
15853.268221569019021.73177843098098
15943.451456875661270.54854312433873
16033.23016669561742-0.230166695617418
16133.25380016812417-0.253800168124167
16243.425493495248230.574506504751773
16333.48901795556219-0.489017955562189
16422.60178464202460-0.601784642024597
16512.77973896192742-1.77973896192742
16613.03803355103566-2.03803355103566
16754.006805766556620.99319423344338
16843.72275529775250.277244702247502
16933.20311190091086-0.203111900910859
17043.604464404482510.395535595517493
17153.60350547384431.3964945261557
17243.596091864191560.403908135808436
17342.405007793462121.59499220653788
17423.34211939143489-1.34211939143489
17533.30298915510907-0.302989155109067
17643.312014248639650.687985751360353
17733.04548299925180-0.0454829992518048
17843.375684448568830.624315551431166
17933.3783165802092-0.378316580209203
18043.051427269282320.948572730717685
18113.17262874550607-2.17262874550607
18223.32924153260512-1.32924153260512
18333.74283318327879-0.742833183278789
18433.35989410893563-0.359894108935633
18553.82943606927161.17056393072840
18644.00527605502908-0.0052760550290807
18733.8861961539305-0.886196153930499
18833.38824036747852-0.388240367478520
18932.921250206376010.0787497936239947
19033.1698491899915-0.169849189991498
19143.084228248116170.915771751883827
19233.15805426554439-0.158054265544392
19322.62202281304329-0.622022813043293
19443.044716957177480.955283042822517
19523.40359518010176-1.40359518010176

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 3.75364371821155 & 0.246356281788452 \tabularnewline
2 & 4 & 3.36323006725487 & 0.636769932745128 \tabularnewline
3 & 5 & 3.58516987159218 & 1.41483012840782 \tabularnewline
4 & 2 & 2.91790493010918 & -0.917904930109179 \tabularnewline
5 & 3 & 2.81052630484053 & 0.189473695159474 \tabularnewline
6 & 5 & 3.57677647685239 & 1.42322352314761 \tabularnewline
7 & 4 & 3.45238041828219 & 0.547619581717807 \tabularnewline
8 & 4 & 3.37121620230626 & 0.628783797693738 \tabularnewline
9 & 4 & 3.11161259581886 & 0.888387404181138 \tabularnewline
10 & 4 & 3.38254755337413 & 0.61745244662587 \tabularnewline
11 & 5 & 3.23985826992617 & 1.76014173007383 \tabularnewline
12 & 4 & 3.13723293909836 & 0.862767060901642 \tabularnewline
13 & 3 & 3.17156988875543 & -0.171569888755432 \tabularnewline
14 & 4 & 3.14433617472278 & 0.855663825277217 \tabularnewline
15 & 3 & 3.21804975649472 & -0.218049756494724 \tabularnewline
16 & 3 & 2.80343887605321 & 0.196561123946788 \tabularnewline
17 & 4 & 2.89392007711487 & 1.10607992288513 \tabularnewline
18 & 3 & 3.21240586890456 & -0.212405868904555 \tabularnewline
19 & 4 & 3.60712509548503 & 0.392874904514971 \tabularnewline
20 & 2 & 2.75479670030096 & -0.754796700300959 \tabularnewline
21 & 4 & 3.42042932695695 & 0.579570673043048 \tabularnewline
22 & 3 & 2.69819957832150 & 0.301800421678495 \tabularnewline
23 & 4 & 3.35801681609768 & 0.64198318390232 \tabularnewline
24 & 4 & 3.22158564201091 & 0.77841435798909 \tabularnewline
25 & 3 & 2.79245206584342 & 0.207547934156575 \tabularnewline
26 & 3 & 3.98490018473423 & -0.984900184734229 \tabularnewline
27 & 2 & 3.09767653554434 & -1.09767653554434 \tabularnewline
28 & 4 & 3.2791359791856 & 0.720864020814397 \tabularnewline
29 & 5 & 3.45510680831703 & 1.54489319168297 \tabularnewline
30 & 4 & 3.05613692758528 & 0.943863072414716 \tabularnewline
31 & 2 & 3.21140337490415 & -1.21140337490415 \tabularnewline
32 & 5 & 3.60566378367653 & 1.39433621632347 \tabularnewline
33 & 4 & 3.37313270958573 & 0.626867290414273 \tabularnewline
34 & 3 & 3.48443304720316 & -0.484433047203162 \tabularnewline
35 & 4 & 3.26840377311303 & 0.731596226886968 \tabularnewline
36 & 2 & 2.84865305798528 & -0.848653057985278 \tabularnewline
37 & 3 & 3.62947857885539 & -0.629478578855389 \tabularnewline
38 & 3 & 3.58881873876079 & -0.588818738760788 \tabularnewline
39 & 3 & 2.80122158653404 & 0.198778413465955 \tabularnewline
40 & 5 & 3.60879215399778 & 1.39120784600222 \tabularnewline
41 & 0 & 2.45734506221047 & -2.45734506221047 \tabularnewline
42 & 4 & 3.61738745068311 & 0.382612549316889 \tabularnewline
43 & 4 & 2.93838936191108 & 1.06161063808892 \tabularnewline
44 & 4 & 3.52291235429798 & 0.477087645702016 \tabularnewline
45 & 2 & 2.8883241948446 & -0.888324194844597 \tabularnewline
46 & 4 & 3.42126182339224 & 0.57873817660776 \tabularnewline
47 & 3 & 3.31972834849149 & -0.319728348491487 \tabularnewline
48 & 4 & 3.17327818573428 & 0.82672181426572 \tabularnewline
49 & 1 & 3.76094140857122 & -2.76094140857122 \tabularnewline
50 & 2 & 3.78001474882545 & -1.78001474882545 \tabularnewline
51 & 2 & 2.85613007999566 & -0.85613007999566 \tabularnewline
52 & 4 & 3.93174212264462 & 0.0682578773553817 \tabularnewline
53 & 3 & 3.15519469471417 & -0.155194694714174 \tabularnewline
54 & 4 & 2.83817683729395 & 1.16182316270605 \tabularnewline
55 & 3 & 3.60174658959194 & -0.601746589591941 \tabularnewline
56 & 3 & 3.16666188904272 & -0.166661889042723 \tabularnewline
57 & 5 & 4.04143653479242 & 0.958563465207578 \tabularnewline
58 & 3 & 3.32070047122651 & -0.320700471226513 \tabularnewline
59 & 2 & 2.58354337094263 & -0.583543370942629 \tabularnewline
60 & 1 & 3.53321023251919 & -2.53321023251919 \tabularnewline
61 & 2 & 3.23052946146232 & -1.23052946146232 \tabularnewline
62 & 5 & 3.14960688003005 & 1.85039311996995 \tabularnewline
63 & 4 & 3.69525708542449 & 0.304742914575507 \tabularnewline
64 & 4 & 3.48569431153061 & 0.514305688469387 \tabularnewline
65 & 3 & 3.64495012833058 & -0.644950128330581 \tabularnewline
66 & 4 & 3.63579622642499 & 0.364203773575012 \tabularnewline
67 & 4 & 3.27864141223836 & 0.721358587761636 \tabularnewline
68 & 2 & 2.95253250674872 & -0.952532506748719 \tabularnewline
69 & 3 & 3.07083049868552 & -0.0708304986855244 \tabularnewline
70 & 4 & 3.00105726309605 & 0.998942736903952 \tabularnewline
71 & 3 & 2.94520415683054 & 0.0547958431694561 \tabularnewline
72 & 2 & 2.40128891354019 & -0.401288913540193 \tabularnewline
73 & 4 & 2.52506036845561 & 1.47493963154439 \tabularnewline
74 & 4 & 3.41099612317589 & 0.589003876824111 \tabularnewline
75 & 3 & 2.87827726911463 & 0.121722730885366 \tabularnewline
76 & 5 & 4.13673403600211 & 0.863265963997893 \tabularnewline
77 & 1 & 2.61092628989287 & -1.61092628989287 \tabularnewline
78 & 3 & 3.73832572235812 & -0.738325722358117 \tabularnewline
79 & 3 & 4.06327627005132 & -1.06327627005132 \tabularnewline
80 & 5 & 3.76722888374438 & 1.23277111625562 \tabularnewline
81 & 2 & 3.38412736487840 & -1.38412736487840 \tabularnewline
82 & 3 & 2.55580809691126 & 0.444191903088743 \tabularnewline
83 & 3 & 3.74968470747056 & -0.749684707470564 \tabularnewline
84 & 4 & 3.63513813827683 & 0.364861861723174 \tabularnewline
85 & 2 & 2.99149131654039 & -0.991491316540394 \tabularnewline
86 & 4 & 3.78426687945417 & 0.215733120545827 \tabularnewline
87 & 3 & 2.65876855975598 & 0.341231440244022 \tabularnewline
88 & 3 & 3.09770696706006 & -0.0977069670600576 \tabularnewline
89 & 3 & 3.22805600383106 & -0.228056003831056 \tabularnewline
90 & 2 & 3.76185677584723 & -1.76185677584723 \tabularnewline
91 & 3 & 3.63001282029384 & -0.630012820293843 \tabularnewline
92 & 2 & 3.09090756665300 & -1.09090756665300 \tabularnewline
93 & 4 & 2.87054152450221 & 1.12945847549779 \tabularnewline
94 & 4 & 3.46454824769335 & 0.535451752306653 \tabularnewline
95 & 2 & 3.42974985231713 & -1.42974985231713 \tabularnewline
96 & 1 & 2.52580681682443 & -1.52580681682443 \tabularnewline
97 & 5 & 3.2594608042237 & 1.7405391957763 \tabularnewline
98 & 4 & 3.121204048759 & 0.878795951241001 \tabularnewline
99 & 4 & 3.58657601761264 & 0.413423982387363 \tabularnewline
100 & 4 & 3.7395784115413 & 0.260421588458703 \tabularnewline
101 & 3 & 3.00860882731488 & -0.00860882731488219 \tabularnewline
102 & 3 & 3.25408229833061 & -0.254082298330611 \tabularnewline
103 & 1 & 2.49944736682413 & -1.49944736682413 \tabularnewline
104 & 5 & 3.17983894432500 & 1.82016105567500 \tabularnewline
105 & 3 & 3.35390698895573 & -0.353906988955732 \tabularnewline
106 & 3 & 3.37153522425452 & -0.371535224254524 \tabularnewline
107 & 2 & 2.79459903959432 & -0.794599039594317 \tabularnewline
108 & 4 & 3.49253292637918 & 0.507467073620822 \tabularnewline
109 & 4 & 2.97133359842930 & 1.02866640157070 \tabularnewline
110 & 3 & 3.73719630685801 & -0.737196306858013 \tabularnewline
111 & 4 & 2.83947745106876 & 1.16052254893124 \tabularnewline
112 & 4 & 3.25858028994318 & 0.741419710056822 \tabularnewline
113 & 2 & 2.53987277880064 & -0.539872778800642 \tabularnewline
114 & 3 & 2.95608537101353 & 0.0439146289864661 \tabularnewline
115 & 3 & 3.7157707752796 & -0.715770775279602 \tabularnewline
116 & 3 & 3.62629693309924 & -0.626296933099238 \tabularnewline
117 & 4 & 3.18436206433501 & 0.815637935664988 \tabularnewline
118 & 5 & 2.38488904745677 & 2.61511095254323 \tabularnewline
119 & 3 & 3.28065199518145 & -0.280651995181448 \tabularnewline
120 & 3 & 3.26004845610290 & -0.260048456102898 \tabularnewline
121 & 2 & 2.49907239445033 & -0.499072394450325 \tabularnewline
122 & 3 & 3.28377844945346 & -0.283778449453464 \tabularnewline
123 & 1 & 1.30155966924746 & -0.301559669247457 \tabularnewline
124 & 4 & 3.11248459718527 & 0.887515402814727 \tabularnewline
125 & 4 & 3.44025394981764 & 0.55974605018236 \tabularnewline
126 & 4 & 2.61358398816806 & 1.38641601183194 \tabularnewline
127 & 3 & 3.63077817620244 & -0.630778176202442 \tabularnewline
128 & 5 & 3.87194607159445 & 1.12805392840555 \tabularnewline
129 & 2 & 3.59680481356183 & -1.59680481356183 \tabularnewline
130 & 2 & 3.29102684628615 & -1.29102684628615 \tabularnewline
131 & 3 & 3.01470838295106 & -0.0147083829510622 \tabularnewline
132 & 3 & 3.37380048411051 & -0.373800484110505 \tabularnewline
133 & 2 & 3.51245628936445 & -1.51245628936445 \tabularnewline
134 & 1 & 3.3093877721919 & -2.30938777219190 \tabularnewline
135 & 3 & 3.08790880754384 & -0.0879088075438377 \tabularnewline
136 & 5 & 3.61914191323911 & 1.38085808676089 \tabularnewline
137 & 4 & 3.21723001413547 & 0.782769985864535 \tabularnewline
138 & 4 & 3.30670754773422 & 0.693292452265785 \tabularnewline
139 & 4 & 3.23495700208418 & 0.76504299791582 \tabularnewline
140 & 3 & 3.16176574378456 & -0.161765743784560 \tabularnewline
141 & 5 & 2.99522979016784 & 2.00477020983216 \tabularnewline
142 & 3 & 3.26072750645867 & -0.260727506458674 \tabularnewline
143 & 3 & 2.97618844518762 & 0.0238115548123835 \tabularnewline
144 & 3 & 3.17958283646325 & -0.179582836463246 \tabularnewline
145 & 3 & 3.38795725170416 & -0.387957251704158 \tabularnewline
146 & 4 & 3.32950398182521 & 0.670496018174786 \tabularnewline
147 & 2 & 3.06873231644301 & -1.06873231644301 \tabularnewline
148 & 2 & 1.67643759723356 & 0.32356240276644 \tabularnewline
149 & 4 & 2.66570577972329 & 1.33429422027671 \tabularnewline
150 & 3 & 2.77638525480916 & 0.223614745190840 \tabularnewline
151 & 3 & 3.55472342364738 & -0.554723423647376 \tabularnewline
152 & 2 & 2.92830572717015 & -0.928305727170146 \tabularnewline
153 & 3 & 3.19357053542211 & -0.193570535422113 \tabularnewline
154 & 3 & 3.40556090593604 & -0.405560905936042 \tabularnewline
155 & 4 & 3.51328025816389 & 0.486719741836113 \tabularnewline
156 & 1 & 2.70783055026909 & -1.70783055026909 \tabularnewline
157 & 1 & 3.19984171439962 & -2.19984171439962 \tabularnewline
158 & 5 & 3.26822156901902 & 1.73177843098098 \tabularnewline
159 & 4 & 3.45145687566127 & 0.54854312433873 \tabularnewline
160 & 3 & 3.23016669561742 & -0.230166695617418 \tabularnewline
161 & 3 & 3.25380016812417 & -0.253800168124167 \tabularnewline
162 & 4 & 3.42549349524823 & 0.574506504751773 \tabularnewline
163 & 3 & 3.48901795556219 & -0.489017955562189 \tabularnewline
164 & 2 & 2.60178464202460 & -0.601784642024597 \tabularnewline
165 & 1 & 2.77973896192742 & -1.77973896192742 \tabularnewline
166 & 1 & 3.03803355103566 & -2.03803355103566 \tabularnewline
167 & 5 & 4.00680576655662 & 0.99319423344338 \tabularnewline
168 & 4 & 3.7227552977525 & 0.277244702247502 \tabularnewline
169 & 3 & 3.20311190091086 & -0.203111900910859 \tabularnewline
170 & 4 & 3.60446440448251 & 0.395535595517493 \tabularnewline
171 & 5 & 3.6035054738443 & 1.3964945261557 \tabularnewline
172 & 4 & 3.59609186419156 & 0.403908135808436 \tabularnewline
173 & 4 & 2.40500779346212 & 1.59499220653788 \tabularnewline
174 & 2 & 3.34211939143489 & -1.34211939143489 \tabularnewline
175 & 3 & 3.30298915510907 & -0.302989155109067 \tabularnewline
176 & 4 & 3.31201424863965 & 0.687985751360353 \tabularnewline
177 & 3 & 3.04548299925180 & -0.0454829992518048 \tabularnewline
178 & 4 & 3.37568444856883 & 0.624315551431166 \tabularnewline
179 & 3 & 3.3783165802092 & -0.378316580209203 \tabularnewline
180 & 4 & 3.05142726928232 & 0.948572730717685 \tabularnewline
181 & 1 & 3.17262874550607 & -2.17262874550607 \tabularnewline
182 & 2 & 3.32924153260512 & -1.32924153260512 \tabularnewline
183 & 3 & 3.74283318327879 & -0.742833183278789 \tabularnewline
184 & 3 & 3.35989410893563 & -0.359894108935633 \tabularnewline
185 & 5 & 3.8294360692716 & 1.17056393072840 \tabularnewline
186 & 4 & 4.00527605502908 & -0.0052760550290807 \tabularnewline
187 & 3 & 3.8861961539305 & -0.886196153930499 \tabularnewline
188 & 3 & 3.38824036747852 & -0.388240367478520 \tabularnewline
189 & 3 & 2.92125020637601 & 0.0787497936239947 \tabularnewline
190 & 3 & 3.1698491899915 & -0.169849189991498 \tabularnewline
191 & 4 & 3.08422824811617 & 0.915771751883827 \tabularnewline
192 & 3 & 3.15805426554439 & -0.158054265544392 \tabularnewline
193 & 2 & 2.62202281304329 & -0.622022813043293 \tabularnewline
194 & 4 & 3.04471695717748 & 0.955283042822517 \tabularnewline
195 & 2 & 3.40359518010176 & -1.40359518010176 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&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]4[/C][C]3.75364371821155[/C][C]0.246356281788452[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]3.36323006725487[/C][C]0.636769932745128[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]3.58516987159218[/C][C]1.41483012840782[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]2.91790493010918[/C][C]-0.917904930109179[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.81052630484053[/C][C]0.189473695159474[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]3.57677647685239[/C][C]1.42322352314761[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.45238041828219[/C][C]0.547619581717807[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]3.37121620230626[/C][C]0.628783797693738[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.11161259581886[/C][C]0.888387404181138[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.38254755337413[/C][C]0.61745244662587[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]3.23985826992617[/C][C]1.76014173007383[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.13723293909836[/C][C]0.862767060901642[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]3.17156988875543[/C][C]-0.171569888755432[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.14433617472278[/C][C]0.855663825277217[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]3.21804975649472[/C][C]-0.218049756494724[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.80343887605321[/C][C]0.196561123946788[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]2.89392007711487[/C][C]1.10607992288513[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]3.21240586890456[/C][C]-0.212405868904555[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.60712509548503[/C][C]0.392874904514971[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]2.75479670030096[/C][C]-0.754796700300959[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.42042932695695[/C][C]0.579570673043048[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.69819957832150[/C][C]0.301800421678495[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]3.35801681609768[/C][C]0.64198318390232[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]3.22158564201091[/C][C]0.77841435798909[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]2.79245206584342[/C][C]0.207547934156575[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]3.98490018473423[/C][C]-0.984900184734229[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]3.09767653554434[/C][C]-1.09767653554434[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.2791359791856[/C][C]0.720864020814397[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]3.45510680831703[/C][C]1.54489319168297[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.05613692758528[/C][C]0.943863072414716[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]3.21140337490415[/C][C]-1.21140337490415[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]3.60566378367653[/C][C]1.39433621632347[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.37313270958573[/C][C]0.626867290414273[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]3.48443304720316[/C][C]-0.484433047203162[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.26840377311303[/C][C]0.731596226886968[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]2.84865305798528[/C][C]-0.848653057985278[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.62947857885539[/C][C]-0.629478578855389[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]3.58881873876079[/C][C]-0.588818738760788[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]2.80122158653404[/C][C]0.198778413465955[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]3.60879215399778[/C][C]1.39120784600222[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]2.45734506221047[/C][C]-2.45734506221047[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.61738745068311[/C][C]0.382612549316889[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]2.93838936191108[/C][C]1.06161063808892[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.52291235429798[/C][C]0.477087645702016[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]2.8883241948446[/C][C]-0.888324194844597[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.42126182339224[/C][C]0.57873817660776[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]3.31972834849149[/C][C]-0.319728348491487[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.17327818573428[/C][C]0.82672181426572[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]3.76094140857122[/C][C]-2.76094140857122[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]3.78001474882545[/C][C]-1.78001474882545[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]2.85613007999566[/C][C]-0.85613007999566[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.93174212264462[/C][C]0.0682578773553817[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]3.15519469471417[/C][C]-0.155194694714174[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]2.83817683729395[/C][C]1.16182316270605[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]3.60174658959194[/C][C]-0.601746589591941[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]3.16666188904272[/C][C]-0.166661889042723[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]4.04143653479242[/C][C]0.958563465207578[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]3.32070047122651[/C][C]-0.320700471226513[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]2.58354337094263[/C][C]-0.583543370942629[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]3.53321023251919[/C][C]-2.53321023251919[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]3.23052946146232[/C][C]-1.23052946146232[/C][/ROW]
[ROW][C]62[/C][C]5[/C][C]3.14960688003005[/C][C]1.85039311996995[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.69525708542449[/C][C]0.304742914575507[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]3.48569431153061[/C][C]0.514305688469387[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]3.64495012833058[/C][C]-0.644950128330581[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.63579622642499[/C][C]0.364203773575012[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.27864141223836[/C][C]0.721358587761636[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]2.95253250674872[/C][C]-0.952532506748719[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]3.07083049868552[/C][C]-0.0708304986855244[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]3.00105726309605[/C][C]0.998942736903952[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]2.94520415683054[/C][C]0.0547958431694561[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]2.40128891354019[/C][C]-0.401288913540193[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]2.52506036845561[/C][C]1.47493963154439[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]3.41099612317589[/C][C]0.589003876824111[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]2.87827726911463[/C][C]0.121722730885366[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]4.13673403600211[/C][C]0.863265963997893[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]2.61092628989287[/C][C]-1.61092628989287[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]3.73832572235812[/C][C]-0.738325722358117[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]4.06327627005132[/C][C]-1.06327627005132[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]3.76722888374438[/C][C]1.23277111625562[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]3.38412736487840[/C][C]-1.38412736487840[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]2.55580809691126[/C][C]0.444191903088743[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]3.74968470747056[/C][C]-0.749684707470564[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]3.63513813827683[/C][C]0.364861861723174[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]2.99149131654039[/C][C]-0.991491316540394[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]3.78426687945417[/C][C]0.215733120545827[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]2.65876855975598[/C][C]0.341231440244022[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]3.09770696706006[/C][C]-0.0977069670600576[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]3.22805600383106[/C][C]-0.228056003831056[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]3.76185677584723[/C][C]-1.76185677584723[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]3.63001282029384[/C][C]-0.630012820293843[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]3.09090756665300[/C][C]-1.09090756665300[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]2.87054152450221[/C][C]1.12945847549779[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]3.46454824769335[/C][C]0.535451752306653[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]3.42974985231713[/C][C]-1.42974985231713[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]2.52580681682443[/C][C]-1.52580681682443[/C][/ROW]
[ROW][C]97[/C][C]5[/C][C]3.2594608042237[/C][C]1.7405391957763[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]3.121204048759[/C][C]0.878795951241001[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.58657601761264[/C][C]0.413423982387363[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.7395784115413[/C][C]0.260421588458703[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]3.00860882731488[/C][C]-0.00860882731488219[/C][/ROW]
[ROW][C]102[/C][C]3[/C][C]3.25408229833061[/C][C]-0.254082298330611[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]2.49944736682413[/C][C]-1.49944736682413[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]3.17983894432500[/C][C]1.82016105567500[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]3.35390698895573[/C][C]-0.353906988955732[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]3.37153522425452[/C][C]-0.371535224254524[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]2.79459903959432[/C][C]-0.794599039594317[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]3.49253292637918[/C][C]0.507467073620822[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]2.97133359842930[/C][C]1.02866640157070[/C][/ROW]
[ROW][C]110[/C][C]3[/C][C]3.73719630685801[/C][C]-0.737196306858013[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]2.83947745106876[/C][C]1.16052254893124[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.25858028994318[/C][C]0.741419710056822[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]2.53987277880064[/C][C]-0.539872778800642[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]2.95608537101353[/C][C]0.0439146289864661[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]3.7157707752796[/C][C]-0.715770775279602[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]3.62629693309924[/C][C]-0.626296933099238[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]3.18436206433501[/C][C]0.815637935664988[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]2.38488904745677[/C][C]2.61511095254323[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]3.28065199518145[/C][C]-0.280651995181448[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]3.26004845610290[/C][C]-0.260048456102898[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]2.49907239445033[/C][C]-0.499072394450325[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]3.28377844945346[/C][C]-0.283778449453464[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.30155966924746[/C][C]-0.301559669247457[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]3.11248459718527[/C][C]0.887515402814727[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]3.44025394981764[/C][C]0.55974605018236[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]2.61358398816806[/C][C]1.38641601183194[/C][/ROW]
[ROW][C]127[/C][C]3[/C][C]3.63077817620244[/C][C]-0.630778176202442[/C][/ROW]
[ROW][C]128[/C][C]5[/C][C]3.87194607159445[/C][C]1.12805392840555[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]3.59680481356183[/C][C]-1.59680481356183[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]3.29102684628615[/C][C]-1.29102684628615[/C][/ROW]
[ROW][C]131[/C][C]3[/C][C]3.01470838295106[/C][C]-0.0147083829510622[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]3.37380048411051[/C][C]-0.373800484110505[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]3.51245628936445[/C][C]-1.51245628936445[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]3.3093877721919[/C][C]-2.30938777219190[/C][/ROW]
[ROW][C]135[/C][C]3[/C][C]3.08790880754384[/C][C]-0.0879088075438377[/C][/ROW]
[ROW][C]136[/C][C]5[/C][C]3.61914191323911[/C][C]1.38085808676089[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.21723001413547[/C][C]0.782769985864535[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]3.30670754773422[/C][C]0.693292452265785[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]3.23495700208418[/C][C]0.76504299791582[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]3.16176574378456[/C][C]-0.161765743784560[/C][/ROW]
[ROW][C]141[/C][C]5[/C][C]2.99522979016784[/C][C]2.00477020983216[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]3.26072750645867[/C][C]-0.260727506458674[/C][/ROW]
[ROW][C]143[/C][C]3[/C][C]2.97618844518762[/C][C]0.0238115548123835[/C][/ROW]
[ROW][C]144[/C][C]3[/C][C]3.17958283646325[/C][C]-0.179582836463246[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]3.38795725170416[/C][C]-0.387957251704158[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]3.32950398182521[/C][C]0.670496018174786[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]3.06873231644301[/C][C]-1.06873231644301[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.67643759723356[/C][C]0.32356240276644[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]2.66570577972329[/C][C]1.33429422027671[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]2.77638525480916[/C][C]0.223614745190840[/C][/ROW]
[ROW][C]151[/C][C]3[/C][C]3.55472342364738[/C][C]-0.554723423647376[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]2.92830572717015[/C][C]-0.928305727170146[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]3.19357053542211[/C][C]-0.193570535422113[/C][/ROW]
[ROW][C]154[/C][C]3[/C][C]3.40556090593604[/C][C]-0.405560905936042[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]3.51328025816389[/C][C]0.486719741836113[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]2.70783055026909[/C][C]-1.70783055026909[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]3.19984171439962[/C][C]-2.19984171439962[/C][/ROW]
[ROW][C]158[/C][C]5[/C][C]3.26822156901902[/C][C]1.73177843098098[/C][/ROW]
[ROW][C]159[/C][C]4[/C][C]3.45145687566127[/C][C]0.54854312433873[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]3.23016669561742[/C][C]-0.230166695617418[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]3.25380016812417[/C][C]-0.253800168124167[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]3.42549349524823[/C][C]0.574506504751773[/C][/ROW]
[ROW][C]163[/C][C]3[/C][C]3.48901795556219[/C][C]-0.489017955562189[/C][/ROW]
[ROW][C]164[/C][C]2[/C][C]2.60178464202460[/C][C]-0.601784642024597[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]2.77973896192742[/C][C]-1.77973896192742[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]3.03803355103566[/C][C]-2.03803355103566[/C][/ROW]
[ROW][C]167[/C][C]5[/C][C]4.00680576655662[/C][C]0.99319423344338[/C][/ROW]
[ROW][C]168[/C][C]4[/C][C]3.7227552977525[/C][C]0.277244702247502[/C][/ROW]
[ROW][C]169[/C][C]3[/C][C]3.20311190091086[/C][C]-0.203111900910859[/C][/ROW]
[ROW][C]170[/C][C]4[/C][C]3.60446440448251[/C][C]0.395535595517493[/C][/ROW]
[ROW][C]171[/C][C]5[/C][C]3.6035054738443[/C][C]1.3964945261557[/C][/ROW]
[ROW][C]172[/C][C]4[/C][C]3.59609186419156[/C][C]0.403908135808436[/C][/ROW]
[ROW][C]173[/C][C]4[/C][C]2.40500779346212[/C][C]1.59499220653788[/C][/ROW]
[ROW][C]174[/C][C]2[/C][C]3.34211939143489[/C][C]-1.34211939143489[/C][/ROW]
[ROW][C]175[/C][C]3[/C][C]3.30298915510907[/C][C]-0.302989155109067[/C][/ROW]
[ROW][C]176[/C][C]4[/C][C]3.31201424863965[/C][C]0.687985751360353[/C][/ROW]
[ROW][C]177[/C][C]3[/C][C]3.04548299925180[/C][C]-0.0454829992518048[/C][/ROW]
[ROW][C]178[/C][C]4[/C][C]3.37568444856883[/C][C]0.624315551431166[/C][/ROW]
[ROW][C]179[/C][C]3[/C][C]3.3783165802092[/C][C]-0.378316580209203[/C][/ROW]
[ROW][C]180[/C][C]4[/C][C]3.05142726928232[/C][C]0.948572730717685[/C][/ROW]
[ROW][C]181[/C][C]1[/C][C]3.17262874550607[/C][C]-2.17262874550607[/C][/ROW]
[ROW][C]182[/C][C]2[/C][C]3.32924153260512[/C][C]-1.32924153260512[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]3.74283318327879[/C][C]-0.742833183278789[/C][/ROW]
[ROW][C]184[/C][C]3[/C][C]3.35989410893563[/C][C]-0.359894108935633[/C][/ROW]
[ROW][C]185[/C][C]5[/C][C]3.8294360692716[/C][C]1.17056393072840[/C][/ROW]
[ROW][C]186[/C][C]4[/C][C]4.00527605502908[/C][C]-0.0052760550290807[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]3.8861961539305[/C][C]-0.886196153930499[/C][/ROW]
[ROW][C]188[/C][C]3[/C][C]3.38824036747852[/C][C]-0.388240367478520[/C][/ROW]
[ROW][C]189[/C][C]3[/C][C]2.92125020637601[/C][C]0.0787497936239947[/C][/ROW]
[ROW][C]190[/C][C]3[/C][C]3.1698491899915[/C][C]-0.169849189991498[/C][/ROW]
[ROW][C]191[/C][C]4[/C][C]3.08422824811617[/C][C]0.915771751883827[/C][/ROW]
[ROW][C]192[/C][C]3[/C][C]3.15805426554439[/C][C]-0.158054265544392[/C][/ROW]
[ROW][C]193[/C][C]2[/C][C]2.62202281304329[/C][C]-0.622022813043293[/C][/ROW]
[ROW][C]194[/C][C]4[/C][C]3.04471695717748[/C][C]0.955283042822517[/C][/ROW]
[ROW][C]195[/C][C]2[/C][C]3.40359518010176[/C][C]-1.40359518010176[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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
143.753643718211550.246356281788452
243.363230067254870.636769932745128
353.585169871592181.41483012840782
422.91790493010918-0.917904930109179
532.810526304840530.189473695159474
653.576776476852391.42322352314761
743.452380418282190.547619581717807
843.371216202306260.628783797693738
943.111612595818860.888387404181138
1043.382547553374130.61745244662587
1153.239858269926171.76014173007383
1243.137232939098360.862767060901642
1333.17156988875543-0.171569888755432
1443.144336174722780.855663825277217
1533.21804975649472-0.218049756494724
1632.803438876053210.196561123946788
1742.893920077114871.10607992288513
1833.21240586890456-0.212405868904555
1943.607125095485030.392874904514971
2022.75479670030096-0.754796700300959
2143.420429326956950.579570673043048
2232.698199578321500.301800421678495
2343.358016816097680.64198318390232
2443.221585642010910.77841435798909
2532.792452065843420.207547934156575
2633.98490018473423-0.984900184734229
2723.09767653554434-1.09767653554434
2843.27913597918560.720864020814397
2953.455106808317031.54489319168297
3043.056136927585280.943863072414716
3123.21140337490415-1.21140337490415
3253.605663783676531.39433621632347
3343.373132709585730.626867290414273
3433.48443304720316-0.484433047203162
3543.268403773113030.731596226886968
3622.84865305798528-0.848653057985278
3733.62947857885539-0.629478578855389
3833.58881873876079-0.588818738760788
3932.801221586534040.198778413465955
4053.608792153997781.39120784600222
4102.45734506221047-2.45734506221047
4243.617387450683110.382612549316889
4342.938389361911081.06161063808892
4443.522912354297980.477087645702016
4522.8883241948446-0.888324194844597
4643.421261823392240.57873817660776
4733.31972834849149-0.319728348491487
4843.173278185734280.82672181426572
4913.76094140857122-2.76094140857122
5023.78001474882545-1.78001474882545
5122.85613007999566-0.85613007999566
5243.931742122644620.0682578773553817
5333.15519469471417-0.155194694714174
5442.838176837293951.16182316270605
5533.60174658959194-0.601746589591941
5633.16666188904272-0.166661889042723
5754.041436534792420.958563465207578
5833.32070047122651-0.320700471226513
5922.58354337094263-0.583543370942629
6013.53321023251919-2.53321023251919
6123.23052946146232-1.23052946146232
6253.149606880030051.85039311996995
6343.695257085424490.304742914575507
6443.485694311530610.514305688469387
6533.64495012833058-0.644950128330581
6643.635796226424990.364203773575012
6743.278641412238360.721358587761636
6822.95253250674872-0.952532506748719
6933.07083049868552-0.0708304986855244
7043.001057263096050.998942736903952
7132.945204156830540.0547958431694561
7222.40128891354019-0.401288913540193
7342.525060368455611.47493963154439
7443.410996123175890.589003876824111
7532.878277269114630.121722730885366
7654.136734036002110.863265963997893
7712.61092628989287-1.61092628989287
7833.73832572235812-0.738325722358117
7934.06327627005132-1.06327627005132
8053.767228883744381.23277111625562
8123.38412736487840-1.38412736487840
8232.555808096911260.444191903088743
8333.74968470747056-0.749684707470564
8443.635138138276830.364861861723174
8522.99149131654039-0.991491316540394
8643.784266879454170.215733120545827
8732.658768559755980.341231440244022
8833.09770696706006-0.0977069670600576
8933.22805600383106-0.228056003831056
9023.76185677584723-1.76185677584723
9133.63001282029384-0.630012820293843
9223.09090756665300-1.09090756665300
9342.870541524502211.12945847549779
9443.464548247693350.535451752306653
9523.42974985231713-1.42974985231713
9612.52580681682443-1.52580681682443
9753.25946080422371.7405391957763
9843.1212040487590.878795951241001
9943.586576017612640.413423982387363
10043.73957841154130.260421588458703
10133.00860882731488-0.00860882731488219
10233.25408229833061-0.254082298330611
10312.49944736682413-1.49944736682413
10453.179838944325001.82016105567500
10533.35390698895573-0.353906988955732
10633.37153522425452-0.371535224254524
10722.79459903959432-0.794599039594317
10843.492532926379180.507467073620822
10942.971333598429301.02866640157070
11033.73719630685801-0.737196306858013
11142.839477451068761.16052254893124
11243.258580289943180.741419710056822
11322.53987277880064-0.539872778800642
11432.956085371013530.0439146289864661
11533.7157707752796-0.715770775279602
11633.62629693309924-0.626296933099238
11743.184362064335010.815637935664988
11852.384889047456772.61511095254323
11933.28065199518145-0.280651995181448
12033.26004845610290-0.260048456102898
12122.49907239445033-0.499072394450325
12233.28377844945346-0.283778449453464
12311.30155966924746-0.301559669247457
12443.112484597185270.887515402814727
12543.440253949817640.55974605018236
12642.613583988168061.38641601183194
12733.63077817620244-0.630778176202442
12853.871946071594451.12805392840555
12923.59680481356183-1.59680481356183
13023.29102684628615-1.29102684628615
13133.01470838295106-0.0147083829510622
13233.37380048411051-0.373800484110505
13323.51245628936445-1.51245628936445
13413.3093877721919-2.30938777219190
13533.08790880754384-0.0879088075438377
13653.619141913239111.38085808676089
13743.217230014135470.782769985864535
13843.306707547734220.693292452265785
13943.234957002084180.76504299791582
14033.16176574378456-0.161765743784560
14152.995229790167842.00477020983216
14233.26072750645867-0.260727506458674
14332.976188445187620.0238115548123835
14433.17958283646325-0.179582836463246
14533.38795725170416-0.387957251704158
14643.329503981825210.670496018174786
14723.06873231644301-1.06873231644301
14821.676437597233560.32356240276644
14942.665705779723291.33429422027671
15032.776385254809160.223614745190840
15133.55472342364738-0.554723423647376
15222.92830572717015-0.928305727170146
15333.19357053542211-0.193570535422113
15433.40556090593604-0.405560905936042
15543.513280258163890.486719741836113
15612.70783055026909-1.70783055026909
15713.19984171439962-2.19984171439962
15853.268221569019021.73177843098098
15943.451456875661270.54854312433873
16033.23016669561742-0.230166695617418
16133.25380016812417-0.253800168124167
16243.425493495248230.574506504751773
16333.48901795556219-0.489017955562189
16422.60178464202460-0.601784642024597
16512.77973896192742-1.77973896192742
16613.03803355103566-2.03803355103566
16754.006805766556620.99319423344338
16843.72275529775250.277244702247502
16933.20311190091086-0.203111900910859
17043.604464404482510.395535595517493
17153.60350547384431.3964945261557
17243.596091864191560.403908135808436
17342.405007793462121.59499220653788
17423.34211939143489-1.34211939143489
17533.30298915510907-0.302989155109067
17643.312014248639650.687985751360353
17733.04548299925180-0.0454829992518048
17843.375684448568830.624315551431166
17933.3783165802092-0.378316580209203
18043.051427269282320.948572730717685
18113.17262874550607-2.17262874550607
18223.32924153260512-1.32924153260512
18333.74283318327879-0.742833183278789
18433.35989410893563-0.359894108935633
18553.82943606927161.17056393072840
18644.00527605502908-0.0052760550290807
18733.8861961539305-0.886196153930499
18833.38824036747852-0.388240367478520
18932.921250206376010.0787497936239947
19033.1698491899915-0.169849189991498
19143.084228248116170.915771751883827
19233.15805426554439-0.158054265544392
19322.62202281304329-0.622022813043293
19443.044716957177480.955283042822517
19523.40359518010176-1.40359518010176






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1120368803851570.2240737607703130.887963119614843
110.06258467452553380.1251693490510680.937415325474466
120.02357307482251950.04714614964503910.97642692517748
130.03539536651229810.07079073302459610.964604633487702
140.01804666314540410.03609332629080810.981953336854596
150.04048790297909770.08097580595819550.959512097020902
160.02382427095137990.04764854190275990.97617572904862
170.01781647217812090.03563294435624190.98218352782188
180.01441924873420750.0288384974684150.985580751265792
190.01437958421164540.02875916842329080.985620415788355
200.007791346500305480.01558269300061100.992208653499695
210.004066791329649460.008133582659298910.99593320867035
220.003328251106190690.006656502212381380.99667174889381
230.001746498994956720.003492997989913440.998253501005043
240.0009016232147158820.001803246429431760.999098376785284
250.0004410418111623880.0008820836223247760.999558958188838
260.005300863571799260.01060172714359850.9946991364282
270.02847953439102520.05695906878205040.971520465608975
280.01962122876044480.03924245752088970.980378771239555
290.02315139865896690.04630279731793390.976848601341033
300.01867333321079590.03734666642159170.981326666789204
310.03553604616212980.07107209232425970.96446395383787
320.04109228750928390.08218457501856790.958907712490716
330.03206362279182760.06412724558365520.967936377208172
340.02484610541261930.04969221082523860.97515389458738
350.01755825679952590.03511651359905190.982441743200474
360.02099106111291690.04198212222583380.979008938887083
370.02611128652795910.05222257305591820.97388871347204
380.02739184696048990.05478369392097990.97260815303951
390.02068034555863230.04136069111726460.979319654441368
400.02312991725481000.04625983450962010.97687008274519
410.1148810941265680.2297621882531360.885118905873432
420.09091813301053460.1818362660210690.909081866989465
430.08720468547351510.1744093709470300.912795314526485
440.06882863432279820.1376572686455960.931171365677202
450.07007064978208710.1401412995641740.929929350217913
460.0554451755073270.1108903510146540.944554824492673
470.05576054872243620.1115210974448720.944239451277564
480.05716086964532580.1143217392906520.942839130354674
490.3272900293584610.6545800587169220.672709970641539
500.5195029421793040.9609941156413910.480497057820696
510.4919076325429590.9838152650859190.508092367457041
520.4490730080952290.8981460161904570.550926991904771
530.411307122316270.822614244632540.58869287768373
540.4765892950554450.953178590110890.523410704944555
550.4813703910159270.9627407820318530.518629608984073
560.4481291432859830.8962582865719660.551870856714017
570.4304252550326240.8608505100652480.569574744967376
580.3885596668840370.7771193337680740.611440333115963
590.3646395628689320.7292791257378630.635360437131068
600.5990425697210520.8019148605578960.400957430278948
610.6094187844046680.7811624311906630.390581215595332
620.7074245358471670.5851509283056660.292575464152833
630.6735781726568780.6528436546862440.326421827343122
640.6415185241371190.7169629517257620.358481475862881
650.6359442623749690.7281114752500620.364055737625031
660.600422294579220.799155410841560.39957770542078
670.5809426643565780.8381146712868430.419057335643422
680.5730011827204820.8539976345590370.426998817279519
690.5295296532718580.9409406934562850.470470346728142
700.5192756093411270.9614487813177470.480724390658873
710.476674288602970.953348577205940.52332571139703
720.4484646449023240.8969292898046480.551535355097676
730.4752111811081690.9504223622163370.524788818891831
740.445883742667910.891767485335820.55411625733209
750.4042263844965270.8084527689930550.595773615503473
760.3864233926065080.7728467852130150.613576607393492
770.4814525125977940.9629050251955870.518547487402206
780.4667317976622570.9334635953245130.533268202337743
790.4705896190448840.941179238089770.529410380955116
800.4898475585621150.979695117124230.510152441437885
810.5553140860613540.8893718278772910.444685913938646
820.5248475797215120.9503048405569760.475152420278488
830.5082141547210440.9835716905579130.491785845278956
840.4704143707539960.9408287415079910.529585629246004
850.4631527241406580.9263054482813150.536847275859342
860.4263120766132830.8526241532265660.573687923386717
870.394430030037020.788860060074040.60556996996298
880.3568867198799910.7137734397599830.643113280120009
890.3238352886720470.6476705773440950.676164711327953
900.4085694876038820.8171389752077640.591430512396118
910.3909574968717910.7819149937435810.609042503128209
920.400852264806630.801704529613260.59914773519337
930.4176116110957620.8352232221915240.582388388904238
940.3896383361982020.7792766723964040.610361663801798
950.4292144955040570.8584289910081140.570785504495943
960.4871488386397070.9742976772794130.512851161360293
970.5686993461116280.8626013077767440.431300653888372
980.5585999818348190.8828000363303620.441400018165181
990.5270027652845210.9459944694309580.472997234715479
1000.4869918857358660.9739837714717320.513008114264134
1010.4456978281881580.8913956563763170.554302171811842
1020.4083067427763450.816613485552690.591693257223655
1030.4629221712973350.9258443425946710.537077828702665
1040.5586200297876780.8827599404246450.441379970212323
1050.521333202907770.957333594184460.47866679709223
1060.4848845411002650.969769082200530.515115458899735
1070.4721058916368940.9442117832737880.527894108363106
1080.4502008972696630.9004017945393260.549799102730337
1090.452398143076710.904796286153420.54760185692329
1100.4289610893805620.8579221787611240.571038910619438
1110.4383398874997670.8766797749995340.561660112500233
1120.4190813757479140.8381627514958280.580918624252086
1130.3919878836497520.7839757672995040.608012116350248
1140.3518288267513460.7036576535026920.648171173248654
1150.3302741275453930.6605482550907860.669725872454607
1160.3069588694670000.6139177389339990.693041130533
1170.2968217223558370.5936434447116740.703178277644163
1180.5487507130481370.9024985739037270.451249286951863
1190.5077411711185430.9845176577629130.492258828881457
1200.4683097571590540.9366195143181080.531690242840946
1210.4393834849837830.8787669699675670.560616515016217
1220.4005383712993930.8010767425987860.599461628700607
1230.3642807652981030.7285615305962060.635719234701897
1240.3542897974476860.7085795948953730.645710202552314
1250.3287555801183410.6575111602366820.671244419881659
1260.3535685247914520.7071370495829050.646431475208548
1270.3302002773553540.6604005547107070.669799722644646
1280.3615615914568490.7231231829136980.638438408543151
1290.4250157845753080.8500315691506160.574984215424692
1300.448301547149730.896603094299460.55169845285027
1310.4053521795136820.8107043590273630.594647820486318
1320.3671114747692030.7342229495384060.632888525230797
1330.4234586948430620.8469173896861230.576541305156938
1340.6170688151651950.765862369669610.382931184834805
1350.5725992635666370.8548014728667270.427400736433363
1360.5952210068131780.8095579863736450.404778993186822
1370.571593911481490.856812177037020.42840608851851
1380.5679333472113530.8641333055772930.432066652788647
1390.5563831256684660.8872337486630670.443616874331534
1400.510737584279540.978524831440920.48926241572046
1410.6612328596468830.6775342807062330.338767140353116
1420.6176836775539940.7646326448920130.382316322446006
1430.5701949472708330.8596101054583340.429805052729167
1440.5365576415983390.9268847168033220.463442358401661
1450.4937514808783020.9875029617566030.506248519121698
1460.4918792110824340.9837584221648680.508120788917566
1470.4673350846954710.9346701693909430.532664915304529
1480.4230376398664920.8460752797329850.576962360133508
1490.5042734532909830.9914530934180350.495726546709017
1500.48625573457510.97251146915020.5137442654249
1510.4598862073042720.9197724146085450.540113792695728
1520.438209009442280.876418018884560.56179099055772
1530.4096109544850280.8192219089700570.590389045514972
1540.3638524968191190.7277049936382380.636147503180881
1550.3213589279367770.6427178558735540.678641072063223
1560.3584797531334140.7169595062668280.641520246866586
1570.5367545933089710.9264908133820570.463245406691029
1580.6149227923817280.7701544152365440.385077207618272
1590.5764416500527020.8471166998945960.423558349947298
1600.5246195738611890.9507608522776220.475380426138811
1610.4688411948400630.9376823896801270.531158805159937
1620.4183584581700660.8367169163401310.581641541829934
1630.3672906028538710.7345812057077430.632709397146129
1640.3205045178527800.6410090357055590.67949548214722
1650.3866325042832450.7732650085664890.613367495716755
1660.6566674909384640.6866650181230720.343332509061536
1670.683087661971240.633824676057520.31691233802876
1680.6364724196344280.7270551607311440.363527580365572
1690.5886558525771490.8226882948457020.411344147422851
1700.526137048520950.94772590295810.47386295147905
1710.5700592237625550.859881552474890.429940776237445
1720.5738527343073810.8522945313852380.426147265692619
1730.6340853658303260.7318292683393480.365914634169674
1740.7195957746138040.5608084507723910.280404225386196
1750.6633465422297880.6733069155404240.336653457770212
1760.6404110067982320.7191779864035370.359588993201768
1770.6069555417075670.7860889165848670.393044458292433
1780.6658383751346290.6683232497307410.334161624865371
1790.7481810899251570.5036378201496860.251818910074843
1800.877633842410860.2447323151782790.122366157589139
1810.8409866704933590.3180266590132830.159013329506641
1820.7850985134533570.4298029730932850.214901486546643
1830.67113849009310.65772301981380.3288615099069
1840.6090558821818240.7818882356363520.390944117818176
1850.5054879596200580.9890240807598850.494512040379942

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.112036880385157 & 0.224073760770313 & 0.887963119614843 \tabularnewline
11 & 0.0625846745255338 & 0.125169349051068 & 0.937415325474466 \tabularnewline
12 & 0.0235730748225195 & 0.0471461496450391 & 0.97642692517748 \tabularnewline
13 & 0.0353953665122981 & 0.0707907330245961 & 0.964604633487702 \tabularnewline
14 & 0.0180466631454041 & 0.0360933262908081 & 0.981953336854596 \tabularnewline
15 & 0.0404879029790977 & 0.0809758059581955 & 0.959512097020902 \tabularnewline
16 & 0.0238242709513799 & 0.0476485419027599 & 0.97617572904862 \tabularnewline
17 & 0.0178164721781209 & 0.0356329443562419 & 0.98218352782188 \tabularnewline
18 & 0.0144192487342075 & 0.028838497468415 & 0.985580751265792 \tabularnewline
19 & 0.0143795842116454 & 0.0287591684232908 & 0.985620415788355 \tabularnewline
20 & 0.00779134650030548 & 0.0155826930006110 & 0.992208653499695 \tabularnewline
21 & 0.00406679132964946 & 0.00813358265929891 & 0.99593320867035 \tabularnewline
22 & 0.00332825110619069 & 0.00665650221238138 & 0.99667174889381 \tabularnewline
23 & 0.00174649899495672 & 0.00349299798991344 & 0.998253501005043 \tabularnewline
24 & 0.000901623214715882 & 0.00180324642943176 & 0.999098376785284 \tabularnewline
25 & 0.000441041811162388 & 0.000882083622324776 & 0.999558958188838 \tabularnewline
26 & 0.00530086357179926 & 0.0106017271435985 & 0.9946991364282 \tabularnewline
27 & 0.0284795343910252 & 0.0569590687820504 & 0.971520465608975 \tabularnewline
28 & 0.0196212287604448 & 0.0392424575208897 & 0.980378771239555 \tabularnewline
29 & 0.0231513986589669 & 0.0463027973179339 & 0.976848601341033 \tabularnewline
30 & 0.0186733332107959 & 0.0373466664215917 & 0.981326666789204 \tabularnewline
31 & 0.0355360461621298 & 0.0710720923242597 & 0.96446395383787 \tabularnewline
32 & 0.0410922875092839 & 0.0821845750185679 & 0.958907712490716 \tabularnewline
33 & 0.0320636227918276 & 0.0641272455836552 & 0.967936377208172 \tabularnewline
34 & 0.0248461054126193 & 0.0496922108252386 & 0.97515389458738 \tabularnewline
35 & 0.0175582567995259 & 0.0351165135990519 & 0.982441743200474 \tabularnewline
36 & 0.0209910611129169 & 0.0419821222258338 & 0.979008938887083 \tabularnewline
37 & 0.0261112865279591 & 0.0522225730559182 & 0.97388871347204 \tabularnewline
38 & 0.0273918469604899 & 0.0547836939209799 & 0.97260815303951 \tabularnewline
39 & 0.0206803455586323 & 0.0413606911172646 & 0.979319654441368 \tabularnewline
40 & 0.0231299172548100 & 0.0462598345096201 & 0.97687008274519 \tabularnewline
41 & 0.114881094126568 & 0.229762188253136 & 0.885118905873432 \tabularnewline
42 & 0.0909181330105346 & 0.181836266021069 & 0.909081866989465 \tabularnewline
43 & 0.0872046854735151 & 0.174409370947030 & 0.912795314526485 \tabularnewline
44 & 0.0688286343227982 & 0.137657268645596 & 0.931171365677202 \tabularnewline
45 & 0.0700706497820871 & 0.140141299564174 & 0.929929350217913 \tabularnewline
46 & 0.055445175507327 & 0.110890351014654 & 0.944554824492673 \tabularnewline
47 & 0.0557605487224362 & 0.111521097444872 & 0.944239451277564 \tabularnewline
48 & 0.0571608696453258 & 0.114321739290652 & 0.942839130354674 \tabularnewline
49 & 0.327290029358461 & 0.654580058716922 & 0.672709970641539 \tabularnewline
50 & 0.519502942179304 & 0.960994115641391 & 0.480497057820696 \tabularnewline
51 & 0.491907632542959 & 0.983815265085919 & 0.508092367457041 \tabularnewline
52 & 0.449073008095229 & 0.898146016190457 & 0.550926991904771 \tabularnewline
53 & 0.41130712231627 & 0.82261424463254 & 0.58869287768373 \tabularnewline
54 & 0.476589295055445 & 0.95317859011089 & 0.523410704944555 \tabularnewline
55 & 0.481370391015927 & 0.962740782031853 & 0.518629608984073 \tabularnewline
56 & 0.448129143285983 & 0.896258286571966 & 0.551870856714017 \tabularnewline
57 & 0.430425255032624 & 0.860850510065248 & 0.569574744967376 \tabularnewline
58 & 0.388559666884037 & 0.777119333768074 & 0.611440333115963 \tabularnewline
59 & 0.364639562868932 & 0.729279125737863 & 0.635360437131068 \tabularnewline
60 & 0.599042569721052 & 0.801914860557896 & 0.400957430278948 \tabularnewline
61 & 0.609418784404668 & 0.781162431190663 & 0.390581215595332 \tabularnewline
62 & 0.707424535847167 & 0.585150928305666 & 0.292575464152833 \tabularnewline
63 & 0.673578172656878 & 0.652843654686244 & 0.326421827343122 \tabularnewline
64 & 0.641518524137119 & 0.716962951725762 & 0.358481475862881 \tabularnewline
65 & 0.635944262374969 & 0.728111475250062 & 0.364055737625031 \tabularnewline
66 & 0.60042229457922 & 0.79915541084156 & 0.39957770542078 \tabularnewline
67 & 0.580942664356578 & 0.838114671286843 & 0.419057335643422 \tabularnewline
68 & 0.573001182720482 & 0.853997634559037 & 0.426998817279519 \tabularnewline
69 & 0.529529653271858 & 0.940940693456285 & 0.470470346728142 \tabularnewline
70 & 0.519275609341127 & 0.961448781317747 & 0.480724390658873 \tabularnewline
71 & 0.47667428860297 & 0.95334857720594 & 0.52332571139703 \tabularnewline
72 & 0.448464644902324 & 0.896929289804648 & 0.551535355097676 \tabularnewline
73 & 0.475211181108169 & 0.950422362216337 & 0.524788818891831 \tabularnewline
74 & 0.44588374266791 & 0.89176748533582 & 0.55411625733209 \tabularnewline
75 & 0.404226384496527 & 0.808452768993055 & 0.595773615503473 \tabularnewline
76 & 0.386423392606508 & 0.772846785213015 & 0.613576607393492 \tabularnewline
77 & 0.481452512597794 & 0.962905025195587 & 0.518547487402206 \tabularnewline
78 & 0.466731797662257 & 0.933463595324513 & 0.533268202337743 \tabularnewline
79 & 0.470589619044884 & 0.94117923808977 & 0.529410380955116 \tabularnewline
80 & 0.489847558562115 & 0.97969511712423 & 0.510152441437885 \tabularnewline
81 & 0.555314086061354 & 0.889371827877291 & 0.444685913938646 \tabularnewline
82 & 0.524847579721512 & 0.950304840556976 & 0.475152420278488 \tabularnewline
83 & 0.508214154721044 & 0.983571690557913 & 0.491785845278956 \tabularnewline
84 & 0.470414370753996 & 0.940828741507991 & 0.529585629246004 \tabularnewline
85 & 0.463152724140658 & 0.926305448281315 & 0.536847275859342 \tabularnewline
86 & 0.426312076613283 & 0.852624153226566 & 0.573687923386717 \tabularnewline
87 & 0.39443003003702 & 0.78886006007404 & 0.60556996996298 \tabularnewline
88 & 0.356886719879991 & 0.713773439759983 & 0.643113280120009 \tabularnewline
89 & 0.323835288672047 & 0.647670577344095 & 0.676164711327953 \tabularnewline
90 & 0.408569487603882 & 0.817138975207764 & 0.591430512396118 \tabularnewline
91 & 0.390957496871791 & 0.781914993743581 & 0.609042503128209 \tabularnewline
92 & 0.40085226480663 & 0.80170452961326 & 0.59914773519337 \tabularnewline
93 & 0.417611611095762 & 0.835223222191524 & 0.582388388904238 \tabularnewline
94 & 0.389638336198202 & 0.779276672396404 & 0.610361663801798 \tabularnewline
95 & 0.429214495504057 & 0.858428991008114 & 0.570785504495943 \tabularnewline
96 & 0.487148838639707 & 0.974297677279413 & 0.512851161360293 \tabularnewline
97 & 0.568699346111628 & 0.862601307776744 & 0.431300653888372 \tabularnewline
98 & 0.558599981834819 & 0.882800036330362 & 0.441400018165181 \tabularnewline
99 & 0.527002765284521 & 0.945994469430958 & 0.472997234715479 \tabularnewline
100 & 0.486991885735866 & 0.973983771471732 & 0.513008114264134 \tabularnewline
101 & 0.445697828188158 & 0.891395656376317 & 0.554302171811842 \tabularnewline
102 & 0.408306742776345 & 0.81661348555269 & 0.591693257223655 \tabularnewline
103 & 0.462922171297335 & 0.925844342594671 & 0.537077828702665 \tabularnewline
104 & 0.558620029787678 & 0.882759940424645 & 0.441379970212323 \tabularnewline
105 & 0.52133320290777 & 0.95733359418446 & 0.47866679709223 \tabularnewline
106 & 0.484884541100265 & 0.96976908220053 & 0.515115458899735 \tabularnewline
107 & 0.472105891636894 & 0.944211783273788 & 0.527894108363106 \tabularnewline
108 & 0.450200897269663 & 0.900401794539326 & 0.549799102730337 \tabularnewline
109 & 0.45239814307671 & 0.90479628615342 & 0.54760185692329 \tabularnewline
110 & 0.428961089380562 & 0.857922178761124 & 0.571038910619438 \tabularnewline
111 & 0.438339887499767 & 0.876679774999534 & 0.561660112500233 \tabularnewline
112 & 0.419081375747914 & 0.838162751495828 & 0.580918624252086 \tabularnewline
113 & 0.391987883649752 & 0.783975767299504 & 0.608012116350248 \tabularnewline
114 & 0.351828826751346 & 0.703657653502692 & 0.648171173248654 \tabularnewline
115 & 0.330274127545393 & 0.660548255090786 & 0.669725872454607 \tabularnewline
116 & 0.306958869467000 & 0.613917738933999 & 0.693041130533 \tabularnewline
117 & 0.296821722355837 & 0.593643444711674 & 0.703178277644163 \tabularnewline
118 & 0.548750713048137 & 0.902498573903727 & 0.451249286951863 \tabularnewline
119 & 0.507741171118543 & 0.984517657762913 & 0.492258828881457 \tabularnewline
120 & 0.468309757159054 & 0.936619514318108 & 0.531690242840946 \tabularnewline
121 & 0.439383484983783 & 0.878766969967567 & 0.560616515016217 \tabularnewline
122 & 0.400538371299393 & 0.801076742598786 & 0.599461628700607 \tabularnewline
123 & 0.364280765298103 & 0.728561530596206 & 0.635719234701897 \tabularnewline
124 & 0.354289797447686 & 0.708579594895373 & 0.645710202552314 \tabularnewline
125 & 0.328755580118341 & 0.657511160236682 & 0.671244419881659 \tabularnewline
126 & 0.353568524791452 & 0.707137049582905 & 0.646431475208548 \tabularnewline
127 & 0.330200277355354 & 0.660400554710707 & 0.669799722644646 \tabularnewline
128 & 0.361561591456849 & 0.723123182913698 & 0.638438408543151 \tabularnewline
129 & 0.425015784575308 & 0.850031569150616 & 0.574984215424692 \tabularnewline
130 & 0.44830154714973 & 0.89660309429946 & 0.55169845285027 \tabularnewline
131 & 0.405352179513682 & 0.810704359027363 & 0.594647820486318 \tabularnewline
132 & 0.367111474769203 & 0.734222949538406 & 0.632888525230797 \tabularnewline
133 & 0.423458694843062 & 0.846917389686123 & 0.576541305156938 \tabularnewline
134 & 0.617068815165195 & 0.76586236966961 & 0.382931184834805 \tabularnewline
135 & 0.572599263566637 & 0.854801472866727 & 0.427400736433363 \tabularnewline
136 & 0.595221006813178 & 0.809557986373645 & 0.404778993186822 \tabularnewline
137 & 0.57159391148149 & 0.85681217703702 & 0.42840608851851 \tabularnewline
138 & 0.567933347211353 & 0.864133305577293 & 0.432066652788647 \tabularnewline
139 & 0.556383125668466 & 0.887233748663067 & 0.443616874331534 \tabularnewline
140 & 0.51073758427954 & 0.97852483144092 & 0.48926241572046 \tabularnewline
141 & 0.661232859646883 & 0.677534280706233 & 0.338767140353116 \tabularnewline
142 & 0.617683677553994 & 0.764632644892013 & 0.382316322446006 \tabularnewline
143 & 0.570194947270833 & 0.859610105458334 & 0.429805052729167 \tabularnewline
144 & 0.536557641598339 & 0.926884716803322 & 0.463442358401661 \tabularnewline
145 & 0.493751480878302 & 0.987502961756603 & 0.506248519121698 \tabularnewline
146 & 0.491879211082434 & 0.983758422164868 & 0.508120788917566 \tabularnewline
147 & 0.467335084695471 & 0.934670169390943 & 0.532664915304529 \tabularnewline
148 & 0.423037639866492 & 0.846075279732985 & 0.576962360133508 \tabularnewline
149 & 0.504273453290983 & 0.991453093418035 & 0.495726546709017 \tabularnewline
150 & 0.4862557345751 & 0.9725114691502 & 0.5137442654249 \tabularnewline
151 & 0.459886207304272 & 0.919772414608545 & 0.540113792695728 \tabularnewline
152 & 0.43820900944228 & 0.87641801888456 & 0.56179099055772 \tabularnewline
153 & 0.409610954485028 & 0.819221908970057 & 0.590389045514972 \tabularnewline
154 & 0.363852496819119 & 0.727704993638238 & 0.636147503180881 \tabularnewline
155 & 0.321358927936777 & 0.642717855873554 & 0.678641072063223 \tabularnewline
156 & 0.358479753133414 & 0.716959506266828 & 0.641520246866586 \tabularnewline
157 & 0.536754593308971 & 0.926490813382057 & 0.463245406691029 \tabularnewline
158 & 0.614922792381728 & 0.770154415236544 & 0.385077207618272 \tabularnewline
159 & 0.576441650052702 & 0.847116699894596 & 0.423558349947298 \tabularnewline
160 & 0.524619573861189 & 0.950760852277622 & 0.475380426138811 \tabularnewline
161 & 0.468841194840063 & 0.937682389680127 & 0.531158805159937 \tabularnewline
162 & 0.418358458170066 & 0.836716916340131 & 0.581641541829934 \tabularnewline
163 & 0.367290602853871 & 0.734581205707743 & 0.632709397146129 \tabularnewline
164 & 0.320504517852780 & 0.641009035705559 & 0.67949548214722 \tabularnewline
165 & 0.386632504283245 & 0.773265008566489 & 0.613367495716755 \tabularnewline
166 & 0.656667490938464 & 0.686665018123072 & 0.343332509061536 \tabularnewline
167 & 0.68308766197124 & 0.63382467605752 & 0.31691233802876 \tabularnewline
168 & 0.636472419634428 & 0.727055160731144 & 0.363527580365572 \tabularnewline
169 & 0.588655852577149 & 0.822688294845702 & 0.411344147422851 \tabularnewline
170 & 0.52613704852095 & 0.9477259029581 & 0.47386295147905 \tabularnewline
171 & 0.570059223762555 & 0.85988155247489 & 0.429940776237445 \tabularnewline
172 & 0.573852734307381 & 0.852294531385238 & 0.426147265692619 \tabularnewline
173 & 0.634085365830326 & 0.731829268339348 & 0.365914634169674 \tabularnewline
174 & 0.719595774613804 & 0.560808450772391 & 0.280404225386196 \tabularnewline
175 & 0.663346542229788 & 0.673306915540424 & 0.336653457770212 \tabularnewline
176 & 0.640411006798232 & 0.719177986403537 & 0.359588993201768 \tabularnewline
177 & 0.606955541707567 & 0.786088916584867 & 0.393044458292433 \tabularnewline
178 & 0.665838375134629 & 0.668323249730741 & 0.334161624865371 \tabularnewline
179 & 0.748181089925157 & 0.503637820149686 & 0.251818910074843 \tabularnewline
180 & 0.87763384241086 & 0.244732315178279 & 0.122366157589139 \tabularnewline
181 & 0.840986670493359 & 0.318026659013283 & 0.159013329506641 \tabularnewline
182 & 0.785098513453357 & 0.429802973093285 & 0.214901486546643 \tabularnewline
183 & 0.6711384900931 & 0.6577230198138 & 0.3288615099069 \tabularnewline
184 & 0.609055882181824 & 0.781888235636352 & 0.390944117818176 \tabularnewline
185 & 0.505487959620058 & 0.989024080759885 & 0.494512040379942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&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]0.112036880385157[/C][C]0.224073760770313[/C][C]0.887963119614843[/C][/ROW]
[ROW][C]11[/C][C]0.0625846745255338[/C][C]0.125169349051068[/C][C]0.937415325474466[/C][/ROW]
[ROW][C]12[/C][C]0.0235730748225195[/C][C]0.0471461496450391[/C][C]0.97642692517748[/C][/ROW]
[ROW][C]13[/C][C]0.0353953665122981[/C][C]0.0707907330245961[/C][C]0.964604633487702[/C][/ROW]
[ROW][C]14[/C][C]0.0180466631454041[/C][C]0.0360933262908081[/C][C]0.981953336854596[/C][/ROW]
[ROW][C]15[/C][C]0.0404879029790977[/C][C]0.0809758059581955[/C][C]0.959512097020902[/C][/ROW]
[ROW][C]16[/C][C]0.0238242709513799[/C][C]0.0476485419027599[/C][C]0.97617572904862[/C][/ROW]
[ROW][C]17[/C][C]0.0178164721781209[/C][C]0.0356329443562419[/C][C]0.98218352782188[/C][/ROW]
[ROW][C]18[/C][C]0.0144192487342075[/C][C]0.028838497468415[/C][C]0.985580751265792[/C][/ROW]
[ROW][C]19[/C][C]0.0143795842116454[/C][C]0.0287591684232908[/C][C]0.985620415788355[/C][/ROW]
[ROW][C]20[/C][C]0.00779134650030548[/C][C]0.0155826930006110[/C][C]0.992208653499695[/C][/ROW]
[ROW][C]21[/C][C]0.00406679132964946[/C][C]0.00813358265929891[/C][C]0.99593320867035[/C][/ROW]
[ROW][C]22[/C][C]0.00332825110619069[/C][C]0.00665650221238138[/C][C]0.99667174889381[/C][/ROW]
[ROW][C]23[/C][C]0.00174649899495672[/C][C]0.00349299798991344[/C][C]0.998253501005043[/C][/ROW]
[ROW][C]24[/C][C]0.000901623214715882[/C][C]0.00180324642943176[/C][C]0.999098376785284[/C][/ROW]
[ROW][C]25[/C][C]0.000441041811162388[/C][C]0.000882083622324776[/C][C]0.999558958188838[/C][/ROW]
[ROW][C]26[/C][C]0.00530086357179926[/C][C]0.0106017271435985[/C][C]0.9946991364282[/C][/ROW]
[ROW][C]27[/C][C]0.0284795343910252[/C][C]0.0569590687820504[/C][C]0.971520465608975[/C][/ROW]
[ROW][C]28[/C][C]0.0196212287604448[/C][C]0.0392424575208897[/C][C]0.980378771239555[/C][/ROW]
[ROW][C]29[/C][C]0.0231513986589669[/C][C]0.0463027973179339[/C][C]0.976848601341033[/C][/ROW]
[ROW][C]30[/C][C]0.0186733332107959[/C][C]0.0373466664215917[/C][C]0.981326666789204[/C][/ROW]
[ROW][C]31[/C][C]0.0355360461621298[/C][C]0.0710720923242597[/C][C]0.96446395383787[/C][/ROW]
[ROW][C]32[/C][C]0.0410922875092839[/C][C]0.0821845750185679[/C][C]0.958907712490716[/C][/ROW]
[ROW][C]33[/C][C]0.0320636227918276[/C][C]0.0641272455836552[/C][C]0.967936377208172[/C][/ROW]
[ROW][C]34[/C][C]0.0248461054126193[/C][C]0.0496922108252386[/C][C]0.97515389458738[/C][/ROW]
[ROW][C]35[/C][C]0.0175582567995259[/C][C]0.0351165135990519[/C][C]0.982441743200474[/C][/ROW]
[ROW][C]36[/C][C]0.0209910611129169[/C][C]0.0419821222258338[/C][C]0.979008938887083[/C][/ROW]
[ROW][C]37[/C][C]0.0261112865279591[/C][C]0.0522225730559182[/C][C]0.97388871347204[/C][/ROW]
[ROW][C]38[/C][C]0.0273918469604899[/C][C]0.0547836939209799[/C][C]0.97260815303951[/C][/ROW]
[ROW][C]39[/C][C]0.0206803455586323[/C][C]0.0413606911172646[/C][C]0.979319654441368[/C][/ROW]
[ROW][C]40[/C][C]0.0231299172548100[/C][C]0.0462598345096201[/C][C]0.97687008274519[/C][/ROW]
[ROW][C]41[/C][C]0.114881094126568[/C][C]0.229762188253136[/C][C]0.885118905873432[/C][/ROW]
[ROW][C]42[/C][C]0.0909181330105346[/C][C]0.181836266021069[/C][C]0.909081866989465[/C][/ROW]
[ROW][C]43[/C][C]0.0872046854735151[/C][C]0.174409370947030[/C][C]0.912795314526485[/C][/ROW]
[ROW][C]44[/C][C]0.0688286343227982[/C][C]0.137657268645596[/C][C]0.931171365677202[/C][/ROW]
[ROW][C]45[/C][C]0.0700706497820871[/C][C]0.140141299564174[/C][C]0.929929350217913[/C][/ROW]
[ROW][C]46[/C][C]0.055445175507327[/C][C]0.110890351014654[/C][C]0.944554824492673[/C][/ROW]
[ROW][C]47[/C][C]0.0557605487224362[/C][C]0.111521097444872[/C][C]0.944239451277564[/C][/ROW]
[ROW][C]48[/C][C]0.0571608696453258[/C][C]0.114321739290652[/C][C]0.942839130354674[/C][/ROW]
[ROW][C]49[/C][C]0.327290029358461[/C][C]0.654580058716922[/C][C]0.672709970641539[/C][/ROW]
[ROW][C]50[/C][C]0.519502942179304[/C][C]0.960994115641391[/C][C]0.480497057820696[/C][/ROW]
[ROW][C]51[/C][C]0.491907632542959[/C][C]0.983815265085919[/C][C]0.508092367457041[/C][/ROW]
[ROW][C]52[/C][C]0.449073008095229[/C][C]0.898146016190457[/C][C]0.550926991904771[/C][/ROW]
[ROW][C]53[/C][C]0.41130712231627[/C][C]0.82261424463254[/C][C]0.58869287768373[/C][/ROW]
[ROW][C]54[/C][C]0.476589295055445[/C][C]0.95317859011089[/C][C]0.523410704944555[/C][/ROW]
[ROW][C]55[/C][C]0.481370391015927[/C][C]0.962740782031853[/C][C]0.518629608984073[/C][/ROW]
[ROW][C]56[/C][C]0.448129143285983[/C][C]0.896258286571966[/C][C]0.551870856714017[/C][/ROW]
[ROW][C]57[/C][C]0.430425255032624[/C][C]0.860850510065248[/C][C]0.569574744967376[/C][/ROW]
[ROW][C]58[/C][C]0.388559666884037[/C][C]0.777119333768074[/C][C]0.611440333115963[/C][/ROW]
[ROW][C]59[/C][C]0.364639562868932[/C][C]0.729279125737863[/C][C]0.635360437131068[/C][/ROW]
[ROW][C]60[/C][C]0.599042569721052[/C][C]0.801914860557896[/C][C]0.400957430278948[/C][/ROW]
[ROW][C]61[/C][C]0.609418784404668[/C][C]0.781162431190663[/C][C]0.390581215595332[/C][/ROW]
[ROW][C]62[/C][C]0.707424535847167[/C][C]0.585150928305666[/C][C]0.292575464152833[/C][/ROW]
[ROW][C]63[/C][C]0.673578172656878[/C][C]0.652843654686244[/C][C]0.326421827343122[/C][/ROW]
[ROW][C]64[/C][C]0.641518524137119[/C][C]0.716962951725762[/C][C]0.358481475862881[/C][/ROW]
[ROW][C]65[/C][C]0.635944262374969[/C][C]0.728111475250062[/C][C]0.364055737625031[/C][/ROW]
[ROW][C]66[/C][C]0.60042229457922[/C][C]0.79915541084156[/C][C]0.39957770542078[/C][/ROW]
[ROW][C]67[/C][C]0.580942664356578[/C][C]0.838114671286843[/C][C]0.419057335643422[/C][/ROW]
[ROW][C]68[/C][C]0.573001182720482[/C][C]0.853997634559037[/C][C]0.426998817279519[/C][/ROW]
[ROW][C]69[/C][C]0.529529653271858[/C][C]0.940940693456285[/C][C]0.470470346728142[/C][/ROW]
[ROW][C]70[/C][C]0.519275609341127[/C][C]0.961448781317747[/C][C]0.480724390658873[/C][/ROW]
[ROW][C]71[/C][C]0.47667428860297[/C][C]0.95334857720594[/C][C]0.52332571139703[/C][/ROW]
[ROW][C]72[/C][C]0.448464644902324[/C][C]0.896929289804648[/C][C]0.551535355097676[/C][/ROW]
[ROW][C]73[/C][C]0.475211181108169[/C][C]0.950422362216337[/C][C]0.524788818891831[/C][/ROW]
[ROW][C]74[/C][C]0.44588374266791[/C][C]0.89176748533582[/C][C]0.55411625733209[/C][/ROW]
[ROW][C]75[/C][C]0.404226384496527[/C][C]0.808452768993055[/C][C]0.595773615503473[/C][/ROW]
[ROW][C]76[/C][C]0.386423392606508[/C][C]0.772846785213015[/C][C]0.613576607393492[/C][/ROW]
[ROW][C]77[/C][C]0.481452512597794[/C][C]0.962905025195587[/C][C]0.518547487402206[/C][/ROW]
[ROW][C]78[/C][C]0.466731797662257[/C][C]0.933463595324513[/C][C]0.533268202337743[/C][/ROW]
[ROW][C]79[/C][C]0.470589619044884[/C][C]0.94117923808977[/C][C]0.529410380955116[/C][/ROW]
[ROW][C]80[/C][C]0.489847558562115[/C][C]0.97969511712423[/C][C]0.510152441437885[/C][/ROW]
[ROW][C]81[/C][C]0.555314086061354[/C][C]0.889371827877291[/C][C]0.444685913938646[/C][/ROW]
[ROW][C]82[/C][C]0.524847579721512[/C][C]0.950304840556976[/C][C]0.475152420278488[/C][/ROW]
[ROW][C]83[/C][C]0.508214154721044[/C][C]0.983571690557913[/C][C]0.491785845278956[/C][/ROW]
[ROW][C]84[/C][C]0.470414370753996[/C][C]0.940828741507991[/C][C]0.529585629246004[/C][/ROW]
[ROW][C]85[/C][C]0.463152724140658[/C][C]0.926305448281315[/C][C]0.536847275859342[/C][/ROW]
[ROW][C]86[/C][C]0.426312076613283[/C][C]0.852624153226566[/C][C]0.573687923386717[/C][/ROW]
[ROW][C]87[/C][C]0.39443003003702[/C][C]0.78886006007404[/C][C]0.60556996996298[/C][/ROW]
[ROW][C]88[/C][C]0.356886719879991[/C][C]0.713773439759983[/C][C]0.643113280120009[/C][/ROW]
[ROW][C]89[/C][C]0.323835288672047[/C][C]0.647670577344095[/C][C]0.676164711327953[/C][/ROW]
[ROW][C]90[/C][C]0.408569487603882[/C][C]0.817138975207764[/C][C]0.591430512396118[/C][/ROW]
[ROW][C]91[/C][C]0.390957496871791[/C][C]0.781914993743581[/C][C]0.609042503128209[/C][/ROW]
[ROW][C]92[/C][C]0.40085226480663[/C][C]0.80170452961326[/C][C]0.59914773519337[/C][/ROW]
[ROW][C]93[/C][C]0.417611611095762[/C][C]0.835223222191524[/C][C]0.582388388904238[/C][/ROW]
[ROW][C]94[/C][C]0.389638336198202[/C][C]0.779276672396404[/C][C]0.610361663801798[/C][/ROW]
[ROW][C]95[/C][C]0.429214495504057[/C][C]0.858428991008114[/C][C]0.570785504495943[/C][/ROW]
[ROW][C]96[/C][C]0.487148838639707[/C][C]0.974297677279413[/C][C]0.512851161360293[/C][/ROW]
[ROW][C]97[/C][C]0.568699346111628[/C][C]0.862601307776744[/C][C]0.431300653888372[/C][/ROW]
[ROW][C]98[/C][C]0.558599981834819[/C][C]0.882800036330362[/C][C]0.441400018165181[/C][/ROW]
[ROW][C]99[/C][C]0.527002765284521[/C][C]0.945994469430958[/C][C]0.472997234715479[/C][/ROW]
[ROW][C]100[/C][C]0.486991885735866[/C][C]0.973983771471732[/C][C]0.513008114264134[/C][/ROW]
[ROW][C]101[/C][C]0.445697828188158[/C][C]0.891395656376317[/C][C]0.554302171811842[/C][/ROW]
[ROW][C]102[/C][C]0.408306742776345[/C][C]0.81661348555269[/C][C]0.591693257223655[/C][/ROW]
[ROW][C]103[/C][C]0.462922171297335[/C][C]0.925844342594671[/C][C]0.537077828702665[/C][/ROW]
[ROW][C]104[/C][C]0.558620029787678[/C][C]0.882759940424645[/C][C]0.441379970212323[/C][/ROW]
[ROW][C]105[/C][C]0.52133320290777[/C][C]0.95733359418446[/C][C]0.47866679709223[/C][/ROW]
[ROW][C]106[/C][C]0.484884541100265[/C][C]0.96976908220053[/C][C]0.515115458899735[/C][/ROW]
[ROW][C]107[/C][C]0.472105891636894[/C][C]0.944211783273788[/C][C]0.527894108363106[/C][/ROW]
[ROW][C]108[/C][C]0.450200897269663[/C][C]0.900401794539326[/C][C]0.549799102730337[/C][/ROW]
[ROW][C]109[/C][C]0.45239814307671[/C][C]0.90479628615342[/C][C]0.54760185692329[/C][/ROW]
[ROW][C]110[/C][C]0.428961089380562[/C][C]0.857922178761124[/C][C]0.571038910619438[/C][/ROW]
[ROW][C]111[/C][C]0.438339887499767[/C][C]0.876679774999534[/C][C]0.561660112500233[/C][/ROW]
[ROW][C]112[/C][C]0.419081375747914[/C][C]0.838162751495828[/C][C]0.580918624252086[/C][/ROW]
[ROW][C]113[/C][C]0.391987883649752[/C][C]0.783975767299504[/C][C]0.608012116350248[/C][/ROW]
[ROW][C]114[/C][C]0.351828826751346[/C][C]0.703657653502692[/C][C]0.648171173248654[/C][/ROW]
[ROW][C]115[/C][C]0.330274127545393[/C][C]0.660548255090786[/C][C]0.669725872454607[/C][/ROW]
[ROW][C]116[/C][C]0.306958869467000[/C][C]0.613917738933999[/C][C]0.693041130533[/C][/ROW]
[ROW][C]117[/C][C]0.296821722355837[/C][C]0.593643444711674[/C][C]0.703178277644163[/C][/ROW]
[ROW][C]118[/C][C]0.548750713048137[/C][C]0.902498573903727[/C][C]0.451249286951863[/C][/ROW]
[ROW][C]119[/C][C]0.507741171118543[/C][C]0.984517657762913[/C][C]0.492258828881457[/C][/ROW]
[ROW][C]120[/C][C]0.468309757159054[/C][C]0.936619514318108[/C][C]0.531690242840946[/C][/ROW]
[ROW][C]121[/C][C]0.439383484983783[/C][C]0.878766969967567[/C][C]0.560616515016217[/C][/ROW]
[ROW][C]122[/C][C]0.400538371299393[/C][C]0.801076742598786[/C][C]0.599461628700607[/C][/ROW]
[ROW][C]123[/C][C]0.364280765298103[/C][C]0.728561530596206[/C][C]0.635719234701897[/C][/ROW]
[ROW][C]124[/C][C]0.354289797447686[/C][C]0.708579594895373[/C][C]0.645710202552314[/C][/ROW]
[ROW][C]125[/C][C]0.328755580118341[/C][C]0.657511160236682[/C][C]0.671244419881659[/C][/ROW]
[ROW][C]126[/C][C]0.353568524791452[/C][C]0.707137049582905[/C][C]0.646431475208548[/C][/ROW]
[ROW][C]127[/C][C]0.330200277355354[/C][C]0.660400554710707[/C][C]0.669799722644646[/C][/ROW]
[ROW][C]128[/C][C]0.361561591456849[/C][C]0.723123182913698[/C][C]0.638438408543151[/C][/ROW]
[ROW][C]129[/C][C]0.425015784575308[/C][C]0.850031569150616[/C][C]0.574984215424692[/C][/ROW]
[ROW][C]130[/C][C]0.44830154714973[/C][C]0.89660309429946[/C][C]0.55169845285027[/C][/ROW]
[ROW][C]131[/C][C]0.405352179513682[/C][C]0.810704359027363[/C][C]0.594647820486318[/C][/ROW]
[ROW][C]132[/C][C]0.367111474769203[/C][C]0.734222949538406[/C][C]0.632888525230797[/C][/ROW]
[ROW][C]133[/C][C]0.423458694843062[/C][C]0.846917389686123[/C][C]0.576541305156938[/C][/ROW]
[ROW][C]134[/C][C]0.617068815165195[/C][C]0.76586236966961[/C][C]0.382931184834805[/C][/ROW]
[ROW][C]135[/C][C]0.572599263566637[/C][C]0.854801472866727[/C][C]0.427400736433363[/C][/ROW]
[ROW][C]136[/C][C]0.595221006813178[/C][C]0.809557986373645[/C][C]0.404778993186822[/C][/ROW]
[ROW][C]137[/C][C]0.57159391148149[/C][C]0.85681217703702[/C][C]0.42840608851851[/C][/ROW]
[ROW][C]138[/C][C]0.567933347211353[/C][C]0.864133305577293[/C][C]0.432066652788647[/C][/ROW]
[ROW][C]139[/C][C]0.556383125668466[/C][C]0.887233748663067[/C][C]0.443616874331534[/C][/ROW]
[ROW][C]140[/C][C]0.51073758427954[/C][C]0.97852483144092[/C][C]0.48926241572046[/C][/ROW]
[ROW][C]141[/C][C]0.661232859646883[/C][C]0.677534280706233[/C][C]0.338767140353116[/C][/ROW]
[ROW][C]142[/C][C]0.617683677553994[/C][C]0.764632644892013[/C][C]0.382316322446006[/C][/ROW]
[ROW][C]143[/C][C]0.570194947270833[/C][C]0.859610105458334[/C][C]0.429805052729167[/C][/ROW]
[ROW][C]144[/C][C]0.536557641598339[/C][C]0.926884716803322[/C][C]0.463442358401661[/C][/ROW]
[ROW][C]145[/C][C]0.493751480878302[/C][C]0.987502961756603[/C][C]0.506248519121698[/C][/ROW]
[ROW][C]146[/C][C]0.491879211082434[/C][C]0.983758422164868[/C][C]0.508120788917566[/C][/ROW]
[ROW][C]147[/C][C]0.467335084695471[/C][C]0.934670169390943[/C][C]0.532664915304529[/C][/ROW]
[ROW][C]148[/C][C]0.423037639866492[/C][C]0.846075279732985[/C][C]0.576962360133508[/C][/ROW]
[ROW][C]149[/C][C]0.504273453290983[/C][C]0.991453093418035[/C][C]0.495726546709017[/C][/ROW]
[ROW][C]150[/C][C]0.4862557345751[/C][C]0.9725114691502[/C][C]0.5137442654249[/C][/ROW]
[ROW][C]151[/C][C]0.459886207304272[/C][C]0.919772414608545[/C][C]0.540113792695728[/C][/ROW]
[ROW][C]152[/C][C]0.43820900944228[/C][C]0.87641801888456[/C][C]0.56179099055772[/C][/ROW]
[ROW][C]153[/C][C]0.409610954485028[/C][C]0.819221908970057[/C][C]0.590389045514972[/C][/ROW]
[ROW][C]154[/C][C]0.363852496819119[/C][C]0.727704993638238[/C][C]0.636147503180881[/C][/ROW]
[ROW][C]155[/C][C]0.321358927936777[/C][C]0.642717855873554[/C][C]0.678641072063223[/C][/ROW]
[ROW][C]156[/C][C]0.358479753133414[/C][C]0.716959506266828[/C][C]0.641520246866586[/C][/ROW]
[ROW][C]157[/C][C]0.536754593308971[/C][C]0.926490813382057[/C][C]0.463245406691029[/C][/ROW]
[ROW][C]158[/C][C]0.614922792381728[/C][C]0.770154415236544[/C][C]0.385077207618272[/C][/ROW]
[ROW][C]159[/C][C]0.576441650052702[/C][C]0.847116699894596[/C][C]0.423558349947298[/C][/ROW]
[ROW][C]160[/C][C]0.524619573861189[/C][C]0.950760852277622[/C][C]0.475380426138811[/C][/ROW]
[ROW][C]161[/C][C]0.468841194840063[/C][C]0.937682389680127[/C][C]0.531158805159937[/C][/ROW]
[ROW][C]162[/C][C]0.418358458170066[/C][C]0.836716916340131[/C][C]0.581641541829934[/C][/ROW]
[ROW][C]163[/C][C]0.367290602853871[/C][C]0.734581205707743[/C][C]0.632709397146129[/C][/ROW]
[ROW][C]164[/C][C]0.320504517852780[/C][C]0.641009035705559[/C][C]0.67949548214722[/C][/ROW]
[ROW][C]165[/C][C]0.386632504283245[/C][C]0.773265008566489[/C][C]0.613367495716755[/C][/ROW]
[ROW][C]166[/C][C]0.656667490938464[/C][C]0.686665018123072[/C][C]0.343332509061536[/C][/ROW]
[ROW][C]167[/C][C]0.68308766197124[/C][C]0.63382467605752[/C][C]0.31691233802876[/C][/ROW]
[ROW][C]168[/C][C]0.636472419634428[/C][C]0.727055160731144[/C][C]0.363527580365572[/C][/ROW]
[ROW][C]169[/C][C]0.588655852577149[/C][C]0.822688294845702[/C][C]0.411344147422851[/C][/ROW]
[ROW][C]170[/C][C]0.52613704852095[/C][C]0.9477259029581[/C][C]0.47386295147905[/C][/ROW]
[ROW][C]171[/C][C]0.570059223762555[/C][C]0.85988155247489[/C][C]0.429940776237445[/C][/ROW]
[ROW][C]172[/C][C]0.573852734307381[/C][C]0.852294531385238[/C][C]0.426147265692619[/C][/ROW]
[ROW][C]173[/C][C]0.634085365830326[/C][C]0.731829268339348[/C][C]0.365914634169674[/C][/ROW]
[ROW][C]174[/C][C]0.719595774613804[/C][C]0.560808450772391[/C][C]0.280404225386196[/C][/ROW]
[ROW][C]175[/C][C]0.663346542229788[/C][C]0.673306915540424[/C][C]0.336653457770212[/C][/ROW]
[ROW][C]176[/C][C]0.640411006798232[/C][C]0.719177986403537[/C][C]0.359588993201768[/C][/ROW]
[ROW][C]177[/C][C]0.606955541707567[/C][C]0.786088916584867[/C][C]0.393044458292433[/C][/ROW]
[ROW][C]178[/C][C]0.665838375134629[/C][C]0.668323249730741[/C][C]0.334161624865371[/C][/ROW]
[ROW][C]179[/C][C]0.748181089925157[/C][C]0.503637820149686[/C][C]0.251818910074843[/C][/ROW]
[ROW][C]180[/C][C]0.87763384241086[/C][C]0.244732315178279[/C][C]0.122366157589139[/C][/ROW]
[ROW][C]181[/C][C]0.840986670493359[/C][C]0.318026659013283[/C][C]0.159013329506641[/C][/ROW]
[ROW][C]182[/C][C]0.785098513453357[/C][C]0.429802973093285[/C][C]0.214901486546643[/C][/ROW]
[ROW][C]183[/C][C]0.6711384900931[/C][C]0.6577230198138[/C][C]0.3288615099069[/C][/ROW]
[ROW][C]184[/C][C]0.609055882181824[/C][C]0.781888235636352[/C][C]0.390944117818176[/C][/ROW]
[ROW][C]185[/C][C]0.505487959620058[/C][C]0.989024080759885[/C][C]0.494512040379942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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
100.1120368803851570.2240737607703130.887963119614843
110.06258467452553380.1251693490510680.937415325474466
120.02357307482251950.04714614964503910.97642692517748
130.03539536651229810.07079073302459610.964604633487702
140.01804666314540410.03609332629080810.981953336854596
150.04048790297909770.08097580595819550.959512097020902
160.02382427095137990.04764854190275990.97617572904862
170.01781647217812090.03563294435624190.98218352782188
180.01441924873420750.0288384974684150.985580751265792
190.01437958421164540.02875916842329080.985620415788355
200.007791346500305480.01558269300061100.992208653499695
210.004066791329649460.008133582659298910.99593320867035
220.003328251106190690.006656502212381380.99667174889381
230.001746498994956720.003492997989913440.998253501005043
240.0009016232147158820.001803246429431760.999098376785284
250.0004410418111623880.0008820836223247760.999558958188838
260.005300863571799260.01060172714359850.9946991364282
270.02847953439102520.05695906878205040.971520465608975
280.01962122876044480.03924245752088970.980378771239555
290.02315139865896690.04630279731793390.976848601341033
300.01867333321079590.03734666642159170.981326666789204
310.03553604616212980.07107209232425970.96446395383787
320.04109228750928390.08218457501856790.958907712490716
330.03206362279182760.06412724558365520.967936377208172
340.02484610541261930.04969221082523860.97515389458738
350.01755825679952590.03511651359905190.982441743200474
360.02099106111291690.04198212222583380.979008938887083
370.02611128652795910.05222257305591820.97388871347204
380.02739184696048990.05478369392097990.97260815303951
390.02068034555863230.04136069111726460.979319654441368
400.02312991725481000.04625983450962010.97687008274519
410.1148810941265680.2297621882531360.885118905873432
420.09091813301053460.1818362660210690.909081866989465
430.08720468547351510.1744093709470300.912795314526485
440.06882863432279820.1376572686455960.931171365677202
450.07007064978208710.1401412995641740.929929350217913
460.0554451755073270.1108903510146540.944554824492673
470.05576054872243620.1115210974448720.944239451277564
480.05716086964532580.1143217392906520.942839130354674
490.3272900293584610.6545800587169220.672709970641539
500.5195029421793040.9609941156413910.480497057820696
510.4919076325429590.9838152650859190.508092367457041
520.4490730080952290.8981460161904570.550926991904771
530.411307122316270.822614244632540.58869287768373
540.4765892950554450.953178590110890.523410704944555
550.4813703910159270.9627407820318530.518629608984073
560.4481291432859830.8962582865719660.551870856714017
570.4304252550326240.8608505100652480.569574744967376
580.3885596668840370.7771193337680740.611440333115963
590.3646395628689320.7292791257378630.635360437131068
600.5990425697210520.8019148605578960.400957430278948
610.6094187844046680.7811624311906630.390581215595332
620.7074245358471670.5851509283056660.292575464152833
630.6735781726568780.6528436546862440.326421827343122
640.6415185241371190.7169629517257620.358481475862881
650.6359442623749690.7281114752500620.364055737625031
660.600422294579220.799155410841560.39957770542078
670.5809426643565780.8381146712868430.419057335643422
680.5730011827204820.8539976345590370.426998817279519
690.5295296532718580.9409406934562850.470470346728142
700.5192756093411270.9614487813177470.480724390658873
710.476674288602970.953348577205940.52332571139703
720.4484646449023240.8969292898046480.551535355097676
730.4752111811081690.9504223622163370.524788818891831
740.445883742667910.891767485335820.55411625733209
750.4042263844965270.8084527689930550.595773615503473
760.3864233926065080.7728467852130150.613576607393492
770.4814525125977940.9629050251955870.518547487402206
780.4667317976622570.9334635953245130.533268202337743
790.4705896190448840.941179238089770.529410380955116
800.4898475585621150.979695117124230.510152441437885
810.5553140860613540.8893718278772910.444685913938646
820.5248475797215120.9503048405569760.475152420278488
830.5082141547210440.9835716905579130.491785845278956
840.4704143707539960.9408287415079910.529585629246004
850.4631527241406580.9263054482813150.536847275859342
860.4263120766132830.8526241532265660.573687923386717
870.394430030037020.788860060074040.60556996996298
880.3568867198799910.7137734397599830.643113280120009
890.3238352886720470.6476705773440950.676164711327953
900.4085694876038820.8171389752077640.591430512396118
910.3909574968717910.7819149937435810.609042503128209
920.400852264806630.801704529613260.59914773519337
930.4176116110957620.8352232221915240.582388388904238
940.3896383361982020.7792766723964040.610361663801798
950.4292144955040570.8584289910081140.570785504495943
960.4871488386397070.9742976772794130.512851161360293
970.5686993461116280.8626013077767440.431300653888372
980.5585999818348190.8828000363303620.441400018165181
990.5270027652845210.9459944694309580.472997234715479
1000.4869918857358660.9739837714717320.513008114264134
1010.4456978281881580.8913956563763170.554302171811842
1020.4083067427763450.816613485552690.591693257223655
1030.4629221712973350.9258443425946710.537077828702665
1040.5586200297876780.8827599404246450.441379970212323
1050.521333202907770.957333594184460.47866679709223
1060.4848845411002650.969769082200530.515115458899735
1070.4721058916368940.9442117832737880.527894108363106
1080.4502008972696630.9004017945393260.549799102730337
1090.452398143076710.904796286153420.54760185692329
1100.4289610893805620.8579221787611240.571038910619438
1110.4383398874997670.8766797749995340.561660112500233
1120.4190813757479140.8381627514958280.580918624252086
1130.3919878836497520.7839757672995040.608012116350248
1140.3518288267513460.7036576535026920.648171173248654
1150.3302741275453930.6605482550907860.669725872454607
1160.3069588694670000.6139177389339990.693041130533
1170.2968217223558370.5936434447116740.703178277644163
1180.5487507130481370.9024985739037270.451249286951863
1190.5077411711185430.9845176577629130.492258828881457
1200.4683097571590540.9366195143181080.531690242840946
1210.4393834849837830.8787669699675670.560616515016217
1220.4005383712993930.8010767425987860.599461628700607
1230.3642807652981030.7285615305962060.635719234701897
1240.3542897974476860.7085795948953730.645710202552314
1250.3287555801183410.6575111602366820.671244419881659
1260.3535685247914520.7071370495829050.646431475208548
1270.3302002773553540.6604005547107070.669799722644646
1280.3615615914568490.7231231829136980.638438408543151
1290.4250157845753080.8500315691506160.574984215424692
1300.448301547149730.896603094299460.55169845285027
1310.4053521795136820.8107043590273630.594647820486318
1320.3671114747692030.7342229495384060.632888525230797
1330.4234586948430620.8469173896861230.576541305156938
1340.6170688151651950.765862369669610.382931184834805
1350.5725992635666370.8548014728667270.427400736433363
1360.5952210068131780.8095579863736450.404778993186822
1370.571593911481490.856812177037020.42840608851851
1380.5679333472113530.8641333055772930.432066652788647
1390.5563831256684660.8872337486630670.443616874331534
1400.510737584279540.978524831440920.48926241572046
1410.6612328596468830.6775342807062330.338767140353116
1420.6176836775539940.7646326448920130.382316322446006
1430.5701949472708330.8596101054583340.429805052729167
1440.5365576415983390.9268847168033220.463442358401661
1450.4937514808783020.9875029617566030.506248519121698
1460.4918792110824340.9837584221648680.508120788917566
1470.4673350846954710.9346701693909430.532664915304529
1480.4230376398664920.8460752797329850.576962360133508
1490.5042734532909830.9914530934180350.495726546709017
1500.48625573457510.97251146915020.5137442654249
1510.4598862073042720.9197724146085450.540113792695728
1520.438209009442280.876418018884560.56179099055772
1530.4096109544850280.8192219089700570.590389045514972
1540.3638524968191190.7277049936382380.636147503180881
1550.3213589279367770.6427178558735540.678641072063223
1560.3584797531334140.7169595062668280.641520246866586
1570.5367545933089710.9264908133820570.463245406691029
1580.6149227923817280.7701544152365440.385077207618272
1590.5764416500527020.8471166998945960.423558349947298
1600.5246195738611890.9507608522776220.475380426138811
1610.4688411948400630.9376823896801270.531158805159937
1620.4183584581700660.8367169163401310.581641541829934
1630.3672906028538710.7345812057077430.632709397146129
1640.3205045178527800.6410090357055590.67949548214722
1650.3866325042832450.7732650085664890.613367495716755
1660.6566674909384640.6866650181230720.343332509061536
1670.683087661971240.633824676057520.31691233802876
1680.6364724196344280.7270551607311440.363527580365572
1690.5886558525771490.8226882948457020.411344147422851
1700.526137048520950.94772590295810.47386295147905
1710.5700592237625550.859881552474890.429940776237445
1720.5738527343073810.8522945313852380.426147265692619
1730.6340853658303260.7318292683393480.365914634169674
1740.7195957746138040.5608084507723910.280404225386196
1750.6633465422297880.6733069155404240.336653457770212
1760.6404110067982320.7191779864035370.359588993201768
1770.6069555417075670.7860889165848670.393044458292433
1780.6658383751346290.6683232497307410.334161624865371
1790.7481810899251570.5036378201496860.251818910074843
1800.877633842410860.2447323151782790.122366157589139
1810.8409866704933590.3180266590132830.159013329506641
1820.7850985134533570.4298029730932850.214901486546643
1830.67113849009310.65772301981380.3288615099069
1840.6090558821818240.7818882356363520.390944117818176
1850.5054879596200580.9890240807598850.494512040379942






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0284090909090909NOK
5% type I error level210.119318181818182NOK
10% type I error level290.164772727272727NOK

\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 & 5 & 0.0284090909090909 & NOK \tabularnewline
5% type I error level & 21 & 0.119318181818182 & NOK \tabularnewline
10% type I error level & 29 & 0.164772727272727 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114929&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]5[/C][C]0.0284090909090909[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.119318181818182[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.164772727272727[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114929&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114929&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 level50.0284090909090909NOK
5% type I error level210.119318181818182NOK
10% type I error level290.164772727272727NOK


Figures (Output of Computation)

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