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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationMon, 06 Dec 2010 19:00:10 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/06/t129166189230dpspr33e6f1o7.htm/, Retrieved Sun, 28 Apr 2024 20:12:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105790, Retrieved Sun, 28 Apr 2024 20:12:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact214

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Standard Deviation-Mean Plot] [Births] [2010-11-29 10:52:49] [b98453cac15ba1066b407e146608df68]
- RMP           [ARIMA Backward Selection] [Births] [2010-11-29 17:42:52] [b98453cac15ba1066b407e146608df68]
F   PD              [ARIMA Backward Selection] [W9] [2010-12-06 19:00:10] [476d588d86fe88306e0383abd6004235] [Current]
-                     [ARIMA Backward Selection] [] [2010-12-15 15:13:49] [2e1e44f0ae3cb9513dc28781dfdb387b]
-                       [ARIMA Backward Selection] [] [2010-12-17 16:50:02] [dc73d270d5d96f29ff77294e1b86f79b]
-   P                 [ARIMA Backward Selection] [ARMA parameters] [2011-12-07 12:53:48] [489eb911c8db04aca1fc54d886fc3144]
-   P                 [ARIMA Backward Selection] [ws9.6] [2011-12-07 13:25:00] [8ae0a4da1b3ee81f40dbba5e42914d07]
-                     [ARIMA Backward Selection] [ARMA parameter] [2011-12-08 08:41:51] [d1852ae60c447c0faaa37b55ffb3cb2b]
- RMP                 [ARIMA Forecasting] [Forecasting] [2011-12-08 08:48:03] [d1852ae60c447c0faaa37b55ffb3cb2b]
-                     [ARIMA Backward Selection] [ARIMA parameters ...] [2011-12-08 14:21:22] [d5821fed422662f85834b1edae505ce2]
-                     [ARIMA Backward Selection] [workshop 9g] [2011-12-08 14:32:47] [bab4e1d4a779bb46523d87231e2a2e96]
-    D                [ARIMA Backward Selection] [workshop 9] [2011-12-08 14:34:12] [b1d8f9e536fbb1cbb331f583f7f5f24b]
-                     [ARIMA Backward Selection] [ARIMA backward] [2011-12-22 13:41:46] [bab4e1d4a779bb46523d87231e2a2e96]
- R                   [ARIMA Backward Selection] [] [2012-11-26 21:01:37] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2010-12-10 13:23:10 [Pascal Wijnen] [reply] 
Persoonlijk denk ik dat de student hier een fout begaat. Normaal gezien zouden alle parameters op nul moeten uitkomen!?
  2010-12-13 21:19:09 [Kirsten Vercammen] [reply] 
Aangezien de student een fout heeft begaan bij de VRM volgens de vorige reviewer kan het natuurlijk wel zijn dat je een fout hebt gemaakt. Misschien is je lambda-waarde niet correct. (Ik heb deze data niet gebruikt voor men workshop). Ik geef je alleen maar mee waar je een fout zou kunnen gemaakt hebben.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 14 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105790&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105790&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.39950.06390.18580.1506-0.7323-0.39710.183
(p-val)(0.4771 )(0.7441 )(0.1433 )(0.7911 )(0.2421 )(0.1575 )(0.7882 )
Estimates ( 2 )-0.25460.09950.17790-0.7335-0.39650.1899
(p-val)(0.0521 )(0.4567 )(0.1662 )(NA )(0.2436 )(0.1556 )(0.7814 )
Estimates ( 3 )-0.24940.10670.16980-0.5607-0.32480
(p-val)(0.0543 )(0.4148 )(0.1758 )(NA )(0 )(0.0447 )(NA )
Estimates ( 4 )-0.27100.14510-0.5702-0.34050
(p-val)(0.0345 )(NA )(0.2359 )(NA )(0 )(0.0326 )(NA )
Estimates ( 5 )-0.2532000-0.574-0.33760
(p-val)(0.0476 )(NA )(NA )(NA )(0 )(0.033 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.3995 & 0.0639 & 0.1858 & 0.1506 & -0.7323 & -0.3971 & 0.183 \tabularnewline
(p-val) & (0.4771 ) & (0.7441 ) & (0.1433 ) & (0.7911 ) & (0.2421 ) & (0.1575 ) & (0.7882 ) \tabularnewline
Estimates ( 2 ) & -0.2546 & 0.0995 & 0.1779 & 0 & -0.7335 & -0.3965 & 0.1899 \tabularnewline
(p-val) & (0.0521 ) & (0.4567 ) & (0.1662 ) & (NA ) & (0.2436 ) & (0.1556 ) & (0.7814 ) \tabularnewline
Estimates ( 3 ) & -0.2494 & 0.1067 & 0.1698 & 0 & -0.5607 & -0.3248 & 0 \tabularnewline
(p-val) & (0.0543 ) & (0.4148 ) & (0.1758 ) & (NA ) & (0 ) & (0.0447 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.271 & 0 & 0.1451 & 0 & -0.5702 & -0.3405 & 0 \tabularnewline
(p-val) & (0.0345 ) & (NA ) & (0.2359 ) & (NA ) & (0 ) & (0.0326 ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.2532 & 0 & 0 & 0 & -0.574 & -0.3376 & 0 \tabularnewline
(p-val) & (0.0476 ) & (NA ) & (NA ) & (NA ) & (0 ) & (0.033 ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105790&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]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.3995[/C][C]0.0639[/C][C]0.1858[/C][C]0.1506[/C][C]-0.7323[/C][C]-0.3971[/C][C]0.183[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4771 )[/C][C](0.7441 )[/C][C](0.1433 )[/C][C](0.7911 )[/C][C](0.2421 )[/C][C](0.1575 )[/C][C](0.7882 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.2546[/C][C]0.0995[/C][C]0.1779[/C][C]0[/C][C]-0.7335[/C][C]-0.3965[/C][C]0.1899[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0521 )[/C][C](0.4567 )[/C][C](0.1662 )[/C][C](NA )[/C][C](0.2436 )[/C][C](0.1556 )[/C][C](0.7814 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.2494[/C][C]0.1067[/C][C]0.1698[/C][C]0[/C][C]-0.5607[/C][C]-0.3248[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0543 )[/C][C](0.4148 )[/C][C](0.1758 )[/C][C](NA )[/C][C](0 )[/C][C](0.0447 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.271[/C][C]0[/C][C]0.1451[/C][C]0[/C][C]-0.5702[/C][C]-0.3405[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0345 )[/C][C](NA )[/C][C](0.2359 )[/C][C](NA )[/C][C](0 )[/C][C](0.0326 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.2532[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.574[/C][C]-0.3376[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0476 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.033 )[/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][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][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][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][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][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][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][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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105790&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105790&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:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.39950.06390.18580.1506-0.7323-0.39710.183
(p-val)(0.4771 )(0.7441 )(0.1433 )(0.7911 )(0.2421 )(0.1575 )(0.7882 )
Estimates ( 2 )-0.25460.09950.17790-0.7335-0.39650.1899
(p-val)(0.0521 )(0.4567 )(0.1662 )(NA )(0.2436 )(0.1556 )(0.7814 )
Estimates ( 3 )-0.24940.10670.16980-0.5607-0.32480
(p-val)(0.0543 )(0.4148 )(0.1758 )(NA )(0 )(0.0447 )(NA )
Estimates ( 4 )-0.27100.14510-0.5702-0.34050
(p-val)(0.0345 )(NA )(0.2359 )(NA )(0 )(0.0326 )(NA )
Estimates ( 5 )-0.2532000-0.574-0.33760
(p-val)(0.0476 )(NA )(NA )(NA )(0 )(0.033 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
867.887509505204
-2250.28069475706
33618.356965784
9954.34444592902
354.191747925620
18882.4059848456
20229.4309143634
268402.415800735
-113346.928018808
-45016.3930870888
35069.8581927414
58531.0974683435
-77256.37719663
-31473.5947870219
-52391.0075621489
32854.9859435725
101107.732677222
-176275.960473942
79531.885537752
-176414.252881799
151290.583018155
167731.591284544
143237.123984833
80251.9641959216
118735.723920888
75035.8242647686
19198.3069299239
-36364.565244121
-36170.5788957657
-109567.395737977
-100783.334905072
-149267.404560060
38947.3539988643
58613.0612764424
16074.4618904611
-41563.0054471834
-15970.5960147386
-47563.9543407556
59595.3586354662
65897.8398819312
-166489.283404707
46312.3277180123
-15952.8741348381
-87780.6497165994
134744.171799582
75232.8110734522
24408.7565122573
-15406.1428224565
-3766.75426023186
27197.2235783702
-46777.2891917652
-82472.8205054209
-35154.7186673461
-46946.8689402549
-43641.5350003634
-54920.7077875747
54905.4050480593
-10509.5838823069
-13706.8037712772
-42347.6091932919
-28990.4681222014

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
867.887509505204 \tabularnewline
-2250.28069475706 \tabularnewline
33618.356965784 \tabularnewline
9954.34444592902 \tabularnewline
354.191747925620 \tabularnewline
18882.4059848456 \tabularnewline
20229.4309143634 \tabularnewline
268402.415800735 \tabularnewline
-113346.928018808 \tabularnewline
-45016.3930870888 \tabularnewline
35069.8581927414 \tabularnewline
58531.0974683435 \tabularnewline
-77256.37719663 \tabularnewline
-31473.5947870219 \tabularnewline
-52391.0075621489 \tabularnewline
32854.9859435725 \tabularnewline
101107.732677222 \tabularnewline
-176275.960473942 \tabularnewline
79531.885537752 \tabularnewline
-176414.252881799 \tabularnewline
151290.583018155 \tabularnewline
167731.591284544 \tabularnewline
143237.123984833 \tabularnewline
80251.9641959216 \tabularnewline
118735.723920888 \tabularnewline
75035.8242647686 \tabularnewline
19198.3069299239 \tabularnewline
-36364.565244121 \tabularnewline
-36170.5788957657 \tabularnewline
-109567.395737977 \tabularnewline
-100783.334905072 \tabularnewline
-149267.404560060 \tabularnewline
38947.3539988643 \tabularnewline
58613.0612764424 \tabularnewline
16074.4618904611 \tabularnewline
-41563.0054471834 \tabularnewline
-15970.5960147386 \tabularnewline
-47563.9543407556 \tabularnewline
59595.3586354662 \tabularnewline
65897.8398819312 \tabularnewline
-166489.283404707 \tabularnewline
46312.3277180123 \tabularnewline
-15952.8741348381 \tabularnewline
-87780.6497165994 \tabularnewline
134744.171799582 \tabularnewline
75232.8110734522 \tabularnewline
24408.7565122573 \tabularnewline
-15406.1428224565 \tabularnewline
-3766.75426023186 \tabularnewline
27197.2235783702 \tabularnewline
-46777.2891917652 \tabularnewline
-82472.8205054209 \tabularnewline
-35154.7186673461 \tabularnewline
-46946.8689402549 \tabularnewline
-43641.5350003634 \tabularnewline
-54920.7077875747 \tabularnewline
54905.4050480593 \tabularnewline
-10509.5838823069 \tabularnewline
-13706.8037712772 \tabularnewline
-42347.6091932919 \tabularnewline
-28990.4681222014 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105790&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]867.887509505204[/C][/ROW]
[ROW][C]-2250.28069475706[/C][/ROW]
[ROW][C]33618.356965784[/C][/ROW]
[ROW][C]9954.34444592902[/C][/ROW]
[ROW][C]354.191747925620[/C][/ROW]
[ROW][C]18882.4059848456[/C][/ROW]
[ROW][C]20229.4309143634[/C][/ROW]
[ROW][C]268402.415800735[/C][/ROW]
[ROW][C]-113346.928018808[/C][/ROW]
[ROW][C]-45016.3930870888[/C][/ROW]
[ROW][C]35069.8581927414[/C][/ROW]
[ROW][C]58531.0974683435[/C][/ROW]
[ROW][C]-77256.37719663[/C][/ROW]
[ROW][C]-31473.5947870219[/C][/ROW]
[ROW][C]-52391.0075621489[/C][/ROW]
[ROW][C]32854.9859435725[/C][/ROW]
[ROW][C]101107.732677222[/C][/ROW]
[ROW][C]-176275.960473942[/C][/ROW]
[ROW][C]79531.885537752[/C][/ROW]
[ROW][C]-176414.252881799[/C][/ROW]
[ROW][C]151290.583018155[/C][/ROW]
[ROW][C]167731.591284544[/C][/ROW]
[ROW][C]143237.123984833[/C][/ROW]
[ROW][C]80251.9641959216[/C][/ROW]
[ROW][C]118735.723920888[/C][/ROW]
[ROW][C]75035.8242647686[/C][/ROW]
[ROW][C]19198.3069299239[/C][/ROW]
[ROW][C]-36364.565244121[/C][/ROW]
[ROW][C]-36170.5788957657[/C][/ROW]
[ROW][C]-109567.395737977[/C][/ROW]
[ROW][C]-100783.334905072[/C][/ROW]
[ROW][C]-149267.404560060[/C][/ROW]
[ROW][C]38947.3539988643[/C][/ROW]
[ROW][C]58613.0612764424[/C][/ROW]
[ROW][C]16074.4618904611[/C][/ROW]
[ROW][C]-41563.0054471834[/C][/ROW]
[ROW][C]-15970.5960147386[/C][/ROW]
[ROW][C]-47563.9543407556[/C][/ROW]
[ROW][C]59595.3586354662[/C][/ROW]
[ROW][C]65897.8398819312[/C][/ROW]
[ROW][C]-166489.283404707[/C][/ROW]
[ROW][C]46312.3277180123[/C][/ROW]
[ROW][C]-15952.8741348381[/C][/ROW]
[ROW][C]-87780.6497165994[/C][/ROW]
[ROW][C]134744.171799582[/C][/ROW]
[ROW][C]75232.8110734522[/C][/ROW]
[ROW][C]24408.7565122573[/C][/ROW]
[ROW][C]-15406.1428224565[/C][/ROW]
[ROW][C]-3766.75426023186[/C][/ROW]
[ROW][C]27197.2235783702[/C][/ROW]
[ROW][C]-46777.2891917652[/C][/ROW]
[ROW][C]-82472.8205054209[/C][/ROW]
[ROW][C]-35154.7186673461[/C][/ROW]
[ROW][C]-46946.8689402549[/C][/ROW]
[ROW][C]-43641.5350003634[/C][/ROW]
[ROW][C]-54920.7077875747[/C][/ROW]
[ROW][C]54905.4050480593[/C][/ROW]
[ROW][C]-10509.5838823069[/C][/ROW]
[ROW][C]-13706.8037712772[/C][/ROW]
[ROW][C]-42347.6091932919[/C][/ROW]
[ROW][C]-28990.4681222014[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105790&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105790&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:

Estimated ARIMA Residuals
Value
867.887509505204
-2250.28069475706
33618.356965784
9954.34444592902
354.191747925620
18882.4059848456
20229.4309143634
268402.415800735
-113346.928018808
-45016.3930870888
35069.8581927414
58531.0974683435
-77256.37719663
-31473.5947870219
-52391.0075621489
32854.9859435725
101107.732677222
-176275.960473942
79531.885537752
-176414.252881799
151290.583018155
167731.591284544
143237.123984833
80251.9641959216
118735.723920888
75035.8242647686
19198.3069299239
-36364.565244121
-36170.5788957657
-109567.395737977
-100783.334905072
-149267.404560060
38947.3539988643
58613.0612764424
16074.4618904611
-41563.0054471834
-15970.5960147386
-47563.9543407556
59595.3586354662
65897.8398819312
-166489.283404707
46312.3277180123
-15952.8741348381
-87780.6497165994
134744.171799582
75232.8110734522
24408.7565122573
-15406.1428224565
-3766.75426023186
27197.2235783702
-46777.2891917652
-82472.8205054209
-35154.7186673461
-46946.8689402549
-43641.5350003634
-54920.7077875747
54905.4050480593
-10509.5838823069
-13706.8037712772
-42347.6091932919
-28990.4681222014


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; 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')