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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 computationTue, 20 Jan 2015 10:01:14 +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/2015/Jan/20/t1421748088auspvn3m1bpyusg.htm/, Retrieved Thu, 31 Oct 2024 22:53:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=274987, Retrieved Thu, 31 Oct 2024 22:53:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [] [2015-01-20 09:47:29] [5efa6717cfe6505454df834acc87b53b]
- R P   [(Partial) Autocorrelation Function] [] [2015-01-20 09:48:31] [5efa6717cfe6505454df834acc87b53b]
- RM        [ARIMA Backward Selection] [] [2015-01-20 10:01:14] [3a47cc92becfffb332a48f98591f891c] [Current]
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Dataseries X:
1775
2197
2920
4240
5415
6136
6719
6234
7152
3646
2165
2803
1615
2350
3350
3536
5834
6767
5993
7276
5641
3477
2247
2466
1567
2237
2598
3729
5715
5776
5852
6878
5488
3583
2054
2282
1552
2261
2446
3519
5161
5085
5711
6057
5224
3363
1899
2115
1491
2061
2419
3430
4778
4862
6176
5664
5529
3418
1941
2402
1579
2146
2462
3695
4831
5134
6250
5760
6249
2917
1741
2359
1511
2059
2635
2867
4403
5720
4502
5749
5627
2846
1762
2429
1169
2154
2249
2687
4359
5382
4459
6398
4596
3024
1887
2070
1351
2218
2461
3028
4784
4975
4607
6249
4809
3157
1910
2228
1594
2467
2222
3607
4685
4962
5770
5480
5000
3228
1993
2288
1588
2105
2191
3591
4668
4885
5822
5599
5340
3082
2010
2301




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 7 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=274987&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=274987&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274987&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 time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.01820.27270.578-0.18710.2014-0.0904-0.813
(p-val)(0.9155 )(3e-04 )(0 )(0.4219 )(0.2398 )(0.497 )(0 )
Estimates ( 2 )00.27470.5858-0.16530.1996-0.0892-0.8102
(p-val)(NA )(2e-04 )(0 )(0.1272 )(0.2435 )(0.5016 )(0 )
Estimates ( 3 )00.27190.5919-0.16980.2470-0.8899
(p-val)(NA )(2e-04 )(0 )(0.1118 )(0.1118 )(NA )(0 )
Estimates ( 4 )00.27620.59500.27620-1
(p-val)(NA )(1e-04 )(0 )(NA )(0.0109 )(NA )(0.0035 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(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 ) & 0.0182 & 0.2727 & 0.578 & -0.1871 & 0.2014 & -0.0904 & -0.813 \tabularnewline
(p-val) & (0.9155 ) & (3e-04 ) & (0 ) & (0.4219 ) & (0.2398 ) & (0.497 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.2747 & 0.5858 & -0.1653 & 0.1996 & -0.0892 & -0.8102 \tabularnewline
(p-val) & (NA ) & (2e-04 ) & (0 ) & (0.1272 ) & (0.2435 ) & (0.5016 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.2719 & 0.5919 & -0.1698 & 0.247 & 0 & -0.8899 \tabularnewline
(p-val) & (NA ) & (2e-04 ) & (0 ) & (0.1118 ) & (0.1118 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.2762 & 0.595 & 0 & 0.2762 & 0 & -1 \tabularnewline
(p-val) & (NA ) & (1e-04 ) & (0 ) & (NA ) & (0.0109 ) & (NA ) & (0.0035 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (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=274987&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]0.0182[/C][C]0.2727[/C][C]0.578[/C][C]-0.1871[/C][C]0.2014[/C][C]-0.0904[/C][C]-0.813[/C][/ROW]
[ROW][C](p-val)[/C][C](0.9155 )[/C][C](3e-04 )[/C][C](0 )[/C][C](0.4219 )[/C][C](0.2398 )[/C][C](0.497 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.2747[/C][C]0.5858[/C][C]-0.1653[/C][C]0.1996[/C][C]-0.0892[/C][C]-0.8102[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](2e-04 )[/C][C](0 )[/C][C](0.1272 )[/C][C](0.2435 )[/C][C](0.5016 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.2719[/C][C]0.5919[/C][C]-0.1698[/C][C]0.247[/C][C]0[/C][C]-0.8899[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](2e-04 )[/C][C](0 )[/C][C](0.1118 )[/C][C](0.1118 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.2762[/C][C]0.595[/C][C]0[/C][C]0.2762[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](1e-04 )[/C][C](0 )[/C][C](NA )[/C][C](0.0109 )[/C][C](NA )[/C][C](0.0035 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/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 ( 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=274987&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274987&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 )0.01820.27270.578-0.18710.2014-0.0904-0.813
(p-val)(0.9155 )(3e-04 )(0 )(0.4219 )(0.2398 )(0.497 )(0 )
Estimates ( 2 )00.27470.5858-0.16530.1996-0.0892-0.8102
(p-val)(NA )(2e-04 )(0 )(0.1272 )(0.2435 )(0.5016 )(0 )
Estimates ( 3 )00.27190.5919-0.16980.2470-0.8899
(p-val)(NA )(2e-04 )(0 )(0.1118 )(0.1118 )(NA )(0 )
Estimates ( 4 )00.27620.59500.27620-1
(p-val)(NA )(1e-04 )(0 )(NA )(0.0109 )(NA )(0.0035 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(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
0.000452101212319012
0.00298814643657628
-0.00198302656694066
-0.00495997645258872
0.00431997749839595
0.00112967245875038
-0.00232821185258389
0.000414462625008296
-0.00271999682686692
0.00810734270282199
0.00268049854642658
3.23107808188679e-05
-0.00140391971074126
0.00165260969355763
0.00130434845441032
0.00554905103271759
-0.000339248606825232
-0.00299248792437661
-0.0010295803182525
0.00226276910705381
-0.000574643112747923
0.000647964988379823
-0.00139891315803451
0.00188030742206528
0.00268802245061609
0.00173849221785849
-0.00293719419495516
0.00220450810687743
0.00258907800845296
0.00270266653190641
0.00288114530333053
0.000348557836666773
0.000563393571638723
0.000146610955128317
0.00030856621715267
0.00106162661984596
0.00274136936711237
0.000828473929192548
-0.000248254870413949
4.92812255526136e-05
0.000108880167839232
0.00150930990938724
0.00272005174545696
-0.00384298491239769
-0.000201423306809019
-0.00216052824289604
0.000123641569994393
-0.00111414559280874
-0.00259509794018491
-0.00195950217182052
-0.000641558758770762
0.00369318992081205
-0.000125352853209593
0.0018614264692623
0.00108823904209858
-0.000665240517317461
0.000351525323117392
-0.00454764081522771
0.00682011431170838
0.00743042587423353
0.00223750993219234
-0.00366786500594596
-0.00219674375866624
-0.00206866204043163
0.00858468150666519
0.00644690394601141
-0.00366736108731272
0.00429641658888327
9.71652186502794e-05
0.000305000055734757
-0.0023502770670041
0.00141980448670042
-0.0035440494373747
0.00859108570452375
-0.000894602181201521
0.0039864861115566
0.00216840356587686
0.00344095695888495
-0.0045113820603612
0.000542535539601645
-0.00550182120921429
0.00535396388153494
-0.00107400102643868
-0.00106594166254373
0.000625123595608407
0.000536557249563046
-0.00301905454309711
-0.00457563808159686
0.000627044097598684
0.000760004020003512
0.00350862390895236
0.00489425558018258
-0.000244518644756271
0.000165342816541489
-0.00258809809617749
-0.00110543406942372
-0.00196393632867674
-0.00543917014822028
-0.0068434214649695
0.00571059269012957
0.000675575343617377
0.00366439704401677
0.0012234447995292
-0.00193170560826342
0.00301998931488584
0.00241966636017297
0.0016078983645631
-0.00492084027011051
-0.0019074729499128
-0.0018278169578166
0.00417851273142209
0.0058309809736514
-0.000721636819751516
-0.00195858493707616
0.00056103054097246
-0.00102013157971888
0.000361737567422847
-0.0015466157436813
0.00251472917713922
-0.00202682282498639
-0.000446908923590404

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.000452101212319012 \tabularnewline
0.00298814643657628 \tabularnewline
-0.00198302656694066 \tabularnewline
-0.00495997645258872 \tabularnewline
0.00431997749839595 \tabularnewline
0.00112967245875038 \tabularnewline
-0.00232821185258389 \tabularnewline
0.000414462625008296 \tabularnewline
-0.00271999682686692 \tabularnewline
0.00810734270282199 \tabularnewline
0.00268049854642658 \tabularnewline
3.23107808188679e-05 \tabularnewline
-0.00140391971074126 \tabularnewline
0.00165260969355763 \tabularnewline
0.00130434845441032 \tabularnewline
0.00554905103271759 \tabularnewline
-0.000339248606825232 \tabularnewline
-0.00299248792437661 \tabularnewline
-0.0010295803182525 \tabularnewline
0.00226276910705381 \tabularnewline
-0.000574643112747923 \tabularnewline
0.000647964988379823 \tabularnewline
-0.00139891315803451 \tabularnewline
0.00188030742206528 \tabularnewline
0.00268802245061609 \tabularnewline
0.00173849221785849 \tabularnewline
-0.00293719419495516 \tabularnewline
0.00220450810687743 \tabularnewline
0.00258907800845296 \tabularnewline
0.00270266653190641 \tabularnewline
0.00288114530333053 \tabularnewline
0.000348557836666773 \tabularnewline
0.000563393571638723 \tabularnewline
0.000146610955128317 \tabularnewline
0.00030856621715267 \tabularnewline
0.00106162661984596 \tabularnewline
0.00274136936711237 \tabularnewline
0.000828473929192548 \tabularnewline
-0.000248254870413949 \tabularnewline
4.92812255526136e-05 \tabularnewline
0.000108880167839232 \tabularnewline
0.00150930990938724 \tabularnewline
0.00272005174545696 \tabularnewline
-0.00384298491239769 \tabularnewline
-0.000201423306809019 \tabularnewline
-0.00216052824289604 \tabularnewline
0.000123641569994393 \tabularnewline
-0.00111414559280874 \tabularnewline
-0.00259509794018491 \tabularnewline
-0.00195950217182052 \tabularnewline
-0.000641558758770762 \tabularnewline
0.00369318992081205 \tabularnewline
-0.000125352853209593 \tabularnewline
0.0018614264692623 \tabularnewline
0.00108823904209858 \tabularnewline
-0.000665240517317461 \tabularnewline
0.000351525323117392 \tabularnewline
-0.00454764081522771 \tabularnewline
0.00682011431170838 \tabularnewline
0.00743042587423353 \tabularnewline
0.00223750993219234 \tabularnewline
-0.00366786500594596 \tabularnewline
-0.00219674375866624 \tabularnewline
-0.00206866204043163 \tabularnewline
0.00858468150666519 \tabularnewline
0.00644690394601141 \tabularnewline
-0.00366736108731272 \tabularnewline
0.00429641658888327 \tabularnewline
9.71652186502794e-05 \tabularnewline
0.000305000055734757 \tabularnewline
-0.0023502770670041 \tabularnewline
0.00141980448670042 \tabularnewline
-0.0035440494373747 \tabularnewline
0.00859108570452375 \tabularnewline
-0.000894602181201521 \tabularnewline
0.0039864861115566 \tabularnewline
0.00216840356587686 \tabularnewline
0.00344095695888495 \tabularnewline
-0.0045113820603612 \tabularnewline
0.000542535539601645 \tabularnewline
-0.00550182120921429 \tabularnewline
0.00535396388153494 \tabularnewline
-0.00107400102643868 \tabularnewline
-0.00106594166254373 \tabularnewline
0.000625123595608407 \tabularnewline
0.000536557249563046 \tabularnewline
-0.00301905454309711 \tabularnewline
-0.00457563808159686 \tabularnewline
0.000627044097598684 \tabularnewline
0.000760004020003512 \tabularnewline
0.00350862390895236 \tabularnewline
0.00489425558018258 \tabularnewline
-0.000244518644756271 \tabularnewline
0.000165342816541489 \tabularnewline
-0.00258809809617749 \tabularnewline
-0.00110543406942372 \tabularnewline
-0.00196393632867674 \tabularnewline
-0.00543917014822028 \tabularnewline
-0.0068434214649695 \tabularnewline
0.00571059269012957 \tabularnewline
0.000675575343617377 \tabularnewline
0.00366439704401677 \tabularnewline
0.0012234447995292 \tabularnewline
-0.00193170560826342 \tabularnewline
0.00301998931488584 \tabularnewline
0.00241966636017297 \tabularnewline
0.0016078983645631 \tabularnewline
-0.00492084027011051 \tabularnewline
-0.0019074729499128 \tabularnewline
-0.0018278169578166 \tabularnewline
0.00417851273142209 \tabularnewline
0.0058309809736514 \tabularnewline
-0.000721636819751516 \tabularnewline
-0.00195858493707616 \tabularnewline
0.00056103054097246 \tabularnewline
-0.00102013157971888 \tabularnewline
0.000361737567422847 \tabularnewline
-0.0015466157436813 \tabularnewline
0.00251472917713922 \tabularnewline
-0.00202682282498639 \tabularnewline
-0.000446908923590404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=274987&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.000452101212319012[/C][/ROW]
[ROW][C]0.00298814643657628[/C][/ROW]
[ROW][C]-0.00198302656694066[/C][/ROW]
[ROW][C]-0.00495997645258872[/C][/ROW]
[ROW][C]0.00431997749839595[/C][/ROW]
[ROW][C]0.00112967245875038[/C][/ROW]
[ROW][C]-0.00232821185258389[/C][/ROW]
[ROW][C]0.000414462625008296[/C][/ROW]
[ROW][C]-0.00271999682686692[/C][/ROW]
[ROW][C]0.00810734270282199[/C][/ROW]
[ROW][C]0.00268049854642658[/C][/ROW]
[ROW][C]3.23107808188679e-05[/C][/ROW]
[ROW][C]-0.00140391971074126[/C][/ROW]
[ROW][C]0.00165260969355763[/C][/ROW]
[ROW][C]0.00130434845441032[/C][/ROW]
[ROW][C]0.00554905103271759[/C][/ROW]
[ROW][C]-0.000339248606825232[/C][/ROW]
[ROW][C]-0.00299248792437661[/C][/ROW]
[ROW][C]-0.0010295803182525[/C][/ROW]
[ROW][C]0.00226276910705381[/C][/ROW]
[ROW][C]-0.000574643112747923[/C][/ROW]
[ROW][C]0.000647964988379823[/C][/ROW]
[ROW][C]-0.00139891315803451[/C][/ROW]
[ROW][C]0.00188030742206528[/C][/ROW]
[ROW][C]0.00268802245061609[/C][/ROW]
[ROW][C]0.00173849221785849[/C][/ROW]
[ROW][C]-0.00293719419495516[/C][/ROW]
[ROW][C]0.00220450810687743[/C][/ROW]
[ROW][C]0.00258907800845296[/C][/ROW]
[ROW][C]0.00270266653190641[/C][/ROW]
[ROW][C]0.00288114530333053[/C][/ROW]
[ROW][C]0.000348557836666773[/C][/ROW]
[ROW][C]0.000563393571638723[/C][/ROW]
[ROW][C]0.000146610955128317[/C][/ROW]
[ROW][C]0.00030856621715267[/C][/ROW]
[ROW][C]0.00106162661984596[/C][/ROW]
[ROW][C]0.00274136936711237[/C][/ROW]
[ROW][C]0.000828473929192548[/C][/ROW]
[ROW][C]-0.000248254870413949[/C][/ROW]
[ROW][C]4.92812255526136e-05[/C][/ROW]
[ROW][C]0.000108880167839232[/C][/ROW]
[ROW][C]0.00150930990938724[/C][/ROW]
[ROW][C]0.00272005174545696[/C][/ROW]
[ROW][C]-0.00384298491239769[/C][/ROW]
[ROW][C]-0.000201423306809019[/C][/ROW]
[ROW][C]-0.00216052824289604[/C][/ROW]
[ROW][C]0.000123641569994393[/C][/ROW]
[ROW][C]-0.00111414559280874[/C][/ROW]
[ROW][C]-0.00259509794018491[/C][/ROW]
[ROW][C]-0.00195950217182052[/C][/ROW]
[ROW][C]-0.000641558758770762[/C][/ROW]
[ROW][C]0.00369318992081205[/C][/ROW]
[ROW][C]-0.000125352853209593[/C][/ROW]
[ROW][C]0.0018614264692623[/C][/ROW]
[ROW][C]0.00108823904209858[/C][/ROW]
[ROW][C]-0.000665240517317461[/C][/ROW]
[ROW][C]0.000351525323117392[/C][/ROW]
[ROW][C]-0.00454764081522771[/C][/ROW]
[ROW][C]0.00682011431170838[/C][/ROW]
[ROW][C]0.00743042587423353[/C][/ROW]
[ROW][C]0.00223750993219234[/C][/ROW]
[ROW][C]-0.00366786500594596[/C][/ROW]
[ROW][C]-0.00219674375866624[/C][/ROW]
[ROW][C]-0.00206866204043163[/C][/ROW]
[ROW][C]0.00858468150666519[/C][/ROW]
[ROW][C]0.00644690394601141[/C][/ROW]
[ROW][C]-0.00366736108731272[/C][/ROW]
[ROW][C]0.00429641658888327[/C][/ROW]
[ROW][C]9.71652186502794e-05[/C][/ROW]
[ROW][C]0.000305000055734757[/C][/ROW]
[ROW][C]-0.0023502770670041[/C][/ROW]
[ROW][C]0.00141980448670042[/C][/ROW]
[ROW][C]-0.0035440494373747[/C][/ROW]
[ROW][C]0.00859108570452375[/C][/ROW]
[ROW][C]-0.000894602181201521[/C][/ROW]
[ROW][C]0.0039864861115566[/C][/ROW]
[ROW][C]0.00216840356587686[/C][/ROW]
[ROW][C]0.00344095695888495[/C][/ROW]
[ROW][C]-0.0045113820603612[/C][/ROW]
[ROW][C]0.000542535539601645[/C][/ROW]
[ROW][C]-0.00550182120921429[/C][/ROW]
[ROW][C]0.00535396388153494[/C][/ROW]
[ROW][C]-0.00107400102643868[/C][/ROW]
[ROW][C]-0.00106594166254373[/C][/ROW]
[ROW][C]0.000625123595608407[/C][/ROW]
[ROW][C]0.000536557249563046[/C][/ROW]
[ROW][C]-0.00301905454309711[/C][/ROW]
[ROW][C]-0.00457563808159686[/C][/ROW]
[ROW][C]0.000627044097598684[/C][/ROW]
[ROW][C]0.000760004020003512[/C][/ROW]
[ROW][C]0.00350862390895236[/C][/ROW]
[ROW][C]0.00489425558018258[/C][/ROW]
[ROW][C]-0.000244518644756271[/C][/ROW]
[ROW][C]0.000165342816541489[/C][/ROW]
[ROW][C]-0.00258809809617749[/C][/ROW]
[ROW][C]-0.00110543406942372[/C][/ROW]
[ROW][C]-0.00196393632867674[/C][/ROW]
[ROW][C]-0.00543917014822028[/C][/ROW]
[ROW][C]-0.0068434214649695[/C][/ROW]
[ROW][C]0.00571059269012957[/C][/ROW]
[ROW][C]0.000675575343617377[/C][/ROW]
[ROW][C]0.00366439704401677[/C][/ROW]
[ROW][C]0.0012234447995292[/C][/ROW]
[ROW][C]-0.00193170560826342[/C][/ROW]
[ROW][C]0.00301998931488584[/C][/ROW]
[ROW][C]0.00241966636017297[/C][/ROW]
[ROW][C]0.0016078983645631[/C][/ROW]
[ROW][C]-0.00492084027011051[/C][/ROW]
[ROW][C]-0.0019074729499128[/C][/ROW]
[ROW][C]-0.0018278169578166[/C][/ROW]
[ROW][C]0.00417851273142209[/C][/ROW]
[ROW][C]0.0058309809736514[/C][/ROW]
[ROW][C]-0.000721636819751516[/C][/ROW]
[ROW][C]-0.00195858493707616[/C][/ROW]
[ROW][C]0.00056103054097246[/C][/ROW]
[ROW][C]-0.00102013157971888[/C][/ROW]
[ROW][C]0.000361737567422847[/C][/ROW]
[ROW][C]-0.0015466157436813[/C][/ROW]
[ROW][C]0.00251472917713922[/C][/ROW]
[ROW][C]-0.00202682282498639[/C][/ROW]
[ROW][C]-0.000446908923590404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=274987&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274987&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
0.000452101212319012
0.00298814643657628
-0.00198302656694066
-0.00495997645258872
0.00431997749839595
0.00112967245875038
-0.00232821185258389
0.000414462625008296
-0.00271999682686692
0.00810734270282199
0.00268049854642658
3.23107808188679e-05
-0.00140391971074126
0.00165260969355763
0.00130434845441032
0.00554905103271759
-0.000339248606825232
-0.00299248792437661
-0.0010295803182525
0.00226276910705381
-0.000574643112747923
0.000647964988379823
-0.00139891315803451
0.00188030742206528
0.00268802245061609
0.00173849221785849
-0.00293719419495516
0.00220450810687743
0.00258907800845296
0.00270266653190641
0.00288114530333053
0.000348557836666773
0.000563393571638723
0.000146610955128317
0.00030856621715267
0.00106162661984596
0.00274136936711237
0.000828473929192548
-0.000248254870413949
4.92812255526136e-05
0.000108880167839232
0.00150930990938724
0.00272005174545696
-0.00384298491239769
-0.000201423306809019
-0.00216052824289604
0.000123641569994393
-0.00111414559280874
-0.00259509794018491
-0.00195950217182052
-0.000641558758770762
0.00369318992081205
-0.000125352853209593
0.0018614264692623
0.00108823904209858
-0.000665240517317461
0.000351525323117392
-0.00454764081522771
0.00682011431170838
0.00743042587423353
0.00223750993219234
-0.00366786500594596
-0.00219674375866624
-0.00206866204043163
0.00858468150666519
0.00644690394601141
-0.00366736108731272
0.00429641658888327
9.71652186502794e-05
0.000305000055734757
-0.0023502770670041
0.00141980448670042
-0.0035440494373747
0.00859108570452375
-0.000894602181201521
0.0039864861115566
0.00216840356587686
0.00344095695888495
-0.0045113820603612
0.000542535539601645
-0.00550182120921429
0.00535396388153494
-0.00107400102643868
-0.00106594166254373
0.000625123595608407
0.000536557249563046
-0.00301905454309711
-0.00457563808159686
0.000627044097598684
0.000760004020003512
0.00350862390895236
0.00489425558018258
-0.000244518644756271
0.000165342816541489
-0.00258809809617749
-0.00110543406942372
-0.00196393632867674
-0.00543917014822028
-0.0068434214649695
0.00571059269012957
0.000675575343617377
0.00366439704401677
0.0012234447995292
-0.00193170560826342
0.00301998931488584
0.00241966636017297
0.0016078983645631
-0.00492084027011051
-0.0019074729499128
-0.0018278169578166
0.00417851273142209
0.0058309809736514
-0.000721636819751516
-0.00195858493707616
0.00056103054097246
-0.00102013157971888
0.000361737567422847
-0.0015466157436813
0.00251472917713922
-0.00202682282498639
-0.000446908923590404



Parameters (Session):
par1 = Default ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = FALSE ; par2 = -0.1 ; 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')