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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationSun, 09 Dec 2012 07:12:08 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/09/t1355055159yt6l9a6icncqjtw.htm/, Retrieved Thu, 31 Oct 2024 23:35:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197826, Retrieved Thu, 31 Oct 2024 23:35:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
-   PD        [ARIMA Backward Selection] [Paper 2012] [2012-12-09 12:12:08] [25a095d064099991cfda4df2228bcf08] [Current]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 17 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197826&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197826&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197826&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.0961-0.136-0.0746-0.72150.431-0.1347-0.9995
(p-val)(0.6315 )(0.4085 )(0.6312 )(0 )(0.0219 )(0.4597 )(6e-04 )
Estimates ( 2 )0-0.1025-0.0425-1.30330.4435-0.1592-0.9997
(p-val)(NA )(0.4917 )(0.7666 )(0 )(0.0162 )(0.3602 )(7e-04 )
Estimates ( 3 )0-0.09220-1.28650.4329-0.1556-1
(p-val)(NA )(0.5287 )(NA )(0 )(0.0175 )(0.3731 )(6e-04 )
Estimates ( 4 )000-1.25110.4276-0.1329-1
(p-val)(NA )(NA )(NA )(0 )(0.0203 )(0.4384 )(6e-04 )
Estimates ( 5 )000-1.24740.45680-0.9995
(p-val)(NA )(NA )(NA )(0 )(0.013 )(NA )(0 )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.0961 & -0.136 & -0.0746 & -0.7215 & 0.431 & -0.1347 & -0.9995 \tabularnewline
(p-val) & (0.6315 ) & (0.4085 ) & (0.6312 ) & (0 ) & (0.0219 ) & (0.4597 ) & (6e-04 ) \tabularnewline
Estimates ( 2 ) & 0 & -0.1025 & -0.0425 & -1.3033 & 0.4435 & -0.1592 & -0.9997 \tabularnewline
(p-val) & (NA ) & (0.4917 ) & (0.7666 ) & (0 ) & (0.0162 ) & (0.3602 ) & (7e-04 ) \tabularnewline
Estimates ( 3 ) & 0 & -0.0922 & 0 & -1.2865 & 0.4329 & -0.1556 & -1 \tabularnewline
(p-val) & (NA ) & (0.5287 ) & (NA ) & (0 ) & (0.0175 ) & (0.3731 ) & (6e-04 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0 & -1.2511 & 0.4276 & -0.1329 & -1 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0.0203 ) & (0.4384 ) & (6e-04 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -1.2474 & 0.4568 & 0 & -0.9995 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0.013 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197826&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.0961[/C][C]-0.136[/C][C]-0.0746[/C][C]-0.7215[/C][C]0.431[/C][C]-0.1347[/C][C]-0.9995[/C][/ROW]
[ROW][C](p-val)[/C][C](0.6315 )[/C][C](0.4085 )[/C][C](0.6312 )[/C][C](0 )[/C][C](0.0219 )[/C][C](0.4597 )[/C][C](6e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]-0.1025[/C][C]-0.0425[/C][C]-1.3033[/C][C]0.4435[/C][C]-0.1592[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.4917 )[/C][C](0.7666 )[/C][C](0 )[/C][C](0.0162 )[/C][C](0.3602 )[/C][C](7e-04 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]-0.0922[/C][C]0[/C][C]-1.2865[/C][C]0.4329[/C][C]-0.1556[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.5287 )[/C][C](NA )[/C][C](0 )[/C][C](0.0175 )[/C][C](0.3731 )[/C][C](6e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.2511[/C][C]0.4276[/C][C]-0.1329[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0203 )[/C][C](0.4384 )[/C][C](6e-04 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.2474[/C][C]0.4568[/C][C]0[/C][C]-0.9995[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.013 )[/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][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197826&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197826&T=1

As an alternative you can also use a QR Code:  

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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.0961-0.136-0.0746-0.72150.431-0.1347-0.9995
(p-val)(0.6315 )(0.4085 )(0.6312 )(0 )(0.0219 )(0.4597 )(6e-04 )
Estimates ( 2 )0-0.1025-0.0425-1.30330.4435-0.1592-0.9997
(p-val)(NA )(0.4917 )(0.7666 )(0 )(0.0162 )(0.3602 )(7e-04 )
Estimates ( 3 )0-0.09220-1.28650.4329-0.1556-1
(p-val)(NA )(0.5287 )(NA )(0 )(0.0175 )(0.3731 )(6e-04 )
Estimates ( 4 )000-1.25110.4276-0.1329-1
(p-val)(NA )(NA )(NA )(0 )(0.0203 )(0.4384 )(6e-04 )
Estimates ( 5 )000-1.24740.45680-0.9995
(p-val)(NA )(NA )(NA )(0 )(0.013 )(NA )(0 )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
-3.60593959722636e-05
2.34799531539502e-05
-1.91555588736802e-05
8.86159973222824e-05
-6.64231657659329e-05
3.02076007889851e-05
0.000212532129136052
0.000187080477290026
0.000128286194854522
-9.68621279767517e-05
-8.42067158427556e-06
-0.000129468461314375
-4.62471497247313e-05
8.30882292376498e-05
-0.000132852400855296
-7.88834081018089e-05
4.01288928840675e-08
0.000117279129773872
-0.000225843212273347
8.49140800997773e-05
-0.000173220737456653
-8.20435645310622e-05
9.46484569607066e-05
-0.000145202707485573
7.4033565171636e-05
-8.61081533010845e-06
-0.000161324441034618
-7.7978342419585e-06
-0.000177222056187428
-7.17967843914367e-05
0.000184776262630884
0.000149917692026811
-0.000171093200505232
-2.3689914138558e-05
-0.000151323112229772
-4.73221724551214e-05
2.66159304229679e-05
-0.000113717863479698
-0.000115547830150541
0.000108467125651959
2.48305481766775e-05
-9.37239585026904e-05
-6.83612137110216e-05
-5.86980381954581e-05
1.85329945252522e-05
0.000206768154527503
-5.94538743016286e-05
-5.28831586715766e-05
-4.93118249933725e-05
7.89184422558866e-05
2.65053278381695e-05
-5.69250959947098e-05
4.53229196956851e-06
-6.16161431076672e-05
8.80127821516968e-05
-0.000154162010350188
-6.33175154743789e-05
2.4689285060649e-05
-1.25741350763591e-05
-0.000140267484935943
-9.43496754474059e-05
-6.27751284641578e-05
0.000207779401925261

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-3.60593959722636e-05 \tabularnewline
2.34799531539502e-05 \tabularnewline
-1.91555588736802e-05 \tabularnewline
8.86159973222824e-05 \tabularnewline
-6.64231657659329e-05 \tabularnewline
3.02076007889851e-05 \tabularnewline
0.000212532129136052 \tabularnewline
0.000187080477290026 \tabularnewline
0.000128286194854522 \tabularnewline
-9.68621279767517e-05 \tabularnewline
-8.42067158427556e-06 \tabularnewline
-0.000129468461314375 \tabularnewline
-4.62471497247313e-05 \tabularnewline
8.30882292376498e-05 \tabularnewline
-0.000132852400855296 \tabularnewline
-7.88834081018089e-05 \tabularnewline
4.01288928840675e-08 \tabularnewline
0.000117279129773872 \tabularnewline
-0.000225843212273347 \tabularnewline
8.49140800997773e-05 \tabularnewline
-0.000173220737456653 \tabularnewline
-8.20435645310622e-05 \tabularnewline
9.46484569607066e-05 \tabularnewline
-0.000145202707485573 \tabularnewline
7.4033565171636e-05 \tabularnewline
-8.61081533010845e-06 \tabularnewline
-0.000161324441034618 \tabularnewline
-7.7978342419585e-06 \tabularnewline
-0.000177222056187428 \tabularnewline
-7.17967843914367e-05 \tabularnewline
0.000184776262630884 \tabularnewline
0.000149917692026811 \tabularnewline
-0.000171093200505232 \tabularnewline
-2.3689914138558e-05 \tabularnewline
-0.000151323112229772 \tabularnewline
-4.73221724551214e-05 \tabularnewline
2.66159304229679e-05 \tabularnewline
-0.000113717863479698 \tabularnewline
-0.000115547830150541 \tabularnewline
0.000108467125651959 \tabularnewline
2.48305481766775e-05 \tabularnewline
-9.37239585026904e-05 \tabularnewline
-6.83612137110216e-05 \tabularnewline
-5.86980381954581e-05 \tabularnewline
1.85329945252522e-05 \tabularnewline
0.000206768154527503 \tabularnewline
-5.94538743016286e-05 \tabularnewline
-5.28831586715766e-05 \tabularnewline
-4.93118249933725e-05 \tabularnewline
7.89184422558866e-05 \tabularnewline
2.65053278381695e-05 \tabularnewline
-5.69250959947098e-05 \tabularnewline
4.53229196956851e-06 \tabularnewline
-6.16161431076672e-05 \tabularnewline
8.80127821516968e-05 \tabularnewline
-0.000154162010350188 \tabularnewline
-6.33175154743789e-05 \tabularnewline
2.4689285060649e-05 \tabularnewline
-1.25741350763591e-05 \tabularnewline
-0.000140267484935943 \tabularnewline
-9.43496754474059e-05 \tabularnewline
-6.27751284641578e-05 \tabularnewline
0.000207779401925261 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197826&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-3.60593959722636e-05[/C][/ROW]
[ROW][C]2.34799531539502e-05[/C][/ROW]
[ROW][C]-1.91555588736802e-05[/C][/ROW]
[ROW][C]8.86159973222824e-05[/C][/ROW]
[ROW][C]-6.64231657659329e-05[/C][/ROW]
[ROW][C]3.02076007889851e-05[/C][/ROW]
[ROW][C]0.000212532129136052[/C][/ROW]
[ROW][C]0.000187080477290026[/C][/ROW]
[ROW][C]0.000128286194854522[/C][/ROW]
[ROW][C]-9.68621279767517e-05[/C][/ROW]
[ROW][C]-8.42067158427556e-06[/C][/ROW]
[ROW][C]-0.000129468461314375[/C][/ROW]
[ROW][C]-4.62471497247313e-05[/C][/ROW]
[ROW][C]8.30882292376498e-05[/C][/ROW]
[ROW][C]-0.000132852400855296[/C][/ROW]
[ROW][C]-7.88834081018089e-05[/C][/ROW]
[ROW][C]4.01288928840675e-08[/C][/ROW]
[ROW][C]0.000117279129773872[/C][/ROW]
[ROW][C]-0.000225843212273347[/C][/ROW]
[ROW][C]8.49140800997773e-05[/C][/ROW]
[ROW][C]-0.000173220737456653[/C][/ROW]
[ROW][C]-8.20435645310622e-05[/C][/ROW]
[ROW][C]9.46484569607066e-05[/C][/ROW]
[ROW][C]-0.000145202707485573[/C][/ROW]
[ROW][C]7.4033565171636e-05[/C][/ROW]
[ROW][C]-8.61081533010845e-06[/C][/ROW]
[ROW][C]-0.000161324441034618[/C][/ROW]
[ROW][C]-7.7978342419585e-06[/C][/ROW]
[ROW][C]-0.000177222056187428[/C][/ROW]
[ROW][C]-7.17967843914367e-05[/C][/ROW]
[ROW][C]0.000184776262630884[/C][/ROW]
[ROW][C]0.000149917692026811[/C][/ROW]
[ROW][C]-0.000171093200505232[/C][/ROW]
[ROW][C]-2.3689914138558e-05[/C][/ROW]
[ROW][C]-0.000151323112229772[/C][/ROW]
[ROW][C]-4.73221724551214e-05[/C][/ROW]
[ROW][C]2.66159304229679e-05[/C][/ROW]
[ROW][C]-0.000113717863479698[/C][/ROW]
[ROW][C]-0.000115547830150541[/C][/ROW]
[ROW][C]0.000108467125651959[/C][/ROW]
[ROW][C]2.48305481766775e-05[/C][/ROW]
[ROW][C]-9.37239585026904e-05[/C][/ROW]
[ROW][C]-6.83612137110216e-05[/C][/ROW]
[ROW][C]-5.86980381954581e-05[/C][/ROW]
[ROW][C]1.85329945252522e-05[/C][/ROW]
[ROW][C]0.000206768154527503[/C][/ROW]
[ROW][C]-5.94538743016286e-05[/C][/ROW]
[ROW][C]-5.28831586715766e-05[/C][/ROW]
[ROW][C]-4.93118249933725e-05[/C][/ROW]
[ROW][C]7.89184422558866e-05[/C][/ROW]
[ROW][C]2.65053278381695e-05[/C][/ROW]
[ROW][C]-5.69250959947098e-05[/C][/ROW]
[ROW][C]4.53229196956851e-06[/C][/ROW]
[ROW][C]-6.16161431076672e-05[/C][/ROW]
[ROW][C]8.80127821516968e-05[/C][/ROW]
[ROW][C]-0.000154162010350188[/C][/ROW]
[ROW][C]-6.33175154743789e-05[/C][/ROW]
[ROW][C]2.4689285060649e-05[/C][/ROW]
[ROW][C]-1.25741350763591e-05[/C][/ROW]
[ROW][C]-0.000140267484935943[/C][/ROW]
[ROW][C]-9.43496754474059e-05[/C][/ROW]
[ROW][C]-6.27751284641578e-05[/C][/ROW]
[ROW][C]0.000207779401925261[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197826&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197826&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
-3.60593959722636e-05
2.34799531539502e-05
-1.91555588736802e-05
8.86159973222824e-05
-6.64231657659329e-05
3.02076007889851e-05
0.000212532129136052
0.000187080477290026
0.000128286194854522
-9.68621279767517e-05
-8.42067158427556e-06
-0.000129468461314375
-4.62471497247313e-05
8.30882292376498e-05
-0.000132852400855296
-7.88834081018089e-05
4.01288928840675e-08
0.000117279129773872
-0.000225843212273347
8.49140800997773e-05
-0.000173220737456653
-8.20435645310622e-05
9.46484569607066e-05
-0.000145202707485573
7.4033565171636e-05
-8.61081533010845e-06
-0.000161324441034618
-7.7978342419585e-06
-0.000177222056187428
-7.17967843914367e-05
0.000184776262630884
0.000149917692026811
-0.000171093200505232
-2.3689914138558e-05
-0.000151323112229772
-4.73221724551214e-05
2.66159304229679e-05
-0.000113717863479698
-0.000115547830150541
0.000108467125651959
2.48305481766775e-05
-9.37239585026904e-05
-6.83612137110216e-05
-5.86980381954581e-05
1.85329945252522e-05
0.000206768154527503
-5.94538743016286e-05
-5.28831586715766e-05
-4.93118249933725e-05
7.89184422558866e-05
2.65053278381695e-05
-5.69250959947098e-05
4.53229196956851e-06
-6.16161431076672e-05
8.80127821516968e-05
-0.000154162010350188
-6.33175154743789e-05
2.4689285060649e-05
-1.25741350763591e-05
-0.000140267484935943
-9.43496754474059e-05
-6.27751284641578e-05
0.000207779401925261



Parameters (Session):
par1 = FALSE ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')