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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 computationThu, 03 Sep 2015 08:43:24 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Sep/03/t1441266239dh8n0djcvs6jemn.htm/, Retrieved Thu, 16 May 2024 17:33:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280543, Retrieved Thu, 16 May 2024 17:33:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact48
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [] [2015-09-03 07:43:24] [6aaa86d036131c368c5c53235820ac38] [Current]
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Dataseries X:
1.4
1.5
1.8
1.8
1.8
1.7
1.5
1.1
1.3
1.6
1.9
1.9
2
2.2
2.2
2
2.3
2.6
3.2
3.2
3.1
2.8
2.3
1.9
1.9
2
2
1.8
1.6
1.4
0.2
0.3
0.4
0.7
1
1.1
0.8
0.8
1
1.1
1
0.8
1.6
1.5
1.6
1.6
1.6
1.9
2
1.9
2
2.1
2.3
2.3
2.6
2.6
2.7
2.6
2.6
2.4
2.5
2.5
2.5
2.4
2.1
2.1
2.3
2.3
2.3
2.9
2.8
2.9
3
3
2.9
2.6
2.8
2.9
3.1
2.8
2.4
1.6
1.5
1.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280543&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280543&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280543&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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.1548-0.1795-0.0232-0.8339-0.9313-0.4806-0.2758
(p-val)(0.4935 )(0.2468 )(0.9132 )(0 )(0.0116 )(0.0944 )(0.5974 )
Estimates ( 2 )0.1385-0.18470-0.8314-1.0571-0.5755-14.0862
(p-val)(0.3743 )(0.2168 )(NA )(0 )(0 )(0 )(0.5137 )
Estimates ( 3 )0.1332-0.17920-0.8318-1.0964-0.59960
(p-val)(0.3923 )(0.2317 )(NA )(0 )(0 )(0 )(NA )
Estimates ( 4 )0-0.21910-0.7727-1.1253-0.6210
(p-val)(NA )(0.121 )(NA )(0 )(0 )(0 )(NA )
Estimates ( 5 )000-0.845-1.0857-0.55890
(p-val)(NA )(NA )(NA )(0 )(0 )(0 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.1548 & -0.1795 & -0.0232 & -0.8339 & -0.9313 & -0.4806 & -0.2758 \tabularnewline
(p-val) & (0.4935 ) & (0.2468 ) & (0.9132 ) & (0 ) & (0.0116 ) & (0.0944 ) & (0.5974 ) \tabularnewline
Estimates ( 2 ) & 0.1385 & -0.1847 & 0 & -0.8314 & -1.0571 & -0.5755 & -14.0862 \tabularnewline
(p-val) & (0.3743 ) & (0.2168 ) & (NA ) & (0 ) & (0 ) & (0 ) & (0.5137 ) \tabularnewline
Estimates ( 3 ) & 0.1332 & -0.1792 & 0 & -0.8318 & -1.0964 & -0.5996 & 0 \tabularnewline
(p-val) & (0.3923 ) & (0.2317 ) & (NA ) & (0 ) & (0 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & -0.2191 & 0 & -0.7727 & -1.1253 & -0.621 & 0 \tabularnewline
(p-val) & (NA ) & (0.121 ) & (NA ) & (0 ) & (0 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -0.845 & -1.0857 & -0.5589 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280543&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]0.1548[/C][C]-0.1795[/C][C]-0.0232[/C][C]-0.8339[/C][C]-0.9313[/C][C]-0.4806[/C][C]-0.2758[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4935 )[/C][C](0.2468 )[/C][C](0.9132 )[/C][C](0 )[/C][C](0.0116 )[/C][C](0.0944 )[/C][C](0.5974 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1385[/C][C]-0.1847[/C][C]0[/C][C]-0.8314[/C][C]-1.0571[/C][C]-0.5755[/C][C]-14.0862[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3743 )[/C][C](0.2168 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0.5137 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1332[/C][C]-0.1792[/C][C]0[/C][C]-0.8318[/C][C]-1.0964[/C][C]-0.5996[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3923 )[/C][C](0.2317 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]-0.2191[/C][C]0[/C][C]-0.7727[/C][C]-1.1253[/C][C]-0.621[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.121 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.845[/C][C]-1.0857[/C][C]-0.5589[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280543&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280543&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 )0.1548-0.1795-0.0232-0.8339-0.9313-0.4806-0.2758
(p-val)(0.4935 )(0.2468 )(0.9132 )(0 )(0.0116 )(0.0944 )(0.5974 )
Estimates ( 2 )0.1385-0.18470-0.8314-1.0571-0.5755-14.0862
(p-val)(0.3743 )(0.2168 )(NA )(0 )(0 )(0 )(0.5137 )
Estimates ( 3 )0.1332-0.17920-0.8318-1.0964-0.59960
(p-val)(0.3923 )(0.2317 )(NA )(0 )(0 )(0 )(NA )
Estimates ( 4 )0-0.21910-0.7727-1.1253-0.6210
(p-val)(NA )(0.121 )(NA )(0 )(0 )(0 )(NA )
Estimates ( 5 )000-0.845-1.0857-0.55890
(p-val)(NA )(NA )(NA )(0 )(0 )(0 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.00767069904897011
-0.238147397323549
-0.0411080900552259
0.27574067480054
0.280201743010444
0.610278571037883
0.111212394002324
-0.348265112910399
-0.591204032764115
-0.736886965071745
-0.301257364065929
0.0162406556992612
0.118551353011305
-0.0558457205173855
0.037680761664163
-0.169848094622167
-0.0348834210480133
-0.946944790733823
0.763850678045413
0.0227551262874297
0.512184933219422
0.383800369405525
0.323249277496104
-0.273958856129523
-0.00298451559878602
0.0903350027211144
0.323072331636278
-0.305752467203612
-0.208859659215967
0.644073482470485
0.141089100164935
0.184010401078512
0.0262869551019209
0.1255891982776
0.627697300241753
-0.177033304992597
-0.404854460256699
0.0815626710558295
0.275891221782471
0.0084472755394966
-0.233723837484594
0.598006848113063
-0.351176034148723
0.183986833797572
-0.257374461449131
0.162741314364681
-0.152798601475383
0.241847611231054
-0.305499152903556
-0.170675306739148
-0.145452552867798
-0.255605100827844
0.262174920737401
0.565225039365953
-0.131043946013129
-0.132725868206285
0.51509750522122
-0.647066652350739
-0.134996332655313
0.157702603970845
-0.0845654204209445
-0.392195684926911
-0.597522498559305
0.220386806395976
0.238372951461879
-0.438774546190372
-0.178822231326732
-0.682253769021857
-0.680829300059262
0.16211655265916
0.412281853993026

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00767069904897011 \tabularnewline
-0.238147397323549 \tabularnewline
-0.0411080900552259 \tabularnewline
0.27574067480054 \tabularnewline
0.280201743010444 \tabularnewline
0.610278571037883 \tabularnewline
0.111212394002324 \tabularnewline
-0.348265112910399 \tabularnewline
-0.591204032764115 \tabularnewline
-0.736886965071745 \tabularnewline
-0.301257364065929 \tabularnewline
0.0162406556992612 \tabularnewline
0.118551353011305 \tabularnewline
-0.0558457205173855 \tabularnewline
0.037680761664163 \tabularnewline
-0.169848094622167 \tabularnewline
-0.0348834210480133 \tabularnewline
-0.946944790733823 \tabularnewline
0.763850678045413 \tabularnewline
0.0227551262874297 \tabularnewline
0.512184933219422 \tabularnewline
0.383800369405525 \tabularnewline
0.323249277496104 \tabularnewline
-0.273958856129523 \tabularnewline
-0.00298451559878602 \tabularnewline
0.0903350027211144 \tabularnewline
0.323072331636278 \tabularnewline
-0.305752467203612 \tabularnewline
-0.208859659215967 \tabularnewline
0.644073482470485 \tabularnewline
0.141089100164935 \tabularnewline
0.184010401078512 \tabularnewline
0.0262869551019209 \tabularnewline
0.1255891982776 \tabularnewline
0.627697300241753 \tabularnewline
-0.177033304992597 \tabularnewline
-0.404854460256699 \tabularnewline
0.0815626710558295 \tabularnewline
0.275891221782471 \tabularnewline
0.0084472755394966 \tabularnewline
-0.233723837484594 \tabularnewline
0.598006848113063 \tabularnewline
-0.351176034148723 \tabularnewline
0.183986833797572 \tabularnewline
-0.257374461449131 \tabularnewline
0.162741314364681 \tabularnewline
-0.152798601475383 \tabularnewline
0.241847611231054 \tabularnewline
-0.305499152903556 \tabularnewline
-0.170675306739148 \tabularnewline
-0.145452552867798 \tabularnewline
-0.255605100827844 \tabularnewline
0.262174920737401 \tabularnewline
0.565225039365953 \tabularnewline
-0.131043946013129 \tabularnewline
-0.132725868206285 \tabularnewline
0.51509750522122 \tabularnewline
-0.647066652350739 \tabularnewline
-0.134996332655313 \tabularnewline
0.157702603970845 \tabularnewline
-0.0845654204209445 \tabularnewline
-0.392195684926911 \tabularnewline
-0.597522498559305 \tabularnewline
0.220386806395976 \tabularnewline
0.238372951461879 \tabularnewline
-0.438774546190372 \tabularnewline
-0.178822231326732 \tabularnewline
-0.682253769021857 \tabularnewline
-0.680829300059262 \tabularnewline
0.16211655265916 \tabularnewline
0.412281853993026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280543&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00767069904897011[/C][/ROW]
[ROW][C]-0.238147397323549[/C][/ROW]
[ROW][C]-0.0411080900552259[/C][/ROW]
[ROW][C]0.27574067480054[/C][/ROW]
[ROW][C]0.280201743010444[/C][/ROW]
[ROW][C]0.610278571037883[/C][/ROW]
[ROW][C]0.111212394002324[/C][/ROW]
[ROW][C]-0.348265112910399[/C][/ROW]
[ROW][C]-0.591204032764115[/C][/ROW]
[ROW][C]-0.736886965071745[/C][/ROW]
[ROW][C]-0.301257364065929[/C][/ROW]
[ROW][C]0.0162406556992612[/C][/ROW]
[ROW][C]0.118551353011305[/C][/ROW]
[ROW][C]-0.0558457205173855[/C][/ROW]
[ROW][C]0.037680761664163[/C][/ROW]
[ROW][C]-0.169848094622167[/C][/ROW]
[ROW][C]-0.0348834210480133[/C][/ROW]
[ROW][C]-0.946944790733823[/C][/ROW]
[ROW][C]0.763850678045413[/C][/ROW]
[ROW][C]0.0227551262874297[/C][/ROW]
[ROW][C]0.512184933219422[/C][/ROW]
[ROW][C]0.383800369405525[/C][/ROW]
[ROW][C]0.323249277496104[/C][/ROW]
[ROW][C]-0.273958856129523[/C][/ROW]
[ROW][C]-0.00298451559878602[/C][/ROW]
[ROW][C]0.0903350027211144[/C][/ROW]
[ROW][C]0.323072331636278[/C][/ROW]
[ROW][C]-0.305752467203612[/C][/ROW]
[ROW][C]-0.208859659215967[/C][/ROW]
[ROW][C]0.644073482470485[/C][/ROW]
[ROW][C]0.141089100164935[/C][/ROW]
[ROW][C]0.184010401078512[/C][/ROW]
[ROW][C]0.0262869551019209[/C][/ROW]
[ROW][C]0.1255891982776[/C][/ROW]
[ROW][C]0.627697300241753[/C][/ROW]
[ROW][C]-0.177033304992597[/C][/ROW]
[ROW][C]-0.404854460256699[/C][/ROW]
[ROW][C]0.0815626710558295[/C][/ROW]
[ROW][C]0.275891221782471[/C][/ROW]
[ROW][C]0.0084472755394966[/C][/ROW]
[ROW][C]-0.233723837484594[/C][/ROW]
[ROW][C]0.598006848113063[/C][/ROW]
[ROW][C]-0.351176034148723[/C][/ROW]
[ROW][C]0.183986833797572[/C][/ROW]
[ROW][C]-0.257374461449131[/C][/ROW]
[ROW][C]0.162741314364681[/C][/ROW]
[ROW][C]-0.152798601475383[/C][/ROW]
[ROW][C]0.241847611231054[/C][/ROW]
[ROW][C]-0.305499152903556[/C][/ROW]
[ROW][C]-0.170675306739148[/C][/ROW]
[ROW][C]-0.145452552867798[/C][/ROW]
[ROW][C]-0.255605100827844[/C][/ROW]
[ROW][C]0.262174920737401[/C][/ROW]
[ROW][C]0.565225039365953[/C][/ROW]
[ROW][C]-0.131043946013129[/C][/ROW]
[ROW][C]-0.132725868206285[/C][/ROW]
[ROW][C]0.51509750522122[/C][/ROW]
[ROW][C]-0.647066652350739[/C][/ROW]
[ROW][C]-0.134996332655313[/C][/ROW]
[ROW][C]0.157702603970845[/C][/ROW]
[ROW][C]-0.0845654204209445[/C][/ROW]
[ROW][C]-0.392195684926911[/C][/ROW]
[ROW][C]-0.597522498559305[/C][/ROW]
[ROW][C]0.220386806395976[/C][/ROW]
[ROW][C]0.238372951461879[/C][/ROW]
[ROW][C]-0.438774546190372[/C][/ROW]
[ROW][C]-0.178822231326732[/C][/ROW]
[ROW][C]-0.682253769021857[/C][/ROW]
[ROW][C]-0.680829300059262[/C][/ROW]
[ROW][C]0.16211655265916[/C][/ROW]
[ROW][C]0.412281853993026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280543&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280543&T=2

As an alternative you can also use a QR Code:  

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

Estimated ARIMA Residuals
Value
0.00767069904897011
-0.238147397323549
-0.0411080900552259
0.27574067480054
0.280201743010444
0.610278571037883
0.111212394002324
-0.348265112910399
-0.591204032764115
-0.736886965071745
-0.301257364065929
0.0162406556992612
0.118551353011305
-0.0558457205173855
0.037680761664163
-0.169848094622167
-0.0348834210480133
-0.946944790733823
0.763850678045413
0.0227551262874297
0.512184933219422
0.383800369405525
0.323249277496104
-0.273958856129523
-0.00298451559878602
0.0903350027211144
0.323072331636278
-0.305752467203612
-0.208859659215967
0.644073482470485
0.141089100164935
0.184010401078512
0.0262869551019209
0.1255891982776
0.627697300241753
-0.177033304992597
-0.404854460256699
0.0815626710558295
0.275891221782471
0.0084472755394966
-0.233723837484594
0.598006848113063
-0.351176034148723
0.183986833797572
-0.257374461449131
0.162741314364681
-0.152798601475383
0.241847611231054
-0.305499152903556
-0.170675306739148
-0.145452552867798
-0.255605100827844
0.262174920737401
0.565225039365953
-0.131043946013129
-0.132725868206285
0.51509750522122
-0.647066652350739
-0.134996332655313
0.157702603970845
-0.0845654204209445
-0.392195684926911
-0.597522498559305
0.220386806395976
0.238372951461879
-0.438774546190372
-0.178822231326732
-0.682253769021857
-0.680829300059262
0.16211655265916
0.412281853993026



Parameters (Session):
par1 = FALSE ; par2 = 1.2 ; par3 = 2 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1.2 ; par3 = 2 ; 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')