Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Wed, 07 Dec 2011 16:48:04 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/07/t1323294604mfgwon2ebcchosa.htm/, Retrieved Thu, 02 May 2024 23:09:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152753, Retrieved Thu, 02 May 2024 23:09:56 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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 Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- RMP         [ARIMA Backward Selection] [ARIMA Backward se...] [2011-12-07 21:48:04] [614dd89c388120cee0dd25886939832b] [Current]
-   PD          [ARIMA Backward Selection] [ARIMA Backward se...] [2011-12-21 11:02:42] [15a5dd358825f04074b70fc847ec6454] 
- RMPD          [Skewness and Kurtosis Test] [Skewness ARIMA Ba...] [2011-12-21 11:26:17] [15a5dd358825f04074b70fc847ec6454] 
- RMPD          [ARIMA Forecasting] [ARIMA forecast] [2011-12-21 12:13:55] [15a5dd358825f04074b70fc847ec6454] 
- RMPD          [One Sample Tests about the Mean] [P-waarde Mean Res...] [2011-12-21 12:24:26] [15a5dd358825f04074b70fc847ec6454] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 13 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152753&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152753&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152753&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ma1	sar1	sar2	sma1
Estimates ( 1 )	-0.1972	0.2672	0.3756	-0.2202	-0.155	-0.4655
(p-val)	(0.3445 )	(0 )	(0.0816 )	(0.0527 )	(0.0704 )	(0 )
Estimates ( 2 )	0	0.2372	0.1759	-0.2174	-0.1539	-0.4625
(p-val)	(NA )	(0 )	(0.0011 )	(0.0581 )	(0.0736 )	(0 )
Estimates ( 3 )	0	0.249	0.1634	-0.0733	0	-0.6023
(p-val)	(NA )	(0 )	(0.0023 )	(0.3679 )	(NA )	(0 )
Estimates ( 4 )	0	0.2493	0.1624	0	0	-0.6436
(p-val)	(NA )	(0 )	(0.0024 )	(NA )	(NA )	(0 )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.1972 & 0.2672 & 0.3756 & -0.2202 & -0.155 & -0.4655 \tabularnewline
(p-val) & (0.3445 ) & (0 ) & (0.0816 ) & (0.0527 ) & (0.0704 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.2372 & 0.1759 & -0.2174 & -0.1539 & -0.4625 \tabularnewline
(p-val) & (NA ) & (0 ) & (0.0011 ) & (0.0581 ) & (0.0736 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.249 & 0.1634 & -0.0733 & 0 & -0.6023 \tabularnewline
(p-val) & (NA ) & (0 ) & (0.0023 ) & (0.3679 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.2493 & 0.1624 & 0 & 0 & -0.6436 \tabularnewline
(p-val) & (NA ) & (0 ) & (0.0024 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152753&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.1972[/C][C]0.2672[/C][C]0.3756[/C][C]-0.2202[/C][C]-0.155[/C][C]-0.4655[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3445 )[/C][C](0 )[/C][C](0.0816 )[/C][C](0.0527 )[/C][C](0.0704 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.2372[/C][C]0.1759[/C][C]-0.2174[/C][C]-0.1539[/C][C]-0.4625[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](0.0011 )[/C][C](0.0581 )[/C][C](0.0736 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.249[/C][C]0.1634[/C][C]-0.0733[/C][C]0[/C][C]-0.6023[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](0.0023 )[/C][C](0.3679 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.2493[/C][C]0.1624[/C][C]0[/C][C]0[/C][C]-0.6436[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](0.0024 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[ROW][C]Estimates ( 8 )[/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][/ROW]
[ROW][C]Estimates ( 9 )[/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][/ROW]
[ROW][C]Estimates ( 10 )[/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][/ROW]
[ROW][C]Estimates ( 11 )[/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152753&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152753&T=1

As an alternative you can also use a QR Code:

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

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ma1	sar1	sar2	sma1
Estimates ( 1 )	-0.1972	0.2672	0.3756	-0.2202	-0.155	-0.4655
(p-val)	(0.3445 )	(0 )	(0.0816 )	(0.0527 )	(0.0704 )	(0 )
Estimates ( 2 )	0	0.2372	0.1759	-0.2174	-0.1539	-0.4625
(p-val)	(NA )	(0 )	(0.0011 )	(0.0581 )	(0.0736 )	(0 )
Estimates ( 3 )	0	0.249	0.1634	-0.0733	0	-0.6023
(p-val)	(NA )	(0 )	(0.0023 )	(0.3679 )	(NA )	(0 )
Estimates ( 4 )	0	0.2493	0.1624	0	0	-0.6436
(p-val)	(NA )	(0 )	(0.0024 )	(NA )	(NA )	(0 )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

Estimated ARIMA Residuals
Value
-5.69125818634438
153.404545269607
86.8052573113031
177.678587180251
1213.3390175033
97.7457484860814
732.819909374387
-974.817261935692
-558.780363629298
1191.33428379609
-1083.25055928752
-56.4012649737862
1137.53368126308
-849.140905721284
-1182.41593492207
-1070.20693590831
-522.031248975964
157.684832456268
-1033.69539409139
-653.540621101504
773.477071896674
-754.887588151068
696.682140203249
-139.294167253701
-1940.24982866273
-801.242649800034
841.527380795645
309.025669025242
-32.4948800747961
-268.198255032012
-306.358617087264
808.197916483968
643.993122513349
-158.381662325412
133.07330747612
-725.943761843786
-726.155078682582
-4.91725085954767
186.204862355087
707.194580552416
226.138089711016
-582.435962846777
98.4055102758542
781.011054567865
-252.603634385802
-213.196128898146
-59.2985758659246
-99.1683153779977
-174.506511889446
-624.254095983475
600.605496473968
721.974522011149
-337.672801528529
-492.500434394621
21.0755546246684
504.240673494961
421.76169911264
147.051627342013
444.379564911949
653.308085278649
1518.39171183081
689.151600649557
190.055149201069
-348.104196764366
-324.796626681026
-489.99117445742
103.041604330493
150.349261331502
10.9050833718297
-1047.83196781848
159.195220687229
-267.946311078638
-28.0513252003064
-584.308138358071
-324.515074924782
382.832480703161
-743.931255768504
171.736833661325
-415.672444597014
336.102551718071
-274.665588353461
480.092256812743
81.2914085276426
-83.265624310665
-775.487288233887
-74.0560540944577
636.88456792596
-570.693737048945
893.150259026221
543.707202530903
-644.543298333958
-813.821264610736
-15.5314803988812
311.709976403422
634.871250050552
-74.5703207877202
-427.148676115448
-421.438943418893
-229.998207516121
302.803653957453
491.856800189114
534.030605856914
-626.133864437512
-99.6169749809487
378.077406609341
310.346605661322
799.665010674464
217.573346729545
1530.54127357361
1786.81137426767
-200.779213149814
-202.314324997762
-638.519633205498
494.311718924116
99.4637572932313
-962.445587342164
-1330.61895247104
-157.572087138145
-577.624955108074
821.486426713696
-106.022312963944
-729.744647939876
-849.248428559885
-1408.90495044368
233.853565680408
633.653573810298
-68.1634809049503
287.806109670011
250.380140174687
473.597337466749
174.923135223061
-973.31189913661
-315.916802976185
-1029.16024970714
1657.01567366902
-560.86054162948
-389.500299583618
1389.48844057488
-578.105248858693
287.936295189159
-381.688682596264
895.637709911661
355.01964462019
1015.41893955457
605.955923413197
569.121494025257
-969.505661473554
-593.756944918196
108.645440390911
734.183454148667
-669.867661114097
-926.477267296986
-153.037395755565
-269.148532840416
-615.341734149281
-94.3723415733578
-248.154195265586
-716.234019170693
30.2058068961398
315.946679051834
873.175300921261
-1146.72048520709
-385.361990806634
1421.16357928292
-280.948127786303
-719.904679222899
757.792761429679
-489.278278723775
428.605524959329
674.212320827032
-1219.79510058714
156.268258077022
422.534182301395
278.212996241946
-429.274275641372
-410.098346929771
374.295237262815
129.069729448022
320.657254020969
-687.774351542897
-54.5153295308085
-347.874943786245
62.7802399153693
381.852701306434
-706.763615993845
1252.54034215665
-1310.32225909432
388.504750431329
421.086137903453
-62.6106155014123
-796.412202340837
164.038011877325
-610.320983411693
584.50831311212
-531.638478896478
733.714176296397
-299.121685037614
286.825986383511
-273.899304817213
-84.1759836771615
154.389609208269
-101.982556543801
-408.587836428454
-343.370331309172
-701.250319864386
-470.844944533489
958.731396760053
453.431258047785
485.915923558768
-225.466057775799
48.5663516459432
91.8286320731172
33.7137807912436
153.251207107826
-464.220398605333
196.098669753289
-394.789323696842
-43.7243814538033
313.04938660092
154.462457201954
-219.124478630576
863.367070575425
463.438778053972
-451.022589307632
652.710915786962
295.530096955881
-887.992257232453
-480.940937737128
-392.547683555353
691.460491681346
-204.108010319212
-333.198248146225
40.6495592449484
967.240138819078
185.025870251011
-713.420917832531
232.535011026026
-67.5277040782914
-181.1079395975
-220.720066526237
-126.288628260779
-21.0109944016558
379.57609043486
374.766755675895
-314.373276237076
-99.4537374974244
788.372825739957
-75.6213388626333
559.497327011602
-284.189878766725
-683.121935890147
162.294279410278
919.379177327279
729.249119731921
179.921549594815
21.9321734649701
-145.744930095926
991.356399852837
375.621331247704
-364.133967072924
409.36922152826
28.3638064445848
933.5148028091
139.059815345398
855.534288989729
-621.507368632965
-606.810640299147
-723.024918005984
-269.714778308271
767.673111384589
275.284529051472
-212.960121578282
-704.264811257005
-584.582720020127
701.135367189048
-213.626582636313
580.537791914279
-563.3379693568
-139.443247415477
-605.699917037588
-383.586231216524
466.931787007076
-103.418026601342
-206.145840147477
-315.93432992797
-115.142476902916
-1078.24909523715
-41.6919003425761
-89.0960177061997
420.785371768443
-393.666986794174
184.625265574151
-235.320388044186
-207.12834227433
-95.2381113399751
-60.541323749804
353.423348774305
-730.399114701629
731.090563307096
354.759194755907
752.491832338693
-53.8646266979655
-723.353387181321
-137.354262836393
596.275812110066
593.164777954613
209.86022915931
-461.874761261099
1384.86482139786
37.0868807603157
1522.42738949785
1443.7942958882
5254.74332275509
-1104.44480910037
-110.875463093869
-1040.32020912807
-144.685727223869
709.4136911784
-967.379706592312
-1029.35896555631
-467.029456432509
-84.5955347744035
-822.015826928184
-276.153137212186
44.1157438409237
-914.797437068488
-1299.04729176591
-458.77889668006
-1020.0505997341
1973.6185094081
685.311826828772
-94.6356273632656
-1255.44790891877
171.683863401485
540.491292994316
-685.941239105504
-1120.88039483305
1326.5848853066
-1207.42151133624
-2010.35601200772
335.548910556906
1218.08770925681
-1381.56907787313
681.226215299578
-544.914580808644
-57.5794690515088
-115.973597699092
-1782.43929324256
96.0958525536029
-566.100927723699
694.24941456724
-134.506376436733
510.671790567481
-1490.94509055079
1687.35382078051
-661.917933196597
3.09372300760508
-191.723122008264
-143.792352482297
1001.08409820926

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-5.69125818634438 \tabularnewline
153.404545269607 \tabularnewline
86.8052573113031 \tabularnewline
177.678587180251 \tabularnewline
1213.3390175033 \tabularnewline
97.7457484860814 \tabularnewline
732.819909374387 \tabularnewline
-974.817261935692 \tabularnewline
-558.780363629298 \tabularnewline
1191.33428379609 \tabularnewline
-1083.25055928752 \tabularnewline
-56.4012649737862 \tabularnewline
1137.53368126308 \tabularnewline
-849.140905721284 \tabularnewline
-1182.41593492207 \tabularnewline
-1070.20693590831 \tabularnewline
-522.031248975964 \tabularnewline
157.684832456268 \tabularnewline
-1033.69539409139 \tabularnewline
-653.540621101504 \tabularnewline
773.477071896674 \tabularnewline
-754.887588151068 \tabularnewline
696.682140203249 \tabularnewline
-139.294167253701 \tabularnewline
-1940.24982866273 \tabularnewline
-801.242649800034 \tabularnewline
841.527380795645 \tabularnewline
309.025669025242 \tabularnewline
-32.4948800747961 \tabularnewline
-268.198255032012 \tabularnewline
-306.358617087264 \tabularnewline
808.197916483968 \tabularnewline
643.993122513349 \tabularnewline
-158.381662325412 \tabularnewline
133.07330747612 \tabularnewline
-725.943761843786 \tabularnewline
-726.155078682582 \tabularnewline
-4.91725085954767 \tabularnewline
186.204862355087 \tabularnewline
707.194580552416 \tabularnewline
226.138089711016 \tabularnewline
-582.435962846777 \tabularnewline
98.4055102758542 \tabularnewline
781.011054567865 \tabularnewline
-252.603634385802 \tabularnewline
-213.196128898146 \tabularnewline
-59.2985758659246 \tabularnewline
-99.1683153779977 \tabularnewline
-174.506511889446 \tabularnewline
-624.254095983475 \tabularnewline
600.605496473968 \tabularnewline
721.974522011149 \tabularnewline
-337.672801528529 \tabularnewline
-492.500434394621 \tabularnewline
21.0755546246684 \tabularnewline
504.240673494961 \tabularnewline
421.76169911264 \tabularnewline
147.051627342013 \tabularnewline
444.379564911949 \tabularnewline
653.308085278649 \tabularnewline
1518.39171183081 \tabularnewline
689.151600649557 \tabularnewline
190.055149201069 \tabularnewline
-348.104196764366 \tabularnewline
-324.796626681026 \tabularnewline
-489.99117445742 \tabularnewline
103.041604330493 \tabularnewline
150.349261331502 \tabularnewline
10.9050833718297 \tabularnewline
-1047.83196781848 \tabularnewline
159.195220687229 \tabularnewline
-267.946311078638 \tabularnewline
-28.0513252003064 \tabularnewline
-584.308138358071 \tabularnewline
-324.515074924782 \tabularnewline
382.832480703161 \tabularnewline
-743.931255768504 \tabularnewline
171.736833661325 \tabularnewline
-415.672444597014 \tabularnewline
336.102551718071 \tabularnewline
-274.665588353461 \tabularnewline
480.092256812743 \tabularnewline
81.2914085276426 \tabularnewline
-83.265624310665 \tabularnewline
-775.487288233887 \tabularnewline
-74.0560540944577 \tabularnewline
636.88456792596 \tabularnewline
-570.693737048945 \tabularnewline
893.150259026221 \tabularnewline
543.707202530903 \tabularnewline
-644.543298333958 \tabularnewline
-813.821264610736 \tabularnewline
-15.5314803988812 \tabularnewline
311.709976403422 \tabularnewline
634.871250050552 \tabularnewline
-74.5703207877202 \tabularnewline
-427.148676115448 \tabularnewline
-421.438943418893 \tabularnewline
-229.998207516121 \tabularnewline
302.803653957453 \tabularnewline
491.856800189114 \tabularnewline
534.030605856914 \tabularnewline
-626.133864437512 \tabularnewline
-99.6169749809487 \tabularnewline
378.077406609341 \tabularnewline
310.346605661322 \tabularnewline
799.665010674464 \tabularnewline
217.573346729545 \tabularnewline
1530.54127357361 \tabularnewline
1786.81137426767 \tabularnewline
-200.779213149814 \tabularnewline
-202.314324997762 \tabularnewline
-638.519633205498 \tabularnewline
494.311718924116 \tabularnewline
99.4637572932313 \tabularnewline
-962.445587342164 \tabularnewline
-1330.61895247104 \tabularnewline
-157.572087138145 \tabularnewline
-577.624955108074 \tabularnewline
821.486426713696 \tabularnewline
-106.022312963944 \tabularnewline
-729.744647939876 \tabularnewline
-849.248428559885 \tabularnewline
-1408.90495044368 \tabularnewline
233.853565680408 \tabularnewline
633.653573810298 \tabularnewline
-68.1634809049503 \tabularnewline
287.806109670011 \tabularnewline
250.380140174687 \tabularnewline
473.597337466749 \tabularnewline
174.923135223061 \tabularnewline
-973.31189913661 \tabularnewline
-315.916802976185 \tabularnewline
-1029.16024970714 \tabularnewline
1657.01567366902 \tabularnewline
-560.86054162948 \tabularnewline
-389.500299583618 \tabularnewline
1389.48844057488 \tabularnewline
-578.105248858693 \tabularnewline
287.936295189159 \tabularnewline
-381.688682596264 \tabularnewline
895.637709911661 \tabularnewline
355.01964462019 \tabularnewline
1015.41893955457 \tabularnewline
605.955923413197 \tabularnewline
569.121494025257 \tabularnewline
-969.505661473554 \tabularnewline
-593.756944918196 \tabularnewline
108.645440390911 \tabularnewline
734.183454148667 \tabularnewline
-669.867661114097 \tabularnewline
-926.477267296986 \tabularnewline
-153.037395755565 \tabularnewline
-269.148532840416 \tabularnewline
-615.341734149281 \tabularnewline
-94.3723415733578 \tabularnewline
-248.154195265586 \tabularnewline
-716.234019170693 \tabularnewline
30.2058068961398 \tabularnewline
315.946679051834 \tabularnewline
873.175300921261 \tabularnewline
-1146.72048520709 \tabularnewline
-385.361990806634 \tabularnewline
1421.16357928292 \tabularnewline
-280.948127786303 \tabularnewline
-719.904679222899 \tabularnewline
757.792761429679 \tabularnewline
-489.278278723775 \tabularnewline
428.605524959329 \tabularnewline
674.212320827032 \tabularnewline
-1219.79510058714 \tabularnewline
156.268258077022 \tabularnewline
422.534182301395 \tabularnewline
278.212996241946 \tabularnewline
-429.274275641372 \tabularnewline
-410.098346929771 \tabularnewline
374.295237262815 \tabularnewline
129.069729448022 \tabularnewline
320.657254020969 \tabularnewline
-687.774351542897 \tabularnewline
-54.5153295308085 \tabularnewline
-347.874943786245 \tabularnewline
62.7802399153693 \tabularnewline
381.852701306434 \tabularnewline
-706.763615993845 \tabularnewline
1252.54034215665 \tabularnewline
-1310.32225909432 \tabularnewline
388.504750431329 \tabularnewline
421.086137903453 \tabularnewline
-62.6106155014123 \tabularnewline
-796.412202340837 \tabularnewline
164.038011877325 \tabularnewline
-610.320983411693 \tabularnewline
584.50831311212 \tabularnewline
-531.638478896478 \tabularnewline
733.714176296397 \tabularnewline
-299.121685037614 \tabularnewline
286.825986383511 \tabularnewline
-273.899304817213 \tabularnewline
-84.1759836771615 \tabularnewline
154.389609208269 \tabularnewline
-101.982556543801 \tabularnewline
-408.587836428454 \tabularnewline
-343.370331309172 \tabularnewline
-701.250319864386 \tabularnewline
-470.844944533489 \tabularnewline
958.731396760053 \tabularnewline
453.431258047785 \tabularnewline
485.915923558768 \tabularnewline
-225.466057775799 \tabularnewline
48.5663516459432 \tabularnewline
91.8286320731172 \tabularnewline
33.7137807912436 \tabularnewline
153.251207107826 \tabularnewline
-464.220398605333 \tabularnewline
196.098669753289 \tabularnewline
-394.789323696842 \tabularnewline
-43.7243814538033 \tabularnewline
313.04938660092 \tabularnewline
154.462457201954 \tabularnewline
-219.124478630576 \tabularnewline
863.367070575425 \tabularnewline
463.438778053972 \tabularnewline
-451.022589307632 \tabularnewline
652.710915786962 \tabularnewline
295.530096955881 \tabularnewline
-887.992257232453 \tabularnewline
-480.940937737128 \tabularnewline
-392.547683555353 \tabularnewline
691.460491681346 \tabularnewline
-204.108010319212 \tabularnewline
-333.198248146225 \tabularnewline
40.6495592449484 \tabularnewline
967.240138819078 \tabularnewline
185.025870251011 \tabularnewline
-713.420917832531 \tabularnewline
232.535011026026 \tabularnewline
-67.5277040782914 \tabularnewline
-181.1079395975 \tabularnewline
-220.720066526237 \tabularnewline
-126.288628260779 \tabularnewline
-21.0109944016558 \tabularnewline
379.57609043486 \tabularnewline
374.766755675895 \tabularnewline
-314.373276237076 \tabularnewline
-99.4537374974244 \tabularnewline
788.372825739957 \tabularnewline
-75.6213388626333 \tabularnewline
559.497327011602 \tabularnewline
-284.189878766725 \tabularnewline
-683.121935890147 \tabularnewline
162.294279410278 \tabularnewline
919.379177327279 \tabularnewline
729.249119731921 \tabularnewline
179.921549594815 \tabularnewline
21.9321734649701 \tabularnewline
-145.744930095926 \tabularnewline
991.356399852837 \tabularnewline
375.621331247704 \tabularnewline
-364.133967072924 \tabularnewline
409.36922152826 \tabularnewline
28.3638064445848 \tabularnewline
933.5148028091 \tabularnewline
139.059815345398 \tabularnewline
855.534288989729 \tabularnewline
-621.507368632965 \tabularnewline
-606.810640299147 \tabularnewline
-723.024918005984 \tabularnewline
-269.714778308271 \tabularnewline
767.673111384589 \tabularnewline
275.284529051472 \tabularnewline
-212.960121578282 \tabularnewline
-704.264811257005 \tabularnewline
-584.582720020127 \tabularnewline
701.135367189048 \tabularnewline
-213.626582636313 \tabularnewline
580.537791914279 \tabularnewline
-563.3379693568 \tabularnewline
-139.443247415477 \tabularnewline
-605.699917037588 \tabularnewline
-383.586231216524 \tabularnewline
466.931787007076 \tabularnewline
-103.418026601342 \tabularnewline
-206.145840147477 \tabularnewline
-315.93432992797 \tabularnewline
-115.142476902916 \tabularnewline
-1078.24909523715 \tabularnewline
-41.6919003425761 \tabularnewline
-89.0960177061997 \tabularnewline
420.785371768443 \tabularnewline
-393.666986794174 \tabularnewline
184.625265574151 \tabularnewline
-235.320388044186 \tabularnewline
-207.12834227433 \tabularnewline
-95.2381113399751 \tabularnewline
-60.541323749804 \tabularnewline
353.423348774305 \tabularnewline
-730.399114701629 \tabularnewline
731.090563307096 \tabularnewline
354.759194755907 \tabularnewline
752.491832338693 \tabularnewline
-53.8646266979655 \tabularnewline
-723.353387181321 \tabularnewline
-137.354262836393 \tabularnewline
596.275812110066 \tabularnewline
593.164777954613 \tabularnewline
209.86022915931 \tabularnewline
-461.874761261099 \tabularnewline
1384.86482139786 \tabularnewline
37.0868807603157 \tabularnewline
1522.42738949785 \tabularnewline
1443.7942958882 \tabularnewline
5254.74332275509 \tabularnewline
-1104.44480910037 \tabularnewline
-110.875463093869 \tabularnewline
-1040.32020912807 \tabularnewline
-144.685727223869 \tabularnewline
709.4136911784 \tabularnewline
-967.379706592312 \tabularnewline
-1029.35896555631 \tabularnewline
-467.029456432509 \tabularnewline
-84.5955347744035 \tabularnewline
-822.015826928184 \tabularnewline
-276.153137212186 \tabularnewline
44.1157438409237 \tabularnewline
-914.797437068488 \tabularnewline
-1299.04729176591 \tabularnewline
-458.77889668006 \tabularnewline
-1020.0505997341 \tabularnewline
1973.6185094081 \tabularnewline
685.311826828772 \tabularnewline
-94.6356273632656 \tabularnewline
-1255.44790891877 \tabularnewline
171.683863401485 \tabularnewline
540.491292994316 \tabularnewline
-685.941239105504 \tabularnewline
-1120.88039483305 \tabularnewline
1326.5848853066 \tabularnewline
-1207.42151133624 \tabularnewline
-2010.35601200772 \tabularnewline
335.548910556906 \tabularnewline
1218.08770925681 \tabularnewline
-1381.56907787313 \tabularnewline
681.226215299578 \tabularnewline
-544.914580808644 \tabularnewline
-57.5794690515088 \tabularnewline
-115.973597699092 \tabularnewline
-1782.43929324256 \tabularnewline
96.0958525536029 \tabularnewline
-566.100927723699 \tabularnewline
694.24941456724 \tabularnewline
-134.506376436733 \tabularnewline
510.671790567481 \tabularnewline
-1490.94509055079 \tabularnewline
1687.35382078051 \tabularnewline
-661.917933196597 \tabularnewline
3.09372300760508 \tabularnewline
-191.723122008264 \tabularnewline
-143.792352482297 \tabularnewline
1001.08409820926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152753&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-5.69125818634438[/C][/ROW]
[ROW][C]153.404545269607[/C][/ROW]
[ROW][C]86.8052573113031[/C][/ROW]
[ROW][C]177.678587180251[/C][/ROW]
[ROW][C]1213.3390175033[/C][/ROW]
[ROW][C]97.7457484860814[/C][/ROW]
[ROW][C]732.819909374387[/C][/ROW]
[ROW][C]-974.817261935692[/C][/ROW]
[ROW][C]-558.780363629298[/C][/ROW]
[ROW][C]1191.33428379609[/C][/ROW]
[ROW][C]-1083.25055928752[/C][/ROW]
[ROW][C]-56.4012649737862[/C][/ROW]
[ROW][C]1137.53368126308[/C][/ROW]
[ROW][C]-849.140905721284[/C][/ROW]
[ROW][C]-1182.41593492207[/C][/ROW]
[ROW][C]-1070.20693590831[/C][/ROW]
[ROW][C]-522.031248975964[/C][/ROW]
[ROW][C]157.684832456268[/C][/ROW]
[ROW][C]-1033.69539409139[/C][/ROW]
[ROW][C]-653.540621101504[/C][/ROW]
[ROW][C]773.477071896674[/C][/ROW]
[ROW][C]-754.887588151068[/C][/ROW]
[ROW][C]696.682140203249[/C][/ROW]
[ROW][C]-139.294167253701[/C][/ROW]
[ROW][C]-1940.24982866273[/C][/ROW]
[ROW][C]-801.242649800034[/C][/ROW]
[ROW][C]841.527380795645[/C][/ROW]
[ROW][C]309.025669025242[/C][/ROW]
[ROW][C]-32.4948800747961[/C][/ROW]
[ROW][C]-268.198255032012[/C][/ROW]
[ROW][C]-306.358617087264[/C][/ROW]
[ROW][C]808.197916483968[/C][/ROW]
[ROW][C]643.993122513349[/C][/ROW]
[ROW][C]-158.381662325412[/C][/ROW]
[ROW][C]133.07330747612[/C][/ROW]
[ROW][C]-725.943761843786[/C][/ROW]
[ROW][C]-726.155078682582[/C][/ROW]
[ROW][C]-4.91725085954767[/C][/ROW]
[ROW][C]186.204862355087[/C][/ROW]
[ROW][C]707.194580552416[/C][/ROW]
[ROW][C]226.138089711016[/C][/ROW]
[ROW][C]-582.435962846777[/C][/ROW]
[ROW][C]98.4055102758542[/C][/ROW]
[ROW][C]781.011054567865[/C][/ROW]
[ROW][C]-252.603634385802[/C][/ROW]
[ROW][C]-213.196128898146[/C][/ROW]
[ROW][C]-59.2985758659246[/C][/ROW]
[ROW][C]-99.1683153779977[/C][/ROW]
[ROW][C]-174.506511889446[/C][/ROW]
[ROW][C]-624.254095983475[/C][/ROW]
[ROW][C]600.605496473968[/C][/ROW]
[ROW][C]721.974522011149[/C][/ROW]
[ROW][C]-337.672801528529[/C][/ROW]
[ROW][C]-492.500434394621[/C][/ROW]
[ROW][C]21.0755546246684[/C][/ROW]
[ROW][C]504.240673494961[/C][/ROW]
[ROW][C]421.76169911264[/C][/ROW]
[ROW][C]147.051627342013[/C][/ROW]
[ROW][C]444.379564911949[/C][/ROW]
[ROW][C]653.308085278649[/C][/ROW]
[ROW][C]1518.39171183081[/C][/ROW]
[ROW][C]689.151600649557[/C][/ROW]
[ROW][C]190.055149201069[/C][/ROW]
[ROW][C]-348.104196764366[/C][/ROW]
[ROW][C]-324.796626681026[/C][/ROW]
[ROW][C]-489.99117445742[/C][/ROW]
[ROW][C]103.041604330493[/C][/ROW]
[ROW][C]150.349261331502[/C][/ROW]
[ROW][C]10.9050833718297[/C][/ROW]
[ROW][C]-1047.83196781848[/C][/ROW]
[ROW][C]159.195220687229[/C][/ROW]
[ROW][C]-267.946311078638[/C][/ROW]
[ROW][C]-28.0513252003064[/C][/ROW]
[ROW][C]-584.308138358071[/C][/ROW]
[ROW][C]-324.515074924782[/C][/ROW]
[ROW][C]382.832480703161[/C][/ROW]
[ROW][C]-743.931255768504[/C][/ROW]
[ROW][C]171.736833661325[/C][/ROW]
[ROW][C]-415.672444597014[/C][/ROW]
[ROW][C]336.102551718071[/C][/ROW]
[ROW][C]-274.665588353461[/C][/ROW]
[ROW][C]480.092256812743[/C][/ROW]
[ROW][C]81.2914085276426[/C][/ROW]
[ROW][C]-83.265624310665[/C][/ROW]
[ROW][C]-775.487288233887[/C][/ROW]
[ROW][C]-74.0560540944577[/C][/ROW]
[ROW][C]636.88456792596[/C][/ROW]
[ROW][C]-570.693737048945[/C][/ROW]
[ROW][C]893.150259026221[/C][/ROW]
[ROW][C]543.707202530903[/C][/ROW]
[ROW][C]-644.543298333958[/C][/ROW]
[ROW][C]-813.821264610736[/C][/ROW]
[ROW][C]-15.5314803988812[/C][/ROW]
[ROW][C]311.709976403422[/C][/ROW]
[ROW][C]634.871250050552[/C][/ROW]
[ROW][C]-74.5703207877202[/C][/ROW]
[ROW][C]-427.148676115448[/C][/ROW]
[ROW][C]-421.438943418893[/C][/ROW]
[ROW][C]-229.998207516121[/C][/ROW]
[ROW][C]302.803653957453[/C][/ROW]
[ROW][C]491.856800189114[/C][/ROW]
[ROW][C]534.030605856914[/C][/ROW]
[ROW][C]-626.133864437512[/C][/ROW]
[ROW][C]-99.6169749809487[/C][/ROW]
[ROW][C]378.077406609341[/C][/ROW]
[ROW][C]310.346605661322[/C][/ROW]
[ROW][C]799.665010674464[/C][/ROW]
[ROW][C]217.573346729545[/C][/ROW]
[ROW][C]1530.54127357361[/C][/ROW]
[ROW][C]1786.81137426767[/C][/ROW]
[ROW][C]-200.779213149814[/C][/ROW]
[ROW][C]-202.314324997762[/C][/ROW]
[ROW][C]-638.519633205498[/C][/ROW]
[ROW][C]494.311718924116[/C][/ROW]
[ROW][C]99.4637572932313[/C][/ROW]
[ROW][C]-962.445587342164[/C][/ROW]
[ROW][C]-1330.61895247104[/C][/ROW]
[ROW][C]-157.572087138145[/C][/ROW]
[ROW][C]-577.624955108074[/C][/ROW]
[ROW][C]821.486426713696[/C][/ROW]
[ROW][C]-106.022312963944[/C][/ROW]
[ROW][C]-729.744647939876[/C][/ROW]
[ROW][C]-849.248428559885[/C][/ROW]
[ROW][C]-1408.90495044368[/C][/ROW]
[ROW][C]233.853565680408[/C][/ROW]
[ROW][C]633.653573810298[/C][/ROW]
[ROW][C]-68.1634809049503[/C][/ROW]
[ROW][C]287.806109670011[/C][/ROW]
[ROW][C]250.380140174687[/C][/ROW]
[ROW][C]473.597337466749[/C][/ROW]
[ROW][C]174.923135223061[/C][/ROW]
[ROW][C]-973.31189913661[/C][/ROW]
[ROW][C]-315.916802976185[/C][/ROW]
[ROW][C]-1029.16024970714[/C][/ROW]
[ROW][C]1657.01567366902[/C][/ROW]
[ROW][C]-560.86054162948[/C][/ROW]
[ROW][C]-389.500299583618[/C][/ROW]
[ROW][C]1389.48844057488[/C][/ROW]
[ROW][C]-578.105248858693[/C][/ROW]
[ROW][C]287.936295189159[/C][/ROW]
[ROW][C]-381.688682596264[/C][/ROW]
[ROW][C]895.637709911661[/C][/ROW]
[ROW][C]355.01964462019[/C][/ROW]
[ROW][C]1015.41893955457[/C][/ROW]
[ROW][C]605.955923413197[/C][/ROW]
[ROW][C]569.121494025257[/C][/ROW]
[ROW][C]-969.505661473554[/C][/ROW]
[ROW][C]-593.756944918196[/C][/ROW]
[ROW][C]108.645440390911[/C][/ROW]
[ROW][C]734.183454148667[/C][/ROW]
[ROW][C]-669.867661114097[/C][/ROW]
[ROW][C]-926.477267296986[/C][/ROW]
[ROW][C]-153.037395755565[/C][/ROW]
[ROW][C]-269.148532840416[/C][/ROW]
[ROW][C]-615.341734149281[/C][/ROW]
[ROW][C]-94.3723415733578[/C][/ROW]
[ROW][C]-248.154195265586[/C][/ROW]
[ROW][C]-716.234019170693[/C][/ROW]
[ROW][C]30.2058068961398[/C][/ROW]
[ROW][C]315.946679051834[/C][/ROW]
[ROW][C]873.175300921261[/C][/ROW]
[ROW][C]-1146.72048520709[/C][/ROW]
[ROW][C]-385.361990806634[/C][/ROW]
[ROW][C]1421.16357928292[/C][/ROW]
[ROW][C]-280.948127786303[/C][/ROW]
[ROW][C]-719.904679222899[/C][/ROW]
[ROW][C]757.792761429679[/C][/ROW]
[ROW][C]-489.278278723775[/C][/ROW]
[ROW][C]428.605524959329[/C][/ROW]
[ROW][C]674.212320827032[/C][/ROW]
[ROW][C]-1219.79510058714[/C][/ROW]
[ROW][C]156.268258077022[/C][/ROW]
[ROW][C]422.534182301395[/C][/ROW]
[ROW][C]278.212996241946[/C][/ROW]
[ROW][C]-429.274275641372[/C][/ROW]
[ROW][C]-410.098346929771[/C][/ROW]
[ROW][C]374.295237262815[/C][/ROW]
[ROW][C]129.069729448022[/C][/ROW]
[ROW][C]320.657254020969[/C][/ROW]
[ROW][C]-687.774351542897[/C][/ROW]
[ROW][C]-54.5153295308085[/C][/ROW]
[ROW][C]-347.874943786245[/C][/ROW]
[ROW][C]62.7802399153693[/C][/ROW]
[ROW][C]381.852701306434[/C][/ROW]
[ROW][C]-706.763615993845[/C][/ROW]
[ROW][C]1252.54034215665[/C][/ROW]
[ROW][C]-1310.32225909432[/C][/ROW]
[ROW][C]388.504750431329[/C][/ROW]
[ROW][C]421.086137903453[/C][/ROW]
[ROW][C]-62.6106155014123[/C][/ROW]
[ROW][C]-796.412202340837[/C][/ROW]
[ROW][C]164.038011877325[/C][/ROW]
[ROW][C]-610.320983411693[/C][/ROW]
[ROW][C]584.50831311212[/C][/ROW]
[ROW][C]-531.638478896478[/C][/ROW]
[ROW][C]733.714176296397[/C][/ROW]
[ROW][C]-299.121685037614[/C][/ROW]
[ROW][C]286.825986383511[/C][/ROW]
[ROW][C]-273.899304817213[/C][/ROW]
[ROW][C]-84.1759836771615[/C][/ROW]
[ROW][C]154.389609208269[/C][/ROW]
[ROW][C]-101.982556543801[/C][/ROW]
[ROW][C]-408.587836428454[/C][/ROW]
[ROW][C]-343.370331309172[/C][/ROW]
[ROW][C]-701.250319864386[/C][/ROW]
[ROW][C]-470.844944533489[/C][/ROW]
[ROW][C]958.731396760053[/C][/ROW]
[ROW][C]453.431258047785[/C][/ROW]
[ROW][C]485.915923558768[/C][/ROW]
[ROW][C]-225.466057775799[/C][/ROW]
[ROW][C]48.5663516459432[/C][/ROW]
[ROW][C]91.8286320731172[/C][/ROW]
[ROW][C]33.7137807912436[/C][/ROW]
[ROW][C]153.251207107826[/C][/ROW]
[ROW][C]-464.220398605333[/C][/ROW]
[ROW][C]196.098669753289[/C][/ROW]
[ROW][C]-394.789323696842[/C][/ROW]
[ROW][C]-43.7243814538033[/C][/ROW]
[ROW][C]313.04938660092[/C][/ROW]
[ROW][C]154.462457201954[/C][/ROW]
[ROW][C]-219.124478630576[/C][/ROW]
[ROW][C]863.367070575425[/C][/ROW]
[ROW][C]463.438778053972[/C][/ROW]
[ROW][C]-451.022589307632[/C][/ROW]
[ROW][C]652.710915786962[/C][/ROW]
[ROW][C]295.530096955881[/C][/ROW]
[ROW][C]-887.992257232453[/C][/ROW]
[ROW][C]-480.940937737128[/C][/ROW]
[ROW][C]-392.547683555353[/C][/ROW]
[ROW][C]691.460491681346[/C][/ROW]
[ROW][C]-204.108010319212[/C][/ROW]
[ROW][C]-333.198248146225[/C][/ROW]
[ROW][C]40.6495592449484[/C][/ROW]
[ROW][C]967.240138819078[/C][/ROW]
[ROW][C]185.025870251011[/C][/ROW]
[ROW][C]-713.420917832531[/C][/ROW]
[ROW][C]232.535011026026[/C][/ROW]
[ROW][C]-67.5277040782914[/C][/ROW]
[ROW][C]-181.1079395975[/C][/ROW]
[ROW][C]-220.720066526237[/C][/ROW]
[ROW][C]-126.288628260779[/C][/ROW]
[ROW][C]-21.0109944016558[/C][/ROW]
[ROW][C]379.57609043486[/C][/ROW]
[ROW][C]374.766755675895[/C][/ROW]
[ROW][C]-314.373276237076[/C][/ROW]
[ROW][C]-99.4537374974244[/C][/ROW]
[ROW][C]788.372825739957[/C][/ROW]
[ROW][C]-75.6213388626333[/C][/ROW]
[ROW][C]559.497327011602[/C][/ROW]
[ROW][C]-284.189878766725[/C][/ROW]
[ROW][C]-683.121935890147[/C][/ROW]
[ROW][C]162.294279410278[/C][/ROW]
[ROW][C]919.379177327279[/C][/ROW]
[ROW][C]729.249119731921[/C][/ROW]
[ROW][C]179.921549594815[/C][/ROW]
[ROW][C]21.9321734649701[/C][/ROW]
[ROW][C]-145.744930095926[/C][/ROW]
[ROW][C]991.356399852837[/C][/ROW]
[ROW][C]375.621331247704[/C][/ROW]
[ROW][C]-364.133967072924[/C][/ROW]
[ROW][C]409.36922152826[/C][/ROW]
[ROW][C]28.3638064445848[/C][/ROW]
[ROW][C]933.5148028091[/C][/ROW]
[ROW][C]139.059815345398[/C][/ROW]
[ROW][C]855.534288989729[/C][/ROW]
[ROW][C]-621.507368632965[/C][/ROW]
[ROW][C]-606.810640299147[/C][/ROW]
[ROW][C]-723.024918005984[/C][/ROW]
[ROW][C]-269.714778308271[/C][/ROW]
[ROW][C]767.673111384589[/C][/ROW]
[ROW][C]275.284529051472[/C][/ROW]
[ROW][C]-212.960121578282[/C][/ROW]
[ROW][C]-704.264811257005[/C][/ROW]
[ROW][C]-584.582720020127[/C][/ROW]
[ROW][C]701.135367189048[/C][/ROW]
[ROW][C]-213.626582636313[/C][/ROW]
[ROW][C]580.537791914279[/C][/ROW]
[ROW][C]-563.3379693568[/C][/ROW]
[ROW][C]-139.443247415477[/C][/ROW]
[ROW][C]-605.699917037588[/C][/ROW]
[ROW][C]-383.586231216524[/C][/ROW]
[ROW][C]466.931787007076[/C][/ROW]
[ROW][C]-103.418026601342[/C][/ROW]
[ROW][C]-206.145840147477[/C][/ROW]
[ROW][C]-315.93432992797[/C][/ROW]
[ROW][C]-115.142476902916[/C][/ROW]
[ROW][C]-1078.24909523715[/C][/ROW]
[ROW][C]-41.6919003425761[/C][/ROW]
[ROW][C]-89.0960177061997[/C][/ROW]
[ROW][C]420.785371768443[/C][/ROW]
[ROW][C]-393.666986794174[/C][/ROW]
[ROW][C]184.625265574151[/C][/ROW]
[ROW][C]-235.320388044186[/C][/ROW]
[ROW][C]-207.12834227433[/C][/ROW]
[ROW][C]-95.2381113399751[/C][/ROW]
[ROW][C]-60.541323749804[/C][/ROW]
[ROW][C]353.423348774305[/C][/ROW]
[ROW][C]-730.399114701629[/C][/ROW]
[ROW][C]731.090563307096[/C][/ROW]
[ROW][C]354.759194755907[/C][/ROW]
[ROW][C]752.491832338693[/C][/ROW]
[ROW][C]-53.8646266979655[/C][/ROW]
[ROW][C]-723.353387181321[/C][/ROW]
[ROW][C]-137.354262836393[/C][/ROW]
[ROW][C]596.275812110066[/C][/ROW]
[ROW][C]593.164777954613[/C][/ROW]
[ROW][C]209.86022915931[/C][/ROW]
[ROW][C]-461.874761261099[/C][/ROW]
[ROW][C]1384.86482139786[/C][/ROW]
[ROW][C]37.0868807603157[/C][/ROW]
[ROW][C]1522.42738949785[/C][/ROW]
[ROW][C]1443.7942958882[/C][/ROW]
[ROW][C]5254.74332275509[/C][/ROW]
[ROW][C]-1104.44480910037[/C][/ROW]
[ROW][C]-110.875463093869[/C][/ROW]
[ROW][C]-1040.32020912807[/C][/ROW]
[ROW][C]-144.685727223869[/C][/ROW]
[ROW][C]709.4136911784[/C][/ROW]
[ROW][C]-967.379706592312[/C][/ROW]
[ROW][C]-1029.35896555631[/C][/ROW]
[ROW][C]-467.029456432509[/C][/ROW]
[ROW][C]-84.5955347744035[/C][/ROW]
[ROW][C]-822.015826928184[/C][/ROW]
[ROW][C]-276.153137212186[/C][/ROW]
[ROW][C]44.1157438409237[/C][/ROW]
[ROW][C]-914.797437068488[/C][/ROW]
[ROW][C]-1299.04729176591[/C][/ROW]
[ROW][C]-458.77889668006[/C][/ROW]
[ROW][C]-1020.0505997341[/C][/ROW]
[ROW][C]1973.6185094081[/C][/ROW]
[ROW][C]685.311826828772[/C][/ROW]
[ROW][C]-94.6356273632656[/C][/ROW]
[ROW][C]-1255.44790891877[/C][/ROW]
[ROW][C]171.683863401485[/C][/ROW]
[ROW][C]540.491292994316[/C][/ROW]
[ROW][C]-685.941239105504[/C][/ROW]
[ROW][C]-1120.88039483305[/C][/ROW]
[ROW][C]1326.5848853066[/C][/ROW]
[ROW][C]-1207.42151133624[/C][/ROW]
[ROW][C]-2010.35601200772[/C][/ROW]
[ROW][C]335.548910556906[/C][/ROW]
[ROW][C]1218.08770925681[/C][/ROW]
[ROW][C]-1381.56907787313[/C][/ROW]
[ROW][C]681.226215299578[/C][/ROW]
[ROW][C]-544.914580808644[/C][/ROW]
[ROW][C]-57.5794690515088[/C][/ROW]
[ROW][C]-115.973597699092[/C][/ROW]
[ROW][C]-1782.43929324256[/C][/ROW]
[ROW][C]96.0958525536029[/C][/ROW]
[ROW][C]-566.100927723699[/C][/ROW]
[ROW][C]694.24941456724[/C][/ROW]
[ROW][C]-134.506376436733[/C][/ROW]
[ROW][C]510.671790567481[/C][/ROW]
[ROW][C]-1490.94509055079[/C][/ROW]
[ROW][C]1687.35382078051[/C][/ROW]
[ROW][C]-661.917933196597[/C][/ROW]
[ROW][C]3.09372300760508[/C][/ROW]
[ROW][C]-191.723122008264[/C][/ROW]
[ROW][C]-143.792352482297[/C][/ROW]
[ROW][C]1001.08409820926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152753&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152753&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
-5.69125818634438
153.404545269607
86.8052573113031
177.678587180251
1213.3390175033
97.7457484860814
732.819909374387
-974.817261935692
-558.780363629298
1191.33428379609
-1083.25055928752
-56.4012649737862
1137.53368126308
-849.140905721284
-1182.41593492207
-1070.20693590831
-522.031248975964
157.684832456268
-1033.69539409139
-653.540621101504
773.477071896674
-754.887588151068
696.682140203249
-139.294167253701
-1940.24982866273
-801.242649800034
841.527380795645
309.025669025242
-32.4948800747961
-268.198255032012
-306.358617087264
808.197916483968
643.993122513349
-158.381662325412
133.07330747612
-725.943761843786
-726.155078682582
-4.91725085954767
186.204862355087
707.194580552416
226.138089711016
-582.435962846777
98.4055102758542
781.011054567865
-252.603634385802
-213.196128898146
-59.2985758659246
-99.1683153779977
-174.506511889446
-624.254095983475
600.605496473968
721.974522011149
-337.672801528529
-492.500434394621
21.0755546246684
504.240673494961
421.76169911264
147.051627342013
444.379564911949
653.308085278649
1518.39171183081
689.151600649557
190.055149201069
-348.104196764366
-324.796626681026
-489.99117445742
103.041604330493
150.349261331502
10.9050833718297
-1047.83196781848
159.195220687229
-267.946311078638
-28.0513252003064
-584.308138358071
-324.515074924782
382.832480703161
-743.931255768504
171.736833661325
-415.672444597014
336.102551718071
-274.665588353461
480.092256812743
81.2914085276426
-83.265624310665
-775.487288233887
-74.0560540944577
636.88456792596
-570.693737048945
893.150259026221
543.707202530903
-644.543298333958
-813.821264610736
-15.5314803988812
311.709976403422
634.871250050552
-74.5703207877202
-427.148676115448
-421.438943418893
-229.998207516121
302.803653957453
491.856800189114
534.030605856914
-626.133864437512
-99.6169749809487
378.077406609341
310.346605661322
799.665010674464
217.573346729545
1530.54127357361
1786.81137426767
-200.779213149814
-202.314324997762
-638.519633205498
494.311718924116
99.4637572932313
-962.445587342164
-1330.61895247104
-157.572087138145
-577.624955108074
821.486426713696
-106.022312963944
-729.744647939876
-849.248428559885
-1408.90495044368
233.853565680408
633.653573810298
-68.1634809049503
287.806109670011
250.380140174687
473.597337466749
174.923135223061
-973.31189913661
-315.916802976185
-1029.16024970714
1657.01567366902
-560.86054162948
-389.500299583618
1389.48844057488
-578.105248858693
287.936295189159
-381.688682596264
895.637709911661
355.01964462019
1015.41893955457
605.955923413197
569.121494025257
-969.505661473554
-593.756944918196
108.645440390911
734.183454148667
-669.867661114097
-926.477267296986
-153.037395755565
-269.148532840416
-615.341734149281
-94.3723415733578
-248.154195265586
-716.234019170693
30.2058068961398
315.946679051834
873.175300921261
-1146.72048520709
-385.361990806634
1421.16357928292
-280.948127786303
-719.904679222899
757.792761429679
-489.278278723775
428.605524959329
674.212320827032
-1219.79510058714
156.268258077022
422.534182301395
278.212996241946
-429.274275641372
-410.098346929771
374.295237262815
129.069729448022
320.657254020969
-687.774351542897
-54.5153295308085
-347.874943786245
62.7802399153693
381.852701306434
-706.763615993845
1252.54034215665
-1310.32225909432
388.504750431329
421.086137903453
-62.6106155014123
-796.412202340837
164.038011877325
-610.320983411693
584.50831311212
-531.638478896478
733.714176296397
-299.121685037614
286.825986383511
-273.899304817213
-84.1759836771615
154.389609208269
-101.982556543801
-408.587836428454
-343.370331309172
-701.250319864386
-470.844944533489
958.731396760053
453.431258047785
485.915923558768
-225.466057775799
48.5663516459432
91.8286320731172
33.7137807912436
153.251207107826
-464.220398605333
196.098669753289
-394.789323696842
-43.7243814538033
313.04938660092
154.462457201954
-219.124478630576
863.367070575425
463.438778053972
-451.022589307632
652.710915786962
295.530096955881
-887.992257232453
-480.940937737128
-392.547683555353
691.460491681346
-204.108010319212
-333.198248146225
40.6495592449484
967.240138819078
185.025870251011
-713.420917832531
232.535011026026
-67.5277040782914
-181.1079395975
-220.720066526237
-126.288628260779
-21.0109944016558
379.57609043486
374.766755675895
-314.373276237076
-99.4537374974244
788.372825739957
-75.6213388626333
559.497327011602
-284.189878766725
-683.121935890147
162.294279410278
919.379177327279
729.249119731921
179.921549594815
21.9321734649701
-145.744930095926
991.356399852837
375.621331247704
-364.133967072924
409.36922152826
28.3638064445848
933.5148028091
139.059815345398
855.534288989729
-621.507368632965
-606.810640299147
-723.024918005984
-269.714778308271
767.673111384589
275.284529051472
-212.960121578282
-704.264811257005
-584.582720020127
701.135367189048
-213.626582636313
580.537791914279
-563.3379693568
-139.443247415477
-605.699917037588
-383.586231216524
466.931787007076
-103.418026601342
-206.145840147477
-315.93432992797
-115.142476902916
-1078.24909523715
-41.6919003425761
-89.0960177061997
420.785371768443
-393.666986794174
184.625265574151
-235.320388044186
-207.12834227433
-95.2381113399751
-60.541323749804
353.423348774305
-730.399114701629
731.090563307096
354.759194755907
752.491832338693
-53.8646266979655
-723.353387181321
-137.354262836393
596.275812110066
593.164777954613
209.86022915931
-461.874761261099
1384.86482139786
37.0868807603157
1522.42738949785
1443.7942958882
5254.74332275509
-1104.44480910037
-110.875463093869
-1040.32020912807
-144.685727223869
709.4136911784
-967.379706592312
-1029.35896555631
-467.029456432509
-84.5955347744035
-822.015826928184
-276.153137212186
44.1157438409237
-914.797437068488
-1299.04729176591
-458.77889668006
-1020.0505997341
1973.6185094081
685.311826828772
-94.6356273632656
-1255.44790891877
171.683863401485
540.491292994316
-685.941239105504
-1120.88039483305
1326.5848853066
-1207.42151133624
-2010.35601200772
335.548910556906
1218.08770925681
-1381.56907787313
681.226215299578
-544.914580808644
-57.5794690515088
-115.973597699092
-1782.43929324256
96.0958525536029
-566.100927723699
694.24941456724
-134.506376436733
510.671790567481
-1490.94509055079
1687.35382078051
-661.917933196597
3.09372300760508
-191.723122008264
-143.792352482297
1001.08409820926

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = FALSE ; par2 = 1.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code