Free Statistics

of Irreproducible Research!

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 computationThu, 01 Dec 2011 08:48:24 -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/01/t1322747332u2yez8oweeg7bng.htm/, Retrieved Fri, 29 Mar 2024 06:50:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149596, Retrieved Fri, 29 Mar 2024 06:50:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [WS 9: Autocorrela...] [2011-12-01 12:39:51] [570fce4db58fd7864ac807c4286d6e49]
- R P   [(Partial) Autocorrelation Function] [WS9: Autocorrelat...] [2011-12-01 12:46:27] [570fce4db58fd7864ac807c4286d6e49]
- RMP     [Spectral Analysis] [WS: Sprectral ana...] [2011-12-01 12:59:39] [570fce4db58fd7864ac807c4286d6e49]
- RMP         [ARIMA Backward Selection] [WS 9: Arima Backw...] [2011-12-01 13:48:24] [8e74b77f6c0ad21b554439c4ef29c61b] [Current]
- RM            [ARIMA Forecasting] [WS 9: Arima Forec...] [2011-12-01 14:10:39] [570fce4db58fd7864ac807c4286d6e49]
- R P             [ARIMA Forecasting] [voorbeeld oplossing] [2012-12-11 09:52:54] [3cf53fa4571e8f9aa5147d4ada4d99ce]
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Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149596&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149596&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149596&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







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=149596&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=149596&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149596&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
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
-538.308879766516
7737.06068464101
1937.35293843064
3.34163163734387
5094.27124531449
4139.59620102801
53754.5804754626
-24955.1175899735
-7277.45355763449
16096.6963079771
6048.17410051161
-17573.9044609804
-4356.52050808393
-11531.0026354526
5421.91016287626
21384.0313404548
-40246.7378107774
20054.0548730578
-33216.6942193556
26493.7223860244
38842.6283775447
23781.6303065519
22996.6548266228
30652.6470206993
18776.2437577688
6552.15405320634
-4697.16822501165
-5604.45482650297
-23416.3904266027
-21407.157274022
-30483.4620069195
6330.46528128495
10143.2273779231
-500.081336390963
-6990.71020049155
-2025.75232290643
-11356.2996772513
12938.8764316561
14022.3080348612
-37986.058950459
13264.3329761091
-3084.88301375964
-23842.4655272307
32125.1962870231
13478.2567234654
1838.17207262059
2340.96080657564
40.1542445126033
6568.47219496602
-10323.4114726533
-18029.7238542377
-6745.95309121852
-11315.0861929685
-11366.6203518145
-11408.623083603
10262.6359884203
-4353.44745998422
-4285.53902328882
-7649.75245821134
-7651.35052619167

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
221.114422853726 \tabularnewline
-538.308879766516 \tabularnewline
7737.06068464101 \tabularnewline
1937.35293843064 \tabularnewline
3.34163163734387 \tabularnewline
5094.27124531449 \tabularnewline
4139.59620102801 \tabularnewline
53754.5804754626 \tabularnewline
-24955.1175899735 \tabularnewline
-7277.45355763449 \tabularnewline
16096.6963079771 \tabularnewline
6048.17410051161 \tabularnewline
-17573.9044609804 \tabularnewline
-4356.52050808393 \tabularnewline
-11531.0026354526 \tabularnewline
5421.91016287626 \tabularnewline
21384.0313404548 \tabularnewline
-40246.7378107774 \tabularnewline
20054.0548730578 \tabularnewline
-33216.6942193556 \tabularnewline
26493.7223860244 \tabularnewline
38842.6283775447 \tabularnewline
23781.6303065519 \tabularnewline
22996.6548266228 \tabularnewline
30652.6470206993 \tabularnewline
18776.2437577688 \tabularnewline
6552.15405320634 \tabularnewline
-4697.16822501165 \tabularnewline
-5604.45482650297 \tabularnewline
-23416.3904266027 \tabularnewline
-21407.157274022 \tabularnewline
-30483.4620069195 \tabularnewline
6330.46528128495 \tabularnewline
10143.2273779231 \tabularnewline
-500.081336390963 \tabularnewline
-6990.71020049155 \tabularnewline
-2025.75232290643 \tabularnewline
-11356.2996772513 \tabularnewline
12938.8764316561 \tabularnewline
14022.3080348612 \tabularnewline
-37986.058950459 \tabularnewline
13264.3329761091 \tabularnewline
-3084.88301375964 \tabularnewline
-23842.4655272307 \tabularnewline
32125.1962870231 \tabularnewline
13478.2567234654 \tabularnewline
1838.17207262059 \tabularnewline
2340.96080657564 \tabularnewline
40.1542445126033 \tabularnewline
6568.47219496602 \tabularnewline
-10323.4114726533 \tabularnewline
-18029.7238542377 \tabularnewline
-6745.95309121852 \tabularnewline
-11315.0861929685 \tabularnewline
-11366.6203518145 \tabularnewline
-11408.623083603 \tabularnewline
10262.6359884203 \tabularnewline
-4353.44745998422 \tabularnewline
-4285.53902328882 \tabularnewline
-7649.75245821134 \tabularnewline
-7651.35052619167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149596&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]221.114422853726[/C][/ROW]
[ROW][C]-538.308879766516[/C][/ROW]
[ROW][C]7737.06068464101[/C][/ROW]
[ROW][C]1937.35293843064[/C][/ROW]
[ROW][C]3.34163163734387[/C][/ROW]
[ROW][C]5094.27124531449[/C][/ROW]
[ROW][C]4139.59620102801[/C][/ROW]
[ROW][C]53754.5804754626[/C][/ROW]
[ROW][C]-24955.1175899735[/C][/ROW]
[ROW][C]-7277.45355763449[/C][/ROW]
[ROW][C]16096.6963079771[/C][/ROW]
[ROW][C]6048.17410051161[/C][/ROW]
[ROW][C]-17573.9044609804[/C][/ROW]
[ROW][C]-4356.52050808393[/C][/ROW]
[ROW][C]-11531.0026354526[/C][/ROW]
[ROW][C]5421.91016287626[/C][/ROW]
[ROW][C]21384.0313404548[/C][/ROW]
[ROW][C]-40246.7378107774[/C][/ROW]
[ROW][C]20054.0548730578[/C][/ROW]
[ROW][C]-33216.6942193556[/C][/ROW]
[ROW][C]26493.7223860244[/C][/ROW]
[ROW][C]38842.6283775447[/C][/ROW]
[ROW][C]23781.6303065519[/C][/ROW]
[ROW][C]22996.6548266228[/C][/ROW]
[ROW][C]30652.6470206993[/C][/ROW]
[ROW][C]18776.2437577688[/C][/ROW]
[ROW][C]6552.15405320634[/C][/ROW]
[ROW][C]-4697.16822501165[/C][/ROW]
[ROW][C]-5604.45482650297[/C][/ROW]
[ROW][C]-23416.3904266027[/C][/ROW]
[ROW][C]-21407.157274022[/C][/ROW]
[ROW][C]-30483.4620069195[/C][/ROW]
[ROW][C]6330.46528128495[/C][/ROW]
[ROW][C]10143.2273779231[/C][/ROW]
[ROW][C]-500.081336390963[/C][/ROW]
[ROW][C]-6990.71020049155[/C][/ROW]
[ROW][C]-2025.75232290643[/C][/ROW]
[ROW][C]-11356.2996772513[/C][/ROW]
[ROW][C]12938.8764316561[/C][/ROW]
[ROW][C]14022.3080348612[/C][/ROW]
[ROW][C]-37986.058950459[/C][/ROW]
[ROW][C]13264.3329761091[/C][/ROW]
[ROW][C]-3084.88301375964[/C][/ROW]
[ROW][C]-23842.4655272307[/C][/ROW]
[ROW][C]32125.1962870231[/C][/ROW]
[ROW][C]13478.2567234654[/C][/ROW]
[ROW][C]1838.17207262059[/C][/ROW]
[ROW][C]2340.96080657564[/C][/ROW]
[ROW][C]40.1542445126033[/C][/ROW]
[ROW][C]6568.47219496602[/C][/ROW]
[ROW][C]-10323.4114726533[/C][/ROW]
[ROW][C]-18029.7238542377[/C][/ROW]
[ROW][C]-6745.95309121852[/C][/ROW]
[ROW][C]-11315.0861929685[/C][/ROW]
[ROW][C]-11366.6203518145[/C][/ROW]
[ROW][C]-11408.623083603[/C][/ROW]
[ROW][C]10262.6359884203[/C][/ROW]
[ROW][C]-4353.44745998422[/C][/ROW]
[ROW][C]-4285.53902328882[/C][/ROW]
[ROW][C]-7649.75245821134[/C][/ROW]
[ROW][C]-7651.35052619167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149596&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149596&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
221.114422853726
-538.308879766516
7737.06068464101
1937.35293843064
3.34163163734387
5094.27124531449
4139.59620102801
53754.5804754626
-24955.1175899735
-7277.45355763449
16096.6963079771
6048.17410051161
-17573.9044609804
-4356.52050808393
-11531.0026354526
5421.91016287626
21384.0313404548
-40246.7378107774
20054.0548730578
-33216.6942193556
26493.7223860244
38842.6283775447
23781.6303065519
22996.6548266228
30652.6470206993
18776.2437577688
6552.15405320634
-4697.16822501165
-5604.45482650297
-23416.3904266027
-21407.157274022
-30483.4620069195
6330.46528128495
10143.2273779231
-500.081336390963
-6990.71020049155
-2025.75232290643
-11356.2996772513
12938.8764316561
14022.3080348612
-37986.058950459
13264.3329761091
-3084.88301375964
-23842.4655272307
32125.1962870231
13478.2567234654
1838.17207262059
2340.96080657564
40.1542445126033
6568.47219496602
-10323.4114726533
-18029.7238542377
-6745.95309121852
-11315.0861929685
-11366.6203518145
-11408.623083603
10262.6359884203
-4353.44745998422
-4285.53902328882
-7649.75245821134
-7651.35052619167



Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ;
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')