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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 computationFri, 17 Dec 2010 15:45:37 +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/17/t1292600644bkd03lyvk70o6ob.htm/, Retrieved Mon, 06 May 2024 12:44:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111537, Retrieved Mon, 06 May 2024 12:44:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
F   PD      [ARIMA Backward Selection] [Workshop 6 'Aanta...] [2010-12-14 18:47:02] [40c8b935cbad1b0be3c22a481f9723f7]
- R P           [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-17 15:45:37] [a4848c79f7a98c5639a543e143e21e11] [Current]
-                 [ARIMA Backward Selection] [Arima backward se...] [2010-12-17 16:26:58] [c895532cb7349383dee5125244983cc8]
-                 [ARIMA Backward Selection] [] [2010-12-17 19:41:15] [916599f00c9c716123aa8433d9efa14f]
-   PD            [ARIMA Backward Selection] [ARIMA BACKWARD Se...] [2010-12-26 13:20:22] [c895532cb7349383dee5125244983cc8]
- RMP               [ARIMA Forecasting] [TUM Arima Forecas...] [2010-12-26 14:35:20] [c895532cb7349383dee5125244983cc8]
- RMP               [ARIMA Forecasting] [] [2010-12-26 14:45:55] [c895532cb7349383dee5125244983cc8]
-                     [ARIMA Forecasting] [TUMBLER] [2010-12-26 14:49:10] [c895532cb7349383dee5125244983cc8]
-                       [ARIMA Forecasting] [TUM (ARIMA forecast)] [2010-12-28 09:30:57] [75b8170d590d2aca2c97c1862bb2167f]
-   P                   [ARIMA Forecasting] [berekening 16] [2010-12-28 14:20:00] [916599f00c9c716123aa8433d9efa14f]
- RM D                [Kendall tau Correlation Matrix] [Pearson correlati...] [2010-12-26 15:06:39] [c895532cb7349383dee5125244983cc8]
-                       [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2010-12-26 15:10:04] [c895532cb7349383dee5125244983cc8]
-    D                    [Kendall tau Correlation Matrix] [Pearson correlati...] [2010-12-26 15:15:56] [c895532cb7349383dee5125244983cc8]
-                           [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2010-12-26 15:18:36] [c895532cb7349383dee5125244983cc8]
-   P                         [Kendall tau Correlation Matrix] [Kendal Tau Correl...] [2010-12-28 08:33:14] [75b8170d590d2aca2c97c1862bb2167f]
-   P                         [Kendall tau Correlation Matrix] [berekening 7] [2010-12-28 13:47:30] [916599f00c9c716123aa8433d9efa14f]
-   P                       [Kendall tau Correlation Matrix] [pearson correlati...] [2010-12-28 08:28:09] [75b8170d590d2aca2c97c1862bb2167f]
-   P                       [Kendall tau Correlation Matrix] [berekening 6] [2010-12-28 13:45:17] [916599f00c9c716123aa8433d9efa14f]
-                   [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-28 09:23:51] [75b8170d590d2aca2c97c1862bb2167f]
-   P               [ARIMA Backward Selection] [berekening 15] [2010-12-28 14:16:57] [916599f00c9c716123aa8433d9efa14f]
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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 time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111537&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111537&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111537&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.39760.07540.20110.1714-0.706-0.39320.1477
(p-val)(0.4482 )(0.6763 )(0.1141 )(0.7473 )(0.2508 )(0.1628 )(0.826 )
Estimates ( 2 )-0.4050.07920.19640.1828-0.5728-0.33710
(p-val)(0.4542 )(0.6611 )(0.1191 )(0.7385 )(0 )(0.037 )(NA )
Estimates ( 3 )-0.22940.11740.18250-0.5663-0.33360
(p-val)(0.0764 )(0.3664 )(0.1455 )(NA )(0 )(0.0389 )(NA )
Estimates ( 4 )-0.250400.15740-0.5771-0.35190
(p-val)(0.0522 )(NA )(0.201 )(NA )(0 )(0.0269 )(NA )
Estimates ( 5 )-0.2308000-0.5816-0.35130
(p-val)(0.0729 )(NA )(NA )(NA )(0 )(0.0258 )(NA )
Estimates ( 6 )0000-0.599-0.40340
(p-val)(NA )(NA )(NA )(NA )(0 )(0.008 )(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 ) & -0.3976 & 0.0754 & 0.2011 & 0.1714 & -0.706 & -0.3932 & 0.1477 \tabularnewline
(p-val) & (0.4482 ) & (0.6763 ) & (0.1141 ) & (0.7473 ) & (0.2508 ) & (0.1628 ) & (0.826 ) \tabularnewline
Estimates ( 2 ) & -0.405 & 0.0792 & 0.1964 & 0.1828 & -0.5728 & -0.3371 & 0 \tabularnewline
(p-val) & (0.4542 ) & (0.6611 ) & (0.1191 ) & (0.7385 ) & (0 ) & (0.037 ) & (NA ) \tabularnewline
Estimates ( 3 ) & -0.2294 & 0.1174 & 0.1825 & 0 & -0.5663 & -0.3336 & 0 \tabularnewline
(p-val) & (0.0764 ) & (0.3664 ) & (0.1455 ) & (NA ) & (0 ) & (0.0389 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.2504 & 0 & 0.1574 & 0 & -0.5771 & -0.3519 & 0 \tabularnewline
(p-val) & (0.0522 ) & (NA ) & (0.201 ) & (NA ) & (0 ) & (0.0269 ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.2308 & 0 & 0 & 0 & -0.5816 & -0.3513 & 0 \tabularnewline
(p-val) & (0.0729 ) & (NA ) & (NA ) & (NA ) & (0 ) & (0.0258 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & -0.599 & -0.4034 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) & (0.008 ) & (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=111537&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]-0.3976[/C][C]0.0754[/C][C]0.2011[/C][C]0.1714[/C][C]-0.706[/C][C]-0.3932[/C][C]0.1477[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4482 )[/C][C](0.6763 )[/C][C](0.1141 )[/C][C](0.7473 )[/C][C](0.2508 )[/C][C](0.1628 )[/C][C](0.826 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.405[/C][C]0.0792[/C][C]0.1964[/C][C]0.1828[/C][C]-0.5728[/C][C]-0.3371[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4542 )[/C][C](0.6611 )[/C][C](0.1191 )[/C][C](0.7385 )[/C][C](0 )[/C][C](0.037 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.2294[/C][C]0.1174[/C][C]0.1825[/C][C]0[/C][C]-0.5663[/C][C]-0.3336[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0764 )[/C][C](0.3664 )[/C][C](0.1455 )[/C][C](NA )[/C][C](0 )[/C][C](0.0389 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.2504[/C][C]0[/C][C]0.1574[/C][C]0[/C][C]-0.5771[/C][C]-0.3519[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0522 )[/C][C](NA )[/C][C](0.201 )[/C][C](NA )[/C][C](0 )[/C][C](0.0269 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.2308[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.5816[/C][C]-0.3513[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0729 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0258 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.599[/C][C]-0.4034[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.008 )[/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=111537&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111537&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 )-0.39760.07540.20110.1714-0.706-0.39320.1477
(p-val)(0.4482 )(0.6763 )(0.1141 )(0.7473 )(0.2508 )(0.1628 )(0.826 )
Estimates ( 2 )-0.4050.07920.19640.1828-0.5728-0.33710
(p-val)(0.4542 )(0.6611 )(0.1191 )(0.7385 )(0 )(0.037 )(NA )
Estimates ( 3 )-0.22940.11740.18250-0.5663-0.33360
(p-val)(0.0764 )(0.3664 )(0.1455 )(NA )(0 )(0.0389 )(NA )
Estimates ( 4 )-0.250400.15740-0.5771-0.35190
(p-val)(0.0522 )(NA )(0.201 )(NA )(0 )(0.0269 )(NA )
Estimates ( 5 )-0.2308000-0.5816-0.35130
(p-val)(0.0729 )(NA )(NA )(NA )(0 )(0.0258 )(NA )
Estimates ( 6 )0000-0.599-0.40340
(p-val)(NA )(NA )(NA )(NA )(0 )(0.008 )(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
221.114422853726
-538.308879951932
7737.06068740495
1937.35293976010
3.34163191259175
5094.27124721966
4139.59620295102
53754.5804953636
-24955.1175945395
-7277.45356148658
16096.6963130391
6048.17410375177
-17573.9044668129
-4356.52051051619
-11531.002639376
5421.9101652659
21384.0313512680
-40246.7378286115
20054.0548834480
-33216.6942175080
26493.7223889768
38842.6283957788
23781.6303249722
22996.6548403874
30652.6470299862
18776.2437635076
6552.15404418888
-4697.1682284801
-5604.45482970198
-23416.3904279392
-21407.1572830589
-30483.4620778166
6330.46531213668
10143.2273826381
-500.081362385127
-6990.71021239593
-2025.75230280327
-11356.2996737013
12938.8764500236
14022.3080302091
-37986.0589762063
13264.3330322636
-3084.88303283765
-23842.4654467707
32125.1962366045
13478.2566694121
1838.17205359612
2340.96078281233
40.1541953518172
6568.47217042808
-10323.4114900420
-18029.7238463970
-6745.95306308515
-11315.0861981536
-11366.620310509
-11408.6230644906
10262.6359942141
-4353.44744418858
-4285.53900274332
-7649.75243185324
-7651.35050224673

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
221.114422853726 \tabularnewline
-538.308879951932 \tabularnewline
7737.06068740495 \tabularnewline
1937.35293976010 \tabularnewline
3.34163191259175 \tabularnewline
5094.27124721966 \tabularnewline
4139.59620295102 \tabularnewline
53754.5804953636 \tabularnewline
-24955.1175945395 \tabularnewline
-7277.45356148658 \tabularnewline
16096.6963130391 \tabularnewline
6048.17410375177 \tabularnewline
-17573.9044668129 \tabularnewline
-4356.52051051619 \tabularnewline
-11531.002639376 \tabularnewline
5421.9101652659 \tabularnewline
21384.0313512680 \tabularnewline
-40246.7378286115 \tabularnewline
20054.0548834480 \tabularnewline
-33216.6942175080 \tabularnewline
26493.7223889768 \tabularnewline
38842.6283957788 \tabularnewline
23781.6303249722 \tabularnewline
22996.6548403874 \tabularnewline
30652.6470299862 \tabularnewline
18776.2437635076 \tabularnewline
6552.15404418888 \tabularnewline
-4697.1682284801 \tabularnewline
-5604.45482970198 \tabularnewline
-23416.3904279392 \tabularnewline
-21407.1572830589 \tabularnewline
-30483.4620778166 \tabularnewline
6330.46531213668 \tabularnewline
10143.2273826381 \tabularnewline
-500.081362385127 \tabularnewline
-6990.71021239593 \tabularnewline
-2025.75230280327 \tabularnewline
-11356.2996737013 \tabularnewline
12938.8764500236 \tabularnewline
14022.3080302091 \tabularnewline
-37986.0589762063 \tabularnewline
13264.3330322636 \tabularnewline
-3084.88303283765 \tabularnewline
-23842.4654467707 \tabularnewline
32125.1962366045 \tabularnewline
13478.2566694121 \tabularnewline
1838.17205359612 \tabularnewline
2340.96078281233 \tabularnewline
40.1541953518172 \tabularnewline
6568.47217042808 \tabularnewline
-10323.4114900420 \tabularnewline
-18029.7238463970 \tabularnewline
-6745.95306308515 \tabularnewline
-11315.0861981536 \tabularnewline
-11366.620310509 \tabularnewline
-11408.6230644906 \tabularnewline
10262.6359942141 \tabularnewline
-4353.44744418858 \tabularnewline
-4285.53900274332 \tabularnewline
-7649.75243185324 \tabularnewline
-7651.35050224673 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111537&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]221.114422853726[/C][/ROW]
[ROW][C]-538.308879951932[/C][/ROW]
[ROW][C]7737.06068740495[/C][/ROW]
[ROW][C]1937.35293976010[/C][/ROW]
[ROW][C]3.34163191259175[/C][/ROW]
[ROW][C]5094.27124721966[/C][/ROW]
[ROW][C]4139.59620295102[/C][/ROW]
[ROW][C]53754.5804953636[/C][/ROW]
[ROW][C]-24955.1175945395[/C][/ROW]
[ROW][C]-7277.45356148658[/C][/ROW]
[ROW][C]16096.6963130391[/C][/ROW]
[ROW][C]6048.17410375177[/C][/ROW]
[ROW][C]-17573.9044668129[/C][/ROW]
[ROW][C]-4356.52051051619[/C][/ROW]
[ROW][C]-11531.002639376[/C][/ROW]
[ROW][C]5421.9101652659[/C][/ROW]
[ROW][C]21384.0313512680[/C][/ROW]
[ROW][C]-40246.7378286115[/C][/ROW]
[ROW][C]20054.0548834480[/C][/ROW]
[ROW][C]-33216.6942175080[/C][/ROW]
[ROW][C]26493.7223889768[/C][/ROW]
[ROW][C]38842.6283957788[/C][/ROW]
[ROW][C]23781.6303249722[/C][/ROW]
[ROW][C]22996.6548403874[/C][/ROW]
[ROW][C]30652.6470299862[/C][/ROW]
[ROW][C]18776.2437635076[/C][/ROW]
[ROW][C]6552.15404418888[/C][/ROW]
[ROW][C]-4697.1682284801[/C][/ROW]
[ROW][C]-5604.45482970198[/C][/ROW]
[ROW][C]-23416.3904279392[/C][/ROW]
[ROW][C]-21407.1572830589[/C][/ROW]
[ROW][C]-30483.4620778166[/C][/ROW]
[ROW][C]6330.46531213668[/C][/ROW]
[ROW][C]10143.2273826381[/C][/ROW]
[ROW][C]-500.081362385127[/C][/ROW]
[ROW][C]-6990.71021239593[/C][/ROW]
[ROW][C]-2025.75230280327[/C][/ROW]
[ROW][C]-11356.2996737013[/C][/ROW]
[ROW][C]12938.8764500236[/C][/ROW]
[ROW][C]14022.3080302091[/C][/ROW]
[ROW][C]-37986.0589762063[/C][/ROW]
[ROW][C]13264.3330322636[/C][/ROW]
[ROW][C]-3084.88303283765[/C][/ROW]
[ROW][C]-23842.4654467707[/C][/ROW]
[ROW][C]32125.1962366045[/C][/ROW]
[ROW][C]13478.2566694121[/C][/ROW]
[ROW][C]1838.17205359612[/C][/ROW]
[ROW][C]2340.96078281233[/C][/ROW]
[ROW][C]40.1541953518172[/C][/ROW]
[ROW][C]6568.47217042808[/C][/ROW]
[ROW][C]-10323.4114900420[/C][/ROW]
[ROW][C]-18029.7238463970[/C][/ROW]
[ROW][C]-6745.95306308515[/C][/ROW]
[ROW][C]-11315.0861981536[/C][/ROW]
[ROW][C]-11366.620310509[/C][/ROW]
[ROW][C]-11408.6230644906[/C][/ROW]
[ROW][C]10262.6359942141[/C][/ROW]
[ROW][C]-4353.44744418858[/C][/ROW]
[ROW][C]-4285.53900274332[/C][/ROW]
[ROW][C]-7649.75243185324[/C][/ROW]
[ROW][C]-7651.35050224673[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111537&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111537&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
221.114422853726
-538.308879951932
7737.06068740495
1937.35293976010
3.34163191259175
5094.27124721966
4139.59620295102
53754.5804953636
-24955.1175945395
-7277.45356148658
16096.6963130391
6048.17410375177
-17573.9044668129
-4356.52051051619
-11531.002639376
5421.9101652659
21384.0313512680
-40246.7378286115
20054.0548834480
-33216.6942175080
26493.7223889768
38842.6283957788
23781.6303249722
22996.6548403874
30652.6470299862
18776.2437635076
6552.15404418888
-4697.1682284801
-5604.45482970198
-23416.3904279392
-21407.1572830589
-30483.4620778166
6330.46531213668
10143.2273826381
-500.081362385127
-6990.71021239593
-2025.75230280327
-11356.2996737013
12938.8764500236
14022.3080302091
-37986.0589762063
13264.3330322636
-3084.88303283765
-23842.4654467707
32125.1962366045
13478.2566694121
1838.17205359612
2340.96078281233
40.1541953518172
6568.47217042808
-10323.4114900420
-18029.7238463970
-6745.95306308515
-11315.0861981536
-11366.620310509
-11408.6230644906
10262.6359942141
-4353.44744418858
-4285.53900274332
-7649.75243185324
-7651.35050224673


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 0.9 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.9 ; 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')