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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

Mon, 15 Dec 2008 13:28:38 -0700

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/2008/Dec/15/t1229373073sczaor5mqrd652y.htm/, Retrieved Wed, 15 May 2024 22:34:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33814, Retrieved Wed, 15 May 2024 22:34:42 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

201

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-12-13 16:08:49] [d134696a922d84037f02d49ded84b0bd]
-   P   [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-12-14 14:45:58] [d134696a922d84037f02d49ded84b0bd]
- RMP     [ARIMA Backward Selection] [Backward Selectio...] [2008-12-14 16:30:33] [d134696a922d84037f02d49ded84b0bd]
-   P       [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-15 20:23:35] [d134696a922d84037f02d49ded84b0bd]
-   P           [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-15 20:28:38] [db9a5fd0f9c3e1245d8075d8bb09236d] [Current]
- RMP             [ARIMA Forecasting] [ARIMA Forecasting] [2008-12-16 09:49:01] [d134696a922d84037f02d49ded84b0bd] 
-                   [ARIMA Forecasting] [ARIMA Forecasting] [2008-12-16 09:51:44] [d134696a922d84037f02d49ded84b0bd] 

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33814&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33814&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33814&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	11 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sar1	sma1
Estimates ( 1 )	0.2434	0.0151	-0.0464	-0.4977	0.1033	-0.9998
(p-val)	(0.4791 )	(0.9015 )	(0.6496 )	(0.1411 )	(0.3076 )	(0 )
Estimates ( 2 )	0.2149	0	-0.0492	-0.468	0.1023	-0.9998
(p-val)	(0.4272 )	(NA )	(0.6158 )	(0.064 )	(0.3102 )	(1e-04 )
Estimates ( 3 )	0.272	0	0	-0.5274	0.1172	-0.9996
(p-val)	(0.3063 )	(NA )	(NA )	(0.0277 )	(0.2222 )	(3e-04 )
Estimates ( 4 )	0	0	0	-0.2641	0.1233	-0.9997
(p-val)	(NA )	(NA )	(NA )	(0.0061 )	(0.1984 )	(0 )
Estimates ( 5 )	0	0	0	-0.2429	0	-0.8946
(p-val)	(NA )	(NA )	(NA )	(0.0111 )	(NA )	(0 )
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 & ar3 & ma1 & sar1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.2434 & 0.0151 & -0.0464 & -0.4977 & 0.1033 & -0.9998 \tabularnewline
(p-val) & (0.4791 ) & (0.9015 ) & (0.6496 ) & (0.1411 ) & (0.3076 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.2149 & 0 & -0.0492 & -0.468 & 0.1023 & -0.9998 \tabularnewline
(p-val) & (0.4272 ) & (NA ) & (0.6158 ) & (0.064 ) & (0.3102 ) & (1e-04 ) \tabularnewline
Estimates ( 3 ) & 0.272 & 0 & 0 & -0.5274 & 0.1172 & -0.9996 \tabularnewline
(p-val) & (0.3063 ) & (NA ) & (NA ) & (0.0277 ) & (0.2222 ) & (3e-04 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0 & -0.2641 & 0.1233 & -0.9997 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.0061 ) & (0.1984 ) & (0 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -0.2429 & 0 & -0.8946 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.0111 ) & (NA ) & (0 ) \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=33814&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]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.2434[/C][C]0.0151[/C][C]-0.0464[/C][C]-0.4977[/C][C]0.1033[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4791 )[/C][C](0.9015 )[/C][C](0.6496 )[/C][C](0.1411 )[/C][C](0.3076 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.2149[/C][C]0[/C][C]-0.0492[/C][C]-0.468[/C][C]0.1023[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4272 )[/C][C](NA )[/C][C](0.6158 )[/C][C](0.064 )[/C][C](0.3102 )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.272[/C][C]0[/C][C]0[/C][C]-0.5274[/C][C]0.1172[/C][C]-0.9996[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3063 )[/C][C](NA )[/C][C](NA )[/C][C](0.0277 )[/C][C](0.2222 )[/C][C](3e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.2641[/C][C]0.1233[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0061 )[/C][C](0.1984 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.2429[/C][C]0[/C][C]-0.8946[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0111 )[/C][C](NA )[/C][C](0 )[/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=33814&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33814&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	ar3	ma1	sar1	sma1
Estimates ( 1 )	0.2434	0.0151	-0.0464	-0.4977	0.1033	-0.9998
(p-val)	(0.4791 )	(0.9015 )	(0.6496 )	(0.1411 )	(0.3076 )	(0 )
Estimates ( 2 )	0.2149	0	-0.0492	-0.468	0.1023	-0.9998
(p-val)	(0.4272 )	(NA )	(0.6158 )	(0.064 )	(0.3102 )	(1e-04 )
Estimates ( 3 )	0.272	0	0	-0.5274	0.1172	-0.9996
(p-val)	(0.3063 )	(NA )	(NA )	(0.0277 )	(0.2222 )	(3e-04 )
Estimates ( 4 )	0	0	0	-0.2641	0.1233	-0.9997
(p-val)	(NA )	(NA )	(NA )	(0.0061 )	(0.1984 )	(0 )
Estimates ( 5 )	0	0	0	-0.2429	0	-0.8946
(p-val)	(NA )	(NA )	(NA )	(0.0111 )	(NA )	(0 )
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
-687.299215587455
634.510651311828
-2989.95387274653
-3333.05086568427
-2266.33030397089
-601.160066132213
-572.951090556036
-740.959346706209
-262.962204925828
462.104257989745
823.68250536494
-152.706024956773
-1746.69644903619
-823.915681181412
-2311.2855684853
1819.17087245978
-1735.1594980118
-2307.61701651483
4.90779046230937
960.391268028267
-689.912314334675
-1225.66845464831
614.674276675942
-978.214007626866
2650.45054480687
-4073.28042939850
-809.079268175477
223.005208833481
-560.61407983169
-1317.50261201739
250.510147888070
-215.534153331981
-144.489990424484
512.280696104591
-900.397389147888
245.564182948959
-3315.56844672157
-3665.4033364372
-19.8004769558522
-1779.67738275633
-128.439300933674
-2085.61293330689
1377.03677074229
-164.080567240688
-640.996216240064
-480.130349526114
838.377128871288
-1795.43018701680
653.808327099296
-373.413638602678
-600.983209791458
-2453.68058100696
581.839037790912
-2282.40151859855
-747.037949745312
-890.638298106612
-1127.60568579086
189.424529472590
-1519.57235471503
-209.692215334553
-221.250067810004
-1338.63393661144
-1710.61752495854
3992.43523908766
-965.64999166399
-1182.52073336020
248.732907574234
-1182.92078941381
1286.65695224083
767.902837322281
1562.34563539543
-2249.70531780720
-1132.38143677965
-511.927567422342
1361.63054736114
-1720.16005735296
3011.4517590451
-96.414390465552
-1867.44783284398
-1528.63425416686
-506.404799838822
-236.230952419378
1293.41655094492
89.7864900490472
2953.05550100405
-3912.38357902194
-287.612552751836
-587.20047824587
246.875511608574
-383.516891402948
346.899795347275
-783.38982458984
81.8379329358533
1525.47712181840
557.686412740294
-408.921662494766
-578.686354054127
-638.190094366125
348.725421443472
-1614.31850809666
1836.23241627799
-2033.31083350997
-1055.18831414857
-1537.15821181930
-1989.39177417962
-977.034733542916
1035.30274285717
644.021573500225
898.020207704394
2584.96361600774
1293.78869915620
1256.12418973997
-4389.90047996021
-3314.81098032824
777.5292255289
-1082.43842316972
6355.89378323041
-181.427628898801
-2759.80055805893
-2405.42021594371
-414.665357057758
-1224.96662018909
2699.97324919353
548.698997245382
-677.146719749133
-3127.25085525612
2207.83484999665
-1041.50706328445
844.219652237964
-1681.16979254851
-336.087576852493
-2824.63619479332
-4283.8868907949
3082.78816502054
1018.37321009155
-995.467196577083
1230.71405115358
-2331.17055693265
-2440.07275764220
1605.96991349513
2622.8730258076
-75.1614469417187
-3482.9600341981
1214.51703851165
140.775735458267
687.967275497079
1584.19074642952
-160.056425647255
166.024707564035
-489.467741380472
-4175.42245409604
-2103.12991175088
-921.41711440308
2090.44506819610
5.50834652991482
-1062.21824330365
940.566212282953
-177.140605721846
-711.744419568331
1208.51965480194
3507.41323541712
-1063.41747034715
1430.67802310181
2493.12467193044
-1466.92843976959
442.32397625832
7125.34676512188
-3362.97578310408
2820.12860039965
655.555742451225
-779.323373245361
1175.91965233318
-3318.74655622830
-76.3482472526865
691.905700199193
-1170.48763498763
-1040.00332992146
-347.5451039882
2840.47341663084
13244.9729149631

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-687.299215587455 \tabularnewline
634.510651311828 \tabularnewline
-2989.95387274653 \tabularnewline
-3333.05086568427 \tabularnewline
-2266.33030397089 \tabularnewline
-601.160066132213 \tabularnewline
-572.951090556036 \tabularnewline
-740.959346706209 \tabularnewline
-262.962204925828 \tabularnewline
462.104257989745 \tabularnewline
823.68250536494 \tabularnewline
-152.706024956773 \tabularnewline
-1746.69644903619 \tabularnewline
-823.915681181412 \tabularnewline
-2311.2855684853 \tabularnewline
1819.17087245978 \tabularnewline
-1735.1594980118 \tabularnewline
-2307.61701651483 \tabularnewline
4.90779046230937 \tabularnewline
960.391268028267 \tabularnewline
-689.912314334675 \tabularnewline
-1225.66845464831 \tabularnewline
614.674276675942 \tabularnewline
-978.214007626866 \tabularnewline
2650.45054480687 \tabularnewline
-4073.28042939850 \tabularnewline
-809.079268175477 \tabularnewline
223.005208833481 \tabularnewline
-560.61407983169 \tabularnewline
-1317.50261201739 \tabularnewline
250.510147888070 \tabularnewline
-215.534153331981 \tabularnewline
-144.489990424484 \tabularnewline
512.280696104591 \tabularnewline
-900.397389147888 \tabularnewline
245.564182948959 \tabularnewline
-3315.56844672157 \tabularnewline
-3665.4033364372 \tabularnewline
-19.8004769558522 \tabularnewline
-1779.67738275633 \tabularnewline
-128.439300933674 \tabularnewline
-2085.61293330689 \tabularnewline
1377.03677074229 \tabularnewline
-164.080567240688 \tabularnewline
-640.996216240064 \tabularnewline
-480.130349526114 \tabularnewline
838.377128871288 \tabularnewline
-1795.43018701680 \tabularnewline
653.808327099296 \tabularnewline
-373.413638602678 \tabularnewline
-600.983209791458 \tabularnewline
-2453.68058100696 \tabularnewline
581.839037790912 \tabularnewline
-2282.40151859855 \tabularnewline
-747.037949745312 \tabularnewline
-890.638298106612 \tabularnewline
-1127.60568579086 \tabularnewline
189.424529472590 \tabularnewline
-1519.57235471503 \tabularnewline
-209.692215334553 \tabularnewline
-221.250067810004 \tabularnewline
-1338.63393661144 \tabularnewline
-1710.61752495854 \tabularnewline
3992.43523908766 \tabularnewline
-965.64999166399 \tabularnewline
-1182.52073336020 \tabularnewline
248.732907574234 \tabularnewline
-1182.92078941381 \tabularnewline
1286.65695224083 \tabularnewline
767.902837322281 \tabularnewline
1562.34563539543 \tabularnewline
-2249.70531780720 \tabularnewline
-1132.38143677965 \tabularnewline
-511.927567422342 \tabularnewline
1361.63054736114 \tabularnewline
-1720.16005735296 \tabularnewline
3011.4517590451 \tabularnewline
-96.414390465552 \tabularnewline
-1867.44783284398 \tabularnewline
-1528.63425416686 \tabularnewline
-506.404799838822 \tabularnewline
-236.230952419378 \tabularnewline
1293.41655094492 \tabularnewline
89.7864900490472 \tabularnewline
2953.05550100405 \tabularnewline
-3912.38357902194 \tabularnewline
-287.612552751836 \tabularnewline
-587.20047824587 \tabularnewline
246.875511608574 \tabularnewline
-383.516891402948 \tabularnewline
346.899795347275 \tabularnewline
-783.38982458984 \tabularnewline
81.8379329358533 \tabularnewline
1525.47712181840 \tabularnewline
557.686412740294 \tabularnewline
-408.921662494766 \tabularnewline
-578.686354054127 \tabularnewline
-638.190094366125 \tabularnewline
348.725421443472 \tabularnewline
-1614.31850809666 \tabularnewline
1836.23241627799 \tabularnewline
-2033.31083350997 \tabularnewline
-1055.18831414857 \tabularnewline
-1537.15821181930 \tabularnewline
-1989.39177417962 \tabularnewline
-977.034733542916 \tabularnewline
1035.30274285717 \tabularnewline
644.021573500225 \tabularnewline
898.020207704394 \tabularnewline
2584.96361600774 \tabularnewline
1293.78869915620 \tabularnewline
1256.12418973997 \tabularnewline
-4389.90047996021 \tabularnewline
-3314.81098032824 \tabularnewline
777.5292255289 \tabularnewline
-1082.43842316972 \tabularnewline
6355.89378323041 \tabularnewline
-181.427628898801 \tabularnewline
-2759.80055805893 \tabularnewline
-2405.42021594371 \tabularnewline
-414.665357057758 \tabularnewline
-1224.96662018909 \tabularnewline
2699.97324919353 \tabularnewline
548.698997245382 \tabularnewline
-677.146719749133 \tabularnewline
-3127.25085525612 \tabularnewline
2207.83484999665 \tabularnewline
-1041.50706328445 \tabularnewline
844.219652237964 \tabularnewline
-1681.16979254851 \tabularnewline
-336.087576852493 \tabularnewline
-2824.63619479332 \tabularnewline
-4283.8868907949 \tabularnewline
3082.78816502054 \tabularnewline
1018.37321009155 \tabularnewline
-995.467196577083 \tabularnewline
1230.71405115358 \tabularnewline
-2331.17055693265 \tabularnewline
-2440.07275764220 \tabularnewline
1605.96991349513 \tabularnewline
2622.8730258076 \tabularnewline
-75.1614469417187 \tabularnewline
-3482.9600341981 \tabularnewline
1214.51703851165 \tabularnewline
140.775735458267 \tabularnewline
687.967275497079 \tabularnewline
1584.19074642952 \tabularnewline
-160.056425647255 \tabularnewline
166.024707564035 \tabularnewline
-489.467741380472 \tabularnewline
-4175.42245409604 \tabularnewline
-2103.12991175088 \tabularnewline
-921.41711440308 \tabularnewline
2090.44506819610 \tabularnewline
5.50834652991482 \tabularnewline
-1062.21824330365 \tabularnewline
940.566212282953 \tabularnewline
-177.140605721846 \tabularnewline
-711.744419568331 \tabularnewline
1208.51965480194 \tabularnewline
3507.41323541712 \tabularnewline
-1063.41747034715 \tabularnewline
1430.67802310181 \tabularnewline
2493.12467193044 \tabularnewline
-1466.92843976959 \tabularnewline
442.32397625832 \tabularnewline
7125.34676512188 \tabularnewline
-3362.97578310408 \tabularnewline
2820.12860039965 \tabularnewline
655.555742451225 \tabularnewline
-779.323373245361 \tabularnewline
1175.91965233318 \tabularnewline
-3318.74655622830 \tabularnewline
-76.3482472526865 \tabularnewline
691.905700199193 \tabularnewline
-1170.48763498763 \tabularnewline
-1040.00332992146 \tabularnewline
-347.5451039882 \tabularnewline
2840.47341663084 \tabularnewline
13244.9729149631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33814&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-687.299215587455[/C][/ROW]
[ROW][C]634.510651311828[/C][/ROW]
[ROW][C]-2989.95387274653[/C][/ROW]
[ROW][C]-3333.05086568427[/C][/ROW]
[ROW][C]-2266.33030397089[/C][/ROW]
[ROW][C]-601.160066132213[/C][/ROW]
[ROW][C]-572.951090556036[/C][/ROW]
[ROW][C]-740.959346706209[/C][/ROW]
[ROW][C]-262.962204925828[/C][/ROW]
[ROW][C]462.104257989745[/C][/ROW]
[ROW][C]823.68250536494[/C][/ROW]
[ROW][C]-152.706024956773[/C][/ROW]
[ROW][C]-1746.69644903619[/C][/ROW]
[ROW][C]-823.915681181412[/C][/ROW]
[ROW][C]-2311.2855684853[/C][/ROW]
[ROW][C]1819.17087245978[/C][/ROW]
[ROW][C]-1735.1594980118[/C][/ROW]
[ROW][C]-2307.61701651483[/C][/ROW]
[ROW][C]4.90779046230937[/C][/ROW]
[ROW][C]960.391268028267[/C][/ROW]
[ROW][C]-689.912314334675[/C][/ROW]
[ROW][C]-1225.66845464831[/C][/ROW]
[ROW][C]614.674276675942[/C][/ROW]
[ROW][C]-978.214007626866[/C][/ROW]
[ROW][C]2650.45054480687[/C][/ROW]
[ROW][C]-4073.28042939850[/C][/ROW]
[ROW][C]-809.079268175477[/C][/ROW]
[ROW][C]223.005208833481[/C][/ROW]
[ROW][C]-560.61407983169[/C][/ROW]
[ROW][C]-1317.50261201739[/C][/ROW]
[ROW][C]250.510147888070[/C][/ROW]
[ROW][C]-215.534153331981[/C][/ROW]
[ROW][C]-144.489990424484[/C][/ROW]
[ROW][C]512.280696104591[/C][/ROW]
[ROW][C]-900.397389147888[/C][/ROW]
[ROW][C]245.564182948959[/C][/ROW]
[ROW][C]-3315.56844672157[/C][/ROW]
[ROW][C]-3665.4033364372[/C][/ROW]
[ROW][C]-19.8004769558522[/C][/ROW]
[ROW][C]-1779.67738275633[/C][/ROW]
[ROW][C]-128.439300933674[/C][/ROW]
[ROW][C]-2085.61293330689[/C][/ROW]
[ROW][C]1377.03677074229[/C][/ROW]
[ROW][C]-164.080567240688[/C][/ROW]
[ROW][C]-640.996216240064[/C][/ROW]
[ROW][C]-480.130349526114[/C][/ROW]
[ROW][C]838.377128871288[/C][/ROW]
[ROW][C]-1795.43018701680[/C][/ROW]
[ROW][C]653.808327099296[/C][/ROW]
[ROW][C]-373.413638602678[/C][/ROW]
[ROW][C]-600.983209791458[/C][/ROW]
[ROW][C]-2453.68058100696[/C][/ROW]
[ROW][C]581.839037790912[/C][/ROW]
[ROW][C]-2282.40151859855[/C][/ROW]
[ROW][C]-747.037949745312[/C][/ROW]
[ROW][C]-890.638298106612[/C][/ROW]
[ROW][C]-1127.60568579086[/C][/ROW]
[ROW][C]189.424529472590[/C][/ROW]
[ROW][C]-1519.57235471503[/C][/ROW]
[ROW][C]-209.692215334553[/C][/ROW]
[ROW][C]-221.250067810004[/C][/ROW]
[ROW][C]-1338.63393661144[/C][/ROW]
[ROW][C]-1710.61752495854[/C][/ROW]
[ROW][C]3992.43523908766[/C][/ROW]
[ROW][C]-965.64999166399[/C][/ROW]
[ROW][C]-1182.52073336020[/C][/ROW]
[ROW][C]248.732907574234[/C][/ROW]
[ROW][C]-1182.92078941381[/C][/ROW]
[ROW][C]1286.65695224083[/C][/ROW]
[ROW][C]767.902837322281[/C][/ROW]
[ROW][C]1562.34563539543[/C][/ROW]
[ROW][C]-2249.70531780720[/C][/ROW]
[ROW][C]-1132.38143677965[/C][/ROW]
[ROW][C]-511.927567422342[/C][/ROW]
[ROW][C]1361.63054736114[/C][/ROW]
[ROW][C]-1720.16005735296[/C][/ROW]
[ROW][C]3011.4517590451[/C][/ROW]
[ROW][C]-96.414390465552[/C][/ROW]
[ROW][C]-1867.44783284398[/C][/ROW]
[ROW][C]-1528.63425416686[/C][/ROW]
[ROW][C]-506.404799838822[/C][/ROW]
[ROW][C]-236.230952419378[/C][/ROW]
[ROW][C]1293.41655094492[/C][/ROW]
[ROW][C]89.7864900490472[/C][/ROW]
[ROW][C]2953.05550100405[/C][/ROW]
[ROW][C]-3912.38357902194[/C][/ROW]
[ROW][C]-287.612552751836[/C][/ROW]
[ROW][C]-587.20047824587[/C][/ROW]
[ROW][C]246.875511608574[/C][/ROW]
[ROW][C]-383.516891402948[/C][/ROW]
[ROW][C]346.899795347275[/C][/ROW]
[ROW][C]-783.38982458984[/C][/ROW]
[ROW][C]81.8379329358533[/C][/ROW]
[ROW][C]1525.47712181840[/C][/ROW]
[ROW][C]557.686412740294[/C][/ROW]
[ROW][C]-408.921662494766[/C][/ROW]
[ROW][C]-578.686354054127[/C][/ROW]
[ROW][C]-638.190094366125[/C][/ROW]
[ROW][C]348.725421443472[/C][/ROW]
[ROW][C]-1614.31850809666[/C][/ROW]
[ROW][C]1836.23241627799[/C][/ROW]
[ROW][C]-2033.31083350997[/C][/ROW]
[ROW][C]-1055.18831414857[/C][/ROW]
[ROW][C]-1537.15821181930[/C][/ROW]
[ROW][C]-1989.39177417962[/C][/ROW]
[ROW][C]-977.034733542916[/C][/ROW]
[ROW][C]1035.30274285717[/C][/ROW]
[ROW][C]644.021573500225[/C][/ROW]
[ROW][C]898.020207704394[/C][/ROW]
[ROW][C]2584.96361600774[/C][/ROW]
[ROW][C]1293.78869915620[/C][/ROW]
[ROW][C]1256.12418973997[/C][/ROW]
[ROW][C]-4389.90047996021[/C][/ROW]
[ROW][C]-3314.81098032824[/C][/ROW]
[ROW][C]777.5292255289[/C][/ROW]
[ROW][C]-1082.43842316972[/C][/ROW]
[ROW][C]6355.89378323041[/C][/ROW]
[ROW][C]-181.427628898801[/C][/ROW]
[ROW][C]-2759.80055805893[/C][/ROW]
[ROW][C]-2405.42021594371[/C][/ROW]
[ROW][C]-414.665357057758[/C][/ROW]
[ROW][C]-1224.96662018909[/C][/ROW]
[ROW][C]2699.97324919353[/C][/ROW]
[ROW][C]548.698997245382[/C][/ROW]
[ROW][C]-677.146719749133[/C][/ROW]
[ROW][C]-3127.25085525612[/C][/ROW]
[ROW][C]2207.83484999665[/C][/ROW]
[ROW][C]-1041.50706328445[/C][/ROW]
[ROW][C]844.219652237964[/C][/ROW]
[ROW][C]-1681.16979254851[/C][/ROW]
[ROW][C]-336.087576852493[/C][/ROW]
[ROW][C]-2824.63619479332[/C][/ROW]
[ROW][C]-4283.8868907949[/C][/ROW]
[ROW][C]3082.78816502054[/C][/ROW]
[ROW][C]1018.37321009155[/C][/ROW]
[ROW][C]-995.467196577083[/C][/ROW]
[ROW][C]1230.71405115358[/C][/ROW]
[ROW][C]-2331.17055693265[/C][/ROW]
[ROW][C]-2440.07275764220[/C][/ROW]
[ROW][C]1605.96991349513[/C][/ROW]
[ROW][C]2622.8730258076[/C][/ROW]
[ROW][C]-75.1614469417187[/C][/ROW]
[ROW][C]-3482.9600341981[/C][/ROW]
[ROW][C]1214.51703851165[/C][/ROW]
[ROW][C]140.775735458267[/C][/ROW]
[ROW][C]687.967275497079[/C][/ROW]
[ROW][C]1584.19074642952[/C][/ROW]
[ROW][C]-160.056425647255[/C][/ROW]
[ROW][C]166.024707564035[/C][/ROW]
[ROW][C]-489.467741380472[/C][/ROW]
[ROW][C]-4175.42245409604[/C][/ROW]
[ROW][C]-2103.12991175088[/C][/ROW]
[ROW][C]-921.41711440308[/C][/ROW]
[ROW][C]2090.44506819610[/C][/ROW]
[ROW][C]5.50834652991482[/C][/ROW]
[ROW][C]-1062.21824330365[/C][/ROW]
[ROW][C]940.566212282953[/C][/ROW]
[ROW][C]-177.140605721846[/C][/ROW]
[ROW][C]-711.744419568331[/C][/ROW]
[ROW][C]1208.51965480194[/C][/ROW]
[ROW][C]3507.41323541712[/C][/ROW]
[ROW][C]-1063.41747034715[/C][/ROW]
[ROW][C]1430.67802310181[/C][/ROW]
[ROW][C]2493.12467193044[/C][/ROW]
[ROW][C]-1466.92843976959[/C][/ROW]
[ROW][C]442.32397625832[/C][/ROW]
[ROW][C]7125.34676512188[/C][/ROW]
[ROW][C]-3362.97578310408[/C][/ROW]
[ROW][C]2820.12860039965[/C][/ROW]
[ROW][C]655.555742451225[/C][/ROW]
[ROW][C]-779.323373245361[/C][/ROW]
[ROW][C]1175.91965233318[/C][/ROW]
[ROW][C]-3318.74655622830[/C][/ROW]
[ROW][C]-76.3482472526865[/C][/ROW]
[ROW][C]691.905700199193[/C][/ROW]
[ROW][C]-1170.48763498763[/C][/ROW]
[ROW][C]-1040.00332992146[/C][/ROW]
[ROW][C]-347.5451039882[/C][/ROW]
[ROW][C]2840.47341663084[/C][/ROW]
[ROW][C]13244.9729149631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33814&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33814&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
-687.299215587455
634.510651311828
-2989.95387274653
-3333.05086568427
-2266.33030397089
-601.160066132213
-572.951090556036
-740.959346706209
-262.962204925828
462.104257989745
823.68250536494
-152.706024956773
-1746.69644903619
-823.915681181412
-2311.2855684853
1819.17087245978
-1735.1594980118
-2307.61701651483
4.90779046230937
960.391268028267
-689.912314334675
-1225.66845464831
614.674276675942
-978.214007626866
2650.45054480687
-4073.28042939850
-809.079268175477
223.005208833481
-560.61407983169
-1317.50261201739
250.510147888070
-215.534153331981
-144.489990424484
512.280696104591
-900.397389147888
245.564182948959
-3315.56844672157
-3665.4033364372
-19.8004769558522
-1779.67738275633
-128.439300933674
-2085.61293330689
1377.03677074229
-164.080567240688
-640.996216240064
-480.130349526114
838.377128871288
-1795.43018701680
653.808327099296
-373.413638602678
-600.983209791458
-2453.68058100696
581.839037790912
-2282.40151859855
-747.037949745312
-890.638298106612
-1127.60568579086
189.424529472590
-1519.57235471503
-209.692215334553
-221.250067810004
-1338.63393661144
-1710.61752495854
3992.43523908766
-965.64999166399
-1182.52073336020
248.732907574234
-1182.92078941381
1286.65695224083
767.902837322281
1562.34563539543
-2249.70531780720
-1132.38143677965
-511.927567422342
1361.63054736114
-1720.16005735296
3011.4517590451
-96.414390465552
-1867.44783284398
-1528.63425416686
-506.404799838822
-236.230952419378
1293.41655094492
89.7864900490472
2953.05550100405
-3912.38357902194
-287.612552751836
-587.20047824587
246.875511608574
-383.516891402948
346.899795347275
-783.38982458984
81.8379329358533
1525.47712181840
557.686412740294
-408.921662494766
-578.686354054127
-638.190094366125
348.725421443472
-1614.31850809666
1836.23241627799
-2033.31083350997
-1055.18831414857
-1537.15821181930
-1989.39177417962
-977.034733542916
1035.30274285717
644.021573500225
898.020207704394
2584.96361600774
1293.78869915620
1256.12418973997
-4389.90047996021
-3314.81098032824
777.5292255289
-1082.43842316972
6355.89378323041
-181.427628898801
-2759.80055805893
-2405.42021594371
-414.665357057758
-1224.96662018909
2699.97324919353
548.698997245382
-677.146719749133
-3127.25085525612
2207.83484999665
-1041.50706328445
844.219652237964
-1681.16979254851
-336.087576852493
-2824.63619479332
-4283.8868907949
3082.78816502054
1018.37321009155
-995.467196577083
1230.71405115358
-2331.17055693265
-2440.07275764220
1605.96991349513
2622.8730258076
-75.1614469417187
-3482.9600341981
1214.51703851165
140.775735458267
687.967275497079
1584.19074642952
-160.056425647255
166.024707564035
-489.467741380472
-4175.42245409604
-2103.12991175088
-921.41711440308
2090.44506819610
5.50834652991482
-1062.21824330365
940.566212282953
-177.140605721846
-711.744419568331
1208.51965480194
3507.41323541712
-1063.41747034715
1430.67802310181
2493.12467193044
-1466.92843976959
442.32397625832
7125.34676512188
-3362.97578310408
2820.12860039965
655.555742451225
-779.323373245361
1175.91965233318
-3318.74655622830
-76.3482472526865
691.905700199193
-1170.48763498763
-1040.00332992146
-347.5451039882
2840.47341663084
13244.9729149631

Figure 1

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Postscript link

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Figure 2

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Figure 3

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Figure 4

PNG link

Postscript link

PDF link

Figure 5

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Figure 6

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Figure 7

PNG link

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PDF link

Parameters (Session):

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

Parameters (R input):

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