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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Wed, 22 Dec 2010 19:34:28 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t1293046400ls3wev8supdoj46.htm/, Retrieved Mon, 06 May 2024 00:09:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114525, Retrieved Mon, 06 May 2024 00:09:38 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

162

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Variance Reduction Matrix] [Unemployment] [2010-11-29 09:29:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Variance Reduction Matrix] [WS9 - Variance Re...] [2010-12-04 11:04:59] [8ef49741e164ec6343c90c7935194465]
-   P     [Variance Reduction Matrix] [WS 9 VRM] [2010-12-05 14:01:21] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD      [(Partial) Autocorrelation Function] [paper ACF] [2010-12-10 10:47:04] [8214fe6d084e5ad7598b249a26cc9f06]
-   P         [(Partial) Autocorrelation Function] [paper acf met D=1] [2010-12-10 11:19:24] [8214fe6d084e5ad7598b249a26cc9f06]
- RMP           [Spectral Analysis] [paper - cum perio...] [2010-12-10 11:22:34] [8214fe6d084e5ad7598b249a26cc9f06]
-   P             [Spectral Analysis] [paper - cum perio...] [2010-12-10 11:27:22] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD              [Spectral Analysis] [cum periodogram 2 ] [2010-12-20 20:28:39] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                [Spectral Analysis] [cum per 2  paper] [2010-12-22 13:46:56] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                  [Spectral Analysis] [cum per 1 middeng...] [2010-12-22 19:06:59] [8214fe6d084e5ad7598b249a26cc9f06]
-   P                     [Spectral Analysis] [cum per 2 middeng...] [2010-12-22 19:08:52] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                      [Spectral Analysis] [cum per 1 hoogges...] [2010-12-22 19:10:38] [8214fe6d084e5ad7598b249a26cc9f06]
-   P                         [Spectral Analysis] [cum per 2 hoogges...] [2010-12-22 19:12:39] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD                          [Standard Deviation-Mean Plot] [sdmp laaggeschoolden] [2010-12-22 19:15:16] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                            [Standard Deviation-Mean Plot] [sdmp middengescho...] [2010-12-22 19:17:33] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD                              [ARIMA Backward Selection] [arima backward se...] [2010-12-22 19:29:00] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                                  [ARIMA Backward Selection] [arima backward se...] [2010-12-22 19:34:28] [b47314d83d48c7bf812ec2bcd743b159] [Current]
-    D                                    [ARIMA Backward Selection] [arima backward se...] [2010-12-22 22:09:56] [8214fe6d084e5ad7598b249a26cc9f06] 

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114525&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114525&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114525&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sma1
Estimates ( 1 )	0.7937	0.2569	-0.1456	-0.798	-0.566
(p-val)	(0 )	(0.0277 )	(0.183 )	(0 )	(0 )
Estimates ( 2 )	0.1288	0.2726	0	-0.1115	-0.5697
(p-val)	(0.794 )	(0.0045 )	(NA )	(0.8319 )	(0 )
Estimates ( 3 )	0.0259	0.2769	0	0	-0.5701
(p-val)	(0.7701 )	(0.0022 )	(NA )	(NA )	(0 )
Estimates ( 4 )	0	0.278	0	0	-0.5669
(p-val)	(NA )	(0.0021 )	(NA )	(NA )	(0 )
Estimates ( 5 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.7937 & 0.2569 & -0.1456 & -0.798 & -0.566 \tabularnewline
(p-val) & (0 ) & (0.0277 ) & (0.183 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.1288 & 0.2726 & 0 & -0.1115 & -0.5697 \tabularnewline
(p-val) & (0.794 ) & (0.0045 ) & (NA ) & (0.8319 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.0259 & 0.2769 & 0 & 0 & -0.5701 \tabularnewline
(p-val) & (0.7701 ) & (0.0022 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.278 & 0 & 0 & -0.5669 \tabularnewline
(p-val) & (NA ) & (0.0021 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114525&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.7937[/C][C]0.2569[/C][C]-0.1456[/C][C]-0.798[/C][C]-0.566[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0277 )[/C][C](0.183 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1288[/C][C]0.2726[/C][C]0[/C][C]-0.1115[/C][C]-0.5697[/C][/ROW]
[ROW][C](p-val)[/C][C](0.794 )[/C][C](0.0045 )[/C][C](NA )[/C][C](0.8319 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.0259[/C][C]0.2769[/C][C]0[/C][C]0[/C][C]-0.5701[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7701 )[/C][C](0.0022 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.278[/C][C]0[/C][C]0[/C][C]-0.5669[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0021 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114525&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114525&T=1

As an alternative you can also use a QR Code:

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

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sma1
Estimates ( 1 )	0.7937	0.2569	-0.1456	-0.798	-0.566
(p-val)	(0 )	(0.0277 )	(0.183 )	(0 )	(0 )
Estimates ( 2 )	0.1288	0.2726	0	-0.1115	-0.5697
(p-val)	(0.794 )	(0.0045 )	(NA )	(0.8319 )	(0 )
Estimates ( 3 )	0.0259	0.2769	0	0	-0.5701
(p-val)	(0.7701 )	(0.0022 )	(NA )	(NA )	(0 )
Estimates ( 4 )	0	0.278	0	0	-0.5669
(p-val)	(NA )	(0.0021 )	(NA )	(NA )	(0 )
Estimates ( 5 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )

Estimated ARIMA Residuals
Value
-204.111005343425
375.716462470718
1363.60140442283
663.620855626287
-186.582877824764
984.698872825405
119.308640293455
-1739.90575792591
5240.44335930055
1683.54667635205
-985.255532717327
666.591195708317
-550.827242380818
-26.5008992128547
1086.06239771469
-1141.13380222409
415.951449786125
1037.46636355519
3624.9725801716
-2976.19070741960
-1732.19439115759
1305.16256588149
1551.70598712848
819.514395458812
458.811888102428
-289.54341300662
-6.7987982750674
1233.1797304406
-185.833475631346
2164.25756597423
2695.68836771278
-2144.43575335811
-2213.2136781487
-308.966835285498
-549.517132922459
847.234905343392
224.002346638446
303.457278327302
-475.346380521525
-1342.56321231477
136.114037050763
799.180482138085
484.486951129422
836.047321873514
-1674.320415942
3249.05197922628
-800.86394680647
-1066.47780176230
-1154.33559412089
-183.321306979955
1052.86885688914
-144.922718515031
-633.435665057468
-93.4965926216568
-355.699135992729
-1603.54156069270
885.58192756533
-1111.23918087092
-1313.80999984403
-150.694295826699
-4900.93022803229
2101.81603585562
400.383722893299
2244.44203932845
365.368443991074
-2208.13947041067
-3084.62340688241
-4043.96110780677
3833.34122486209
-5171.49165908579
-686.89122157336
-847.315861717433
2341.70566946805
-2356.01985857369
-1550.74600599895
-631.931267035364
-3.10185883907091
617.519745614417
-71.763944992993
-2803.82113216134
-2190.48681588939
2127.08216762723
2076.85866265731
1164.38711243214
-203.643755290904
-775.353226697436
1181.67768805748
140.326659305790
-1894.81226947906
2501.50863448056
-1455.77137634689
-1953.05748911830
1668.25316930157
2453.88861402476
2077.38374918143
2859.98160937324
3387.28683184575
1934.8186140022
1574.60830822403
-936.005919507582
408.38295753287
1787.93858174247
-1372.43048291006
-266.765646108852
514.137024162693
991.25952269867
1145.22486907121
3702.6908154436
-3822.8074066021
-931.187110013866
-1335.87000255598
-408.332704856568
-196.734760393621
-1211.18966306875
875.82063230116
-629.64169904142
-1500.37696375183
112.642682915076
-803.22796526992

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-204.111005343425 \tabularnewline
375.716462470718 \tabularnewline
1363.60140442283 \tabularnewline
663.620855626287 \tabularnewline
-186.582877824764 \tabularnewline
984.698872825405 \tabularnewline
119.308640293455 \tabularnewline
-1739.90575792591 \tabularnewline
5240.44335930055 \tabularnewline
1683.54667635205 \tabularnewline
-985.255532717327 \tabularnewline
666.591195708317 \tabularnewline
-550.827242380818 \tabularnewline
-26.5008992128547 \tabularnewline
1086.06239771469 \tabularnewline
-1141.13380222409 \tabularnewline
415.951449786125 \tabularnewline
1037.46636355519 \tabularnewline
3624.9725801716 \tabularnewline
-2976.19070741960 \tabularnewline
-1732.19439115759 \tabularnewline
1305.16256588149 \tabularnewline
1551.70598712848 \tabularnewline
819.514395458812 \tabularnewline
458.811888102428 \tabularnewline
-289.54341300662 \tabularnewline
-6.7987982750674 \tabularnewline
1233.1797304406 \tabularnewline
-185.833475631346 \tabularnewline
2164.25756597423 \tabularnewline
2695.68836771278 \tabularnewline
-2144.43575335811 \tabularnewline
-2213.2136781487 \tabularnewline
-308.966835285498 \tabularnewline
-549.517132922459 \tabularnewline
847.234905343392 \tabularnewline
224.002346638446 \tabularnewline
303.457278327302 \tabularnewline
-475.346380521525 \tabularnewline
-1342.56321231477 \tabularnewline
136.114037050763 \tabularnewline
799.180482138085 \tabularnewline
484.486951129422 \tabularnewline
836.047321873514 \tabularnewline
-1674.320415942 \tabularnewline
3249.05197922628 \tabularnewline
-800.86394680647 \tabularnewline
-1066.47780176230 \tabularnewline
-1154.33559412089 \tabularnewline
-183.321306979955 \tabularnewline
1052.86885688914 \tabularnewline
-144.922718515031 \tabularnewline
-633.435665057468 \tabularnewline
-93.4965926216568 \tabularnewline
-355.699135992729 \tabularnewline
-1603.54156069270 \tabularnewline
885.58192756533 \tabularnewline
-1111.23918087092 \tabularnewline
-1313.80999984403 \tabularnewline
-150.694295826699 \tabularnewline
-4900.93022803229 \tabularnewline
2101.81603585562 \tabularnewline
400.383722893299 \tabularnewline
2244.44203932845 \tabularnewline
365.368443991074 \tabularnewline
-2208.13947041067 \tabularnewline
-3084.62340688241 \tabularnewline
-4043.96110780677 \tabularnewline
3833.34122486209 \tabularnewline
-5171.49165908579 \tabularnewline
-686.89122157336 \tabularnewline
-847.315861717433 \tabularnewline
2341.70566946805 \tabularnewline
-2356.01985857369 \tabularnewline
-1550.74600599895 \tabularnewline
-631.931267035364 \tabularnewline
-3.10185883907091 \tabularnewline
617.519745614417 \tabularnewline
-71.763944992993 \tabularnewline
-2803.82113216134 \tabularnewline
-2190.48681588939 \tabularnewline
2127.08216762723 \tabularnewline
2076.85866265731 \tabularnewline
1164.38711243214 \tabularnewline
-203.643755290904 \tabularnewline
-775.353226697436 \tabularnewline
1181.67768805748 \tabularnewline
140.326659305790 \tabularnewline
-1894.81226947906 \tabularnewline
2501.50863448056 \tabularnewline
-1455.77137634689 \tabularnewline
-1953.05748911830 \tabularnewline
1668.25316930157 \tabularnewline
2453.88861402476 \tabularnewline
2077.38374918143 \tabularnewline
2859.98160937324 \tabularnewline
3387.28683184575 \tabularnewline
1934.8186140022 \tabularnewline
1574.60830822403 \tabularnewline
-936.005919507582 \tabularnewline
408.38295753287 \tabularnewline
1787.93858174247 \tabularnewline
-1372.43048291006 \tabularnewline
-266.765646108852 \tabularnewline
514.137024162693 \tabularnewline
991.25952269867 \tabularnewline
1145.22486907121 \tabularnewline
3702.6908154436 \tabularnewline
-3822.8074066021 \tabularnewline
-931.187110013866 \tabularnewline
-1335.87000255598 \tabularnewline
-408.332704856568 \tabularnewline
-196.734760393621 \tabularnewline
-1211.18966306875 \tabularnewline
875.82063230116 \tabularnewline
-629.64169904142 \tabularnewline
-1500.37696375183 \tabularnewline
112.642682915076 \tabularnewline
-803.22796526992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114525&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-204.111005343425[/C][/ROW]
[ROW][C]375.716462470718[/C][/ROW]
[ROW][C]1363.60140442283[/C][/ROW]
[ROW][C]663.620855626287[/C][/ROW]
[ROW][C]-186.582877824764[/C][/ROW]
[ROW][C]984.698872825405[/C][/ROW]
[ROW][C]119.308640293455[/C][/ROW]
[ROW][C]-1739.90575792591[/C][/ROW]
[ROW][C]5240.44335930055[/C][/ROW]
[ROW][C]1683.54667635205[/C][/ROW]
[ROW][C]-985.255532717327[/C][/ROW]
[ROW][C]666.591195708317[/C][/ROW]
[ROW][C]-550.827242380818[/C][/ROW]
[ROW][C]-26.5008992128547[/C][/ROW]
[ROW][C]1086.06239771469[/C][/ROW]
[ROW][C]-1141.13380222409[/C][/ROW]
[ROW][C]415.951449786125[/C][/ROW]
[ROW][C]1037.46636355519[/C][/ROW]
[ROW][C]3624.9725801716[/C][/ROW]
[ROW][C]-2976.19070741960[/C][/ROW]
[ROW][C]-1732.19439115759[/C][/ROW]
[ROW][C]1305.16256588149[/C][/ROW]
[ROW][C]1551.70598712848[/C][/ROW]
[ROW][C]819.514395458812[/C][/ROW]
[ROW][C]458.811888102428[/C][/ROW]
[ROW][C]-289.54341300662[/C][/ROW]
[ROW][C]-6.7987982750674[/C][/ROW]
[ROW][C]1233.1797304406[/C][/ROW]
[ROW][C]-185.833475631346[/C][/ROW]
[ROW][C]2164.25756597423[/C][/ROW]
[ROW][C]2695.68836771278[/C][/ROW]
[ROW][C]-2144.43575335811[/C][/ROW]
[ROW][C]-2213.2136781487[/C][/ROW]
[ROW][C]-308.966835285498[/C][/ROW]
[ROW][C]-549.517132922459[/C][/ROW]
[ROW][C]847.234905343392[/C][/ROW]
[ROW][C]224.002346638446[/C][/ROW]
[ROW][C]303.457278327302[/C][/ROW]
[ROW][C]-475.346380521525[/C][/ROW]
[ROW][C]-1342.56321231477[/C][/ROW]
[ROW][C]136.114037050763[/C][/ROW]
[ROW][C]799.180482138085[/C][/ROW]
[ROW][C]484.486951129422[/C][/ROW]
[ROW][C]836.047321873514[/C][/ROW]
[ROW][C]-1674.320415942[/C][/ROW]
[ROW][C]3249.05197922628[/C][/ROW]
[ROW][C]-800.86394680647[/C][/ROW]
[ROW][C]-1066.47780176230[/C][/ROW]
[ROW][C]-1154.33559412089[/C][/ROW]
[ROW][C]-183.321306979955[/C][/ROW]
[ROW][C]1052.86885688914[/C][/ROW]
[ROW][C]-144.922718515031[/C][/ROW]
[ROW][C]-633.435665057468[/C][/ROW]
[ROW][C]-93.4965926216568[/C][/ROW]
[ROW][C]-355.699135992729[/C][/ROW]
[ROW][C]-1603.54156069270[/C][/ROW]
[ROW][C]885.58192756533[/C][/ROW]
[ROW][C]-1111.23918087092[/C][/ROW]
[ROW][C]-1313.80999984403[/C][/ROW]
[ROW][C]-150.694295826699[/C][/ROW]
[ROW][C]-4900.93022803229[/C][/ROW]
[ROW][C]2101.81603585562[/C][/ROW]
[ROW][C]400.383722893299[/C][/ROW]
[ROW][C]2244.44203932845[/C][/ROW]
[ROW][C]365.368443991074[/C][/ROW]
[ROW][C]-2208.13947041067[/C][/ROW]
[ROW][C]-3084.62340688241[/C][/ROW]
[ROW][C]-4043.96110780677[/C][/ROW]
[ROW][C]3833.34122486209[/C][/ROW]
[ROW][C]-5171.49165908579[/C][/ROW]
[ROW][C]-686.89122157336[/C][/ROW]
[ROW][C]-847.315861717433[/C][/ROW]
[ROW][C]2341.70566946805[/C][/ROW]
[ROW][C]-2356.01985857369[/C][/ROW]
[ROW][C]-1550.74600599895[/C][/ROW]
[ROW][C]-631.931267035364[/C][/ROW]
[ROW][C]-3.10185883907091[/C][/ROW]
[ROW][C]617.519745614417[/C][/ROW]
[ROW][C]-71.763944992993[/C][/ROW]
[ROW][C]-2803.82113216134[/C][/ROW]
[ROW][C]-2190.48681588939[/C][/ROW]
[ROW][C]2127.08216762723[/C][/ROW]
[ROW][C]2076.85866265731[/C][/ROW]
[ROW][C]1164.38711243214[/C][/ROW]
[ROW][C]-203.643755290904[/C][/ROW]
[ROW][C]-775.353226697436[/C][/ROW]
[ROW][C]1181.67768805748[/C][/ROW]
[ROW][C]140.326659305790[/C][/ROW]
[ROW][C]-1894.81226947906[/C][/ROW]
[ROW][C]2501.50863448056[/C][/ROW]
[ROW][C]-1455.77137634689[/C][/ROW]
[ROW][C]-1953.05748911830[/C][/ROW]
[ROW][C]1668.25316930157[/C][/ROW]
[ROW][C]2453.88861402476[/C][/ROW]
[ROW][C]2077.38374918143[/C][/ROW]
[ROW][C]2859.98160937324[/C][/ROW]
[ROW][C]3387.28683184575[/C][/ROW]
[ROW][C]1934.8186140022[/C][/ROW]
[ROW][C]1574.60830822403[/C][/ROW]
[ROW][C]-936.005919507582[/C][/ROW]
[ROW][C]408.38295753287[/C][/ROW]
[ROW][C]1787.93858174247[/C][/ROW]
[ROW][C]-1372.43048291006[/C][/ROW]
[ROW][C]-266.765646108852[/C][/ROW]
[ROW][C]514.137024162693[/C][/ROW]
[ROW][C]991.25952269867[/C][/ROW]
[ROW][C]1145.22486907121[/C][/ROW]
[ROW][C]3702.6908154436[/C][/ROW]
[ROW][C]-3822.8074066021[/C][/ROW]
[ROW][C]-931.187110013866[/C][/ROW]
[ROW][C]-1335.87000255598[/C][/ROW]
[ROW][C]-408.332704856568[/C][/ROW]
[ROW][C]-196.734760393621[/C][/ROW]
[ROW][C]-1211.18966306875[/C][/ROW]
[ROW][C]875.82063230116[/C][/ROW]
[ROW][C]-629.64169904142[/C][/ROW]
[ROW][C]-1500.37696375183[/C][/ROW]
[ROW][C]112.642682915076[/C][/ROW]
[ROW][C]-803.22796526992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114525&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114525&T=2

As an alternative you can also use a QR Code:

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

Estimated ARIMA Residuals
Value
-204.111005343425
375.716462470718
1363.60140442283
663.620855626287
-186.582877824764
984.698872825405
119.308640293455
-1739.90575792591
5240.44335930055
1683.54667635205
-985.255532717327
666.591195708317
-550.827242380818
-26.5008992128547
1086.06239771469
-1141.13380222409
415.951449786125
1037.46636355519
3624.9725801716
-2976.19070741960
-1732.19439115759
1305.16256588149
1551.70598712848
819.514395458812
458.811888102428
-289.54341300662
-6.7987982750674
1233.1797304406
-185.833475631346
2164.25756597423
2695.68836771278
-2144.43575335811
-2213.2136781487
-308.966835285498
-549.517132922459
847.234905343392
224.002346638446
303.457278327302
-475.346380521525
-1342.56321231477
136.114037050763
799.180482138085
484.486951129422
836.047321873514
-1674.320415942
3249.05197922628
-800.86394680647
-1066.47780176230
-1154.33559412089
-183.321306979955
1052.86885688914
-144.922718515031
-633.435665057468
-93.4965926216568
-355.699135992729
-1603.54156069270
885.58192756533
-1111.23918087092
-1313.80999984403
-150.694295826699
-4900.93022803229
2101.81603585562
400.383722893299
2244.44203932845
365.368443991074
-2208.13947041067
-3084.62340688241
-4043.96110780677
3833.34122486209
-5171.49165908579
-686.89122157336
-847.315861717433
2341.70566946805
-2356.01985857369
-1550.74600599895
-631.931267035364
-3.10185883907091
617.519745614417
-71.763944992993
-2803.82113216134
-2190.48681588939
2127.08216762723
2076.85866265731
1164.38711243214
-203.643755290904
-775.353226697436
1181.67768805748
140.326659305790
-1894.81226947906
2501.50863448056
-1455.77137634689
-1953.05748911830
1668.25316930157
2453.88861402476
2077.38374918143
2859.98160937324
3387.28683184575
1934.8186140022
1574.60830822403
-936.005919507582
408.38295753287
1787.93858174247
-1372.43048291006
-266.765646108852
514.137024162693
991.25952269867
1145.22486907121
3702.6908154436
-3822.8074066021
-931.187110013866
-1335.87000255598
-408.332704856568
-196.734760393621
-1211.18966306875
875.82063230116
-629.64169904142
-1500.37696375183
112.642682915076
-803.22796526992

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 1 ;

R code (references can be found in the software module):

library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame  <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names  <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval  <- arimaSelect.out[[2]][1:k,]
aic   <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft  = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop   = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01)                            symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1)  & (pval[j,i]>=.05)) symb = 'red'
else                                         symb = 'black'
polygon(c(i+.5   ,i+.2   ,i+.5   ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix)  {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv  <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i   ,1+2*i   ,1+2*i-.5   ,1+2*i),
c(.4      ,.7      , .4        , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code