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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Nov 2012 12:49:40 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t13534339319pb6315xocwetzy.htm/, Retrieved Mon, 29 Apr 2024 21:39:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191210, Retrieved Mon, 29 Apr 2024 21:39:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [WS7 - multiple li...] [2012-11-20 17:49:40] [0ab7a1f5c5e2896cc7067de738ff1e9d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,593	1,613	1,524
1,619	1,638	1,486
1,619	1,638	1,486
1,618	1,638	1,486
1,618	1,638	1,486
1,618	1,638	1,486
1,653	1,673	1,523
1,653	1,673	1,523
1,653	1,673	1,523
1,653	1,673	1,523
1,653	1,673	1,523
1,653	1,673	1,523
1,653	1,673	1,523
1,653	1,673	1,55
1,667	1,688	1,55
1,667	1,688	1,55
1,667	1,688	1,55
1,667	1,688	1,55
1,667	1,688	1,55
1,667	1,688	1,55
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,516
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,498
1,667	1,688	1,542
1,667	1,688	1,542
1,667	1,688	1,526
1,687	1,707	1,526
1,687	1,707	1,526
1,687	1,707	1,526
1,687	1,707	1,526
1,687	1,707	1,526
1,687	1,707	1,526
1,687	1,707	1,55
1,687	1,707	1,55
1,687	1,707	1,55
1,687	1,707	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,713	1,733	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,55
1,741	1,76	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,769	1,789	1,579
1,743	1,762	1,552
1,743	1,762	1,552
1,743	1,762	1,552
1,743	1,762	1,552
1,743	1,762	1,552
1,743	1,762	1,552
1,789	1,808	1,552
1,789	1,808	1,552
1,789	1,808	1,552
1,789	1,808	1,552
1,789	1,809	1,553
1,789	1,809	1,553
1,789	1,809	1,553
1,789	1,809	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,804	1,824	1,553
1,777	1,797	1,553
1,777	1,797	1,538
1,777	1,797	1,538
1,777	1,797	1,538
1,777	1,797	1,538
1,777	1,797	1,538
1,777	1,797	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,736	1,756	1,538
1,719	1,759	1,538
1,719	1,759	1,538
1,719	1,759	1,538
1,719	1,759	1,538
1,719	1,759	1,538
1,719	1,759	1,503
1,719	1,759	1,503
1,719	1,759	1,503
1,719	1,759	1,503
1,719	1,759	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,693	1,733	1,503
1,71	1,752	1,503
1,71	1,752	1,503
1,71	1,752	1,503
1,71	1,752	1,503
1,71	1,752	1,503
1,71	1,752	1,467
1,71	1,752	1,467
1,687	1,729	1,467
1,687	1,729	1,467
1,687	1,729	1,467
1,687	1,729	1,467
1,687	1,729	1,467
1,687	1,729	1,467
1,659	1,7	1,467
1,659	1,7	1,467
1,659	1,7	1,467
1,659	1,7	1,467
1,659	1,7	1,467
1,659	1,7	1,467
1,659	1,7	1,467
1,659	1,7	1,45
1,659	1,7	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45
1,641	1,682	1,45

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Diesel[t] = + 1.00093134404964 + 2.36645810706491Super95[t] -2.02959961349787Super98[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Diesel[t] =  +  1.00093134404964 +  2.36645810706491Super95[t] -2.02959961349787Super98[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Diesel[t] =  +  1.00093134404964 +  2.36645810706491Super95[t] -2.02959961349787Super98[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&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
Diesel[t] = + 1.00093134404964 + 2.36645810706491Super95[t] -2.02959961349787Super98[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.000931344049640.04713121.237100
Super952.366458107064910.13184417.94900
Super98-2.029599613497870.138208-14.685100

\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) & 1.00093134404964 & 0.047131 & 21.2371 & 0 & 0 \tabularnewline
Super95 & 2.36645810706491 & 0.131844 & 17.949 & 0 & 0 \tabularnewline
Super98 & -2.02959961349787 & 0.138208 & -14.6851 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&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]1.00093134404964[/C][C]0.047131[/C][C]21.2371[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Super95[/C][C]2.36645810706491[/C][C]0.131844[/C][C]17.949[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Super98[/C][C]-2.02959961349787[/C][C]0.138208[/C][C]-14.6851[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&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)1.000931344049640.04713121.237100
Super952.366458107064910.13184417.94900
Super98-2.029599613497870.138208-14.685100






Multiple Linear Regression - Regression Statistics
Multiple R0.868969173009457
R-squared0.75510742364074
Adjusted R-squared0.752386395014526
F-TEST (value)277.508077778434
F-TEST (DF numerator)2
F-TEST (DF denominator)180
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0169882281053616
Sum Squared Residuals0.0519479809487637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.868969173009457 \tabularnewline
R-squared & 0.75510742364074 \tabularnewline
Adjusted R-squared & 0.752386395014526 \tabularnewline
F-TEST (value) & 277.508077778434 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 180 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0169882281053616 \tabularnewline
Sum Squared Residuals & 0.0519479809487637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.868969173009457[/C][/ROW]
[ROW][C]R-squared[/C][C]0.75510742364074[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.752386395014526[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]277.508077778434[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]180[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0169882281053616[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0519479809487637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&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 R0.868969173009457
R-squared0.75510742364074
Adjusted R-squared0.752386395014526
F-TEST (value)277.508077778434
F-TEST (DF numerator)2
F-TEST (DF denominator)180
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0169882281053616
Sum Squared Residuals0.0519479809487637






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.5241.496954932031990.0270450679680139
21.4861.50774285247823-0.0217428524782296
31.4861.50774285247823-0.0217428524782296
41.4861.50537639437116-0.019376394371165
51.4861.50537639437116-0.019376394371165
61.4861.50537639437116-0.019376394371165
71.5231.517166441646010.00583355835398873
81.5231.517166441646010.00583355835398873
91.5231.517166441646010.00583355835398873
101.5231.517166441646010.00583355835398873
111.5231.517166441646010.00583355835398873
121.5231.517166441646010.00583355835398873
131.5231.517166441646010.00583355835398873
141.551.517166441646010.0328335583539889
151.551.519852860942450.0301471390575478
161.551.519852860942450.0301471390575478
171.551.519852860942450.0301471390575478
181.551.519852860942450.0301471390575478
191.551.519852860942450.0301471390575478
201.551.519852860942450.0301471390575478
211.5161.51985286094245-0.0038528609424522
221.5161.51985286094245-0.0038528609424522
231.5161.51985286094245-0.0038528609424522
241.5161.51985286094245-0.0038528609424522
251.5161.51985286094245-0.0038528609424522
261.5161.51985286094245-0.0038528609424522
271.5161.51985286094245-0.0038528609424522
281.5161.51985286094245-0.0038528609424522
291.4981.51985286094245-0.0218528609424522
301.4981.51985286094245-0.0218528609424522
311.4981.51985286094245-0.0218528609424522
321.4981.51985286094245-0.0218528609424522
331.4981.51985286094245-0.0218528609424522
341.4981.51985286094245-0.0218528609424522
351.4981.51985286094245-0.0218528609424522
361.4981.51985286094245-0.0218528609424522
371.4981.51985286094245-0.0218528609424522
381.4981.51985286094245-0.0218528609424522
391.4981.51985286094245-0.0218528609424522
401.5421.519852860942450.0221471390575478
411.5421.519852860942450.0221471390575478
421.5261.519852860942450.00614713905754781
431.5261.52861963042729-0.00261963042729079
441.5261.52861963042729-0.00261963042729079
451.5261.52861963042729-0.00261963042729079
461.5261.52861963042729-0.00261963042729079
471.5261.52861963042729-0.00261963042729079
481.5261.52861963042729-0.00261963042729079
491.551.528619630427290.0213803695727092
501.551.528619630427290.0213803695727092
511.551.528619630427290.0213803695727092
521.551.528619630427290.0213803695727092
531.551.537377951260030.012622048739966
541.551.537377951260030.012622048739966
551.551.537377951260030.012622048739966
561.551.537377951260030.012622048739966
571.551.537377951260030.012622048739966
581.551.537377951260030.012622048739966
591.551.537377951260030.012622048739966
601.551.537377951260030.012622048739966
611.551.537377951260030.012622048739966
621.551.537377951260030.012622048739966
631.551.537377951260030.012622048739966
641.551.537377951260030.012622048739966
651.551.537377951260030.012622048739966
661.551.537377951260030.012622048739966
671.551.548839588693410.00116041130659054
681.551.548839588693410.00116041130659054
691.551.548839588693410.00116041130659054
701.551.548839588693410.00116041130659054
711.551.548839588693410.00116041130659054
721.551.548839588693410.00116041130659054
731.551.548839588693410.00116041130659054
741.551.548839588693410.00116041130659054
751.551.548839588693410.00116041130659054
761.5791.548839588693410.0301604113065904
771.5791.556242026899790.0227579731002113
781.5791.556242026899790.0227579731002113
791.5791.556242026899790.0227579731002113
801.5791.556242026899790.0227579731002113
811.5791.556242026899790.0227579731002113
821.5791.556242026899790.0227579731002113
831.5791.556242026899790.0227579731002113
841.5791.556242026899790.0227579731002113
851.5521.549513305680540.00248669431945646
861.5521.549513305680540.00248669431945646
871.5521.549513305680540.00248669431945646
881.5521.549513305680540.00248669431945646
891.5521.549513305680540.00248669431945646
901.5521.549513305680540.00248669431945646
911.5521.56500879638463-0.0130087963846272
921.5521.56500879638463-0.0130087963846272
931.5521.56500879638463-0.0130087963846272
941.5521.56500879638463-0.0130087963846272
951.5531.56297919677113-0.0099791967711297
961.5531.56297919677113-0.0099791967711297
971.5531.56297919677113-0.0099791967711297
981.5531.56297919677113-0.0099791967711297
991.5531.56803207417464-0.0150320741746355
1001.5531.56803207417464-0.0150320741746355
1011.5531.56803207417464-0.0150320741746355
1021.5531.56803207417464-0.0150320741746355
1031.5531.56803207417464-0.0150320741746355
1041.5531.56803207417464-0.0150320741746355
1051.5531.56803207417464-0.0150320741746355
1061.5531.56803207417464-0.0150320741746355
1071.5531.56803207417464-0.0150320741746355
1081.5531.56803207417464-0.0150320741746355
1091.5531.56803207417464-0.0150320741746355
1101.5531.56803207417464-0.0150320741746355
1111.5531.55893689484833-0.00593689484832512
1121.5381.55893689484833-0.020936894848325
1131.5381.55893689484833-0.020936894848325
1141.5381.55893689484833-0.020936894848325
1151.5381.55893689484833-0.020936894848325
1161.5381.55893689484833-0.020936894848325
1171.5381.55893689484833-0.020936894848325
1181.5381.54512569661208-0.0071256966120761
1191.5381.54512569661208-0.0071256966120761
1201.5381.54512569661208-0.0071256966120761
1211.5381.54512569661208-0.0071256966120761
1221.5381.54512569661208-0.0071256966120761
1231.5381.54512569661208-0.0071256966120761
1241.5381.54512569661208-0.0071256966120761
1251.5381.54512569661208-0.0071256966120761
1261.5381.54512569661208-0.0071256966120761
1271.5381.498807109951480.0391928900485206
1281.5381.498807109951480.0391928900485206
1291.5381.498807109951480.0391928900485206
1301.5381.498807109951480.0391928900485206
1311.5381.498807109951480.0391928900485206
1321.5031.498807109951480.00419289004852043
1331.5031.498807109951480.00419289004852043
1341.5031.498807109951480.00419289004852043
1351.5031.498807109951480.00419289004852043
1361.5031.498807109951480.00419289004852043
1371.5031.490048789118740.0129512108812641
1381.5031.490048789118740.0129512108812641
1391.5031.490048789118740.0129512108812641
1401.5031.490048789118740.0129512108812641
1411.5031.490048789118740.0129512108812641
1421.5031.490048789118740.0129512108812641
1431.5031.490048789118740.0129512108812641
1441.5031.490048789118740.0129512108812641
1451.5031.490048789118740.0129512108812641
1461.5031.490048789118740.0129512108812641
1471.5031.490048789118740.0129512108812641
1481.5031.490048789118740.0129512108812641
1491.5031.490048789118740.0129512108812641
1501.5031.490048789118740.0129512108812641
1511.5031.490048789118740.0129512108812641
1521.5031.490048789118740.0129512108812641
1531.5031.491716184282380.0112838157176201
1541.5031.491716184282380.0112838157176201
1551.5031.491716184282380.0112838157176201
1561.5031.491716184282380.0112838157176201
1571.5031.491716184282380.0112838157176201
1581.4671.49171618428238-0.0247161842823797
1591.4671.49171618428238-0.0247161842823797
1601.4671.48396843893034-0.0169684389303377
1611.4671.48396843893034-0.0169684389303377
1621.4671.48396843893034-0.0169684389303377
1631.4671.48396843893034-0.0169684389303377
1641.4671.48396843893034-0.0169684389303377
1651.4671.48396843893034-0.0169684389303377
1661.4671.47656600072396-0.00956600072395842
1671.4671.47656600072396-0.00956600072395842
1681.4671.47656600072396-0.00956600072395842
1691.4671.47656600072396-0.00956600072395842
1701.4671.47656600072396-0.00956600072395842
1711.4671.47656600072396-0.00956600072395842
1721.4671.47656600072396-0.00956600072395842
1731.451.47656600072396-0.0265660007239585
1741.451.47656600072396-0.0265660007239585
1751.451.47050254783975-0.0205025478397517
1761.451.47050254783975-0.0205025478397517
1771.451.47050254783975-0.0205025478397517
1781.451.47050254783975-0.0205025478397517
1791.451.47050254783975-0.0205025478397517
1801.451.47050254783975-0.0205025478397517
1811.451.47050254783975-0.0205025478397517
1821.451.47050254783975-0.0205025478397517
1831.451.47050254783975-0.0205025478397517

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.524 & 1.49695493203199 & 0.0270450679680139 \tabularnewline
2 & 1.486 & 1.50774285247823 & -0.0217428524782296 \tabularnewline
3 & 1.486 & 1.50774285247823 & -0.0217428524782296 \tabularnewline
4 & 1.486 & 1.50537639437116 & -0.019376394371165 \tabularnewline
5 & 1.486 & 1.50537639437116 & -0.019376394371165 \tabularnewline
6 & 1.486 & 1.50537639437116 & -0.019376394371165 \tabularnewline
7 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
8 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
9 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
10 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
11 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
12 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
13 & 1.523 & 1.51716644164601 & 0.00583355835398873 \tabularnewline
14 & 1.55 & 1.51716644164601 & 0.0328335583539889 \tabularnewline
15 & 1.55 & 1.51985286094245 & 0.0301471390575478 \tabularnewline
16 & 1.55 & 1.51985286094245 & 0.0301471390575478 \tabularnewline
17 & 1.55 & 1.51985286094245 & 0.0301471390575478 \tabularnewline
18 & 1.55 & 1.51985286094245 & 0.0301471390575478 \tabularnewline
19 & 1.55 & 1.51985286094245 & 0.0301471390575478 \tabularnewline
20 & 1.55 & 1.51985286094245 & 0.0301471390575478 \tabularnewline
21 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
22 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
23 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
24 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
25 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
26 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
27 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
28 & 1.516 & 1.51985286094245 & -0.0038528609424522 \tabularnewline
29 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
30 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
31 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
32 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
33 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
34 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
35 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
36 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
37 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
38 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
39 & 1.498 & 1.51985286094245 & -0.0218528609424522 \tabularnewline
40 & 1.542 & 1.51985286094245 & 0.0221471390575478 \tabularnewline
41 & 1.542 & 1.51985286094245 & 0.0221471390575478 \tabularnewline
42 & 1.526 & 1.51985286094245 & 0.00614713905754781 \tabularnewline
43 & 1.526 & 1.52861963042729 & -0.00261963042729079 \tabularnewline
44 & 1.526 & 1.52861963042729 & -0.00261963042729079 \tabularnewline
45 & 1.526 & 1.52861963042729 & -0.00261963042729079 \tabularnewline
46 & 1.526 & 1.52861963042729 & -0.00261963042729079 \tabularnewline
47 & 1.526 & 1.52861963042729 & -0.00261963042729079 \tabularnewline
48 & 1.526 & 1.52861963042729 & -0.00261963042729079 \tabularnewline
49 & 1.55 & 1.52861963042729 & 0.0213803695727092 \tabularnewline
50 & 1.55 & 1.52861963042729 & 0.0213803695727092 \tabularnewline
51 & 1.55 & 1.52861963042729 & 0.0213803695727092 \tabularnewline
52 & 1.55 & 1.52861963042729 & 0.0213803695727092 \tabularnewline
53 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
54 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
55 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
56 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
57 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
58 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
59 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
60 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
61 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
62 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
63 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
64 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
65 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
66 & 1.55 & 1.53737795126003 & 0.012622048739966 \tabularnewline
67 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
68 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
69 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
70 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
71 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
72 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
73 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
74 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
75 & 1.55 & 1.54883958869341 & 0.00116041130659054 \tabularnewline
76 & 1.579 & 1.54883958869341 & 0.0301604113065904 \tabularnewline
77 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
78 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
79 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
80 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
81 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
82 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
83 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
84 & 1.579 & 1.55624202689979 & 0.0227579731002113 \tabularnewline
85 & 1.552 & 1.54951330568054 & 0.00248669431945646 \tabularnewline
86 & 1.552 & 1.54951330568054 & 0.00248669431945646 \tabularnewline
87 & 1.552 & 1.54951330568054 & 0.00248669431945646 \tabularnewline
88 & 1.552 & 1.54951330568054 & 0.00248669431945646 \tabularnewline
89 & 1.552 & 1.54951330568054 & 0.00248669431945646 \tabularnewline
90 & 1.552 & 1.54951330568054 & 0.00248669431945646 \tabularnewline
91 & 1.552 & 1.56500879638463 & -0.0130087963846272 \tabularnewline
92 & 1.552 & 1.56500879638463 & -0.0130087963846272 \tabularnewline
93 & 1.552 & 1.56500879638463 & -0.0130087963846272 \tabularnewline
94 & 1.552 & 1.56500879638463 & -0.0130087963846272 \tabularnewline
95 & 1.553 & 1.56297919677113 & -0.0099791967711297 \tabularnewline
96 & 1.553 & 1.56297919677113 & -0.0099791967711297 \tabularnewline
97 & 1.553 & 1.56297919677113 & -0.0099791967711297 \tabularnewline
98 & 1.553 & 1.56297919677113 & -0.0099791967711297 \tabularnewline
99 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
100 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
101 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
102 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
103 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
104 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
105 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
106 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
107 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
108 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
109 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
110 & 1.553 & 1.56803207417464 & -0.0150320741746355 \tabularnewline
111 & 1.553 & 1.55893689484833 & -0.00593689484832512 \tabularnewline
112 & 1.538 & 1.55893689484833 & -0.020936894848325 \tabularnewline
113 & 1.538 & 1.55893689484833 & -0.020936894848325 \tabularnewline
114 & 1.538 & 1.55893689484833 & -0.020936894848325 \tabularnewline
115 & 1.538 & 1.55893689484833 & -0.020936894848325 \tabularnewline
116 & 1.538 & 1.55893689484833 & -0.020936894848325 \tabularnewline
117 & 1.538 & 1.55893689484833 & -0.020936894848325 \tabularnewline
118 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
119 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
120 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
121 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
122 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
123 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
124 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
125 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
126 & 1.538 & 1.54512569661208 & -0.0071256966120761 \tabularnewline
127 & 1.538 & 1.49880710995148 & 0.0391928900485206 \tabularnewline
128 & 1.538 & 1.49880710995148 & 0.0391928900485206 \tabularnewline
129 & 1.538 & 1.49880710995148 & 0.0391928900485206 \tabularnewline
130 & 1.538 & 1.49880710995148 & 0.0391928900485206 \tabularnewline
131 & 1.538 & 1.49880710995148 & 0.0391928900485206 \tabularnewline
132 & 1.503 & 1.49880710995148 & 0.00419289004852043 \tabularnewline
133 & 1.503 & 1.49880710995148 & 0.00419289004852043 \tabularnewline
134 & 1.503 & 1.49880710995148 & 0.00419289004852043 \tabularnewline
135 & 1.503 & 1.49880710995148 & 0.00419289004852043 \tabularnewline
136 & 1.503 & 1.49880710995148 & 0.00419289004852043 \tabularnewline
137 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
138 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
139 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
140 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
141 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
142 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
143 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
144 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
145 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
146 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
147 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
148 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
149 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
150 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
151 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
152 & 1.503 & 1.49004878911874 & 0.0129512108812641 \tabularnewline
153 & 1.503 & 1.49171618428238 & 0.0112838157176201 \tabularnewline
154 & 1.503 & 1.49171618428238 & 0.0112838157176201 \tabularnewline
155 & 1.503 & 1.49171618428238 & 0.0112838157176201 \tabularnewline
156 & 1.503 & 1.49171618428238 & 0.0112838157176201 \tabularnewline
157 & 1.503 & 1.49171618428238 & 0.0112838157176201 \tabularnewline
158 & 1.467 & 1.49171618428238 & -0.0247161842823797 \tabularnewline
159 & 1.467 & 1.49171618428238 & -0.0247161842823797 \tabularnewline
160 & 1.467 & 1.48396843893034 & -0.0169684389303377 \tabularnewline
161 & 1.467 & 1.48396843893034 & -0.0169684389303377 \tabularnewline
162 & 1.467 & 1.48396843893034 & -0.0169684389303377 \tabularnewline
163 & 1.467 & 1.48396843893034 & -0.0169684389303377 \tabularnewline
164 & 1.467 & 1.48396843893034 & -0.0169684389303377 \tabularnewline
165 & 1.467 & 1.48396843893034 & -0.0169684389303377 \tabularnewline
166 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
167 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
168 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
169 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
170 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
171 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
172 & 1.467 & 1.47656600072396 & -0.00956600072395842 \tabularnewline
173 & 1.45 & 1.47656600072396 & -0.0265660007239585 \tabularnewline
174 & 1.45 & 1.47656600072396 & -0.0265660007239585 \tabularnewline
175 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
176 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
177 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
178 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
179 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
180 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
181 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
182 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
183 & 1.45 & 1.47050254783975 & -0.0205025478397517 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&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]1.524[/C][C]1.49695493203199[/C][C]0.0270450679680139[/C][/ROW]
[ROW][C]2[/C][C]1.486[/C][C]1.50774285247823[/C][C]-0.0217428524782296[/C][/ROW]
[ROW][C]3[/C][C]1.486[/C][C]1.50774285247823[/C][C]-0.0217428524782296[/C][/ROW]
[ROW][C]4[/C][C]1.486[/C][C]1.50537639437116[/C][C]-0.019376394371165[/C][/ROW]
[ROW][C]5[/C][C]1.486[/C][C]1.50537639437116[/C][C]-0.019376394371165[/C][/ROW]
[ROW][C]6[/C][C]1.486[/C][C]1.50537639437116[/C][C]-0.019376394371165[/C][/ROW]
[ROW][C]7[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]8[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]9[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]10[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]11[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]12[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]13[/C][C]1.523[/C][C]1.51716644164601[/C][C]0.00583355835398873[/C][/ROW]
[ROW][C]14[/C][C]1.55[/C][C]1.51716644164601[/C][C]0.0328335583539889[/C][/ROW]
[ROW][C]15[/C][C]1.55[/C][C]1.51985286094245[/C][C]0.0301471390575478[/C][/ROW]
[ROW][C]16[/C][C]1.55[/C][C]1.51985286094245[/C][C]0.0301471390575478[/C][/ROW]
[ROW][C]17[/C][C]1.55[/C][C]1.51985286094245[/C][C]0.0301471390575478[/C][/ROW]
[ROW][C]18[/C][C]1.55[/C][C]1.51985286094245[/C][C]0.0301471390575478[/C][/ROW]
[ROW][C]19[/C][C]1.55[/C][C]1.51985286094245[/C][C]0.0301471390575478[/C][/ROW]
[ROW][C]20[/C][C]1.55[/C][C]1.51985286094245[/C][C]0.0301471390575478[/C][/ROW]
[ROW][C]21[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]22[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]23[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]24[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]25[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]26[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]27[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]28[/C][C]1.516[/C][C]1.51985286094245[/C][C]-0.0038528609424522[/C][/ROW]
[ROW][C]29[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]30[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]31[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]32[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]33[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]34[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]35[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]36[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]37[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]38[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]39[/C][C]1.498[/C][C]1.51985286094245[/C][C]-0.0218528609424522[/C][/ROW]
[ROW][C]40[/C][C]1.542[/C][C]1.51985286094245[/C][C]0.0221471390575478[/C][/ROW]
[ROW][C]41[/C][C]1.542[/C][C]1.51985286094245[/C][C]0.0221471390575478[/C][/ROW]
[ROW][C]42[/C][C]1.526[/C][C]1.51985286094245[/C][C]0.00614713905754781[/C][/ROW]
[ROW][C]43[/C][C]1.526[/C][C]1.52861963042729[/C][C]-0.00261963042729079[/C][/ROW]
[ROW][C]44[/C][C]1.526[/C][C]1.52861963042729[/C][C]-0.00261963042729079[/C][/ROW]
[ROW][C]45[/C][C]1.526[/C][C]1.52861963042729[/C][C]-0.00261963042729079[/C][/ROW]
[ROW][C]46[/C][C]1.526[/C][C]1.52861963042729[/C][C]-0.00261963042729079[/C][/ROW]
[ROW][C]47[/C][C]1.526[/C][C]1.52861963042729[/C][C]-0.00261963042729079[/C][/ROW]
[ROW][C]48[/C][C]1.526[/C][C]1.52861963042729[/C][C]-0.00261963042729079[/C][/ROW]
[ROW][C]49[/C][C]1.55[/C][C]1.52861963042729[/C][C]0.0213803695727092[/C][/ROW]
[ROW][C]50[/C][C]1.55[/C][C]1.52861963042729[/C][C]0.0213803695727092[/C][/ROW]
[ROW][C]51[/C][C]1.55[/C][C]1.52861963042729[/C][C]0.0213803695727092[/C][/ROW]
[ROW][C]52[/C][C]1.55[/C][C]1.52861963042729[/C][C]0.0213803695727092[/C][/ROW]
[ROW][C]53[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]54[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]55[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]56[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]57[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]58[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]59[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]60[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]61[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]62[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]63[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]64[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]65[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]66[/C][C]1.55[/C][C]1.53737795126003[/C][C]0.012622048739966[/C][/ROW]
[ROW][C]67[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]68[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]69[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]70[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]71[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]72[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]73[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]74[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]75[/C][C]1.55[/C][C]1.54883958869341[/C][C]0.00116041130659054[/C][/ROW]
[ROW][C]76[/C][C]1.579[/C][C]1.54883958869341[/C][C]0.0301604113065904[/C][/ROW]
[ROW][C]77[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]78[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]79[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]80[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]81[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]82[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]83[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]84[/C][C]1.579[/C][C]1.55624202689979[/C][C]0.0227579731002113[/C][/ROW]
[ROW][C]85[/C][C]1.552[/C][C]1.54951330568054[/C][C]0.00248669431945646[/C][/ROW]
[ROW][C]86[/C][C]1.552[/C][C]1.54951330568054[/C][C]0.00248669431945646[/C][/ROW]
[ROW][C]87[/C][C]1.552[/C][C]1.54951330568054[/C][C]0.00248669431945646[/C][/ROW]
[ROW][C]88[/C][C]1.552[/C][C]1.54951330568054[/C][C]0.00248669431945646[/C][/ROW]
[ROW][C]89[/C][C]1.552[/C][C]1.54951330568054[/C][C]0.00248669431945646[/C][/ROW]
[ROW][C]90[/C][C]1.552[/C][C]1.54951330568054[/C][C]0.00248669431945646[/C][/ROW]
[ROW][C]91[/C][C]1.552[/C][C]1.56500879638463[/C][C]-0.0130087963846272[/C][/ROW]
[ROW][C]92[/C][C]1.552[/C][C]1.56500879638463[/C][C]-0.0130087963846272[/C][/ROW]
[ROW][C]93[/C][C]1.552[/C][C]1.56500879638463[/C][C]-0.0130087963846272[/C][/ROW]
[ROW][C]94[/C][C]1.552[/C][C]1.56500879638463[/C][C]-0.0130087963846272[/C][/ROW]
[ROW][C]95[/C][C]1.553[/C][C]1.56297919677113[/C][C]-0.0099791967711297[/C][/ROW]
[ROW][C]96[/C][C]1.553[/C][C]1.56297919677113[/C][C]-0.0099791967711297[/C][/ROW]
[ROW][C]97[/C][C]1.553[/C][C]1.56297919677113[/C][C]-0.0099791967711297[/C][/ROW]
[ROW][C]98[/C][C]1.553[/C][C]1.56297919677113[/C][C]-0.0099791967711297[/C][/ROW]
[ROW][C]99[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]100[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]101[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]102[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]103[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]104[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]105[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]106[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]107[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]108[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]109[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]110[/C][C]1.553[/C][C]1.56803207417464[/C][C]-0.0150320741746355[/C][/ROW]
[ROW][C]111[/C][C]1.553[/C][C]1.55893689484833[/C][C]-0.00593689484832512[/C][/ROW]
[ROW][C]112[/C][C]1.538[/C][C]1.55893689484833[/C][C]-0.020936894848325[/C][/ROW]
[ROW][C]113[/C][C]1.538[/C][C]1.55893689484833[/C][C]-0.020936894848325[/C][/ROW]
[ROW][C]114[/C][C]1.538[/C][C]1.55893689484833[/C][C]-0.020936894848325[/C][/ROW]
[ROW][C]115[/C][C]1.538[/C][C]1.55893689484833[/C][C]-0.020936894848325[/C][/ROW]
[ROW][C]116[/C][C]1.538[/C][C]1.55893689484833[/C][C]-0.020936894848325[/C][/ROW]
[ROW][C]117[/C][C]1.538[/C][C]1.55893689484833[/C][C]-0.020936894848325[/C][/ROW]
[ROW][C]118[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]119[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]120[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]121[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]122[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]123[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]124[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]125[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]126[/C][C]1.538[/C][C]1.54512569661208[/C][C]-0.0071256966120761[/C][/ROW]
[ROW][C]127[/C][C]1.538[/C][C]1.49880710995148[/C][C]0.0391928900485206[/C][/ROW]
[ROW][C]128[/C][C]1.538[/C][C]1.49880710995148[/C][C]0.0391928900485206[/C][/ROW]
[ROW][C]129[/C][C]1.538[/C][C]1.49880710995148[/C][C]0.0391928900485206[/C][/ROW]
[ROW][C]130[/C][C]1.538[/C][C]1.49880710995148[/C][C]0.0391928900485206[/C][/ROW]
[ROW][C]131[/C][C]1.538[/C][C]1.49880710995148[/C][C]0.0391928900485206[/C][/ROW]
[ROW][C]132[/C][C]1.503[/C][C]1.49880710995148[/C][C]0.00419289004852043[/C][/ROW]
[ROW][C]133[/C][C]1.503[/C][C]1.49880710995148[/C][C]0.00419289004852043[/C][/ROW]
[ROW][C]134[/C][C]1.503[/C][C]1.49880710995148[/C][C]0.00419289004852043[/C][/ROW]
[ROW][C]135[/C][C]1.503[/C][C]1.49880710995148[/C][C]0.00419289004852043[/C][/ROW]
[ROW][C]136[/C][C]1.503[/C][C]1.49880710995148[/C][C]0.00419289004852043[/C][/ROW]
[ROW][C]137[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]138[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]139[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]140[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]141[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]142[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]143[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]144[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]145[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]146[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]147[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]148[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]149[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]150[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]151[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]152[/C][C]1.503[/C][C]1.49004878911874[/C][C]0.0129512108812641[/C][/ROW]
[ROW][C]153[/C][C]1.503[/C][C]1.49171618428238[/C][C]0.0112838157176201[/C][/ROW]
[ROW][C]154[/C][C]1.503[/C][C]1.49171618428238[/C][C]0.0112838157176201[/C][/ROW]
[ROW][C]155[/C][C]1.503[/C][C]1.49171618428238[/C][C]0.0112838157176201[/C][/ROW]
[ROW][C]156[/C][C]1.503[/C][C]1.49171618428238[/C][C]0.0112838157176201[/C][/ROW]
[ROW][C]157[/C][C]1.503[/C][C]1.49171618428238[/C][C]0.0112838157176201[/C][/ROW]
[ROW][C]158[/C][C]1.467[/C][C]1.49171618428238[/C][C]-0.0247161842823797[/C][/ROW]
[ROW][C]159[/C][C]1.467[/C][C]1.49171618428238[/C][C]-0.0247161842823797[/C][/ROW]
[ROW][C]160[/C][C]1.467[/C][C]1.48396843893034[/C][C]-0.0169684389303377[/C][/ROW]
[ROW][C]161[/C][C]1.467[/C][C]1.48396843893034[/C][C]-0.0169684389303377[/C][/ROW]
[ROW][C]162[/C][C]1.467[/C][C]1.48396843893034[/C][C]-0.0169684389303377[/C][/ROW]
[ROW][C]163[/C][C]1.467[/C][C]1.48396843893034[/C][C]-0.0169684389303377[/C][/ROW]
[ROW][C]164[/C][C]1.467[/C][C]1.48396843893034[/C][C]-0.0169684389303377[/C][/ROW]
[ROW][C]165[/C][C]1.467[/C][C]1.48396843893034[/C][C]-0.0169684389303377[/C][/ROW]
[ROW][C]166[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]167[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]168[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]169[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]170[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]171[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]172[/C][C]1.467[/C][C]1.47656600072396[/C][C]-0.00956600072395842[/C][/ROW]
[ROW][C]173[/C][C]1.45[/C][C]1.47656600072396[/C][C]-0.0265660007239585[/C][/ROW]
[ROW][C]174[/C][C]1.45[/C][C]1.47656600072396[/C][C]-0.0265660007239585[/C][/ROW]
[ROW][C]175[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]176[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]177[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]178[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]179[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]180[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]181[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]182[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[ROW][C]183[/C][C]1.45[/C][C]1.47050254783975[/C][C]-0.0205025478397517[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&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
11.5241.496954932031990.0270450679680139
21.4861.50774285247823-0.0217428524782296
31.4861.50774285247823-0.0217428524782296
41.4861.50537639437116-0.019376394371165
51.4861.50537639437116-0.019376394371165
61.4861.50537639437116-0.019376394371165
71.5231.517166441646010.00583355835398873
81.5231.517166441646010.00583355835398873
91.5231.517166441646010.00583355835398873
101.5231.517166441646010.00583355835398873
111.5231.517166441646010.00583355835398873
121.5231.517166441646010.00583355835398873
131.5231.517166441646010.00583355835398873
141.551.517166441646010.0328335583539889
151.551.519852860942450.0301471390575478
161.551.519852860942450.0301471390575478
171.551.519852860942450.0301471390575478
181.551.519852860942450.0301471390575478
191.551.519852860942450.0301471390575478
201.551.519852860942450.0301471390575478
211.5161.51985286094245-0.0038528609424522
221.5161.51985286094245-0.0038528609424522
231.5161.51985286094245-0.0038528609424522
241.5161.51985286094245-0.0038528609424522
251.5161.51985286094245-0.0038528609424522
261.5161.51985286094245-0.0038528609424522
271.5161.51985286094245-0.0038528609424522
281.5161.51985286094245-0.0038528609424522
291.4981.51985286094245-0.0218528609424522
301.4981.51985286094245-0.0218528609424522
311.4981.51985286094245-0.0218528609424522
321.4981.51985286094245-0.0218528609424522
331.4981.51985286094245-0.0218528609424522
341.4981.51985286094245-0.0218528609424522
351.4981.51985286094245-0.0218528609424522
361.4981.51985286094245-0.0218528609424522
371.4981.51985286094245-0.0218528609424522
381.4981.51985286094245-0.0218528609424522
391.4981.51985286094245-0.0218528609424522
401.5421.519852860942450.0221471390575478
411.5421.519852860942450.0221471390575478
421.5261.519852860942450.00614713905754781
431.5261.52861963042729-0.00261963042729079
441.5261.52861963042729-0.00261963042729079
451.5261.52861963042729-0.00261963042729079
461.5261.52861963042729-0.00261963042729079
471.5261.52861963042729-0.00261963042729079
481.5261.52861963042729-0.00261963042729079
491.551.528619630427290.0213803695727092
501.551.528619630427290.0213803695727092
511.551.528619630427290.0213803695727092
521.551.528619630427290.0213803695727092
531.551.537377951260030.012622048739966
541.551.537377951260030.012622048739966
551.551.537377951260030.012622048739966
561.551.537377951260030.012622048739966
571.551.537377951260030.012622048739966
581.551.537377951260030.012622048739966
591.551.537377951260030.012622048739966
601.551.537377951260030.012622048739966
611.551.537377951260030.012622048739966
621.551.537377951260030.012622048739966
631.551.537377951260030.012622048739966
641.551.537377951260030.012622048739966
651.551.537377951260030.012622048739966
661.551.537377951260030.012622048739966
671.551.548839588693410.00116041130659054
681.551.548839588693410.00116041130659054
691.551.548839588693410.00116041130659054
701.551.548839588693410.00116041130659054
711.551.548839588693410.00116041130659054
721.551.548839588693410.00116041130659054
731.551.548839588693410.00116041130659054
741.551.548839588693410.00116041130659054
751.551.548839588693410.00116041130659054
761.5791.548839588693410.0301604113065904
771.5791.556242026899790.0227579731002113
781.5791.556242026899790.0227579731002113
791.5791.556242026899790.0227579731002113
801.5791.556242026899790.0227579731002113
811.5791.556242026899790.0227579731002113
821.5791.556242026899790.0227579731002113
831.5791.556242026899790.0227579731002113
841.5791.556242026899790.0227579731002113
851.5521.549513305680540.00248669431945646
861.5521.549513305680540.00248669431945646
871.5521.549513305680540.00248669431945646
881.5521.549513305680540.00248669431945646
891.5521.549513305680540.00248669431945646
901.5521.549513305680540.00248669431945646
911.5521.56500879638463-0.0130087963846272
921.5521.56500879638463-0.0130087963846272
931.5521.56500879638463-0.0130087963846272
941.5521.56500879638463-0.0130087963846272
951.5531.56297919677113-0.0099791967711297
961.5531.56297919677113-0.0099791967711297
971.5531.56297919677113-0.0099791967711297
981.5531.56297919677113-0.0099791967711297
991.5531.56803207417464-0.0150320741746355
1001.5531.56803207417464-0.0150320741746355
1011.5531.56803207417464-0.0150320741746355
1021.5531.56803207417464-0.0150320741746355
1031.5531.56803207417464-0.0150320741746355
1041.5531.56803207417464-0.0150320741746355
1051.5531.56803207417464-0.0150320741746355
1061.5531.56803207417464-0.0150320741746355
1071.5531.56803207417464-0.0150320741746355
1081.5531.56803207417464-0.0150320741746355
1091.5531.56803207417464-0.0150320741746355
1101.5531.56803207417464-0.0150320741746355
1111.5531.55893689484833-0.00593689484832512
1121.5381.55893689484833-0.020936894848325
1131.5381.55893689484833-0.020936894848325
1141.5381.55893689484833-0.020936894848325
1151.5381.55893689484833-0.020936894848325
1161.5381.55893689484833-0.020936894848325
1171.5381.55893689484833-0.020936894848325
1181.5381.54512569661208-0.0071256966120761
1191.5381.54512569661208-0.0071256966120761
1201.5381.54512569661208-0.0071256966120761
1211.5381.54512569661208-0.0071256966120761
1221.5381.54512569661208-0.0071256966120761
1231.5381.54512569661208-0.0071256966120761
1241.5381.54512569661208-0.0071256966120761
1251.5381.54512569661208-0.0071256966120761
1261.5381.54512569661208-0.0071256966120761
1271.5381.498807109951480.0391928900485206
1281.5381.498807109951480.0391928900485206
1291.5381.498807109951480.0391928900485206
1301.5381.498807109951480.0391928900485206
1311.5381.498807109951480.0391928900485206
1321.5031.498807109951480.00419289004852043
1331.5031.498807109951480.00419289004852043
1341.5031.498807109951480.00419289004852043
1351.5031.498807109951480.00419289004852043
1361.5031.498807109951480.00419289004852043
1371.5031.490048789118740.0129512108812641
1381.5031.490048789118740.0129512108812641
1391.5031.490048789118740.0129512108812641
1401.5031.490048789118740.0129512108812641
1411.5031.490048789118740.0129512108812641
1421.5031.490048789118740.0129512108812641
1431.5031.490048789118740.0129512108812641
1441.5031.490048789118740.0129512108812641
1451.5031.490048789118740.0129512108812641
1461.5031.490048789118740.0129512108812641
1471.5031.490048789118740.0129512108812641
1481.5031.490048789118740.0129512108812641
1491.5031.490048789118740.0129512108812641
1501.5031.490048789118740.0129512108812641
1511.5031.490048789118740.0129512108812641
1521.5031.490048789118740.0129512108812641
1531.5031.491716184282380.0112838157176201
1541.5031.491716184282380.0112838157176201
1551.5031.491716184282380.0112838157176201
1561.5031.491716184282380.0112838157176201
1571.5031.491716184282380.0112838157176201
1581.4671.49171618428238-0.0247161842823797
1591.4671.49171618428238-0.0247161842823797
1601.4671.48396843893034-0.0169684389303377
1611.4671.48396843893034-0.0169684389303377
1621.4671.48396843893034-0.0169684389303377
1631.4671.48396843893034-0.0169684389303377
1641.4671.48396843893034-0.0169684389303377
1651.4671.48396843893034-0.0169684389303377
1661.4671.47656600072396-0.00956600072395842
1671.4671.47656600072396-0.00956600072395842
1681.4671.47656600072396-0.00956600072395842
1691.4671.47656600072396-0.00956600072395842
1701.4671.47656600072396-0.00956600072395842
1711.4671.47656600072396-0.00956600072395842
1721.4671.47656600072396-0.00956600072395842
1731.451.47656600072396-0.0265660007239585
1741.451.47656600072396-0.0265660007239585
1751.451.47050254783975-0.0205025478397517
1761.451.47050254783975-0.0205025478397517
1771.451.47050254783975-0.0205025478397517
1781.451.47050254783975-0.0205025478397517
1791.451.47050254783975-0.0205025478397517
1801.451.47050254783975-0.0205025478397517
1811.451.47050254783975-0.0205025478397517
1821.451.47050254783975-0.0205025478397517
1831.451.47050254783975-0.0205025478397517






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
61.66364341112035e-513.3272868222407e-511
70.7878598950390130.4242802099219740.212140104960987
80.7366334176013360.5267331647973280.263366582398664
90.6505820863285250.6988358273429490.349417913671475
100.5516522580148780.8966954839702430.448347741985122
110.4502961483563920.9005922967127850.549703851643608
120.3542286131867930.7084572263735860.645771386813207
130.2688108622046270.5376217244092530.731189137795373
140.4498903124342950.8997806248685910.550109687565705
150.378296830550230.7565936611004590.62170316944977
160.3143678048359610.6287356096719210.685632195164039
170.2588566719331410.5177133438662810.741143328066859
180.2118071759109970.4236143518219940.788192824089003
190.172757281316240.3455145626324790.82724271868376
200.1409353964496670.2818707928993340.859064603550333
210.3160815706364290.6321631412728590.683918429363571
220.4444539636422650.888907927284530.555546036357735
230.5244217309781710.9511565380436590.475578269021829
240.5706628504779470.8586742990441050.429337149522053
250.5945922720445470.8108154559109060.405407727955453
260.6033625579002830.7932748841994330.396637442099716
270.6013288420340460.7973423159319080.398671157965954
280.591212216744950.8175755665101010.40878778325505
290.7195114749831030.5609770500337940.280488525016897
300.8017750251773980.3964499496452040.198224974822602
310.8544289265395160.2911421469209690.145571073460485
320.8888648444222930.2222703111554140.111135155577707
330.912093759387830.1758124812243390.0879062406121695
340.9283171117728330.1433657764543340.0716828882271672
350.9400730561336790.1198538877326420.0599269438663212
360.948930639326020.1021387213479590.0510693606739797
370.9558889347620250.08822213047595030.0441110652379752
380.9616042858235070.07679142835298550.0383957141764927
390.9665200520449740.06695989591005230.0334799479550262
400.971058880836450.05788223832709950.0289411191635498
410.9745171187365230.05096576252695420.0254828812634771
420.9670469049346260.06590619013074710.0329530950653735
430.9579572532707170.08408549345856640.0420427467292832
440.9465643770684960.1068712458630080.0534356229315041
450.9327413568918820.1345172862162350.0672586431081175
460.9163110625467290.1673778749065420.0836889374532712
470.8971162591610630.2057674816778740.102883740838937
480.8750481788010110.2499036423979780.124951821198989
490.8820492245501830.2359015508996340.117950775449817
500.8850720120403280.2298559759193440.114927987959672
510.8858470348588440.2283059302823120.114152965141156
520.8854823938373290.2290352123253420.114517606162671
530.8659762712636440.2680474574727130.134023728736356
540.8447561731651070.3104876536697850.155243826834893
550.8219751655913510.3560496688172990.178024834408649
560.7978370339834040.4043259320331910.202162966016596
570.7725944836997680.4548110326004630.227405516300232
580.7465453732298930.5069092535402140.253454626770107
590.7200276958188340.5599446083623310.279972304181166
600.6934140397107110.6131719205785770.306585960289289
610.6671063535936020.6657872928127970.332893646406398
620.6415319661187710.7169360677624570.358468033881229
630.6171419551340150.765716089731970.382858044865985
640.5944131587292990.8111736825414010.405586841270701
650.5738554132731470.8522891734537060.426144586726853
660.5560260631784020.8879478736431970.443973936821598
670.549985748964630.9000285020707390.45001425103537
680.5343735731267060.9312528537465890.465626426873294
690.5128961019035460.9742077961929080.487103898096454
700.4876644154465160.9753288308930330.512335584553484
710.4600390836617370.9200781673234750.539960916338263
720.4309883206028790.8619766412057590.569011679397121
730.4012531131319850.8025062262639690.598746886868015
740.371427832397450.7428556647949010.62857216760255
750.3420011228060270.6840022456120540.657998877193973
760.4405707828168040.8811415656336080.559429217183196
770.4577102299689430.9154204599378870.542289770031057
780.4778639406834590.9557278813669180.522136059316541
790.5018163947454750.996367210509050.498183605254525
800.5302841959469780.9394316081060440.469715804053022
810.5639047911142830.8721904177714340.436095208885717
820.6031722480690340.7936555038619310.396827751930966
830.6483067099059150.7033865801881690.351693290094085
840.6990410731390170.6019178537219670.300958926860983
850.6989335122157850.6021329755684290.301066487784215
860.7013597750109530.5972804499780940.298640224989047
870.7069720033776080.5860559932447850.293027996622392
880.7164792123662390.5670415752675230.283520787633761
890.7306387722284610.5387224555430790.269361227771539
900.7502171468737930.4995657062524130.249782853126207
910.7792961123670650.4414077752658690.220703887632935
920.7975217001565990.4049565996868020.202478299843401
930.8081552366342360.3836895267315270.191844763365764
940.8132208949629550.373558210074090.186779105037045
950.8145130665509440.3709738668981120.185486933449056
960.8114515878776880.3770968242446240.188548412122312
970.8048038098646680.3903923802706630.195196190135332
980.7950369691472360.4099260617055290.204963030852764
990.8022244570925370.3955510858149270.197775542907463
1000.8056218190643950.3887563618712110.194378180935605
1010.8063731411849230.3872537176301550.193626858815077
1020.8053331373976430.3893337252047130.194666862602357
1030.8032108989525850.3935782020948310.196789101047415
1040.8006652210352730.3986695579294540.199334778964727
1050.7983760714989910.4032478570020180.201623928501009
1060.797107084553270.4057858308934590.20289291544673
1070.7977690425150470.4044619149699070.202230957484953
1080.801490797479880.397018405040240.19850920252012
1090.8096973774082430.3806052451835150.190302622591757
1100.8241733651718250.351653269656350.175826634828175
1110.7965874767225830.4068250465548330.203412523277417
1120.8154211248475060.3691577503049870.184578875152494
1130.8383483275820190.3233033448359630.161651672417981
1140.8660731464262550.267853707147490.133926853573745
1150.8986544790649980.2026910418700030.101345520935002
1160.934490336547810.1310193269043810.0655096634521905
1170.9686418612875010.06271627742499820.0313581387124991
1180.9603469416371680.07930611672566360.0396530583628318
1190.9503681552967040.09926368940659220.0496318447032961
1200.9385399293333980.1229201413332050.0614600706666025
1210.9247666543144570.1504666913710850.0752333456855427
1220.9091144454089160.1817711091821670.0908855545910836
1230.8920593010086110.2158813979827790.107940698991389
1240.8753962380636970.2492075238726050.124603761936303
1250.8678188134184970.2643623731630060.132181186581503
1260.9997842343747890.0004315312504228330.000215765625211417
1270.9998758003745430.0002483992509147440.000124199625457372
1280.999913425237220.0001731495255590358.65747627795174e-05
1290.9999482544188910.000103491162218185.174558110909e-05
1300.9999754154810324.91690379368452e-052.45845189684226e-05
1310.9999917320641311.65358717373646e-058.26793586868228e-06
1320.9999954339377659.13212447091171e-064.56606223545585e-06
1330.9999975574749944.88505001276969e-062.44252500638485e-06
1340.999998887548782.22490244098838e-061.11245122049419e-06
1350.9999996333984397.33203122141927e-073.66601561070963e-07
1360.9999999336705121.32658976990259e-076.63294884951293e-08
1370.9999998616778232.76644353917515e-071.38322176958757e-07
1380.9999997134859515.73028097587024e-072.86514048793512e-07
1390.9999994117470931.17650581431362e-065.88252907156812e-07
1400.9999988047261842.39054763304203e-061.19527381652101e-06
1410.9999975993745954.80125081053426e-062.40062540526713e-06
1420.9999952389683559.52206328963597e-064.76103164481798e-06
1430.9999906840453771.86319092454253e-059.31595462271264e-06
1440.9999820289865853.59420268308892e-051.79710134154446e-05
1450.9999658473516756.8305296650516e-053.4152648325258e-05
1460.999936105095470.0001277898090599756.38949045299876e-05
1470.9998824173540730.000235165291853270.000117582645926635
1480.9997873852696660.0004252294606677050.000212614730333852
1490.9996228924674720.0007542150650562580.000377107532528129
1500.9993464522830520.001307095433895430.000653547716947714
1510.9989088368061590.002182326387681640.00109116319384082
1520.9985377306525960.002924538694807030.00146226934740351
1530.9986582591457620.002683481708476510.00134174085423825
1540.9990125473843430.001974905231313310.000987452615656655
1550.9995190899038130.0009618201923741380.000480910096187069
1560.9999092718876360.0001814562247282599.07281123641297e-05
1570.9999993619602351.27607953069809e-066.38039765349043e-07
1580.9999996788687746.42262450979602e-073.21131225489801e-07
1590.9999999750852654.98294702154663e-082.49147351077331e-08
1600.99999993142091.37158200609563e-076.85791003047814e-08
1610.9999998082127773.83574446065242e-071.91787223032621e-07
1620.9999994585871711.08282565706655e-065.41412828533276e-07
1630.99999846595923.06808160094826e-061.53404080047413e-06
1640.9999956597982048.68040359124811e-064.34020179562405e-06
1650.9999877962891822.44074216351956e-051.22037108175978e-05
1660.9999717919398375.64161203269039e-052.82080601634519e-05
1670.9999386976482260.0001226047035475136.13023517737563e-05
1680.999878193067760.0002436138644791270.000121806932239563
1690.9997907685113320.0004184629773356480.000209231488667824
1700.9997299254869670.0005401490260668070.000270074513033403
1710.9998440074012040.0003119851975916030.000155992598795801
17212.87560192226581e-1141.43780096113291e-114
17311.01792064688891e-2125.08960323444453e-213
17411.42725798214978e-837.13628991074892e-84
17511.69337243438397e-708.46686217191986e-71
17619.48406749358026e-574.74203374679013e-57
17712.33735966442174e-441.16867983221087e-44

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 1.66364341112035e-51 & 3.3272868222407e-51 & 1 \tabularnewline
7 & 0.787859895039013 & 0.424280209921974 & 0.212140104960987 \tabularnewline
8 & 0.736633417601336 & 0.526733164797328 & 0.263366582398664 \tabularnewline
9 & 0.650582086328525 & 0.698835827342949 & 0.349417913671475 \tabularnewline
10 & 0.551652258014878 & 0.896695483970243 & 0.448347741985122 \tabularnewline
11 & 0.450296148356392 & 0.900592296712785 & 0.549703851643608 \tabularnewline
12 & 0.354228613186793 & 0.708457226373586 & 0.645771386813207 \tabularnewline
13 & 0.268810862204627 & 0.537621724409253 & 0.731189137795373 \tabularnewline
14 & 0.449890312434295 & 0.899780624868591 & 0.550109687565705 \tabularnewline
15 & 0.37829683055023 & 0.756593661100459 & 0.62170316944977 \tabularnewline
16 & 0.314367804835961 & 0.628735609671921 & 0.685632195164039 \tabularnewline
17 & 0.258856671933141 & 0.517713343866281 & 0.741143328066859 \tabularnewline
18 & 0.211807175910997 & 0.423614351821994 & 0.788192824089003 \tabularnewline
19 & 0.17275728131624 & 0.345514562632479 & 0.82724271868376 \tabularnewline
20 & 0.140935396449667 & 0.281870792899334 & 0.859064603550333 \tabularnewline
21 & 0.316081570636429 & 0.632163141272859 & 0.683918429363571 \tabularnewline
22 & 0.444453963642265 & 0.88890792728453 & 0.555546036357735 \tabularnewline
23 & 0.524421730978171 & 0.951156538043659 & 0.475578269021829 \tabularnewline
24 & 0.570662850477947 & 0.858674299044105 & 0.429337149522053 \tabularnewline
25 & 0.594592272044547 & 0.810815455910906 & 0.405407727955453 \tabularnewline
26 & 0.603362557900283 & 0.793274884199433 & 0.396637442099716 \tabularnewline
27 & 0.601328842034046 & 0.797342315931908 & 0.398671157965954 \tabularnewline
28 & 0.59121221674495 & 0.817575566510101 & 0.40878778325505 \tabularnewline
29 & 0.719511474983103 & 0.560977050033794 & 0.280488525016897 \tabularnewline
30 & 0.801775025177398 & 0.396449949645204 & 0.198224974822602 \tabularnewline
31 & 0.854428926539516 & 0.291142146920969 & 0.145571073460485 \tabularnewline
32 & 0.888864844422293 & 0.222270311155414 & 0.111135155577707 \tabularnewline
33 & 0.91209375938783 & 0.175812481224339 & 0.0879062406121695 \tabularnewline
34 & 0.928317111772833 & 0.143365776454334 & 0.0716828882271672 \tabularnewline
35 & 0.940073056133679 & 0.119853887732642 & 0.0599269438663212 \tabularnewline
36 & 0.94893063932602 & 0.102138721347959 & 0.0510693606739797 \tabularnewline
37 & 0.955888934762025 & 0.0882221304759503 & 0.0441110652379752 \tabularnewline
38 & 0.961604285823507 & 0.0767914283529855 & 0.0383957141764927 \tabularnewline
39 & 0.966520052044974 & 0.0669598959100523 & 0.0334799479550262 \tabularnewline
40 & 0.97105888083645 & 0.0578822383270995 & 0.0289411191635498 \tabularnewline
41 & 0.974517118736523 & 0.0509657625269542 & 0.0254828812634771 \tabularnewline
42 & 0.967046904934626 & 0.0659061901307471 & 0.0329530950653735 \tabularnewline
43 & 0.957957253270717 & 0.0840854934585664 & 0.0420427467292832 \tabularnewline
44 & 0.946564377068496 & 0.106871245863008 & 0.0534356229315041 \tabularnewline
45 & 0.932741356891882 & 0.134517286216235 & 0.0672586431081175 \tabularnewline
46 & 0.916311062546729 & 0.167377874906542 & 0.0836889374532712 \tabularnewline
47 & 0.897116259161063 & 0.205767481677874 & 0.102883740838937 \tabularnewline
48 & 0.875048178801011 & 0.249903642397978 & 0.124951821198989 \tabularnewline
49 & 0.882049224550183 & 0.235901550899634 & 0.117950775449817 \tabularnewline
50 & 0.885072012040328 & 0.229855975919344 & 0.114927987959672 \tabularnewline
51 & 0.885847034858844 & 0.228305930282312 & 0.114152965141156 \tabularnewline
52 & 0.885482393837329 & 0.229035212325342 & 0.114517606162671 \tabularnewline
53 & 0.865976271263644 & 0.268047457472713 & 0.134023728736356 \tabularnewline
54 & 0.844756173165107 & 0.310487653669785 & 0.155243826834893 \tabularnewline
55 & 0.821975165591351 & 0.356049668817299 & 0.178024834408649 \tabularnewline
56 & 0.797837033983404 & 0.404325932033191 & 0.202162966016596 \tabularnewline
57 & 0.772594483699768 & 0.454811032600463 & 0.227405516300232 \tabularnewline
58 & 0.746545373229893 & 0.506909253540214 & 0.253454626770107 \tabularnewline
59 & 0.720027695818834 & 0.559944608362331 & 0.279972304181166 \tabularnewline
60 & 0.693414039710711 & 0.613171920578577 & 0.306585960289289 \tabularnewline
61 & 0.667106353593602 & 0.665787292812797 & 0.332893646406398 \tabularnewline
62 & 0.641531966118771 & 0.716936067762457 & 0.358468033881229 \tabularnewline
63 & 0.617141955134015 & 0.76571608973197 & 0.382858044865985 \tabularnewline
64 & 0.594413158729299 & 0.811173682541401 & 0.405586841270701 \tabularnewline
65 & 0.573855413273147 & 0.852289173453706 & 0.426144586726853 \tabularnewline
66 & 0.556026063178402 & 0.887947873643197 & 0.443973936821598 \tabularnewline
67 & 0.54998574896463 & 0.900028502070739 & 0.45001425103537 \tabularnewline
68 & 0.534373573126706 & 0.931252853746589 & 0.465626426873294 \tabularnewline
69 & 0.512896101903546 & 0.974207796192908 & 0.487103898096454 \tabularnewline
70 & 0.487664415446516 & 0.975328830893033 & 0.512335584553484 \tabularnewline
71 & 0.460039083661737 & 0.920078167323475 & 0.539960916338263 \tabularnewline
72 & 0.430988320602879 & 0.861976641205759 & 0.569011679397121 \tabularnewline
73 & 0.401253113131985 & 0.802506226263969 & 0.598746886868015 \tabularnewline
74 & 0.37142783239745 & 0.742855664794901 & 0.62857216760255 \tabularnewline
75 & 0.342001122806027 & 0.684002245612054 & 0.657998877193973 \tabularnewline
76 & 0.440570782816804 & 0.881141565633608 & 0.559429217183196 \tabularnewline
77 & 0.457710229968943 & 0.915420459937887 & 0.542289770031057 \tabularnewline
78 & 0.477863940683459 & 0.955727881366918 & 0.522136059316541 \tabularnewline
79 & 0.501816394745475 & 0.99636721050905 & 0.498183605254525 \tabularnewline
80 & 0.530284195946978 & 0.939431608106044 & 0.469715804053022 \tabularnewline
81 & 0.563904791114283 & 0.872190417771434 & 0.436095208885717 \tabularnewline
82 & 0.603172248069034 & 0.793655503861931 & 0.396827751930966 \tabularnewline
83 & 0.648306709905915 & 0.703386580188169 & 0.351693290094085 \tabularnewline
84 & 0.699041073139017 & 0.601917853721967 & 0.300958926860983 \tabularnewline
85 & 0.698933512215785 & 0.602132975568429 & 0.301066487784215 \tabularnewline
86 & 0.701359775010953 & 0.597280449978094 & 0.298640224989047 \tabularnewline
87 & 0.706972003377608 & 0.586055993244785 & 0.293027996622392 \tabularnewline
88 & 0.716479212366239 & 0.567041575267523 & 0.283520787633761 \tabularnewline
89 & 0.730638772228461 & 0.538722455543079 & 0.269361227771539 \tabularnewline
90 & 0.750217146873793 & 0.499565706252413 & 0.249782853126207 \tabularnewline
91 & 0.779296112367065 & 0.441407775265869 & 0.220703887632935 \tabularnewline
92 & 0.797521700156599 & 0.404956599686802 & 0.202478299843401 \tabularnewline
93 & 0.808155236634236 & 0.383689526731527 & 0.191844763365764 \tabularnewline
94 & 0.813220894962955 & 0.37355821007409 & 0.186779105037045 \tabularnewline
95 & 0.814513066550944 & 0.370973866898112 & 0.185486933449056 \tabularnewline
96 & 0.811451587877688 & 0.377096824244624 & 0.188548412122312 \tabularnewline
97 & 0.804803809864668 & 0.390392380270663 & 0.195196190135332 \tabularnewline
98 & 0.795036969147236 & 0.409926061705529 & 0.204963030852764 \tabularnewline
99 & 0.802224457092537 & 0.395551085814927 & 0.197775542907463 \tabularnewline
100 & 0.805621819064395 & 0.388756361871211 & 0.194378180935605 \tabularnewline
101 & 0.806373141184923 & 0.387253717630155 & 0.193626858815077 \tabularnewline
102 & 0.805333137397643 & 0.389333725204713 & 0.194666862602357 \tabularnewline
103 & 0.803210898952585 & 0.393578202094831 & 0.196789101047415 \tabularnewline
104 & 0.800665221035273 & 0.398669557929454 & 0.199334778964727 \tabularnewline
105 & 0.798376071498991 & 0.403247857002018 & 0.201623928501009 \tabularnewline
106 & 0.79710708455327 & 0.405785830893459 & 0.20289291544673 \tabularnewline
107 & 0.797769042515047 & 0.404461914969907 & 0.202230957484953 \tabularnewline
108 & 0.80149079747988 & 0.39701840504024 & 0.19850920252012 \tabularnewline
109 & 0.809697377408243 & 0.380605245183515 & 0.190302622591757 \tabularnewline
110 & 0.824173365171825 & 0.35165326965635 & 0.175826634828175 \tabularnewline
111 & 0.796587476722583 & 0.406825046554833 & 0.203412523277417 \tabularnewline
112 & 0.815421124847506 & 0.369157750304987 & 0.184578875152494 \tabularnewline
113 & 0.838348327582019 & 0.323303344835963 & 0.161651672417981 \tabularnewline
114 & 0.866073146426255 & 0.26785370714749 & 0.133926853573745 \tabularnewline
115 & 0.898654479064998 & 0.202691041870003 & 0.101345520935002 \tabularnewline
116 & 0.93449033654781 & 0.131019326904381 & 0.0655096634521905 \tabularnewline
117 & 0.968641861287501 & 0.0627162774249982 & 0.0313581387124991 \tabularnewline
118 & 0.960346941637168 & 0.0793061167256636 & 0.0396530583628318 \tabularnewline
119 & 0.950368155296704 & 0.0992636894065922 & 0.0496318447032961 \tabularnewline
120 & 0.938539929333398 & 0.122920141333205 & 0.0614600706666025 \tabularnewline
121 & 0.924766654314457 & 0.150466691371085 & 0.0752333456855427 \tabularnewline
122 & 0.909114445408916 & 0.181771109182167 & 0.0908855545910836 \tabularnewline
123 & 0.892059301008611 & 0.215881397982779 & 0.107940698991389 \tabularnewline
124 & 0.875396238063697 & 0.249207523872605 & 0.124603761936303 \tabularnewline
125 & 0.867818813418497 & 0.264362373163006 & 0.132181186581503 \tabularnewline
126 & 0.999784234374789 & 0.000431531250422833 & 0.000215765625211417 \tabularnewline
127 & 0.999875800374543 & 0.000248399250914744 & 0.000124199625457372 \tabularnewline
128 & 0.99991342523722 & 0.000173149525559035 & 8.65747627795174e-05 \tabularnewline
129 & 0.999948254418891 & 0.00010349116221818 & 5.174558110909e-05 \tabularnewline
130 & 0.999975415481032 & 4.91690379368452e-05 & 2.45845189684226e-05 \tabularnewline
131 & 0.999991732064131 & 1.65358717373646e-05 & 8.26793586868228e-06 \tabularnewline
132 & 0.999995433937765 & 9.13212447091171e-06 & 4.56606223545585e-06 \tabularnewline
133 & 0.999997557474994 & 4.88505001276969e-06 & 2.44252500638485e-06 \tabularnewline
134 & 0.99999888754878 & 2.22490244098838e-06 & 1.11245122049419e-06 \tabularnewline
135 & 0.999999633398439 & 7.33203122141927e-07 & 3.66601561070963e-07 \tabularnewline
136 & 0.999999933670512 & 1.32658976990259e-07 & 6.63294884951293e-08 \tabularnewline
137 & 0.999999861677823 & 2.76644353917515e-07 & 1.38322176958757e-07 \tabularnewline
138 & 0.999999713485951 & 5.73028097587024e-07 & 2.86514048793512e-07 \tabularnewline
139 & 0.999999411747093 & 1.17650581431362e-06 & 5.88252907156812e-07 \tabularnewline
140 & 0.999998804726184 & 2.39054763304203e-06 & 1.19527381652101e-06 \tabularnewline
141 & 0.999997599374595 & 4.80125081053426e-06 & 2.40062540526713e-06 \tabularnewline
142 & 0.999995238968355 & 9.52206328963597e-06 & 4.76103164481798e-06 \tabularnewline
143 & 0.999990684045377 & 1.86319092454253e-05 & 9.31595462271264e-06 \tabularnewline
144 & 0.999982028986585 & 3.59420268308892e-05 & 1.79710134154446e-05 \tabularnewline
145 & 0.999965847351675 & 6.8305296650516e-05 & 3.4152648325258e-05 \tabularnewline
146 & 0.99993610509547 & 0.000127789809059975 & 6.38949045299876e-05 \tabularnewline
147 & 0.999882417354073 & 0.00023516529185327 & 0.000117582645926635 \tabularnewline
148 & 0.999787385269666 & 0.000425229460667705 & 0.000212614730333852 \tabularnewline
149 & 0.999622892467472 & 0.000754215065056258 & 0.000377107532528129 \tabularnewline
150 & 0.999346452283052 & 0.00130709543389543 & 0.000653547716947714 \tabularnewline
151 & 0.998908836806159 & 0.00218232638768164 & 0.00109116319384082 \tabularnewline
152 & 0.998537730652596 & 0.00292453869480703 & 0.00146226934740351 \tabularnewline
153 & 0.998658259145762 & 0.00268348170847651 & 0.00134174085423825 \tabularnewline
154 & 0.999012547384343 & 0.00197490523131331 & 0.000987452615656655 \tabularnewline
155 & 0.999519089903813 & 0.000961820192374138 & 0.000480910096187069 \tabularnewline
156 & 0.999909271887636 & 0.000181456224728259 & 9.07281123641297e-05 \tabularnewline
157 & 0.999999361960235 & 1.27607953069809e-06 & 6.38039765349043e-07 \tabularnewline
158 & 0.999999678868774 & 6.42262450979602e-07 & 3.21131225489801e-07 \tabularnewline
159 & 0.999999975085265 & 4.98294702154663e-08 & 2.49147351077331e-08 \tabularnewline
160 & 0.9999999314209 & 1.37158200609563e-07 & 6.85791003047814e-08 \tabularnewline
161 & 0.999999808212777 & 3.83574446065242e-07 & 1.91787223032621e-07 \tabularnewline
162 & 0.999999458587171 & 1.08282565706655e-06 & 5.41412828533276e-07 \tabularnewline
163 & 0.9999984659592 & 3.06808160094826e-06 & 1.53404080047413e-06 \tabularnewline
164 & 0.999995659798204 & 8.68040359124811e-06 & 4.34020179562405e-06 \tabularnewline
165 & 0.999987796289182 & 2.44074216351956e-05 & 1.22037108175978e-05 \tabularnewline
166 & 0.999971791939837 & 5.64161203269039e-05 & 2.82080601634519e-05 \tabularnewline
167 & 0.999938697648226 & 0.000122604703547513 & 6.13023517737563e-05 \tabularnewline
168 & 0.99987819306776 & 0.000243613864479127 & 0.000121806932239563 \tabularnewline
169 & 0.999790768511332 & 0.000418462977335648 & 0.000209231488667824 \tabularnewline
170 & 0.999729925486967 & 0.000540149026066807 & 0.000270074513033403 \tabularnewline
171 & 0.999844007401204 & 0.000311985197591603 & 0.000155992598795801 \tabularnewline
172 & 1 & 2.87560192226581e-114 & 1.43780096113291e-114 \tabularnewline
173 & 1 & 1.01792064688891e-212 & 5.08960323444453e-213 \tabularnewline
174 & 1 & 1.42725798214978e-83 & 7.13628991074892e-84 \tabularnewline
175 & 1 & 1.69337243438397e-70 & 8.46686217191986e-71 \tabularnewline
176 & 1 & 9.48406749358026e-57 & 4.74203374679013e-57 \tabularnewline
177 & 1 & 2.33735966442174e-44 & 1.16867983221087e-44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C]1.66364341112035e-51[/C][C]3.3272868222407e-51[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0.787859895039013[/C][C]0.424280209921974[/C][C]0.212140104960987[/C][/ROW]
[ROW][C]8[/C][C]0.736633417601336[/C][C]0.526733164797328[/C][C]0.263366582398664[/C][/ROW]
[ROW][C]9[/C][C]0.650582086328525[/C][C]0.698835827342949[/C][C]0.349417913671475[/C][/ROW]
[ROW][C]10[/C][C]0.551652258014878[/C][C]0.896695483970243[/C][C]0.448347741985122[/C][/ROW]
[ROW][C]11[/C][C]0.450296148356392[/C][C]0.900592296712785[/C][C]0.549703851643608[/C][/ROW]
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[ROW][C]156[/C][C]0.999909271887636[/C][C]0.000181456224728259[/C][C]9.07281123641297e-05[/C][/ROW]
[ROW][C]157[/C][C]0.999999361960235[/C][C]1.27607953069809e-06[/C][C]6.38039765349043e-07[/C][/ROW]
[ROW][C]158[/C][C]0.999999678868774[/C][C]6.42262450979602e-07[/C][C]3.21131225489801e-07[/C][/ROW]
[ROW][C]159[/C][C]0.999999975085265[/C][C]4.98294702154663e-08[/C][C]2.49147351077331e-08[/C][/ROW]
[ROW][C]160[/C][C]0.9999999314209[/C][C]1.37158200609563e-07[/C][C]6.85791003047814e-08[/C][/ROW]
[ROW][C]161[/C][C]0.999999808212777[/C][C]3.83574446065242e-07[/C][C]1.91787223032621e-07[/C][/ROW]
[ROW][C]162[/C][C]0.999999458587171[/C][C]1.08282565706655e-06[/C][C]5.41412828533276e-07[/C][/ROW]
[ROW][C]163[/C][C]0.9999984659592[/C][C]3.06808160094826e-06[/C][C]1.53404080047413e-06[/C][/ROW]
[ROW][C]164[/C][C]0.999995659798204[/C][C]8.68040359124811e-06[/C][C]4.34020179562405e-06[/C][/ROW]
[ROW][C]165[/C][C]0.999987796289182[/C][C]2.44074216351956e-05[/C][C]1.22037108175978e-05[/C][/ROW]
[ROW][C]166[/C][C]0.999971791939837[/C][C]5.64161203269039e-05[/C][C]2.82080601634519e-05[/C][/ROW]
[ROW][C]167[/C][C]0.999938697648226[/C][C]0.000122604703547513[/C][C]6.13023517737563e-05[/C][/ROW]
[ROW][C]168[/C][C]0.99987819306776[/C][C]0.000243613864479127[/C][C]0.000121806932239563[/C][/ROW]
[ROW][C]169[/C][C]0.999790768511332[/C][C]0.000418462977335648[/C][C]0.000209231488667824[/C][/ROW]
[ROW][C]170[/C][C]0.999729925486967[/C][C]0.000540149026066807[/C][C]0.000270074513033403[/C][/ROW]
[ROW][C]171[/C][C]0.999844007401204[/C][C]0.000311985197591603[/C][C]0.000155992598795801[/C][/ROW]
[ROW][C]172[/C][C]1[/C][C]2.87560192226581e-114[/C][C]1.43780096113291e-114[/C][/ROW]
[ROW][C]173[/C][C]1[/C][C]1.01792064688891e-212[/C][C]5.08960323444453e-213[/C][/ROW]
[ROW][C]174[/C][C]1[/C][C]1.42725798214978e-83[/C][C]7.13628991074892e-84[/C][/ROW]
[ROW][C]175[/C][C]1[/C][C]1.69337243438397e-70[/C][C]8.46686217191986e-71[/C][/ROW]
[ROW][C]176[/C][C]1[/C][C]9.48406749358026e-57[/C][C]4.74203374679013e-57[/C][/ROW]
[ROW][C]177[/C][C]1[/C][C]2.33735966442174e-44[/C][C]1.16867983221087e-44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&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
61.66364341112035e-513.3272868222407e-511
70.7878598950390130.4242802099219740.212140104960987
80.7366334176013360.5267331647973280.263366582398664
90.6505820863285250.6988358273429490.349417913671475
100.5516522580148780.8966954839702430.448347741985122
110.4502961483563920.9005922967127850.549703851643608
120.3542286131867930.7084572263735860.645771386813207
130.2688108622046270.5376217244092530.731189137795373
140.4498903124342950.8997806248685910.550109687565705
150.378296830550230.7565936611004590.62170316944977
160.3143678048359610.6287356096719210.685632195164039
170.2588566719331410.5177133438662810.741143328066859
180.2118071759109970.4236143518219940.788192824089003
190.172757281316240.3455145626324790.82724271868376
200.1409353964496670.2818707928993340.859064603550333
210.3160815706364290.6321631412728590.683918429363571
220.4444539636422650.888907927284530.555546036357735
230.5244217309781710.9511565380436590.475578269021829
240.5706628504779470.8586742990441050.429337149522053
250.5945922720445470.8108154559109060.405407727955453
260.6033625579002830.7932748841994330.396637442099716
270.6013288420340460.7973423159319080.398671157965954
280.591212216744950.8175755665101010.40878778325505
290.7195114749831030.5609770500337940.280488525016897
300.8017750251773980.3964499496452040.198224974822602
310.8544289265395160.2911421469209690.145571073460485
320.8888648444222930.2222703111554140.111135155577707
330.912093759387830.1758124812243390.0879062406121695
340.9283171117728330.1433657764543340.0716828882271672
350.9400730561336790.1198538877326420.0599269438663212
360.948930639326020.1021387213479590.0510693606739797
370.9558889347620250.08822213047595030.0441110652379752
380.9616042858235070.07679142835298550.0383957141764927
390.9665200520449740.06695989591005230.0334799479550262
400.971058880836450.05788223832709950.0289411191635498
410.9745171187365230.05096576252695420.0254828812634771
420.9670469049346260.06590619013074710.0329530950653735
430.9579572532707170.08408549345856640.0420427467292832
440.9465643770684960.1068712458630080.0534356229315041
450.9327413568918820.1345172862162350.0672586431081175
460.9163110625467290.1673778749065420.0836889374532712
470.8971162591610630.2057674816778740.102883740838937
480.8750481788010110.2499036423979780.124951821198989
490.8820492245501830.2359015508996340.117950775449817
500.8850720120403280.2298559759193440.114927987959672
510.8858470348588440.2283059302823120.114152965141156
520.8854823938373290.2290352123253420.114517606162671
530.8659762712636440.2680474574727130.134023728736356
540.8447561731651070.3104876536697850.155243826834893
550.8219751655913510.3560496688172990.178024834408649
560.7978370339834040.4043259320331910.202162966016596
570.7725944836997680.4548110326004630.227405516300232
580.7465453732298930.5069092535402140.253454626770107
590.7200276958188340.5599446083623310.279972304181166
600.6934140397107110.6131719205785770.306585960289289
610.6671063535936020.6657872928127970.332893646406398
620.6415319661187710.7169360677624570.358468033881229
630.6171419551340150.765716089731970.382858044865985
640.5944131587292990.8111736825414010.405586841270701
650.5738554132731470.8522891734537060.426144586726853
660.5560260631784020.8879478736431970.443973936821598
670.549985748964630.9000285020707390.45001425103537
680.5343735731267060.9312528537465890.465626426873294
690.5128961019035460.9742077961929080.487103898096454
700.4876644154465160.9753288308930330.512335584553484
710.4600390836617370.9200781673234750.539960916338263
720.4309883206028790.8619766412057590.569011679397121
730.4012531131319850.8025062262639690.598746886868015
740.371427832397450.7428556647949010.62857216760255
750.3420011228060270.6840022456120540.657998877193973
760.4405707828168040.8811415656336080.559429217183196
770.4577102299689430.9154204599378870.542289770031057
780.4778639406834590.9557278813669180.522136059316541
790.5018163947454750.996367210509050.498183605254525
800.5302841959469780.9394316081060440.469715804053022
810.5639047911142830.8721904177714340.436095208885717
820.6031722480690340.7936555038619310.396827751930966
830.6483067099059150.7033865801881690.351693290094085
840.6990410731390170.6019178537219670.300958926860983
850.6989335122157850.6021329755684290.301066487784215
860.7013597750109530.5972804499780940.298640224989047
870.7069720033776080.5860559932447850.293027996622392
880.7164792123662390.5670415752675230.283520787633761
890.7306387722284610.5387224555430790.269361227771539
900.7502171468737930.4995657062524130.249782853126207
910.7792961123670650.4414077752658690.220703887632935
920.7975217001565990.4049565996868020.202478299843401
930.8081552366342360.3836895267315270.191844763365764
940.8132208949629550.373558210074090.186779105037045
950.8145130665509440.3709738668981120.185486933449056
960.8114515878776880.3770968242446240.188548412122312
970.8048038098646680.3903923802706630.195196190135332
980.7950369691472360.4099260617055290.204963030852764
990.8022244570925370.3955510858149270.197775542907463
1000.8056218190643950.3887563618712110.194378180935605
1010.8063731411849230.3872537176301550.193626858815077
1020.8053331373976430.3893337252047130.194666862602357
1030.8032108989525850.3935782020948310.196789101047415
1040.8006652210352730.3986695579294540.199334778964727
1050.7983760714989910.4032478570020180.201623928501009
1060.797107084553270.4057858308934590.20289291544673
1070.7977690425150470.4044619149699070.202230957484953
1080.801490797479880.397018405040240.19850920252012
1090.8096973774082430.3806052451835150.190302622591757
1100.8241733651718250.351653269656350.175826634828175
1110.7965874767225830.4068250465548330.203412523277417
1120.8154211248475060.3691577503049870.184578875152494
1130.8383483275820190.3233033448359630.161651672417981
1140.8660731464262550.267853707147490.133926853573745
1150.8986544790649980.2026910418700030.101345520935002
1160.934490336547810.1310193269043810.0655096634521905
1170.9686418612875010.06271627742499820.0313581387124991
1180.9603469416371680.07930611672566360.0396530583628318
1190.9503681552967040.09926368940659220.0496318447032961
1200.9385399293333980.1229201413332050.0614600706666025
1210.9247666543144570.1504666913710850.0752333456855427
1220.9091144454089160.1817711091821670.0908855545910836
1230.8920593010086110.2158813979827790.107940698991389
1240.8753962380636970.2492075238726050.124603761936303
1250.8678188134184970.2643623731630060.132181186581503
1260.9997842343747890.0004315312504228330.000215765625211417
1270.9998758003745430.0002483992509147440.000124199625457372
1280.999913425237220.0001731495255590358.65747627795174e-05
1290.9999482544188910.000103491162218185.174558110909e-05
1300.9999754154810324.91690379368452e-052.45845189684226e-05
1310.9999917320641311.65358717373646e-058.26793586868228e-06
1320.9999954339377659.13212447091171e-064.56606223545585e-06
1330.9999975574749944.88505001276969e-062.44252500638485e-06
1340.999998887548782.22490244098838e-061.11245122049419e-06
1350.9999996333984397.33203122141927e-073.66601561070963e-07
1360.9999999336705121.32658976990259e-076.63294884951293e-08
1370.9999998616778232.76644353917515e-071.38322176958757e-07
1380.9999997134859515.73028097587024e-072.86514048793512e-07
1390.9999994117470931.17650581431362e-065.88252907156812e-07
1400.9999988047261842.39054763304203e-061.19527381652101e-06
1410.9999975993745954.80125081053426e-062.40062540526713e-06
1420.9999952389683559.52206328963597e-064.76103164481798e-06
1430.9999906840453771.86319092454253e-059.31595462271264e-06
1440.9999820289865853.59420268308892e-051.79710134154446e-05
1450.9999658473516756.8305296650516e-053.4152648325258e-05
1460.999936105095470.0001277898090599756.38949045299876e-05
1470.9998824173540730.000235165291853270.000117582645926635
1480.9997873852696660.0004252294606677050.000212614730333852
1490.9996228924674720.0007542150650562580.000377107532528129
1500.9993464522830520.001307095433895430.000653547716947714
1510.9989088368061590.002182326387681640.00109116319384082
1520.9985377306525960.002924538694807030.00146226934740351
1530.9986582591457620.002683481708476510.00134174085423825
1540.9990125473843430.001974905231313310.000987452615656655
1550.9995190899038130.0009618201923741380.000480910096187069
1560.9999092718876360.0001814562247282599.07281123641297e-05
1570.9999993619602351.27607953069809e-066.38039765349043e-07
1580.9999996788687746.42262450979602e-073.21131225489801e-07
1590.9999999750852654.98294702154663e-082.49147351077331e-08
1600.99999993142091.37158200609563e-076.85791003047814e-08
1610.9999998082127773.83574446065242e-071.91787223032621e-07
1620.9999994585871711.08282565706655e-065.41412828533276e-07
1630.99999846595923.06808160094826e-061.53404080047413e-06
1640.9999956597982048.68040359124811e-064.34020179562405e-06
1650.9999877962891822.44074216351956e-051.22037108175978e-05
1660.9999717919398375.64161203269039e-052.82080601634519e-05
1670.9999386976482260.0001226047035475136.13023517737563e-05
1680.999878193067760.0002436138644791270.000121806932239563
1690.9997907685113320.0004184629773356480.000209231488667824
1700.9997299254869670.0005401490260668070.000270074513033403
1710.9998440074012040.0003119851975916030.000155992598795801
17212.87560192226581e-1141.43780096113291e-114
17311.01792064688891e-2125.08960323444453e-213
17411.42725798214978e-837.13628991074892e-84
17511.69337243438397e-708.46686217191986e-71
17619.48406749358026e-574.74203374679013e-57
17712.33735966442174e-441.16867983221087e-44






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level530.308139534883721NOK
5% type I error level530.308139534883721NOK
10% type I error level630.366279069767442NOK

\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 & 53 & 0.308139534883721 & NOK \tabularnewline
5% type I error level & 53 & 0.308139534883721 & NOK \tabularnewline
10% type I error level & 63 & 0.366279069767442 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191210&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]53[/C][C]0.308139534883721[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]53[/C][C]0.308139534883721[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.366279069767442[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191210&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191210&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 level530.308139534883721NOK
5% type I error level530.308139534883721NOK
10% type I error level630.366279069767442NOK


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 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}