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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 13:31:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324665113u39107nvkki3hff.htm/, Retrieved Mon, 29 Apr 2024 18:04:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160629, Retrieved Mon, 29 Apr 2024 18:04:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Multiple regression] [2011-12-23 18:31:26] [0b94335bf72158573fe52322b9537409] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9/06/2002	169498	2,47	0,97	0,80	5,06
16/06/2002	125451	2,51	0,95	0,80	5,07
23/06/2002	140449	2,51	0,96	0,80	5,04
30/06/2002	141653	2,51	0,97	0,81	5,08
7/07/2002	136394	2,49	0,96	0,81	5,15
14/07/2002	167588	2,52	0,96	0,83	5,16
21/07/2002	191807	2,51	0,94	0,79	5,09
28/07/2002	149736	2,54	0,95	0,80	5,14
4/08/2002	196066	2,53	0,95	0,81	5,17
11/08/2002	239155	2,55	0,95	0,80	5,13
18/08/2002	178421	2,50	0,95	0,81	5,19
25/08/2002	139871	2,59	0,97	0,81	5,17
1/09/2002	118159	2,56	0,97	0,82	5,17
8/09/2002	109763	2,59	0,95	0,81	5,21
15/09/2002	97415	2,58	0,96	0,82	5,21
22/09/2002	119190	2,62	0,96	0,82	5,23
29/09/2002	97903	2,59	0,94	0,80	5,17
6/10/2002	96953	2,58	0,96	0,79	5,16
13/10/2002	87888	2,57	0,98	0,79	5,15
20/10/2002	84637	2,57	0,97	0,80	5,12
27/10/2002	90549	2,55	0,96	0,80	5,12
3/11/2002	95680	2,51	0,95	0,80	5,10
10/11/2002	99371	2,50	0,96	0,80	5,13
17/11/2002	79984	2,59	0,96	0,80	5,14
24/11/2002	86752	2,63	0,97	0,80	5,16
1/12/2002	85733	2,63	0,96	0,80	5,17
8/12/2002	84906	2,61	0,95	0,78	5,15
15/12/2002	78356	2,64	0,95	0,78	5,12
22/12/2002	108895	2,67	0,94	0,76	5,08
29/12/2002	101768	2,63	0,94	0,77	5,07
5/01/2003	73285	2,58	0,98	0,82	5,16
12/01/2003	65724	2,56	0,93	0,81	5,14
19/01/2003	67457	2,57	0,93	0,81	5,13
26/01/2003	67203	2,55	0,96	0,80	5,11
2/02/2003	69273	2,58	0,97	0,79	5,12
9/02/2003	80807	2,50	0,97	0,81	5,09
16/02/2003	75129	2,56	0,95	0,81	5,10
23/02/2003	74991	2,62	0,95	0,81	4,95
2/03/2003	68157	2,71	0,96	0,81	5,11
9/03/2003	73858	2,74	0,98	0,82	5,11
16/03/2003	71349	2,76	0,98	0,81	5,10
23/03/2003	85634	2,66	0,97	0,80	5,11
30/03/2003	91624	2,61	0,98	0,83	5,12
6/04/2003	116014	2,68	0,98	0,81	5,09
13/04/2003	120033	2,70	0,99	0,83	5,10
20/04/2003	108651	2,70	0,99	0,84	5,06
27/04/2003	105378	2,72	0,97	0,84	5,03
4/05/2003	138939	2,77	0,98	0,85	5,05
11/05/2003	132974	2,76	0,97	0,85	5,04
18/05/2003	135277	2,72	0,97	0,86	5,02
25/05/2003	152741	2,69	0,97	0,85	4,97
1/06/2003	158417	2,70	0,98	0,87	4,91
8/06/2003	157460	2,69	0,97	0,86	4,91
15/06/2003	193997	2,66	0,97	0,87	4,98
22/06/2003	154089	2,74	0,98	0,87	4,98
29/06/2003	147570	2,76	0,98	0,86	4,97
6/07/2003	162924	2,79	0,95	0,87	4,97
13/07/2003	153629	2,78	0,97	0,88	4,90
20/07/2003	155907	2,80	0,97	0,87	4,91
27/07/2003	197675	2,78	0,97	0,87	4,88
3/08/2003	250708	2,76	0,97	0,86	4,86
10/08/2003	266652	2,73	0,98	0,86	4,87
17/08/2003	209842	2,72	0,98	0,88	4,86
24/08/2003	165826	2,73	0,98	0,88	4,89
31/08/2003	137152	2,74	0,96	0,87	4,90
7/09/2003	150581	2,72	0,98	0,88	4,88
14/09/2003	145973	2,71	1,00	0,89	4,85
21/09/2003	126532	2,66	1,01	0,89	4,85
28/09/2003	115437	2,68	1,02	0,88	4,84
5/10/2003	119526	2,67	1,01	0,88	4,91
12/10/2003	110856	2,68	1,01	0,88	4,94
19/10/2003	97243	2,67	1,02	0,88	4,92
26/10/2003	103876	2,71	1,01	0,87	4,93
2/11/2003	116370	2,69	1,01	0,87	4,97
9/11/2003	109616	2,64	1,01	0,86	4,89
16/11/2003	98365	2,66	1,02	0,88	4,88
23/11/2003	90440	2,70	1,02	0,87	4,93
30/11/2003	88899	2,69	1,02	0,86	4,94
7/12/2003	92358	2,71	1,01	0,85	4,99
14/12/2003	88394	2,74	1,01	0,86	5,00
21/12/2003	98219	2,78	0,99	0,84	5,02
28/12/2003	113546	2,79	1,00	0,85	5,06
4/01/2004	107168	2,75	1,01	0,88	5,01
11/01/2004	77540	2,69	0,99	0,88	5,02
18/01/2004	74944	2,69	1,00	0,89	4,97
25/01/2004	75641	2,69	1,02	0,88	4,96
1/02/2004	75910	2,72	1,01	0,88	4,95
8/02/2004	87384	2,69	1,01	0,88	4,92
15/02/2004	84615	2,70	1,01	0,89	4,88
22/02/2004	80420	2,68	1,03	0,89	4,86
29/02/2004	80784	2,70	1,02	0,89	4,94
7/03/2004	79933	2,72	1,02	0,88	4,83
14/03/2004	82118	2,70	1,03	0,89	4,95
21/03/2004	91420	2,66	1,03	0,89	4,95
28/03/2004	112426	2,68	1,02	0,89	4,94
4/04/2004	114528	2,65	1,02	0,89	4,93
11/04/2004	131025	2,69	1,02	0,90	4,97
18/04/2004	116460	2,66	1,02	0,88	4,95
25/04/2004	111258	2,69	1,03	0,90	4,92
2/05/2004	155318	2,69	1,02	0,88	4,82
9/05/2004	155078	2,65	1,02	0,90	4,82
16/05/2004	134794	2,66	1,02	0,89	4,84
23/05/2004	139985	2,63	1,03	0,89	4,83
30/05/2004	198778	2,65	1,02	0,88	4,79
6/06/2004	172436	2,60	1,02	0,89	4,81
13/06/2004	169585	2,57	1,02	0,91	4,85
20/06/2004	203702	2,65	1,02	0,91	4,84
27/06/2004	282392	2,69	1,02	0,90	4,82
4/07/2004	220658	2,71	1,00	0,93	4,92
11/07/2004	194472	2,72	1,04	0,94	4,92
18/07/2004	269246	2,73	1,04	0,95	4,90
25/07/2004	215340	2,72	1,03	0,95	4,91
1/08/2004	218319	2,73	1,02	0,93	4,85
8/08/2004	195724	2,72	1,04	0,95	4,86
15/08/2004	174614	2,70	1,05	0,95	4,88
22/08/2004	172085	2,72	1,03	0,94	4,85
29/08/2004	152347	2,70	0,99	0,92	4,91
5/09/2004	189615	2,72	1,03	0,94	4,89
12/09/2004	173804	2,70	1,08	0,95	4,92
19/09/2004	145683	2,65	1,09	0,97	4,82
26/09/2004	133550	2,66	1,08	0,96	4,82
3/10/2004	121156	2,69	1,05	0,92	4,87
10/10/2004	112040	2,70	1,06	0,94	4,88
17/10/2004	120767	2,71	1,04	0,94	4,90
24/10/2004	127019	2,69	1,06	0,92	4,88
31/10/2004	136295	2,72	1,06	0,91	4,89
7/11/2004	113425	2,71	1,07	0,93	4,88
14/11/2004	107815	2,71	1,08	0,93	4,87
21/11/2004	100298	2,74	1,08	0,94	4,85
28/11/2004	97048	2,82	1,05	0,92	4,87
5/12/2004	98750	2,76	1,04	0,91	4,88
12/12/2004	98235	2,77	1,04	0,91	4,87
19/12/2004	101254	2,77	1,04	0,90	4,93
26/12/2004	139589	2,81	1,04	0,89	4,93
2/01/2005	134921	2,77	1,06	0,91	4,74
9/01/2005	80355	2,76	1,08	0,93	4,77
16/01/2005	80396	2,73	1,08	0,94	4,81
23/01/2005	82183	2,72	1,08	0,93	4,82
30/01/2005	79709	2,73	1,07	0,91	4,79
6/02/2005	90781	2,71	1,06	0,92	4,75

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Tijd[t] = + 0.0496851142277171 -2.6411583586197e-08QBEFRU[t] -0.00554889904037299PBEFRU[t] -0.0181255447103948PSOCOLA[t] + 0.0162608122186263PSOORA[t] -0.00513429676003876PSTIM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tijd[t] =  +  0.0496851142277171 -2.6411583586197e-08QBEFRU[t] -0.00554889904037299PBEFRU[t] -0.0181255447103948PSOCOLA[t] +  0.0162608122186263PSOORA[t] -0.00513429676003876PSTIM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tijd[t] =  +  0.0496851142277171 -2.6411583586197e-08QBEFRU[t] -0.00554889904037299PBEFRU[t] -0.0181255447103948PSOCOLA[t] +  0.0162608122186263PSOORA[t] -0.00513429676003876PSTIM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&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
Tijd[t] = + 0.0496851142277171 -2.6411583586197e-08QBEFRU[t] -0.00554889904037299PBEFRU[t] -0.0181255447103948PSOCOLA[t] + 0.0162608122186263PSOORA[t] -0.00513429676003876PSTIM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04968511422771710.0258871.91930.0570690.028534
QBEFRU-2.6411583586197e-080-4.85123e-062e-06
PBEFRU-0.005548899040372990.003346-1.65840.0995830.049792
PSOCOLA-0.01812554471039480.01277-1.41940.1581070.079053
PSOORA0.01626081221862630.0118981.36670.1740230.087011
PSTIM-0.005134296760038760.003246-1.58150.116110.058055

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.0496851142277171 & 0.025887 & 1.9193 & 0.057069 & 0.028534 \tabularnewline
QBEFRU & -2.6411583586197e-08 & 0 & -4.8512 & 3e-06 & 2e-06 \tabularnewline
PBEFRU & -0.00554889904037299 & 0.003346 & -1.6584 & 0.099583 & 0.049792 \tabularnewline
PSOCOLA & -0.0181255447103948 & 0.01277 & -1.4194 & 0.158107 & 0.079053 \tabularnewline
PSOORA & 0.0162608122186263 & 0.011898 & 1.3667 & 0.174023 & 0.087011 \tabularnewline
PSTIM & -0.00513429676003876 & 0.003246 & -1.5815 & 0.11611 & 0.058055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.0496851142277171[/C][C]0.025887[/C][C]1.9193[/C][C]0.057069[/C][C]0.028534[/C][/ROW]
[ROW][C]QBEFRU[/C][C]-2.6411583586197e-08[/C][C]0[/C][C]-4.8512[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]PBEFRU[/C][C]-0.00554889904037299[/C][C]0.003346[/C][C]-1.6584[/C][C]0.099583[/C][C]0.049792[/C][/ROW]
[ROW][C]PSOCOLA[/C][C]-0.0181255447103948[/C][C]0.01277[/C][C]-1.4194[/C][C]0.158107[/C][C]0.079053[/C][/ROW]
[ROW][C]PSOORA[/C][C]0.0162608122186263[/C][C]0.011898[/C][C]1.3667[/C][C]0.174023[/C][C]0.087011[/C][/ROW]
[ROW][C]PSTIM[/C][C]-0.00513429676003876[/C][C]0.003246[/C][C]-1.5815[/C][C]0.11611[/C][C]0.058055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04968511422771710.0258871.91930.0570690.028534
QBEFRU-2.6411583586197e-080-4.85123e-062e-06
PBEFRU-0.005548899040372990.003346-1.65840.0995830.049792
PSOCOLA-0.01812554471039480.01277-1.41940.1581070.079053
PSOORA0.01626081221862630.0118981.36670.1740230.087011
PSTIM-0.005134296760038760.003246-1.58150.116110.058055






Multiple Linear Regression - Regression Statistics
Multiple R0.408675886319258
R-squared0.167015980058831
Adjusted R-squared0.135934486777445
F-TEST (value)5.37348635558797
F-TEST (DF numerator)5
F-TEST (DF denominator)134
p-value0.000155287617872535
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.00237224861758171
Sum Squared Residuals0.000754093509484857

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.408675886319258 \tabularnewline
R-squared & 0.167015980058831 \tabularnewline
Adjusted R-squared & 0.135934486777445 \tabularnewline
F-TEST (value) & 5.37348635558797 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 0.000155287617872535 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.00237224861758171 \tabularnewline
Sum Squared Residuals & 0.000754093509484857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.408675886319258[/C][/ROW]
[ROW][C]R-squared[/C][C]0.167015980058831[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.135934486777445[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.37348635558797[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]134[/C][/ROW]
[ROW][C]p-value[/C][C]0.000155287617872535[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.00237224861758171[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.000754093509484857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&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 R0.408675886319258
R-squared0.167015980058831
Adjusted R-squared0.135934486777445
F-TEST (value)5.37348635558797
F-TEST (DF numerator)5
F-TEST (DF denominator)134
p-value0.000155287617872535
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.00237224861758171
Sum Squared Residuals0.000754093509484857






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.0007492507492507490.000949952803324658-0.000200702054073908
20.001332001332001330.00220251579053839-0.00087051445853706
30.001914751914751910.001779168315609820.000135583599142091
40.00249750249750250.001523349573652810.000974152923849689
50.00049950049950050.00159508074644131-0.00109558024694081
60.0009990009990009990.0008786041136144290.00012039688538657
70.00149850149850150.0003659101398096130.00113259135869189
80.0019980019980020.00103524273873370.000962759259268303
90.00024975024975025-0.0001243377190259830.000374087968776233
100.000686813186813187-0.00133060067676380.00201741386357699
110.001123876123876120.0004054757093628750.000718400414513249
120.001560939060939060.0006644163839700840.000896522676968977
135.55000555000555e-050.00156693978019104-0.00151143972469099
140.0004440004440004440.00161675536638965-0.00117275492238921
150.0008325008325008330.00197972726599805-0.00114722643349722
160.001221001221001220.001079973136592920.000141028084408306
170.001609501609501610.00215401594304118-0.000544514333539572
180.00029970029970030.00176081988905802-0.00146111958935772
190.0006493506493506490.00174456195806312-0.00109521130871247
200.0009990009990009990.00232831848839322-0.00132931748939222
210.001348651348651350.00246440663414304-0.00111575528549169
220.0001362274089546820.0028347861426819-0.00269855873372722
230.0004540913631822720.00245750562816387-0.00200341426498159
240.0007719553174098630.00241880311791551-0.00164684780050565
250.001089819271637450.00173415217628449-0.000644332904647034
264.16250416250416e-050.00189097805946238-0.00184935301783734
270.0003330003330003330.00198252355782782-0.00164952322482749
280.0006243756243756240.00214308136190739-0.00151870573753176
290.0009157509157509160.0012314421126903-0.000315691196939383
300.001207126207126210.00185558452031069-0.000648458313184487
310.002496255616575140.00251124272172703-1.49871051518905e-05
320.005991013479780330.003668273734563980.00232273974521635
330.009485771342985520.003618356437405760.00586741490557977
340.01298052920619070.003132354432146780.00984817477404394
350.0004992511233150270.00251600894602156-0.00201675782270653
360.002246630054917620.00313453481134189-0.000887904756424271
370.003994008986520220.003262733767129450.000731275219390769
380.005741387918122820.003703589137247780.00203779878087504
390.0003328340822100180.00238194205713213-0.00204910797492211
400.001497753369945080.0018649998758744-0.000367246505929313
410.002662672657680150.001709023403698840.000953649253981311
420.003827591945415210.00185392819352460.00197366375189061
430.004992511233150270.002228393711716390.00276411752143389
440.0007488766849725410.00102460491365157-0.000275728228679028
450.001622566150773840.0009000966080793740.000722469542694465
460.002496255616575140.001568693245045280.000927562371529855
470.003369945082376440.00206070017432450.00130924490805193
480.0003994008986520220.000775522805451037-0.000376121906799015
490.001098352471293060.00122115530665077-0.000122802835357707
500.00179730404393410.001647579448653710.000149724595280385
510.002496255616575140.001446901239931230.0010493543766439
528.32085205525046e-050.00169351870396315-0.00161031018341065
530.0006656681644200370.00179293090477656-0.00112726274035652
540.001248127808287570.0007976051954824140.000450522612805155
550.00183058745215510.001226471302906580.000604116149248523
560.002413047096022630.001176405280911670.00123664181511097
570.0004279295342700240.00131078931881611-0.00088285978454609
580.0009271806575850510.00177127197983462-0.000844091322249573
590.001426431780900080.001386177321831164.02544590689214e-05
600.001925682904215110.0005480251822115030.0013776577220036
610.000187219171243135-0.0008016045362933140.000988823707536449
620.000624063904143784-0.001288842268484790.00191290617262857
630.001060908637044430.0006436479974237090.000417260639620724
640.001497753369945080.00159666236734887-9.89089974037876e-05
650.001934598102845730.00244705892911699-0.000512460826271263
660.0003883064292450210.00210613891712456-0.00171783248787954
670.0007766128584900430.00223745861547302-0.00146084575698297
680.001164919287735060.00284711571688697-0.00168219642915191
690.001553225716980090.00273665365427855-0.00118342793729846
700.0002496255616575140.00250600035329955-0.00225637479164204
710.0005991013479780330.00252547088978698-0.00192636954180895
720.0009485771342985520.00286193125564644-0.00191335412134789
730.001298052920619070.00243209161742158-0.00113403869680251
749.07729315118232e-050.00200771140250154-0.00191693847098972
750.0004084781918032040.0027116758086782-0.002303197616875
760.0007261834520945850.00309315831966801-0.00236697486757342
770.001043888712385970.0026611911977855-0.00161730248539953
780.001361593972677350.00254342934870891-0.00118183537603156
790.0002912298219337660.00210302618719254-0.00181179636525877
800.0005824596438675320.00215251988790291-0.00157006024403538
810.0008736894658012980.0016056788321882-0.000731989366386906
820.001164919287735060.0009213603048395950.000243558982895469
830.001996007984031940.001875053104024060.000120954880007877
840.005489021956087820.003301677371545790.00218734458454203
850.008982035928143710.003608309355619810.0053737265725239
860.01247504990019960.003116124433066460.00935892546713314
870.0002495009980039920.00317515116057492-0.00292565016257093
880.001996007984031940.00319260052451925-0.00119659254048731
890.003742514970059880.003578225201653510.000164289768406369
900.005489021956087820.003540174816597940.00194884713948988
910.007235528942115770.003190094725665950.00404543421644981
920.00116433799068530.00350375752390835-0.00233941953322306
930.002328675981370590.00292226325845763-0.000593587277087042
940.003493013972055890.002898538669553750.000594475302502139
950.004657351962741180.002465357378638970.00219199458410222
960.0004990019960079840.00262765016875236-0.00212864817274438
970.001372255489021960.00192721856450067-0.000554963075478709
980.002245508982035930.00225583994147306-1.0330959437132e-05
990.00311876247504990.002524755728147010.000594006746902889
1000.0001996007984031940.00173053023407447-0.00153092943567127
1010.0008982035928143710.0022840412201226-0.00138583762730823
1020.001596806387225550.00249899073379425-0.000902184346568704
1030.002295409181636730.00239844269510593-0.000103033513469207
1040.00299401197604790.0009586676758344270.00203534430021348
1050.0004990019960079840.00199176874966617-0.00149276675365818
1060.001081170991350630.00235337951965258-0.00127220852830195
1070.001663339986693280.001059726566812850.000603613419880435
1080.00224550898203593-0.001300479094185410.00354598807622134
1090.0002851439977188480.000555941210880231-0.000270797213161383
1100.0007841459937268320.0006296522820351250.000154493711691707
1110.00128314798973482-0.001135442402055860.00241859039179068
1120.00178214998574280.0004737018926489630.00130844809309384
1136.2375249500998e-050.000503629803075705-0.000441254553574707
1140.0004990019960079840.00106725090717379-0.000568248911165809
1150.000935628742514970.00145183603518115-0.000516207292666181
1160.001372255489021960.00176158462408598-0.000389329135064024
1170.001808882235528940.00248562218015874-0.000676739944629794
1180.0002772233311155470.0010932176934184-0.000815994362302849
1190.0006653359946773120.000724091206172576-5.8755211495264e-05
1200.001053448658239080.00240164677349113-0.00134819811525205
1210.001441561321800840.00268525685165641-0.00124369552985557
1220.0001497005988023950.0024827540619774-0.00233305346317501
1230.0004990019960079840.00276065089721363-0.00226164890120565
1240.0008483033932135730.00273449297586028-0.00188618958264671
1250.001197604790419160.00209530453270719-0.000897699742288032
1260.001546906187624750.001469892622363797.70135652609615e-05
1270.0003175467247323530.00232471829425281-0.00200717156952046
1280.0006350934494647070.00234297479866782-0.00170788134920311
1290.000952640174197060.00264033775866111-0.00168769758446405
1300.001270186898929410.00239812764382495-0.00112794074489554
1310.000207917498336660.00265341342830092-0.00244549592996426
1320.0004990019960079840.00266286937104447-0.00216386737503648
1330.0007900864936793080.00211246687240915-0.00132238037872984
1340.001081170991350630.0007154147318311070.000365756259519525
1350.0009975062344139650.00199888170019839-0.00100137546578442
1360.004488778054862840.003304221607930020.00118455644693283
1370.007980049875311720.003426841955998880.00455320791931284
1380.01147132169576060.003221182356747430.00825013933901317
1390.01496259351620950.003241103729668530.0117214897865409
1400.001496259351620950.00360888809670138-0.00211262874508043

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.000749250749250749 & 0.000949952803324658 & -0.000200702054073908 \tabularnewline
2 & 0.00133200133200133 & 0.00220251579053839 & -0.00087051445853706 \tabularnewline
3 & 0.00191475191475191 & 0.00177916831560982 & 0.000135583599142091 \tabularnewline
4 & 0.0024975024975025 & 0.00152334957365281 & 0.000974152923849689 \tabularnewline
5 & 0.0004995004995005 & 0.00159508074644131 & -0.00109558024694081 \tabularnewline
6 & 0.000999000999000999 & 0.000878604113614429 & 0.00012039688538657 \tabularnewline
7 & 0.0014985014985015 & 0.000365910139809613 & 0.00113259135869189 \tabularnewline
8 & 0.001998001998002 & 0.0010352427387337 & 0.000962759259268303 \tabularnewline
9 & 0.00024975024975025 & -0.000124337719025983 & 0.000374087968776233 \tabularnewline
10 & 0.000686813186813187 & -0.0013306006767638 & 0.00201741386357699 \tabularnewline
11 & 0.00112387612387612 & 0.000405475709362875 & 0.000718400414513249 \tabularnewline
12 & 0.00156093906093906 & 0.000664416383970084 & 0.000896522676968977 \tabularnewline
13 & 5.55000555000555e-05 & 0.00156693978019104 & -0.00151143972469099 \tabularnewline
14 & 0.000444000444000444 & 0.00161675536638965 & -0.00117275492238921 \tabularnewline
15 & 0.000832500832500833 & 0.00197972726599805 & -0.00114722643349722 \tabularnewline
16 & 0.00122100122100122 & 0.00107997313659292 & 0.000141028084408306 \tabularnewline
17 & 0.00160950160950161 & 0.00215401594304118 & -0.000544514333539572 \tabularnewline
18 & 0.0002997002997003 & 0.00176081988905802 & -0.00146111958935772 \tabularnewline
19 & 0.000649350649350649 & 0.00174456195806312 & -0.00109521130871247 \tabularnewline
20 & 0.000999000999000999 & 0.00232831848839322 & -0.00132931748939222 \tabularnewline
21 & 0.00134865134865135 & 0.00246440663414304 & -0.00111575528549169 \tabularnewline
22 & 0.000136227408954682 & 0.0028347861426819 & -0.00269855873372722 \tabularnewline
23 & 0.000454091363182272 & 0.00245750562816387 & -0.00200341426498159 \tabularnewline
24 & 0.000771955317409863 & 0.00241880311791551 & -0.00164684780050565 \tabularnewline
25 & 0.00108981927163745 & 0.00173415217628449 & -0.000644332904647034 \tabularnewline
26 & 4.16250416250416e-05 & 0.00189097805946238 & -0.00184935301783734 \tabularnewline
27 & 0.000333000333000333 & 0.00198252355782782 & -0.00164952322482749 \tabularnewline
28 & 0.000624375624375624 & 0.00214308136190739 & -0.00151870573753176 \tabularnewline
29 & 0.000915750915750916 & 0.0012314421126903 & -0.000315691196939383 \tabularnewline
30 & 0.00120712620712621 & 0.00185558452031069 & -0.000648458313184487 \tabularnewline
31 & 0.00249625561657514 & 0.00251124272172703 & -1.49871051518905e-05 \tabularnewline
32 & 0.00599101347978033 & 0.00366827373456398 & 0.00232273974521635 \tabularnewline
33 & 0.00948577134298552 & 0.00361835643740576 & 0.00586741490557977 \tabularnewline
34 & 0.0129805292061907 & 0.00313235443214678 & 0.00984817477404394 \tabularnewline
35 & 0.000499251123315027 & 0.00251600894602156 & -0.00201675782270653 \tabularnewline
36 & 0.00224663005491762 & 0.00313453481134189 & -0.000887904756424271 \tabularnewline
37 & 0.00399400898652022 & 0.00326273376712945 & 0.000731275219390769 \tabularnewline
38 & 0.00574138791812282 & 0.00370358913724778 & 0.00203779878087504 \tabularnewline
39 & 0.000332834082210018 & 0.00238194205713213 & -0.00204910797492211 \tabularnewline
40 & 0.00149775336994508 & 0.0018649998758744 & -0.000367246505929313 \tabularnewline
41 & 0.00266267265768015 & 0.00170902340369884 & 0.000953649253981311 \tabularnewline
42 & 0.00382759194541521 & 0.0018539281935246 & 0.00197366375189061 \tabularnewline
43 & 0.00499251123315027 & 0.00222839371171639 & 0.00276411752143389 \tabularnewline
44 & 0.000748876684972541 & 0.00102460491365157 & -0.000275728228679028 \tabularnewline
45 & 0.00162256615077384 & 0.000900096608079374 & 0.000722469542694465 \tabularnewline
46 & 0.00249625561657514 & 0.00156869324504528 & 0.000927562371529855 \tabularnewline
47 & 0.00336994508237644 & 0.0020607001743245 & 0.00130924490805193 \tabularnewline
48 & 0.000399400898652022 & 0.000775522805451037 & -0.000376121906799015 \tabularnewline
49 & 0.00109835247129306 & 0.00122115530665077 & -0.000122802835357707 \tabularnewline
50 & 0.0017973040439341 & 0.00164757944865371 & 0.000149724595280385 \tabularnewline
51 & 0.00249625561657514 & 0.00144690123993123 & 0.0010493543766439 \tabularnewline
52 & 8.32085205525046e-05 & 0.00169351870396315 & -0.00161031018341065 \tabularnewline
53 & 0.000665668164420037 & 0.00179293090477656 & -0.00112726274035652 \tabularnewline
54 & 0.00124812780828757 & 0.000797605195482414 & 0.000450522612805155 \tabularnewline
55 & 0.0018305874521551 & 0.00122647130290658 & 0.000604116149248523 \tabularnewline
56 & 0.00241304709602263 & 0.00117640528091167 & 0.00123664181511097 \tabularnewline
57 & 0.000427929534270024 & 0.00131078931881611 & -0.00088285978454609 \tabularnewline
58 & 0.000927180657585051 & 0.00177127197983462 & -0.000844091322249573 \tabularnewline
59 & 0.00142643178090008 & 0.00138617732183116 & 4.02544590689214e-05 \tabularnewline
60 & 0.00192568290421511 & 0.000548025182211503 & 0.0013776577220036 \tabularnewline
61 & 0.000187219171243135 & -0.000801604536293314 & 0.000988823707536449 \tabularnewline
62 & 0.000624063904143784 & -0.00128884226848479 & 0.00191290617262857 \tabularnewline
63 & 0.00106090863704443 & 0.000643647997423709 & 0.000417260639620724 \tabularnewline
64 & 0.00149775336994508 & 0.00159666236734887 & -9.89089974037876e-05 \tabularnewline
65 & 0.00193459810284573 & 0.00244705892911699 & -0.000512460826271263 \tabularnewline
66 & 0.000388306429245021 & 0.00210613891712456 & -0.00171783248787954 \tabularnewline
67 & 0.000776612858490043 & 0.00223745861547302 & -0.00146084575698297 \tabularnewline
68 & 0.00116491928773506 & 0.00284711571688697 & -0.00168219642915191 \tabularnewline
69 & 0.00155322571698009 & 0.00273665365427855 & -0.00118342793729846 \tabularnewline
70 & 0.000249625561657514 & 0.00250600035329955 & -0.00225637479164204 \tabularnewline
71 & 0.000599101347978033 & 0.00252547088978698 & -0.00192636954180895 \tabularnewline
72 & 0.000948577134298552 & 0.00286193125564644 & -0.00191335412134789 \tabularnewline
73 & 0.00129805292061907 & 0.00243209161742158 & -0.00113403869680251 \tabularnewline
74 & 9.07729315118232e-05 & 0.00200771140250154 & -0.00191693847098972 \tabularnewline
75 & 0.000408478191803204 & 0.0027116758086782 & -0.002303197616875 \tabularnewline
76 & 0.000726183452094585 & 0.00309315831966801 & -0.00236697486757342 \tabularnewline
77 & 0.00104388871238597 & 0.0026611911977855 & -0.00161730248539953 \tabularnewline
78 & 0.00136159397267735 & 0.00254342934870891 & -0.00118183537603156 \tabularnewline
79 & 0.000291229821933766 & 0.00210302618719254 & -0.00181179636525877 \tabularnewline
80 & 0.000582459643867532 & 0.00215251988790291 & -0.00157006024403538 \tabularnewline
81 & 0.000873689465801298 & 0.0016056788321882 & -0.000731989366386906 \tabularnewline
82 & 0.00116491928773506 & 0.000921360304839595 & 0.000243558982895469 \tabularnewline
83 & 0.00199600798403194 & 0.00187505310402406 & 0.000120954880007877 \tabularnewline
84 & 0.00548902195608782 & 0.00330167737154579 & 0.00218734458454203 \tabularnewline
85 & 0.00898203592814371 & 0.00360830935561981 & 0.0053737265725239 \tabularnewline
86 & 0.0124750499001996 & 0.00311612443306646 & 0.00935892546713314 \tabularnewline
87 & 0.000249500998003992 & 0.00317515116057492 & -0.00292565016257093 \tabularnewline
88 & 0.00199600798403194 & 0.00319260052451925 & -0.00119659254048731 \tabularnewline
89 & 0.00374251497005988 & 0.00357822520165351 & 0.000164289768406369 \tabularnewline
90 & 0.00548902195608782 & 0.00354017481659794 & 0.00194884713948988 \tabularnewline
91 & 0.00723552894211577 & 0.00319009472566595 & 0.00404543421644981 \tabularnewline
92 & 0.0011643379906853 & 0.00350375752390835 & -0.00233941953322306 \tabularnewline
93 & 0.00232867598137059 & 0.00292226325845763 & -0.000593587277087042 \tabularnewline
94 & 0.00349301397205589 & 0.00289853866955375 & 0.000594475302502139 \tabularnewline
95 & 0.00465735196274118 & 0.00246535737863897 & 0.00219199458410222 \tabularnewline
96 & 0.000499001996007984 & 0.00262765016875236 & -0.00212864817274438 \tabularnewline
97 & 0.00137225548902196 & 0.00192721856450067 & -0.000554963075478709 \tabularnewline
98 & 0.00224550898203593 & 0.00225583994147306 & -1.0330959437132e-05 \tabularnewline
99 & 0.0031187624750499 & 0.00252475572814701 & 0.000594006746902889 \tabularnewline
100 & 0.000199600798403194 & 0.00173053023407447 & -0.00153092943567127 \tabularnewline
101 & 0.000898203592814371 & 0.0022840412201226 & -0.00138583762730823 \tabularnewline
102 & 0.00159680638722555 & 0.00249899073379425 & -0.000902184346568704 \tabularnewline
103 & 0.00229540918163673 & 0.00239844269510593 & -0.000103033513469207 \tabularnewline
104 & 0.0029940119760479 & 0.000958667675834427 & 0.00203534430021348 \tabularnewline
105 & 0.000499001996007984 & 0.00199176874966617 & -0.00149276675365818 \tabularnewline
106 & 0.00108117099135063 & 0.00235337951965258 & -0.00127220852830195 \tabularnewline
107 & 0.00166333998669328 & 0.00105972656681285 & 0.000603613419880435 \tabularnewline
108 & 0.00224550898203593 & -0.00130047909418541 & 0.00354598807622134 \tabularnewline
109 & 0.000285143997718848 & 0.000555941210880231 & -0.000270797213161383 \tabularnewline
110 & 0.000784145993726832 & 0.000629652282035125 & 0.000154493711691707 \tabularnewline
111 & 0.00128314798973482 & -0.00113544240205586 & 0.00241859039179068 \tabularnewline
112 & 0.0017821499857428 & 0.000473701892648963 & 0.00130844809309384 \tabularnewline
113 & 6.2375249500998e-05 & 0.000503629803075705 & -0.000441254553574707 \tabularnewline
114 & 0.000499001996007984 & 0.00106725090717379 & -0.000568248911165809 \tabularnewline
115 & 0.00093562874251497 & 0.00145183603518115 & -0.000516207292666181 \tabularnewline
116 & 0.00137225548902196 & 0.00176158462408598 & -0.000389329135064024 \tabularnewline
117 & 0.00180888223552894 & 0.00248562218015874 & -0.000676739944629794 \tabularnewline
118 & 0.000277223331115547 & 0.0010932176934184 & -0.000815994362302849 \tabularnewline
119 & 0.000665335994677312 & 0.000724091206172576 & -5.8755211495264e-05 \tabularnewline
120 & 0.00105344865823908 & 0.00240164677349113 & -0.00134819811525205 \tabularnewline
121 & 0.00144156132180084 & 0.00268525685165641 & -0.00124369552985557 \tabularnewline
122 & 0.000149700598802395 & 0.0024827540619774 & -0.00233305346317501 \tabularnewline
123 & 0.000499001996007984 & 0.00276065089721363 & -0.00226164890120565 \tabularnewline
124 & 0.000848303393213573 & 0.00273449297586028 & -0.00188618958264671 \tabularnewline
125 & 0.00119760479041916 & 0.00209530453270719 & -0.000897699742288032 \tabularnewline
126 & 0.00154690618762475 & 0.00146989262236379 & 7.70135652609615e-05 \tabularnewline
127 & 0.000317546724732353 & 0.00232471829425281 & -0.00200717156952046 \tabularnewline
128 & 0.000635093449464707 & 0.00234297479866782 & -0.00170788134920311 \tabularnewline
129 & 0.00095264017419706 & 0.00264033775866111 & -0.00168769758446405 \tabularnewline
130 & 0.00127018689892941 & 0.00239812764382495 & -0.00112794074489554 \tabularnewline
131 & 0.00020791749833666 & 0.00265341342830092 & -0.00244549592996426 \tabularnewline
132 & 0.000499001996007984 & 0.00266286937104447 & -0.00216386737503648 \tabularnewline
133 & 0.000790086493679308 & 0.00211246687240915 & -0.00132238037872984 \tabularnewline
134 & 0.00108117099135063 & 0.000715414731831107 & 0.000365756259519525 \tabularnewline
135 & 0.000997506234413965 & 0.00199888170019839 & -0.00100137546578442 \tabularnewline
136 & 0.00448877805486284 & 0.00330422160793002 & 0.00118455644693283 \tabularnewline
137 & 0.00798004987531172 & 0.00342684195599888 & 0.00455320791931284 \tabularnewline
138 & 0.0114713216957606 & 0.00322118235674743 & 0.00825013933901317 \tabularnewline
139 & 0.0149625935162095 & 0.00324110372966853 & 0.0117214897865409 \tabularnewline
140 & 0.00149625935162095 & 0.00360888809670138 & -0.00211262874508043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&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]0.000749250749250749[/C][C]0.000949952803324658[/C][C]-0.000200702054073908[/C][/ROW]
[ROW][C]2[/C][C]0.00133200133200133[/C][C]0.00220251579053839[/C][C]-0.00087051445853706[/C][/ROW]
[ROW][C]3[/C][C]0.00191475191475191[/C][C]0.00177916831560982[/C][C]0.000135583599142091[/C][/ROW]
[ROW][C]4[/C][C]0.0024975024975025[/C][C]0.00152334957365281[/C][C]0.000974152923849689[/C][/ROW]
[ROW][C]5[/C][C]0.0004995004995005[/C][C]0.00159508074644131[/C][C]-0.00109558024694081[/C][/ROW]
[ROW][C]6[/C][C]0.000999000999000999[/C][C]0.000878604113614429[/C][C]0.00012039688538657[/C][/ROW]
[ROW][C]7[/C][C]0.0014985014985015[/C][C]0.000365910139809613[/C][C]0.00113259135869189[/C][/ROW]
[ROW][C]8[/C][C]0.001998001998002[/C][C]0.0010352427387337[/C][C]0.000962759259268303[/C][/ROW]
[ROW][C]9[/C][C]0.00024975024975025[/C][C]-0.000124337719025983[/C][C]0.000374087968776233[/C][/ROW]
[ROW][C]10[/C][C]0.000686813186813187[/C][C]-0.0013306006767638[/C][C]0.00201741386357699[/C][/ROW]
[ROW][C]11[/C][C]0.00112387612387612[/C][C]0.000405475709362875[/C][C]0.000718400414513249[/C][/ROW]
[ROW][C]12[/C][C]0.00156093906093906[/C][C]0.000664416383970084[/C][C]0.000896522676968977[/C][/ROW]
[ROW][C]13[/C][C]5.55000555000555e-05[/C][C]0.00156693978019104[/C][C]-0.00151143972469099[/C][/ROW]
[ROW][C]14[/C][C]0.000444000444000444[/C][C]0.00161675536638965[/C][C]-0.00117275492238921[/C][/ROW]
[ROW][C]15[/C][C]0.000832500832500833[/C][C]0.00197972726599805[/C][C]-0.00114722643349722[/C][/ROW]
[ROW][C]16[/C][C]0.00122100122100122[/C][C]0.00107997313659292[/C][C]0.000141028084408306[/C][/ROW]
[ROW][C]17[/C][C]0.00160950160950161[/C][C]0.00215401594304118[/C][C]-0.000544514333539572[/C][/ROW]
[ROW][C]18[/C][C]0.0002997002997003[/C][C]0.00176081988905802[/C][C]-0.00146111958935772[/C][/ROW]
[ROW][C]19[/C][C]0.000649350649350649[/C][C]0.00174456195806312[/C][C]-0.00109521130871247[/C][/ROW]
[ROW][C]20[/C][C]0.000999000999000999[/C][C]0.00232831848839322[/C][C]-0.00132931748939222[/C][/ROW]
[ROW][C]21[/C][C]0.00134865134865135[/C][C]0.00246440663414304[/C][C]-0.00111575528549169[/C][/ROW]
[ROW][C]22[/C][C]0.000136227408954682[/C][C]0.0028347861426819[/C][C]-0.00269855873372722[/C][/ROW]
[ROW][C]23[/C][C]0.000454091363182272[/C][C]0.00245750562816387[/C][C]-0.00200341426498159[/C][/ROW]
[ROW][C]24[/C][C]0.000771955317409863[/C][C]0.00241880311791551[/C][C]-0.00164684780050565[/C][/ROW]
[ROW][C]25[/C][C]0.00108981927163745[/C][C]0.00173415217628449[/C][C]-0.000644332904647034[/C][/ROW]
[ROW][C]26[/C][C]4.16250416250416e-05[/C][C]0.00189097805946238[/C][C]-0.00184935301783734[/C][/ROW]
[ROW][C]27[/C][C]0.000333000333000333[/C][C]0.00198252355782782[/C][C]-0.00164952322482749[/C][/ROW]
[ROW][C]28[/C][C]0.000624375624375624[/C][C]0.00214308136190739[/C][C]-0.00151870573753176[/C][/ROW]
[ROW][C]29[/C][C]0.000915750915750916[/C][C]0.0012314421126903[/C][C]-0.000315691196939383[/C][/ROW]
[ROW][C]30[/C][C]0.00120712620712621[/C][C]0.00185558452031069[/C][C]-0.000648458313184487[/C][/ROW]
[ROW][C]31[/C][C]0.00249625561657514[/C][C]0.00251124272172703[/C][C]-1.49871051518905e-05[/C][/ROW]
[ROW][C]32[/C][C]0.00599101347978033[/C][C]0.00366827373456398[/C][C]0.00232273974521635[/C][/ROW]
[ROW][C]33[/C][C]0.00948577134298552[/C][C]0.00361835643740576[/C][C]0.00586741490557977[/C][/ROW]
[ROW][C]34[/C][C]0.0129805292061907[/C][C]0.00313235443214678[/C][C]0.00984817477404394[/C][/ROW]
[ROW][C]35[/C][C]0.000499251123315027[/C][C]0.00251600894602156[/C][C]-0.00201675782270653[/C][/ROW]
[ROW][C]36[/C][C]0.00224663005491762[/C][C]0.00313453481134189[/C][C]-0.000887904756424271[/C][/ROW]
[ROW][C]37[/C][C]0.00399400898652022[/C][C]0.00326273376712945[/C][C]0.000731275219390769[/C][/ROW]
[ROW][C]38[/C][C]0.00574138791812282[/C][C]0.00370358913724778[/C][C]0.00203779878087504[/C][/ROW]
[ROW][C]39[/C][C]0.000332834082210018[/C][C]0.00238194205713213[/C][C]-0.00204910797492211[/C][/ROW]
[ROW][C]40[/C][C]0.00149775336994508[/C][C]0.0018649998758744[/C][C]-0.000367246505929313[/C][/ROW]
[ROW][C]41[/C][C]0.00266267265768015[/C][C]0.00170902340369884[/C][C]0.000953649253981311[/C][/ROW]
[ROW][C]42[/C][C]0.00382759194541521[/C][C]0.0018539281935246[/C][C]0.00197366375189061[/C][/ROW]
[ROW][C]43[/C][C]0.00499251123315027[/C][C]0.00222839371171639[/C][C]0.00276411752143389[/C][/ROW]
[ROW][C]44[/C][C]0.000748876684972541[/C][C]0.00102460491365157[/C][C]-0.000275728228679028[/C][/ROW]
[ROW][C]45[/C][C]0.00162256615077384[/C][C]0.000900096608079374[/C][C]0.000722469542694465[/C][/ROW]
[ROW][C]46[/C][C]0.00249625561657514[/C][C]0.00156869324504528[/C][C]0.000927562371529855[/C][/ROW]
[ROW][C]47[/C][C]0.00336994508237644[/C][C]0.0020607001743245[/C][C]0.00130924490805193[/C][/ROW]
[ROW][C]48[/C][C]0.000399400898652022[/C][C]0.000775522805451037[/C][C]-0.000376121906799015[/C][/ROW]
[ROW][C]49[/C][C]0.00109835247129306[/C][C]0.00122115530665077[/C][C]-0.000122802835357707[/C][/ROW]
[ROW][C]50[/C][C]0.0017973040439341[/C][C]0.00164757944865371[/C][C]0.000149724595280385[/C][/ROW]
[ROW][C]51[/C][C]0.00249625561657514[/C][C]0.00144690123993123[/C][C]0.0010493543766439[/C][/ROW]
[ROW][C]52[/C][C]8.32085205525046e-05[/C][C]0.00169351870396315[/C][C]-0.00161031018341065[/C][/ROW]
[ROW][C]53[/C][C]0.000665668164420037[/C][C]0.00179293090477656[/C][C]-0.00112726274035652[/C][/ROW]
[ROW][C]54[/C][C]0.00124812780828757[/C][C]0.000797605195482414[/C][C]0.000450522612805155[/C][/ROW]
[ROW][C]55[/C][C]0.0018305874521551[/C][C]0.00122647130290658[/C][C]0.000604116149248523[/C][/ROW]
[ROW][C]56[/C][C]0.00241304709602263[/C][C]0.00117640528091167[/C][C]0.00123664181511097[/C][/ROW]
[ROW][C]57[/C][C]0.000427929534270024[/C][C]0.00131078931881611[/C][C]-0.00088285978454609[/C][/ROW]
[ROW][C]58[/C][C]0.000927180657585051[/C][C]0.00177127197983462[/C][C]-0.000844091322249573[/C][/ROW]
[ROW][C]59[/C][C]0.00142643178090008[/C][C]0.00138617732183116[/C][C]4.02544590689214e-05[/C][/ROW]
[ROW][C]60[/C][C]0.00192568290421511[/C][C]0.000548025182211503[/C][C]0.0013776577220036[/C][/ROW]
[ROW][C]61[/C][C]0.000187219171243135[/C][C]-0.000801604536293314[/C][C]0.000988823707536449[/C][/ROW]
[ROW][C]62[/C][C]0.000624063904143784[/C][C]-0.00128884226848479[/C][C]0.00191290617262857[/C][/ROW]
[ROW][C]63[/C][C]0.00106090863704443[/C][C]0.000643647997423709[/C][C]0.000417260639620724[/C][/ROW]
[ROW][C]64[/C][C]0.00149775336994508[/C][C]0.00159666236734887[/C][C]-9.89089974037876e-05[/C][/ROW]
[ROW][C]65[/C][C]0.00193459810284573[/C][C]0.00244705892911699[/C][C]-0.000512460826271263[/C][/ROW]
[ROW][C]66[/C][C]0.000388306429245021[/C][C]0.00210613891712456[/C][C]-0.00171783248787954[/C][/ROW]
[ROW][C]67[/C][C]0.000776612858490043[/C][C]0.00223745861547302[/C][C]-0.00146084575698297[/C][/ROW]
[ROW][C]68[/C][C]0.00116491928773506[/C][C]0.00284711571688697[/C][C]-0.00168219642915191[/C][/ROW]
[ROW][C]69[/C][C]0.00155322571698009[/C][C]0.00273665365427855[/C][C]-0.00118342793729846[/C][/ROW]
[ROW][C]70[/C][C]0.000249625561657514[/C][C]0.00250600035329955[/C][C]-0.00225637479164204[/C][/ROW]
[ROW][C]71[/C][C]0.000599101347978033[/C][C]0.00252547088978698[/C][C]-0.00192636954180895[/C][/ROW]
[ROW][C]72[/C][C]0.000948577134298552[/C][C]0.00286193125564644[/C][C]-0.00191335412134789[/C][/ROW]
[ROW][C]73[/C][C]0.00129805292061907[/C][C]0.00243209161742158[/C][C]-0.00113403869680251[/C][/ROW]
[ROW][C]74[/C][C]9.07729315118232e-05[/C][C]0.00200771140250154[/C][C]-0.00191693847098972[/C][/ROW]
[ROW][C]75[/C][C]0.000408478191803204[/C][C]0.0027116758086782[/C][C]-0.002303197616875[/C][/ROW]
[ROW][C]76[/C][C]0.000726183452094585[/C][C]0.00309315831966801[/C][C]-0.00236697486757342[/C][/ROW]
[ROW][C]77[/C][C]0.00104388871238597[/C][C]0.0026611911977855[/C][C]-0.00161730248539953[/C][/ROW]
[ROW][C]78[/C][C]0.00136159397267735[/C][C]0.00254342934870891[/C][C]-0.00118183537603156[/C][/ROW]
[ROW][C]79[/C][C]0.000291229821933766[/C][C]0.00210302618719254[/C][C]-0.00181179636525877[/C][/ROW]
[ROW][C]80[/C][C]0.000582459643867532[/C][C]0.00215251988790291[/C][C]-0.00157006024403538[/C][/ROW]
[ROW][C]81[/C][C]0.000873689465801298[/C][C]0.0016056788321882[/C][C]-0.000731989366386906[/C][/ROW]
[ROW][C]82[/C][C]0.00116491928773506[/C][C]0.000921360304839595[/C][C]0.000243558982895469[/C][/ROW]
[ROW][C]83[/C][C]0.00199600798403194[/C][C]0.00187505310402406[/C][C]0.000120954880007877[/C][/ROW]
[ROW][C]84[/C][C]0.00548902195608782[/C][C]0.00330167737154579[/C][C]0.00218734458454203[/C][/ROW]
[ROW][C]85[/C][C]0.00898203592814371[/C][C]0.00360830935561981[/C][C]0.0053737265725239[/C][/ROW]
[ROW][C]86[/C][C]0.0124750499001996[/C][C]0.00311612443306646[/C][C]0.00935892546713314[/C][/ROW]
[ROW][C]87[/C][C]0.000249500998003992[/C][C]0.00317515116057492[/C][C]-0.00292565016257093[/C][/ROW]
[ROW][C]88[/C][C]0.00199600798403194[/C][C]0.00319260052451925[/C][C]-0.00119659254048731[/C][/ROW]
[ROW][C]89[/C][C]0.00374251497005988[/C][C]0.00357822520165351[/C][C]0.000164289768406369[/C][/ROW]
[ROW][C]90[/C][C]0.00548902195608782[/C][C]0.00354017481659794[/C][C]0.00194884713948988[/C][/ROW]
[ROW][C]91[/C][C]0.00723552894211577[/C][C]0.00319009472566595[/C][C]0.00404543421644981[/C][/ROW]
[ROW][C]92[/C][C]0.0011643379906853[/C][C]0.00350375752390835[/C][C]-0.00233941953322306[/C][/ROW]
[ROW][C]93[/C][C]0.00232867598137059[/C][C]0.00292226325845763[/C][C]-0.000593587277087042[/C][/ROW]
[ROW][C]94[/C][C]0.00349301397205589[/C][C]0.00289853866955375[/C][C]0.000594475302502139[/C][/ROW]
[ROW][C]95[/C][C]0.00465735196274118[/C][C]0.00246535737863897[/C][C]0.00219199458410222[/C][/ROW]
[ROW][C]96[/C][C]0.000499001996007984[/C][C]0.00262765016875236[/C][C]-0.00212864817274438[/C][/ROW]
[ROW][C]97[/C][C]0.00137225548902196[/C][C]0.00192721856450067[/C][C]-0.000554963075478709[/C][/ROW]
[ROW][C]98[/C][C]0.00224550898203593[/C][C]0.00225583994147306[/C][C]-1.0330959437132e-05[/C][/ROW]
[ROW][C]99[/C][C]0.0031187624750499[/C][C]0.00252475572814701[/C][C]0.000594006746902889[/C][/ROW]
[ROW][C]100[/C][C]0.000199600798403194[/C][C]0.00173053023407447[/C][C]-0.00153092943567127[/C][/ROW]
[ROW][C]101[/C][C]0.000898203592814371[/C][C]0.0022840412201226[/C][C]-0.00138583762730823[/C][/ROW]
[ROW][C]102[/C][C]0.00159680638722555[/C][C]0.00249899073379425[/C][C]-0.000902184346568704[/C][/ROW]
[ROW][C]103[/C][C]0.00229540918163673[/C][C]0.00239844269510593[/C][C]-0.000103033513469207[/C][/ROW]
[ROW][C]104[/C][C]0.0029940119760479[/C][C]0.000958667675834427[/C][C]0.00203534430021348[/C][/ROW]
[ROW][C]105[/C][C]0.000499001996007984[/C][C]0.00199176874966617[/C][C]-0.00149276675365818[/C][/ROW]
[ROW][C]106[/C][C]0.00108117099135063[/C][C]0.00235337951965258[/C][C]-0.00127220852830195[/C][/ROW]
[ROW][C]107[/C][C]0.00166333998669328[/C][C]0.00105972656681285[/C][C]0.000603613419880435[/C][/ROW]
[ROW][C]108[/C][C]0.00224550898203593[/C][C]-0.00130047909418541[/C][C]0.00354598807622134[/C][/ROW]
[ROW][C]109[/C][C]0.000285143997718848[/C][C]0.000555941210880231[/C][C]-0.000270797213161383[/C][/ROW]
[ROW][C]110[/C][C]0.000784145993726832[/C][C]0.000629652282035125[/C][C]0.000154493711691707[/C][/ROW]
[ROW][C]111[/C][C]0.00128314798973482[/C][C]-0.00113544240205586[/C][C]0.00241859039179068[/C][/ROW]
[ROW][C]112[/C][C]0.0017821499857428[/C][C]0.000473701892648963[/C][C]0.00130844809309384[/C][/ROW]
[ROW][C]113[/C][C]6.2375249500998e-05[/C][C]0.000503629803075705[/C][C]-0.000441254553574707[/C][/ROW]
[ROW][C]114[/C][C]0.000499001996007984[/C][C]0.00106725090717379[/C][C]-0.000568248911165809[/C][/ROW]
[ROW][C]115[/C][C]0.00093562874251497[/C][C]0.00145183603518115[/C][C]-0.000516207292666181[/C][/ROW]
[ROW][C]116[/C][C]0.00137225548902196[/C][C]0.00176158462408598[/C][C]-0.000389329135064024[/C][/ROW]
[ROW][C]117[/C][C]0.00180888223552894[/C][C]0.00248562218015874[/C][C]-0.000676739944629794[/C][/ROW]
[ROW][C]118[/C][C]0.000277223331115547[/C][C]0.0010932176934184[/C][C]-0.000815994362302849[/C][/ROW]
[ROW][C]119[/C][C]0.000665335994677312[/C][C]0.000724091206172576[/C][C]-5.8755211495264e-05[/C][/ROW]
[ROW][C]120[/C][C]0.00105344865823908[/C][C]0.00240164677349113[/C][C]-0.00134819811525205[/C][/ROW]
[ROW][C]121[/C][C]0.00144156132180084[/C][C]0.00268525685165641[/C][C]-0.00124369552985557[/C][/ROW]
[ROW][C]122[/C][C]0.000149700598802395[/C][C]0.0024827540619774[/C][C]-0.00233305346317501[/C][/ROW]
[ROW][C]123[/C][C]0.000499001996007984[/C][C]0.00276065089721363[/C][C]-0.00226164890120565[/C][/ROW]
[ROW][C]124[/C][C]0.000848303393213573[/C][C]0.00273449297586028[/C][C]-0.00188618958264671[/C][/ROW]
[ROW][C]125[/C][C]0.00119760479041916[/C][C]0.00209530453270719[/C][C]-0.000897699742288032[/C][/ROW]
[ROW][C]126[/C][C]0.00154690618762475[/C][C]0.00146989262236379[/C][C]7.70135652609615e-05[/C][/ROW]
[ROW][C]127[/C][C]0.000317546724732353[/C][C]0.00232471829425281[/C][C]-0.00200717156952046[/C][/ROW]
[ROW][C]128[/C][C]0.000635093449464707[/C][C]0.00234297479866782[/C][C]-0.00170788134920311[/C][/ROW]
[ROW][C]129[/C][C]0.00095264017419706[/C][C]0.00264033775866111[/C][C]-0.00168769758446405[/C][/ROW]
[ROW][C]130[/C][C]0.00127018689892941[/C][C]0.00239812764382495[/C][C]-0.00112794074489554[/C][/ROW]
[ROW][C]131[/C][C]0.00020791749833666[/C][C]0.00265341342830092[/C][C]-0.00244549592996426[/C][/ROW]
[ROW][C]132[/C][C]0.000499001996007984[/C][C]0.00266286937104447[/C][C]-0.00216386737503648[/C][/ROW]
[ROW][C]133[/C][C]0.000790086493679308[/C][C]0.00211246687240915[/C][C]-0.00132238037872984[/C][/ROW]
[ROW][C]134[/C][C]0.00108117099135063[/C][C]0.000715414731831107[/C][C]0.000365756259519525[/C][/ROW]
[ROW][C]135[/C][C]0.000997506234413965[/C][C]0.00199888170019839[/C][C]-0.00100137546578442[/C][/ROW]
[ROW][C]136[/C][C]0.00448877805486284[/C][C]0.00330422160793002[/C][C]0.00118455644693283[/C][/ROW]
[ROW][C]137[/C][C]0.00798004987531172[/C][C]0.00342684195599888[/C][C]0.00455320791931284[/C][/ROW]
[ROW][C]138[/C][C]0.0114713216957606[/C][C]0.00322118235674743[/C][C]0.00825013933901317[/C][/ROW]
[ROW][C]139[/C][C]0.0149625935162095[/C][C]0.00324110372966853[/C][C]0.0117214897865409[/C][/ROW]
[ROW][C]140[/C][C]0.00149625935162095[/C][C]0.00360888809670138[/C][C]-0.00211262874508043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&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
10.0007492507492507490.000949952803324658-0.000200702054073908
20.001332001332001330.00220251579053839-0.00087051445853706
30.001914751914751910.001779168315609820.000135583599142091
40.00249750249750250.001523349573652810.000974152923849689
50.00049950049950050.00159508074644131-0.00109558024694081
60.0009990009990009990.0008786041136144290.00012039688538657
70.00149850149850150.0003659101398096130.00113259135869189
80.0019980019980020.00103524273873370.000962759259268303
90.00024975024975025-0.0001243377190259830.000374087968776233
100.000686813186813187-0.00133060067676380.00201741386357699
110.001123876123876120.0004054757093628750.000718400414513249
120.001560939060939060.0006644163839700840.000896522676968977
135.55000555000555e-050.00156693978019104-0.00151143972469099
140.0004440004440004440.00161675536638965-0.00117275492238921
150.0008325008325008330.00197972726599805-0.00114722643349722
160.001221001221001220.001079973136592920.000141028084408306
170.001609501609501610.00215401594304118-0.000544514333539572
180.00029970029970030.00176081988905802-0.00146111958935772
190.0006493506493506490.00174456195806312-0.00109521130871247
200.0009990009990009990.00232831848839322-0.00132931748939222
210.001348651348651350.00246440663414304-0.00111575528549169
220.0001362274089546820.0028347861426819-0.00269855873372722
230.0004540913631822720.00245750562816387-0.00200341426498159
240.0007719553174098630.00241880311791551-0.00164684780050565
250.001089819271637450.00173415217628449-0.000644332904647034
264.16250416250416e-050.00189097805946238-0.00184935301783734
270.0003330003330003330.00198252355782782-0.00164952322482749
280.0006243756243756240.00214308136190739-0.00151870573753176
290.0009157509157509160.0012314421126903-0.000315691196939383
300.001207126207126210.00185558452031069-0.000648458313184487
310.002496255616575140.00251124272172703-1.49871051518905e-05
320.005991013479780330.003668273734563980.00232273974521635
330.009485771342985520.003618356437405760.00586741490557977
340.01298052920619070.003132354432146780.00984817477404394
350.0004992511233150270.00251600894602156-0.00201675782270653
360.002246630054917620.00313453481134189-0.000887904756424271
370.003994008986520220.003262733767129450.000731275219390769
380.005741387918122820.003703589137247780.00203779878087504
390.0003328340822100180.00238194205713213-0.00204910797492211
400.001497753369945080.0018649998758744-0.000367246505929313
410.002662672657680150.001709023403698840.000953649253981311
420.003827591945415210.00185392819352460.00197366375189061
430.004992511233150270.002228393711716390.00276411752143389
440.0007488766849725410.00102460491365157-0.000275728228679028
450.001622566150773840.0009000966080793740.000722469542694465
460.002496255616575140.001568693245045280.000927562371529855
470.003369945082376440.00206070017432450.00130924490805193
480.0003994008986520220.000775522805451037-0.000376121906799015
490.001098352471293060.00122115530665077-0.000122802835357707
500.00179730404393410.001647579448653710.000149724595280385
510.002496255616575140.001446901239931230.0010493543766439
528.32085205525046e-050.00169351870396315-0.00161031018341065
530.0006656681644200370.00179293090477656-0.00112726274035652
540.001248127808287570.0007976051954824140.000450522612805155
550.00183058745215510.001226471302906580.000604116149248523
560.002413047096022630.001176405280911670.00123664181511097
570.0004279295342700240.00131078931881611-0.00088285978454609
580.0009271806575850510.00177127197983462-0.000844091322249573
590.001426431780900080.001386177321831164.02544590689214e-05
600.001925682904215110.0005480251822115030.0013776577220036
610.000187219171243135-0.0008016045362933140.000988823707536449
620.000624063904143784-0.001288842268484790.00191290617262857
630.001060908637044430.0006436479974237090.000417260639620724
640.001497753369945080.00159666236734887-9.89089974037876e-05
650.001934598102845730.00244705892911699-0.000512460826271263
660.0003883064292450210.00210613891712456-0.00171783248787954
670.0007766128584900430.00223745861547302-0.00146084575698297
680.001164919287735060.00284711571688697-0.00168219642915191
690.001553225716980090.00273665365427855-0.00118342793729846
700.0002496255616575140.00250600035329955-0.00225637479164204
710.0005991013479780330.00252547088978698-0.00192636954180895
720.0009485771342985520.00286193125564644-0.00191335412134789
730.001298052920619070.00243209161742158-0.00113403869680251
749.07729315118232e-050.00200771140250154-0.00191693847098972
750.0004084781918032040.0027116758086782-0.002303197616875
760.0007261834520945850.00309315831966801-0.00236697486757342
770.001043888712385970.0026611911977855-0.00161730248539953
780.001361593972677350.00254342934870891-0.00118183537603156
790.0002912298219337660.00210302618719254-0.00181179636525877
800.0005824596438675320.00215251988790291-0.00157006024403538
810.0008736894658012980.0016056788321882-0.000731989366386906
820.001164919287735060.0009213603048395950.000243558982895469
830.001996007984031940.001875053104024060.000120954880007877
840.005489021956087820.003301677371545790.00218734458454203
850.008982035928143710.003608309355619810.0053737265725239
860.01247504990019960.003116124433066460.00935892546713314
870.0002495009980039920.00317515116057492-0.00292565016257093
880.001996007984031940.00319260052451925-0.00119659254048731
890.003742514970059880.003578225201653510.000164289768406369
900.005489021956087820.003540174816597940.00194884713948988
910.007235528942115770.003190094725665950.00404543421644981
920.00116433799068530.00350375752390835-0.00233941953322306
930.002328675981370590.00292226325845763-0.000593587277087042
940.003493013972055890.002898538669553750.000594475302502139
950.004657351962741180.002465357378638970.00219199458410222
960.0004990019960079840.00262765016875236-0.00212864817274438
970.001372255489021960.00192721856450067-0.000554963075478709
980.002245508982035930.00225583994147306-1.0330959437132e-05
990.00311876247504990.002524755728147010.000594006746902889
1000.0001996007984031940.00173053023407447-0.00153092943567127
1010.0008982035928143710.0022840412201226-0.00138583762730823
1020.001596806387225550.00249899073379425-0.000902184346568704
1030.002295409181636730.00239844269510593-0.000103033513469207
1040.00299401197604790.0009586676758344270.00203534430021348
1050.0004990019960079840.00199176874966617-0.00149276675365818
1060.001081170991350630.00235337951965258-0.00127220852830195
1070.001663339986693280.001059726566812850.000603613419880435
1080.00224550898203593-0.001300479094185410.00354598807622134
1090.0002851439977188480.000555941210880231-0.000270797213161383
1100.0007841459937268320.0006296522820351250.000154493711691707
1110.00128314798973482-0.001135442402055860.00241859039179068
1120.00178214998574280.0004737018926489630.00130844809309384
1136.2375249500998e-050.000503629803075705-0.000441254553574707
1140.0004990019960079840.00106725090717379-0.000568248911165809
1150.000935628742514970.00145183603518115-0.000516207292666181
1160.001372255489021960.00176158462408598-0.000389329135064024
1170.001808882235528940.00248562218015874-0.000676739944629794
1180.0002772233311155470.0010932176934184-0.000815994362302849
1190.0006653359946773120.000724091206172576-5.8755211495264e-05
1200.001053448658239080.00240164677349113-0.00134819811525205
1210.001441561321800840.00268525685165641-0.00124369552985557
1220.0001497005988023950.0024827540619774-0.00233305346317501
1230.0004990019960079840.00276065089721363-0.00226164890120565
1240.0008483033932135730.00273449297586028-0.00188618958264671
1250.001197604790419160.00209530453270719-0.000897699742288032
1260.001546906187624750.001469892622363797.70135652609615e-05
1270.0003175467247323530.00232471829425281-0.00200717156952046
1280.0006350934494647070.00234297479866782-0.00170788134920311
1290.000952640174197060.00264033775866111-0.00168769758446405
1300.001270186898929410.00239812764382495-0.00112794074489554
1310.000207917498336660.00265341342830092-0.00244549592996426
1320.0004990019960079840.00266286937104447-0.00216386737503648
1330.0007900864936793080.00211246687240915-0.00132238037872984
1340.001081170991350630.0007154147318311070.000365756259519525
1350.0009975062344139650.00199888170019839-0.00100137546578442
1360.004488778054862840.003304221607930020.00118455644693283
1370.007980049875311720.003426841955998880.00455320791931284
1380.01147132169576060.003221182356747430.00825013933901317
1390.01496259351620950.003241103729668530.0117214897865409
1400.001496259351620950.00360888809670138-0.00211262874508043






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02249054790024630.04498109580049270.977509452099754
100.007781733622205370.01556346724441070.992218266377795
110.004528563355926280.009057126711852560.995471436644074
120.001435381923599130.002870763847198260.998564618076401
130.001714077311727450.00342815462345490.998285922688273
140.0006291280517603070.001258256103520610.99937087194824
150.0001749397418421380.0003498794836842770.999825060258158
165.41741667789873e-050.0001083483335579750.999945825833221
171.41230258417177e-052.82460516834355e-050.999985876974158
186.25590208247369e-061.25118041649474e-050.999993744097918
191.58055714625251e-063.16111429250501e-060.999998419442854
204.15649861787179e-078.31299723574357e-070.999999584350138
219.73132629081324e-081.94626525816265e-070.999999902686737
221.18194533409645e-072.3638906681929e-070.999999881805467
233.10238671649076e-086.20477343298151e-080.999999968976133
249.64097537970394e-091.92819507594079e-080.999999990359025
252.28911009620492e-094.57822019240983e-090.99999999771089
262.08250221895147e-094.16500443790295e-090.999999997917498
276.13634319044889e-101.22726863808978e-090.999999999386366
281.83739599603992e-103.67479199207985e-100.99999999981626
294.38558570655495e-118.77117141310991e-110.999999999956144
301.02122316252004e-112.04244632504008e-110.999999999989788
312.03190975817914e-114.06381951635828e-110.999999999979681
327.42457345281369e-081.48491469056274e-070.999999925754265
339.09026490921955e-050.0001818052981843910.999909097350908
340.4508450751807270.9016901503614530.549154924819274
350.4065368700038570.8130737400077130.593463129996143
360.3616625394209160.7233250788418310.638337460579084
370.3164091310464040.6328182620928090.683590868953596
380.2975886678801720.5951773357603440.702411332119828
390.2972275580357970.5944551160715930.702772441964203
400.2526222018581310.5052444037162610.747377798141869
410.2295248634351430.4590497268702860.770475136564857
420.2303379197096740.4606758394193480.769662080290326
430.2346199693455370.4692399386910740.765380030654463
440.1944888231064910.3889776462129820.805511176893509
450.1587180482995060.3174360965990130.841281951700494
460.1301136430777390.2602272861554790.869886356922261
470.1126074881546150.2252149763092310.887392511845385
480.1056011192214040.2112022384428080.894398880778596
490.09416864174250590.1883372834850120.905831358257494
500.08241812815735680.1648362563147140.917581871842643
510.06657777749279040.1331555549855810.93342222250721
520.07573262922578270.1514652584515650.924267370774217
530.06897120211351520.137942404227030.931028797886485
540.05405125983960220.1081025196792040.945948740160398
550.04151350195039090.08302700390078170.958486498049609
560.03317642532603850.0663528506520770.966823574673962
570.02919943592374890.05839887184749780.970800564076251
580.02376711108307640.04753422216615290.976232888916924
590.01737371064361590.03474742128723170.982626289356384
600.01378512878102050.02757025756204110.986214871218979
610.01041811928030820.02083623856061640.989581880719692
620.00941225854428950.0188245170885790.99058774145571
630.00683229589815840.01366459179631680.993167704101842
640.004915925284001630.009831850568003260.995084074715998
650.003722651231938090.007445302463876190.996277348768062
660.003261716678663720.006523433357327450.996738283321336
670.002480002023109450.004960004046218910.997519997976891
680.00183462012088530.00366924024177060.998165379879115
690.001243144440293790.002486288880587590.998756855559706
700.0009878371671486590.001975674334297320.999012162832851
710.0007171433511943620.001434286702388720.999282856648806
720.0005078579494748910.001015715898949780.999492142050525
730.0003314610040384150.0006629220080768310.999668538995962
740.0002348986345913330.0004697972691826660.999765101365409
750.0001808871920156450.0003617743840312890.999819112807984
760.0001404996840003980.0002809993680007960.999859500316
779.71228596266055e-050.0001942457192532110.999902877140373
786.62957494330812e-050.0001325914988661620.999933704250567
794.95741816885285e-059.91483633770569e-050.999950425818312
803.56216661515395e-057.12433323030789e-050.999964378333848
812.3364420107663e-054.6728840215326e-050.999976635579892
821.56536970008431e-053.13073940016862e-050.999984346302999
831.02462716801984e-052.04925433603967e-050.99998975372832
841.10054492769833e-052.20108985539666e-050.999988994550723
850.0001770775150432590.0003541550300865170.999822922484957
860.06570997198117840.1314199439623570.934290028018822
870.06705773400529120.1341154680105820.932942265994709
880.05330114328296520.106602286565930.946698856717035
890.04161108303547210.08322216607094420.958388916964528
900.0415169080420750.08303381608415010.958483091957925
910.08420636102767220.1684127220553440.915793638972328
920.07733368700954260.1546673740190850.922666312990457
930.0601265891262740.1202531782525480.939873410873726
940.04982390161941510.09964780323883010.950176098380585
950.05770282584851080.1154056516970220.942297174151489
960.04825720845113720.09651441690227440.951742791548863
970.03796287045104530.07592574090209050.962037129548955
980.03060172300193740.06120344600387470.969398276998063
990.02667760154025290.05335520308050590.973322398459747
1000.02251454011276980.04502908022553960.97748545988723
1010.01728380479490660.03456760958981310.982716195205093
1020.01230685967242010.02461371934484030.98769314032758
1030.008615343336016440.01723068667203290.991384656663984
1040.007448229124005310.01489645824801060.992551770875995
1050.006008708811565910.01201741762313180.993991291188434
1060.004304989634558380.008609979269116750.995695010365442
1070.002858520316863380.005717040633726750.997141479683137
1080.002766888302079470.005533776604158930.997233111697921
1090.001967479743338020.003934959486676050.998032520256662
1100.001286901084534140.002573802169068280.998713098915466
1110.001332243453046570.002664486906093140.998667756546953
1120.001576548827834070.003153097655668140.998423451172166
1130.001049492519471930.002098985038943860.998950507480528
1140.000732971608563750.00146594321712750.999267028391436
1150.0004992991734462140.0009985983468924290.999500700826554
1160.0003661762476074310.0007323524952148630.999633823752393
1170.0005590613961627060.001118122792325410.999440938603837
1180.004079212356724960.008158424713449930.995920787643275
1190.00476290699248980.00952581398497960.99523709300751
1200.003523939665686830.007047879331373650.996476060334313
1210.002993153800487840.005986307600975680.997006846199512
1220.001868752360807050.003737504721614110.998131247639193
1230.00107022660552920.002140453211058390.998929773394471
1240.04926735599527370.09853471199054730.950732644004726
1250.05176782163135950.1035356432627190.94823217836864
1260.0408647591793360.0817295183586720.959135240820664
1270.02832418614907130.05664837229814260.971675813850929
1280.04218743847674160.08437487695348330.957812561523258
1290.04963545331716180.09927090663432360.950364546682838
1300.06912161879073490.138243237581470.930878381209265
1310.04016322349745280.08032644699490550.959836776502547

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0224905479002463 & 0.0449810958004927 & 0.977509452099754 \tabularnewline
10 & 0.00778173362220537 & 0.0155634672444107 & 0.992218266377795 \tabularnewline
11 & 0.00452856335592628 & 0.00905712671185256 & 0.995471436644074 \tabularnewline
12 & 0.00143538192359913 & 0.00287076384719826 & 0.998564618076401 \tabularnewline
13 & 0.00171407731172745 & 0.0034281546234549 & 0.998285922688273 \tabularnewline
14 & 0.000629128051760307 & 0.00125825610352061 & 0.99937087194824 \tabularnewline
15 & 0.000174939741842138 & 0.000349879483684277 & 0.999825060258158 \tabularnewline
16 & 5.41741667789873e-05 & 0.000108348333557975 & 0.999945825833221 \tabularnewline
17 & 1.41230258417177e-05 & 2.82460516834355e-05 & 0.999985876974158 \tabularnewline
18 & 6.25590208247369e-06 & 1.25118041649474e-05 & 0.999993744097918 \tabularnewline
19 & 1.58055714625251e-06 & 3.16111429250501e-06 & 0.999998419442854 \tabularnewline
20 & 4.15649861787179e-07 & 8.31299723574357e-07 & 0.999999584350138 \tabularnewline
21 & 9.73132629081324e-08 & 1.94626525816265e-07 & 0.999999902686737 \tabularnewline
22 & 1.18194533409645e-07 & 2.3638906681929e-07 & 0.999999881805467 \tabularnewline
23 & 3.10238671649076e-08 & 6.20477343298151e-08 & 0.999999968976133 \tabularnewline
24 & 9.64097537970394e-09 & 1.92819507594079e-08 & 0.999999990359025 \tabularnewline
25 & 2.28911009620492e-09 & 4.57822019240983e-09 & 0.99999999771089 \tabularnewline
26 & 2.08250221895147e-09 & 4.16500443790295e-09 & 0.999999997917498 \tabularnewline
27 & 6.13634319044889e-10 & 1.22726863808978e-09 & 0.999999999386366 \tabularnewline
28 & 1.83739599603992e-10 & 3.67479199207985e-10 & 0.99999999981626 \tabularnewline
29 & 4.38558570655495e-11 & 8.77117141310991e-11 & 0.999999999956144 \tabularnewline
30 & 1.02122316252004e-11 & 2.04244632504008e-11 & 0.999999999989788 \tabularnewline
31 & 2.03190975817914e-11 & 4.06381951635828e-11 & 0.999999999979681 \tabularnewline
32 & 7.42457345281369e-08 & 1.48491469056274e-07 & 0.999999925754265 \tabularnewline
33 & 9.09026490921955e-05 & 0.000181805298184391 & 0.999909097350908 \tabularnewline
34 & 0.450845075180727 & 0.901690150361453 & 0.549154924819274 \tabularnewline
35 & 0.406536870003857 & 0.813073740007713 & 0.593463129996143 \tabularnewline
36 & 0.361662539420916 & 0.723325078841831 & 0.638337460579084 \tabularnewline
37 & 0.316409131046404 & 0.632818262092809 & 0.683590868953596 \tabularnewline
38 & 0.297588667880172 & 0.595177335760344 & 0.702411332119828 \tabularnewline
39 & 0.297227558035797 & 0.594455116071593 & 0.702772441964203 \tabularnewline
40 & 0.252622201858131 & 0.505244403716261 & 0.747377798141869 \tabularnewline
41 & 0.229524863435143 & 0.459049726870286 & 0.770475136564857 \tabularnewline
42 & 0.230337919709674 & 0.460675839419348 & 0.769662080290326 \tabularnewline
43 & 0.234619969345537 & 0.469239938691074 & 0.765380030654463 \tabularnewline
44 & 0.194488823106491 & 0.388977646212982 & 0.805511176893509 \tabularnewline
45 & 0.158718048299506 & 0.317436096599013 & 0.841281951700494 \tabularnewline
46 & 0.130113643077739 & 0.260227286155479 & 0.869886356922261 \tabularnewline
47 & 0.112607488154615 & 0.225214976309231 & 0.887392511845385 \tabularnewline
48 & 0.105601119221404 & 0.211202238442808 & 0.894398880778596 \tabularnewline
49 & 0.0941686417425059 & 0.188337283485012 & 0.905831358257494 \tabularnewline
50 & 0.0824181281573568 & 0.164836256314714 & 0.917581871842643 \tabularnewline
51 & 0.0665777774927904 & 0.133155554985581 & 0.93342222250721 \tabularnewline
52 & 0.0757326292257827 & 0.151465258451565 & 0.924267370774217 \tabularnewline
53 & 0.0689712021135152 & 0.13794240422703 & 0.931028797886485 \tabularnewline
54 & 0.0540512598396022 & 0.108102519679204 & 0.945948740160398 \tabularnewline
55 & 0.0415135019503909 & 0.0830270039007817 & 0.958486498049609 \tabularnewline
56 & 0.0331764253260385 & 0.066352850652077 & 0.966823574673962 \tabularnewline
57 & 0.0291994359237489 & 0.0583988718474978 & 0.970800564076251 \tabularnewline
58 & 0.0237671110830764 & 0.0475342221661529 & 0.976232888916924 \tabularnewline
59 & 0.0173737106436159 & 0.0347474212872317 & 0.982626289356384 \tabularnewline
60 & 0.0137851287810205 & 0.0275702575620411 & 0.986214871218979 \tabularnewline
61 & 0.0104181192803082 & 0.0208362385606164 & 0.989581880719692 \tabularnewline
62 & 0.0094122585442895 & 0.018824517088579 & 0.99058774145571 \tabularnewline
63 & 0.0068322958981584 & 0.0136645917963168 & 0.993167704101842 \tabularnewline
64 & 0.00491592528400163 & 0.00983185056800326 & 0.995084074715998 \tabularnewline
65 & 0.00372265123193809 & 0.00744530246387619 & 0.996277348768062 \tabularnewline
66 & 0.00326171667866372 & 0.00652343335732745 & 0.996738283321336 \tabularnewline
67 & 0.00248000202310945 & 0.00496000404621891 & 0.997519997976891 \tabularnewline
68 & 0.0018346201208853 & 0.0036692402417706 & 0.998165379879115 \tabularnewline
69 & 0.00124314444029379 & 0.00248628888058759 & 0.998756855559706 \tabularnewline
70 & 0.000987837167148659 & 0.00197567433429732 & 0.999012162832851 \tabularnewline
71 & 0.000717143351194362 & 0.00143428670238872 & 0.999282856648806 \tabularnewline
72 & 0.000507857949474891 & 0.00101571589894978 & 0.999492142050525 \tabularnewline
73 & 0.000331461004038415 & 0.000662922008076831 & 0.999668538995962 \tabularnewline
74 & 0.000234898634591333 & 0.000469797269182666 & 0.999765101365409 \tabularnewline
75 & 0.000180887192015645 & 0.000361774384031289 & 0.999819112807984 \tabularnewline
76 & 0.000140499684000398 & 0.000280999368000796 & 0.999859500316 \tabularnewline
77 & 9.71228596266055e-05 & 0.000194245719253211 & 0.999902877140373 \tabularnewline
78 & 6.62957494330812e-05 & 0.000132591498866162 & 0.999933704250567 \tabularnewline
79 & 4.95741816885285e-05 & 9.91483633770569e-05 & 0.999950425818312 \tabularnewline
80 & 3.56216661515395e-05 & 7.12433323030789e-05 & 0.999964378333848 \tabularnewline
81 & 2.3364420107663e-05 & 4.6728840215326e-05 & 0.999976635579892 \tabularnewline
82 & 1.56536970008431e-05 & 3.13073940016862e-05 & 0.999984346302999 \tabularnewline
83 & 1.02462716801984e-05 & 2.04925433603967e-05 & 0.99998975372832 \tabularnewline
84 & 1.10054492769833e-05 & 2.20108985539666e-05 & 0.999988994550723 \tabularnewline
85 & 0.000177077515043259 & 0.000354155030086517 & 0.999822922484957 \tabularnewline
86 & 0.0657099719811784 & 0.131419943962357 & 0.934290028018822 \tabularnewline
87 & 0.0670577340052912 & 0.134115468010582 & 0.932942265994709 \tabularnewline
88 & 0.0533011432829652 & 0.10660228656593 & 0.946698856717035 \tabularnewline
89 & 0.0416110830354721 & 0.0832221660709442 & 0.958388916964528 \tabularnewline
90 & 0.041516908042075 & 0.0830338160841501 & 0.958483091957925 \tabularnewline
91 & 0.0842063610276722 & 0.168412722055344 & 0.915793638972328 \tabularnewline
92 & 0.0773336870095426 & 0.154667374019085 & 0.922666312990457 \tabularnewline
93 & 0.060126589126274 & 0.120253178252548 & 0.939873410873726 \tabularnewline
94 & 0.0498239016194151 & 0.0996478032388301 & 0.950176098380585 \tabularnewline
95 & 0.0577028258485108 & 0.115405651697022 & 0.942297174151489 \tabularnewline
96 & 0.0482572084511372 & 0.0965144169022744 & 0.951742791548863 \tabularnewline
97 & 0.0379628704510453 & 0.0759257409020905 & 0.962037129548955 \tabularnewline
98 & 0.0306017230019374 & 0.0612034460038747 & 0.969398276998063 \tabularnewline
99 & 0.0266776015402529 & 0.0533552030805059 & 0.973322398459747 \tabularnewline
100 & 0.0225145401127698 & 0.0450290802255396 & 0.97748545988723 \tabularnewline
101 & 0.0172838047949066 & 0.0345676095898131 & 0.982716195205093 \tabularnewline
102 & 0.0123068596724201 & 0.0246137193448403 & 0.98769314032758 \tabularnewline
103 & 0.00861534333601644 & 0.0172306866720329 & 0.991384656663984 \tabularnewline
104 & 0.00744822912400531 & 0.0148964582480106 & 0.992551770875995 \tabularnewline
105 & 0.00600870881156591 & 0.0120174176231318 & 0.993991291188434 \tabularnewline
106 & 0.00430498963455838 & 0.00860997926911675 & 0.995695010365442 \tabularnewline
107 & 0.00285852031686338 & 0.00571704063372675 & 0.997141479683137 \tabularnewline
108 & 0.00276688830207947 & 0.00553377660415893 & 0.997233111697921 \tabularnewline
109 & 0.00196747974333802 & 0.00393495948667605 & 0.998032520256662 \tabularnewline
110 & 0.00128690108453414 & 0.00257380216906828 & 0.998713098915466 \tabularnewline
111 & 0.00133224345304657 & 0.00266448690609314 & 0.998667756546953 \tabularnewline
112 & 0.00157654882783407 & 0.00315309765566814 & 0.998423451172166 \tabularnewline
113 & 0.00104949251947193 & 0.00209898503894386 & 0.998950507480528 \tabularnewline
114 & 0.00073297160856375 & 0.0014659432171275 & 0.999267028391436 \tabularnewline
115 & 0.000499299173446214 & 0.000998598346892429 & 0.999500700826554 \tabularnewline
116 & 0.000366176247607431 & 0.000732352495214863 & 0.999633823752393 \tabularnewline
117 & 0.000559061396162706 & 0.00111812279232541 & 0.999440938603837 \tabularnewline
118 & 0.00407921235672496 & 0.00815842471344993 & 0.995920787643275 \tabularnewline
119 & 0.0047629069924898 & 0.0095258139849796 & 0.99523709300751 \tabularnewline
120 & 0.00352393966568683 & 0.00704787933137365 & 0.996476060334313 \tabularnewline
121 & 0.00299315380048784 & 0.00598630760097568 & 0.997006846199512 \tabularnewline
122 & 0.00186875236080705 & 0.00373750472161411 & 0.998131247639193 \tabularnewline
123 & 0.0010702266055292 & 0.00214045321105839 & 0.998929773394471 \tabularnewline
124 & 0.0492673559952737 & 0.0985347119905473 & 0.950732644004726 \tabularnewline
125 & 0.0517678216313595 & 0.103535643262719 & 0.94823217836864 \tabularnewline
126 & 0.040864759179336 & 0.081729518358672 & 0.959135240820664 \tabularnewline
127 & 0.0283241861490713 & 0.0566483722981426 & 0.971675813850929 \tabularnewline
128 & 0.0421874384767416 & 0.0843748769534833 & 0.957812561523258 \tabularnewline
129 & 0.0496354533171618 & 0.0992709066343236 & 0.950364546682838 \tabularnewline
130 & 0.0691216187907349 & 0.13824323758147 & 0.930878381209265 \tabularnewline
131 & 0.0401632234974528 & 0.0803264469949055 & 0.959836776502547 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0224905479002463[/C][C]0.0449810958004927[/C][C]0.977509452099754[/C][/ROW]
[ROW][C]10[/C][C]0.00778173362220537[/C][C]0.0155634672444107[/C][C]0.992218266377795[/C][/ROW]
[ROW][C]11[/C][C]0.00452856335592628[/C][C]0.00905712671185256[/C][C]0.995471436644074[/C][/ROW]
[ROW][C]12[/C][C]0.00143538192359913[/C][C]0.00287076384719826[/C][C]0.998564618076401[/C][/ROW]
[ROW][C]13[/C][C]0.00171407731172745[/C][C]0.0034281546234549[/C][C]0.998285922688273[/C][/ROW]
[ROW][C]14[/C][C]0.000629128051760307[/C][C]0.00125825610352061[/C][C]0.99937087194824[/C][/ROW]
[ROW][C]15[/C][C]0.000174939741842138[/C][C]0.000349879483684277[/C][C]0.999825060258158[/C][/ROW]
[ROW][C]16[/C][C]5.41741667789873e-05[/C][C]0.000108348333557975[/C][C]0.999945825833221[/C][/ROW]
[ROW][C]17[/C][C]1.41230258417177e-05[/C][C]2.82460516834355e-05[/C][C]0.999985876974158[/C][/ROW]
[ROW][C]18[/C][C]6.25590208247369e-06[/C][C]1.25118041649474e-05[/C][C]0.999993744097918[/C][/ROW]
[ROW][C]19[/C][C]1.58055714625251e-06[/C][C]3.16111429250501e-06[/C][C]0.999998419442854[/C][/ROW]
[ROW][C]20[/C][C]4.15649861787179e-07[/C][C]8.31299723574357e-07[/C][C]0.999999584350138[/C][/ROW]
[ROW][C]21[/C][C]9.73132629081324e-08[/C][C]1.94626525816265e-07[/C][C]0.999999902686737[/C][/ROW]
[ROW][C]22[/C][C]1.18194533409645e-07[/C][C]2.3638906681929e-07[/C][C]0.999999881805467[/C][/ROW]
[ROW][C]23[/C][C]3.10238671649076e-08[/C][C]6.20477343298151e-08[/C][C]0.999999968976133[/C][/ROW]
[ROW][C]24[/C][C]9.64097537970394e-09[/C][C]1.92819507594079e-08[/C][C]0.999999990359025[/C][/ROW]
[ROW][C]25[/C][C]2.28911009620492e-09[/C][C]4.57822019240983e-09[/C][C]0.99999999771089[/C][/ROW]
[ROW][C]26[/C][C]2.08250221895147e-09[/C][C]4.16500443790295e-09[/C][C]0.999999997917498[/C][/ROW]
[ROW][C]27[/C][C]6.13634319044889e-10[/C][C]1.22726863808978e-09[/C][C]0.999999999386366[/C][/ROW]
[ROW][C]28[/C][C]1.83739599603992e-10[/C][C]3.67479199207985e-10[/C][C]0.99999999981626[/C][/ROW]
[ROW][C]29[/C][C]4.38558570655495e-11[/C][C]8.77117141310991e-11[/C][C]0.999999999956144[/C][/ROW]
[ROW][C]30[/C][C]1.02122316252004e-11[/C][C]2.04244632504008e-11[/C][C]0.999999999989788[/C][/ROW]
[ROW][C]31[/C][C]2.03190975817914e-11[/C][C]4.06381951635828e-11[/C][C]0.999999999979681[/C][/ROW]
[ROW][C]32[/C][C]7.42457345281369e-08[/C][C]1.48491469056274e-07[/C][C]0.999999925754265[/C][/ROW]
[ROW][C]33[/C][C]9.09026490921955e-05[/C][C]0.000181805298184391[/C][C]0.999909097350908[/C][/ROW]
[ROW][C]34[/C][C]0.450845075180727[/C][C]0.901690150361453[/C][C]0.549154924819274[/C][/ROW]
[ROW][C]35[/C][C]0.406536870003857[/C][C]0.813073740007713[/C][C]0.593463129996143[/C][/ROW]
[ROW][C]36[/C][C]0.361662539420916[/C][C]0.723325078841831[/C][C]0.638337460579084[/C][/ROW]
[ROW][C]37[/C][C]0.316409131046404[/C][C]0.632818262092809[/C][C]0.683590868953596[/C][/ROW]
[ROW][C]38[/C][C]0.297588667880172[/C][C]0.595177335760344[/C][C]0.702411332119828[/C][/ROW]
[ROW][C]39[/C][C]0.297227558035797[/C][C]0.594455116071593[/C][C]0.702772441964203[/C][/ROW]
[ROW][C]40[/C][C]0.252622201858131[/C][C]0.505244403716261[/C][C]0.747377798141869[/C][/ROW]
[ROW][C]41[/C][C]0.229524863435143[/C][C]0.459049726870286[/C][C]0.770475136564857[/C][/ROW]
[ROW][C]42[/C][C]0.230337919709674[/C][C]0.460675839419348[/C][C]0.769662080290326[/C][/ROW]
[ROW][C]43[/C][C]0.234619969345537[/C][C]0.469239938691074[/C][C]0.765380030654463[/C][/ROW]
[ROW][C]44[/C][C]0.194488823106491[/C][C]0.388977646212982[/C][C]0.805511176893509[/C][/ROW]
[ROW][C]45[/C][C]0.158718048299506[/C][C]0.317436096599013[/C][C]0.841281951700494[/C][/ROW]
[ROW][C]46[/C][C]0.130113643077739[/C][C]0.260227286155479[/C][C]0.869886356922261[/C][/ROW]
[ROW][C]47[/C][C]0.112607488154615[/C][C]0.225214976309231[/C][C]0.887392511845385[/C][/ROW]
[ROW][C]48[/C][C]0.105601119221404[/C][C]0.211202238442808[/C][C]0.894398880778596[/C][/ROW]
[ROW][C]49[/C][C]0.0941686417425059[/C][C]0.188337283485012[/C][C]0.905831358257494[/C][/ROW]
[ROW][C]50[/C][C]0.0824181281573568[/C][C]0.164836256314714[/C][C]0.917581871842643[/C][/ROW]
[ROW][C]51[/C][C]0.0665777774927904[/C][C]0.133155554985581[/C][C]0.93342222250721[/C][/ROW]
[ROW][C]52[/C][C]0.0757326292257827[/C][C]0.151465258451565[/C][C]0.924267370774217[/C][/ROW]
[ROW][C]53[/C][C]0.0689712021135152[/C][C]0.13794240422703[/C][C]0.931028797886485[/C][/ROW]
[ROW][C]54[/C][C]0.0540512598396022[/C][C]0.108102519679204[/C][C]0.945948740160398[/C][/ROW]
[ROW][C]55[/C][C]0.0415135019503909[/C][C]0.0830270039007817[/C][C]0.958486498049609[/C][/ROW]
[ROW][C]56[/C][C]0.0331764253260385[/C][C]0.066352850652077[/C][C]0.966823574673962[/C][/ROW]
[ROW][C]57[/C][C]0.0291994359237489[/C][C]0.0583988718474978[/C][C]0.970800564076251[/C][/ROW]
[ROW][C]58[/C][C]0.0237671110830764[/C][C]0.0475342221661529[/C][C]0.976232888916924[/C][/ROW]
[ROW][C]59[/C][C]0.0173737106436159[/C][C]0.0347474212872317[/C][C]0.982626289356384[/C][/ROW]
[ROW][C]60[/C][C]0.0137851287810205[/C][C]0.0275702575620411[/C][C]0.986214871218979[/C][/ROW]
[ROW][C]61[/C][C]0.0104181192803082[/C][C]0.0208362385606164[/C][C]0.989581880719692[/C][/ROW]
[ROW][C]62[/C][C]0.0094122585442895[/C][C]0.018824517088579[/C][C]0.99058774145571[/C][/ROW]
[ROW][C]63[/C][C]0.0068322958981584[/C][C]0.0136645917963168[/C][C]0.993167704101842[/C][/ROW]
[ROW][C]64[/C][C]0.00491592528400163[/C][C]0.00983185056800326[/C][C]0.995084074715998[/C][/ROW]
[ROW][C]65[/C][C]0.00372265123193809[/C][C]0.00744530246387619[/C][C]0.996277348768062[/C][/ROW]
[ROW][C]66[/C][C]0.00326171667866372[/C][C]0.00652343335732745[/C][C]0.996738283321336[/C][/ROW]
[ROW][C]67[/C][C]0.00248000202310945[/C][C]0.00496000404621891[/C][C]0.997519997976891[/C][/ROW]
[ROW][C]68[/C][C]0.0018346201208853[/C][C]0.0036692402417706[/C][C]0.998165379879115[/C][/ROW]
[ROW][C]69[/C][C]0.00124314444029379[/C][C]0.00248628888058759[/C][C]0.998756855559706[/C][/ROW]
[ROW][C]70[/C][C]0.000987837167148659[/C][C]0.00197567433429732[/C][C]0.999012162832851[/C][/ROW]
[ROW][C]71[/C][C]0.000717143351194362[/C][C]0.00143428670238872[/C][C]0.999282856648806[/C][/ROW]
[ROW][C]72[/C][C]0.000507857949474891[/C][C]0.00101571589894978[/C][C]0.999492142050525[/C][/ROW]
[ROW][C]73[/C][C]0.000331461004038415[/C][C]0.000662922008076831[/C][C]0.999668538995962[/C][/ROW]
[ROW][C]74[/C][C]0.000234898634591333[/C][C]0.000469797269182666[/C][C]0.999765101365409[/C][/ROW]
[ROW][C]75[/C][C]0.000180887192015645[/C][C]0.000361774384031289[/C][C]0.999819112807984[/C][/ROW]
[ROW][C]76[/C][C]0.000140499684000398[/C][C]0.000280999368000796[/C][C]0.999859500316[/C][/ROW]
[ROW][C]77[/C][C]9.71228596266055e-05[/C][C]0.000194245719253211[/C][C]0.999902877140373[/C][/ROW]
[ROW][C]78[/C][C]6.62957494330812e-05[/C][C]0.000132591498866162[/C][C]0.999933704250567[/C][/ROW]
[ROW][C]79[/C][C]4.95741816885285e-05[/C][C]9.91483633770569e-05[/C][C]0.999950425818312[/C][/ROW]
[ROW][C]80[/C][C]3.56216661515395e-05[/C][C]7.12433323030789e-05[/C][C]0.999964378333848[/C][/ROW]
[ROW][C]81[/C][C]2.3364420107663e-05[/C][C]4.6728840215326e-05[/C][C]0.999976635579892[/C][/ROW]
[ROW][C]82[/C][C]1.56536970008431e-05[/C][C]3.13073940016862e-05[/C][C]0.999984346302999[/C][/ROW]
[ROW][C]83[/C][C]1.02462716801984e-05[/C][C]2.04925433603967e-05[/C][C]0.99998975372832[/C][/ROW]
[ROW][C]84[/C][C]1.10054492769833e-05[/C][C]2.20108985539666e-05[/C][C]0.999988994550723[/C][/ROW]
[ROW][C]85[/C][C]0.000177077515043259[/C][C]0.000354155030086517[/C][C]0.999822922484957[/C][/ROW]
[ROW][C]86[/C][C]0.0657099719811784[/C][C]0.131419943962357[/C][C]0.934290028018822[/C][/ROW]
[ROW][C]87[/C][C]0.0670577340052912[/C][C]0.134115468010582[/C][C]0.932942265994709[/C][/ROW]
[ROW][C]88[/C][C]0.0533011432829652[/C][C]0.10660228656593[/C][C]0.946698856717035[/C][/ROW]
[ROW][C]89[/C][C]0.0416110830354721[/C][C]0.0832221660709442[/C][C]0.958388916964528[/C][/ROW]
[ROW][C]90[/C][C]0.041516908042075[/C][C]0.0830338160841501[/C][C]0.958483091957925[/C][/ROW]
[ROW][C]91[/C][C]0.0842063610276722[/C][C]0.168412722055344[/C][C]0.915793638972328[/C][/ROW]
[ROW][C]92[/C][C]0.0773336870095426[/C][C]0.154667374019085[/C][C]0.922666312990457[/C][/ROW]
[ROW][C]93[/C][C]0.060126589126274[/C][C]0.120253178252548[/C][C]0.939873410873726[/C][/ROW]
[ROW][C]94[/C][C]0.0498239016194151[/C][C]0.0996478032388301[/C][C]0.950176098380585[/C][/ROW]
[ROW][C]95[/C][C]0.0577028258485108[/C][C]0.115405651697022[/C][C]0.942297174151489[/C][/ROW]
[ROW][C]96[/C][C]0.0482572084511372[/C][C]0.0965144169022744[/C][C]0.951742791548863[/C][/ROW]
[ROW][C]97[/C][C]0.0379628704510453[/C][C]0.0759257409020905[/C][C]0.962037129548955[/C][/ROW]
[ROW][C]98[/C][C]0.0306017230019374[/C][C]0.0612034460038747[/C][C]0.969398276998063[/C][/ROW]
[ROW][C]99[/C][C]0.0266776015402529[/C][C]0.0533552030805059[/C][C]0.973322398459747[/C][/ROW]
[ROW][C]100[/C][C]0.0225145401127698[/C][C]0.0450290802255396[/C][C]0.97748545988723[/C][/ROW]
[ROW][C]101[/C][C]0.0172838047949066[/C][C]0.0345676095898131[/C][C]0.982716195205093[/C][/ROW]
[ROW][C]102[/C][C]0.0123068596724201[/C][C]0.0246137193448403[/C][C]0.98769314032758[/C][/ROW]
[ROW][C]103[/C][C]0.00861534333601644[/C][C]0.0172306866720329[/C][C]0.991384656663984[/C][/ROW]
[ROW][C]104[/C][C]0.00744822912400531[/C][C]0.0148964582480106[/C][C]0.992551770875995[/C][/ROW]
[ROW][C]105[/C][C]0.00600870881156591[/C][C]0.0120174176231318[/C][C]0.993991291188434[/C][/ROW]
[ROW][C]106[/C][C]0.00430498963455838[/C][C]0.00860997926911675[/C][C]0.995695010365442[/C][/ROW]
[ROW][C]107[/C][C]0.00285852031686338[/C][C]0.00571704063372675[/C][C]0.997141479683137[/C][/ROW]
[ROW][C]108[/C][C]0.00276688830207947[/C][C]0.00553377660415893[/C][C]0.997233111697921[/C][/ROW]
[ROW][C]109[/C][C]0.00196747974333802[/C][C]0.00393495948667605[/C][C]0.998032520256662[/C][/ROW]
[ROW][C]110[/C][C]0.00128690108453414[/C][C]0.00257380216906828[/C][C]0.998713098915466[/C][/ROW]
[ROW][C]111[/C][C]0.00133224345304657[/C][C]0.00266448690609314[/C][C]0.998667756546953[/C][/ROW]
[ROW][C]112[/C][C]0.00157654882783407[/C][C]0.00315309765566814[/C][C]0.998423451172166[/C][/ROW]
[ROW][C]113[/C][C]0.00104949251947193[/C][C]0.00209898503894386[/C][C]0.998950507480528[/C][/ROW]
[ROW][C]114[/C][C]0.00073297160856375[/C][C]0.0014659432171275[/C][C]0.999267028391436[/C][/ROW]
[ROW][C]115[/C][C]0.000499299173446214[/C][C]0.000998598346892429[/C][C]0.999500700826554[/C][/ROW]
[ROW][C]116[/C][C]0.000366176247607431[/C][C]0.000732352495214863[/C][C]0.999633823752393[/C][/ROW]
[ROW][C]117[/C][C]0.000559061396162706[/C][C]0.00111812279232541[/C][C]0.999440938603837[/C][/ROW]
[ROW][C]118[/C][C]0.00407921235672496[/C][C]0.00815842471344993[/C][C]0.995920787643275[/C][/ROW]
[ROW][C]119[/C][C]0.0047629069924898[/C][C]0.0095258139849796[/C][C]0.99523709300751[/C][/ROW]
[ROW][C]120[/C][C]0.00352393966568683[/C][C]0.00704787933137365[/C][C]0.996476060334313[/C][/ROW]
[ROW][C]121[/C][C]0.00299315380048784[/C][C]0.00598630760097568[/C][C]0.997006846199512[/C][/ROW]
[ROW][C]122[/C][C]0.00186875236080705[/C][C]0.00373750472161411[/C][C]0.998131247639193[/C][/ROW]
[ROW][C]123[/C][C]0.0010702266055292[/C][C]0.00214045321105839[/C][C]0.998929773394471[/C][/ROW]
[ROW][C]124[/C][C]0.0492673559952737[/C][C]0.0985347119905473[/C][C]0.950732644004726[/C][/ROW]
[ROW][C]125[/C][C]0.0517678216313595[/C][C]0.103535643262719[/C][C]0.94823217836864[/C][/ROW]
[ROW][C]126[/C][C]0.040864759179336[/C][C]0.081729518358672[/C][C]0.959135240820664[/C][/ROW]
[ROW][C]127[/C][C]0.0283241861490713[/C][C]0.0566483722981426[/C][C]0.971675813850929[/C][/ROW]
[ROW][C]128[/C][C]0.0421874384767416[/C][C]0.0843748769534833[/C][C]0.957812561523258[/C][/ROW]
[ROW][C]129[/C][C]0.0496354533171618[/C][C]0.0992709066343236[/C][C]0.950364546682838[/C][/ROW]
[ROW][C]130[/C][C]0.0691216187907349[/C][C]0.13824323758147[/C][C]0.930878381209265[/C][/ROW]
[ROW][C]131[/C][C]0.0401632234974528[/C][C]0.0803264469949055[/C][C]0.959836776502547[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02249054790024630.04498109580049270.977509452099754
100.007781733622205370.01556346724441070.992218266377795
110.004528563355926280.009057126711852560.995471436644074
120.001435381923599130.002870763847198260.998564618076401
130.001714077311727450.00342815462345490.998285922688273
140.0006291280517603070.001258256103520610.99937087194824
150.0001749397418421380.0003498794836842770.999825060258158
165.41741667789873e-050.0001083483335579750.999945825833221
171.41230258417177e-052.82460516834355e-050.999985876974158
186.25590208247369e-061.25118041649474e-050.999993744097918
191.58055714625251e-063.16111429250501e-060.999998419442854
204.15649861787179e-078.31299723574357e-070.999999584350138
219.73132629081324e-081.94626525816265e-070.999999902686737
221.18194533409645e-072.3638906681929e-070.999999881805467
233.10238671649076e-086.20477343298151e-080.999999968976133
249.64097537970394e-091.92819507594079e-080.999999990359025
252.28911009620492e-094.57822019240983e-090.99999999771089
262.08250221895147e-094.16500443790295e-090.999999997917498
276.13634319044889e-101.22726863808978e-090.999999999386366
281.83739599603992e-103.67479199207985e-100.99999999981626
294.38558570655495e-118.77117141310991e-110.999999999956144
301.02122316252004e-112.04244632504008e-110.999999999989788
312.03190975817914e-114.06381951635828e-110.999999999979681
327.42457345281369e-081.48491469056274e-070.999999925754265
339.09026490921955e-050.0001818052981843910.999909097350908
340.4508450751807270.9016901503614530.549154924819274
350.4065368700038570.8130737400077130.593463129996143
360.3616625394209160.7233250788418310.638337460579084
370.3164091310464040.6328182620928090.683590868953596
380.2975886678801720.5951773357603440.702411332119828
390.2972275580357970.5944551160715930.702772441964203
400.2526222018581310.5052444037162610.747377798141869
410.2295248634351430.4590497268702860.770475136564857
420.2303379197096740.4606758394193480.769662080290326
430.2346199693455370.4692399386910740.765380030654463
440.1944888231064910.3889776462129820.805511176893509
450.1587180482995060.3174360965990130.841281951700494
460.1301136430777390.2602272861554790.869886356922261
470.1126074881546150.2252149763092310.887392511845385
480.1056011192214040.2112022384428080.894398880778596
490.09416864174250590.1883372834850120.905831358257494
500.08241812815735680.1648362563147140.917581871842643
510.06657777749279040.1331555549855810.93342222250721
520.07573262922578270.1514652584515650.924267370774217
530.06897120211351520.137942404227030.931028797886485
540.05405125983960220.1081025196792040.945948740160398
550.04151350195039090.08302700390078170.958486498049609
560.03317642532603850.0663528506520770.966823574673962
570.02919943592374890.05839887184749780.970800564076251
580.02376711108307640.04753422216615290.976232888916924
590.01737371064361590.03474742128723170.982626289356384
600.01378512878102050.02757025756204110.986214871218979
610.01041811928030820.02083623856061640.989581880719692
620.00941225854428950.0188245170885790.99058774145571
630.00683229589815840.01366459179631680.993167704101842
640.004915925284001630.009831850568003260.995084074715998
650.003722651231938090.007445302463876190.996277348768062
660.003261716678663720.006523433357327450.996738283321336
670.002480002023109450.004960004046218910.997519997976891
680.00183462012088530.00366924024177060.998165379879115
690.001243144440293790.002486288880587590.998756855559706
700.0009878371671486590.001975674334297320.999012162832851
710.0007171433511943620.001434286702388720.999282856648806
720.0005078579494748910.001015715898949780.999492142050525
730.0003314610040384150.0006629220080768310.999668538995962
740.0002348986345913330.0004697972691826660.999765101365409
750.0001808871920156450.0003617743840312890.999819112807984
760.0001404996840003980.0002809993680007960.999859500316
779.71228596266055e-050.0001942457192532110.999902877140373
786.62957494330812e-050.0001325914988661620.999933704250567
794.95741816885285e-059.91483633770569e-050.999950425818312
803.56216661515395e-057.12433323030789e-050.999964378333848
812.3364420107663e-054.6728840215326e-050.999976635579892
821.56536970008431e-053.13073940016862e-050.999984346302999
831.02462716801984e-052.04925433603967e-050.99998975372832
841.10054492769833e-052.20108985539666e-050.999988994550723
850.0001770775150432590.0003541550300865170.999822922484957
860.06570997198117840.1314199439623570.934290028018822
870.06705773400529120.1341154680105820.932942265994709
880.05330114328296520.106602286565930.946698856717035
890.04161108303547210.08322216607094420.958388916964528
900.0415169080420750.08303381608415010.958483091957925
910.08420636102767220.1684127220553440.915793638972328
920.07733368700954260.1546673740190850.922666312990457
930.0601265891262740.1202531782525480.939873410873726
940.04982390161941510.09964780323883010.950176098380585
950.05770282584851080.1154056516970220.942297174151489
960.04825720845113720.09651441690227440.951742791548863
970.03796287045104530.07592574090209050.962037129548955
980.03060172300193740.06120344600387470.969398276998063
990.02667760154025290.05335520308050590.973322398459747
1000.02251454011276980.04502908022553960.97748545988723
1010.01728380479490660.03456760958981310.982716195205093
1020.01230685967242010.02461371934484030.98769314032758
1030.008615343336016440.01723068667203290.991384656663984
1040.007448229124005310.01489645824801060.992551770875995
1050.006008708811565910.01201741762313180.993991291188434
1060.004304989634558380.008609979269116750.995695010365442
1070.002858520316863380.005717040633726750.997141479683137
1080.002766888302079470.005533776604158930.997233111697921
1090.001967479743338020.003934959486676050.998032520256662
1100.001286901084534140.002573802169068280.998713098915466
1110.001332243453046570.002664486906093140.998667756546953
1120.001576548827834070.003153097655668140.998423451172166
1130.001049492519471930.002098985038943860.998950507480528
1140.000732971608563750.00146594321712750.999267028391436
1150.0004992991734462140.0009985983468924290.999500700826554
1160.0003661762476074310.0007323524952148630.999633823752393
1170.0005590613961627060.001118122792325410.999440938603837
1180.004079212356724960.008158424713449930.995920787643275
1190.00476290699248980.00952581398497960.99523709300751
1200.003523939665686830.007047879331373650.996476060334313
1210.002993153800487840.005986307600975680.997006846199512
1220.001868752360807050.003737504721614110.998131247639193
1230.00107022660552920.002140453211058390.998929773394471
1240.04926735599527370.09853471199054730.950732644004726
1250.05176782163135950.1035356432627190.94823217836864
1260.0408647591793360.0817295183586720.959135240820664
1270.02832418614907130.05664837229814260.971675813850929
1280.04218743847674160.08437487695348330.957812561523258
1290.04963545331716180.09927090663432360.950364546682838
1300.06912161879073490.138243237581470.930878381209265
1310.04016322349745280.08032644699490550.959836776502547






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level630.51219512195122NOK
5% type I error level770.626016260162602NOK
10% type I error level930.75609756097561NOK

\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 & 63 & 0.51219512195122 & NOK \tabularnewline
5% type I error level & 77 & 0.626016260162602 & NOK \tabularnewline
10% type I error level & 93 & 0.75609756097561 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160629&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]63[/C][C]0.51219512195122[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]77[/C][C]0.626016260162602[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]93[/C][C]0.75609756097561[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160629&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160629&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level630.51219512195122NOK
5% type I error level770.626016260162602NOK
10% type I error level930.75609756097561NOK


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')
}