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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 13:08:30 -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/t13534349501pejdenslxppxt1.htm/, Retrieved Mon, 29 Apr 2024 21:07:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191212, Retrieved Mon, 29 Apr 2024 21:07:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7: Beer] [2012-11-20 18:08:30] [ac36efda2e34453d278f09f8b1b9a536] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4264830	1,23	2,35	1,80	0,48	0,37	0,95
3924674	1,22	2,32	1,79	0,48	0,37	0,97
3734753	1,21	2,36	1,78	0,49	0,38	0,97
3762290	1,22	2,28	1,80	0,49	0,38	0,95
3609739	1,21	2,26	1,79	0,49	0,38	0,96
3877594	1,22	2,31	1,76	0,49	0,38	0,96
3636415	1,21	2,28	1,76	0,49	0,38	0,94
3578195	1,20	2,20	1,77	0,49	0,38	0,96
3604342	1,18	2,24	1,76	0,49	0,38	0,98
3459513	1,19	2,33	1,74	0,49	0,38	0,97
3366571	1,20	2,36	1,75	0,49	0,37	0,96
3371277	1,19	2,23	1,70	0,49	0,39	0,95
3724848	1,19	2,31	1,71	0,49	0,39	0,96
3350830	1,20	2,27	1,77	0,49	0,37	0,96
3305159	1,21	2,28	1,77	0,50	0,37	0,97
3390736	1,20	2,31	1,77	0,49	0,37	0,96
3349758	1,20	2,43	1,77	0,50	0,37	0,95
3253655	1,20	2,46	1,77	0,50	0,36	0,95
3734250	1,21	2,48	1,77	0,51	0,36	0,94
3455433	1,21	2,45	1,79	0,51	0,36	0,94
2966726	1,21	2,44	1,79	0,51	0,37	0,98
2993716	1,20	2,36	1,76	0,50	0,37	0,93
3009320	1,21	2,34	1,76	0,50	0,37	0,93
3169713	1,21	2,16	1,78	0,50	0,38	0,96
3170061	1,21	2,23	1,81	0,49	0,37	0,97
3368934	1,20	2,16	1,77	0,49	0,37	0,97
3292638	1,19	2,20	1,75	0,50	0,37	0,95
3337344	1,20	2,26	1,77	0,50	0,37	0,95
3208306	1,20	2,23	1,72	0,50	0,37	0,96
3359130	1,20	2,32	1,80	0,50	0,37	0,98
3223078	1,22	2,34	1,77	0,50	0,37	0,98
3437159	1,22	2,33	1,80	0,50	0,39	0,97
3400156	1,21	2,30	1,79	0,49	0,39	0,98
3657576	1,25	2,32	1,82	0,50	0,38	0,98
3765613	1,25	2,31	1,81	0,50	0,39	0,99
3481921	1,27	2,32	1,80	0,49	0,38	0,99
3604800	1,28	2,32	1,76	0,50	0,39	0,97
3981340	1,27	2,30	1,73	0,50	0,38	0,98
3734078	1,28	2,33	1,77	0,50	0,39	0,97
4018173	1,29	2,34	1,78	0,51	0,40	0,97
3887417	1,26	2,32	1,77	0,51	0,40	0,97
3919880	1,27	2,32	1,75	0,50	0,39	0,98
4014466	1,25	2,32	1,75	0,51	0,39	0,97
4197758	1,27	2,33	1,78	0,51	0,40	0,97
3896531	1,27	2,36	1,76	0,51	0,39	0,98
3964742	1,27	2,37	1,73	0,51	0,39	0,98
4201847	1,29	2,35	1,77	0,51	0,38	0,95
4050512	1,26	2,28	1,78	0,51	0,39	0,97
3997402	1,27	2,21	1,80	0,51	0,38	0,97
4314479	1,27	2,26	1,81	0,51	0,38	0,97
4925744	1,28	2,32	1,79	0,51	0,39	0,97
5130631	1,28	2,33	1,79	0,51	0,38	0,98
4444855	1,28	2,36	1,79	0,51	0,38	0,98
3967319	1,27	2,36	1,76	0,52	0,38	0,98
3931250	1,24	2,35	1,78	0,51	0,39	0,96
4235952	1,25	2,34	1,81	0,52	0,39	0,98
4169219	1,25	2,32	1,82	0,51	0,39	1,00
3779064	1,24	2,27	1,80	0,52	0,39	1,01
3558810	1,24	2,30	1,78	0,51	0,39	1,02
3699466	1,23	2,29	1,76	0,51	0,39	1,01
3650693	1,24	2,30	1,76	0,51	0,38	1,01
3525633	1,23	2,30	1,76	0,51	0,38	1,02
3470276	1,24	2,36	1,77	0,51	0,37	1,01
3859094	1,24	2,35	1,78	0,50	0,37	1,01
3661155	1,24	2,35	1,78	0,50	0,37	1,01
3356365	1,25	2,30	1,79	0,50	0,38	1,02
3344440	1,26	2,24	1,84	0,50	0,37	1,02
3338684	1,26	2,22	1,91	0,51	0,37	1,02
3404294	1,27	2,27	1,92	0,51	0,37	1,01
3289319	1,26	2,37	1,86	0,51	0,37	1,01
3469252	1,28	2,38	1,76	0,53	0,37	0,99
3571850	1,29	2,41	1,80	0,52	0,37	1,00
3639914	1,28	2,37	1,81	0,50	0,37	1,01
3091730	1,27	2,37	1,80	0,50	0,38	0,99
3078149	1,30	2,36	1,81	0,50	0,38	1,00
3188115	1,30	2,30	1,80	0,50	0,38	1,02
3246082	1,28	2,18	1,80	0,50	0,38	1,01
3486992	1,29	2,22	1,76	0,51	0,38	1,01
3378187	1,27	2,24	1,76	0,50	0,38	1,01
3282306	1,26	2,17	1,76	0,50	0,38	1,03
3288345	1,27	2,23	1,78	0,51	0,38	1,02
3325749	1,27	2,27	1,77	0,52	0,39	1,02
3352262	1,27	2,25	1,80	0,52	0,39	1,03
3531954	1,28	2,25	1,80	0,52	0,39	1,03
3722622	1,29	2,27	1,81	0,52	0,39	1,02
3809365	1,28	2,28	1,79	0,50	0,39	1,02
3750617	1,30	2,32	1,81	0,49	0,38	1,02
3615286	1,30	2,33	1,81	0,49	0,39	1,02
3696556	1,30	2,30	1,79	0,49	0,39	1,03
4123959	1,29	2,30	1,79	0,49	0,39	1,02
4136163	1,30	2,20	1,79	0,50	0,40	1,02
3933392	1,29	2,15	1,79	0,51	0,40	1,02
4035576	1,28	2,15	1,80	0,50	0,40	1,03
4551202	1,30	2,13	1,86	0,50	0,40	1,02
4032195	1,30	2,12	1,93	0,50	0,41	1,02
3970893	1,31	2,17	1,81	0,50	0,40	1,02
4489016	1,32	2,20	1,70	0,49	0,40	1,02
5426127	1,33	2,21	1,74	0,49	0,39	1,02
4578224	1,32	2,28	1,74	0,49	0,39	1,00
4126390	1,30	2,32	1,73	0,49	0,39	1,04
4892100	1,31	2,32	1,76	0,48	0,39	1,04
4128697	1,30	2,33	1,75	0,49	0,40	1,03
4408721	1,30	2,32	1,79	0,49	0,39	1,02
4199465	1,30	2,31	1,79	0,49	0,39	1,04
4074767	1,29	2,33	1,83	0,49	0,39	1,05
4161758	1,29	2,31	1,82	0,48	0,39	1,03
3891319	1,30	2,19	1,85	0,48	0,39	0,99
4470302	1,30	2,13	1,85	0,48	0,40	1,03
4283111	1,29	2,15	1,86	0,48	0,39	1,08
3845962	1,27	2,25	1,83	0,49	0,39	1,09
3911471	1,26	2,29	1,87	0,48	0,39	1,08
3798478	1,25	2,29	1,88	0,48	0,39	1,05
3644313	1,26	2,29	1,92	0,50	0,39	1,06
3784029	1,27	2,32	1,91	0,48	0,39	1,04
3647134	1,26	2,32	1,93	0,48	0,39	1,06
3994662	1,25	2,34	1,90	0,48	0,38	1,06
3607836	1,25	2,32	1,91	0,47	0,38	1,07
3566008	1,25	2,16	1,89	0,48	0,38	1,08
3511412	1,26	1,88	1,91	0,48	0,39	1,08
3258665	1,26	2,02	1,92	0,48	0,39	1,05
3486573	1,26	2,07	1,91	0,48	0,39	1,04
3369443	1,27	2,22	1,95	0,48	0,39	1,04
3465544	1,28	2,40	1,95	0,49	0,39	1,04
3905224	1,29	2,47	1,97	0,50	0,39	1,04
3733881	1,30	2,43	1,97	0,49	0,39	1,06
3220642	1,26	2,39	1,94	0,49	0,40	1,08
3225812	1,25	2,39	1,93	0,52	0,44	1,08
3354461	1,26	2,39	1,92	0,52	0,44	1,08
3352261	1,25	2,36	1,93	0,51	0,44	1,07
3450652	1,24	2,32	1,91	0,54	0,45	1,06

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
QBEPIL[t] = + 937628.048735503 + 8056925.15259668PBEPIL[t] + 235921.324377866PBEABD[t] -1529426.31137177PBEWIT[t] -9625944.17979685PWABR[t] + 8079834.06250011PWAPL[t] -3415028.9892527PSOCOLA[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEPIL[t] =  +  937628.048735503 +  8056925.15259668PBEPIL[t] +  235921.324377866PBEABD[t] -1529426.31137177PBEWIT[t] -9625944.17979685PWABR[t] +  8079834.06250011PWAPL[t] -3415028.9892527PSOCOLA[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEPIL[t] =  +  937628.048735503 +  8056925.15259668PBEPIL[t] +  235921.324377866PBEABD[t] -1529426.31137177PBEWIT[t] -9625944.17979685PWABR[t] +  8079834.06250011PWAPL[t] -3415028.9892527PSOCOLA[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&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
QBEPIL[t] = + 937628.048735503 + 8056925.15259668PBEPIL[t] + 235921.324377866PBEABD[t] -1529426.31137177PBEWIT[t] -9625944.17979685PWABR[t] + 8079834.06250011PWAPL[t] -3415028.9892527PSOCOLA[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)937628.0487355031901642.0980180.49310.6228480.311424
PBEPIL8056925.152596681020057.2504547.898500
PBEABD235921.324377866363468.5605440.64910.5174950.258747
PBEWIT-1529426.31137177671513.069409-2.27760.0244780.012239
PWABR-9625944.179796852669247.442907-3.60620.000450.000225
PWAPL8079834.062500112613468.7015543.09160.0024630.001232
PSOCOLA-3415028.98925271297500.524522-2.6320.0095760.004788

\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) & 937628.048735503 & 1901642.098018 & 0.4931 & 0.622848 & 0.311424 \tabularnewline
PBEPIL & 8056925.15259668 & 1020057.250454 & 7.8985 & 0 & 0 \tabularnewline
PBEABD & 235921.324377866 & 363468.560544 & 0.6491 & 0.517495 & 0.258747 \tabularnewline
PBEWIT & -1529426.31137177 & 671513.069409 & -2.2776 & 0.024478 & 0.012239 \tabularnewline
PWABR & -9625944.17979685 & 2669247.442907 & -3.6062 & 0.00045 & 0.000225 \tabularnewline
PWAPL & 8079834.06250011 & 2613468.701554 & 3.0916 & 0.002463 & 0.001232 \tabularnewline
PSOCOLA & -3415028.9892527 & 1297500.524522 & -2.632 & 0.009576 & 0.004788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&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]937628.048735503[/C][C]1901642.098018[/C][C]0.4931[/C][C]0.622848[/C][C]0.311424[/C][/ROW]
[ROW][C]PBEPIL[/C][C]8056925.15259668[/C][C]1020057.250454[/C][C]7.8985[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PBEABD[/C][C]235921.324377866[/C][C]363468.560544[/C][C]0.6491[/C][C]0.517495[/C][C]0.258747[/C][/ROW]
[ROW][C]PBEWIT[/C][C]-1529426.31137177[/C][C]671513.069409[/C][C]-2.2776[/C][C]0.024478[/C][C]0.012239[/C][/ROW]
[ROW][C]PWABR[/C][C]-9625944.17979685[/C][C]2669247.442907[/C][C]-3.6062[/C][C]0.00045[/C][C]0.000225[/C][/ROW]
[ROW][C]PWAPL[/C][C]8079834.06250011[/C][C]2613468.701554[/C][C]3.0916[/C][C]0.002463[/C][C]0.001232[/C][/ROW]
[ROW][C]PSOCOLA[/C][C]-3415028.9892527[/C][C]1297500.524522[/C][C]-2.632[/C][C]0.009576[/C][C]0.004788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&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)937628.0487355031901642.0980180.49310.6228480.311424
PBEPIL8056925.152596681020057.2504547.898500
PBEABD235921.324377866363468.5605440.64910.5174950.258747
PBEWIT-1529426.31137177671513.069409-2.27760.0244780.012239
PWABR-9625944.179796852669247.442907-3.60620.000450.000225
PWAPL8079834.062500112613468.7015543.09160.0024630.001232
PSOCOLA-3415028.98925271297500.524522-2.6320.0095760.004788






Multiple Linear Regression - Regression Statistics
Multiple R0.660347895790128
R-squared0.43605934347445
Adjusted R-squared0.40855004315613
F-TEST (value)15.8513425797333
F-TEST (DF numerator)6
F-TEST (DF denominator)123
p-value1.97508676080815e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation338579.761706063
Sum Squared Residuals14100259369542.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.660347895790128 \tabularnewline
R-squared & 0.43605934347445 \tabularnewline
Adjusted R-squared & 0.40855004315613 \tabularnewline
F-TEST (value) & 15.8513425797333 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 123 \tabularnewline
p-value & 1.97508676080815e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 338579.761706063 \tabularnewline
Sum Squared Residuals & 14100259369542.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.660347895790128[/C][/ROW]
[ROW][C]R-squared[/C][C]0.43605934347445[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.40855004315613[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.8513425797333[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]123[/C][/ROW]
[ROW][C]p-value[/C][C]1.97508676080815e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]338579.761706063[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14100259369542.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&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 R0.660347895790128
R-squared0.43605934347445
Adjusted R-squared0.40855004315613
F-TEST (value)15.8513425797333
F-TEST (DF numerator)6
F-TEST (DF denominator)123
p-value1.97508676080815e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation338579.761706063
Sum Squared Residuals14100259369542.9






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
142648303773901.59528071490928.404719285
239246743633248.38735207291425.612647932
337347533561949.15074197172803.849258034
437622903661356.74987532100933.250124678
536097393557213.0450829952525.9549170115
638775943695461.152169182132.847830998
736364153676114.84069675-39699.8406967531
835781953493077.0403217985117.9596782147
936043423288369.07357363315972.92642637
1034595133454910.060413574602.93958643298
1133665713480614.63782468-114043.637824678
1233712773641593.90084071-270316.900840706
1337248483611023.05378469113824.946215309
1433508303428793.19240323-77963.1924032347
1533051593381311.92548248-76152.9254824845
1633907363438230.04537835-47494.0453783493
1733497583404431.45239825-54673.4523982517
1832536553330710.75150459-77055.7515045864
1937342503353889.27761267380360.722387331
2034554333316223.1116539139209.888346102
2129667263258061.07946501-291335.079465012
2229937163471511.80259057-477795.802590572
2330093203547362.62762898-538042.627628982
2431697133452655.73396095-282942.733960951
2531700613404598.24860669-234537.248606689
2633689343368691.55682914242.443170857564
2732926383300188.82249281-7550.82249281102
2833373443364324.82725401-26980.8272540144
2932083063399568.21319874-191262.21319874
3033591303230146.44769795128983.552302048
3132230783441886.1665786-218808.166578596
3234371593589391.13513619-152232.135136194
3334001563579147.65889805-178991.65889805
3436575763683202.51972535-25626.5197253523
3537656133742785.6203277722827.3796722345
3634819213937038.70091016-455117.700910163
3736048004131624.48350309-526824.483503087
3839813403977270.964313194069.03568681212
3937340784118689.43363315-384611.433633148
4040181734170862.53411621-152689.534116208
4138874173939730.61616447-52313.6161644683
4239198804032199.20519831-112319.205198311
4340144663808951.55024094205514.449759064
4441977584007364.8178205190393.182179504
4538965313930082.35326174-33551.3532617393
4639647423978324.35584667-13582.3558466711
4742018474095219.90900876106627.090991243
4840505123834201.15945063216310.840549365
4939974023786869.05141771210532.948582285
5043144793783370.85452289531108.14547711
5149257443989482.25236397936261.747636035
5251306313876892.835090221253738.16490978
5344448553883970.47482155560884.525178448
5439673193753024.57083877214294.42916123
5539312503723727.43899768207522.561002321
5642359523591494.66635569644457.333644309
5741692193599440.83876733569778.161232669
5837790643407254.31555941371809.68444059
5935588103507029.6334236251780.3665763767
6036994663488839.98477384210626.01522616
6136506933490970.10891858159722.891081415
6235256333376250.56750009149382.432499909
6334702763409032.7846425461243.2153574623
6438590943487638.75008301371455.24991699
6536611553487638.75008301173516.24991699
6633563653587765.72300884-231400.72300884
6733444403496910.03887855-152470.038878545
6833386843288872.32879749811.6712030041
6934042943400093.673320674200.32667933453
7032893193434882.13291479-145563.132914791
7134692523627104.1765368-157852.176536797
7235718503715683.16724467-143833.167244671
7336399143768751.39333328-128837.393333282
7430917303852575.32533109-760845.325331088
7530781494042479.31365896-964330.313658965
7631881153975317.71752496-787202.717524957
7732460823820018.94544021-573936.945440206
7834869923874942.66059819-387950.66059819
7933781873814782.02583178-436595.025831782
8032823063649397.70181431-367091.70181431
8132883453651424.55467007-363079.554670072
8233257493660694.56958594-334945.569585937
8333522623575943.0638647-223681.0638647
8435319543656512.31539067-124558.315390667
8537226223760656.020183-38034.0201829999
8638093653905553.39172418-96188.3917241844
8737506174061001.32269676-310384.322696765
8836152864144158.87656554-528872.876565545
8936965564133519.47316912-436963.473169117
9041239594087100.5115356836858.4884643229
9141361634128616.529450897546.47054910995
9239333923939991.76990806-6599.76990806121
9340355763906237.40717382129338.592826182
9445512024005042.19494842546159.805051584
9540321953976421.4805336155773.5194663858
9639708934171519.61501809-200626.615018086
9744890164523662.84232425-34646.8423242516
9854261274464615.91401413961511.085985875
9945782244468861.73497966109362.265020337
10041263904195853.18844645-69463.1884464534
10148921004326799.09242924565300.907570764
10241286974282572.50598032-153875.505980325
10344087214172388.1895492236332.810450799
10441994654101728.3965203797736.6034796313
10540747673930550.22913456144216.770865439
10641617584105686.0873437456071.9126562556
10738913194248663.15017332-357344.150173322
10844703024178705.05176554291596.948234457
10942831113836010.17352578447100.82647422
11038459623613936.86056229232025.13943771
11139114713612037.14124706299433.858752937
11237984783618624.49628496179853.50371504
11336443133411347.52186759232965.478132408
11437840293775108.13961968920.86038039648
11536471343595649.7820811551484.2179188527
11639946623484883.40575889509778.59424111
11736078363526979.8680630680856.1319369438
11835660083389411.25069954176596.749300463
11935114123454132.3457972757279.6542027324
12032586653574317.93777403-315652.937774032
12134865733635558.55699917-148985.55699917
12233694433690338.95472695-320895.954726946
12334655443717114.60284296-251570.60284296
12439052243687350.37904997217873.620950026
12537338813786441.63961374-52560.6396137406
12632206423513108.33071586-292466.330715859
12732258123482248.37940971-256436.379409708
12833544613578111.89404939-223650.894049392
12933522613605580.47136887-253319.471368867
13034506523372333.1982188478318.8017811562

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4264830 & 3773901.59528071 & 490928.404719285 \tabularnewline
2 & 3924674 & 3633248.38735207 & 291425.612647932 \tabularnewline
3 & 3734753 & 3561949.15074197 & 172803.849258034 \tabularnewline
4 & 3762290 & 3661356.74987532 & 100933.250124678 \tabularnewline
5 & 3609739 & 3557213.04508299 & 52525.9549170115 \tabularnewline
6 & 3877594 & 3695461.152169 & 182132.847830998 \tabularnewline
7 & 3636415 & 3676114.84069675 & -39699.8406967531 \tabularnewline
8 & 3578195 & 3493077.04032179 & 85117.9596782147 \tabularnewline
9 & 3604342 & 3288369.07357363 & 315972.92642637 \tabularnewline
10 & 3459513 & 3454910.06041357 & 4602.93958643298 \tabularnewline
11 & 3366571 & 3480614.63782468 & -114043.637824678 \tabularnewline
12 & 3371277 & 3641593.90084071 & -270316.900840706 \tabularnewline
13 & 3724848 & 3611023.05378469 & 113824.946215309 \tabularnewline
14 & 3350830 & 3428793.19240323 & -77963.1924032347 \tabularnewline
15 & 3305159 & 3381311.92548248 & -76152.9254824845 \tabularnewline
16 & 3390736 & 3438230.04537835 & -47494.0453783493 \tabularnewline
17 & 3349758 & 3404431.45239825 & -54673.4523982517 \tabularnewline
18 & 3253655 & 3330710.75150459 & -77055.7515045864 \tabularnewline
19 & 3734250 & 3353889.27761267 & 380360.722387331 \tabularnewline
20 & 3455433 & 3316223.1116539 & 139209.888346102 \tabularnewline
21 & 2966726 & 3258061.07946501 & -291335.079465012 \tabularnewline
22 & 2993716 & 3471511.80259057 & -477795.802590572 \tabularnewline
23 & 3009320 & 3547362.62762898 & -538042.627628982 \tabularnewline
24 & 3169713 & 3452655.73396095 & -282942.733960951 \tabularnewline
25 & 3170061 & 3404598.24860669 & -234537.248606689 \tabularnewline
26 & 3368934 & 3368691.55682914 & 242.443170857564 \tabularnewline
27 & 3292638 & 3300188.82249281 & -7550.82249281102 \tabularnewline
28 & 3337344 & 3364324.82725401 & -26980.8272540144 \tabularnewline
29 & 3208306 & 3399568.21319874 & -191262.21319874 \tabularnewline
30 & 3359130 & 3230146.44769795 & 128983.552302048 \tabularnewline
31 & 3223078 & 3441886.1665786 & -218808.166578596 \tabularnewline
32 & 3437159 & 3589391.13513619 & -152232.135136194 \tabularnewline
33 & 3400156 & 3579147.65889805 & -178991.65889805 \tabularnewline
34 & 3657576 & 3683202.51972535 & -25626.5197253523 \tabularnewline
35 & 3765613 & 3742785.62032777 & 22827.3796722345 \tabularnewline
36 & 3481921 & 3937038.70091016 & -455117.700910163 \tabularnewline
37 & 3604800 & 4131624.48350309 & -526824.483503087 \tabularnewline
38 & 3981340 & 3977270.96431319 & 4069.03568681212 \tabularnewline
39 & 3734078 & 4118689.43363315 & -384611.433633148 \tabularnewline
40 & 4018173 & 4170862.53411621 & -152689.534116208 \tabularnewline
41 & 3887417 & 3939730.61616447 & -52313.6161644683 \tabularnewline
42 & 3919880 & 4032199.20519831 & -112319.205198311 \tabularnewline
43 & 4014466 & 3808951.55024094 & 205514.449759064 \tabularnewline
44 & 4197758 & 4007364.8178205 & 190393.182179504 \tabularnewline
45 & 3896531 & 3930082.35326174 & -33551.3532617393 \tabularnewline
46 & 3964742 & 3978324.35584667 & -13582.3558466711 \tabularnewline
47 & 4201847 & 4095219.90900876 & 106627.090991243 \tabularnewline
48 & 4050512 & 3834201.15945063 & 216310.840549365 \tabularnewline
49 & 3997402 & 3786869.05141771 & 210532.948582285 \tabularnewline
50 & 4314479 & 3783370.85452289 & 531108.14547711 \tabularnewline
51 & 4925744 & 3989482.25236397 & 936261.747636035 \tabularnewline
52 & 5130631 & 3876892.83509022 & 1253738.16490978 \tabularnewline
53 & 4444855 & 3883970.47482155 & 560884.525178448 \tabularnewline
54 & 3967319 & 3753024.57083877 & 214294.42916123 \tabularnewline
55 & 3931250 & 3723727.43899768 & 207522.561002321 \tabularnewline
56 & 4235952 & 3591494.66635569 & 644457.333644309 \tabularnewline
57 & 4169219 & 3599440.83876733 & 569778.161232669 \tabularnewline
58 & 3779064 & 3407254.31555941 & 371809.68444059 \tabularnewline
59 & 3558810 & 3507029.63342362 & 51780.3665763767 \tabularnewline
60 & 3699466 & 3488839.98477384 & 210626.01522616 \tabularnewline
61 & 3650693 & 3490970.10891858 & 159722.891081415 \tabularnewline
62 & 3525633 & 3376250.56750009 & 149382.432499909 \tabularnewline
63 & 3470276 & 3409032.78464254 & 61243.2153574623 \tabularnewline
64 & 3859094 & 3487638.75008301 & 371455.24991699 \tabularnewline
65 & 3661155 & 3487638.75008301 & 173516.24991699 \tabularnewline
66 & 3356365 & 3587765.72300884 & -231400.72300884 \tabularnewline
67 & 3344440 & 3496910.03887855 & -152470.038878545 \tabularnewline
68 & 3338684 & 3288872.328797 & 49811.6712030041 \tabularnewline
69 & 3404294 & 3400093.67332067 & 4200.32667933453 \tabularnewline
70 & 3289319 & 3434882.13291479 & -145563.132914791 \tabularnewline
71 & 3469252 & 3627104.1765368 & -157852.176536797 \tabularnewline
72 & 3571850 & 3715683.16724467 & -143833.167244671 \tabularnewline
73 & 3639914 & 3768751.39333328 & -128837.393333282 \tabularnewline
74 & 3091730 & 3852575.32533109 & -760845.325331088 \tabularnewline
75 & 3078149 & 4042479.31365896 & -964330.313658965 \tabularnewline
76 & 3188115 & 3975317.71752496 & -787202.717524957 \tabularnewline
77 & 3246082 & 3820018.94544021 & -573936.945440206 \tabularnewline
78 & 3486992 & 3874942.66059819 & -387950.66059819 \tabularnewline
79 & 3378187 & 3814782.02583178 & -436595.025831782 \tabularnewline
80 & 3282306 & 3649397.70181431 & -367091.70181431 \tabularnewline
81 & 3288345 & 3651424.55467007 & -363079.554670072 \tabularnewline
82 & 3325749 & 3660694.56958594 & -334945.569585937 \tabularnewline
83 & 3352262 & 3575943.0638647 & -223681.0638647 \tabularnewline
84 & 3531954 & 3656512.31539067 & -124558.315390667 \tabularnewline
85 & 3722622 & 3760656.020183 & -38034.0201829999 \tabularnewline
86 & 3809365 & 3905553.39172418 & -96188.3917241844 \tabularnewline
87 & 3750617 & 4061001.32269676 & -310384.322696765 \tabularnewline
88 & 3615286 & 4144158.87656554 & -528872.876565545 \tabularnewline
89 & 3696556 & 4133519.47316912 & -436963.473169117 \tabularnewline
90 & 4123959 & 4087100.51153568 & 36858.4884643229 \tabularnewline
91 & 4136163 & 4128616.52945089 & 7546.47054910995 \tabularnewline
92 & 3933392 & 3939991.76990806 & -6599.76990806121 \tabularnewline
93 & 4035576 & 3906237.40717382 & 129338.592826182 \tabularnewline
94 & 4551202 & 4005042.19494842 & 546159.805051584 \tabularnewline
95 & 4032195 & 3976421.48053361 & 55773.5194663858 \tabularnewline
96 & 3970893 & 4171519.61501809 & -200626.615018086 \tabularnewline
97 & 4489016 & 4523662.84232425 & -34646.8423242516 \tabularnewline
98 & 5426127 & 4464615.91401413 & 961511.085985875 \tabularnewline
99 & 4578224 & 4468861.73497966 & 109362.265020337 \tabularnewline
100 & 4126390 & 4195853.18844645 & -69463.1884464534 \tabularnewline
101 & 4892100 & 4326799.09242924 & 565300.907570764 \tabularnewline
102 & 4128697 & 4282572.50598032 & -153875.505980325 \tabularnewline
103 & 4408721 & 4172388.1895492 & 236332.810450799 \tabularnewline
104 & 4199465 & 4101728.39652037 & 97736.6034796313 \tabularnewline
105 & 4074767 & 3930550.22913456 & 144216.770865439 \tabularnewline
106 & 4161758 & 4105686.08734374 & 56071.9126562556 \tabularnewline
107 & 3891319 & 4248663.15017332 & -357344.150173322 \tabularnewline
108 & 4470302 & 4178705.05176554 & 291596.948234457 \tabularnewline
109 & 4283111 & 3836010.17352578 & 447100.82647422 \tabularnewline
110 & 3845962 & 3613936.86056229 & 232025.13943771 \tabularnewline
111 & 3911471 & 3612037.14124706 & 299433.858752937 \tabularnewline
112 & 3798478 & 3618624.49628496 & 179853.50371504 \tabularnewline
113 & 3644313 & 3411347.52186759 & 232965.478132408 \tabularnewline
114 & 3784029 & 3775108.1396196 & 8920.86038039648 \tabularnewline
115 & 3647134 & 3595649.78208115 & 51484.2179188527 \tabularnewline
116 & 3994662 & 3484883.40575889 & 509778.59424111 \tabularnewline
117 & 3607836 & 3526979.86806306 & 80856.1319369438 \tabularnewline
118 & 3566008 & 3389411.25069954 & 176596.749300463 \tabularnewline
119 & 3511412 & 3454132.34579727 & 57279.6542027324 \tabularnewline
120 & 3258665 & 3574317.93777403 & -315652.937774032 \tabularnewline
121 & 3486573 & 3635558.55699917 & -148985.55699917 \tabularnewline
122 & 3369443 & 3690338.95472695 & -320895.954726946 \tabularnewline
123 & 3465544 & 3717114.60284296 & -251570.60284296 \tabularnewline
124 & 3905224 & 3687350.37904997 & 217873.620950026 \tabularnewline
125 & 3733881 & 3786441.63961374 & -52560.6396137406 \tabularnewline
126 & 3220642 & 3513108.33071586 & -292466.330715859 \tabularnewline
127 & 3225812 & 3482248.37940971 & -256436.379409708 \tabularnewline
128 & 3354461 & 3578111.89404939 & -223650.894049392 \tabularnewline
129 & 3352261 & 3605580.47136887 & -253319.471368867 \tabularnewline
130 & 3450652 & 3372333.19821884 & 78318.8017811562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&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]4264830[/C][C]3773901.59528071[/C][C]490928.404719285[/C][/ROW]
[ROW][C]2[/C][C]3924674[/C][C]3633248.38735207[/C][C]291425.612647932[/C][/ROW]
[ROW][C]3[/C][C]3734753[/C][C]3561949.15074197[/C][C]172803.849258034[/C][/ROW]
[ROW][C]4[/C][C]3762290[/C][C]3661356.74987532[/C][C]100933.250124678[/C][/ROW]
[ROW][C]5[/C][C]3609739[/C][C]3557213.04508299[/C][C]52525.9549170115[/C][/ROW]
[ROW][C]6[/C][C]3877594[/C][C]3695461.152169[/C][C]182132.847830998[/C][/ROW]
[ROW][C]7[/C][C]3636415[/C][C]3676114.84069675[/C][C]-39699.8406967531[/C][/ROW]
[ROW][C]8[/C][C]3578195[/C][C]3493077.04032179[/C][C]85117.9596782147[/C][/ROW]
[ROW][C]9[/C][C]3604342[/C][C]3288369.07357363[/C][C]315972.92642637[/C][/ROW]
[ROW][C]10[/C][C]3459513[/C][C]3454910.06041357[/C][C]4602.93958643298[/C][/ROW]
[ROW][C]11[/C][C]3366571[/C][C]3480614.63782468[/C][C]-114043.637824678[/C][/ROW]
[ROW][C]12[/C][C]3371277[/C][C]3641593.90084071[/C][C]-270316.900840706[/C][/ROW]
[ROW][C]13[/C][C]3724848[/C][C]3611023.05378469[/C][C]113824.946215309[/C][/ROW]
[ROW][C]14[/C][C]3350830[/C][C]3428793.19240323[/C][C]-77963.1924032347[/C][/ROW]
[ROW][C]15[/C][C]3305159[/C][C]3381311.92548248[/C][C]-76152.9254824845[/C][/ROW]
[ROW][C]16[/C][C]3390736[/C][C]3438230.04537835[/C][C]-47494.0453783493[/C][/ROW]
[ROW][C]17[/C][C]3349758[/C][C]3404431.45239825[/C][C]-54673.4523982517[/C][/ROW]
[ROW][C]18[/C][C]3253655[/C][C]3330710.75150459[/C][C]-77055.7515045864[/C][/ROW]
[ROW][C]19[/C][C]3734250[/C][C]3353889.27761267[/C][C]380360.722387331[/C][/ROW]
[ROW][C]20[/C][C]3455433[/C][C]3316223.1116539[/C][C]139209.888346102[/C][/ROW]
[ROW][C]21[/C][C]2966726[/C][C]3258061.07946501[/C][C]-291335.079465012[/C][/ROW]
[ROW][C]22[/C][C]2993716[/C][C]3471511.80259057[/C][C]-477795.802590572[/C][/ROW]
[ROW][C]23[/C][C]3009320[/C][C]3547362.62762898[/C][C]-538042.627628982[/C][/ROW]
[ROW][C]24[/C][C]3169713[/C][C]3452655.73396095[/C][C]-282942.733960951[/C][/ROW]
[ROW][C]25[/C][C]3170061[/C][C]3404598.24860669[/C][C]-234537.248606689[/C][/ROW]
[ROW][C]26[/C][C]3368934[/C][C]3368691.55682914[/C][C]242.443170857564[/C][/ROW]
[ROW][C]27[/C][C]3292638[/C][C]3300188.82249281[/C][C]-7550.82249281102[/C][/ROW]
[ROW][C]28[/C][C]3337344[/C][C]3364324.82725401[/C][C]-26980.8272540144[/C][/ROW]
[ROW][C]29[/C][C]3208306[/C][C]3399568.21319874[/C][C]-191262.21319874[/C][/ROW]
[ROW][C]30[/C][C]3359130[/C][C]3230146.44769795[/C][C]128983.552302048[/C][/ROW]
[ROW][C]31[/C][C]3223078[/C][C]3441886.1665786[/C][C]-218808.166578596[/C][/ROW]
[ROW][C]32[/C][C]3437159[/C][C]3589391.13513619[/C][C]-152232.135136194[/C][/ROW]
[ROW][C]33[/C][C]3400156[/C][C]3579147.65889805[/C][C]-178991.65889805[/C][/ROW]
[ROW][C]34[/C][C]3657576[/C][C]3683202.51972535[/C][C]-25626.5197253523[/C][/ROW]
[ROW][C]35[/C][C]3765613[/C][C]3742785.62032777[/C][C]22827.3796722345[/C][/ROW]
[ROW][C]36[/C][C]3481921[/C][C]3937038.70091016[/C][C]-455117.700910163[/C][/ROW]
[ROW][C]37[/C][C]3604800[/C][C]4131624.48350309[/C][C]-526824.483503087[/C][/ROW]
[ROW][C]38[/C][C]3981340[/C][C]3977270.96431319[/C][C]4069.03568681212[/C][/ROW]
[ROW][C]39[/C][C]3734078[/C][C]4118689.43363315[/C][C]-384611.433633148[/C][/ROW]
[ROW][C]40[/C][C]4018173[/C][C]4170862.53411621[/C][C]-152689.534116208[/C][/ROW]
[ROW][C]41[/C][C]3887417[/C][C]3939730.61616447[/C][C]-52313.6161644683[/C][/ROW]
[ROW][C]42[/C][C]3919880[/C][C]4032199.20519831[/C][C]-112319.205198311[/C][/ROW]
[ROW][C]43[/C][C]4014466[/C][C]3808951.55024094[/C][C]205514.449759064[/C][/ROW]
[ROW][C]44[/C][C]4197758[/C][C]4007364.8178205[/C][C]190393.182179504[/C][/ROW]
[ROW][C]45[/C][C]3896531[/C][C]3930082.35326174[/C][C]-33551.3532617393[/C][/ROW]
[ROW][C]46[/C][C]3964742[/C][C]3978324.35584667[/C][C]-13582.3558466711[/C][/ROW]
[ROW][C]47[/C][C]4201847[/C][C]4095219.90900876[/C][C]106627.090991243[/C][/ROW]
[ROW][C]48[/C][C]4050512[/C][C]3834201.15945063[/C][C]216310.840549365[/C][/ROW]
[ROW][C]49[/C][C]3997402[/C][C]3786869.05141771[/C][C]210532.948582285[/C][/ROW]
[ROW][C]50[/C][C]4314479[/C][C]3783370.85452289[/C][C]531108.14547711[/C][/ROW]
[ROW][C]51[/C][C]4925744[/C][C]3989482.25236397[/C][C]936261.747636035[/C][/ROW]
[ROW][C]52[/C][C]5130631[/C][C]3876892.83509022[/C][C]1253738.16490978[/C][/ROW]
[ROW][C]53[/C][C]4444855[/C][C]3883970.47482155[/C][C]560884.525178448[/C][/ROW]
[ROW][C]54[/C][C]3967319[/C][C]3753024.57083877[/C][C]214294.42916123[/C][/ROW]
[ROW][C]55[/C][C]3931250[/C][C]3723727.43899768[/C][C]207522.561002321[/C][/ROW]
[ROW][C]56[/C][C]4235952[/C][C]3591494.66635569[/C][C]644457.333644309[/C][/ROW]
[ROW][C]57[/C][C]4169219[/C][C]3599440.83876733[/C][C]569778.161232669[/C][/ROW]
[ROW][C]58[/C][C]3779064[/C][C]3407254.31555941[/C][C]371809.68444059[/C][/ROW]
[ROW][C]59[/C][C]3558810[/C][C]3507029.63342362[/C][C]51780.3665763767[/C][/ROW]
[ROW][C]60[/C][C]3699466[/C][C]3488839.98477384[/C][C]210626.01522616[/C][/ROW]
[ROW][C]61[/C][C]3650693[/C][C]3490970.10891858[/C][C]159722.891081415[/C][/ROW]
[ROW][C]62[/C][C]3525633[/C][C]3376250.56750009[/C][C]149382.432499909[/C][/ROW]
[ROW][C]63[/C][C]3470276[/C][C]3409032.78464254[/C][C]61243.2153574623[/C][/ROW]
[ROW][C]64[/C][C]3859094[/C][C]3487638.75008301[/C][C]371455.24991699[/C][/ROW]
[ROW][C]65[/C][C]3661155[/C][C]3487638.75008301[/C][C]173516.24991699[/C][/ROW]
[ROW][C]66[/C][C]3356365[/C][C]3587765.72300884[/C][C]-231400.72300884[/C][/ROW]
[ROW][C]67[/C][C]3344440[/C][C]3496910.03887855[/C][C]-152470.038878545[/C][/ROW]
[ROW][C]68[/C][C]3338684[/C][C]3288872.328797[/C][C]49811.6712030041[/C][/ROW]
[ROW][C]69[/C][C]3404294[/C][C]3400093.67332067[/C][C]4200.32667933453[/C][/ROW]
[ROW][C]70[/C][C]3289319[/C][C]3434882.13291479[/C][C]-145563.132914791[/C][/ROW]
[ROW][C]71[/C][C]3469252[/C][C]3627104.1765368[/C][C]-157852.176536797[/C][/ROW]
[ROW][C]72[/C][C]3571850[/C][C]3715683.16724467[/C][C]-143833.167244671[/C][/ROW]
[ROW][C]73[/C][C]3639914[/C][C]3768751.39333328[/C][C]-128837.393333282[/C][/ROW]
[ROW][C]74[/C][C]3091730[/C][C]3852575.32533109[/C][C]-760845.325331088[/C][/ROW]
[ROW][C]75[/C][C]3078149[/C][C]4042479.31365896[/C][C]-964330.313658965[/C][/ROW]
[ROW][C]76[/C][C]3188115[/C][C]3975317.71752496[/C][C]-787202.717524957[/C][/ROW]
[ROW][C]77[/C][C]3246082[/C][C]3820018.94544021[/C][C]-573936.945440206[/C][/ROW]
[ROW][C]78[/C][C]3486992[/C][C]3874942.66059819[/C][C]-387950.66059819[/C][/ROW]
[ROW][C]79[/C][C]3378187[/C][C]3814782.02583178[/C][C]-436595.025831782[/C][/ROW]
[ROW][C]80[/C][C]3282306[/C][C]3649397.70181431[/C][C]-367091.70181431[/C][/ROW]
[ROW][C]81[/C][C]3288345[/C][C]3651424.55467007[/C][C]-363079.554670072[/C][/ROW]
[ROW][C]82[/C][C]3325749[/C][C]3660694.56958594[/C][C]-334945.569585937[/C][/ROW]
[ROW][C]83[/C][C]3352262[/C][C]3575943.0638647[/C][C]-223681.0638647[/C][/ROW]
[ROW][C]84[/C][C]3531954[/C][C]3656512.31539067[/C][C]-124558.315390667[/C][/ROW]
[ROW][C]85[/C][C]3722622[/C][C]3760656.020183[/C][C]-38034.0201829999[/C][/ROW]
[ROW][C]86[/C][C]3809365[/C][C]3905553.39172418[/C][C]-96188.3917241844[/C][/ROW]
[ROW][C]87[/C][C]3750617[/C][C]4061001.32269676[/C][C]-310384.322696765[/C][/ROW]
[ROW][C]88[/C][C]3615286[/C][C]4144158.87656554[/C][C]-528872.876565545[/C][/ROW]
[ROW][C]89[/C][C]3696556[/C][C]4133519.47316912[/C][C]-436963.473169117[/C][/ROW]
[ROW][C]90[/C][C]4123959[/C][C]4087100.51153568[/C][C]36858.4884643229[/C][/ROW]
[ROW][C]91[/C][C]4136163[/C][C]4128616.52945089[/C][C]7546.47054910995[/C][/ROW]
[ROW][C]92[/C][C]3933392[/C][C]3939991.76990806[/C][C]-6599.76990806121[/C][/ROW]
[ROW][C]93[/C][C]4035576[/C][C]3906237.40717382[/C][C]129338.592826182[/C][/ROW]
[ROW][C]94[/C][C]4551202[/C][C]4005042.19494842[/C][C]546159.805051584[/C][/ROW]
[ROW][C]95[/C][C]4032195[/C][C]3976421.48053361[/C][C]55773.5194663858[/C][/ROW]
[ROW][C]96[/C][C]3970893[/C][C]4171519.61501809[/C][C]-200626.615018086[/C][/ROW]
[ROW][C]97[/C][C]4489016[/C][C]4523662.84232425[/C][C]-34646.8423242516[/C][/ROW]
[ROW][C]98[/C][C]5426127[/C][C]4464615.91401413[/C][C]961511.085985875[/C][/ROW]
[ROW][C]99[/C][C]4578224[/C][C]4468861.73497966[/C][C]109362.265020337[/C][/ROW]
[ROW][C]100[/C][C]4126390[/C][C]4195853.18844645[/C][C]-69463.1884464534[/C][/ROW]
[ROW][C]101[/C][C]4892100[/C][C]4326799.09242924[/C][C]565300.907570764[/C][/ROW]
[ROW][C]102[/C][C]4128697[/C][C]4282572.50598032[/C][C]-153875.505980325[/C][/ROW]
[ROW][C]103[/C][C]4408721[/C][C]4172388.1895492[/C][C]236332.810450799[/C][/ROW]
[ROW][C]104[/C][C]4199465[/C][C]4101728.39652037[/C][C]97736.6034796313[/C][/ROW]
[ROW][C]105[/C][C]4074767[/C][C]3930550.22913456[/C][C]144216.770865439[/C][/ROW]
[ROW][C]106[/C][C]4161758[/C][C]4105686.08734374[/C][C]56071.9126562556[/C][/ROW]
[ROW][C]107[/C][C]3891319[/C][C]4248663.15017332[/C][C]-357344.150173322[/C][/ROW]
[ROW][C]108[/C][C]4470302[/C][C]4178705.05176554[/C][C]291596.948234457[/C][/ROW]
[ROW][C]109[/C][C]4283111[/C][C]3836010.17352578[/C][C]447100.82647422[/C][/ROW]
[ROW][C]110[/C][C]3845962[/C][C]3613936.86056229[/C][C]232025.13943771[/C][/ROW]
[ROW][C]111[/C][C]3911471[/C][C]3612037.14124706[/C][C]299433.858752937[/C][/ROW]
[ROW][C]112[/C][C]3798478[/C][C]3618624.49628496[/C][C]179853.50371504[/C][/ROW]
[ROW][C]113[/C][C]3644313[/C][C]3411347.52186759[/C][C]232965.478132408[/C][/ROW]
[ROW][C]114[/C][C]3784029[/C][C]3775108.1396196[/C][C]8920.86038039648[/C][/ROW]
[ROW][C]115[/C][C]3647134[/C][C]3595649.78208115[/C][C]51484.2179188527[/C][/ROW]
[ROW][C]116[/C][C]3994662[/C][C]3484883.40575889[/C][C]509778.59424111[/C][/ROW]
[ROW][C]117[/C][C]3607836[/C][C]3526979.86806306[/C][C]80856.1319369438[/C][/ROW]
[ROW][C]118[/C][C]3566008[/C][C]3389411.25069954[/C][C]176596.749300463[/C][/ROW]
[ROW][C]119[/C][C]3511412[/C][C]3454132.34579727[/C][C]57279.6542027324[/C][/ROW]
[ROW][C]120[/C][C]3258665[/C][C]3574317.93777403[/C][C]-315652.937774032[/C][/ROW]
[ROW][C]121[/C][C]3486573[/C][C]3635558.55699917[/C][C]-148985.55699917[/C][/ROW]
[ROW][C]122[/C][C]3369443[/C][C]3690338.95472695[/C][C]-320895.954726946[/C][/ROW]
[ROW][C]123[/C][C]3465544[/C][C]3717114.60284296[/C][C]-251570.60284296[/C][/ROW]
[ROW][C]124[/C][C]3905224[/C][C]3687350.37904997[/C][C]217873.620950026[/C][/ROW]
[ROW][C]125[/C][C]3733881[/C][C]3786441.63961374[/C][C]-52560.6396137406[/C][/ROW]
[ROW][C]126[/C][C]3220642[/C][C]3513108.33071586[/C][C]-292466.330715859[/C][/ROW]
[ROW][C]127[/C][C]3225812[/C][C]3482248.37940971[/C][C]-256436.379409708[/C][/ROW]
[ROW][C]128[/C][C]3354461[/C][C]3578111.89404939[/C][C]-223650.894049392[/C][/ROW]
[ROW][C]129[/C][C]3352261[/C][C]3605580.47136887[/C][C]-253319.471368867[/C][/ROW]
[ROW][C]130[/C][C]3450652[/C][C]3372333.19821884[/C][C]78318.8017811562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&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
142648303773901.59528071490928.404719285
239246743633248.38735207291425.612647932
337347533561949.15074197172803.849258034
437622903661356.74987532100933.250124678
536097393557213.0450829952525.9549170115
638775943695461.152169182132.847830998
736364153676114.84069675-39699.8406967531
835781953493077.0403217985117.9596782147
936043423288369.07357363315972.92642637
1034595133454910.060413574602.93958643298
1133665713480614.63782468-114043.637824678
1233712773641593.90084071-270316.900840706
1337248483611023.05378469113824.946215309
1433508303428793.19240323-77963.1924032347
1533051593381311.92548248-76152.9254824845
1633907363438230.04537835-47494.0453783493
1733497583404431.45239825-54673.4523982517
1832536553330710.75150459-77055.7515045864
1937342503353889.27761267380360.722387331
2034554333316223.1116539139209.888346102
2129667263258061.07946501-291335.079465012
2229937163471511.80259057-477795.802590572
2330093203547362.62762898-538042.627628982
2431697133452655.73396095-282942.733960951
2531700613404598.24860669-234537.248606689
2633689343368691.55682914242.443170857564
2732926383300188.82249281-7550.82249281102
2833373443364324.82725401-26980.8272540144
2932083063399568.21319874-191262.21319874
3033591303230146.44769795128983.552302048
3132230783441886.1665786-218808.166578596
3234371593589391.13513619-152232.135136194
3334001563579147.65889805-178991.65889805
3436575763683202.51972535-25626.5197253523
3537656133742785.6203277722827.3796722345
3634819213937038.70091016-455117.700910163
3736048004131624.48350309-526824.483503087
3839813403977270.964313194069.03568681212
3937340784118689.43363315-384611.433633148
4040181734170862.53411621-152689.534116208
4138874173939730.61616447-52313.6161644683
4239198804032199.20519831-112319.205198311
4340144663808951.55024094205514.449759064
4441977584007364.8178205190393.182179504
4538965313930082.35326174-33551.3532617393
4639647423978324.35584667-13582.3558466711
4742018474095219.90900876106627.090991243
4840505123834201.15945063216310.840549365
4939974023786869.05141771210532.948582285
5043144793783370.85452289531108.14547711
5149257443989482.25236397936261.747636035
5251306313876892.835090221253738.16490978
5344448553883970.47482155560884.525178448
5439673193753024.57083877214294.42916123
5539312503723727.43899768207522.561002321
5642359523591494.66635569644457.333644309
5741692193599440.83876733569778.161232669
5837790643407254.31555941371809.68444059
5935588103507029.6334236251780.3665763767
6036994663488839.98477384210626.01522616
6136506933490970.10891858159722.891081415
6235256333376250.56750009149382.432499909
6334702763409032.7846425461243.2153574623
6438590943487638.75008301371455.24991699
6536611553487638.75008301173516.24991699
6633563653587765.72300884-231400.72300884
6733444403496910.03887855-152470.038878545
6833386843288872.32879749811.6712030041
6934042943400093.673320674200.32667933453
7032893193434882.13291479-145563.132914791
7134692523627104.1765368-157852.176536797
7235718503715683.16724467-143833.167244671
7336399143768751.39333328-128837.393333282
7430917303852575.32533109-760845.325331088
7530781494042479.31365896-964330.313658965
7631881153975317.71752496-787202.717524957
7732460823820018.94544021-573936.945440206
7834869923874942.66059819-387950.66059819
7933781873814782.02583178-436595.025831782
8032823063649397.70181431-367091.70181431
8132883453651424.55467007-363079.554670072
8233257493660694.56958594-334945.569585937
8333522623575943.0638647-223681.0638647
8435319543656512.31539067-124558.315390667
8537226223760656.020183-38034.0201829999
8638093653905553.39172418-96188.3917241844
8737506174061001.32269676-310384.322696765
8836152864144158.87656554-528872.876565545
8936965564133519.47316912-436963.473169117
9041239594087100.5115356836858.4884643229
9141361634128616.529450897546.47054910995
9239333923939991.76990806-6599.76990806121
9340355763906237.40717382129338.592826182
9445512024005042.19494842546159.805051584
9540321953976421.4805336155773.5194663858
9639708934171519.61501809-200626.615018086
9744890164523662.84232425-34646.8423242516
9854261274464615.91401413961511.085985875
9945782244468861.73497966109362.265020337
10041263904195853.18844645-69463.1884464534
10148921004326799.09242924565300.907570764
10241286974282572.50598032-153875.505980325
10344087214172388.1895492236332.810450799
10441994654101728.3965203797736.6034796313
10540747673930550.22913456144216.770865439
10641617584105686.0873437456071.9126562556
10738913194248663.15017332-357344.150173322
10844703024178705.05176554291596.948234457
10942831113836010.17352578447100.82647422
11038459623613936.86056229232025.13943771
11139114713612037.14124706299433.858752937
11237984783618624.49628496179853.50371504
11336443133411347.52186759232965.478132408
11437840293775108.13961968920.86038039648
11536471343595649.7820811551484.2179188527
11639946623484883.40575889509778.59424111
11736078363526979.8680630680856.1319369438
11835660083389411.25069954176596.749300463
11935114123454132.3457972757279.6542027324
12032586653574317.93777403-315652.937774032
12134865733635558.55699917-148985.55699917
12233694433690338.95472695-320895.954726946
12334655443717114.60284296-251570.60284296
12439052243687350.37904997217873.620950026
12537338813786441.63961374-52560.6396137406
12632206423513108.33071586-292466.330715859
12732258123482248.37940971-256436.379409708
12833544613578111.89404939-223650.894049392
12933522613605580.47136887-253319.471368867
13034506523372333.1982188478318.8017811562






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1268807810993260.2537615621986530.873119218900674
110.04834315794864990.09668631589729990.95165684205135
120.02856750245282230.05713500490564460.971432497547178
130.01672579096209890.03345158192419780.983274209037901
140.006152452011207220.01230490402241440.993847547988793
150.003217359391386660.006434718782773310.996782640608613
160.001134404256746150.002268808513492310.998865595743254
170.0006020236802313330.001204047360462670.999397976319769
180.0002538533799406550.000507706759881310.999746146620059
190.007853899767268450.01570779953453690.992146100232732
200.00399451433431680.00798902866863360.996005485665683
210.008861169992050840.01772233998410170.991138830007949
220.02212922055790580.04425844111581170.977870779442094
230.03374643493866970.06749286987733940.96625356506133
240.02165210288505630.04330420577011260.978347897114944
250.02147136021038580.04294272042077160.978528639789614
260.01499972917703260.02999945835406520.985000270822967
270.01485417262088410.02970834524176820.985145827379116
280.009993978214462340.01998795642892470.990006021785538
290.006171557243086730.01234311448617350.993828442756913
300.003772830097620210.007545660195240420.99622716990238
310.003119712483736630.006239424967473260.996880287516263
320.001956232897155650.003912465794311290.998043767102844
330.001661714334390180.003323428668780370.99833828566561
340.0009605447801726270.001921089560345250.999039455219827
350.0005789290573908240.001157858114781650.999421070942609
360.0012759003837290.002551800767458010.998724099616271
370.0009809035368272170.001961807073654430.999019096463173
380.001546769037731440.003093538075462870.998453230962269
390.001129452553389430.002258905106778860.998870547446611
400.001190706715601050.00238141343120210.998809293284399
410.001089253251499540.002178506502999080.9989107467485
420.0007352459190722540.001470491838144510.999264754080928
430.001261501215179050.00252300243035810.998738498784821
440.001467766067989910.002935532135979830.99853223393201
450.0009757689081766710.001951537816353340.999024231091823
460.0006609547739718210.001321909547943640.999339045226028
470.0006150099974622870.001230019994924570.999384990002538
480.0006231605228976350.001246321045795270.999376839477102
490.000588014813096170.001176029626192340.999411985186904
500.001398645224895410.002797290449790820.998601354775105
510.02182327030388490.04364654060776980.978176729696115
520.3336739433331520.6673478866663050.666326056666848
530.3884596082220970.7769192164441940.611540391777903
540.3550719537713940.7101439075427890.644928046228606
550.3213012494352480.6426024988704950.678698750564752
560.4299989364245010.8599978728490010.570001063575499
570.5069916065155360.9860167869689270.493008393484464
580.516269504986470.967460990027060.48373049501353
590.4782472733251930.9564945466503870.521752726674807
600.4576433341629320.9152866683258650.542356665837068
610.4315881637084360.8631763274168720.568411836291564
620.4047325597368620.8094651194737240.595267440263138
630.3816154607421770.7632309214843540.618384539257823
640.4618596047446640.9237192094893290.538140395255336
650.5105119733268220.9789760533463560.489488026673178
660.5142342652585220.9715314694829570.485765734741478
670.519844671241520.9603106575169590.48015532875848
680.5302654574852520.9394690850294950.469734542514748
690.5371988601444750.9256022797110490.462801139855525
700.5479536971598850.9040926056802290.452046302840115
710.5813953779093360.8372092441813290.418604622090664
720.5866775446616110.8266449106767780.413322455338389
730.5677253878833270.8645492242333460.432274612116673
740.6598688961214590.6802622077570820.340131103878541
750.8573931588171770.2852136823656450.142606841182823
760.9440654567590290.1118690864819430.0559345432409714
770.9508938416413810.09821231671723870.0491061583586194
780.9443680358791530.1112639282416950.0556319641208475
790.9333122351089560.1333755297820880.0666877648910439
800.9219270741896840.1561458516206330.0780729258103164
810.9117767810715340.1764464378569320.0882232189284658
820.905265009679940.1894699806401210.0947349903200605
830.8919685750961730.2160628498076550.108031424903827
840.8720080576345150.2559838847309690.127991942365485
850.8434510828045570.3130978343908850.156548917195443
860.810078348297440.379843303405120.18992165170256
870.8217510945631320.3564978108737350.178248905436868
880.885562968798410.228874062403180.11443703120159
890.9377906125535390.1244187748929220.0622093874464609
900.9250106308545510.1499787382908980.074989369145449
910.9073730683753220.1852538632493560.0926269316246782
920.8933863353342080.2132273293315840.106613664665792
930.8674602888995780.2650794222008440.132539711100422
940.915779327764670.168441344470660.0842206722353299
950.907863608871040.1842727822579210.0921363911289604
960.9070959367141970.1858081265716070.0929040632858033
970.9138086591708360.1723826816583280.086191340829164
980.9937378057048360.01252438859032750.00626219429516377
990.9902764353254210.01944712934915710.00972356467457855
1000.9943436698592550.01131266028148980.00565633014074488
1010.9970679562669160.005864087466168690.00293204373308435
1020.9977849210147050.004430157970590980.00221507898529549
1030.9961715087827630.00765698243447370.00382849121723685
1040.995486870856280.009026258287439150.00451312914371957
1050.993911487323080.01217702535383950.00608851267691975
1060.9912246777764360.01755064444712880.00877532222356438
1070.9981838665712490.00363226685750130.00181613342875065
1080.997140564320820.005718871358359990.00285943567917999
1090.9987321225399680.002535754920063690.00126787746003184
1100.999041625733030.001916748533939710.000958374266969853
1110.9977665155094270.004466968981145740.00223348449057287
1120.994959014202410.01008197159518020.00504098579759008
1130.9901059041621080.01978819167578430.00989409583789213
1140.9792544565152820.04149108696943530.0207455434847176
1150.9669933237656660.06601335246866750.0330066762343337
1160.9767887802164510.04642243956709840.0232112197835492
1170.9799526971971080.04009460560578460.0200473028028923
1180.9740217407842160.05195651843156720.0259782592157836
1190.9574218532160450.08515629356790920.0425781467839546
1200.9028294252288530.1943411495422950.0971705747711474

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.126880781099326 & 0.253761562198653 & 0.873119218900674 \tabularnewline
11 & 0.0483431579486499 & 0.0966863158972999 & 0.95165684205135 \tabularnewline
12 & 0.0285675024528223 & 0.0571350049056446 & 0.971432497547178 \tabularnewline
13 & 0.0167257909620989 & 0.0334515819241978 & 0.983274209037901 \tabularnewline
14 & 0.00615245201120722 & 0.0123049040224144 & 0.993847547988793 \tabularnewline
15 & 0.00321735939138666 & 0.00643471878277331 & 0.996782640608613 \tabularnewline
16 & 0.00113440425674615 & 0.00226880851349231 & 0.998865595743254 \tabularnewline
17 & 0.000602023680231333 & 0.00120404736046267 & 0.999397976319769 \tabularnewline
18 & 0.000253853379940655 & 0.00050770675988131 & 0.999746146620059 \tabularnewline
19 & 0.00785389976726845 & 0.0157077995345369 & 0.992146100232732 \tabularnewline
20 & 0.0039945143343168 & 0.0079890286686336 & 0.996005485665683 \tabularnewline
21 & 0.00886116999205084 & 0.0177223399841017 & 0.991138830007949 \tabularnewline
22 & 0.0221292205579058 & 0.0442584411158117 & 0.977870779442094 \tabularnewline
23 & 0.0337464349386697 & 0.0674928698773394 & 0.96625356506133 \tabularnewline
24 & 0.0216521028850563 & 0.0433042057701126 & 0.978347897114944 \tabularnewline
25 & 0.0214713602103858 & 0.0429427204207716 & 0.978528639789614 \tabularnewline
26 & 0.0149997291770326 & 0.0299994583540652 & 0.985000270822967 \tabularnewline
27 & 0.0148541726208841 & 0.0297083452417682 & 0.985145827379116 \tabularnewline
28 & 0.00999397821446234 & 0.0199879564289247 & 0.990006021785538 \tabularnewline
29 & 0.00617155724308673 & 0.0123431144861735 & 0.993828442756913 \tabularnewline
30 & 0.00377283009762021 & 0.00754566019524042 & 0.99622716990238 \tabularnewline
31 & 0.00311971248373663 & 0.00623942496747326 & 0.996880287516263 \tabularnewline
32 & 0.00195623289715565 & 0.00391246579431129 & 0.998043767102844 \tabularnewline
33 & 0.00166171433439018 & 0.00332342866878037 & 0.99833828566561 \tabularnewline
34 & 0.000960544780172627 & 0.00192108956034525 & 0.999039455219827 \tabularnewline
35 & 0.000578929057390824 & 0.00115785811478165 & 0.999421070942609 \tabularnewline
36 & 0.001275900383729 & 0.00255180076745801 & 0.998724099616271 \tabularnewline
37 & 0.000980903536827217 & 0.00196180707365443 & 0.999019096463173 \tabularnewline
38 & 0.00154676903773144 & 0.00309353807546287 & 0.998453230962269 \tabularnewline
39 & 0.00112945255338943 & 0.00225890510677886 & 0.998870547446611 \tabularnewline
40 & 0.00119070671560105 & 0.0023814134312021 & 0.998809293284399 \tabularnewline
41 & 0.00108925325149954 & 0.00217850650299908 & 0.9989107467485 \tabularnewline
42 & 0.000735245919072254 & 0.00147049183814451 & 0.999264754080928 \tabularnewline
43 & 0.00126150121517905 & 0.0025230024303581 & 0.998738498784821 \tabularnewline
44 & 0.00146776606798991 & 0.00293553213597983 & 0.99853223393201 \tabularnewline
45 & 0.000975768908176671 & 0.00195153781635334 & 0.999024231091823 \tabularnewline
46 & 0.000660954773971821 & 0.00132190954794364 & 0.999339045226028 \tabularnewline
47 & 0.000615009997462287 & 0.00123001999492457 & 0.999384990002538 \tabularnewline
48 & 0.000623160522897635 & 0.00124632104579527 & 0.999376839477102 \tabularnewline
49 & 0.00058801481309617 & 0.00117602962619234 & 0.999411985186904 \tabularnewline
50 & 0.00139864522489541 & 0.00279729044979082 & 0.998601354775105 \tabularnewline
51 & 0.0218232703038849 & 0.0436465406077698 & 0.978176729696115 \tabularnewline
52 & 0.333673943333152 & 0.667347886666305 & 0.666326056666848 \tabularnewline
53 & 0.388459608222097 & 0.776919216444194 & 0.611540391777903 \tabularnewline
54 & 0.355071953771394 & 0.710143907542789 & 0.644928046228606 \tabularnewline
55 & 0.321301249435248 & 0.642602498870495 & 0.678698750564752 \tabularnewline
56 & 0.429998936424501 & 0.859997872849001 & 0.570001063575499 \tabularnewline
57 & 0.506991606515536 & 0.986016786968927 & 0.493008393484464 \tabularnewline
58 & 0.51626950498647 & 0.96746099002706 & 0.48373049501353 \tabularnewline
59 & 0.478247273325193 & 0.956494546650387 & 0.521752726674807 \tabularnewline
60 & 0.457643334162932 & 0.915286668325865 & 0.542356665837068 \tabularnewline
61 & 0.431588163708436 & 0.863176327416872 & 0.568411836291564 \tabularnewline
62 & 0.404732559736862 & 0.809465119473724 & 0.595267440263138 \tabularnewline
63 & 0.381615460742177 & 0.763230921484354 & 0.618384539257823 \tabularnewline
64 & 0.461859604744664 & 0.923719209489329 & 0.538140395255336 \tabularnewline
65 & 0.510511973326822 & 0.978976053346356 & 0.489488026673178 \tabularnewline
66 & 0.514234265258522 & 0.971531469482957 & 0.485765734741478 \tabularnewline
67 & 0.51984467124152 & 0.960310657516959 & 0.48015532875848 \tabularnewline
68 & 0.530265457485252 & 0.939469085029495 & 0.469734542514748 \tabularnewline
69 & 0.537198860144475 & 0.925602279711049 & 0.462801139855525 \tabularnewline
70 & 0.547953697159885 & 0.904092605680229 & 0.452046302840115 \tabularnewline
71 & 0.581395377909336 & 0.837209244181329 & 0.418604622090664 \tabularnewline
72 & 0.586677544661611 & 0.826644910676778 & 0.413322455338389 \tabularnewline
73 & 0.567725387883327 & 0.864549224233346 & 0.432274612116673 \tabularnewline
74 & 0.659868896121459 & 0.680262207757082 & 0.340131103878541 \tabularnewline
75 & 0.857393158817177 & 0.285213682365645 & 0.142606841182823 \tabularnewline
76 & 0.944065456759029 & 0.111869086481943 & 0.0559345432409714 \tabularnewline
77 & 0.950893841641381 & 0.0982123167172387 & 0.0491061583586194 \tabularnewline
78 & 0.944368035879153 & 0.111263928241695 & 0.0556319641208475 \tabularnewline
79 & 0.933312235108956 & 0.133375529782088 & 0.0666877648910439 \tabularnewline
80 & 0.921927074189684 & 0.156145851620633 & 0.0780729258103164 \tabularnewline
81 & 0.911776781071534 & 0.176446437856932 & 0.0882232189284658 \tabularnewline
82 & 0.90526500967994 & 0.189469980640121 & 0.0947349903200605 \tabularnewline
83 & 0.891968575096173 & 0.216062849807655 & 0.108031424903827 \tabularnewline
84 & 0.872008057634515 & 0.255983884730969 & 0.127991942365485 \tabularnewline
85 & 0.843451082804557 & 0.313097834390885 & 0.156548917195443 \tabularnewline
86 & 0.81007834829744 & 0.37984330340512 & 0.18992165170256 \tabularnewline
87 & 0.821751094563132 & 0.356497810873735 & 0.178248905436868 \tabularnewline
88 & 0.88556296879841 & 0.22887406240318 & 0.11443703120159 \tabularnewline
89 & 0.937790612553539 & 0.124418774892922 & 0.0622093874464609 \tabularnewline
90 & 0.925010630854551 & 0.149978738290898 & 0.074989369145449 \tabularnewline
91 & 0.907373068375322 & 0.185253863249356 & 0.0926269316246782 \tabularnewline
92 & 0.893386335334208 & 0.213227329331584 & 0.106613664665792 \tabularnewline
93 & 0.867460288899578 & 0.265079422200844 & 0.132539711100422 \tabularnewline
94 & 0.91577932776467 & 0.16844134447066 & 0.0842206722353299 \tabularnewline
95 & 0.90786360887104 & 0.184272782257921 & 0.0921363911289604 \tabularnewline
96 & 0.907095936714197 & 0.185808126571607 & 0.0929040632858033 \tabularnewline
97 & 0.913808659170836 & 0.172382681658328 & 0.086191340829164 \tabularnewline
98 & 0.993737805704836 & 0.0125243885903275 & 0.00626219429516377 \tabularnewline
99 & 0.990276435325421 & 0.0194471293491571 & 0.00972356467457855 \tabularnewline
100 & 0.994343669859255 & 0.0113126602814898 & 0.00565633014074488 \tabularnewline
101 & 0.997067956266916 & 0.00586408746616869 & 0.00293204373308435 \tabularnewline
102 & 0.997784921014705 & 0.00443015797059098 & 0.00221507898529549 \tabularnewline
103 & 0.996171508782763 & 0.0076569824344737 & 0.00382849121723685 \tabularnewline
104 & 0.99548687085628 & 0.00902625828743915 & 0.00451312914371957 \tabularnewline
105 & 0.99391148732308 & 0.0121770253538395 & 0.00608851267691975 \tabularnewline
106 & 0.991224677776436 & 0.0175506444471288 & 0.00877532222356438 \tabularnewline
107 & 0.998183866571249 & 0.0036322668575013 & 0.00181613342875065 \tabularnewline
108 & 0.99714056432082 & 0.00571887135835999 & 0.00285943567917999 \tabularnewline
109 & 0.998732122539968 & 0.00253575492006369 & 0.00126787746003184 \tabularnewline
110 & 0.99904162573303 & 0.00191674853393971 & 0.000958374266969853 \tabularnewline
111 & 0.997766515509427 & 0.00446696898114574 & 0.00223348449057287 \tabularnewline
112 & 0.99495901420241 & 0.0100819715951802 & 0.00504098579759008 \tabularnewline
113 & 0.990105904162108 & 0.0197881916757843 & 0.00989409583789213 \tabularnewline
114 & 0.979254456515282 & 0.0414910869694353 & 0.0207455434847176 \tabularnewline
115 & 0.966993323765666 & 0.0660133524686675 & 0.0330066762343337 \tabularnewline
116 & 0.976788780216451 & 0.0464224395670984 & 0.0232112197835492 \tabularnewline
117 & 0.979952697197108 & 0.0400946056057846 & 0.0200473028028923 \tabularnewline
118 & 0.974021740784216 & 0.0519565184315672 & 0.0259782592157836 \tabularnewline
119 & 0.957421853216045 & 0.0851562935679092 & 0.0425781467839546 \tabularnewline
120 & 0.902829425228853 & 0.194341149542295 & 0.0971705747711474 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.126880781099326[/C][C]0.253761562198653[/C][C]0.873119218900674[/C][/ROW]
[ROW][C]11[/C][C]0.0483431579486499[/C][C]0.0966863158972999[/C][C]0.95165684205135[/C][/ROW]
[ROW][C]12[/C][C]0.0285675024528223[/C][C]0.0571350049056446[/C][C]0.971432497547178[/C][/ROW]
[ROW][C]13[/C][C]0.0167257909620989[/C][C]0.0334515819241978[/C][C]0.983274209037901[/C][/ROW]
[ROW][C]14[/C][C]0.00615245201120722[/C][C]0.0123049040224144[/C][C]0.993847547988793[/C][/ROW]
[ROW][C]15[/C][C]0.00321735939138666[/C][C]0.00643471878277331[/C][C]0.996782640608613[/C][/ROW]
[ROW][C]16[/C][C]0.00113440425674615[/C][C]0.00226880851349231[/C][C]0.998865595743254[/C][/ROW]
[ROW][C]17[/C][C]0.000602023680231333[/C][C]0.00120404736046267[/C][C]0.999397976319769[/C][/ROW]
[ROW][C]18[/C][C]0.000253853379940655[/C][C]0.00050770675988131[/C][C]0.999746146620059[/C][/ROW]
[ROW][C]19[/C][C]0.00785389976726845[/C][C]0.0157077995345369[/C][C]0.992146100232732[/C][/ROW]
[ROW][C]20[/C][C]0.0039945143343168[/C][C]0.0079890286686336[/C][C]0.996005485665683[/C][/ROW]
[ROW][C]21[/C][C]0.00886116999205084[/C][C]0.0177223399841017[/C][C]0.991138830007949[/C][/ROW]
[ROW][C]22[/C][C]0.0221292205579058[/C][C]0.0442584411158117[/C][C]0.977870779442094[/C][/ROW]
[ROW][C]23[/C][C]0.0337464349386697[/C][C]0.0674928698773394[/C][C]0.96625356506133[/C][/ROW]
[ROW][C]24[/C][C]0.0216521028850563[/C][C]0.0433042057701126[/C][C]0.978347897114944[/C][/ROW]
[ROW][C]25[/C][C]0.0214713602103858[/C][C]0.0429427204207716[/C][C]0.978528639789614[/C][/ROW]
[ROW][C]26[/C][C]0.0149997291770326[/C][C]0.0299994583540652[/C][C]0.985000270822967[/C][/ROW]
[ROW][C]27[/C][C]0.0148541726208841[/C][C]0.0297083452417682[/C][C]0.985145827379116[/C][/ROW]
[ROW][C]28[/C][C]0.00999397821446234[/C][C]0.0199879564289247[/C][C]0.990006021785538[/C][/ROW]
[ROW][C]29[/C][C]0.00617155724308673[/C][C]0.0123431144861735[/C][C]0.993828442756913[/C][/ROW]
[ROW][C]30[/C][C]0.00377283009762021[/C][C]0.00754566019524042[/C][C]0.99622716990238[/C][/ROW]
[ROW][C]31[/C][C]0.00311971248373663[/C][C]0.00623942496747326[/C][C]0.996880287516263[/C][/ROW]
[ROW][C]32[/C][C]0.00195623289715565[/C][C]0.00391246579431129[/C][C]0.998043767102844[/C][/ROW]
[ROW][C]33[/C][C]0.00166171433439018[/C][C]0.00332342866878037[/C][C]0.99833828566561[/C][/ROW]
[ROW][C]34[/C][C]0.000960544780172627[/C][C]0.00192108956034525[/C][C]0.999039455219827[/C][/ROW]
[ROW][C]35[/C][C]0.000578929057390824[/C][C]0.00115785811478165[/C][C]0.999421070942609[/C][/ROW]
[ROW][C]36[/C][C]0.001275900383729[/C][C]0.00255180076745801[/C][C]0.998724099616271[/C][/ROW]
[ROW][C]37[/C][C]0.000980903536827217[/C][C]0.00196180707365443[/C][C]0.999019096463173[/C][/ROW]
[ROW][C]38[/C][C]0.00154676903773144[/C][C]0.00309353807546287[/C][C]0.998453230962269[/C][/ROW]
[ROW][C]39[/C][C]0.00112945255338943[/C][C]0.00225890510677886[/C][C]0.998870547446611[/C][/ROW]
[ROW][C]40[/C][C]0.00119070671560105[/C][C]0.0023814134312021[/C][C]0.998809293284399[/C][/ROW]
[ROW][C]41[/C][C]0.00108925325149954[/C][C]0.00217850650299908[/C][C]0.9989107467485[/C][/ROW]
[ROW][C]42[/C][C]0.000735245919072254[/C][C]0.00147049183814451[/C][C]0.999264754080928[/C][/ROW]
[ROW][C]43[/C][C]0.00126150121517905[/C][C]0.0025230024303581[/C][C]0.998738498784821[/C][/ROW]
[ROW][C]44[/C][C]0.00146776606798991[/C][C]0.00293553213597983[/C][C]0.99853223393201[/C][/ROW]
[ROW][C]45[/C][C]0.000975768908176671[/C][C]0.00195153781635334[/C][C]0.999024231091823[/C][/ROW]
[ROW][C]46[/C][C]0.000660954773971821[/C][C]0.00132190954794364[/C][C]0.999339045226028[/C][/ROW]
[ROW][C]47[/C][C]0.000615009997462287[/C][C]0.00123001999492457[/C][C]0.999384990002538[/C][/ROW]
[ROW][C]48[/C][C]0.000623160522897635[/C][C]0.00124632104579527[/C][C]0.999376839477102[/C][/ROW]
[ROW][C]49[/C][C]0.00058801481309617[/C][C]0.00117602962619234[/C][C]0.999411985186904[/C][/ROW]
[ROW][C]50[/C][C]0.00139864522489541[/C][C]0.00279729044979082[/C][C]0.998601354775105[/C][/ROW]
[ROW][C]51[/C][C]0.0218232703038849[/C][C]0.0436465406077698[/C][C]0.978176729696115[/C][/ROW]
[ROW][C]52[/C][C]0.333673943333152[/C][C]0.667347886666305[/C][C]0.666326056666848[/C][/ROW]
[ROW][C]53[/C][C]0.388459608222097[/C][C]0.776919216444194[/C][C]0.611540391777903[/C][/ROW]
[ROW][C]54[/C][C]0.355071953771394[/C][C]0.710143907542789[/C][C]0.644928046228606[/C][/ROW]
[ROW][C]55[/C][C]0.321301249435248[/C][C]0.642602498870495[/C][C]0.678698750564752[/C][/ROW]
[ROW][C]56[/C][C]0.429998936424501[/C][C]0.859997872849001[/C][C]0.570001063575499[/C][/ROW]
[ROW][C]57[/C][C]0.506991606515536[/C][C]0.986016786968927[/C][C]0.493008393484464[/C][/ROW]
[ROW][C]58[/C][C]0.51626950498647[/C][C]0.96746099002706[/C][C]0.48373049501353[/C][/ROW]
[ROW][C]59[/C][C]0.478247273325193[/C][C]0.956494546650387[/C][C]0.521752726674807[/C][/ROW]
[ROW][C]60[/C][C]0.457643334162932[/C][C]0.915286668325865[/C][C]0.542356665837068[/C][/ROW]
[ROW][C]61[/C][C]0.431588163708436[/C][C]0.863176327416872[/C][C]0.568411836291564[/C][/ROW]
[ROW][C]62[/C][C]0.404732559736862[/C][C]0.809465119473724[/C][C]0.595267440263138[/C][/ROW]
[ROW][C]63[/C][C]0.381615460742177[/C][C]0.763230921484354[/C][C]0.618384539257823[/C][/ROW]
[ROW][C]64[/C][C]0.461859604744664[/C][C]0.923719209489329[/C][C]0.538140395255336[/C][/ROW]
[ROW][C]65[/C][C]0.510511973326822[/C][C]0.978976053346356[/C][C]0.489488026673178[/C][/ROW]
[ROW][C]66[/C][C]0.514234265258522[/C][C]0.971531469482957[/C][C]0.485765734741478[/C][/ROW]
[ROW][C]67[/C][C]0.51984467124152[/C][C]0.960310657516959[/C][C]0.48015532875848[/C][/ROW]
[ROW][C]68[/C][C]0.530265457485252[/C][C]0.939469085029495[/C][C]0.469734542514748[/C][/ROW]
[ROW][C]69[/C][C]0.537198860144475[/C][C]0.925602279711049[/C][C]0.462801139855525[/C][/ROW]
[ROW][C]70[/C][C]0.547953697159885[/C][C]0.904092605680229[/C][C]0.452046302840115[/C][/ROW]
[ROW][C]71[/C][C]0.581395377909336[/C][C]0.837209244181329[/C][C]0.418604622090664[/C][/ROW]
[ROW][C]72[/C][C]0.586677544661611[/C][C]0.826644910676778[/C][C]0.413322455338389[/C][/ROW]
[ROW][C]73[/C][C]0.567725387883327[/C][C]0.864549224233346[/C][C]0.432274612116673[/C][/ROW]
[ROW][C]74[/C][C]0.659868896121459[/C][C]0.680262207757082[/C][C]0.340131103878541[/C][/ROW]
[ROW][C]75[/C][C]0.857393158817177[/C][C]0.285213682365645[/C][C]0.142606841182823[/C][/ROW]
[ROW][C]76[/C][C]0.944065456759029[/C][C]0.111869086481943[/C][C]0.0559345432409714[/C][/ROW]
[ROW][C]77[/C][C]0.950893841641381[/C][C]0.0982123167172387[/C][C]0.0491061583586194[/C][/ROW]
[ROW][C]78[/C][C]0.944368035879153[/C][C]0.111263928241695[/C][C]0.0556319641208475[/C][/ROW]
[ROW][C]79[/C][C]0.933312235108956[/C][C]0.133375529782088[/C][C]0.0666877648910439[/C][/ROW]
[ROW][C]80[/C][C]0.921927074189684[/C][C]0.156145851620633[/C][C]0.0780729258103164[/C][/ROW]
[ROW][C]81[/C][C]0.911776781071534[/C][C]0.176446437856932[/C][C]0.0882232189284658[/C][/ROW]
[ROW][C]82[/C][C]0.90526500967994[/C][C]0.189469980640121[/C][C]0.0947349903200605[/C][/ROW]
[ROW][C]83[/C][C]0.891968575096173[/C][C]0.216062849807655[/C][C]0.108031424903827[/C][/ROW]
[ROW][C]84[/C][C]0.872008057634515[/C][C]0.255983884730969[/C][C]0.127991942365485[/C][/ROW]
[ROW][C]85[/C][C]0.843451082804557[/C][C]0.313097834390885[/C][C]0.156548917195443[/C][/ROW]
[ROW][C]86[/C][C]0.81007834829744[/C][C]0.37984330340512[/C][C]0.18992165170256[/C][/ROW]
[ROW][C]87[/C][C]0.821751094563132[/C][C]0.356497810873735[/C][C]0.178248905436868[/C][/ROW]
[ROW][C]88[/C][C]0.88556296879841[/C][C]0.22887406240318[/C][C]0.11443703120159[/C][/ROW]
[ROW][C]89[/C][C]0.937790612553539[/C][C]0.124418774892922[/C][C]0.0622093874464609[/C][/ROW]
[ROW][C]90[/C][C]0.925010630854551[/C][C]0.149978738290898[/C][C]0.074989369145449[/C][/ROW]
[ROW][C]91[/C][C]0.907373068375322[/C][C]0.185253863249356[/C][C]0.0926269316246782[/C][/ROW]
[ROW][C]92[/C][C]0.893386335334208[/C][C]0.213227329331584[/C][C]0.106613664665792[/C][/ROW]
[ROW][C]93[/C][C]0.867460288899578[/C][C]0.265079422200844[/C][C]0.132539711100422[/C][/ROW]
[ROW][C]94[/C][C]0.91577932776467[/C][C]0.16844134447066[/C][C]0.0842206722353299[/C][/ROW]
[ROW][C]95[/C][C]0.90786360887104[/C][C]0.184272782257921[/C][C]0.0921363911289604[/C][/ROW]
[ROW][C]96[/C][C]0.907095936714197[/C][C]0.185808126571607[/C][C]0.0929040632858033[/C][/ROW]
[ROW][C]97[/C][C]0.913808659170836[/C][C]0.172382681658328[/C][C]0.086191340829164[/C][/ROW]
[ROW][C]98[/C][C]0.993737805704836[/C][C]0.0125243885903275[/C][C]0.00626219429516377[/C][/ROW]
[ROW][C]99[/C][C]0.990276435325421[/C][C]0.0194471293491571[/C][C]0.00972356467457855[/C][/ROW]
[ROW][C]100[/C][C]0.994343669859255[/C][C]0.0113126602814898[/C][C]0.00565633014074488[/C][/ROW]
[ROW][C]101[/C][C]0.997067956266916[/C][C]0.00586408746616869[/C][C]0.00293204373308435[/C][/ROW]
[ROW][C]102[/C][C]0.997784921014705[/C][C]0.00443015797059098[/C][C]0.00221507898529549[/C][/ROW]
[ROW][C]103[/C][C]0.996171508782763[/C][C]0.0076569824344737[/C][C]0.00382849121723685[/C][/ROW]
[ROW][C]104[/C][C]0.99548687085628[/C][C]0.00902625828743915[/C][C]0.00451312914371957[/C][/ROW]
[ROW][C]105[/C][C]0.99391148732308[/C][C]0.0121770253538395[/C][C]0.00608851267691975[/C][/ROW]
[ROW][C]106[/C][C]0.991224677776436[/C][C]0.0175506444471288[/C][C]0.00877532222356438[/C][/ROW]
[ROW][C]107[/C][C]0.998183866571249[/C][C]0.0036322668575013[/C][C]0.00181613342875065[/C][/ROW]
[ROW][C]108[/C][C]0.99714056432082[/C][C]0.00571887135835999[/C][C]0.00285943567917999[/C][/ROW]
[ROW][C]109[/C][C]0.998732122539968[/C][C]0.00253575492006369[/C][C]0.00126787746003184[/C][/ROW]
[ROW][C]110[/C][C]0.99904162573303[/C][C]0.00191674853393971[/C][C]0.000958374266969853[/C][/ROW]
[ROW][C]111[/C][C]0.997766515509427[/C][C]0.00446696898114574[/C][C]0.00223348449057287[/C][/ROW]
[ROW][C]112[/C][C]0.99495901420241[/C][C]0.0100819715951802[/C][C]0.00504098579759008[/C][/ROW]
[ROW][C]113[/C][C]0.990105904162108[/C][C]0.0197881916757843[/C][C]0.00989409583789213[/C][/ROW]
[ROW][C]114[/C][C]0.979254456515282[/C][C]0.0414910869694353[/C][C]0.0207455434847176[/C][/ROW]
[ROW][C]115[/C][C]0.966993323765666[/C][C]0.0660133524686675[/C][C]0.0330066762343337[/C][/ROW]
[ROW][C]116[/C][C]0.976788780216451[/C][C]0.0464224395670984[/C][C]0.0232112197835492[/C][/ROW]
[ROW][C]117[/C][C]0.979952697197108[/C][C]0.0400946056057846[/C][C]0.0200473028028923[/C][/ROW]
[ROW][C]118[/C][C]0.974021740784216[/C][C]0.0519565184315672[/C][C]0.0259782592157836[/C][/ROW]
[ROW][C]119[/C][C]0.957421853216045[/C][C]0.0851562935679092[/C][C]0.0425781467839546[/C][/ROW]
[ROW][C]120[/C][C]0.902829425228853[/C][C]0.194341149542295[/C][C]0.0971705747711474[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1268807810993260.2537615621986530.873119218900674
110.04834315794864990.09668631589729990.95165684205135
120.02856750245282230.05713500490564460.971432497547178
130.01672579096209890.03345158192419780.983274209037901
140.006152452011207220.01230490402241440.993847547988793
150.003217359391386660.006434718782773310.996782640608613
160.001134404256746150.002268808513492310.998865595743254
170.0006020236802313330.001204047360462670.999397976319769
180.0002538533799406550.000507706759881310.999746146620059
190.007853899767268450.01570779953453690.992146100232732
200.00399451433431680.00798902866863360.996005485665683
210.008861169992050840.01772233998410170.991138830007949
220.02212922055790580.04425844111581170.977870779442094
230.03374643493866970.06749286987733940.96625356506133
240.02165210288505630.04330420577011260.978347897114944
250.02147136021038580.04294272042077160.978528639789614
260.01499972917703260.02999945835406520.985000270822967
270.01485417262088410.02970834524176820.985145827379116
280.009993978214462340.01998795642892470.990006021785538
290.006171557243086730.01234311448617350.993828442756913
300.003772830097620210.007545660195240420.99622716990238
310.003119712483736630.006239424967473260.996880287516263
320.001956232897155650.003912465794311290.998043767102844
330.001661714334390180.003323428668780370.99833828566561
340.0009605447801726270.001921089560345250.999039455219827
350.0005789290573908240.001157858114781650.999421070942609
360.0012759003837290.002551800767458010.998724099616271
370.0009809035368272170.001961807073654430.999019096463173
380.001546769037731440.003093538075462870.998453230962269
390.001129452553389430.002258905106778860.998870547446611
400.001190706715601050.00238141343120210.998809293284399
410.001089253251499540.002178506502999080.9989107467485
420.0007352459190722540.001470491838144510.999264754080928
430.001261501215179050.00252300243035810.998738498784821
440.001467766067989910.002935532135979830.99853223393201
450.0009757689081766710.001951537816353340.999024231091823
460.0006609547739718210.001321909547943640.999339045226028
470.0006150099974622870.001230019994924570.999384990002538
480.0006231605228976350.001246321045795270.999376839477102
490.000588014813096170.001176029626192340.999411985186904
500.001398645224895410.002797290449790820.998601354775105
510.02182327030388490.04364654060776980.978176729696115
520.3336739433331520.6673478866663050.666326056666848
530.3884596082220970.7769192164441940.611540391777903
540.3550719537713940.7101439075427890.644928046228606
550.3213012494352480.6426024988704950.678698750564752
560.4299989364245010.8599978728490010.570001063575499
570.5069916065155360.9860167869689270.493008393484464
580.516269504986470.967460990027060.48373049501353
590.4782472733251930.9564945466503870.521752726674807
600.4576433341629320.9152866683258650.542356665837068
610.4315881637084360.8631763274168720.568411836291564
620.4047325597368620.8094651194737240.595267440263138
630.3816154607421770.7632309214843540.618384539257823
640.4618596047446640.9237192094893290.538140395255336
650.5105119733268220.9789760533463560.489488026673178
660.5142342652585220.9715314694829570.485765734741478
670.519844671241520.9603106575169590.48015532875848
680.5302654574852520.9394690850294950.469734542514748
690.5371988601444750.9256022797110490.462801139855525
700.5479536971598850.9040926056802290.452046302840115
710.5813953779093360.8372092441813290.418604622090664
720.5866775446616110.8266449106767780.413322455338389
730.5677253878833270.8645492242333460.432274612116673
740.6598688961214590.6802622077570820.340131103878541
750.8573931588171770.2852136823656450.142606841182823
760.9440654567590290.1118690864819430.0559345432409714
770.9508938416413810.09821231671723870.0491061583586194
780.9443680358791530.1112639282416950.0556319641208475
790.9333122351089560.1333755297820880.0666877648910439
800.9219270741896840.1561458516206330.0780729258103164
810.9117767810715340.1764464378569320.0882232189284658
820.905265009679940.1894699806401210.0947349903200605
830.8919685750961730.2160628498076550.108031424903827
840.8720080576345150.2559838847309690.127991942365485
850.8434510828045570.3130978343908850.156548917195443
860.810078348297440.379843303405120.18992165170256
870.8217510945631320.3564978108737350.178248905436868
880.885562968798410.228874062403180.11443703120159
890.9377906125535390.1244187748929220.0622093874464609
900.9250106308545510.1499787382908980.074989369145449
910.9073730683753220.1852538632493560.0926269316246782
920.8933863353342080.2132273293315840.106613664665792
930.8674602888995780.2650794222008440.132539711100422
940.915779327764670.168441344470660.0842206722353299
950.907863608871040.1842727822579210.0921363911289604
960.9070959367141970.1858081265716070.0929040632858033
970.9138086591708360.1723826816583280.086191340829164
980.9937378057048360.01252438859032750.00626219429516377
990.9902764353254210.01944712934915710.00972356467457855
1000.9943436698592550.01131266028148980.00565633014074488
1010.9970679562669160.005864087466168690.00293204373308435
1020.9977849210147050.004430157970590980.00221507898529549
1030.9961715087827630.00765698243447370.00382849121723685
1040.995486870856280.009026258287439150.00451312914371957
1050.993911487323080.01217702535383950.00608851267691975
1060.9912246777764360.01755064444712880.00877532222356438
1070.9981838665712490.00363226685750130.00181613342875065
1080.997140564320820.005718871358359990.00285943567917999
1090.9987321225399680.002535754920063690.00126787746003184
1100.999041625733030.001916748533939710.000958374266969853
1110.9977665155094270.004466968981145740.00223348449057287
1120.994959014202410.01008197159518020.00504098579759008
1130.9901059041621080.01978819167578430.00989409583789213
1140.9792544565152820.04149108696943530.0207455434847176
1150.9669933237656660.06601335246866750.0330066762343337
1160.9767887802164510.04642243956709840.0232112197835492
1170.9799526971971080.04009460560578460.0200473028028923
1180.9740217407842160.05195651843156720.0259782592157836
1190.9574218532160450.08515629356790920.0425781467839546
1200.9028294252288530.1943411495422950.0971705747711474






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level350.315315315315315NOK
5% type I error level570.513513513513513NOK
10% type I error level640.576576576576577NOK

\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 & 35 & 0.315315315315315 & NOK \tabularnewline
5% type I error level & 57 & 0.513513513513513 & NOK \tabularnewline
10% type I error level & 64 & 0.576576576576577 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191212&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]35[/C][C]0.315315315315315[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]57[/C][C]0.513513513513513[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]64[/C][C]0.576576576576577[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191212&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191212&T=6

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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 level350.315315315315315NOK
5% type I error level570.513513513513513NOK
10% type I error level640.576576576576577NOK


Figures (Output of Computation)

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Input Parameters & R Code

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