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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 15 Dec 2009 11:35:49 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/15/t1260902218jn12y9sbi5j1jf3.htm/, Retrieved Wed, 08 May 2024 05:47:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68071, Retrieved Wed, 08 May 2024 05:47:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSDHW, DSHW
Estimated Impact113
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [Paper Bivariate E...] [2009-12-13 14:39:24] [143cbdcaf7333bdd9926a1dde50d1082]
- RMPD    [ARIMA Backward Selection] [Paper-ARIMAbackw-Yt] [2009-12-15 18:35:49] [36295456a56d4c7dcc9b9537ce63463b] [Current]
- R PD      [ARIMA Backward Selection] [Paper-ARIMAbackw-Xt] [2009-12-18 10:42:00] [143cbdcaf7333bdd9926a1dde50d1082]
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Dataseries X:
128
502
629.7
595.9
823.7
498.7
766.9
1611.3
329.7
1378.9
1159.4
790.1
-189.6
862.4
426.6
852
834.7
1026.7
1052.8
1280.9
-243.6
976
908.2
416
610.7
728
520.8
905.8
768.9
479.3
1054.2
1411.9
-131
1526.2
1049.5
550.8
168.5
458.2
297
616.3
762.7
693.1
512.7
1169.2
-915.1
1384.2
1368.9
-275.1
-408.9
-37.5
171.5
671.8
-18.5
231.6
747.5
1505.7
-83.6
1173.2
1452.1
777
-52.8
861.2
735.2
1073.6
966.9
1189.8
1093.5
1782.7
-70.4
1471.6
1273.8
900.8
-910.2
299.8
460.2
677.2
937.1
1265.4
1275.6
1582.6
-154.2
1667.7
1083.1
891.7
-26.5
423.4
662.8
711.4
993.3
1133.2
343.9
1415.8
-531.8
1193.6
1201.3
805.6
-164.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 12 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68071&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68071&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68071&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 time12 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.30030.26210.24240.55470.15250.1486-1
(p-val)(0.3714 )(0.0694 )(0.0307 )(0.104 )(0.2556 )(0.2897 )(0.0028 )
Estimates ( 2 )00.18220.19950.24920.1360.1275-0.9999
(p-val)(NA )(0.0918 )(0.0645 )(0.0262 )(0.3009 )(0.3472 )(0.0053 )
Estimates ( 3 )00.17820.18980.24670.05590-0.8116
(p-val)(NA )(0.1009 )(0.0869 )(0.0273 )(0.7755 )(NA )(0.0034 )
Estimates ( 4 )00.17510.19890.245400-0.7552
(p-val)(NA )(0.1043 )(0.0612 )(0.028 )(NA )(NA )(0 )
Estimates ( 5 )000.21050.213300-0.7746
(p-val)(NA )(NA )(0.0547 )(0.0279 )(NA )(NA )(0 )
Estimates ( 6 )0000.242100-0.8333
(p-val)(NA )(NA )(NA )(0.0165 )(NA )(NA )(0 )
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 ) & -0.3003 & 0.2621 & 0.2424 & 0.5547 & 0.1525 & 0.1486 & -1 \tabularnewline
(p-val) & (0.3714 ) & (0.0694 ) & (0.0307 ) & (0.104 ) & (0.2556 ) & (0.2897 ) & (0.0028 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.1822 & 0.1995 & 0.2492 & 0.136 & 0.1275 & -0.9999 \tabularnewline
(p-val) & (NA ) & (0.0918 ) & (0.0645 ) & (0.0262 ) & (0.3009 ) & (0.3472 ) & (0.0053 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.1782 & 0.1898 & 0.2467 & 0.0559 & 0 & -0.8116 \tabularnewline
(p-val) & (NA ) & (0.1009 ) & (0.0869 ) & (0.0273 ) & (0.7755 ) & (NA ) & (0.0034 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.1751 & 0.1989 & 0.2454 & 0 & 0 & -0.7552 \tabularnewline
(p-val) & (NA ) & (0.1043 ) & (0.0612 ) & (0.028 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0.2105 & 0.2133 & 0 & 0 & -0.7746 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0547 ) & (0.0279 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0.2421 & 0 & 0 & -0.8333 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.0165 ) & (NA ) & (NA ) & (0 ) \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=68071&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]-0.3003[/C][C]0.2621[/C][C]0.2424[/C][C]0.5547[/C][C]0.1525[/C][C]0.1486[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3714 )[/C][C](0.0694 )[/C][C](0.0307 )[/C][C](0.104 )[/C][C](0.2556 )[/C][C](0.2897 )[/C][C](0.0028 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.1822[/C][C]0.1995[/C][C]0.2492[/C][C]0.136[/C][C]0.1275[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0918 )[/C][C](0.0645 )[/C][C](0.0262 )[/C][C](0.3009 )[/C][C](0.3472 )[/C][C](0.0053 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.1782[/C][C]0.1898[/C][C]0.2467[/C][C]0.0559[/C][C]0[/C][C]-0.8116[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1009 )[/C][C](0.0869 )[/C][C](0.0273 )[/C][C](0.7755 )[/C][C](NA )[/C][C](0.0034 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.1751[/C][C]0.1989[/C][C]0.2454[/C][C]0[/C][C]0[/C][C]-0.7552[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1043 )[/C][C](0.0612 )[/C][C](0.028 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0.2105[/C][C]0.2133[/C][C]0[/C][C]0[/C][C]-0.7746[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0547 )[/C][C](0.0279 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2421[/C][C]0[/C][C]0[/C][C]-0.8333[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0165 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=68071&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68071&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 )-0.30030.26210.24240.55470.15250.1486-1
(p-val)(0.3714 )(0.0694 )(0.0307 )(0.104 )(0.2556 )(0.2897 )(0.0028 )
Estimates ( 2 )00.18220.19950.24920.1360.1275-0.9999
(p-val)(NA )(0.0918 )(0.0645 )(0.0262 )(0.3009 )(0.3472 )(0.0053 )
Estimates ( 3 )00.17820.18980.24670.05590-0.8116
(p-val)(NA )(0.1009 )(0.0869 )(0.0273 )(0.7755 )(NA )(0.0034 )
Estimates ( 4 )00.17510.19890.245400-0.7552
(p-val)(NA )(0.1043 )(0.0612 )(0.028 )(NA )(NA )(0 )
Estimates ( 5 )000.21050.213300-0.7746
(p-val)(NA )(NA )(0.0547 )(0.0279 )(NA )(NA )(0 )
Estimates ( 6 )0000.242100-0.8333
(p-val)(NA )(NA )(NA )(0.0165 )(NA )(NA )(0 )
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
0.790098685760698
-240.286776919260
328.591811860112
-213.635876932857
298.457994577103
-113.615925692757
473.145463405171
77.4372152539838
-273.753162261419
-493.691709801928
-287.176634354493
-55.7290037608449
-246.999614512969
688.338508855857
-78.5266784100142
71.8890910158688
22.6063222701892
-66.3498228167505
-249.238981173394
146.496067837964
-42.4452033659016
-87.1260123449437
317.39931966006
-27.8801552718772
-15.9787636083003
-93.3727851559264
-218.574620322313
-156.954932698832
-127.881676108871
34.2690911282097
65.2513815299818
-408.118699611051
-150.154403903611
-819.302516241956
345.582495219391
301.515656151425
-710.719206213384
-450.654877576726
-617.054918157008
26.5610562181256
50.9341672625952
-663.625979709982
-229.554420346569
-16.6469346749634
319.86138203698
224.907314990613
-182.781514931011
314.627721060879
319.636925277973
-117.528571240253
365.812781148711
183.202451634886
324.296599044965
230.914652987281
501.866375342385
101.494503156155
281.265536238965
-28.2772472728003
127.924820948259
-54.5614951194853
417.606688473757
-1036.60939906537
-40.6019157853152
-90.7097605829532
79.5489787704607
296.927377001688
484.620406903424
316.547667114668
-31.0220971002515
-68.5794563985398
260.958359821794
-223.965982053063
360.739995021891
38.2771501009297
-45.0885708420886
137.615815726068
-134.319302164646
298.086617829280
180.572353284010
-651.709666946478
-10.8084715004994
-402.734049642139
-2.50947226402265
24.5095731091008
227.242822745546
1.25190834832096

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.790098685760698 \tabularnewline
-240.286776919260 \tabularnewline
328.591811860112 \tabularnewline
-213.635876932857 \tabularnewline
298.457994577103 \tabularnewline
-113.615925692757 \tabularnewline
473.145463405171 \tabularnewline
77.4372152539838 \tabularnewline
-273.753162261419 \tabularnewline
-493.691709801928 \tabularnewline
-287.176634354493 \tabularnewline
-55.7290037608449 \tabularnewline
-246.999614512969 \tabularnewline
688.338508855857 \tabularnewline
-78.5266784100142 \tabularnewline
71.8890910158688 \tabularnewline
22.6063222701892 \tabularnewline
-66.3498228167505 \tabularnewline
-249.238981173394 \tabularnewline
146.496067837964 \tabularnewline
-42.4452033659016 \tabularnewline
-87.1260123449437 \tabularnewline
317.39931966006 \tabularnewline
-27.8801552718772 \tabularnewline
-15.9787636083003 \tabularnewline
-93.3727851559264 \tabularnewline
-218.574620322313 \tabularnewline
-156.954932698832 \tabularnewline
-127.881676108871 \tabularnewline
34.2690911282097 \tabularnewline
65.2513815299818 \tabularnewline
-408.118699611051 \tabularnewline
-150.154403903611 \tabularnewline
-819.302516241956 \tabularnewline
345.582495219391 \tabularnewline
301.515656151425 \tabularnewline
-710.719206213384 \tabularnewline
-450.654877576726 \tabularnewline
-617.054918157008 \tabularnewline
26.5610562181256 \tabularnewline
50.9341672625952 \tabularnewline
-663.625979709982 \tabularnewline
-229.554420346569 \tabularnewline
-16.6469346749634 \tabularnewline
319.86138203698 \tabularnewline
224.907314990613 \tabularnewline
-182.781514931011 \tabularnewline
314.627721060879 \tabularnewline
319.636925277973 \tabularnewline
-117.528571240253 \tabularnewline
365.812781148711 \tabularnewline
183.202451634886 \tabularnewline
324.296599044965 \tabularnewline
230.914652987281 \tabularnewline
501.866375342385 \tabularnewline
101.494503156155 \tabularnewline
281.265536238965 \tabularnewline
-28.2772472728003 \tabularnewline
127.924820948259 \tabularnewline
-54.5614951194853 \tabularnewline
417.606688473757 \tabularnewline
-1036.60939906537 \tabularnewline
-40.6019157853152 \tabularnewline
-90.7097605829532 \tabularnewline
79.5489787704607 \tabularnewline
296.927377001688 \tabularnewline
484.620406903424 \tabularnewline
316.547667114668 \tabularnewline
-31.0220971002515 \tabularnewline
-68.5794563985398 \tabularnewline
260.958359821794 \tabularnewline
-223.965982053063 \tabularnewline
360.739995021891 \tabularnewline
38.2771501009297 \tabularnewline
-45.0885708420886 \tabularnewline
137.615815726068 \tabularnewline
-134.319302164646 \tabularnewline
298.086617829280 \tabularnewline
180.572353284010 \tabularnewline
-651.709666946478 \tabularnewline
-10.8084715004994 \tabularnewline
-402.734049642139 \tabularnewline
-2.50947226402265 \tabularnewline
24.5095731091008 \tabularnewline
227.242822745546 \tabularnewline
1.25190834832096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68071&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.790098685760698[/C][/ROW]
[ROW][C]-240.286776919260[/C][/ROW]
[ROW][C]328.591811860112[/C][/ROW]
[ROW][C]-213.635876932857[/C][/ROW]
[ROW][C]298.457994577103[/C][/ROW]
[ROW][C]-113.615925692757[/C][/ROW]
[ROW][C]473.145463405171[/C][/ROW]
[ROW][C]77.4372152539838[/C][/ROW]
[ROW][C]-273.753162261419[/C][/ROW]
[ROW][C]-493.691709801928[/C][/ROW]
[ROW][C]-287.176634354493[/C][/ROW]
[ROW][C]-55.7290037608449[/C][/ROW]
[ROW][C]-246.999614512969[/C][/ROW]
[ROW][C]688.338508855857[/C][/ROW]
[ROW][C]-78.5266784100142[/C][/ROW]
[ROW][C]71.8890910158688[/C][/ROW]
[ROW][C]22.6063222701892[/C][/ROW]
[ROW][C]-66.3498228167505[/C][/ROW]
[ROW][C]-249.238981173394[/C][/ROW]
[ROW][C]146.496067837964[/C][/ROW]
[ROW][C]-42.4452033659016[/C][/ROW]
[ROW][C]-87.1260123449437[/C][/ROW]
[ROW][C]317.39931966006[/C][/ROW]
[ROW][C]-27.8801552718772[/C][/ROW]
[ROW][C]-15.9787636083003[/C][/ROW]
[ROW][C]-93.3727851559264[/C][/ROW]
[ROW][C]-218.574620322313[/C][/ROW]
[ROW][C]-156.954932698832[/C][/ROW]
[ROW][C]-127.881676108871[/C][/ROW]
[ROW][C]34.2690911282097[/C][/ROW]
[ROW][C]65.2513815299818[/C][/ROW]
[ROW][C]-408.118699611051[/C][/ROW]
[ROW][C]-150.154403903611[/C][/ROW]
[ROW][C]-819.302516241956[/C][/ROW]
[ROW][C]345.582495219391[/C][/ROW]
[ROW][C]301.515656151425[/C][/ROW]
[ROW][C]-710.719206213384[/C][/ROW]
[ROW][C]-450.654877576726[/C][/ROW]
[ROW][C]-617.054918157008[/C][/ROW]
[ROW][C]26.5610562181256[/C][/ROW]
[ROW][C]50.9341672625952[/C][/ROW]
[ROW][C]-663.625979709982[/C][/ROW]
[ROW][C]-229.554420346569[/C][/ROW]
[ROW][C]-16.6469346749634[/C][/ROW]
[ROW][C]319.86138203698[/C][/ROW]
[ROW][C]224.907314990613[/C][/ROW]
[ROW][C]-182.781514931011[/C][/ROW]
[ROW][C]314.627721060879[/C][/ROW]
[ROW][C]319.636925277973[/C][/ROW]
[ROW][C]-117.528571240253[/C][/ROW]
[ROW][C]365.812781148711[/C][/ROW]
[ROW][C]183.202451634886[/C][/ROW]
[ROW][C]324.296599044965[/C][/ROW]
[ROW][C]230.914652987281[/C][/ROW]
[ROW][C]501.866375342385[/C][/ROW]
[ROW][C]101.494503156155[/C][/ROW]
[ROW][C]281.265536238965[/C][/ROW]
[ROW][C]-28.2772472728003[/C][/ROW]
[ROW][C]127.924820948259[/C][/ROW]
[ROW][C]-54.5614951194853[/C][/ROW]
[ROW][C]417.606688473757[/C][/ROW]
[ROW][C]-1036.60939906537[/C][/ROW]
[ROW][C]-40.6019157853152[/C][/ROW]
[ROW][C]-90.7097605829532[/C][/ROW]
[ROW][C]79.5489787704607[/C][/ROW]
[ROW][C]296.927377001688[/C][/ROW]
[ROW][C]484.620406903424[/C][/ROW]
[ROW][C]316.547667114668[/C][/ROW]
[ROW][C]-31.0220971002515[/C][/ROW]
[ROW][C]-68.5794563985398[/C][/ROW]
[ROW][C]260.958359821794[/C][/ROW]
[ROW][C]-223.965982053063[/C][/ROW]
[ROW][C]360.739995021891[/C][/ROW]
[ROW][C]38.2771501009297[/C][/ROW]
[ROW][C]-45.0885708420886[/C][/ROW]
[ROW][C]137.615815726068[/C][/ROW]
[ROW][C]-134.319302164646[/C][/ROW]
[ROW][C]298.086617829280[/C][/ROW]
[ROW][C]180.572353284010[/C][/ROW]
[ROW][C]-651.709666946478[/C][/ROW]
[ROW][C]-10.8084715004994[/C][/ROW]
[ROW][C]-402.734049642139[/C][/ROW]
[ROW][C]-2.50947226402265[/C][/ROW]
[ROW][C]24.5095731091008[/C][/ROW]
[ROW][C]227.242822745546[/C][/ROW]
[ROW][C]1.25190834832096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68071&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68071&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
0.790098685760698
-240.286776919260
328.591811860112
-213.635876932857
298.457994577103
-113.615925692757
473.145463405171
77.4372152539838
-273.753162261419
-493.691709801928
-287.176634354493
-55.7290037608449
-246.999614512969
688.338508855857
-78.5266784100142
71.8890910158688
22.6063222701892
-66.3498228167505
-249.238981173394
146.496067837964
-42.4452033659016
-87.1260123449437
317.39931966006
-27.8801552718772
-15.9787636083003
-93.3727851559264
-218.574620322313
-156.954932698832
-127.881676108871
34.2690911282097
65.2513815299818
-408.118699611051
-150.154403903611
-819.302516241956
345.582495219391
301.515656151425
-710.719206213384
-450.654877576726
-617.054918157008
26.5610562181256
50.9341672625952
-663.625979709982
-229.554420346569
-16.6469346749634
319.86138203698
224.907314990613
-182.781514931011
314.627721060879
319.636925277973
-117.528571240253
365.812781148711
183.202451634886
324.296599044965
230.914652987281
501.866375342385
101.494503156155
281.265536238965
-28.2772472728003
127.924820948259
-54.5614951194853
417.606688473757
-1036.60939906537
-40.6019157853152
-90.7097605829532
79.5489787704607
296.927377001688
484.620406903424
316.547667114668
-31.0220971002515
-68.5794563985398
260.958359821794
-223.965982053063
360.739995021891
38.2771501009297
-45.0885708420886
137.615815726068
-134.319302164646
298.086617829280
180.572353284010
-651.709666946478
-10.8084715004994
-402.734049642139
-2.50947226402265
24.5095731091008
227.242822745546
1.25190834832096



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